UCLAUCLA Electronic Theses and Dissertations

TitleCharacterizing Neurotrauma and Astroglial Injury Biomarkers by Proteomics and Mass Spectrometry

Permalinkhttps://escholarship.org/uc/item/70p4t7v8

AuthorShen, Sean

Publication Date2016 Peer reviewed|Thesis/dissertation

eScholarship.org Powered by the California Digital LibraryUniversity of California

UNIVERSITY OF CALIFORNIA

Los Angeles

Characterizing Neurotrauma and Astroglial Injury Biomarkers

by Proteomics and Mass Spectrometry

A dissertation submitted in partial satisfaction

of the requirements for the degree Doctor of Philosophy

in Biochemistry and Molecular Biology

by

Sean Shen

2016

© Copyright by

Sean Shen

2016

ii

ABSTRACT OF THE DISSERTATION

Characterizing Neurotrauma and Astroglial Injury Biomarkers

by Proteomics and Mass Spectrometry

by

Sean Shen

Doctor of Philosophy in Biochemistry and Molecular Biology

University of California, Los Angeles, 2016

Professor Joseph Ambrose Loo, Chair

Neurotraumatic injury has long been a leading cause of death and disability

worldwide. Recently, the debilitating long term effects of chronic, mild traumatic brain

injuries (TBIs) have gained increased public attention. In order to protect individuals most

at risk (e.g. military personnel and athletes) from such injuries, improved diagnostics in

the form of a biomarker panel capable of rapidly and sensitively detecting mild TBIs are

needed. Despite the large number of TBI biomarker studies in the literature, the

development of a clinically relevant protein signature remains elusive.

In contrast to diseases with singular mechanistic dysfunction, neurotrauma is

characterized by the disruption to multiple cellular pathways that contribute to the

sequelae of secondary pathophysiology that determines patient outcome and recovery.

This complexity has been a confounding factor in the identification of effective biomarkers.

In an effort to circumvent this hurdle, our group implemented a central nervous system

iii

(CNS) specific cell injury model to examine preferentially released injury related proteins

as candidate diagnostics.

Comparative analysis of a TBI CSF proteome and preferentially released proteins

from our injury model revealed a panel of astroglial injury related candidate biomarkers

including aldolase C (ALDOC), brain lipid binding protein (BLBP), glutamine synthetase

(GS), astrocytic phosphoprotein PEA15 (PEA15), and glial fibrillary acidic protein (GFAP)

and its trauma-generated breakdown products (BDPs). Immediate and robust release of

ALDOC, BLBP, and PEA15 were associated more with acute cell wounding than cell

death observed after biomechanical injury. In contrast, GFAP release correlated primarily

with cell death. The sensitivity and selectivity of our biomarkers for neurotrauma were

evaluated in human TBI and Yucatan swine spinal cord injury (SCI) CSF samples.

Verification studies demonstrated the ability of our astroglial biomarker panel to

differentiate injury from non-injury with elevated signals detectable an hour after injury.

Additionally, differential CSF concentration kinetics were observed over a 1-week period

post-injury indicative of a long diagnostic window. CSF concentration of biomarkers

GFAP, ALDOC, and BLBP correlated strongly with the extent of tissue loss after SCI at 7

days. Taken together, our data demonstrates the successful application of proteomics to

the identification and verification of new neurotrauma biomarkers that exhibit potential for

not only detecting but defining injury severity.

iv

This dissertation of Sean Shen is approved.

Gal Bitan

Jorge Torres

Joseph Ambrose Loo, Committee Chair

University of California, Los Angeles

2016

v

TABLE OF CONTENTS

CHAPTER 1: TRAUMATIC BRAIN INJURY – CLINICAL AND MOLECULAR

PATHOLOGIES .............................................................................................................. 1

1.1 INTRODUCTION ................................................................................................... 1

1.2 CLASSIFICATON OF TRAUMATIC BRAIN INJURY ............................................. 3

1.3 CLINICAL PATHOLOGIES OF NEUROTRAUMA ................................................. 6

1.4 THE MOLECULAR PATHOPHYSIOLOGY OF NEUROTRAUMA ....................... 10

1.5 CONCLUSION ..................................................................................................... 18

1.6 REFERENCES .................................................................................................... 20

CHAPTER 2: ADDRESSING THE NEEDS OF TRAUMATIC BRAIN INJURY WITH

CLINICAL PROTEOMICS ........................................................................................... 30

2.1 INTRODUCTION ................................................................................................ 30

2.2 DISCUSSION ....................................................................................................... 32

2.3 CONCLUSIONS ................................................................................................. 53

2.4 FIGURES ............................................................................................................. 54

2.5 TABLES ............................................................................................................... 55

2.6 REFERENCES ................................................................................................... 56

CHAPTER 3: NEW ASTROGLIAL INJURY DEFINED BIOMARKERS FOR

NEUROTRAUMA ASSESSMENT ................................................................................. 78

3.1 INTRODUCTION ................................................................................................. 78

3.2 RESULTS ............................................................................................................ 81

2.3 DISCUSSION ....................................................................................................... 93

3.4 METHODS ......................................................................................................... 102

2.5 FIGURES ........................................................................................................... 112

3.6 TABLES ............................................................................................................. 129

3.7 SUPPLEMENTAL FIGURES ............................................................................. 158

3.8 REFERENCES .................................................................................................. 174

CHAPTER 4: ASSESSMENT OF ASTROGLIAL INJURY DEFINED BIOMARKERS IN

SPINAL CORD INJURY .............................................................................................. 198

4.1 INTRODUCTION ............................................................................................... 198

4.2 RESULTS .......................................................................................................... 201

4.3 DISCUSSION ..................................................................................................... 207

vi

4.4 METHODS ......................................................................................................... 209

4.5 FIGURES ........................................................................................................... 213

4.6 TABLES ............................................................................................................. 233

4.7 REFERENCES .................................................................................................. 239

CHAPTER 5: CHARACTERIZING THE PREFERENTIAL RELEASE OF PROTEIN

SUBPOPULATIONS BY INJURED ASTROCYTES .................................................... 243

5.1 INTRODUCTION ............................................................................................... 243

5.2 RESULTS .......................................................................................................... 245

5.3 DISCUSSION ..................................................................................................... 250

5.4 METHODS ......................................................................................................... 255

5.5 FIGURES ........................................................................................................... 259

5.6 TABLES ............................................................................................................. 269

5.7 REFERENCES .................................................................................................. 312

CHAPTER 6: FUTURE DIRECTIONS FOR SPINAL CORD AND HEAD TRAUMA ... 320

6.1 INTRODUCTION ............................................................................................... 320

6.2 RESULTS .......................................................................................................... 321

6.3 DISCUSSION ..................................................................................................... 325

6.4 CONCLUDING REMARKS ................................................................................ 328

6.5 METHODS ......................................................................................................... 333

6.6 FIGURES ........................................................................................................... 335

6.7 TABLES ............................................................................................................. 337

6.8 REFERENCES .................................................................................................. 352

vii

LIST OF FIGURES AND TABLES

Figure 2.1: ..................................................................................................................... 54 Table 2.1: ...................................................................................................................... 55 Figure 3.1: ................................................................................................................... 112 Figure 3.2: ................................................................................................................... 113 Figure 3.3: ................................................................................................................... 116 Figure 3.4: ................................................................................................................... 118 Figure 3.5: ................................................................................................................... 120 Figure 3.6: ................................................................................................................... 123 Figure 3.7: ................................................................................................................... 125 Figure 3.8: ................................................................................................................... 128 Table 3.1: .................................................................................................................... 130 Table 3.2: .................................................................................................................... 131 Table 3.3: .................................................................................................................... 133 Table 3.4: .................................................................................................................... 148 Table 3.5: .................................................................................................................... 149 Table 3.6: .................................................................................................................... 152 Table 3.7: .................................................................................................................... 154 Table 3.8: .................................................................................................................... 155 Table 3.9: .................................................................................................................... 157 S3.1: ............................................................................................................................ 158 S3.2: ............................................................................................................................ 160 S3.3: ............................................................................................................................ 161 S3.4: ............................................................................................................................ 162 S3.5: ............................................................................................................................ 163 S3.6: ............................................................................................................................ 164 S3.7: ............................................................................................................................ 165 S3.8: ............................................................................................................................ 167 S3.9: ............................................................................................................................ 168 S3.10: .......................................................................................................................... 170 S3.11: .......................................................................................................................... 171 S3.12: .......................................................................................................................... 173 Figure 4.1: ................................................................................................................... 213 Figure 4.2: ................................................................................................................... 214 Figure 4.3: ................................................................................................................... 215 Figure 4.4: ................................................................................................................... 216 Figure 4.5: ................................................................................................................... 218 Figure 4.6: ................................................................................................................... 219 Figure 4.7: ................................................................................................................... 220 Figure 4.8: ................................................................................................................... 222 Figure 4.9: ................................................................................................................... 224 Figure 4.10: ................................................................................................................. 225 Figure 4.11: ................................................................................................................. 226 Figure 4.12: ................................................................................................................. 228 Figure 4.13: ................................................................................................................. 230 Figure 4.14: ................................................................................................................. 232

viii

Table 4.1: .................................................................................................................... 233 Table 4.2: .................................................................................................................... 235 Table 4.3: .................................................................................................................... 236 Table 4.4: .................................................................................................................... 238 Figure 5.1: ................................................................................................................... 259 Figure 5.2: ................................................................................................................... 261 Figure 5.3: ................................................................................................................... 262 Figure 5.4: ................................................................................................................... 263 Figure 5.5: ................................................................................................................... 265 Figure 5.6: ................................................................................................................... 266 Figure 5.7: ................................................................................................................... 267 Figure 5.8: ................................................................................................................... 268 Table 5.1: .................................................................................................................... 269 Table 5.2: .................................................................................................................... 271 Table 5.3: .................................................................................................................... 273 Table 5.4: .................................................................................................................... 274 Table 5.5: .................................................................................................................... 276 Table 5.6: .................................................................................................................... 289 Table 5.7: .................................................................................................................... 299 Table 5.8: .................................................................................................................... 311 Figure 6.1: ................................................................................................................... 335 Figure 6.2: ................................................................................................................... 336 Table 6.1: .................................................................................................................... 338 Table 6.2: .................................................................................................................... 339 Table 6.3: .................................................................................................................... 341 Table 6.4: .................................................................................................................... 344 Table 6.5: .................................................................................................................... 351

ix

DEDICATION

I would like to dedicate this work to my mother (Xinfang), father (Zhongnan), and to all

my family and friends who have supported me throughout this long journey.

To my WindRose family, thanks for your constant encouragement.

To the memory of Daniel Cho, my brother from another mother.

x

ACKNOWLEDGEMENTS

I would like to thank my mentor, Professor Joseph A. Loo, for giving me this

research opportunity and for his constant support and guidance throughout my graduate

studies. I am forever thankful to Joe for taking a chance on me and trusting me to not only

work with top of the line mass spectrometers but also to be so closely involved in the

installation of new instruments. I will remember the encouraging, insightful, and kind

demeanor that Joe conducts himself as something to aspire toward.

I would like to thank Ina-Beate Wanner for the opportunity to work on her traumatic

brain injury project. Her words of encouragement, criticisms, and patience have pushed

me throughout my graduate training. I have met few people in my life with as much

passion and dedication to her work. I would like to also thank members of the Wanner lab

Julia Halford and Jacklynn Levine for all their help on the neurobiology side of the project

and collaborative discussions.

Additionally, I would like to thank Rachel Loo for all the knowledge and expertise

she has shared with me over the years on sample preparation and biochemical methods.

To past and present Loo lab members, I would like to extend my appreciation for

your support, both professional and personal. I would especially like to thank Carly

Ferguson, Hong Nguyen, Pete Wongkongkathep, Huilin Li, Reid O’Brien Johnson, Dyna

Shirasaki, and my undergraduate student Eric Wang. I would also like to express my

sincere gratitude to my good friends Keith Cheung and Subhajit Poddar for their advice

and input throughout my graduate work.

xi

Finally, I would like to thank my committee members, Professors Gal Bitan,

Catherine Clarke, Jorge Torres, James Wohlschlegel, and Joe Loo for their guidance and

advice.

xii

Chapter 2 of this dissertation is version of a version of S. Shen, R. R. O. Loo, I. B.

Wanner, J. A. Loo, Addressing the needs of traumatic brain injury with clinical proteomics.

Clinical proteomics 11, 1-13 (2014), reprinted with permissions. I would like to

acknowledge my co-authors Rachel R. Ogorzalek Loo, Ina B. Wanner, and Joseph A.

Loo.

Chapter 3 of this dissertation is a manuscript in preparation entitled “New Astroglial

Injury Defined Biomarkers for Neurotrauma Assessment.” I would like to acknowledge Ina

B. Wanner, Julia Halford, Kyohei Itamura, and Jacklynn Levine their work on cellular

characterization and immunoblot studies in addition to data analysis, assembly, and figure

preparation; Gregg Czerwieniec for work on MRM-MS and mass spectrometry studies;

Dalton Dietrich, Paul Vespa, David Hovda for their input on traumatic brain injury (TBI)

pathophysiology; and Ross Bullock, Paul Vespa, Thomas Glenn, and Stefania Mondella

for providing TBI patient biofluid samples and input on clinical analysis.

I would like to acknowledge Ina B. Wanner, Julia Halford, Kyohei Itamura, and

Jacklynn Levine for their work on preparing stretch injured astrocytes, immunoblot

analysis of swine SCI CSF samples, and immunohistology of spinal cords described in

Chapters 4 and 5 of this dissertation.

I would like to acknowledge our Department of Defense collaborators Rachel

Kinsler, Andrew Mayer, Jonathan DeShaw, and Salam Rahmatalla for their work in

developing and executing the spinal cord injury model, cerebrospinal fluid (CSF)

collection, post-injury transportation, and surgery which contributed to the elucidation of

astroglial injury defined biomarkers in assessing swine spinal cord injury and

characterization of swine SCI CSF proteomics in Chapters 4 and 6 respectively.

xiii

I would also like to acknowledge my undergraduate student Eric Wang for his

contributions to sample preparation for the work presented in Chapter 4.

xiv

VITA

2003 – 2007 Bachelors of Arts, University of California, Berkeley

Molecular Cell Biology

2007 – 2009 Research Associate I, WindRose Analytica

2009 – 2010 Research Associate II, Ajinomoto Althea

2011 – 2012 Teaching Assistant, UCLA

2014 - 2015 Technology Fellow, UCLA Office of Intellectual Property

2015 Excellence in Biochemical Research Fellowship

xv

PUBLICATIONS

Shen, S., Loo, R. R. O., Wanner, I.-B. & Loo, J. A. Addressing the Needs of Traumatic Brain Injury with Clinical Proteomics. Clinical Proteomics 2014, 11:11. Dzialo, M.C., Travaglini, K.J., Shen, S., Loo, J.A., Clarke, S.G. A New Type of Protein Lysine Methyltransferase Trimethylates Lys-79 of Elongation Factor 1A. Biochemical and Biophysical Research Communications 2014, 455:382-389 Dzialo, M.C., Travaglini, K.J., Shen, S., Roy, K., Chanfreau, G.F., Loo, J.A., Clarke, S.G. Translational Roles of Elongation Factor 2 Protein Lysine Methylation. Journal of Biological Chemistry 2014, 289:30511-30524 Buehler, D.*, Marsden, M.*, Shen, S., Toso, D. B., Wu, X., Loo, J. A., Zhou, Z. H., Kickhoefer, V. A., Wender, P. A., Zack, J. A., & Rome, L. H. Bioengineered Vaults: Self-Assembling Protein Shell-Lipophilic Core Nanoparticles for Drug Delivery. ACS Nano 2014, 8:7723-7732

PRESENTATIONS

Shen, S., Halford, J., Wanner, I.B., Loo, J.A. Characterizing Traumatic Brain Injury with New Astroglial Injury Biomarkers Measured by Targeted MS. American Society of Mass Spectrometry Annual Conference. San Antonio, TX. June 2016 Shen, S., Itamura, K., Halford, J., Wanner, I.B., Loo, J.A. Measuring Acute Traumatic Brain Injury Biomarkers by Targeted Mass Spectrometry. American Society of Mass Spectrometry Annual Conference. St. Louis, MO. June 2015 Shen, S., Wanner, I.B., Loo, J.A. Discovery and Verification of Neurotrauma Markers by High Mass Accuracy/High Resolution Mass Spectrometry. American Society of Mass Spectrometry Annual Conference. Baltimore, MA. June 2014 Shen, S., Wanner, I.B., Czerwieniec, G., Loo, J.A. Selection and Quantification of Neurotrauma Markers by Mass Spectrometry. American Society of Mass Spectrometry Annual Conference. Minneapolis, MN. June 2013 Shen, S., Ferguson, C., Loo, R.R.O., Loo, J.A. Highly Multiple Charging with 2-nitrophloronolgluncinol by MALDI Time-of-Flight Mass Spectrometry. American Society of Mass Spectrometry Annual Conference. Vancouver, CA. May 2012

1

CHAPTER 1: TRAUMATIC BRAIN INJURY – CLINICAL AND MOLECULAR

PATHOLOGIES

1.1 INTRODUCTION

Impact and Healthcare Significance of Traumatic Brain Injury (TBI)

Neurotrauma to the central nervous system (CNS) is a serious public health

worldwide. Most commonly, neurotrauma is experienced in the form a traumatic brain

injury or TBI. Examining the US alone, TBI is most common in infants and toddlers,

adolescents and the elderly (1). The US National Institute of Neurological Disorders and

Stroke estimates that 2.5-6.5 million Americans have had one or multiple TBIs. In the US

military there were over 212,000 service men and women diagnosed with some form of

TBI between January 2000-May 2011, roughly accounting for one-third of all injured US

soldiers, making TBI the signature injury of the wars in Iraq and Afghanistan compared to

past wars (2). TBI contributes to over one third of all injury-related deaths, yet 75-90% of

all brain trauma cases are considered to be mild TBI (mTBI), many without visible wounds

that often are undiagnosed (3). The documented long term disability associated with

repeated head trauma coupled with inadequate diagnostic measures highlight the

immediate need for increased understanding of TBI pathology and how to treat it. Better

diagnostic tools are needed to detect head injuries, especially mTBI, as well as to confirm

and monitor the severity of TBI in order to determine the best course of action acutely

and later post-injury. This is of special urgency for military personnel and athletes of all

kinds who are most at risk for repeated head injury. This introduction presents TBI as a

biomechanical injury and characterizes the clinical pathologies and their underlying

molecular processes. Chapter 2 will discuss some of the challenges to discovering new

2

biomarkers. In Chapters 3-6, results of proteomic efforts in this field will be discussed as

they relate to insights that can be gleamed for future study and development of

neurotrauma diagnostics and therapeutics.

3

1.2 CLASSIFICATON OF TRAUMATIC BRAIN INJURY

TBI is a Biomechanical Injury

Traumatic brain injury is defined as focal or diffuse brain damage from external

trauma. The principle mechanisms of focal brain damage include contusion, laceration,

and intracranial hemorrhage. Diffuse brain injury occurs from abrupt acceleration and

deceleration type injuries that result in diffuse axonal damage and brain edema. While a

TBI is initiated from one of the above two types of primary injury, assessment and

treatment is further complicated by the onset of secondary non-mechanical damage (4-

6). No treatment exists for primary injuries outside of preventative measures. Secondary

pathologies ranging from ischemic events to edema, however, are sensitive to therapeutic

interventions. Proper management of secondary sequelae is essential to positive long

term patient outcome and brain function.

Classification of TBI severity

Classification of the severity of traumatic brain injury is of clinical interest as it

directly affects the type of acute and post-acute medical care administered. Typically, TBI

severity is determined based on single indicators such as the Glasgow Coma Scale

(GCS), duration of post-traumatic amnesia, and loss of consciousness. The GCS is a

neurocognitive examination of eye function, response to verbal commands, and motor

function graded on a scale of 1-14 where scores of 13 or higher correlate with a mild brain

injury, 9-12 to a moderate injury, and 8 or lower a severe injury (7). And while these

measures correlate with severity and outcome, each may be influenced by indirect

factors. Early sedation and patient intoxication have both been demonstrated to have

4

suppressive effects on GCS values (8, 9). Classification schemes that combine single

indicators of GCS, loss of consciousness, amnesia as well as a myriad of other clinical

criteria such as patient survival, presentation of hematoma/hemorrhage, and patient

reported symptoms have demonstrated effectiveness ex post facto (10). However,

classification after the fact offers little benefit to patient treatment and outcome. Coupled

with the frequent lack of complete documentation of severity indicators, standardization

of classification for clinical and research purposes necessitates a simple yet

unencumbered diagnostic.

Concussive Neurotrauma

Perhaps the most impactful type of TBI is concussive injury. While concussive

injuries are classified as a mild TBI (mTBI), they nonetheless possess the potential to

affect a patient’s long term mental status. Studies estimate that anywhere between 1.5

and 4 million US athletes suffer a concussive mTBI annually (11, 12). Early symptoms of

a concussion include but are not limited to changes in behavior, loss of emotional control,

impairments to memory/attention, headache, and in rare cases catastrophic brain injury

known as the second impact syndrome (13). Second impact syndrome (SIS) is defined

as when a patient sustains a head injury, most commonly a mTBI, and subsequently

endures a second injury before the symptoms of the first have fully cleared, resulting in

catastrophic and typically fatal brain swelling (14, 15). However, it is scientifically unclear

whether it is the repeated injury or delayed onset of cerebral swelling form the initial blow

to the head that is responsible for these rare occurrences (16). Despite this controversy,

the anecdotal reports of SIS have brought attention to the dangers of chronic head

5

trauma. Studies to this effect have identified a positive correlation of greater symptom

severity, time to recover, and the earlier onset of age associated cognitive decline (also

known as chronic traumatic encephalopathy (CTE)) in patients with a history of repeated

concussions. These findings refuted the previous perception of concussive sports injuries

as benign. Public awareness and outcry from this new information has been instrumental

in changing how our sports medicine professionals treat and manage those individuals

most at risk. Major sports organizations, most prominently the National Football League

(NFL), have been at the forefront of this backlash resulting in the institution of new

protocols and procedures to ensure player safety.

6

1.3 CLINICAL PATHOLOGIES OF NEUROTRAUMA

Diffuse Axonal Injury

Biomechanical stretching of neuronal axons causes membrane disruption and

depolarization. This increase in axolemmal permeability has been shown to persist for 6

hours post-injury (17, 18) with the influx of calcium. As a result, neurofilaments undergo

compaction by calcium activated calpain proteolysis or neurofilament phosphorylation,

leading to loss of stability and breakdown (19, 20). As axons begin to develop

abnormalities and breaks, an accumulation of organelles occurs at the site of damage

due to the continued transport along intact segments. Signs of axotomy or axonal

severing can be observed as early as 4 hours post-injury and persist for days to weeks

(21).

Edema and Elevated Intracranial Pressure

Brain edema, or swelling, is a critical pathophysiology resulting from neurotrauma

(TBI, ischemia, etc.). Brain edema is defined as the abnormal accumulation of fluid within

the parenchyma. In most organ systems the parenchyma refers to structural and

connective tissues. In the brain, however, the parenchyma is comprised of the functional

tissue consisting of neurons and glial cells. Edema is categorized as either vasogenic or

cytotoxic (22). Vasogenic edema occurs when excess fluid accumulates in the brain

around cells, usually originating from blood vessels. This is believed to occur following a

traumatic compromise of the blood brain barrier (23). Swelling resulting from the

accumulation of fluid within cells is classified as cytotoxic edema. Cytotoxic edema most

commonly results from ischemic events, where inadequate oxygen and glucose content

7

impede cell survival. Both forms of edema are experienced in TBI, making treatment of

resulting neuropathologies challenging.

Edema is well documented in TBI to raise intracranial pressure (ICP), a secondary

pathology of the initial mechanical insult that is frequently associated with death and poor

prognosis among TBI survivors (24). Assessing the extent of swelling by computed

tomography (CT) scans (25) shortly after injury has demonstrated high correlation

between patient outcome to severity of brain swelling. In severe cases, patient mortality

may occur in as little at 36 hours despite aggressive clinical interventions (22). A study of

the proteomic alterations may reveal trends between protein levels and patient ICP. Such

a multidisciplinary study could identify signature proteins that may act as a less invasive

surrogate measure for ICP, traditionally monitored by insertion of a catheter into different

areas of the brain. Furthermore, potential surrogate protein markers could be monitored

during treatment (both clinical and experimentally) as an indication of the modality’s

effectiveness.

Neuroinflammation

Following the onset of cell death, a complex interplay of immunological and

inflammatory responses is observed in neurotrauma. Both the primary insult and resulting

secondary sequelae activate cellular mediators ranging from proinflammatory cytokines,

prostaglandins, and components of the complement system. These mediators then

induce chemokines and adhesion molecules, recruit immune cells, and activate glial cells

(26). While, many of these components of the inflammatory response contribute to acute

8

and chronic neurological detriment, the immune response is also responsible for long-

term repair and recovery post-trauma.

Delayed CNS injury is another hallmark of neurotrauma with inflammatory

response mediators implicated in the process. Upregulation of cellular adhesion

molecules are responsible for tissue infiltration by leukocytes. These leukocytes are then

responsible for the elimination of injured but also adjacent healthy tissues based on

spreading depressions. This occurs on a time scale ranging from hours to weeks as

astrocytes and microglial begin to synthesize the structural filament components of the

neuroscar (26).

Acutely after injury proinflammatory enzymes tumor necrosis factor (TNFα),

interleukin-1 (IL-1), and interleukin-6 (IL-6) are upregulated. IL-1, released immediately

following CNS damage from activated glial cells, induces a variety of beneficial actions

involved in restoring ionic balance through reduction in EAA glutamate release,

enhancement of gamma-amino butyric acid (GABA), the primary neuroinhibitory signaling

amino acid, and modulation of NMDA. IL-1 also upregulates the production of nitric oxide

(NO) which contributes both protective and neurotoxic effects (27). TNFα is a key

mediator of tissue inflammation implicated in the development of an assortment of

neurological conditions. TNF related signaling pathways function through two receptors,

p55 and p75. While the functions of p75 in the brain are unknown, activation of p55 is

responsible for the induction of apoptosis in the CNS. IL-6 is involved in various signaling

pathways that lead to gene activation related to recovery processes (26, 28).

Modulation of these inflammatory mediators have shown positive experimental

evidence for in the CNS damages at both acute and late (48h) time-points post-TBI.

9

Agents inhibiting TNFα improve short and long-term outcome in rats (29) while studies

have shown IL-6 to have neuroprotective effects by increasing CNS healing (30).

Transgenic mice lacking complement C3 or C5 exhibit reduced secondary damage

compared to control (26). While not necessarily specific to TBI neurotrauma, changes in

neuroimmune responses are critical to treatment of neurotrauma sequelae.

Chemical/biochemical agents with the ability to modulate these immune responses may

represent potential therapies in TBI management.

Cerebral Blood Flow

In the healthy brain, cerebral blood flow (CBF) is tightly coupled to cerebral glucose

metabolism. However, post-trauma, cerebral blood flow is deregulated leading to a

decoupling of blood and oxygen flow with cellular energy requirements. Experimental

evidence in rat fluid procession models have shown a decrease in CBF by as much as

50% of normal levels in the post-traumatic state. This reduction in CBF limits the oxygen

available to cerebral tissue necessary to meet the metabolic needs of injured and

recovering cells in a damaging energy crisis (31, 32).

10

1.4 THE MOLECULAR PATHOPHYSIOLOGY OF NEUROTRAUMA

General Molecular Pathophysiology of TBI

Upon sustaining a TBI, mechanical shear and deformation forces initiate a complex

cascade of neurochemical and metabolic events. These events begin with the impairment

of cerebral blood flow (CBF) resulting in ischemic conditions (33, 34). The resulting

anaerobic conditions and increased cellular metabolism results in an energy crisis, As

energy dependent ion pumps fail, ionic balance is disrupted resulting in the indiscriminate

release of excitatory amino acids (EEA) that only furthers ion imbalances and causes the

activation of signaling pathways for cell death (4).

Ionic Imbalance and Neurotransmitter Release

Acutely following mechanical trauma to brain tissue, neuronal membranes become

compromised, axons are stretched, and voltage-gated potassium channels are opened.

Increases in potassium extracellular potassium concentrations cause nonspecific axon

depolarization and results in the indiscriminant release of excitatory amino acid (EEA)

glutamate which further increases extracellular potassium concentrations through the

activation of, N-methyl-D-aspartate (NMDA), α-amino-3-hydroxy-5-methyl-4-

isoxazolpropionate (AMPA). This massive excitation of neurons is followed by neuronal

suppression, a phenomenon known as spreading depression (35) where the sections of

the brain undergo waves of electrophysiological hyperactivity followed by a waves of

inactivity. Distinct from classical spreading depression, post-traumatic spreading

depression affects both focal and diffuse areas of injury simultaneously. Early loss of

11

consciousness, memory loss, and confusion may be manifestations of a spreading

depression state post-injury (32).

Under normal conditions, elevated extracellular potassium are absorbed by

surrounding astroglial cells (36). Potassium released by neurons causes a passive influx

of potassium into surrounding astrocytes. This causes an astrocyte depolarization and

leads to current conduction along the cell and to cells coupled to them. As this potassium

generated current is propagated to the endfeet of astrocytes which terminate on the

surface of cerebral arterioles, potassium is siphoned from astrocyte feet onto their

adjacent arteriole walls. The increase in potassium content in arterioles causes

vasodilation and is important to the regulation of cerebral blood flow (36). While this

process is sufficient in accommodating mild perturbations in extracellular potassium, it is

unable to compensate for the larger levels of ionic imbalance generated from injury as

post-traumatic astrocytes exhibit reduction/loss of inward potassium uptake and

subsequent conduction (37). This loss of ionic homeostasis is likely involved in the

impairments to learning and memory that patients experience after a TBI.

Neurons restore ionic balance by activating energy dependent sodium/potassium

pumps. In post-TBI conditions however, energy stores are rapidly depleted resulting in

rapid by inefficient anaerobic glycolysis acutely 30 minutes to 4 hours after injury in rats

(38). This increase in glycolysis known as hyper-metabolism results from diminished CBF

and the disparity between cellular glucose supply and demand creates an energy crisis.

It is hypothesized that this energy deficient state is responsible for post-injury vulnerability

where cerebral tissue is less equipped to respond adequately to subsequent injury

leading to increased trauma severity and extended post-acute deficits (32). Additionally,

12

increased anaerobic production of energy results in an extracellular accumulation of

lactate which contributes to acidosis, membrane compromise, and cerebral swelling (13).

Mitochondrial Dysfunction in TBI

In addition to increased potassium ion levels, calcium accumulation is also

observed in the wake of neurotrauma (39). Elevated extracellular potassium ion

concentrations in the post-traumatic state, triggers the unregulated release of excitatory

amino acids that bind and activate NMDA receptors. Activated NMDA receptors create a

pore that allows calcium ions to enter the cells. Calcium is key to the pathophysiology of

trauma induced secondary sequela. When intracellular calcium increases above normal

homeostatic levels, attack and digestion of cellular proteins, lipids and DNA occur as a

result of the activation of proteases, lipases, and nucleases (40). As a result of increased

calcium dependent enzymatic activation, cells are subject to an overproduction of

neurotoxin free radicals, disruption of cytoskeletal organization, and or signaling

cascades leading to cell death.

Neurons and glia cells respond to this increase in intracellular calcium by

sequestering the excess within the mitochondria (41). Under normal conditions, calcium

provides benefit to the mitochondrial by stimulating oxidation-phosphorylation and ATP

synthesis. However, overloaded mitochondrial calcium has been shown to activate the

production of reactive oxygen species. Increases in ROS can further modulate calcium

dynamics by augmenting the calcium surge, thus generating a self-amplified loop of

cellular damage through calcium dependent initiation of apoptosis or necrosis.

13

Additionally, calcium stimulates oxidative phosphorylation by allosteric activation

of tricarboxylic acid (TCA) cycle enzymes leading to faster respiratory chain activity and

increased oxygen consumption that is restricted under ischemic conditions experienced

in the post-TBI state. Additionally, calcium concentrations in excess of physiological

conditions disrupt the respiration process by increasing cytochrome c dislocation from the

inner membrane through either competitive inhibition of negatively charged cadiolipin

binding sites or activation of cyctochrome c release pathways (42).

Altered Glucose Metabolism in TBI

The human brain functions primary on glucose and the energy it generates through

the glycolytic and tricarboxylic acid pathways. Alterations in cerebral glucose metabolism

(CMRglc) is a hallmark response to neurotrauma. Chemically labeled (18F-DG and 14C-

DG) glucose analogs have been used extensively in the study of glucose metabolism.

Once cleaved, these isotopes are trapped in cells allowing glucose accumulation to be

monitored. Using autoradiographic visualization, rapid glucose uptake is observed

acutely in the post-traumatic state followed by an extensive period of glucose metabolism

depression (38). Immediately following head injury, large increases in cerebral glucose

metabolism are observed. This increase has been shown to be attenuated by

administration of kynurenic acid, an inhibitor of NMDA receptors involved in proliferation

of cellular ionic imbalances (43). Consequently, this initial increase in glucose metabolism

is believed to be in response to the higher cellular energy demands necessary to restore

ionic homeostasis and neuronal membrane potential. This period of hyperglycolysis has

been observed for up to 8 days in severe TBI patients (44). The described acute CMRglc

14

period is followed by a period of metabolic depression in both animal and human studies

(45) that correlate with the magnitude of injury severity. Consistent with experimental

data, glucose metabolism rates in select regions of the brain – thalamus, cerebellum, and

brain stem – showed significant positive correlation with levels of consciousness

measured by the Glasgow Coma Scale (GCS) (46)

This delayed wave of glucose metabolism is believed to be the result of the

contributions of changes in cerebral blood flow, defects in glucose transporter function,

and or decreased metabolic demand for glucose. The rapid increase in glucose

metabolism in the acute phase of injury correlates to increased consumption during a

period of blood flow decline (41) generating an energy crisis. One explanation is that the

increased energy burden quickly depletes glucose stores and in the presence of

insufficient glucose replenishment from blood, glycolysis pathways are unable to keep up

and CMRglc rates decline. However, experimental data proving this is so far unclear. In

contrast, experimental studies rats showed no change to blood glucose after injury

suggesting no substrate limitation (47).

A second reason is proposed to be related to decreases in neuronal glucose

transporter GLUT1 (48) resulting in impaired glucose transport from blood to brain cells.

Hattori et al (46) demonstrated lower glucose accumulation in brain regions within

contusion sites. It is possible that inhibition of glucose transport across the blood brain

barrier is substantially affected in the post-TBI state.

Lastly, cells may experience a decreased metabolic demand as it prioritizes other

repair related functions in response to trauma. As it relates to glycolytic processing, proton

nuclear magnetic resonance (NMR) studies have uncovered increases in the amount of

15

glucose diverted to the synthesis of nucleic acid precursors in the acute time phase (3-24

hours) post-injury (49). This increase in DNA synthesis is likely a cellular response to both

DNA damage and upregulation of genes involved in repair and recovery pathways. In

support of this, nicotinamide dinucleotide (NAD+), essential electron acceptors in the

respiratory pathways, concentrations have been shown to decrease after injury (50). This

can be explained by higher NAD+ consumption from DNA repair enzymes as Poly-ADP

ribose polymerase (PARP) (51) in response to the elevated cellular levels of ROS. Thus,

reductions in NAD+ levels may be responsible for glycolytic inhibition as the cell

reorganizes its needs in the aftermath of traumatic injury.

Astrocytes and their Response to Injury

Astrocytes are an abundant class of glial cell in the central nervous system that

provides both structural and functional support to neurons. In healthy tissue, astrocytes

play crucial roles in functions related to energy provision, blood flow, regulation,

maintenance of ionic balance, and neurotransmitter recycling (52, 53).

In healthy tissue, astrocytes regulate important and related functions between

cerebral blood flow and the metabolic demand of neurons. Studies have demonstrated

the ability of astrocytes to elicit bidirectional vasculature changes in adjacent blood

vessels through activation of calcium sensitive signaling pathways (54). Astrocytes

regulate changes in CBF in response to the metabolic needs of neurons. An increase in

astrocytic anaerobic glycolysis is observed under reduced oxygen conditions, leading to

increases in lactate release that result in increased vasodilation (55). Astrocytes also play

crucial roles in maintaining ionic homeostasis through uptake and release of water in

16

response to neuropeptide signals and bidirectional aquaporin channels (56). Regulation

of EAA glutamate is another central role of astrocytes in their neuronal interactions. EEAs

are cleared from neuronal synapses by astrocytes via glutamate transporters, recycled

back to glutamine, and then released and re-absorbed by neurons (57).

Perhaps most interesting, is the key role astrocytes play in response to injury.

Following CNS insult such as mechanical trauma, infection, ischemia, and

neurodegenerative disease, astrocytes undergo a changes to molecular expression and

morphology known collectively as reactive astrogliosis. Reactive astrocytes are

characterized by increased expression of glial fibrillary acidic protein (GFAP) among other

intermediate filament proteins involved in the hallmark star-like morphological change

associated with cellular hypertrophy (52). New evidence has defined the mechanism of

reactive astrogliosis to be a graded one. Changes to gene expression and

intra/intercellular signaling are proportional to the severity of injury. Despite the

appearance of hypertrophy, mild and moderate cases of astrogliosis are believed to be

recoverable after injury resolution (53). In severe cases, higher activation of astrocytes is

documented to result in the formation of a glial scar that creates a barrier that limits the

spread of inflammation (58).

While reactive astrogliosis has traditionally been associated with the formation of

a glial scar that inhibits axonal regeneration, new research has identified a myriad of

beneficial and essential injury responses (59). Reactive astrocytes confer neuronal

protection through the uptake of excitotoxic levels of glutamate that accompanies

indiscriminant membrane depolarization (60-62). Ablation experiments have also

implicated reactive astrocytes in limiting the infiltration of inflammatory cells, repair of the

17

blood brain barrier (63), protection against immune related demyelination (63), and

reduction of hydrocephalus (60, 64). Molecular mediators of reactive astrogliosis are

released by a number of CNS cells and while much is still unknown, considerable

evidence suggest that different signaling mechanisms (STAT3, interleukin-6, leukemia

inhibitory factor) may initiate functional changes proportionate to the extent of injury (62,

65, 66).

18

1.5 CONCLUSION

Traumatic brain injury is a major healthcare crisis for which there is currently

inadequate diagnostic, let alone therapeutic, measures. In order to tackle this silent

epidemic, we must first develop conclusive metrics for injury identification and

classification for both clinical and research standardization. Currently, there is no clinically

validated biofluid marker for TBI/neurotrauma despite the volume of TBI biomarker

studies. And while the list of potential candidate biomarkers identified by proteomics is

encouraging to the mission, it raises the question of which potential biomarkers should

be prioritized for verification purposes. It is here that systems biology and an

understanding of the underlying molecular mechanisms associated with observed clinical

pathologies could provide an additional level of selection on top of biofluid abundance

and tissue enrichment to aid researchers in discriminating the top candidates for

validation studies.

While an effective biomarker does not necessarily require a direct relationship to

the biology of injury, biomarkers with biological relevance to the resulting molecular

sequelae have been shown to be the most promising. An example of such markers is

GFAP, an astroglial-specific intermediate filament, whose expression increases shortly

after injury as astrocytes attempt to maintain homeostasis and promote recovery (67).

Mediators of this processes such as IL-1β may offer insights into potential therapeutic

targets for future study (68). In our project, we have benefited from the understanding of

calcium mediated calpain activation resulting from ionic imbalances in membrane

compromised cells that lead to proteolytic breakdown products (69) that may offer

additional nuance into the severity and progression of TBI. Other examples include

19

elevated post-trauma levels of proteins involved in free radical clearance (70), stress

response (71), and immune response (26) associated with altered metabolic, signaling,

and repair functions.

A more complete understanding of the mechanisms involved in injury response is

also an essential tool for researchers to better design injury models to isolate specific

responses within the complex web of neurotraumatic insult. In our research, this has

helped us to select astrocytes as an in vitro model given their involvement in so many

regulatory elements ranging from maintenance of ionic and fluid homeostasis to

metabolism to blood flow. Alterations in signaling pathways, molecular pathologies of

membrane permeability, and a graded reactivity lead us to believe that investigation of

astroglial responses to injury may help to identify proteomic signatures of injury that are

released proportionate to the severity of injury.

20

1.6 REFERENCES

1. M. Faul, L. Xu, M. M. Wald, V. G. Coronado, Traumatic Brain Injury in the United

States: Emergency Department Visits, Hospitalizations and Deaths 2002–2006.

(Centers for Disease Control and Prevention, National Center for Injury Prevention

and Control, Atlanta, GA, 2010).

2. J. E. Risdall, D. K. Menon, Traumatic brain injury. Phil Trans Royal Soc London,

Series B, Biol Sci 366, (2011).

3. W. A. Gordon, M. Brown, M. Sliwinski, M. R. Hibbard, N. Patti, M. J. Weiss, R.

Kalinsky, M. Sheerer, The enigma of "hidden" traumatic brain injury. J Head

Trauma Rehabil 13, (1998).

4. C. Werner, K. Engelhard, Pathophysiology of traumatic brain injury. British journal

of anaesthesia 99, 4-9 (2007); published online EpubJul (10.1093/bja/aem131).

5. L. F. Marshall, Head injury: recent past, present, and future. Neurosurgery 47, 546-

561 (2000).

6. J. Nortje, D. K. Menon, Traumatic brain injury: physiology, mechanisms, and

outcome. Current Opinion in Neurology 17, 711-718 (2004).

7. G. Teasdale, B. Jennett, Assessment of coma and impaired consciousness: a

practical scale. The Lancet 304, 81-84 (1974).

8. R. D. Zafonte, F. M. Hammond, N. R. Mann, D. L. Wood, K. L. Black, S. R. Millis,

Relationship between glasgow coma scale and functional outcome. American

journal of physical medicine & rehabilitation 75, 364-369 (1996).

21

9. M. P. Kelly, C. T. Johnson, N. Knoller, D. A. Drubach, M. M. Winslow, Substance

abuse, traumatic brain injury and neuropsychological outcome. Brain injury 11,

391-402 (1997).

10. J. F. Malec, A. W. Brown, C. L. Leibson, J. T. Flaada, J. N. Mandrekar, N. N. Diehl,

P. K. Perkins, The mayo classification system for traumatic brain injury severity. J

Neurotrauma 24, 1417-1424 (2007); published online EpubSep

(10.1089/neu.2006.0245).

11. J. A. Langlois, W. Rutland-Brown, M. M. Wald, The Epidemiology and Impact of

Traumatic Brain Injury. Journal of Head Trauma Rehabilitation 21, 375-378 (2006).

12. K. M. Guskiewicz, M. McCrea, S. W. Marshall, Cumulative Effects Associated With

Recurrent Concussion in Collegiate Football Players The NCAA Concussion

Study. JAMA 19, 2549-2555 (2003).

13. G. Barkhoudarian, D. A. Hovda, C. C. Giza, The molecular pathophysiology of

concussive brain injury. Clinics in sports medicine 30, 33-48, vii-iii (2011);

published online EpubJan (10.1016/j.csm.2010.09.001).

14. R. C. Cantu, Second-impact syndrome. Clinics in sports medicine 17, 37-44

(1998).

15. P. McCrory, Does second impact syndrome exist? Clinical Journal of Sport

Medicine 11, 144-149 (2001).

16. P. McCory, G. Davis, M. Makdissi, Second Impact Syndrome or Cerebral Swelling

after Sporting Head Injury. Current Sports Medicine Reports 11, 21-23 (2012).

17. J. T. Povlishock, E. Pettus, in Mechanisms of Secondary Brain Damage in

Cerebral Ischemia and Trauma. (Springer, 1996), pp. 81-86.

22

18. E. H. Pettus, C. W. Christman, M. L. Giebel, J. T. Povlishock, Traumatically

Induced Altered Membrane Permeability: Its Relationship to Traumatically Induced

Reactive Axonal Change. Journal of Neurotrauma 11, 507-522 (1994).

19. G. V. W. Johnson, J. A. Greenwood, A. C. Costello, J. C. Troncoso, The regulatory

role of calmodulin in the proteolysis of individual neurofilament proteins by calpain.

Neurochemical Research 16, 869-873 (1991)10.1007/bf00965535).

20. R. A. Nixon, The Regulation of Neurofilament Protein Dynamics by

Phosphorylation: Clues to Neurofibrillary Pathobiology Brain Pathology 3, 29-38

(1993).

21. J. T. Povlishock, C. W. Christman, The pathobiology of traumatically induced

axonal injury in animals and human: a review of current thoughts. Journal of

Neurotrauma 12, 555-564 (1995).

22. A. Marmarou, A review of progress in understanding the pathophysiology and

treatment of brain edema. Neurosurgery Focus 22, 1-10 (2007).

23. P. Barzó, A. Marmarou, P. Fatouros, F. Corwin, J. Dunbar, Magnetic resonance

imaging-monitored acute blood-brain barrier changes in experimental traumatic

brain injury. Journal of neurosurgery 85, 1113-1121 (1996).

24. H. Feldmann, G. Klages, F. Gärtner, J. Scharfenberg, in Proceedings of the 6th

European Congress of Neurosurgery. (Springer, 1979), pp. 74-77.

25. H. M. Eisenberg, H. E. Gary Jr, E. F. Aldrich, C. Saydjari, B. Turner, M. A. Foulkes,

J. A. Jane, A. Marmarou, L. F. Marshall, H. F. Young, Initial CT findings in 753

patients with severe head injury: a report from the NIH Traumatic Coma Data Bank.

Journal of neurosurgery 73, 688-698 (1990).

23

26. S. M. Lucas, N. J. Rothwell, R. M. Gibson, The role of inflammation in CNS injury

and disease. British journal of pharmacology 147 Suppl 1, S232-240 (2006);

published online EpubJan (10.1038/sj.bjp.0706400).

27. C. Bogdon, Nitric oxide and the immune response. Nature 2, 907-916 (2001).

28. N. J. Van Wagoner, E. N. Benveniste, Interleukin-6 expression and regulation in

astrocytes. Journal of neuroimmunology 100, 124-139 (1999).

29. E. Tobinick, N. M. Kim, G. Reyzin, H. Rodriguez-Romanacce, V. DePuy, Selective

TNF Inhibition for Chronic Stroke and Traumatic Brain Injury. CNS drugs 26, 1051-

1070 (2012).

30. S. A. Loddick, A. V. Turnbull, N. J. Rothwell, Cerebral interleukin-6 is

neuroprotective during permanent focal cerebral ischemia in the rat. Journal of

Cerebral Blood Flow & Metabolism 18, 176-179 (1998).

31. F. Velarde, D. Fisher, D. Hovda, P. Adelson, D. Becker, Fluid percussion injury

induces prolonged changes in cerebral blood flow. J Neurotrauma 9, 402 (1992).

32. C. C. Giza, D. A. Hovda, The Neurometabolic Cascade of Concussion. Journal of

Athletic Training 36, 228-235 (2001).

33. D. A. Bruce, A. Alavi, L. Bilaniuk, C. Dolinskas, W. Obrist, B. Uzzell, Diffuse

cerebral swelling following head injuries in children: the syndrome of “malignant

brain edema”. Journal of neurosurgery 54, 170-178 (1981).

34. H. K. Richards, S. Simac, S. Piechnik, J. D. Pickard, Uncoupling of cerebral blood

flow and metabolism after cerebral contusion in the rat. Journal of Cerebral Blood

Flow & Metabolism 21, 779-781 (2001).

24

35. A. J. Church, R. D. Andrew, Spreading Depression Expands Traumatic Injury in

Neocortical Brain Slices. Journal of Neurotrauma 22, 277-290 (2005).

36. O. B. Paulson, E. A. Newman, Does the Release of Potassium from Astrocyte

Endfeet Regulate Cerebral Blood Flow? Science 237, 896-898 (1987).

37. R. D'Ambrosio, D. O. Maris, M. S. Grady, R. H. Winn, D. Janigro, Impaired K+

Homeostasis and Altered Electrophysiological Properties of Post-Traumatic

Hippocampal Glia. Journal of Neuroscience 19, 8152-8162 (1999).

38. A. Yoshino, D. A. Hovda, T. Kawamata, Y. Katayama, D. P. Becker, Dynamic

changes in local cerebral glucose utilization following cerebral concussion in rats:

evidence of a hyper-and subsequent hypometabolic state. Brain research 561,

106-119 (1991).

39. C. L. Osteen, A. H. Moore, M. L. Prins, D. A. Hovda, Age-Dependency of

45Calcium Accumulation Following Lateral Fluid Percussion: Acute and Delayed

Patterns. Journal of Neurotrauma 18, 141-162 (2001).

40. W. Young, I. Koreh, Potassium and Calcium Changes in Injured Spinal Cords.

Brain research 365, 42-53 (1985).

41. M. Prins, T. Greco, D. Alexander, C. C. Giza, The pathophysiology of traumatic

brain injury at a glance. Disease models & mechanisms 6, 1307-1315 (2013);

published online EpubNov (10.1242/dmm.011585).

42. T. I. Peng, M. J. Jou, Oxidative stress caused by mitochondrial calcium overload.

Ann N Y Acad Sci 1201, 183-188 (2010); published online EpubJul

(10.1111/j.1749-6632.2010.05634.x).

25

43. T. Kawamata, Y. Katayama, D. A. Hovda, A. Yoshino, D. P. Becker, Administration

of excitatory amino acid antagonists via microdialysis attenuates the increase in

glucose utilization seen following concussive brain injury. Journal of Cerebral

Blood Flow & Metabolism 12, 12-24 (1992).

44. M. Bergsneider, D. A. Hovda, E. Shalmon, D. F. Kelly, P. M. Vespa, N. A. Martin,

M. E. Phelps, D. L. McArthur, M. J. Caron, J. F. Kraus, Cerebral hyperglycolysis

following severe traumatic brain injury in humans: a positron emission tomography

study. Journal of neurosurgery 86, 241-251 (1997).

45. M. O’Connell, A. Seal, J. Nortje, P. Al-Rawi, J. Coles, T. Fryer, D. Menon, J.

Pickard, P. Hutchinson, in Intracranial Pressure and Brain Monitoring XII.

(Springer, 2005), pp. 165-168.

46. N. Hattori, S.-C. Huang, H.-M. Wu, E. Yeh, T. C. Glenn, P. M. Vespa, D. McArthur,

M. E. Phelps, D. A. Hovda, M. Bergsneider, Correlation of regional metabolic rates

of glucose with Glasgow Coma Scale after traumatic brain injury. Journal of

Nuclear Medicine 44, 1709-1716 (2003).

47. M. L. Prins, D. A. Hovda, Mapping cerebral glucose metabolism during spatial

learning: interactions of development and traumatic brain injury. Journal of

neurotrauma 18, 31-46 (2001).

48. R. Balabanov, H. Goldman, S. Murphy, G. Pellizon, C. Owen, J. Rafols, P. Dore-

Duffy, Endothelial cell activation following moderate traumatic brain injury.

Neurological research, (2013).

49. B. L. Bartnik, R. L. Sutton, M. Fukushima, N. G. Harris, D. A. Hovda, S. M. Lee,

Upregulation of pentose phosphate pathway and preservation of tricarboxylic acid

26

cycle flux after experimental brain injury. Journal of neurotrauma 22, 1052-1065

(2005).

50. M. A. Satchell, X. Zhang, P. M. Kochanek, C. E. Dixon, L. W. Jenkins, J. Melick,

C. Szabó, R. S. Clark, A dual role for poly‐ADP‐ribosylation in spatial memory

acquisition after traumatic brain injury in mice involving NAD+ depletion and

ribosylation of 14‐3‐3γ. Journal of neurochemistry 85, 697-708 (2003).

51. C. C. Alano, P. Garnier, W. Ying, Y. Higashi, T. M. Kauppinen, R. A. Swanson,

NAD+ Depletion Is Necessary and Sufficient forPoly (ADP-Ribose) Polymerase-1-

Mediated Neuronal Death. The Journal of Neuroscience 30, 2967-2978 (2010).

52. Z. Yang, K. K. W. Wang, Glial fibrillary acidic protein: from intermediate filament

assembly and gliosis to neurobiomarker. Cell 38, 364-374 (2015).

53. M. V. Sofroniew, Molecular dissection of reactive astrogliosis and glial scar

formation. Trends in neurosciences 32, 638-647 (2009).

54. G. R. Gordon, S. J. Mulligan, B. A. MacVicar, Astrocyte control of the

cerebrovasculature. Glia 55, 1214-1221 (2007).

55. G. R. Gordon, H. B. Choi, R. L. Rungta, G. C. Ellis-Davies, B. A. MacVicar, Brain

metabolism dictates the polarity of astrocyte control over arterioles. Nature 456,

745-749 (2008).

56. M. Simard, M. Nedergaard, The neurobiology of glia in the context of water and

ion homeostasis. Neuroscience 129, 877-896 (2004).

57. K. Kam, R. Nicoll, Excitatory synaptic transmission persists independently of the

glutamate–glutamine cycle. The Journal of Neuroscience 27, 9192-9200 (2007).

27

58. R. R. Voskuhl, R. S. Peterson, B. Song, Y. Ao, L. B. J. Morales, S. Tiwari-Woodruff,

M. V. Sofroniew, Reactive astrocytes form scar-like perivascular barriers to

leukocytes during adaptive immune inflammation of the CNS. The Journal of

neuroscience 29, 11511-11522 (2009).

59. M. V. Sofroniew, H. V. Vinters, Astrocytes: biology and pathology. Acta

Neuropathol 119, (2010).

60. T. G. Bush, N. Puvanachandra, C. H. Horner, A. Polito, T. Ostenfeld, C. N.

Svendsen, L. Mucke, M. H. Johnson, M. V. Sofroniew, Leukocyte infiltration,

neuronal degeneration, and neurite outgrowth after ablation of scar-forming,

reactive astrocytes in adult transgenic mice. Neuron 23, 297-308 (1999).

61. J. D. Rothstein, M. Dykes-Hoberg, C. A. Pardo, L. A. Bristol, L. Jin, R. W. Kuncl,

Y. Kanai, M. A. Hediger, Y. Wang, J. P. Schielke, Knockout of glutamate

transporters reveals a major role for astroglial transport in excitotoxicity and

clearance of glutamate. Neuron 16, 675-686 (1996).

62. R. A. Swanson, W. Ying, T. M. Kauppinen, Astrocyte influences on ischemic

neuronal death. Current molecular medicine 4, 193-205 (2004).

63. J. R. Faulkner, J. E. Herrmann, M. J. Woo, K. E. Tansey, N. B. Doan, M. V.

Sofroniew, Reactive astrocytes protect tissue and preserve function after spinal

cord injury. The Journal of Neuroscience 24, 2143-2155 (2004).

64. Z. Zador, S. Stiver, V. Wang, G. T. Manley, in Aquaporins. (Springer, 2009), pp.

159-170.

65. S. Okada, M. Nakamura, H. Katoh, T. Miyao, T. Shimazaki, K. Ishii, J. Yamane, A.

Yoshimura, Y. Iwamoto, Y. Toyama, Conditional ablation of Stat3 or Socs3

28

discloses a dual role for reactive astrocytes after spinal cord injury. Nature

medicine 12, 829-834 (2006).

66. A. D. R. Garcia, N. B. Doan, T. Imura, T. G. Bush, M. V. Sofroniew, GFAP-

expressing progenitors are the principal source of constitutive neurogenesis in

adult mouse forebrain. Nature neuroscience 7, 1233-1241 (2004).

67. R. Hausmann, R. Riess, A. Fieguth, P. Betz, Immunohistochemical investigations

on the course of astroglial GFAP expression following human brain injury.

International journal of legal medicine 113, 70-75 (2000).

68. L. M. Herx, V. W. Yong, Interleukin-1β is required for the early evolution of reactive

astrogliosis following CNS lesion. Journal of Neuropathology & Experimental

Neurology 60, 961-971 (2001).

69. L. Papa, L. M. Lewis, J. L. Falk, Z. Zhang, S. Silvestri, P. Giordano, G. M. Brophy,

J. A. Demery, N. K. Dixit, I. Ferguson, M. C. Liu, J. Mo, L. Akinyi, K. Schmid, S.

Mondello, C. S. Robertson, F. C. Tortella, R. L. Hayes, K. K. Wang, Elevated levels

of serum glial fibrillary acidic protein breakdown products in mild and moderate

traumatic brain injury are associated with intracranial lesions and neurosurgical

intervention. Ann Emerg Med 59, 471-483 (2012); published online EpubJun

(10.1016/j.annemergmed.2011.08.021).

70. A. Lewen, P. Matz, P. H. CHAN, Free radical pathways in CNS injury. Journal of

neurotrauma 17, 871-890 (2000).

71. J. Truettner, R. Schmidt-Kastner, R. Busto, O. Alonso, J. Loor, W. D. Dietrich, M.

Ginsberg, Expression of brain-derived neurotrophic factor, nerve growth factor,

29

and heat shock protein HSP70 following fluid percussion brain injury in rats.

Journal of neurotrauma 16, 471-486 (1999).

30

CHAPTER 2: ADDRESSING THE NEEDS OF TRAUMATIC BRAIN INJURY

WITH CLINICAL PROTEOMICS

2.1 INTRODUCTION

A general goal of “proteomics” is to comprehend the relationship between the

body’s proteins and how they change by disease to understand human

pathophysiology, and ultimately to provide therapeutic and diagnostic tools. The

completion of the human genome provided researchers with the blueprint for life;

proteomics offers the potential means for analyzing the expressed genome.

Proteomics attempts to determine how genes function within the genome and how

they communicate with each other to (hopefully) lead to important new insights into

disease mechanisms. The potential of proteomics to advance biomedical research is

high because the key functional components of biochemical systems and the cellular

targets of therapeutic agents, namely proteins, are being studied. Mapping

proteomes from injured tissues, cells and biofluids can potentially reveal new protein

targets to explore mechanisms of insults and to provide candidate lists for new

disease indicators or injury biomarkers as diagnostic or prognostic tools for the clinic.

A biomarker could be simply a molecule, such as a protein whose presence or

abundance in a biological sample signals a disease or insult to an organ. Thus, they

are quantifiable molecules that indicate a pathophysiological process. A biomarker in

accessible body fluids or tissues could greatly enhance our ability to identify patients

at risk, with invisible wounds or predict outcome of serious injury. A sensitive and

specific disease or injury marker such as an early protein abnormality could provide

31

a warning sign prior to being symptomatic, and hence could result in more effective

preventative care or treatment options to improve outcome.

32

2.2 DISCUSSION

The challenges of clinical proteomics and biomarkers

The goal of clinical proteomics to discover new disease or injury biomarkers is

challenging. Beyond the number of human genes coding for proteins, proteins are

processed and modified, comprising an important dimension of information to which

present proteomic technologies have but limited access. The total mRNA population,

accounting for alternate splicing, RNA editing, and use of alternate promoters could

contain 250,000 transcripts, while various protein modifications could increase the

size of the human proteome to over 500,000 members (1). Cellular proteins and their

post-translational modifications (PTMs) change with the cell cycle, environmental

conditions, developmental stage, and metabolic state. Independent of these

variables, biomarkers should reliably detect changes in health status, a specific

disease, or indicate whether an insult like a toxic exposure or trauma has occurred.

Clearly, we need proteomic approaches that advance beyond identifying proteins to

elucidating their co- and post-translational modifications, to following the dynamics of

those modifications, and to linking those modifications to specific diseases or cellular

responses to an insult that inflicted an organ. Despite all of the significant advances

in technologies in proteomics since its inception in the mid-1990s, with the

development of more sensitive mass spectrometry detectors and more selective and

specific strategies for sample processing and handling, no clinically validated disease

biomarker has been discovered by proteomics to date (2).

33

Meeting the challenge with targeted screens, focused selection strategies, and

clinical validation

What are the major factors that hindered finding robust disease and injury

biomarkers and how can these be overcome? The complexity of clinical samples

themselves is a significant limiting factor. Plasma and serum, i.e., blood, have been

biofluids of choice for measuring levels of proteins and other biomolecules for clinical

testing, as they can be sampled noninvasively. Plasma is a protein-rich information

source containing what blood circulation has encountered on its journey

throughout the body and tissue perfusion. The tremendous analytical challenge

of the large number of plasma proteins lays in their unbalanced abundance:

albumin constitutes over 50% of the plasma proteins (at 30–50 mg/mL) and the

most abundant 22 proteins in plasma represent approximately 99% of the total

protein content in plasma leaving the majority of proteins at very low abundance.

The estimated dynamic range of protein concentrations in human plasma may be

up to 12 orders of magnitude (3).

Disease or insults trigger acute events, secondary and chronic sequelae,

including inflammation, wound healing, and adaptive changes that the compromised

body undergoes in response to the unhealthy state. In an effort to identify original

disease causes or injury factors a simple experimental model can facilitate a

targeted screen circumventing secondary, less disease-specific events. As such,

scientific experimental model design follows controlled strategies for reproducibility

and simplicity that can facilitate the initial discovery by limiting candidate markers to

those proteins that are related to a disease origin or injury cause (4,5). One common

34

proteomics workflow involves a 2-dimensional separation prior to protein

identification to reduce sample complexity (Figure 1). Proteins can be sorted by

charge (isoelectric point) and size using two-dimensional polyacrylamide gel

electrophoresis (2D-PAGE) and can be enzymatically digested within the gel matrix.

Despite being developed over 3 decades ago (6,7), 2D-PAGE remains one of the

most powerful separation techniques for proteomic workflows and was instrumental

in early protein biomarker research. Following separation, gels are stained and

differentially expressed protein spots excised, enzymatically digested with trypsin,

and identified by MS requiring only sufficiently accurate mass measurements (low

part-per million range) performed on one or two tryptic peptides to identify silver

stained protein spots (8).

A second strategy advocates first enzymatically (e.g., with trypsin) or chemically

cleaving (“breaking”) a complex mixture of cellular proteins, and then “sorting” the

peptides by one or more steps of chromatography. MS analyzes the recovered

fragments as in the previous approach, and software matches the fragments to the

proteins from which they are derived. Examples of this experimental approach

include multidimensional protein identification technology (MudPIT) that couples two

or more dimensions of chromatographic separations, e.g., strong cation exchange

(SCX) with reversed phase chromatography (9,10). While the outlined approaches

have been instrumental in biomarker discovery research, the extensive sample

preparation and time required in gel fractionation and long HPLC LC-MS/MS

analyses make discovery proteomics feasible for only limited numbers of samples

per project (11,12). A simplified disease or injury model using a controlled

35

experimental design may help to relieve a proteomic screen from confounding

complexities of clinical samples (4,13-15).

A straightforward selection of suitable marker candidates from the ‘long list’ of

identified injury or disease specifically changed proteins should arrive at a

manageable ‘short list’ of possible disease marker candidates. A tailored selection

strategy will consider injury cause, marker candidates with the necessary reporting

power for the cause as well as organ specificity and exclusion of proteins normally

present in healthy plasma and tissues. The subsequent validation of selected disease

or injury markers from a group of candidates may occur stepwise starting with a

preclinical smaller cohort of patients and controls, allowing to test for normality (16).

Following initial confirmation, a larger subject cohort can be enrolled in clinical trials

allowing for receiver operating characteristic curve analyses that will establish the

basis for biomarker suitability in the clinic (17). Currently, the majority of biomarker

validation studies have been performed by enzyme-linked immunosorbent assay

(ELISA). This highly sensitive method is limited for use early in the verification

process, as antibody pairs have to be optimized for specificity and sensitivity for each

marker separately. As mass spectrometry measurements improve in sensitivity to

match immunoassay detection limits (pg/ mL), a targeted and quantitative mass

spectrometry application can provide multiplex capacity and absolute specificity by

gas-phase sequence determination, making it an ideal alternative for assessing

validity of selected marker candidates.

The need for markers of Traumatic Brain Injury (TBI)

36

Neurotrauma to the central nervous system (CNS) is a serious public health

problem in the US; among US civilians, TBI is most common in infants and toddlers,

adolescents and the elderly (18). The US National Institute of Neurological Disorders

and Stroke estimates that 2.5-6.5 million Americans have had one or multiple TBIs

(19). In the US military there were over 212,000 service men and women diagnosed

with some form of TBI between January 2000-May 2011, roughly accounting for one-

third of all injured US soldiers, making TBI the signature injury of the wars in Iraq and

Afghanistan compared to past wars (20). TBI contributes to over one third of all injury-

related deaths, yet 75-90% of all brain trauma cases are considered to be mild TBI

(mTBI), many without visible wounds that often are undiagnosed (21). Better

diagnostic tools are needed to detect head injuries, especially mTBI, to confirm and

to monitor the severity of TBI in order to determine the best course of action acutely

and later post-injury. The neurotrauma field has currently still no chemical diagnostic

marker in clinical use. Here we will outline briefly the spectrum of TBI and give

examples where a surrogate chemical marker assay for TBI would be of great benefit

to patients, high risk populations, their families and doctors.

Head injuries can be classified into penetrating and non-penetrating TBI.

Penetrating TBI involves physical compromise of the skull by an external object

resulting in specific, focused injury most commonly characterized by hemorrhages

and lesions. Non-penetrating TBI, is much more difficult to assess, as injuries may

not be visible or located precisely. Closed head injuries are caused by rapid

acceleration and deceleration of the brain within the skull and inflict shear and

deformation forces on gray matter tissue and white matter tracts (22). Each trauma

37

patient is a unique injury case with individual complexity, thus the field distinguishes

mainly between severe and mild TBI (mTBI) as opposite ends of a clinical spectrum

of manifestations. Evaluating and predicting outcome in severe TBI is often

problematic, especially for patients without visible wounds such as infants.

Diagnostic neurotrauma tools include imaging techniques, neurocognitive

examinations, and for severe TBI patients, the determination of post-traumatic

amnesia, but they provide only estimates of the dynamically evolving injury process.

Functional MRI (fMRI) and the detection of regional blood flow changes (e.g., PET

scans) are not always available, cannot be obtained in critically ill patients, and are

not definitive. Radiological brain scans on infants and toddlers are widely

considered problematic because the radiation dose endangers the developing brain.

Absence of imaging in the pediatric clinical praxis prevents distinguishing brain injury

from frequent intestinal flu or even infant irritability (23). Non-accidental head injury,

or “Shaken Baby” syndrome, caused by rotation-acceleration strains on the brain

in the still loosely connected infant skull causes bleeding and swelling that can lead

to catastrophic intracranial damage and can severely impair normal brain

development (and can even lead to death) (23). Undiagnosed victims may be sent

back to continued abuse. On the other hand, imaging does not distinguish inflicted

head injury from non-traumatic bleeding, originating from a trauma independent

condition – a situation in which legal authorities, parents and care-givers would

greatly benefit from an assay for brain trauma-specific chemicals (24,25).

Mechanical impacts traumatizing the brain obviously need to be clinically

differentiated from trauma in other organs or from other non-traumatic brain injuries

38

like stroke, ischemia, bleeding diseases, poisoning, epilepsy or chronic

degenerative diseases for proper treatment and activities in the operating room and

the courtrooms (26). Monitoring daily progression of a severe TBI patient by

repeated imaging can be quite impractical, considering life supporting intensive care

instrumentation. A fluid derived chemical marker for compromised brain cell viability

will be a useful added measure of the patients evolving status and could aid in

outcome prognosis.

For the vast majority of mTBI/concussion patients, there are no objective

diagnostic or prognostic tools (27). A ready diagnostic tool at point of care acutely

after TBI is needed especially for high-risk individuals (e.g., athletes, military

personnel). An objective and unambiguous trauma biochemical assay would be

valuable for legal authorities in forensic cases that currently rely on

neuropsychological testing that lacks premorbid base rates and is subject to

malingering and subjective interpretation. Thus, for high-risk groups, for mild and

severe TBI cases as well as for all pediatric neurotrauma patients, there is an urgent

need for an accurate, unambiguous chemical measure indicating that a significant

impact to the brain had occurred.

Moreover, a second hit to a concussed, vulnerable brain can, in rare cases,

have a catastrophic outcome with permanent brain damage or even death (known

as the second impact syndrome) (28). Several repeated concussions over time can

in later years cumulate in irreversible brain damage with devastating psychological

and cognitive decline, a pathological condition now defined as chronic traumatic

encephalopathy (CTE) (29,30). Military personnel and veterans with mild TBI often

39

suffer from post-traumatic stress disorder (PTSD) after being exposed to blast

waves from explosive devices (31,32).

Certain areas of the brain may be more susceptible to concussive trauma. A

recent study investigated longitudinal changes in global and regional brain volume

in patients one year after mTBI and correlated such changes with clinical and

neurocognitive metrics. Magnetic resonance imaging data showed measureable

global brain atrophy, larger than that in control subjects one year after mTBI.

Atrophy was found in specific regions of the cingulate cortex independent of the site

of initial trauma. The cingulate cortex’s role in rational cognitive functions such as

empathy, impulse control, and emotion correlate strongly with the patient’s observed

clinical symptoms of increased depression and anxiety (33). These finding are

supported by an independent study of National Football League players and

referees using positron emission tomography (PET) with a tau specific tracer that

showed higher densities of tau tangles in regions of the brain involved in a nearby

region (caudate nucleus) that is also associated with learning, memory, emotion,

and language comprehension. The deposition of tau tangles is consistent with

those observed in CTE autopsy patients (34).

Current evaluation of concussion is basically an assessment of neurocognitive

deficits, often not immediate and requires extensive neuropsychological testing that

is subject to motivational confounds, while critical care treatment decisions have to

be made immediately by emergency clinical personnel and surgeons. Severity

classification of TBI patients relies on assessing the level of consciousness,

commonly with the Glasgow Coma Scale (GCS), which is an insensitive measure.

40

Testing relies on verbal communication, and proper motor control and eye function,

which are often impaired after TBI. Brain function-altering substances such as drugs,

alcohol, pain medication, sedatives, or even induced coma as part of emergency and

intensive care routine obviously compromise the use of memory recall and the GCS.

Although predictors of TBI exist, such as the Standardized Assessment of

Concussion test, these tools offer little insight into the pathology of the disease

beyond determining whether a concussion has occurred or not. Because of this lack

of insight into TBI, licensed health care providers of concussive sports injury are

conservative in their approach to player safety after injury with the hope that

coordination between sideline and clinical practitioners will aid in improving our

understanding of the extent of impairment for various types of sports related

concussions (35).

Current potential TBI biomarker candidates

An ideal biomarker should be both specific to head trauma as well as

sufficiently sensitive to be measured and quantified reproducibly in patient blood or

other peripheral or proximal fluid samples (such as CSF) by an assay of choice.

These markers should be acutely released into the fluids following injury and show a

distinct temporal signal pattern. The identification of a unique TBI biomarker(s) or

surrogate brain cell injury markers that meet these criteria would provide physicians

with an objective method for early diagnosis of brain injury and enable early

assessment of severity, intervention, and monitoring disease progression (36).

Multiple neurotrauma signature markers would allow for correlation analyses with

41

improved statistical power using multivariant logistic regression or similar analyses

(37). Finding candidate TBI markers is pursued typically by these strategies: (1)

Classical deduction chooses proteins with literature reported association to brain

injury or its secondary events like inflammation, axonal degeneration or reactive

astrogliosis. (2) Hypothesis driven animal trauma model studies report changes in

specific proteins using available antibodies or pathway tailored kits (38). (3) Discovery

of trauma associated proteins using a proteomic screen of samples derived from

animal injury models or small patient cohorts (39-43). Surprisingly, few screens address

the impact of mechanical trauma on brain cells, i.e., cell death (13,14,44). After briefly

summarizing currently investigated candidate TBI markers, we will evaluate challenges

and alternatives in identifying TBI markers.

Inflammatory markers

Part of the pathology of CNS injury is characterized by secondary effects,

including the inflammatory response to TBI. Cytokines are key mediators in the

process of (neuro)inflammation (45) and increased concentrations of these

compounds have been associated with severe CNS injury as well as post-traumatic

hypoxia (46,47). For example, elevated levels of interleukin-10 (IL-10), an anti-

inflammatory cytokine, was measured in low pg/mL levels in CSF and low-mid pg/mL

levels in serum (Table 1) and correlated with severe TBI determined by the GCS

(46,48-50). Higher Il-10 serum levels illustrate the systemic nature of an

inflammatory response. Such responses are systemic in nature and not specific to

TBI, but occur with any insult, hence inflammatory markers are not ‘pointing to brain

42

injury’.

Neuronal markers

With their elongated axonal and dendritic processes, neurons are exposed to

shear forces associated with the whiplash trauma of a concussion. Acute plasma

membrane permeablility, or mechanoporation, compromises cell integrity and is

linked to diffuse axonal injury in response to a mechanical impact (71-73). Tau

protein is a member of microtubule-associated proteins involved in maintaining

cytoskeletal structure and axonal transport. It is expressed by CNS neurons and

oligodendrocytes and found primarily in axons (74). Traditionally used in the

diagnosis of Alzheimer’s disease, elevated levels of Tau in CSF and serum have

been linked to CNS insults like TBI and stroke (62,75). CSF and serum studies of

TBI patients have measured elevated Tau protein concentrations in the 1000 ng/mL

range in young adult TBI patients, whereas it is three orders of magnitude lower in

neonates with brain insults (59,65,66). Because of Tau’s chronic accumulation after

various CNS insults, it seems less useful as an acute head trauma marker.

Mylein basic protein (MBP) is released with myelin debris that accumulates

with axonal damage in the injured brain or spinal cord. MBP is one of three proteins

comprising the myelin sheath essential for axonal impulse conduction (76). MBP

markers have shown promise in the appraisal of TBI with serum levels in the low-

mid ng/mL range (77,78). Similar to GFAP (vide infra), studies have demonstrated

degradation of MBP isoforms as a result of TBI (79,80).

Neuron Specific Enolase (NSE), Microtubule-associated protein 2 (MAP-2)

43

and ubiquitin C-term hydrolase L1 (UCH-L1) all display differential expression

patterns in TBI patients. NSE, a glycolytic enzyme isoform of neurons, has been

documented to increase following head trauma (77), but has a slow elimination

process, making it difficult to distinguish between primary and secondary injuries

(81). Additionally, NSE is released during the process of hemolysis, making it difficult

to pin down the source of injury (82).

Microtubule-associated protein 2 (MAP-2) is a cytoskeletal-associated protein

localized to dendrites of neurons that is believed to function in the growth and

maturation of dendrites as well as cytoskeletal organization (83). Previous studies

have demonstrated that MAP-2 is absent from damaged regions of the brain and that

serum levels increase early after injury (84). Mondello et al. assessed the long-term

release of MAP-2 in blood 6 months post trauma by ELISA immunoassay and found

that severe TBI patients had significantly higher serums levels of MAP-2 compared to

normal non-TBI patients. TBI patients in a vegetative state, as assessed by the GCS,

however, showed no increase in serum MAP-2 versus controls. This suggests that

MAP-2 could provide insight into the mechanism of neuronal remodeling as well as

discriminate between patients with deficits in consciousness and increased risk of

unfavorable outcomes (70).

Ubiquitin C-terminal hydrolase-L1 (UCH-L1) has been identified in a cell death

culture assay and is verified by ELISA to be significantly increased in TBI patients

(85). Neurodegenerative marker UCH-L1 fluid levels are also elevated in ischemia,

vasospasm, infarction, and carbon monoxide poisoning (86-88). UCH-L1 is a

proteolytically stable, abundant neuronal protein (69,70,85,89). Future studies will

44

show whether these proteins would be present in mTBI subjects without significant

brain cell death.

Trauma specific breakdown products of neuronal and glial cytoskeletal proteins

Spectrin breakdown products (SBDPs) have been identified as potential TBI

biomarkers in rat CSF fluid (90). αII-Spectrin is the submembraneous cortical

cytoskeleton of neurons and astroglia, sharing 50-59% homology with the abundant

erythroid α-spectrin (44,91). Cell-death associated spectrin fragments of molecular

weight 150 kDa (SBDP150) and two N-terminal fragments at 145 kDa (SBDP145)

and 120 kDa (SBDP120) cleaved by calpain and caspase-3 have been identified in

a cell death culture model (87,92,93). Using a sandwich ELISA methodology,

Mondello et al. showed both SBDP145 and SBDP120 increased in patients post-

TBI, with SBDP145 present immediately post-trauma and SBDP120 most accurately

measured 24 hr post-injury. SBDP CSF levels greater than specific thresholds

were shown to correlate with poor outcome and mortality and the temporal

expression of SBDP for non-surviving patients differed from that of surviving

patients. Thus, if cross-reactivity and breakdown specificity is controlled, SBDPs in

CSF may aid to predict the severity of injury and mortality (69).

Astroglial markers

Astroglia are the most abundant cells in the human cerebral cortex (94) and

respond to insult by becoming reactive, a process that involves gene expression,

morphological changes, proliferation, and the formation of a glial scar around lesions

45

(95-99). However, astrocytes are also trauma victims as they are especially

vulnerable to acidosis, pressure elevation, and hypoxic/ischemic damage, known co-

morbidities of TBI (100-103). Human astrocytes display very long thin processes that

cross through several laminae from the pia to the ventricular walls, so called

interlaminar processes and are hence vulnerable to shear and deformation forces

similar to those that cause diffuse axonal injury in white matter tracks (104,105). Two

of the most well studied TBI marker candidates are S100β and glial fibrillary acidic

protein (GFAP), both glial proteins. S100β is a calcium binding protein that is

predominantly produced by astrocytes within the CNS. Because S100β is also

produced in a variety of non-CNS cells (e.g., lymphocytes, bone marrow, adipocytes,

and glia of peripheral nerves), brain specificity is its problem (106). It has, however,

been reported that the few extracranial sources of S100β are short lived compared

to S100β from cerebral lesions (107,108). Elevated S100β concentrations have been

measured in the ng/mL and pg/mL range in TBI patient CSF and serum, respectively

(51,53). Despite the immediate spike in S100β levels, it has been found that S100β

measurements taken 24 hours post TBI offer the most prognostic value for patient

outcome due to initial interference from external S100β (52). S100β is released into

the perivascular space immediately following blood brain barrier (BBB) compromise

and may serve as a BBB-permeability marker (109). Additionally, higher levels of

S100β have been correlated to patients suffering from post-traumatic hypoxia,

demonstrating the interrelation between secondary effects and amplified biomarker

expulsion (46).

GFAP is an intermediate filament that is highly enriched in CNS astroglia, but

46

is also expressed in Schwann cells and olfactory ensheathing glia of peripheral

nerves (110-112). GFAP levels are persistently elevated after severe TBI in CSF and

serum, relate to poor outcome, and are predictive for mortality (54,55,113). Serum

levels of GFAP show high variability or no elevation after mTBI, yet reports are

confounded by varying delimitation of ‘mild’ as to include more moderate cases with

lesions and positive imaging signals or not. Thus the discriminative power of GFAP

as a mild neurotrauma biomarker is conflicted (56,114). Measured CSF levels of

several biomarkers in boxers acutely after one or repeated blows to the head as

well as after 14 days, revealed elevated levels of GFAP with large variations among

the boxers suffering a concussion (56). GFAP breakdown products are found after

TBI and are being explored as insult-specific markers (114-116).

Strategies for addressing the challenges in identifying and validating new TBI

biomarkers

For a brain cell specific protein to be a trauma marker, either it should be

selectively expressed in response to the trauma and then discharged into fluids, or

cytosolic proteins released solely from injured neurons and glia with compromised

membrane integrity or dying brain cells (73,117). A suitable study design to identify

fluid-derived trauma specific proteins would employ a targeted proteomic screen on

a defined trauma model. Experimental animal injury models were developed with

the effort to mimic human TBI as closely as possible while underlying cellular and

molecular mechanisms of acute trauma are still scantly investigated. The predominant

criterion is to recapitulate the clinical manifestation of TBI over studies using

47

simplified reproducible trauma models with the goal to determine primary cellular

injury consequences (4,5). Most commonly used injury models include focal injuries

with the animal’s head in a fixed position, like fluid percussion and controlled cortical

impact, which produce a focal contusion with hematoma and hemorrhage while the

dura remains intact (118,119). Also used is Marmarou’s weight drop model where

distributed forces cause diffuse injury with the animal’s head unrestrained in a helmet

and the brain is therefore subjected to rotational forces as well (120,121). Blast

injury models historically use shock tubes and larger animals, but have been

adapted recently to rodents as well as investigated for milder blast effects from

explosion exposures in the field (122).

Developing biochemical markers of TBI by proteomics and mass spectrometry

Proteomic studies of injured brain or spinal cord tissue are being done in

these injury models and are providing lists of protein changes that are difficult to

interpret due to the complexity of events at and around a dynamically changing

lesion site and variations between models (39,40,42). Injury zones are not

reproducibly defined from lab to lab as histopathological analyses have for long not

followed standardized analysis and reporting criteria (5). Tissue derived protein

signals are products of a changing composition of viable, injured, and dead cells as

well as infiltrating non-neural cells, that complicate the interpretation of proteomic

studies (97,99). An effort has been made in recent years to standardize and

compare severities of commonly used TBI animal models across centers (123).

Defining common data elements for collection, analysis protocols, and reporting of

48

fluid samples and histopathological defining features of injury models will help this

field in interpreting proteomic and biomarker preclinical studies as well as clinical

data collection and interpretation (124-126).

Proteomic TBI marker projects on biofluids using rodent injury models have

been few due to naturally limited available fluid amounts (42,127), but biofluid

neurotrauma marker candidates have been studied in pig blast injury models (128-

132). Human proteomic analyses have started from severe trauma patient’s CSF and

plasma from individual patients (41,133). Bioinformatics analysis tools are expected

to facilitate systems level understanding of neurotrauma protein changes (134,135).

While bioinformatics tools are indispensable for classification, consensus-based data

collection, and data mining, they will not make the bottleneck of biomarker candidate

selection much easier.

Hanrieder et al. describes a workflow using matrix-assisted laser desorption

ionization time-of-flight (MALDI- TOF) MS/MS in conjunction with off-line nano-LC

sample fractionation (136). In their study, ventricular CSF samples from 3 severe TBI

patients displaying different symptoms were taken at various time points post-trauma

and analyzed by nano-LC MALDI-TOF MS/MS to determine temporal protein

expression changes. CSF samples were digested with trypsin and labeled with

isobaric tags for relative and absolute quantitation using the iTRAQ method

(137,138). Labeled tryptic digests were then separated on a nano-flow LC system

equipped with online fraction collection capable of depositing fractions directly onto

MALDI sample plates for MALDI-TOF MS/MS-based identification and quantification.

Several proteins were increased after injury. Additionally, relative quantification using

49

iTRAQ labeling revealed temporal changes in protein expression for several

inflammation-related proteins (e.g., serum amyloid, fibroinogen alpha chain,

ceruloplasmin) as well as known neurotrauma-related proteins (GFAP, NSE).

Due to the confounding complexity of clinical TBI and clinic-resembling animal

injury models, we propose a targeted proteomic screen using a well-characterize in

vitro cell-based trauma model as a starting point for TBI marker candidate

identification (139-144). This will limit protein changes to those directly related to an

acute mechanical trauma by applying an abrupt pressure pulse inflicting shear forces

and deformation onto cortical brain cells in a reproducible fashion at various severities

(142). We are finding robust cellular release patterns that correlate with cell injury

and cell death of rodent and human astrocytes matured and stretched in a prototype

of this injury model (139). A suitable selection strategy needs to be applied to any

trauma-release protein list to eliminate proteins found in healthy human plasma and

to focus on brain-specific proteins (145,146).

Verifying biochemical markers for TBI by proteomics and mass spectrometry

One analytical challenge that is unique to TBI for measuring candidate

biomarkers lays in the unpredictably fluctuating protein concentrations among CSF

samples from different TBI patients (low microgram/ml to several mg/ml range). This

may be due to as variables such as the patient’s varying blood–brain barrier integrity,

hemorrhage, brain cell protein leakage, as well as waves of brain cell death. This is

unlike healthy CSF or plasma with constant and physiologically controlled protein

levels allowing for sample preparation with reproducible protein amounts (147).

50

These injury specific variables can be addressed only by relating all measurements

to raw, unprocessed sample volume regardless of depletion and other processing

steps including optimizing protein amounts for trypsin digestion or immunoassay

applications. There are also injury-related but not-trauma specific secondary

changes in protein composition in trauma CSF that could be caused by secondary

infection due to hospitalization that could reduce protein amounts or bacterial

proteins present in the samples. Such samples should be omitted from an initial

biomarker validation study.

The accepted “gold standard” of single-protein measurements is the ELISA

immunoassay, which takes advantage of the specificity and diversity of IgG antigen

recognition. Yet, while ELISA is well touted for its high sensitivity (~1 pg/mL), it is

not without limitations (148). ELISA methods rely on antibodies for protein detection

and assay development ideally uses two antibodies against different epitopes of the

candidate TBI marker. Non-specific binding of immunoglobulins to abundant plasma

proteins may contribute to a background problem, limiting the availability of suitable

highly specific antibodies ideally from different host animals to cancel out non-

specific binding. The availability of such antibody pairs often requires de novo

generation, lengthening the assay development time. Thus, the lack of multiplex

capacity may exclude using the ELISA platform as initial validation tool of candidate

TBI markers in patient samples (149).

By not relying on antibody-antigen binding, quantitative mass spectrometry is

well suited to meet the challenge of overcoming the verification bottleneck where

immunoassays cannot be applied. MRM-MS is quickly becoming the preferred

51

method of candidate biomarker verification because of the discriminating power of

mass analyzers to accurately measure and quantify multiple specific proteins within

a single sample set. Specific peptide fragments (via trypsin digestion) corresponding

to the candidate proteins are selected to act as stoichiometric representatives (or

surrogates) within a complex patient CSF or blood sample. The mass spectrometer

(usually a triple quadrupole analyzer) is then set to scan for the precursor peptide

ion, fragment the precursor in the collision cell, and then select for a specific

precursor fragment (known as a transition). Because the mass spectrometer is not

expending resources scanning through all the ions within a complex patient sample,

the signal from less abundant peptides are no longer being masked by highly

abundant ions, partially addressing the problems with high dynamic range

limitations. Additionally, MRM provides a more cost-effective alternative for

quantification compared to traditional ELISA methods by using stable isotope-

labeled internal standards of the selective candidate peptides. Using the method of

isotope dilution (150), isotopically labeled peptides are spiked into digested CSF or

blood samples and the relative peak heights between the endogenous peptides and

isotope-labeled peptides are used to quantify selected candidate biomarkers. This

approach has been greatly aided by the increased availability of stable isotope-

labeled standard (SIS) peptides manufactured and sold by life science companies

(151). MRM-MS has long been a method of choice for detecting marker metabolites

for amino acids, organic acids, and fatty acid disorders in newborns (152). The

success of these quantitative methods in candidate biomarker discovery/verification

has been well documented in a variety of samples such as synovial fluid (153), CSF

52

(154), and plasma (155).

The MRM-MS platform is ideally suited to address the challenge of validating

several marker candidates at once (multiplexing) and measuring their levels together

with candidate TBI markers reported in the literature. This is in large part due to

advances in in pre-analysis enrichment methods (156) as well as improvements in

both sensitivity and speed of modern mass spectrometers that allow for detection and

quantitation in the low-mid ng/mL concentration range. Hybrid Orbitrap mass

spectrometers such as the Q-Exactive have demonstrated the ability to detect up to

10 amol of heavy SIS peptides in the presence of 10 ng- 1 ug of yeast tryptic digest

background with up to 10 ppm mass accuracy (157). Coupled with the high re- solving

power of Orbitrap detectors (up to 140 K for the Q-Exactive) and fast duty cycles to

collect full MS/MS spectra, these instruments should be able to confidently identify

surrogate peptides. When comparing the low cost of SIS peptide generation from

commercial sources to the cost of antibody generation and capacity to multiplex more

than ten within a single analytical sample, the mass spectrometry platform is a

feasible choice for TBI candidate marker verification for the early preclinical validation

stage. Following this initial verification, antibodies will be generated only for the most

robustly detected TBI marker candidates for ELISA assay development for future

clinical trials and diagnostic use.

53

2.3 CONCLUSIONS

Combining a targeted screen, a focused selection strategy, and a stepwise

approach from preclinical validation towards clinical translation offers a feasible

pipeline for candidate TBI marker identification and preparation for its diagnostic

use. Validation through a stepwise increasing sample cohort and moving from

severe TBI CSF to matching plasma samples and then to mTBI plasma samples will

provide verification where experimental analyses and patient samples are matched

with appropriate positive controls along the way.

Moreover, the emergence of targeted MS-technologies brings promise to the

development of an efficient biomarker discovery to verification pipeline for TBI. This

pipeline could consist of the initial application of proteomics technologies in the form

of comparative 2D-PAGE and shotgun LC-MS/MS to identify and discover candidate

biomarkers from trauma and healthy subject samples. This is followed by the

development of quantitative MRM-MS to assess the biological significance of these

markers followed by clinical validation in a larger scale. With the possibility of

multiplexing using proteomic methods such as MRM-MS, the time required for pre-

clinical verification can be reduced as tens of marker candidate proteins can be

monitored concurrently in clinical samples. This process will help narrow the pool

of potential surrogates from which the most specific and easily measured candidates

can be chosen for clinical validation and assay development.

54

2.4 FIGURES

Figure 2.1: Candidate Biomarker Discovery and Verification Workflow.

Bottom-up proteomics strategies, such as shotgun proteomics (multidimensional LC-

MS/MS) and 2D-PAGE/MS, can be applied to identify putative candidate markers (left).

Candidate protein markers can be subsequently verified and confirmed by targeted

proteomics using standard ELISA methods or multiple reaction monitoring (MRM)-MS

(right). MRM-MS offers the advantages of an antibody-independent platform with

capabilities for multiplexing.

55

2.5 TABLES

Surrogate Marker Process or Source, Cell Type

Concentration in TBI Biofluids (ng/mL)

CSF Serum

IL-10 Inflammation 0.001-0.060 (children [36]) 0.002-0.005 (adult [,37])

0.050-0150 [38]

S100B Astroglia 1.0-15.0 [39] 0.01-0.70 [37,40,41]

GFAP Astroglia 9.0-22.0 [42] 0.14-15.0 [43]

NFL/NFH/ P-NFH Axon 0.13-2.5 and 49–562 [44,45] NA

MBP Axon/oligodendrocytes NA 0.50-100.0 [37,46]

Tau / amyloid β Axon Tau: 0.035-5.72 (neonate, [47]) 0.91- 5.1 [54,55]

1,519.6 – 2,308 (adult, [48-52])

Aβ 1–42: 1.17 (adult, [53])

NSE Neuron 10-30 [37] 10-20 [46]

UCH-L1 Neuron 20-300 [56] 1.0-15 [56]

α-spectrin-II BDP Neuron + astroglia 0.0-100 [57] NA

MAP-2 Neuron NA 0.04-0.06 [58]

Table 2.1: Candidate Marker Biofluid Concentrations

Selected examples of reported TBI markers for which some concentrations were found;

NA – not available.

56

2.6 REFERENCES

1. Alam SL, Atkins JF, Gesteland RF: Programmed ribosomal frameshifting: much

ado about knotting! Proc Natl Acad Sci U S A 1999, 96:14177–14179.

2. Poste G: Bring on the biomarkers. Nature 2011, 469:156–157.

3. Anderson L: Candidate-based proteomics in the search for biomarkers of

cardiovascular disease. J Physiol 2005, 563:23–60.

4. Spaethling JM, Geddes-Klein DM, Miller WJ, von Reyn CR, Singh P, Mesfin M,

Bernstein SJ, Meaney DF: Linking impact to cellular and molecular sequelae of

CNS injury: modeling in vivo complexity with in vitro simplicity. Prog Brain Res

2007, 161:27–39.

5. Kazanis I: CNS injury research; reviewing the last decade: methodological errors

and a proposal for a new strategy. Brain Res Brain Res Rev 2005, 50:377–386.

6. Klose J: Protein mapping by combined isoelectric focusing and electrophoresis of

mouse tissues. Humangenetik 1975, 26:231–243.

7. O'Farrell PH: High resolution two-dimensional electrophoresis of proteins.

J Biol Chem 1975, 250:4007–4021.

8. Nielsen ML, Bennett KL, Larsen B, Moniatte M, Mann M: Peptide end sequencing

by orthogonal MALDI tandem mass spectrometry. J Proteome Res 2002, 1:63–

71.

9. Washburn MP, Wolters D, Yates JR: Large-scale analysis of the yeast proteome

by multidimensional protein identification technology. Nature Biotechnol 2001,

19:242–247.

10. Wolters DA, Washburn MP, Yates JR III: An automated multidimensional protein

57

identification technology for shotgun proteomics. Anal Chem 2001, 73:5683–

5690.

11. Matt P, Fu Z, Fu Q, Van Eyk JE: Biomarker discovery: proteome fractionation and

separation in biological samples. Physiol Genomics 2007, 33:12–17.

12. Parker CE, Pearson TW, Anderson NL, Borchers CH: Mass-spectrometry-based

clinical proteomics – a review and prospective. Analyst 1830, 2010:135.

13. Siman R, McIntosh TK, Soltesz KM, Chen Z, Neumar RW, Roberts VL: Proteins

released from degenerating neurons are surrogate markers for acute brain

damage. Neurobiol Dis 2004, 16:311–320.

14. Siman R, Toraskar N, Dang A, McNeil E, McGarvey M, Plaum J, Maloney E,

Grady MS: A panel of neuron-enriched proteins as markers for traumatic brain

injury in humans. J Neurotrauma 2009, 26:1867–1877.

15. Rifai N, Gillette MA, Carr SA: Protein biomarker discovery and validation: the long

and uncertain path to clinical utility. Nat Biotechnol 2006, 24:971–983.

16. Shapiro SS, Wilk MB: An analysis of variance test for normality (complete

samples). Biometrika 1965, 52:591–611.

17. Borchers CH, Parker CE: Improving the biomarker pipeline. Clin Chem 2010,

56:1786–1788.

18. Faul M, Xu L, Wald MM, Coronado VG: Traumatic Brain Injury in the United

States: Emergency Department Visits, Hospitalizations and Deaths 2002–2006.

Atlanta, GA: Centers for Disease Control and Prevention, National Center for

Injury Prevention and Control; 2010. http://www.cdc.gov/traumaticbraininjury/

pdf/blue_book.pdf.

58

19. National Institute of Neurological Disorders and Stroke: Traumatic Brain Injury:

Hope Through Research. 2002. http://www.ninds.nih.gov/disorders/

tbi/tbi_htr.pdf.

20. Risdall JE, Menon DK: Traumatic brain injury. Phil Trans Royal Soc London,

Series B, Biol Sci 2011, 366:241–250.

21. Gordon WA, Brown M, Sliwinski M, Hibbard MR, Patti N, Weiss MJ, Kalinsky R,

Sheerer M: The enigma of "hidden" traumatic brain injury. J Head Trauma Rehabil

1998, 13:39–56.

22. North SH, Shriver-Lake LC, Taitt CR, Ligler FS: Rapid Analytical Methods for On-

Site Triage for Traumatic Brain Injury. Ann Rev Anal Chem 2012, 5:35–56.

23. Squier W: The "Shaken Baby" syndrome: pathology and mechanisms.

Acta Neuropathol 2011, 122:519–542.

24. Geddes JF, Tasker RC, Hackshaw AK, Nickols CD, Adams GGW, Whitwell HL,

Scheimberg I: Dural haemorrhage in non-traumatic infant deaths: does it explain

the bleeding in 'shaken baby syndrome'? Neuropath Appl Neuro 2003, 29:14–22.

25. Laposata ME, Laposata M: Children with signs of abuse: when is it not child

abuse? Am J Clin Pathol 2005, 123 (Suppl): S119–S124.

26. Yokobori S, Hosein K, Burks S, Sharma I, Gajavelli S, Bullock R: Biomarkers for

the clinical differential diagnosis in traumatic brain injury–a systematic review.

CNS Neurosci Ther 2013, 19:556–565.

27. Bettermann K, Slocomb JE: Clinical Relevance of Biomarkers for Traumatic Brain

Injury. In Biomarkers for Traumatic Brain Injury. Edited by Thurston D.

Cambridge: Royal Society of Chemistry; 2012:1–18.

59

28. Cobb S, Battin B: Second-impact syndrome. J Sch Nurs 2004, 20:262–267.

29. Baugh CM, Stamm JM, Riley DO, Gavett BE, Shenton ME, Lin A, Nowinski CJ,

Cantu RC, McKee AC, Stern RA: Chronic traumatic encephalopathy:

neurodegeneration following repetitive concussive and subconcussive brain

trauma. Brain Imaging Behav 2012, 6:244–254.

30. Stein TD, Alvarez VE, McKee AC: Chronic traumatic encephalopathy: a spectrum

of neuropathological changes following repetitive brain trauma in athletes and

military personnel. Alzheimers Res Ther 2014, 6:4.

31. Kennedy JE, Leal FO, Lewis JD, Cullen MA, Amador RR: Posttraumatic stress

symptoms in OIF/OEF service members with blast-related and non-blast-related

mild TBI. Neurorehabil 2010, 26:223–231.

32. Cifu DX, Taylor BC, Carne WF, Bidelspach D, Sayer NA, Scholten J, Campbell

EH: Traumatic brain injury, posttraumatic stress disorder, and pain diagnoses in

OIF/OEF/OND Veterans. J Rehabil Res Dev 2014, 50:1169–1176.

33. Zhou Y, Kierans A, Kenul D, Ge Y, Rath J, Reaume J, Grossman RI, Lui YW: Mild

traumatic brain injury: longitudinal regional brain volume changes. Radiology

2013, 267:880–890.

34. Small GW, Kepe V, Siddarth P, Ercoli LM, Merrill DA, Donoghue N, Bookheimer

SY, Martinez J, Omalu B, Bailes J, Carrio J: PET scanning of brain tau in retired

national football league players: preliminary findings. Am J Geriat Psych 2013,

12:138–144.

35. Giza CC, Kutcher JS, Ashwal S, Barth J, Getchius TSD, Gioia GA, Gronseth GS,

Guskiewicz K, Mandel S, Manley G, McKeag DB, Thurman DJ, Zafonte R:

60

Summary of evidence-based guideline update: evaluation and management of

concussion in sports: Report of the Guideline Development Subcommittee of the

American Academy of Neurology. Neurology 2013, 80:2250–2257.

36. Bakay RAE, Ward AA Jr: Enzymatic changes in serum and cerebrospinal fluid in

neurological I njury. J Neurosurg 1983, 58:27–37.

37. Diaz-Arrastia R, Wang KK, Papa L, Sorani MD, Yue JK, Puccio AM, McMahon

PJ, Inoue T, Yuh EL, Lingsma HF, Maas AI, Valadka AB, Okonkwo DO, Manley,

The Track-Tbi Investigators GT, Casey IS, Cheong M, Cooper SR, Dams-

O'Connor K, Gordon WA, Hricik AJ, Menon DK, Mukherjee P, Schnyer DM, Sinha

TK, Vassar MJ: Acute biomarkers of traumatic brain injury: relationship between

plasma levels of ubiquitin C-Terminal Hydrolase-L1 and Glial Fibrillary Acidic

Protein. J Neurotrauma 2014, 31:19–25.

38. Light M, Minor KH, DeWitt P, Jasper KH, Davies SJ: Multiplex array proteomics

detects increased MMP-8 in CSF after spinal cord injury. J Neuroinflammation

2012, 9:122.

39. Yan X, Liu J, Luo Z, Ding Q, Mao X, Yan M, Yang S, Hu X, Huang J, Luo Z:

Proteomic profiling of proteins in rat spinal cord induced by contusion injury.

Neurochem Int 2010, 56:971–983.

40. Boutte AM, Yao C, Kobeissy F, May Lu XC, Zhang Z, Wang KK, Schmid K,

Tortella FC, Dave JR: Proteomic analysis and brain-specific systems biology in a

rodent model of penetrating ballistic-like brain injury. Electrophoresis 2012,

33:3693–3704.

41. Sjodin MO, Bergquist J, Wetterhall M: Mining ventricular cerebrospinal fluid from

61

patients with traumatic brain injury using hexapeptide ligand libraries to search

for trauma biomarkers. J Chromatogr B Analyt Technol Biomed Life Sci 2010,

878:2003. 2012.

42. Crawford F, Crynen G, Reed J, Mouzon B, Bishop A, Katz B, Ferguson S, Phillips

J, Ganapathi V, Mathura V, Roses A, Mullan M: Identification of plasma

biomarkers of TBI outcome using proteomic approaches in an APOE mouse

model. J Neurotrauma 2012, 29:246–260.

43. Ottens AK, Bustamante L, Golden EC, Yao C, Hayes RL, Wang KK, Tortella FC,

Dave JR: Neuroproteomics: a biochemical means to discriminate the extent and

modality of brain injury. J Neurotrauma 2010, 27:1837–1852.

44. Guingab-Cagmat JD, Newsom K, Vakulenko A, Cagmat EB, Kobeissy FH,

Zoltewicz S, Wang KK, Anagli J: In vitro MS-based proteomic analysis and

absolute quantification of neuronal-glial injury biomarkers in cell culture system.

Electrophoresis 2012, 33:3786–3797.

45. Woodcock T, Morganti-Kossmann MC: The Role of Markers of Inflammation in

Traumatic Brain Injury. Front Neurol 2013, 4:18.

46. Yan EB, Satgunaseelan L, Paul E, Bye N, Nguyen P, Agyapomaa D, Kossmann

T, Rosenfeld JV, Morganti-Kossmann MC: Post-Traumatic Hypoxia Is Associated

with Prolonged Cerebral Cytokine Production, Higher Serum Biomarker Levels,

and Poor Outcome in Patients with Severe Traumatic Brain Injury. J Neurotrauma

2014. doi:10.1089/neu.2013.3087.

47. Kadhim HJ, Duchateau J, Sebire G: Cytokines and brain injury: invited review. J

Intensive Care Med 2008, 23:236–249.

62

48. Bell MJ, Kochanek PM, Doughty LA, Carcillo JA, Adelson PD, Clark RS,

Wisniewski SR, Whalen MJ, DeKosky ST: Interleukin-6 and interleukin-10 in

cerebropinal fluid after severe traumatic brain injury in children. J Neurotrauma

1997, 14:451–457.

49. Schneider Soares FM, Menezes de Souza N, Libório Schwarzbold M, Paim Diaz

A, Costa Nunes J, Hohl A, Nunes Abreu da Silva P, Vieira J, Lisboa de Souza R,

Moré Bertotti M, Schoder Prediger RD, Neves Linhares M, Bafica A, Walz R:

Interleukin-10 Is an independent biomarker of severe traumatic brain injury

prognosis. Neuroimmunomodulation 2012, 19:377–385.

50. Kamm K, VanderKolk W, Lawrence C, Jonker M, Davis AT: The effect of

traumatic brain injury upon the concentration and expression of interleukin-1β and

Interleukin-10 in the Rat. J Trauma: Injury, Infection, Critical Care 2006, 60:152–

157.

51. Goyal A, Failla MD, Niyonkuru C, Amin K, Fabio A, Berger RP, Wagner AK:

S100b as a prognostic biomarker in outcome prediction for patients with severe

traumatic brain injury. J Neurotraum 2013, 30:946–957.

52. Egea-Guerrero JJ, Murillo-Cabezas F, Gordillo-Escobar E, Rodríguez-Rodríguez

A, Enamorado-Enamorado J, Revuelto-Rey J, Pacheco-Sánchez M, León-Justel

A, Domínguez-Roldán JM, Vilches-Arenas A: S100B protein may detect brain

death development after severe traumatic brain injury. J Neurotraum 2013,

30:1762–1769.

53. Petzold A, Keir G, Lim D, Smith M, Thompson EJ: Cerebrospinal fluid (CSF) and

serum S100B: release and wash-out pattern. Brain Research Bulletin 2003,

63

61:281–285.

54. Fraser DD, Close TE, Rose KL, Ward R, Mehl M, Farrell C, Lacroix J, Creery D,

Kesselman M, Stanimirovic D, Hutchison JS: Severe traumatic brain injury in

children elevates glial fibrillary acidic protein in cerebrospinal fluid and serum*.

Pediatric Critical Care Medicine 2011, 12:319–324.

55. Nylén K, Öst M, Csajbok LZ, Nilsson I, Blennow K, Nellgård B, Rosengren L:

Increased serum-GFAP in patients with severe traumatic brain injury is related to

outcome. J Neurol Sci 2006, 240:85–91.

56. Neselius S, Brisby H, Theodorsson A, Blennow K, Zetterberg H, Marcusson J:

CSF-biomarkers in Olympic boxing: diagnosis and effects of repetitive head

trauma. PLoS One 2012, 7: e33606.

57. Neselius S, Zetterberg H, Blennow K, Marcusson J, Brisby H: Increased CSF

levels of phosphorylated neurofilament heavy protein following bout in amateur

boxers. PLoS One 2013, 8: e81249.

58. Berger RP, Adelson PD, Pierce MC, Dulani T, Cassidy LD, Kochanek PM: Serum

neuron-specific enolase, S100B, and myelin basic protein concentrations after

inflicted and noninflicted traumatic brain injury in children. J Neurosurg (Pediatrics

1) 2005, 103:61–68.

59. Takahashi K, Hasegawa S, Maeba S, Fukunaga S, Motoyama M, Hamano H,

Ichiyama T: Serum tau protein level serves as a predictive factor for neurological

prognosis in neonatal asphyxia. Brain and Development 2013.

10.1016/j.braindev.2013.10.007.

60. Liliang P-C, Liang C-L, Weng H-C, Lu K, Wang K-W, Chen H-J, Chuang J-H: Tau

64

proteins in serum predict outcome after severe traumatic brain injury. J Surg Res

2010, 160:302–307.

61. Neselius S, Zetterberg H, Blennow K, Randall J, Wilson D, Marcusson J, Brisby

H: Olympic boxing is associated with elevated levels of the neuronal protein tau

in plasma. Brain Inj 2013, 27:425–433.

62. Franz G, Beer R, Kampfl A, Engelhardt K, Schmutzhard E, Ulmer H,

Deisenhammer F: Amyloid beta 1–42 and tau in cerebrospinal fluid after severe

traumatic brain injury. Neurology 2003, 60:1457–1461.

63. Shiiya N, Kunihara T, Miyatake T, Matsuzaki K, Yasuda K: Tau protein in the

cerebrospinal fluid is a marker of brain injury after aortic surgery. Ann Thorac Surg

2004, 77:2034–2038.

64. Tsitsopoulos PP, Marklund N: Amyloid-beta peptides and tau protein as

biomarkers in cerebrospinal and interstitial fluid following traumatic brain injury: a

review of experimental and clinical studies. Front Neurol 2013, 4:79.

65. Zemlan FP, Rosenberg WS, Luebbe PA, Campbell TA, Dean GE, Weiner NE,

Cohen JA, Rudick RA, Woo D: Quantification of axonal damage in traumatic brain

injury: affinity purification and characterization of cerebrospinal fluid tau proteins.

J Neurochem 1999, 72:741–750.

66. Emmerling MR, Morganti-Kossmann MC, Kossmann T, Stahel PF, Watson MD,

Evans LM, Mehta PD, Spiegel K, Kuo YM, Roher AE, Raby CA: Traumatic brain

injury elevates the Alzheimer's amyloid peptide A beta 42 in human CSF. A

possible role for nerve cell injury. Ann N Y Acad Sci 2000, 903:118–122.

67. Raby CA, Morganti-Kossmann MC, Kossmann T, Stahel PF, Watson MD, Evans

65

LM, Mehta PD, Spiegel K, Kuo YM, Roher AE, Emmerling MR: Traumatic brain

injury increases beta-amyloid peptide 1–42 in cerebrospinal fluid. J Neurochem

1998, 71:2505–2509.

68. Mondello S, Akinyi L, Buki A, Robicsek SA, Gabrielli A, Tepas J, Papa L, Brophy

GM, Tortella F, Hayes RL, Wang KKW: Clinical Utility of Serum Levels of

Ubiquitin-C Terminal Hydrolase as a Biomarker for Severe Traumatic Brain Injury.

Neurosurgery 2012, 70:666–675.

69. Mondello S, Robicsek SA, Gabrielli A, Brophy GM, Papa L, Tepas J, Robertson

C, Buki A, Scharf D, Jixiang M, Akinyi L, Muller U, Wang KKW, Hayes RL: αII-

Spectrin Breakdown Products (SBDPs): Diagnosis and Outcome in Severe

Traumatic Brain Injury Patients. J Neurotrauma 2010, 27:1203–1213.

70. Mondello S, Gabrielli A, Catani S, D’Ippolito M, Jeromin A, Ciaramella A, Bossù

P, Schmid K, Tortella F, Wang KKW, Hayes RL, Formisano R: Increased levels

of serum MAP-2 at 6-months correlate with improved outcome in survivors of

severe traumatic brain injury. Brain Injury 2012, 26:1629–1635.

71. Whalen MJ, Dalkara T, You Z, Qiu J, Bermpohl D, Mehta N, Suter B, Bhide PG,

Lo EH, Ericsson M, Moskowitz MA: Acute plasmalemma permeability and

protracted clearance of injured cells after controlled cortical impact in mice. J

Cereb Blood Flow Metab 2008, 28:490–505.

72. Farkas O, Lifshitz J, Povlishock JT: Mechanoporation induced by diffuse

traumatic brain injury: an irreversible or reversible response to injury? J Neurosci

2006, 26:3130–3140.

73. Barbee KA: Mechanical cell injury. Ann N Y Acad Sci 2005, 1066:67–84.

66

74. Binder LI, Frankfurter A, Rebhun LI: The Distribution of Tau in the Mammalian

Central Nervous System. J Cell Biol 1985, 101:1371–1378.

75. Bitsch A, Horn C, Kemmling Y, Seipelt M, Hellenbrand U, Stiefel M, Ciesielczyk

B, Cepek L, Bahn E, Ratzka P, Prange H, Otto M: Serum Tau protein level as a

marker of axonal damage in acute ischemic stroke. European Neurology 2001,

47:45–51.

76. Arroyo EJ, Schere SS: On the molecular architecture of myelinated fibers.

Histochem Cell Biol 2000, 133:1–18.

77. Woertgen C, Rothoerl RD, Holzschuh M, Metz C, Brawanski A: Comparison of

serial S-100 and NSE serum measurements after severe head injury. Acta

Neurochirurgica 1997, 139:1161–1165.

78. Yamazaki Y, Yada K, Morii S, Kitahara T, Ohwada T: Diagnostic significance of

serum neuron-specific enolase and myelin basic protein assay in patients with

acute head injury. Surgical Neurology 1995, 43:267–270.

79. Liu MC, Akle V, Zheng W, Kitlen J, O'Steen B, Larner SF, Dave JR, Tortella FC,

Hayes RL, Wang KKW: Extensive degradation of myelin basic protein isoforms

by calpain following traumatic brain injury. J Neurochem 2006, 98:700–712.

80. Ottens AK, Golden EC, Bustamante L, Hayes RL, Denslow ND, Wang KKW:

Proteolysis of multiple myelin basic protein isoforms after neurotrauma:

characterization by mass spectrometry. J Neurochem 2008, 104:1404–1414.

81. Ross SA, Cunningham RT, Johnston CF, Rowlands BJ: Neuron-specific enolase

as an aid to outcome predicition in head injury. Brit J Neurosurg 1996, 10:471–

476.

67

82. Johnsson P: Markers of cerebral ischemia after cardiac surgery.

J Cardiothorac Vasc Anesth 1996, 10:120–126.

83. Caceres A, Payne MR, Binder LI, Steward O: Immunocytochemical Localization

of Actin and Microtubule-Associated Protein MAP2 in Dendritic Spines. Proc Natl

Acad Sci USA 1983, 80:1738–1742.

84. Kitagawa K, Matsumoto M, Niinobe M, Mikoshiba K, Hata R, Ueda H, Handa N,

Fukunaga R, Isaka Y, Kimura K, Kamada T: Microtubule-Associated Protein 2 as

a sensitive marker for cerebral ischemic damage-immunohistochemical

invetigation of dendritic damage. Neuroscience 1989, 31:401–411.

85. Berger RP, Hayes RL, Richichi R, Beers SR, Wang KKW: Serum Concentrations

of Ubiquitin C-Terminal Hydrolase-L1 and αII-Spectrin Breakdown Product 145

kDa Correlate with Outcome after Pediatric TBI. J Neurotrauma 2012, 29:162–

167.

86. Liu MC, Akinyi L, Scharf D, Mo JX, Larner SF, Muller U, Oli MW, Zheng WR,

Kobeissy F, Papa L, Lu XC, Dave JR, Tortella FC, Hayes RL, Wang KKW:

Ubiquitin C-terminal hydrolase-L1 as a biomarker for ischemic and traumatic brain

injury in rats. Eur J Neurosci 2010, 31:722–732.

87. Siman R, Giovannone N, Toraskar N, Frangos S, Stein SC, Levine JM, Kumar

MA: Evidence that a panel of neurodegeneration biomarkers predicts vasospasm,

infarction, and outcome in aneurysmal subarachnoid hemorrhage. PLoS One

2011, 6:e28938.

88. Pang L, Wu Y, Dong N, Xu DH, Wang DW, Wang ZH, Li XL, Bian M, Zhao HJ,

Liu XL, Zhang N: Elevated serum ubiquitin C-terminal hydrolase-L1 levels in

68

patients with carbon monoxide poisoning. Clin Biochem 2014, 47:72–76.

89. Papa L, Akinyi L, Liu MC, Pineda JA, Tepas JJ, Oli MW, Zheng W, Robinson G,

Robicsek SA, Gabrielli A, Heaton SC, Hannay HJ, Demery JA, Brophy GM, Layon

J, Robertson CS, Hayes RL, Wang KKW: Ubiquitin C-terminal hydrolase is a

novel biomarker in humans for severe traumatic brain injury. Crit Care Med 2010,

38:138–144.

90. Pike BR, Flint J, Dutta S, Johnson E, Wang KKW, Hayes RL: Accumulation of

non-erythroid αII-spectrin and calpain-cleaved αII-spectrin breakdown products in

cerebrospinal fluid after traumatic brain injury in rats. J Neurochem 2001,

78:1297–1306.

91. Tomas M, Marin MP, Portoles M, Megias L, Gomez-Lechon MJ, Renau-Piqueras

J: Ethanol affects calmodulin and the calmodulin-binding proteins neuronal nitric

oxide synthase and alphaII-spectrin (alpha-fodrin) in the nucleus of growing and

differentiated rat astrocytes in primary culture. Toxicol In Vitro 2007, 21:1039–

1049.

92. Siman R, Giovannone N, Hanten G, Wilde EA, McCauley SR, Hunter JV, Li X,

Levin HS, Smith DH: Evidence That the Blood Biomarker SNTF Predicts Brain

Imaging Changes and Persistent Cognitive Dysfunction in Mild TBI Patients.

Front Neurol 2013, 4:190.

93. Riederer BM, Zagon IS, Goodman SR: Brain spectrin (240/235) and brain

spectrin(240/235E): two distinct spectrin subtypes with different locations within

Mammalian neural cells. J Cell Biol 1986, 102:2088–2097.

94. Azevedo FA, Carvalho LR, Grinberg LT, Farfel JM, Ferretti RE, Leite RE, Jacob

69

Filho W, Lent R, Herculano-Houzel S: Equal numbers of neuronal and

nonneuronal cells make the human brain an isometrically scaled-up primate

brain. J Comp Neurol 2009, 513:532–541.

95. Reier PJ, Houle JD: The glial scar: its bearing on axonal elongation and

transplantation approaches to CNS repair. Adv Neurol 1988, 47:87–138.

96. McGraw J, Hiebert GW, Steeves JD: Modulating astrogliosis after neurotrauma.

J Neurosci Res 2001, 63:109–115.

97. Wanner IB, Anderson MA, Song B, Levine J, Fernandez A, Gray-Thompson Z,

Ao Y, Sofroniew MV: Glial scar borders are formed by newly proliferated,

elongated astrocytes that interact to corral inflammatory and fibrotic cells via

STAT3-dependent mechanisms after spinal cord injury. J Neurosci 2013,

33:12870–12886.

98. Sofroniew MV, Vinters HV: Astrocytes: biology and pathology. Acta Neuropathol

2010, 119:7–35.

99. Burda JE, Sofroniew MV: Reactive gliosis and the multicellular response to CNS

damage and disease. Neuron 2014, 81:229–248.

100. Li DR, Zhang F, Wang Y, Tan XH, Qiao DF, Wang HJ, Michiue T, Maeda H:

Quantitative analysis of GFAP- and S100 protein-immunopositive astrocytes to

investigate the severity of traumatic brain injury. Legal Med 2012, 14:84–92.

101. Gelot A, Villapol S, Billette de Villemeur T, Renolleau S, Charriaut-Marlangue C:

Astrocytic demise in the developing rat and human brain after hypoxic-ischemic

damage. Dev Neurosci 2009, 31:459–470.

102. Chen Y, Swanson RA: Astrocytes and brain injury. J Cereb Blood Flow Metab

70

2003, 23:137–149.

103. Liu D, Smith CL, Barone FC, Ellison JA, Lysko PG, Li K, Simpson IA: Astrocytic

demise precedes delayed neuronal death in focal ischemic rat brain. Brain Res

Mol Brain Res 1999, 68:29–41.

104. Colombo JA, Yanez A, Lipina SJ: Interlaminar astroglial processes in the cerebral

cortex of non human primates: response to injury. J Hirnforsch 1997, 38:503–

512.

105. Colombo JA, Yanez A, Puissant V, Lipina S: Long, interlaminar astroglial cell

processes in the cortex of adult monkeys. J Neurosci Res 1995, 40:551–556.

106. Shakeri M, Mahdkhah A, Panahi F: S100B protein as a post-traumatic biomarker

for prediction of brain death in association with patient outcomes. Arch Trauma

Res 2013, 2:76–80.

107. Korfias S, Stranjalis G, Psachoulia C, Vasiliadis C, Pitaridis M, Boviatsis E, Sakas

DE: Slight and short-lasting increase of serum S-100B protein in extra-cranial

trauma. Brain Injury 2006, 20:867–872.

108. Thelin EP, Johannesson L, Nelson D, Bellander B-M: S100B is an important

outcome predictor in traumatic brain injury. J Neurotraum 2013, 30:519–528.

109. Marchi N, Cavaglia M, Fazio V, Bhudia S, Hallene K, Janigro D: Peripheral

markers of blood–brain barrier damage. Clinica Chimica Acta 2004, 342:1–12.

110. Mancardi GL, Cadoni A, Tabaton M, Schenone A, Zicca A, De Martini I, Bianchini

D, Damiani G, Zaccheo D: Schwann cell GFAP expression increases in axonal

neuropathies. J Neurol Sci 1991, 102:177–183.

111. Pellitteri R, Spatuzza M, Stanzani S, Zaccheo D: Biomarkers expression in rat

71

olfactory ensheathing cells. Front Biosci 2010, 2:289–298.

112. Eng LF, Ghirnikar RS: GFAP and astrogliosis. Brain Pathol 1994, 4:229–237.

113. Lumpkins KM, Bochicchio GV, Keledjian K, Simard JM, McCunn M, Scalea T:

Glial fibrillary acidic protein is highly correlated with brain injury. J Trauma 2008,

65:778–782. discussion 782–774.

114. Papa L, Lewis LM, Falk JL, Zhang Z, Silvestri S, Giordano P, Brophy GM, Demery

JA, Dixit NK, Ferguson I, Liu MC, Mo J, Akinyi L, Schmid K, Mondello S,

Robertson CS, Tortella FC, Hayes RL, Wang KKW: Elevated levels of serum glial

fibrillary acidic protein breakdown products in mild and moderate traumatic brain

injury are associated with intracranial lesions and neurosurgical intervention. Ann

Emerg Med 2012, 59:471–483.

115. Okonkwo DO, Yue JK, Puccio AM, Panczykowski DM, Inoue T, McMahon PJ,

Sorani MD, Yuh EL, Lingsma HF, Maas AI, Valadka AB, Manley, Transforming

R, Clinical Knowledge In Traumatic Brain Injury Investigators Including GT, Casey

SS, Cheong M, Cooper SR, Dams-O'Connor K, Gordon WA, Hricik AJ,

Hochberger K, Menon DK, Mukherjee P, Sinha TK, Schnyer DM, Vassar MJ:

GFAP-BDP as an acute diagnostic marker in traumatic brain injury: results from

the prospective transforming research and clinical knowledge in traumatic brain

injury study. J Neurotrauma 2013, 30:1490–1497.

116. Zoltewicz JS, Scharf D, Yang B, Chawla A, Newsom KJ, Fang L: Characterization

of Antibodies that Detect Human GFAP after Traumatic Brain Injury. Biomarker

Insights 2012, 7:71–79.

117. Lafrenaye AD, McGinn MJ, Povlishock JT: Increased intracranial pressure after

72

diffuse traumatic brain injury exacerbates neuronal somatic membrane poration

but not axonal injury: evidence for primary intracranial pressure-induced neuronal

perturbation. J Cereb Blood Flow Metab 2012, 32:1919–1932.

118. Thompson HJ, Lifshitz J, Marklund N, Grady MS, Graham DI, Hovda DA,

McIntosh TK: Lateral fluid percussion brain injury: a 15-year review and

evaluation. J Neurotrauma 2005, 22:42–75.

119. Smith DH, Soares HD, Pierce JS, Perlman KG, Saatman KE, Meaney DF, Dixon

CE, McIntosh TK: A model of parasagittal controlled cortical impact in the mouse:

cognitive and histopathologic effects. J Neurotrauma 1995, 12:169–178.

120. Foda MA, Marmarou A: A new model of diffuse brain injury in rats. Part II:

Morphological characterization. J Neurosurg 1994, 80:301–313.

121. Marmarou A, Foda MA, van den Brink W, Campbell J, Kita H, Demetriadou K: A

new model of diffuse brain injury in rats. Part I: Pathophysiology and

biomechanics. J Neurosurg 1994, 80:291–300.

122. Risling M, Davidsson J: Experimental animal models for studies on the

mechanisms of blast-induced neurotrauma. Front Neurol 2012, 3:30.

123. Kochanek PM, Bramlett H, Dietrich WD, Dixon CE, Hayes RL, Povlishock J,

Tortella FC, Wang KK: A novel multicenter preclinical drug screening and

biomarker consortium for experimental traumatic brain injury: operation brain

trauma therapy. J Trauma 2011, 71: S15–S24.

124. Yue JK, Vassar MJ, Lingsma HF, Cooper SR, Okonkwo DO, Valadka AB, Gordon

WA, Maas AI, Mukherjee P, Yuh EL, Puccio AM, Schnyer DM, Manley GT, Track-

Tbi I, Casey SS, Cheong M, Dams-O'Connor K, Hricik AJ, Knight EE, Kulubya

73

ES, Menon DK, Morabito DJ, Pacheco JL, Sinha TK: Transforming research and

clinical knowledge in traumatic brain injury pilot: multicenter implementation of the

common data elements for traumatic brain injury. J Neurotrauma 2013, 30:1831–

1844.

125. Hicks R, Giacino J, Harrison-Felix C, Manley G, Valadka A, Wilde EA: Progress

in developing common data elements for traumatic brain injury research: version

two–the end of the beginning. J Neurotrauma 2013, 30:1852–1861.

126. Manley GT, Diaz-Arrastia R, Brophy M, Engel D, Goodman C, Gwinn K, Veenstra

TD, Ling G, Ottens AK, Tortella F, Hayes RL: Common data elements for

traumatic brain injury: recommendations from the biospecimens and biomarkers

working group. Arch Phys Med Rehabil 2010, 91:1667–1672.

127. Lubieniecka JM, Streijger F, Lee JH, Stoynov N, Liu J, Mottus R, Pfeifer T, Kwon

BK, Coorssen JR, Foster LJ, Grigliatti TA, Tetzlaff W: Biomarkers for severity of

spinal cord injury in the cerebrospinal fluid of rats. PLoS One 2011, 6: e19247.

128. Svetlov SI, Prima V, Kirk DR, Gutierrez H, Curley KC, Hayes RL, Wang KKW:

Morphologic and biochemical characterization of brain injury in a model of

controlled blast overpressure exposure. J Trauma: Injury, Infection, Critical Care

2010, 69:795–804.

129. Svetlov SI, Prima V, Glushakova O, Svetlov A, Kirk DR, Gutierrez H, Serebruany

VL, Curley KC, Wang KK, Hayes RL: Neuro-glial and systemic mechanisms of

pathological responses in rat models of primary blast overpressure compared to

"composite" blast. Front Neurol 2012, 3:15.

74

130. Ahmed FA, Kamnaksh A, Kovesdi E, Long JB, Agoston DV: Long-term

consequences of single and multiple mild blast exposure on select physiological

parameters and blood-based biomarkers. Electrophoresis 2013, 34:2229–2233.

131. Ahmed F, Gyorgy A, Kamnaksh A, Ling G, Tong L, Parks S, Agoston D:

Time-dependent changes of protein biomarker levels in the cerebrospinal fluid

after blast traumatic brain injury. Electrophoresis 2012, 33:3705–3711.

132. Gyorgy A, Ling G, Wingo D, Walker J, Tong L, Parks S, Januszkiewicz A, Baumann

R, Agoston DV: Time-dependent changes in serum biomarker levels after blast

traumatic brain injury. J Neurotrauma 2011, 28:1121–1126.

133. Zetterberg H, Smith DH, Blennow K: Biomarkers of mild traumatic brain injury in

cerebrospinal fluid and blood. Nature Rev Neurol 2013, 9:201–210.

134. Guingab-Cagmat JD, Cagmat EB, Hayes RL, Anagli J: Integration of proteomics,

bioinformatics, and systems biology in traumatic brain injury biomarker discovery.

Front Neurol 2013, 4:61.

135. Agoston DV, Risling M, Bellander BM: Bench-to-bedside and bedside back to the

bench; coordinating clinical and experimental traumatic brain injury studies. Front

Neurol 2012, 3:3.

136. Hanrieder J, Wetterhall M, Enblad P, Hillered L, Bergquist J: Temporally resolved

differential proteomic analysis of human ventricular CSF for monitoring traumatic

brain injury biomarker candidates. J Neurosci Meth 2009, 177:469–478.

137. Ross PL: Multiplexed Protein Quantitation in Saccharomyces cerevisiae Using

Amine-reactive Isobaric Tagging Reagents. Mol Cell Proteomics 2004, 3:1154–

1169.

75

138. Wiese S, Reidegeld KA, Meyer HE, Warscheid B: Protein labeling by iTRAQ: A

new tool for quantitative mass spectrometry in proteome research. Proteomics

2007, 7:340–350.

139. Wanner IB: An in vitro trauma model to study rodent and human astrocyte

reactivity. Methods Mol Biol 2012, 814:189–219.

140. Wanner IB, Deik M, Torres M, Rosendahl AR, Neary JT, Lemmon VP, Bixby JL: A

new in vitro model of the glial scar inhibits axon growth. Glia 2008, 56:1691–1709.

141. Sondej M, Doran P, Loo JA, Wanner I: Sample preparation of primary astrocyte

cellular and released proteins for 2-D gel electrophoresis and protein identification

by mass spectrometry. In Sample preparation in biological mass spectrometry.

Edited by Ivanov A, Lazarev A. Dordrecht: Springer; 2011:829–849.

142. Ellis EF, McKinney JS, Willoughby KA, Liang S, Povlishock JT: A new model for

rapid stretch-induced injury of cells in culture: characterization of the model using

astrocytes. J Neurotrauma 1995, 12:325–339.

143. Ellis EF, Willoughby KA, Sparks SA, Chen T: S100B protein is released from rat

neonatal neurons, astrocytes, and microglia by in vitro trauma and anti-S100

increases trauma-induced delayed neuronal injury and negates the protective

effect of exogenous S100B on neurons. J Neurochem 2007, 101:1463–1470.

144. Rzigalinski BA, Weber JT, Willoughby KA, Ellis EF: Intracellular free calcium

dynamics in stretch-injured astrocytes. J Neurochem 1998, 70:2377–2385.

145. Schenk S, Schoenhals GJ, de Souza G, Mann M: A high confidence, manually

validated human blood plasma protein reference set. BMC Med Genomics 2008,

1:41.

76

146. Omenn GS, States DJ, Adamski M, Blackwell TW, Menon R, Hermjakob H,

Apweiler R, Haab BB, Simpson RJ, Eddes JS, Kapp EA, Moritz RL, Chan DW, Rai

AJ, Admon A, Aebersold R, Eng J, Hancock WS, Hefta SA, Meyer H, Paik YK, Yoo

JS, Ping P, Pounds J, Adkins J, Qian X, Wang R, Wasinger V, Wu CY, Zhao X,

Zeng R, Archakov A, Tsugita A, Beer I, Pandey A, Pisano M, Andrews P, Tammen

H, Speicher DW, Hanash SM: Overview of the HUPO Plasma Proteome Project:

results from the pilot phase with 35 collaborating laboratories and multiple

analytical groups, generating a core dataset of 3020 proteins and a publicly-

available database. Proteomics 2005, 5:3226–3245.

147. Waybright TJ: Preparation of human cerebrospinal fluid for proteomics biomarker

analysis. Methods Mol Biol 2013, 1002:61–70.

148. Theilacker N, Roller EE, Barbee KD, Franzreb M, Huang X: Multiplexed protein

analysis using encoded antibody-conjugated microbeads. J Royal Soc Interf 2011,

8:1104–1113.

149. Kingsmore SF: Multiplexed protein measurement: technologies and applications

of protein and antibody arrays. Nat Rev Drug Discov 2006, 5:310–320.

150. Barr JR, Maggio VL, Patterson DG Jr, Cooper GR, Henderson LO, Turner WE,

Smith SJ, Hannon H, Needham LL, Sampson EJ: Isotope dilution-mass

spectrometric quantification of specific proteins: model application with

apoliprotein A-1. Clin Chem 1996, 42:1672–1682.

151. Gerber SA, Rush J, Stemman O, Kirschner MW, Gygi SP: Absolute quantification

of proteins and phosphoproteins from cell lysates by tandem MS. Proc Natl Acad

Sci U S A 2003, 100:6940–6945.

77

152. Roschinger W, Olgemoller B, Fingerhut R, Liebl B, Roscher AA: Advances in

analytical mass spectrometry to improve screening for inherited metabolic

diseases. Eur J Pediat 2003, 162: S67–S76.

153. Liao H, Wu J, Kuhn E, Chin W, Chang B, Jones MD, O'Neil S, Clauser KR, Karl J,

Hasler F, Roubenoff R, Zolg W, Guild BC: Use of mass spectrometry to identify

protein biomarkers of disease severity in the synovial fluid and serum of patients

with rheumatoid arthritis. Arth Rheum 2004, 50:3792–3803.

154. Struys EA, Jansen EEW, De Meer K, Jakobs C: Determination of S-

Adenosylmethionine and S-Adenosulhomocysteine in Plasma and Cerebrospinal

Fluid by Stable-Isotope DIlution Tandem Mass Spectrometry. Clin Chem 2000,

46:1650–1656.

155. Anderson L, Anderson NG, Haines LR, Hardie DB, Olafson RW, Pearson TW:

Mass Spectrometric Quantification of Peptides and Proteins Using Stable Isotope

Standards and Capture by Anti-peptide Antibodies (SISCAPA). J Proteome Res

2004, 3:235–244.

156. Ahn YH, Lee YJ, Lee YJ, Kim Y-S, Ko JH, Yoo JS: Quantitative Analysis of an

Aberrant Glycoform of TIMP1 from Colon Cancer Serum by L-PHA-Enrichment

and SISCAPA with MRM Mass Spectrometry. J Proteome Res 2009, 8:4216–

4224.

157. Zhang Y, Hao Z, Kellmann M, Huhmer A: HR/AM Targeted Peptide Quantitation

on a Q Exactive MS: a Unique Combination of High Selectivity, Sensitivity, and

Throughput. 2012. http://planetorbitrap.com/data/uploads/ZFS1334248563484_A

N554_63517_HRAM-Q_Exactive_0412S.pdf

78

CHAPTER 3: NEW ASTROGLIAL INJURY DEFINED BIOMARKERS FOR

NEUROTRAUMA ASSESSMENT

3.1 INTRODUCTION

Traumatic brain injury (TBI) is “the most complex disease known to man” (1). It is

a global public health concern affecting over 2.5 million cases per year in the United

States alone and is the leading cause of death and disability among the youth (2). The

spectrum of TBI covers a wide range of severities with multiple adverse outcomes (3).

Severe TBI, characterized by extended periods of coma, results in variable degrees of

brain dysfunction that are difficult to predict (4). The most common TBIs are mild, and

occur frequently particularly during sports practice and routine military operations. Some

mild TBI patients develop persistent or even permanent neurological deficits, which would

be desirable to predict (5, 6). Repeated sub-concussive impacts and cumulative mild TBIs

can increase the risk for neurological deficits, so real-time diagnosis is essential for safe

return-to play/duty to prevent repeated exposure of at-risk individuals (7).

Injury evolution and eventual outcome are difficult to prognosticate, and current

approaches to assess TBI patients, based on Glasgow Coma Scale (GCS) scores or

computed tomography (CT) scans, are often insufficient to adequately capture the TBI

complexity (8-10). While CT scans are common, and readily report macroscopic brain

lesions, the detection of diffuse microstructural injuries and metabolic dysfunction after

TBI require more refined structural and functional imaging techniques that are less

commonly available (11, 12).

79

Information about microstructural injuries such as fiber damage and cell membrane

permeability (mechanoporation) can provide insight into very early cellular and structural

pathophysiology and are accompanied by protein alterations and degradation at the

molecular level (13-19). We believe that further understanding of cellular injury

mechanisms, and their protein signatures are a resource for sensitive and acute

diagnostic tools, needed for accurate assessment of neurotrauma injury magnitude (20).

Multiple studies have been conducted to identify biomarker candidates that can

offer superior diagnostic and prognostic information. GFAP, neuron specific enolase,

neurofilaments, tau, ubiquitin C-terminal hydrolase (UCHL1) and S100β have been

chosen based on neuropathological presence or known expression in neurons or glia

without elucidating their trauma-induced release mechanisms. The utility of these

biomarkers is limited by short-lived biofluid presence, extra-cranial sources, delayed

circulatory appearance and age-dependent liabilities, all of which hamper their individual

clinical translation (21-23). Most protein biomarker mining studies have drawbacks,

including failure to address fluid changes, differences in time-scales between rodent and

human pathophysiology and proteomic analytical challenges that obstruct the

identification of new, low abundance biomarkers (24-28). Furthermore, selection of

suitable biomarker candidates from untargeted (i.e., global) proteomic discovery

experiments are difficult and are typically unsuccessful (29-32). These examples

emphasize the need for a new class of biomarkers associated directly and immediately

with a traumatic impact to the brain.

In this study, astrocytes were chosen, given their central roles in the neuro-

vascular unit, brain metabolism, blood-flow and blood-brain barrier (BBB) (33-37).

80

Astrocytes outnumber neurons in the human neocortical white matter, are vulnerable and

responsive to injury and thereby participate to white matter injury (16, 38-41). Significant

amounts of cellular proteins were found to be released into fluids minutes after abrupt

astrocyte stretch-injury, suggesting astrocytes as the ideal carriers for neurotrauma

biomarkers (32).

In the present work, we determined a TBI CSF proteome and a strategy to

overcome the typical ‘proteomics bottlenecks’. This selection strategy capitalized on

astrocyte-enrichment and our previous work on trauma-released proteins in a simple

culture injury model to identify a panel of new biomarker candidates (32, 37, 42). The

study identified astroglial injury-defined (AID) biomarker release from traumatized human

astrocytes, documents their elevation in TBI patients during the first post-injury days, and

explores their presence in serum of mild TBI patients. Importantly, establishing

biomarkers of cell wounding and cell death may provide future biosignatures of brain cell

compromise and demise that could facilitate our understanding of TBI pathophysiology.

81

3.2 RESULTS

Cerebrospinal fluid of TBI patients carries a signature of trauma-released astroglial

proteins.

Analytical liquid chromatography-tandem mass spectrometry (LC-MS/MS)

identified proteins in CSF of 19 TBI patients and 9 control subjects. Among the 484

proteins identified in TBI CSF, 232 were unique to TBI, while 252 proteins overlapped

with the control CSF proteome (Figure 3.1). To select neurotrauma biomarker candidates,

we determined the overlap with previously published trauma-released proteins

determined from fluids of stretch-injured mouse astrocytes using a 2D gel analysis

approach (Supplement Figure S3.1) (32). To improve specificity, we then determined

astrocyte-enriched proteins using gene array data, and excluded proteins present in

healthy plasma or abundant in extracranial tissues (42-45). Four candidate astroglial

injury-associated proteins were identified: aldolase C (ALDOC) which is one of the most

astrocyte-enriched proteins and also one of the highest expressed proteins in the brain

(32, 46), glutamine synthetase (GS), brain lipid binding protein (BLBP), and astrocytic

phosphoprotein 15 (PEA15).

Traumatized human astrocytes show membrane wounding, reactivity and cell death at

different times post-injury.

Differentiated, serum-free human astrocytes grown on deformable membranes

received pressure-pulses that produced diffuse shear and stretch injuries reminiscent of

an abrupt traumatic force (32, 47, 48). Subpopulations of traumatized human astrocytes

displayed membrane wounding, died or underwent reactivity by acquiring star-shaped

82

morphology and GFAP up-regulation (Figure 3.2) as previously shown (48-50). Wounded

cells displayed beaded, fragmented and amputated processes with elevated GFAP

signals as soon as 30min post-stretching, earlier than reported gene expression changes

(Figure 3.2C) (50, 51). By analyzing nuclear shape and membrane integrity, intact,

wounded and demised astrocytes could be distinguished (see Methods, Figure 2D-F). In

both mild and severely stretched cultures the population of wounded cells, increased 16-

fold over controls 30min after injury, constituting a fraction of 20% of stretched astrocytes

(Figure 3.2E, S3.2A). By two days after stretching, this population decreased

substantially. By contrast, the rate of cell death was low at 30min post- injury and

continued to rise over time until two days post-injury, when cell death differed significantly

between mild and severe pressure pulsed cultures (Figure 3.2F, S3.2B). Mechanical

trauma caused severity-independent acute membrane wounding and protracted severity-

dependent cell death.

Different biomarkers and release kinetics correlate with astrocyte wounding and cell

death.

Fluid immunoblotting from control and stretched cultures provided release kinetics

of AID biomarkers over time (Figure 3.3A). GFAP, ALDOC, BLBP and PEA15 levels

showed logarithmic release levels up to three orders of magnitude (Figure 3.3B-E). GFAP

fluid levels show clear temporal and severity differences, while ALDOC, BLBP and PEA15

levels remained similarly elevated at each timepoint and across the two severities (Figure

3.3B-E). GFAP fluid levels were 5-7 fold elevated between 30min to 1-2 days post-injury

(p<0.013, Figure 3.3B). After mild stretching, GFAP levels at 30min rose only three-fold,

83

which was not significantly different from control levels. In contrast, fluid levels of ALDOC,

BLBP and PEA15 at 30min after mild stretching rose much higher and significantly over

those of controls (60-460x; p<0.0001). Their release after mild stretching was not

significantly different from severe stretching at any timepoint post-injury (2-3x). Yet, GFAP

release levels were significantly higher 5h after severe versus those after mild stretching

(5x, p=0.042). The data suggest the release of cytosolic biomarkers ALDOC, BLBP and

PEA15 relates to both early cell wounding and later cell death, while the release of

cytoskeletal GFAP, particularly its small proteolytic fragments, follows the slow

accumulation of dead cells. We tested for an association between astroglial biomarker

release and cell fates by plotting each biomarker’s levels against cell wounding and cell

death rates from cultures of all conditions, and determining their Spearman correlations

(Figure 3.3F-I). ALDOC, BLBP, and PEA15 associated with cell wounding. ALDOC and

BLBP also correlated with cell death. GFAP had the strongest correlation with cell death,

while it did not correlate with cell wounding. ALDOC and PEA15 release also correlated

well with each other while all other marker pairs had moderate correlation (Table 3.5).

This is the first report of the early, robust release of ALDOC, BLBP and PEA15 from

human astrocytes after mild and severe injury.

Characterization of glial fibrillary acidic protein break-down products

We also identified and measured new trauma-generated small GFAP proteolytic

breakdown products (BDPs, 18, 20 and 25kD) that only appeared 1-2 days after injury

(S3.3). Intact GFAP and its BDPs were further characterized through immunoprecipitation

from stretched astrocyte conditioned media and whole cell lysates. Peptide mapping of

84

immunoprecipitated products revealed a core sequence of amino acids common to all

observed breakdown products (including our new lower MW species) starting at alanine

residue 71 (S3.4). Generation of smaller BDPs appears to result from additional C-

terminal cleavage. These N and C-terminal cleavages are believed to be the result of

calpain and caspase activation following injury and are consistent with the reported

cleavage sites reported in the literature (52). However, we were unable to identify BDP

unique peptides by PRM-MS based on the reported non-tryptic N-terminal cleavage site.

This leaves some room for doubt for the exact sequence of these BDPs.

Trauma caused astroglial biomarker depletion and disassembly in wounded and dying

cell populations.

Cell analyses using dye uptake and biomarker immunofluorescence further

substantiated the correlation between biomarker release and cell fate. Viable GFAP-

expressing control astrocytes displayed cytoskeletal filaments (Figure 3.4A1). Acutely

after stretching, a population of GFAP-positive cells lost cytoskeletal fiber definition

(Figure 3.4A2). While the number of GFAP-positive cells did not decrease significantly

after injury, stretching did significantly increase the fraction of astrocytes with non-fibrous

GFAP (Figure 3.4I). GFAP filament disruption was associated with cell integrity

compromise (Figure 3.4E).

Control images show robust BLBP expression in GFAP-positive astrocytes. By

30min after stretching, bright BLBP signal decreased, while the remaining GFAP signal

distribution were altered (S3.5). The population of cells with bright BLBP signals nearly

disappeared 30min after stretching (Figure 3.4C2, G). ALDOC and PEA15 were

85

ubiquitously expressed in control astrocytes (Figures 3.4B, D). Membrane wounding, with

occasional blebbing 30min after stretching, was associated with ALDOC and PEA15

signal decreases (Figures 3.4B2, and D2, arrowheads and arrows) and occurred in 29-

39% of leaky cells, and in 11-14% of cells with intact membranes (Figure 3.4F, H). Rapid

post-injury resealing of mechanoporated cells could explain the depletion of markers from

cells with intact membranes. Altogether, ~17% of astrocytes were depleted of PEA15 and

ALDOC acutely after stretching. Cell fluorescence measurements 30min after injury

confirmed acute protein loss by demonstrating significant signal intensity reduction from

control levels for ALDOC (by 34%), BLBP (by 29%), and PEA15 (by 43%) in

subpopulations of stretched astrocytes (Figure 3.4J, p<0.001).

This human trauma model documents hyper-acute release of cytosolic markers

ALDOC, BLBP and PEA15 was associated with cytosolic protein loss in a subpopulation

of wounded astrocytes, likely through plasmalemmal irregularities as was also suggested

by previous time-lapse studies (32). In contrast, GFAP was temporarily retained,

undergoing cytoskeletal filament loss and redistribution followed by delayed release with

further fragmentation. The two trauma-inflicted release kinetics highlight different, cell-

fate associated astroglial biomarker classes.

Clinical study documents distinct AID biomarker CSF and blood profiles in TBI patients

across all severities.

We measured AID biomarkers, apolipoprotein B (APOB), a serum protein,

prostaglandin synthase (PTGDS), a CSF standard, and known biomarkers GFAP and

S100β in CSF of 26 severe TBI patients and 13 control subjects (Table 3.6, Figure 3.5).

86

ALDOC, BLBP, PEA15 and small BDPs of GFAP presence in clinical blood samples was

established in 22 severe and 15 mild TBI patients along with 12 control subjects (Figure

3.8). The average age was 42 among severe and mild TBI patients, and 39 among control

subjects. Severe and mild TBI patients included 71% male, and 59% of control subjects

were male. Severe TBI patients had on average a GCS score of 5.5, ranging between 3

to 11 and the survival rate was 85%. Most severe TBI patients (92%) had intracerebral

hemorrhage with one or more neuroimaging findings of contusions, subdural hematoma,

subarachnoid or intraventricular hemorrhage. Diffuse axonal injury was reported in 42%,

epidural hematomas and edema were each found in 21%, ischemia in 8% and midline

shift in 4% of the severe TBI patients. Injury causes among all TBI patients involved motor

vehicles in 43%, falls in 41% and other causes including violence and football in 16% of

the cases (Table 3.6). Multiple samples per patient from different post-injury times were

analyzed from 8 TBI patients (6 severe and 3 mild TBI patients), and data were separated

by day post-injury except post-injury days 4 and 5, which were averaged because

individual immunoblot biomarker optical density (OD) measures did not significantly differ

(not shown). Variances in biomarker levels were larger between TBI patients than within

subjects.

Boxplots of normalized immunoblot densities show significantly elevated TBI CSF

levels for GFAP, ALDOC, BLBP, GS, PEA15 and S100B, which were 2-4 orders of

magnitude greater than control levels, or controls had no measurable signal as for PEA15,

small (18-25 kD) GFAP BDPs and ALDOC BDP (Figures 3.5 D-K, Table 3.7, S3.6 and

not shown). Longitudinal trajectories differed between new and known astroglial

biomarkers in TBI CSF, as GFAP and S100β signals decreased significantly after the first

87

day post-injury, with mean GFAP levels over 11-fold lower on later post-injury days

(Figure 3.5A, D). In contrast, ALDOC and GS means remained elevated across all days

post-injury (Figure 3.5A, F, G). BLBP and PEA15 signals fluctuated over time and across

patients without significant mean decreases across days post-injury (Figure 3.5A, H, I).

About 50% of all TBI CSF samples displayed a new trauma-generated 38kD ALDOC

fragment, found predominantly on later post-injury days (Figure 3.5C). The temporal

differences in enzymatic cleavage pattern between GFAP and ALDOC as well as the

short half-lives of BLBP and PEA15 document highly variable proteolysis and clearance

kinetics. Overall, AID biomarkers, including their BDPs, were robustly elevated after TBI

and had an extended detection window when compared to GFAP and S100. Time after

injury responses were also measured by MRM-MS which demonstrated similar trends

compared to immunoblot densities for GFAP, ALDOC, BLBP, and GS (S3.7)

Analyses included also evaluation of fluid standards. The CSF standard PTGDS

was secreted at high levels in healthy controls and was decreased over ten-fold in TBI

patients; it was often undetectable on injury day and early post-injury days suggesting

dysregulated CSF balance (53, 54) (Figure 3.5A, K). Serum protein apolipoprotein B

(APOB), absent in control CSF, was significantly elevated acutely after TBI with

concentrations decreasing during subsequent post-injury days (Figure 3.5A, J). A

reported ischemic episode on the third post-injury day in one severe TBI patient was

accompanied by a secondary CSF peak of APOB as well as BLBP and PEA15 levels, the

latter two documenting secondary astroglial damage (Figure 3.5A). These are the first

quantitative APOB measurements used as intraventricular bleeding marker in human TBI,

88

although APOB has been documented previously in a rodent spinal cord injury CSF

proteomic study (25).

Preliminary correlation of AID biomarkers with TBI patient survival were explored

for three markers. Acutely depleted PTGDS levels were later restored to higher, near

control, levels in TBI survivors but recovered less or not at all in TBI patients who had

died (S3.8A). Mean PEA15 levels for surviving TBI patients were over one-thousand fold

higher than those of non-surviving TBI patients (S3.8B). Small GFAP fragments (25 kD

doublet, 20 kD, 18 kD BDP) were measured in TBI patients for the first time (Figure 3.5A,

B, S3.8C). Small GFAP BDP amounts differed between survivors and non-survivors by

an order of magnitude more than total GFAP levels (S3.8C). Each TBI patient had

different large and small GFAP fragment profiles, which is a first indication for individual

degradation kinetics reflecting patient heterogeneity. Together with the unique

association to cell death, small GFAP fragments add specificity to TBI severity

assessment. While we were unable to measure GFAP BDPs by MRM-MS, intact GFAP,

ALDOC, BLBP, and GS concentrations were assessed in relation to TBI patient survival

as well (S3.9). Similarly, elevated biomarker concentrations were observed in deceased

patients compared to survivors. However, given the low number of mortalities compared

to survivors, additional clinical studies are needed to determine outcome correlations of

this panel.

AID biomarker concentrations were quantified using antibodies and quantitative mass

spectrometry.

89

Our work represents the first use of multiple reaction monitoring (MRM)-MS to

systematically quantify levels of neurotrauma biomarkers in TBI patients (Figure 3.6).

MRM-MS of GFAP, ALDOC and BLBP show higher levels in TBI than control CSF (Figure

3.6A). Biomarker concentrations were determined using the ratio of the amount of CSF-

derived endogenous peptides to the known amount of isotope-labeled standard peptides

(Figure 3.6 B-D). GFAP immunoblot densities and MRM measurements showed high

correlations between the two independent methods (Figure 3.6B). A comparison of

immunoblot and MRM longitudinal CSF measurements shows matching profiles for

astroglial biomarkers in one severe TBI survivor from as early as 3h to 5 days post-injury

(S3.10).

MRM-MS provides antibody-independent concentration comparisons among AID

biomarkers in TBI patients’ CSF (Figures 3.6C, D). Highest levels were measured for

ALDOC, followed by GFAP (2.5-fold lower) on injury day, and both were significantly

higher than levels for BLBP (29-70-fold lower) and GS (3 orders of magnitude less, Figure

3.6C, Table 3.7). By 3 days post-TBI, ALDOC concentrations were significantly higher

GFAP concentrations, differing by an order of magnitude, documenting prolonged

ALDOC stability in CSF over that of GFAP (Figure 3.6D). MRM and immunoblot pure

protein measurements resulted in similar detection limits for ALDOC, BLBP and GFAP

(Table 3.8). Matching MRM and immunoblot interquartile concentration ranges show a

wide dynamic range in AID biomarker levels after TBI (Tables 3.7, 3.8).

Multivariate Factor analysis of AID biomarkers documents TBI patient diversity.

90

Spearman correlation coefficients between biomarker pairs are listed (Table 3.9).

Strongest correlations were between cell death-associated, lower GFAP fragments,

serum protein APOB (0.9), and S100 (0.87-0.85) that may reflect an association

between intraventricular bleeding and astroglial demise. BLBP and PEA15 correlated

robustly (0.8), suggesting similar CSF profiles. Markers with different temporal profiles

and stability tended to correlate poorly like ALDOC and GS levels with those of GFAP, its

small BDPs, and S100β (Figure 3.5A, Table 3.9). Astroglial biomarker levels related

negatively with PTGDS indicating diverging profiles (53, 54). Proteolytic fragments for

ALDOC and GFAP did not co-vary, suggesting different proteolytic degradation patterns

(see Discussion). The correlations support the diversity of this panel and reflect

differences in biomarker appearance and clearance after TBI.

We used an exploratory machine-learning Factor analysis for unsupervised

grouping of AID biomarker profiles into ‘Factors’ based on Spearman correlations and

known for revealing common underlying trends (55). The algorithm sorted AID biomarkers

into two factors. These two factors and PTGDS accounted for 84% of the cohort’s

biomarker variance. Factor A was comprised of GFAP, its lower BDPs, S100β and APOB.

‘Factor B’ contained ALDOC, its 38kD fragment, BLBP, GS and PEA15 (Figure 3.7A).

Thus, Factor A reflected markers of astroglial demise and bleeding corresponding with

tissue loss, whereas Factor B represented markers of astroglial wounding, associating

with tissue compromise. The resulting categories were reliable, as they had high

communality expressed by factor correlation coefficients (Figure 3.7A, Cronbach’s ).

This TBI patient based, unbiased factor classification independently confirmed a grouping

of astroglial biomarkers based on release from wounded or dying astrocytes in vitro

91

(Figures 3.2-3.4). Factors A and B separated TBI patients between controls and non-

survivors, (Figure 3.7B). Exploratory classification tree analysis partitioned controls and

TBI patients using a Factor B boundary and a Factor A boundary separated TBI survivors

from non-survivors with one outlier (Figure 3.7B, C) (56). Both factors were robustly

elevated in TBI patients versus controls (Figure 3.7D, E). Factor A temporal CSF profiles

decreased over post-injury days and differed between TBI survivors and non-survivors,

while Factor B profiles were more stable over time and indifferent to survival status

(Figures 3.7D, E).

AID biomarkers are elevated in the blood after severe TBI and in a subgroup of mild TBI

patients.

ALDOC, BLBP, PEA15 and GFAP BDPs were detected in blood samples depleted

of abundant proteins. All four markers were robustly elevated in 50 plasma and serum

samples of 22 severe TBI patients compared to 12 control subjects (Figure 3.8A-E, Table

3.6). Their concentrations reached up to 20 ng/ml (Table 3.8). Cell wounding markers

ALDOC, BLBP and PEA15 were significantly elevated over controls as early as 3h on

injury day in blood of severe TBI patients (Figure 3.8A-E, S8). Blood ALDOC levels rose

significantly between injury day (88-fold over controls) and subsequent two post-injury

days (over 300-fold above controls, Figure 3.8A, C). Mean injury day blood levels for

BLBP and PEA15 were elevated over control levels (122-fold and 40-fold, respectively).

(Figure 3.8A, D, E). For the same TBI patient, BLBP and PEA15 were elevated in serum

prior to their presence in CSF (S3.11). In contrast, a 25 kD GFAP fragment was

consistently absent on injury day and appeared robustly on the first post-injury day in

92

blood of severe TBI patients, beginning as early as 22h post-TBI, GFAP was first elevated

in CSF followed by overnight decrease and appearance in the circulation, with levels up

to 4 orders of magnitude higher than those of controls in CSF (Figure 3.8A, B, S3.11).

ALDOC levels showed at 3 and 34h in CSF and in blood (S3.11A). These same patient

observations illustrate that different fluid kinetics for these four astroglial biomarkers can

exist.

The presence of AID biomarkers was explored in 15 mild TBI patients within the

first injury day, a relevant time window for mild TBI diagnosis (57). Preliminary data show

elevation of ALDOC, BLBP and PEA15 in serum as early as one hour after mild TBI

compared to control serum and at similar levels as found after severe TBI (Figure 3.8F,

S3.12). ALDOC was present in 80%, PEA15 in 60% and BLBP in 47% of this cohort of

mild TBI patients irrespective of CT status. In contrast, GFAP BDPs (37, 25 or 20 kD)

were only found in 27% of the samples (S3.12). In some mild TBI patients, sera signals

appeared in the same range as those in severe TBI sera, consistent with similarly

observed in vitro release profiles from wounded human astrocytes after mild and severe

stretching.

93

2.3 DISCUSSION

A new panel of astroglial, injury-defined (AID) biomarkers from the TBI CSF

proteome and a list of trauma-released, astrocyte-enriched proteins is presented. AID

biomarkers are qualified by release from wounded and dying human traumatized

astrocytes. Clinical confirmation in TBI patients shows robust AID biomarker elevation in

CSF and blood of TBI patients with broad post-injury profiles and provides a new concept

for a biosignature of brain cell compromise and demise.

There are unmet requirements for assessing TBI patients.

Presently, the initial assessment of TBI patients relies on the GCS scores and on

CT scans, which both correlate poorly with outcome and functional compromise after TBI

(58, 59). Mild TBI victims are assessed using behavioral testing and cognitive

questionnaires (60). These tests rely on subjective self-reporting and require baseline

assessment. An unbiased brain injury signature is needed that can assess TBI and

identify complicated injuries among mild TBI patients (5, 57). On-site diagnosis of TBI

patients, particularly of athletes, military personnel and urgent care situations, would

provide early information for advising on initial treatment decisions or transportation

choices. Instant release with the primary trauma event and concomitant presence in the

circulation are prerequisites for future real-time neurotrauma biomarkers. Based on this

rationale, connecting acute cellular injury processes and biomarker presence can

facilitate this goal. Few animal studies address primary cellular injury events or early

biomarker release (61-64). After mild TBI, GFAP’s passage into the circulation is delayed,

making its use as urgent care tool less helpful (65). In contrast, UCHL1 declines on injury

94

day limiting interpretation of samples collected at various times after injury (65, 66). Thus,

limitations exist among current neurotrauma biomarker candidates (67, 68). This study

provides biomarkers of brain cell wounding that address current limitations, as they were

released instantly and robustly after severe and mild TBI.

Overcoming proteomic bottlenecks of biomarker identification and a multiplex

standardized TBI assay.

Proteomics provides a comprehensive view of protein changes after neurotrauma,

yet clinically useful neurotrauma biomarkers remain elusive (29, 31). A major hurdle has

been the selection of clinically relevant biomarkers from extensive lists of identified

proteins, which our controlled human trauma model and selection strategy have cleared

(31, 69) Typically, one or two biomarkers are investigated, resulting in diverse profiles

and sensitivities due to non-standardized assays (23, 70). Further, efficient throughput

quantifying multiple biomarker candidates requires a standardized assay to enable

biomarker comparisons (71, 72) (23, 70). MRM-MS is favored as a multiplex, antibody-

independent assay for standardized simultaneous measurement of multiple candidate

biomarkers, but until the present study, had not yet been applied systematically in the

neurotrauma field (57, 70, 73).

Astrocyte trauma responses are heterogeneous.

We previously documented molecular heterogeneity among human and mouse

astrocytes based on variable expression of astroglial markers and their different trauma

responses (32, 48). Morphological signs of astrocyte wounding were found early and

95

scattered in stretch-injured cultures, which could indicate both, selective astrocyte

vulnerability and ‘hot spots’ of focal tensile forces. Distinct subpopulations of wounded

human astrocytes underwent depletion of cytosolic proteins and GFAP filament

disassembly, while adjacent cells seemed unchanged, illustrating the diffuse injury

distribution in this trauma model. Rapid post-injury GFAP filament disassembly and

brighter immunofluorescence associated with cell membrane wounding were found prior

to reported gene expression changes (32, 51). Such GFAP changes are similar to

reported alteration in GFAP antigenicity after acid treatment, mediated by rapidly elevated

calcium, and calpain activation, but are new in mechanically wounded astrocytes (74, 75).

The depletion of key metabolic proteins, together with cytoskeletal filament disassembly

in mechanoporated astrocytes, likely exacerbates their compromise, making a

subpopulation of traumatized astrocytes vulnerable to a second mechanical blow or other

stressors that can lead to cell death.

Mechanical trauma-induced reactivity with characteristic shape changes and

upregulation of astroglial markers occurred within hours and evolved over days post-injury

in human astrocytes (48). Trauma-induced activation of signal transducer and activator

of transcription 3 increases oxidative metabolism and upregulates expression of GFAP,

ALDOC, BLBP and PEA15 during reactive gliosis, boosting astroglial resilience and could

amplify delayed release in case of a secondary insult, as was observed in one TBI patient

(32, 39, 76-78). Thus, acute membrane wounding, neuroprotective astrogliosis as well as

delayed astroglial demise document cell fate heterogeneity that is reflected in diverse

astroglial markers, their release, and expression after neurotrauma.

96

AID biomarkers monitor acute trauma pathology of astroglial membrane wounding and

fiber damage.

Plasmalemmal permeability (mechanoporation) is an early and enduring pathology

in acutely traumatized brain and spinal cord tissues, and mechanoporation is a hallmark

of diffuse axonal injury, which is characterized by process beading and fragmentation (13,

14, 79, 80). Diffuse axonal damage is also a morbidity in mild TBI patients with post-

concussive symptoms (81, 82). How long mechanoporated cells endure in a

compromised state, and whether they recover or undergo protracted cell death are open

questions (83). The present study documents profiles of astroglial biomarker release after

pressure-pulse stretching that correlated with cellular features of human astroglial

wounding, mechanoporation, and delayed cell death. We show human astroglial fiber

damage, including beading and process disintegration, shortly after mechanical trauma.

Astroglial fiber damage is also seen in vivo early after mouse spinal cord crush injury, in

the traumatically injured primate cortex, reported as clasmatodendrosis, and in human

cerebral cortex after traumatic intracranial injury where it is associated with protein

degradation markers (16, 32, 84). This histopathology is particularly relevant for human

white matter injury, because astrocytes outnumber neurons in the human neocortical

white matter, and human astrocytes carry over-sized processes (38, 41, 85, 86). Thus,

astroglial wounding-released biomarkers may provide biofluid-accessible tools to

investigate diffuse glial fiber damage acutely after neurotrauma.

AID biomarkers may have possible roles as biosignatures and for manifesting metabolic

depression.

97

Astrocytes maintain a high rate of active oxidative glucose metabolism, express

overall high levels of glycolytic and tricarboxyl acid (TCA) cycle enzymes and carry a large

number of mitochondria in their perisynaptic processes (37, 42). Astroglial GS and BLBP

have important roles for synaptic plasticity at the tripartite synapse complex in glutamate

recycling and regulation of fatty acid uptake (87, 88). ALDOC is a central glycolytic

enzyme providing the substrate for lactate and ATP production and its product

glyceraldehyde-3-phosphate controls cell fate and astrocyte-neuron crosstalk (36).

PEA15 is a main regulator of glucose metabolism, and high PEA15 levels make cells

resistant to glucose deprivation by adapting to different metabolic states (89, 90).

Astrocytes couple synaptic metabolic demand as they adjust local blood flow by

ensheathing both compartments with their endfeet (88, 91). Vital astroglial metabolism is

essential for maintaining neuro-metabolic coupling and brain energy homeostasis.

The majority of TBI patients undergo metabolic depression indicated by reduced

cerebral oxidative metabolism and associated with an imbalance of lactate, pyruvate,

glutamate and glucose (11, 92, 93). Decreased cerebral glucose metabolism is measured

in mild and severe TBI patients using positron emission tomography scanning (11, 58).

Reduced oxidative glucose metabolism is demonstrated after lateral fluid percussion, a

rat cerebral concussion model with astrocyte metabolism being initially reduced and sub-

acutely supportive for restoring metabolic homeostasis (94-96). One reason for impaired

energetic needs of injured cells may be selective depletion of several glycolytic and TCA

cycle enzymes as well as GS, PEA15 and BLBP as they are acutely released in vitro.

ALDOC and PEA15 are reduced in vivo and in perilesional wounded astrocytes acutely

after mouse crush spinal cord injury (32). The present study links fluid release of BLBP,

98

ALDOC and PEA15 with concomitant cellular protein loss in traumatized human

astrocytes, supports a concept of cellular compromise and identifies biofluid AID markers

signals as possible candidate indicators of metabolic depression after TBI.

GFAP release and degradation associate with astrocyte death after severe TBI

It has been assumed that biomarkers are released due to cell death after

neurotrauma (23). Severe TBI with lesions and contusions are associated with tissue

demise, vascular damage, and perilesional, irreversible astrocyte swelling leading to

cytotoxic edema (97-99). Astrocyte demise is reported after lateral fluid percussion in the

rat cortex and in the peri-lesional mouse spinal cord one day after contusion (100, 101).

Human post-mortem cerebral and hippocampal cell counts document progressive

astroglial death by means of different mortality times after TBI, that relate to injury severity

(102). Our human trauma model shows delayed and severity-dependent astroglial cell

death preceded by GFAP release and associated with the appearance of small GFAP

BDPs. Similar findings, albeit only considering total GFAP signal, were obtained by

stretching rat hippocampal slice cultures (103). Thus, trauma-inflicted astroglial cell death

can be monitored using GFAP, particularly by its small, more selectively cell death-

associated fragments. GFAP degradation is related to caspase activation in models of

Alzheimer’s and Alexander disease (104-106). The present study is the first to associate

small GFAP fragment generation with cell death in traumatized human astrocytes. Our

explorative clinical data indicates small GFAP BDP levels differ substantially between

survivors and non-survivors compared to those of total GFAP amounts. Thus, small

GFAP fragments in biofluids may help to specifically monitor astroglial demise after TBI.

99

Kinetic diversity among astroglial neurotrauma biomarkers

Biomarker profiles fluctuate given injury complexity and irregular secondary events

in severe TBI patients (107). Astroglial biomarkers also displayed marker-specific

kinetics. ALDOC had remarkable CSF stability over time after TBI and is reported to last

up to three weeks in sheep and cow blood (108, 109). A 38kD ALDOC BDP of later post-

injury days could be a product of proteolysis by calpain or cathepsin (110-112). In

contrast, GFAP displayed massive degradation into large fragments that had been

previously detected after TBI, amyotrophic lateral sclerosis and oxidative frontotemporal

lobe degeneration (113-115), and also into small fragments associated with caspase

activation, which have not been measured after TBI (104-106). We quantified large and

small GFAP fragments in TBI patients in this study, and observed that overall GFAP CSF

levels decreased drastically from the second post-injury day onwards, concurrent with

fragments appearing transiently in blood. Delayed biomarker passage into the circulation

may occur via glial-mediated overnight CSF clearance (116). In contrast, BLBP and

PEA15 signals were more variable, which could reflect short biofluid stability, (117).

Overall, these observations reveal a new diversity in biofluid kinetics among different

astroglial biomarkers.

Hyper-acute presence of AID markers indicates astroglial release immediate to the

traumatic impact and documents direct passage into the circulation. This may be possible,

since perivascular astrocyte sheets cover endothelial tubes nearly completely in brain

microvessels and are part of the blood-brain diffusion barrier (118, 119). Astrocytes

function as gatekeepers in the neuro-vascular unit and their damage results in BBB

100

permeability (33, 120). TBI causes microvasculature disruption and astroglial fiber

damage (16, 80, 121, 122). ALDOC is present in astroglial process endings and could be

directly released with their rupture (32). Animal and clinical studies show BBB disruption

in the early hours post-injury after mild TBI, blast shock waves, and mild fluid percussion

injury (123-125). Blood elevation of cytosolic astroglial marker, S100, indicates BBB

permeability and is elevated after mild TBI including repeated sub-concussive events

(126-128). Hence, cytosolic astroglial proteins are situated for immediate release into the

circulation upon BBB disruption after TBI.

AID biomarker panel limitation, uniqueness and significance for future TBI patient

assessment and monitoring

Confounding variables of age and gender were matched in this study, while

medications and comorbidities were not controlled for. Methodological rigor is provided

by using two technically independent assays to validate biomarker measurements. Data

analysis was separated by day, yet considering the short-lived nature of some

biomarkers, future finer resolved kinetic studies are advised.

Selection of candidate biomarkers was achieved using astrocyte enrichment and

trauma-release, not warranting brain exclusiveness. All markers are highly enriched in

the CNS; ALDOC is one of the most abundant brain proteins and is highly brain enriched

(46, 129). BLBP, GS and PEA15 are also highest expressed in the CNS, with selective

presence in other tissues (S1). To our knowledge, the combined biofluid elevation of any

two or more astrocyte-enriched biomarkers presented here points exclusively and

sensitively to brain injury.

101

Biomarker panels are anticipated to improve TBI patient assessment compared to

a single biomarker (130). Several neurotrauma biomarkers have been previously

combined to evaluate patients with brain or spinal cord injuries (131, 132). Unsupervised

factor analysis has been used for psychological and cognitive self-rated scores to assess

TBI patients (133, 134). To our knowledge, this is the first study applying Factor analysis

to a small neurotrauma biomarker panel. AID biomarker factors derived from TBI patients

coincided with cell fate assignment, thereby clinically validating the trauma model’s

classification. Aldolases (ALDOC) and fatty acid binding proteins (BLBP/FABP7) have

been previously considered as biomarkers of brain injury and cerebrovascular disease,

but isoforms were not always distinguished (117, 135). Linking cell fate and biomarkers

is unique to this study and delivers novel fluid biosignatures for traumatized brain tissue.

Correlating biomarkers and cell fates and documenting differences in individual

biomarker’s kinetic are new observations with significance for future neurotrauma

biomarker studies. Overall, our translational and exploratory clinical studies document

elevation of AID biomarkers in CSF and blood of severe and in serum of mild TBI patients,

supporting their further study for TBI patient assessment.

102

3.4 METHODS

Donors, patients and samples

All CSF and plasma samples were collected prospectively under protocols

approved by the local ethics committee of all the sites involved and stored at UCLA.

Written informed consent was obtained from patients or legal authorized representatives

before enrollment. The CSF samples from TBI subjects were collected directly from

ventriculostomy catheters, every 6h up to a maximum of 5d following injury. Blood

samples were collected by venipuncture. CSF and blood samples were aliquoted, and

stored at−80°C until the time of analysis.

Adult patients with severe head injury and requiring a ventricular catheter for

intracranial pressure monitoring were included in the discovery set. Inclusion criteria were

a Glasgow Coma Scale (GCS) score of eight or less post-resuscitation or on presentation.

Exclusion criteria were no informed consent, patients younger than 18 years of age,

female patients that were or may have been pregnant, known history of neurological

disease, and Injury Severity Score greater than 15. Treatment of patients, according to

international guidelines, was targeted at a normal ICP and maintaining cerebral perfusion

pressure.

The CSF control samples were obtained by lumbar drain from patients with an

unruptured aneurysm or was donated from healthy subjects (Precision Med). This study

group included also adult patients presenting to the hospital emergency department within

4 hours after sustaining blunt trauma to the head resulting in mild TBI (GCS 13-15) or

moderate TBI (GCS 9-12). Individuals under the age of 18 years, pregnant women,

103

prisoners, subjects who did not require a CT scan as part of their clinical evaluation or

with previous history of psychotic illness or neurological disease were excluded.

CSF Proteomics

CSF volumes from TBI patients and healthy subjects corresponding to 50-300µg

of total protein content measured by BCA assay was dried down by vacuum centrifugation

and reconstituted in 100 µL of 50 mM ammonium bicarbonate pH 8.3 solution (Sigma-

Aldrich),0.1% deoxycholatic acid. Cysteine disulfides were reduced by addition of tris(2-

carboxyethyl)-phosphine (10 mM, Thermo Scientific) and incubated at 50°C for 1h then

adjusted to room temperature. Free cysteines were alkylated with iodoacetamide (20mM,

Sigma-Aldrich). for 30 min at 37°C in the dark. Trypsin (500ng/µL 50 mM ammonium

bicarbonate, sequencing grade, Promega) was added to CSF samples at a 1:25 enzyme

to protein ratio and digested for 16-18h overnight at 37°C. Samples were acidified with

5% formic acid (v/v) and centrifuged at 13K rpm to pellet deoxycholic acid precipitate. The

supernatant was then transferred to a separate microcentrifuge tube and dried by vacuum

centrifugation.

CSF tryptic digests were reconstituted in 100µL of 0.1% formic acid, 3%

acetonitrile for LC-MS/MS. Samples were desalted using a C18 trap column connected

to C18 PepMap reversed phase HPLC column for peptide separation. CSF samples were

analyzed using a LTQ-Orbitrap or Q-Exactive Orbitrap mass spectrometer (Thermo).

Peptide separation was done in a 60 or 120min gradient from 5-35% of mobile phase

(100% acetonitrile, 0.1% formic acid). Analysis on the Q-Exactive was performed in the

positive ion mode with settings: resolution –70,000; m/z range–300-2000; maximum MS1

104

injection time–50ms; MS automatic gain control (AGC) target–1x106. Acquisition was set

to record up to 10 confirmatory product ion spectra (MS2) per full scan spectrum by

selecting precursor ions of decreasing signal intensity with 30sec dynamic and charge

state exclusions to exclude signals with unassigned charge, charge +1, and charges >+5.

MS2 instrument settings were: resolution – 35,000; maximum MS2 injection time –

100ms; MS2 AGC target – 2x105; fixed first mass m/z–100.

The data was searched using Mascot (Matrix Science) against the human subset

of the SwissProt database. Oxidation of methionine was set as a variable modification

with carbamidomethylation of cysteine was set as a fixed modification. Enzyme specificity

was set to C-terminal cleavage at arginine and lysine with up to 2 mixed cleavages

allowed. Strict m/z error tolerances were set to 15 ppm in MS mode and 0.01Da in MS2

mode. Peptide spectral matches were validated against a decoy database using the

percolator algorithm at a 5% false discovery rate.

Human astroglial injury model, cell permeability and viability assay, immunocytochemistry

Primary human astrocytes were prepared from donated, de-identified human fetal

cerebral neocortex at 16-19 gestational weeks as described (48). Briefly, in calcium and

magnesium-free Hank’s buffered saline solution (HBSS) mechanically dissociated tissue

was filtered through 70 µm and 10 µm nylon meshes (Nitex) into culture medium (DMEM-

F12) with 10% fetal bovine serum (FBS, Atlanta Biol.). Neural progenitor cells were

removed by 30mincentrifugation at 30,000 X g in a HBSS-buffered 33% Percoll gradient

(Sigma). The top fraction was washed and diluted in DMEM/F12, 10% FBS and

astrocytes cultured in T150 cell culture-treated plastic flasks (Corning). Confluent cultures

105

were shaken for 4 days at 200rpm on a shaker in an incubator. Astrocytes were treated

in 0.25% trypsin/EDTA followed by gently mechanical dissociation, and washed cells

were seeded onto collagen I-coated silastic membrane culture plates (6 well Bioflex,

Flexcell Intl.) at a density of ~ 135,000 human cells / 962mm2. Upon confluence, medium

was replaced by DMEM/F12 with 10% heat-inactivated horse serum (Atlanta Biol.) that

was then stepwise reduced. Serum-free astrocytes in 2ml DMEM/F12 were stretch-

injured using one mild (2.6-4.0psi) or severe (4.4-5.3psi) 50ms nitrogen pressure pulse

with the CIC II pressure controller (Custom Design and Fabrication Inc.). Cell death rates

significantly differed between mild and severe pulses, providing 2 outcome defined

distinct severities in this human trauma model (see Results, Figure 3.2).

Cells were incubated with 0.025 µg/ml propidium iodide (PI) in Leibowitz’ L15

(Gibco) for 10min at 37°C followed by four rinses in L15. Dye was crosslinked to DNA of

leaky cells by 5min exposure to UV light. Cells were fixed in freshly depolymerized 4%

paraformaldehyde in Tris-buffered saline for 30min at 4°C. Rinsed cells were

permeabilized with 0.3% Triton in buffer and blocked in 5% normal donkey serum in

buffer, followed by overnight, incubation with primary antibody at 4°C diluted in blocking

solution (Table 3.1). After rinsing, secondary antibodies (Table 3.2) were applied in

blocking solution for 1h at room temperature. Cultures were rinsed and stained in

bisbenzimide nuclear dye (Hoechst, 1:75 in distilled water) for 5min, rinsed, dried and

coverslipped (Fluorogel, Biomedia). Hardened cultures were mounted on slides (32, 48).

Immunoblotting, sub-saturated densitometry and technical variance

106

Culture trauma fluids, conditioned medium, was collected from 6 cultures (12mL)

and samples prepared as described (32). Briefly, protease inhibitors (Roche) and

dithiothreitol (5mM, Calbiochem) were added and fluids concentrated by ultrafiltration to

one twentieth of the original volume (Vivaspin, VWR). Clinical biofluid samples were

treated following common data element recommendations (136). CSF and blood samples

were thawed and supplied with EDTA (pH7.4 to 1mM) and protease inhibitors bestatin

(40µM), pepstatin A (10µM) and phosphoramidon (10µM). Samples were centrifuged for

10min at 16,060g at 4ºC to remove lipids. Plasma and serum samples were depleted of

albumin and immunoglobulins (IgGs) using immunoaffinity columns (ProteoPrep,

PROTIA Sigma) and concentrated by ultrafiltration (Vivaspin, VWR). Depletion removed

~ 85% of original protein concentration, including ~10-15% non-albumin or non-IgG

protein.

Samples were denatured (5min at 100ºC followed by ice), biofluids reduced, by

adding 1% β mercaptoethanol and all samples adjusted to Laemmli Sample Buffer

(2%SDS, 125mM TrisHCL pH6.8, 10% glycerol, 0.6% bromphenol blue) followed by

immunoblotting as previously described (32). Fluid analyses were normalized by volume

(30μL/lane). Proteins were separated in 200mM glycine 25mM Tris 0.1% SDS for 30min

at 100V followed by 1h at 120V in and transferred onto nitrocellulose (Hybond-ECL,

Amersham) with 20% methanol in same buffer. Proteins were reversibly stained using

0.1% Ponceau S in 5% acetic acid. Protein concentrations were determined by assay

(Pierce 660) against bovine serum albumin dilutions. Proteins > 30kD and 10-25kD were

separated on 10% and 15% polyacrylamide, 2% SDS Tris-based gels. Respectively. A

molecular weight standard (Precision Plus Kaleidoscope, Bio-Rad) and His-tagged pure

107

proteins ALDOC, BLBP, PEA15 (EnCor Biotech. Inc) and GFAP full size, 37 and 20kD

fragments (Abbott Diagnostics) at various concentrations diluted in 0.5% bovine serum

albumin were analyzed in parallel.

Blots were blocked for 30min with 10% non-fat milk in Tris-buffered saline with

0.05% Tween-20 (TBST) before overnight incubation at 4ºC with primary antibodies

diluted in 5% BSA in TBST (Table 3.1) (32). Isoform specific antibodies were used for

ALDOC and BLBP, because organs outside the CNS express other isoforms of these

proteins that are released after injury (137, 138). Washed blots were incubated for 1hr at

room temperature with peroxidase-conjugated secondary antibody (Thermo, Table 3.2).

Washed blots were incubated for 5min in enhanced chemiluminescence substrate (West

Pico ECL, Thermo Scientific). Film (Denville) captured signal, using same sequence of

exposure lengths consistently.

Signal levels were measured from scanned films using a bio-imaging and analysis

system with background correction (Autochemie Systems, UVP). Post-hoc normalization

of sub-saturated signals across multiple exposures covered 2-4 orders of magnitude. The

relative standard deviation for scaled exposure readings ranged from 11-33%. Wet

experimental replicates produced signals that varied from the sample mean by 20±14%.

Overall, analysis variance fell one order of magnitude below significant cross-condition

differences. In the trauma model, variation in release of each biomarker due to base

astroglial expression varied no more than 1-2 z-scores (not shown).

We controlled for the combined use of serum and plasma samples by comparing

serum and plasma marker signals in the same subject, which resulted in similar results

for all blood-compatible biomarkers, except for GFAP, which showed additional non-

108

specific bands in plasma compared to serum samples (not shown). Hence, only the new

and specific 25 kDa GFAP BDP was quantified in blood.

Quantitation of Biomarkers in CSF using multiple reaction monitoring mass-spectrometry

Peptides specific to each biomarker were designed and same, synthetic standard

peptides with heavy isotope labeled arginine (6C144N15) and lysine (6C132N15) were

purchased (Thermo Scientific). Peptide standards were prepared in 5% acetonitrile

(5pmol/µL) and spiked into CSF samples to concentrations between 25-75pmol per mL

of CSF. CSF samples are then reduced, alkylated, and digested as described above.

Digested CSF peptides were dried by vacuum centrifugation and reconstituted in

0.1% formic acid, 3% acetonitrile in water. Samples were desalted using an on-line C18

trap column prior to LC-MS/MS analysis. Peptides were separated on a 5%-35% gradient

of mobile phase B (0.1% formic acid in acetonitrile) over 40 min on a C18 PepMap

reversed phase HPLC column. Samples were analyzed using either a Q-Exactive

Orbitrap MS or a 4000 QTRAP triple quadrupole MS (AB Sciex). MRM-MS analysis was

performed with the Q-Exactive (by parallel reaction monitoring) targeting an inclusion list

of precursor peptide ions (Table 3.3) for MS2 analysis with the following parameters:

resolution 17500, AGC target 2x105, maximum ion injection time 50ms, isolation window

3.0Da, fixed first mass 100, normalized collision energy 27.

Biomarker specific precursor peptide ions are listed in Table 3.3. These precursor

ions were fragmented by higher energy collisional dissociation or collision activated

dissociation depending on the MS instrument, into their component product ions.

Biomarker abundance was calculated based on the area under the curve (AUC) of the

109

precursor to product ion transitions for each biomarker specific peptide using Pinpoint

(Thermo) and Skyline (MacCoss Lab). 3 transitions were summed per biomarker specific

peptide and the ratio of the endogenous peptide signal to its heavy labeled counterpart

was determined. Biomarker concentrations were calculated based on each peptide’s

endogenous to heavy standard signal ratio, heavy standard concentration, protein

molecular weight (MW), and a dimensional conversion factor according to the formula:

Endogenous protein concentration (ng/mL) = 𝑒𝑛𝑑𝑜𝑔𝑒𝑛𝑜𝑢𝑠

𝑠𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑟𝑎𝑡𝑖𝑜 ×

25𝑓𝑚𝑜𝑙

µ𝐿 × 𝑝𝑟𝑜𝑡𝑒𝑖𝑛 𝑀𝑊 ×

1

1000 .

Statistical Analyses

Optical density (OD) measurements were log-transformed for normal distribution,

standardized and replicates were averaged. Multiple replicates of each MRM sample

were measured (analytical replicates). Same day patient samples were independently

prepared 3 times assuring experimental MRM-MS and immunoblotting consistencies

(experimental replicates). Same day patient replicates with different draw times were

analyzed in parallel and averaged for graphs and statistical analyses and are shown.

Signal specificity was validated using multiple specific antibodies for ALDOC, GFAP and

BLBP (Table 3.1).

Cell fate (death, wounding) mean differences between early and late post-injury

and between different pressure pulse severities were determined using mixed model

analysis of variance (ANOVA) allowing for non-constant variance and random donors

effects. Percent leaky/wounded and dead cells were normally distributed but had

dissimilar standard deviations. Donor-paired log-transformed biomarker culture fluid

110

levels were compared over time and at two different stretch severities using repeated

measures ANOVA, mixed model with homogeneous variances over time (139). The

associations between biomarker levels and astroglial fate were quantified using the

Spearman correlation coefficient. (rs and p, Sigmaplot). Single cell immunofluorescence

densities were compared between stretched and control astrocytes using Student’s or

Welch’s t-tests or Mann-Whitney U test depending on data distribution. TBI patient CSF

and blood biomarker densitometry levels were compared to controls and over time by

ANOVA with independence of each timepoint (for CSF) and repeated measures ANOVA,

mixed model with non-constant intra-class variances over time.

Spearman correlation of ranks was determined between biomarker immunoblot

densities and MRM-MS biomarker specific peptide measures. A quantile-quantile plot

was generated assessing the strength of the Spearman correlation. MRM-MS

concentration differences across biomarkers were compared using repeated measures

ANOVA mixed model with non-constant variance.

A multivariate factor analysis based on Spearman correlations, was conducted for

CSF samples across all injury days. Signals for GFAP (50-37kDa) and total GFAP

(Spearman correlation 0.988) differed only slightly, as did those of 40 kDa ALDOC and

total ALDOC (0.983), so one entry ‘ALDOC’ and ‘GFAP’ was used (Table 3.9). GFAP

small BDPs (25-18 kD) and ALDOC 38 kD BDP signals varied from their main bands and

were treated as additional biomarkers. Factor extraction was made for maximal

differences between factors (varimax criterion). Factor values were computed by adding

each marker after multiplying each biomarkers’ signal with its weight (loading). Biomarker

111

weight cutoff for loading was 0.51. Factor values were only computed for CSF samples

with available readings for all factor components (biomarkers).

Classification tree analysis partitioned the CSF sample cohort by determining

factor thresholds using Factors A and B amounts calculated of TBI survivors, non-

survivors and Controls (56). Statistical analyses were conducted using Sigmaplot, Excel,

Instat (Graphpad), JMP and SAS version 9.4.

112

2.5 FIGURES

Figure 3.1: Venn diagram shows astrocyte proteomic signature of neurotrauma in

cerebrospinal fluid

Venn diagram documents LC-MS/MS proteomes of CSF from 19 severe TBI patients (484

proteins) and 9 healthy subjects (402 proteins, Crl, Table 3.4). A published astrocyte

trauma-release proteome of 59 proteins showed 38 proteins overlapped (64%, purple

outline) with the clinical CSF proteomes (32). A subset of 14 proteins of the CSF and

trauma model proteomes were 2-fold astrocyte enriched (black outline, (42). Proteins also

present in healthy plasma or abundant in tissues outside the CNS were excluded. Thus,

Aldolase C (ALDOC) was identified among 5 candidates present in TBI and control CSF.

Among 4 proteins exclusive in TBI CSF was glutamine synthetase (GS). Among the

additional 5 trauma-released, astrocyte enriched proteins were astrocytic phosphoprotein

15 (PEA15) and brain-lipid binding protein (BLBP), which were considered despite

absence in CSF proteomes, due to limited LC-MS/MS sensitivity.

113

Figure 3.2: Mechanical trauma causes acute membrane wounding, reactivity and

delayed cell death in human astrocytes

(A) GFAP (green) is weakly expressed in uninjured, differentiated neocortical astrocytes.

(B) Reactive astrocytes 1d post-injury were star-shaped with enlarged processes and

114

upregulated GFAP. (C) Acutely wounded, mechanoporated astrocytes 30min after

stretching had taken up PI (red), had bright GFAP signals and beaded (arrows),

disintegrated or amputated (asterisks) processes. (D) Nuclear morphologies differentiate

(D1) viable astrocytes with large, oval-shaped pale Höchst-positive nuclei (blue); (D2)

membrane-wounded astrocytes show large pale Höchst stained, non-pyknotic nuclei with

PI-positive nucleoli (pink); and (D3) dead astrocytes with condensed chromatin, bright

Höchst and PI-positive small, pyknotic nuclei (pink). E) After mild (2.6 – 4.0 PSI, small red

dot) and severe (4.4 - 5.3 PSI, large red dot p<0.001) pressure-pulse stretching, median

fraction of leaky cells was elevated at 30min (p<0.0001) and 2d post-injury (P<0.01, mild)

and was decreased between 30min and 2d post-injury (triangles, P< 0.01). (F) Cell death

fractions were elevated at 30min (p<0.05, mild; p<0.01 severe; *) and 2d post-injury

(p<0.01, mild; p<0.001 severe; *) in stretched cultures. Severe stretched cultures had

higher cell death rates than mild ones at 2d post-injury (black dot, p<0.001). The increase

between early and late cell death rates was significant for both severities (triangle,

p<0.0001).

115

116

Figure 3.3: Human astroglial biomarker release is defined by membrane wounding

and cell death after mechanical trauma

(A) Immunoblots for GFAP, ALDOC, BLBP and PEA15 from conditioned medium (fluid)

samples of unstretched (control, Crl), mild (2.6-4 psi, small red dot) and severe (4.4-5.3

psi, large red dot) stretched astrocyte cultures at 30min (30’) and 2d post- injury. Small

GFAP BDPs (25-18 kD) are absent at 30min and appear 2d post- injury whereas ALDOC,

BLBP and PEA15 fluid signals are present 30min post-injury and at 2d. Ponceau S shows

protein amounts of same volumes per lane. B-E: Fluid sample geometric means of optical

densities (OD) for total GFAP (B), ALDOC (C), BLBP (D) and PEA15 (E) of unstretched

and at 30min, 5h, 1d and 2d after mild and severe stretching. Asterisks indicate significant

differences between stretch-injured and control (GFAP 5h mild stretch: p = 0.005, all

others p < 0.001; number of donors on x-axis). GFAP levels increased between early and

later time-points (triangles, among mild stretched between 30min and 1d: p=0.018,

between 30min and 2d p = 0.012; among severe stretched between 30min and 1d: p =

0.013, between 30min and 2d: p = 0.01). GFAP release differed at 5h between mild and

severe stretching (black dot, p=0.042). F-J: Biplots correlate biomarker levels for GFAP

(F), ALDOC (G) BLBP (H) and PEA15 (I) on the y-axes with percent membrane wounded

astrocytes (red) and percent cell death (black) on x-axes. Spearman correlations (rs) are

given with p-values and best fit lines for significant ones.

117

118

Figure 3.4: Acute cell wounding is associated with depletion of astroglial markers

and GFAP filament disruption

Human astrocytes show GFAP (white, A), ALDOC (B), BLBP (C) and PEA15 (D) signals

(green) in unstretched (control) and 30min post-stretched cultures. (A1) PI-negative,

intact astrocytes display filament assembled, fibrous GFAP. (A2) Membrane-wounded,

PI-positive (pink) astrocytes had homogeneous, non-fibrous GFAP. (B1) Intact astrocytes

express ALDOC. (B2) Leaky astrocytes (PI-positive, red) show plasmalemma blebbling

(arrowheads) and dim ALDOC. (C1) Group of intact astrocytes with bright BLBP

expression. (C2) Membrane-wounded astrocytes had dim BLBP signal. (D1) Intact

astrocytes expressed PEA15 homogeneously. (D2) PEA15 was depleted from PI-positive

astrocytes (red nuclei, arrows). (E) Proportions of fibrous (striped) and non-fibrous (gray)

GFAP in intact (blue) and leaky (pink) astrocytes with percentage of each population

listed. Stretching increased non-fibrous GFAP signals in intact (p=0.006) and leaky

populations (p<0.001, n=5). (F) Intact astrocytes had strong ALDOC expression (bright,

green). Stretching increased the fraction of ALDOC depleted cells (dim), in intact (p=0.02)

and leaky (p=0.03) astrocytes, and signal loss was greater in leaky than intact stretched

cells (p<0.001, n=6). (G) Brightly BLBP-stained GFAP-positive intact astrocyte population

decreased 30min after stretching (p=0.007) and almost disappeared from leaky cells

(n=5). (H) The majority of intact astrocytes were PEA15-positive and their percentage

diminished 30min after stretching in intact and leaky astrocytes (p<0.01), with greater

signal loss in leaky than intact stretched cells (p<0.0001, n=6). (I) Percent GFAP

expressing astrocytes with fibrous and non-fibrous GFAP cell populations. The shift from

fibrous to non-fibrous GFAP fraction changed acutely post-injury (p<0.01, asterisk),

119

without significant reduction in GFAP-expressing cells. (J) Percentages of bright and dim

cells differed between control and 30min post-stretch cultures for ALDOC (p=0.001),

BLBP (p=0.007) and PEA15 (p=0.003). (K) Stretching reduced cellular ALDOC, BLBP,

and PEA15 fluorescence intensities 30 min post-injury (p<0.001, n=3-5).

120

Figure 3.5: CSF profiles of marker panel in TBI patients on injury day and

consecutive 5 days are diverse

121

(A) Immunoblots of GFAP (50kD with BDPs 37, 25, 20 and 18kD), S100β (10kD), ALDOC

(40kD), GS (45kD), BLBP (15kD) and PEA15 (15kD) of 30 µl CSF samples from injury

day (i) and subsequent 5 post-injury days (i+1 to i+5) of a severe 54 year old male TBI

patient (1a.-1f.) alongside 30µl control CSF of a 24 year old male (I.). Bleeding indicator

APOB (130 and 250kD) had variable intensity over time post-injury and was absent from

healthy CSF; CSF marker PTGDS (22kD) had robust signal in Crl CSF but was absent

acutely after TBI and 1d post-injury, and signals recovered stepwise on subsequent post-

injury days. (B) Six CSF samples (30µl/lane) from four TBI patients (2.-5.) show variable

signals of GFAP and large BDPs (50-37kD), and new small GFAP BDPs (25/23kD

doublet, 20kD, 18kD) on injury day (patients 2., 3., 4a.) and 1d post-injury (4b., 5.) and

control CSF of a 22 year old male (II.). (C) CSF immunoblots (30µl/lane) show full size

ALDOC (40kD) in five TBI patients (6.-10.) and variable intensity of 38kD ALDOC BDP

on four days post-injury in three TBI patients (8.-10.) while a Crl subject showed no

ALDOC (III.). (D-K) Jitterplots (replicates averaged) and box-and-whisker plots, median

(line) and geometric mean (dashed) show logarithmic scaled immunoblot optical densities

(OD) of GFAP, S100β and AID biomarker CSF signals of 20-25 TBI patients on injury day

and subsequent 5 post-injury days and 8-11 Controls (n: subjects numbers per day). (D)

Total GFAP (separate BDPs, S5) was elevated on all TBI days (black *, p<0.05) and

declined over time (red *, p<0.001, i+4/5 p=0.002). (E) S100β was increased on each TBI

day versus controls. (F) ALDOC (p< 0.004) and (G) GS (p < 0.001) were elevated on

each day in TBI CSF versus Crls without significant decline. (H) BLBP (p< 0.03) and (I)

PEA15 (p< 0.004) had elevated mean levels in TBI on indicated days. Serum protein

APOB (J) was elevated in TBI versus Crl CSF (p<0.005). CSF standard PTGDS was

122

decreased in TBI versus Crl (p< 0.004) with levels depleted to various extents followed

by recovery.

123

Figure 3.6: MRM mass spectrometry provides concentration comparison of AID

biomarkers

(A) MRM-MS traces of specific peptides for GFAP, ALDOC and BLBP of three product

ion traces (y #) with given mass over charge (m/z) values and their retention time (min,

x-axis) of biomarker specific precursor ions of m/z 549.816 (for GFAP-specific peptide),

m/z 526.970 (ALDOC) and m/z 446.256 (BLBP). Traces are of CSF samples from a 21

year old male severe TBI patient (left) and a 24 year old male Control (right). (B) Biplot

124

shows log MRM values of endogenous/standard GFAP peptide ion ratios (x-axis) over

log GFAP immunoblot densities in TBI patients’ CSF samples with regression line and

Spearman correlation (r.s.=0.874, p<0.0001). Insert scatterplot of normal distributed

residuals (y-axis) over normal quantiles (x-axis, Pearson coefficient R2=0.991, p<0.0001)

validates accuracy of the two independent methods. (C) Mean MRM concentrations for

GFAP, BLBP, GS and ALDOC on injury day in TBI patients. ALDOC had 2.5-fold higher

concentrations than GFAP. GFAP and ALDOC levels were over two orders larger than

those of BLBP (p<0.001) and over three orders higher than GS (p<0.002). (D) Mean CSF

concentrations on the third post-injury day of ALDOC were 10-fold higher than those of

GFAP (p=0.008). BLBP levels were lower than ALDOC and GFAP levels (p<0.001) and

mean ALDOC levels were three orders of magnitude higher than those of GS (p=0.02).

125

Figure 3.7: Unsupervised multivariate biomarker analysis stratifies TBI patients

using factors

(A) Unsupervised factor analysis grouped biomarkers S100, GFAP, small GFAP BDPs

and APOB into Factor A (gray), ALDOC, 38 kD ALDOC BDP, BLBP, GS and PEA15 into

Factor B (green), given with their respective loading and Cronbach’s coefficients for

each factors reliability. (B) Scatterplot shows Factors A (x-axis) and Factor B (y-axis) CSF

biomarker levels (z-units) from 12 subjects with signals for all biomarkers and they were

partitioned between control and TBI (green dashed line) and between survivors and non-

survivors (gray line). N=number of observations. (C) Classification tree boundaries that

126

partitioned controls, survivors and non-survivors of TBI using Factor thresholds. (D)

Standardized means of Factor A plotted over time post-injury show difference between

TBI survivor and non-survivors on several post-injury days (p<0.03) and decreases over

days post-injury in both TBI patient groups (P<0.005). (E) Factor B means differed

between TBI and Controls (P<0.001) but means did not differ significantly in survival or

temporal profiles. n=number of subjects (D, E).

127

128

Figure 3.8: AID biomarkers are elevated in severe and mild TBI patient’s blood with

unique kinetics

(A) Immunoblot signals for GFAP (25 kD BDP), aldolase A+C, ALDO (mab E9), PEA15

and BLBP in depleted 30µl plasma samples of a control subject (VI.) and 3 severe TBI

patients on injury day and following 2-4 post-injury days. B-E) Scatterplots show levels in

plasma with temporal profiles for GFAP 25 kD BDP (B), ALDOC (mab 5C9) (C), BLBP

(D) and PEA15 (E). Same patient data shown in A are connected by gray lines. (B) GFAP

25 kD BDP was absent on injury day and elevated on post-injury days 1-5 (p<0.0001).

(C) ALDOC levels were elevated in TBI (p<0.027) on injury day, first and second post-

injury days (p<0.009) followed by decrease thereafter (between i+1 and i+4/5 p=0.041).

(D) Mean BLBP levels were increased on injury day in TBI (p=0.0067), stayed elevated

on the first post-injury day and decreased subsequently (p< 0.017). (E) PEA15 levels

were increased on injury day in TBI (p=0.024) and decreased thereafter (p<0.036). (F)

Pilot data show acute post-injury serum presence (see post-injury h) of ALDO (mab E9),

BLBP and PEA15 in CT-positive and CT-negative mild TBI patients while GFAP BDPs

were absent or weak.

129

3.6 TABLES

Name Antibodies (Company) Epitope

GFAP

Rabbit polyclonal anti-GFAP (DAKO,

Z0334)

Whole cow GFAP; recognizes full size

GFAP and large and small breakdown

products (BDPs).

Chicken polyclonal anti GFAP

(ThermoFisher Scientific, PA1-10004)

Whole bovine GFAP; Recognizes full

size GFAP, large and small BDPs

ALDOC

Rabbit affinity purified polyclonal anti-

ALDOC (Genetex, GTX102284)

Recombinant ALDOC fragment amino

acids 10-163 (P09972)

Rabbit Serum 88 (Encor, gift) Recombinant whole ALDOC and BDP

Several monoclonal ALDOC

antibodies (Encor): IgG1 mab 1A1

(MCA-1A1), IgG1 mab E9 (MCA-E9),

IgG1 mab 4A9 (MCA-4A9), IgG1 mab

5C9

Mab 1A1: C-terminal peptide

Mab E9: Recombinant whole protein

Mab 4A9: N-terminal peptide

(MPHSYPALSAEQKKELS)

Mab 5C9: N-terminus

GS

Rabbit IgG fraction polyclonal anti GS

(Sigma, G2781)

GS peptide amino acids 357-373,

Mouse mab IgG2A to GS clone 6 (BD

Transduction, 610517)

Full size GS

PEA15 Rabbit polyclonal affinity purified anti

PEA15 (Cell Signaling)

Human PEA15 peptide surrounding

Leu60

BLBP Affinity purified rabbit polyclonal anti –

FABP7 (Millipore)

GST-tagged recombinant full size

human FABP7, specific to BLBP

130

= FABP7

Affinity purified rabbit polyclonal anti-

FABP7 clone RB22973(Abgent)

C-terminal human FABP7 peptide

amino acids 104-132, specific to BLBP

APOB

Rabbit affinity purified polyclonal IgG

anti-APOB (PTGlab, 20578-1-AP)

Unspecified APOB peptide

APOB 120-130 kD observed band, full

size 516 kD

PTGDS Rabbit affinity purified IgG anti-PTGDS

(USBiological, P9053-24D)

Synthetic human PTGDS peptide amino

acids 120-190

Table 3.1: Primary antibodies

Listed are primary antibodies, commercial source, and epitopes used for Western blotting

and immunocytochemistry.

131

Application Host, target, conjugate Dilution Company Catalog # Im

mu

no-

blo

ttin

g

Goat anti-rabbit IgG, HRP 1:10,000 Thermo Fisher 31460

Goat anti-mouse IgG, HRP 1:10,000 Thermo Fisher 31430

Goat anti-chicken IgY, HRP 1:10,000 Thermo Fisher SA1-72012

Imm

unocyto

che

mis

try

Donkey anti-rabbit IgG, AlexaFluor 488 1:150 JacksonImmuno 711-545-

152

Donkey anti-rabbit IgG, AlexaFluor 647 1:150 JacksonImmuno 711-605-

152

Donkey anti-rabbit IgG, Cy 3 1:250 JacksonImmuno 711-165-

152

Donkey anti-mouse IgG, AlexaFluor

488 1:200 JacksonImmuno

715-545-

151

Donkey anti-mouse IgG, Cy 3 1:150 JacksonImmuno 715-165-

151

Donkey anti-chicken IgY, AlexaFluor

647 1:80 JacksonImmuno

703-605-

155

Donkey anti-goat IgG, AlexaFluor 488 1:100 JacksonImmuno 705-545-

003

Donkey anti-rat IgG, AlexaFluor 594 1:250 JacksonImmuno 712-585-

150

Table 3.2: Secondary antibodies

Listed are secondary detection antibodies used for Western blots and

immunocytochemistry with dilution and commercial sources.

132

Name Peptide Sequence Measured MRM Transition

GFAP ALAAELNQLR(Heavy) 554.821 (2+) --> 924.514 (1+, y8)

554.821 (2+) --> 853.477 (1+, y7)

554.821 (2+) --> 782.439 (1+, y6)

ALAAELNQLR(Light) 549.816 (2+) --> 914.505 (1+, y8)

549.816 (2+) --> 843.468 (1+, y8)

549.816 (2+) --> 722.431 (1+, y8)

LADVYQAELR (Heavy) 594.758 (2+) --> 1003.508 (1+, y8)

594.758 (2+) --> 789.413 (1+, y6)

594.758 (2+) --> 626.350 (1+, y5)

LADVYQAELR (Light) 589.314 (2+) --> 993.500 (1+, y8)

589.314 (2+) --> 779.405 (1+, y6)

589.314 (2+) --> 616.341 (1+, y5)

ALDOC TPSALAILENANVLAR (Heavy) 831.974 (2+) --> 1193.688 (1+ y11)

831.974 (2+) --> 1122.651 (1+ y10)

831.974 (2+) --> 1009.566 (1+ y9)

TPSALAILENANVLAR (Light) 826.970 (2+) --> 1183.679 (1+, y11)

826.970 (2+) --> 1112.642 (1+, y10)

826.970 (2+) --> 999.558 (1+, y9)

GS DIVEAHYR (Heavy) 506.758 (2+) --> 784.398 (1+, y6)

506.758 (2+) --> 685.329 (1+, y5)

506.758 (2+) --> 556.287 (1+, y4)

DIVEAHYR (Light) 501.753 (2+) --> 774.389 (1+, y6)

501.753 (2+) --> 675.321 (1+, y5)

501.753 (2+) --> 546.278 (1+, y4)

133

BLBP ALGVGFATR (Heavy) 451.260 (2+) --> 717.392 (1+, y7)

= FABP7 451.260 (2+) --> 660.370 (1+, y6)

451.260 (2+) --> 561.302 (1+, y5)

ALGVGFATR (Light) 446.256 (2+) --> 707.384 (1+, y7)

446.256 (2+) --> 650.362 (1+, y6)

446.256 (2+) --> 551.294 (1+, y5)

APOB SPAFTDLHLR (Heavy) 389.545 (3+) --> 764.429 (1+, y6)

389.545 (3+) --> 663.381 (1+, y5)

389.545 (3+) --> 491.771 (2+, y8)

SPAFTDLHLR (Light) 386.208 (3+) --> 754.421 (1+ y6)

386.208 (3+) --> 653.373 (1+ y5)

386.208 (3+) --> 486.767 (2+ y8)

PTGDS APEAQVSVQPNFQQDK (Heavy) 897.449 (2+) --> 1297.663 (1+, y11)

897.449 (2+) --> 1198.594 (1+, y10)

897.449 (2+) --> 884.435 (1+, y7)

APEAQVSVQPNFQQDK (Light) 893.442 (2+) --> 1289.648 (1+, y11)

893.442 (2+) --> 1190.580 (1+, y10)

893.442 (2+) --> 876.421 (1+, y7)

Table 3.3: MRM peptides and ion transitions

Human CSF biomarker-specific peptide precursor ions were selected for MRM-MS based

on the above peptide and ion transition list. MRM-MS was operated in positive ion mode.

m/z and charge state (CS [z]) values for each peptide listed.

134

Accession Protein Name

TBI Only CSF Proteins

O94760 N(G),N(G)-dimethylarginine dimethylaminohydrolase 1

P11142 Heat shock cognate 71 kDa protein

P18206 Vinculin

P15104 Glutamine synthetase (GS) (EC 6.3.1.2)

P12277 Creatine kinase B-type

Q06830 Peroxiredoxin-1

P31946 14-3-3 protein beta/alpha

P62258 14-3-3 protein epsilon

P61981 14-3-3 protein gamma

P63104 14-3-3 protein zeta/delta

P23528 Cofilin-1

O75874 Isocitrate dehydrogenase cytoplasmic

P00558 Phosphoglycerate kinase 1

P13796 Plastin-2

P67936 Tropomyosin alpha-4 chain

Q13885 Tubulin beta-2A chain

P27348 14-3-3 protein theta

Q16555 Dihydropyrimidinase-related protein 2

P21333 Filamin-A

Q12765 Secernin-1

P06753 Tropomyosin alpha-3 chain

P14136 Glial fibrillary acidic protein

P30041 Peroxiredoxin-6

P08670 Vimentin

P80108 Phosphatidylinositol-glycan-specific phospholipase D

P00491 Purine nucleoside phosphorylase

P25713 Metallothionein-3

P00918 Carbonic anhydrase 2

Q01469 Fatty acid-binding protein, epidermal

P30043 Flavin reductase

Q06033 Inter-alpha-trypsin inhibitor heavy chain H3

P02545 Prelamin-A/C

P26447 Protein S100-A4

P09382 Galectin-1

P09429 High mobility group protein B1

P26583 High mobility group protein B2

P18669 Phosphoglycerate mutase 1

Q71U36 Tubulin alpha-1A chain

P04040 Catalase

P21291 Cysteine and glycine-rich protein 1

P26038 Moesin

P06703 Protein S100-A6

135

P28799 Granulins

Heat shock-related 70 kDa protein 2

P35998 26S protease regulatory subunit 7

P68032 Actin, alpha cardiac muscle 1

P63261 Actin, cytoplasmic 2

P00568 Adenylate kinase isoenzyme 1

Q01518 Adenylyl cyclase-associated protein 1

P12814 Alpha-actinin-1

P04083 Annexin A1

P04114 Apolipoprotein B-100

P02655 Apolipoprotein C-II

Q13790 Apolipoprotein F

O14791 Apolipoprotein L1

O95445 Apolipoprotein M

P08519 Apolipoprotein(a)

P07738 Bisphosphoglycerate mutase

P04003 C4b-binding protein alpha chain

P20851 C4b-binding protein beta chain

P05937 Calbindin

P00915 Carbonic anhydrase 1

P16152 Carbonyl reductase

P15169 Carboxypeptidase N catalytic chain

P22792 Carboxypeptidase N subunit 2

P49913 Cathelicidin antimicrobial peptide

O43866 CD5 antigen-like

P06276 Cholinesterase

P00740 Coagulation factor IX

P05160 Coagulation factor XIII B chain

P02745 Complement C1q subcomponent subunit A

P31146 Coronin-1A

P02741 C-reactive protein

P06732 Creatine kinase M-type

P13716 Delta-aminolevulinic acid dehydratase

P81605 Dermcidin

P15090 Fatty acid-binding protein, adipocyte

P02792 Ferritin light chain

Q9UGM5 Fetuin-B

O75636 Ficolin-3

P05062 Fructose-bisphosphate aldolase B

P06744 Glucose-6-phosphate isomerase

P35754 Glutaredoxin-1

P78417 Glutathione S-transferase omega-1

P09211 Glutathione S-transferase P

P69891 Hemoglobin subunit gamma-1

P26927 Hepatocyte growth factor-like protein

136

P10412 Histone H1.4

P16401 Histone H1.5

P62805 Histone H4

Q86YZ3 Hornerin

Q14520 Hyaluronan-binding protein 2

P01591 Immunoglobulin J chain

P02533 Keratin, type I cytoskeletal 14

P35908 Keratin, type II cytoskeletal 2 epidermal

P13647 Keratin, type II cytoskeletal 5

P02788 Lactotransferrin

P30740 Leukocyte elastase inhibitor

P18428 Lipopolysaccharide-binding protein

P00338 L-lactate dehydrogenase A chain

P14151 L-selectin

Q9Y5Y7 Lymphatic vessel endothelial hyaluronic acid receptor 1

P14174 Macrophage migration inhibitory factor

P14780 Matrix metalloproteinase-9

P11137 Microtubule-associated protein 2

P19105 Myosin regulatory light chain 12A

P12882 Myosin-1

P12883 Myosin-7

P35579 Myosin-9

P59665 Neutrophil defensin 1

P80188 Neutrophil gelatinase-associated lipocalin

P30044 Peroxiredoxin-5, mitochondrial

P02775 Platelet basic protein

P02776 Platelet factor 4

P20742 Pregnancy zone protein

P07737 Profilin-1

P27918 Properdin

P25786 Proteasome subunit alpha type-1

P28072 Proteasome subunit beta type-6

P05109 Protein S100-A8

Q9UK55 Protein Z-dependent protease inhibitor

Q92954 Proteoglycan 4

P31150 Rab GDP dissociation inhibitor alpha

P52565 Rho GDP-dissociation inhibitor 1

P52566 Rho GDP-dissociation inhibitor 2

P0DJI8 Serum amyloid A-1 protein

P0DJI9 Serum amyloid A-2 protein

P02743 Serum amyloid P-component

P04278 Sex hormone-binding globulin

Q9H299 SH3 domain-binding glutamic acid-rich-like protein 3

P10599 Thioredoxin

P07996 Thrombospondin-1

137

P62328 Thymosin beta-4

P37837 Transaldolase

P29401 Transketolase

P68363 Tubulin alpha-1B chain

P68366 Tubulin alpha-4A chain

P07437 Tubulin beta chain

P68371 Tubulin beta-4B chain

P09936 Ubiquitin carboxyl-terminal hydrolase isozyme L1

P04275 von Willebrand factor

P61604 10 kDa heat shock protein, mitochondrial

P62191 26S protease regulatory subunit 4

P17980 26S protease regulatory subunit 6A

P43686 26S protease regulatory subunit 6B

P62195 26S protease regulatory subunit 8

Q13200 26S proteasome non-ATPase regulatory subunit 2

P51665 26S proteasome non-ATPase regulatory subunit 7

P52209 6-phosphogluconate dehydrogenase, decarboxylating

P00325 Alcohol dehydrogenase 1B

Q9NZD4 Alpha-hemoglobin-stabilizing protein

P20160 Azurocidin

P02730 Band 3 anion transport protein

Q562R1 Beta-actin-like protein 2

Q13938 Calcyphosin

P62158 Calmodulin

P08311 Cathepsin G

P29762 Cellular retinoic acid-binding protein 1

Q15782 Chitinase-3-like protein 2

O43405 Cochlin

P32320 Cytidine deaminase

P19957 Elafin

P12724 Eosinophil cationic protein

P02794 Ferritin heavy chain

Q05315 Galectin-10

P00739 Haptoglobin-related protein

P0DMV8 Heat shock 70 kDa protein 1A

P08238 Heat shock protein HSP 90-beta

P69892 Hemoglobin subunit gamma-2

Q14103 Heterogeneous nuclear ribonucleoprotein D0

P22492 Histone H1t

P20671 Histone H2A type 1-D

O60814 Histone H2B type 1-K

P68431 Histone H3.1

P01877 Ig alpha-2 chain C region

P01880 Ig delta chain C region

P01743 Ig heavy chain V-I region HG3

138

P23083 Ig heavy chain V-I region V35

P06331 Ig heavy chain V-II region ARH-77

P01824 Ig heavy chain V-II region WAH

P01769 Ig heavy chain V-III region GA

P01762 Ig heavy chain V-III region TRO

P01779 Ig heavy chain V-III region TUR

P01594 Ig kappa chain V-I region AU

P01604 Ig kappa chain V-I region Kue

P01605 Ig kappa chain V-I region Lay

P01608 Ig kappa chain V-I region Roy

P01610 Ig kappa chain V-I region WEA

P01611 Ig kappa chain V-I region Wes

P01616 Ig kappa chain V-II region MIL

P04206 Ig kappa chain V-III region GOL

P06311 Ig kappa chain V-III region IARC/BL41

P01624 Ig kappa chain V-III region POM

P01623 Ig kappa chain V-III region WOL

P04211 Ig lambda chain V region 4A

P01701 Ig lambda chain V-I region NEW

P01702 Ig lambda chain V-I region NIG-64

P04208 Ig lambda chain V-I region WAH

P06889 Ig lambda chain V-IV region MOL

A0M8Q6 Ig lambda-7 chain C region

P04220 Ig mu heavy chain disease protein

P09960 Leukotriene A-4 hydrolase

P08637 Low affinity immunoglobulin gamma Fc region receptor III-A

Q9BZG9 Ly-6/neurotoxin-like protein 1

P40121 Macrophage-capping protein

P08493 Matrix Gla protein

P02686 Myelin basic protein

P20916 Myelin-associated glycoprotein

P24158 Myeloblastin

P05164 Myeloperoxidase

P60660 Myosin light polypeptide 6

P29966 Myristoylated alanine-rich C-kinase substrate

P22894 Neutrophil collagenase

P59666 Neutrophil defensin 3

P08246 Neutrophil elastase

P10153 Non-secretory ribonuclease

P20472 Parvalbumin alpha

O75594 Peptidoglycan recognition protein 1

P15259 Phosphoglycerate mutase 2

P0CG48 Polyubiquitin-C

P31949 Protein S100-A11

P80511 Protein S100-A12

139

P04271 Protein S100-B

P48539 Purkinje cell protein 4

P48741 Putative heat shock 70 kDa protein 7

Q9HD89 Resistin

P63313 Thymosin beta-10

P07951 Tropomyosin beta chain

Q13509 Tubulin beta-3 chain

P04350 Tubulin beta-4A chain

Q9BW30 Tubulin polymerization-promoting protein family member 3

Q9Y279 V-set and immunoglobulin domain-containing protein 4

TBI and Control CSF Proteins

P02649 Apolipoprotein E

P10909 Clusterin

P09972 Fructose-bisphosphate aldolase C

P60709 Actin, cytoplasmic 1

P07195 L-lactate dehydrogenase B chain

P13645 Keratin, type I cytoskeletal 10

P00441 Superoxide dismutase

P06733 Alpha-enolase

P06396 Gelsolin

P19823 Inter-alpha-trypsin inhibitor heavy chain H2

P30086 Phosphatidylethanolamine-binding protein 1

P04075 Fructose-bisphosphate aldolase A

P40925 Malate dehydrogenase, cytoplasmic

P32119 Peroxiredoxin-2

P04406 Glyceraldehyde-3-phosphate dehydrogenase

P00751 Complement factor B

P62937 Peptidyl-prolyl cis-trans isomerase A

P01023 Alpha-2-macroglobulin

P01019 Angiotensinogen

P16070 CD44 antigen

P0C0L5 Complement C4-B

P01034 Cystatin-C

Q12805 EGF-containing fibulin-like extracellular matrix protein 1

P18065 Insulin-like growth factor-binding protein 2

P36955 Pigment epithelium-derived factor

Q13228 Selenium-binding protein 1

Q14515 SPARC-like protein 1

P19320 Vascular cell adhesion protein 1

P36222 Chitinase-3-like protein 1

Q9UBP4 Dickkopf-related protein 3

O14594 Neurocan core protein

O00584 Ribonuclease T2

O14498 Immunoglobulin superfamily containing leucine-rich repeat protein

140

P01871 Ig mu chain C region

P06681 Complement C2

P07108 Acyl-CoA-binding protein

P07225 Vitamin K-dependent protein S

P08294 Extracellular superoxide dismutase

P09486 SPARC

P12259 Coagulation factor V

P13473 Lysosome-associated membrane glycoprotein 2

P17900 Ganglioside GM2 activator

P19022 Cadherin-2

P43251 Biotinidase

P49908 Selenoprotein P

P78324 Tyrosine-protein phosphatase non-receptor type substrate 1

Q08380 Galectin-3-binding protein

Q12841 Follistatin-related protein 1

Q13449 Limbic system-associated membrane protein

Q13740 CD166 antigen

Q14118 Dystroglycan

Q8WXD2 Secretogranin-3

Q96GW7 Brevican core protein

Q9P121 Neurotrimin

P02768 Serum albumin

P02787 Serotransferrin

P01024 Complement C3

P00450 Ceruloplasmin

P01008 Antithrombin-III

P00738 Haptoglobin

P02656 Apolipoprotein C-III

P02790 Hemopexin

P07339 Cathepsin D

P07602 Prosaposin

P07858 Cathepsin B

P08571 Monocyte differentiation antigen CD14

P08697 Alpha-2-antiplasmin

P09871 Complement C1s subcomponent

P13591 Neural cell adhesion molecule 1

P16870 Carboxypeptidase E

P23142 Fibulin-1

P43652 Afamin

P61769 Beta-2-microglobulin

Q12907 Vesicular integral-membrane protein VIP36

P02763 Alpha-1-acid glycoprotein 1

P19652 Alpha-1-acid glycoprotein 2

P01011 Alpha-1-antichymotrypsin

P01009 Alpha-1-antitrypsin

141

P04217 Alpha-1B-glycoprotein

P02765 Alpha-2-HS-glycoprotein

P02647 Apolipoprotein A-I

P02652 Apolipoprotein A-II

P06727 Apolipoprotein A-IV

P02654 Apolipoprotein C-I

P05090 Apolipoprotein D

P17174 Aspartate aminotransferase, cytoplasmic

O75882 Attractin

P98160 Basement membrane-specific heparan sulfate proteoglycan core protein

P02749 Beta-2-glycoprotein 1

Q96KN2 Beta-Ala-His dipeptidase

P55290 Cadherin-13

O94985 Calsyntenin-1

Q96IY4 Carboxypeptidase B2

Q9NQ79 Cartilage acidic protein 1

P43121 Cell surface glycoprotein MUC18

P00742 Coagulation factor X

P00748 Coagulation factor XII

P02452 Collagen alpha-1

P08123 Collagen alpha-2

P02746 Complement C1q subcomponent subunit B

P02747 Complement C1q subcomponent subunit C

P00736 Complement C1r subcomponent

Q9NZP8 Complement C1r subcomponent-like protein

P0C0L4 Complement C4-A

P01031 Complement C5

P13671 Complement component C6

P10643 Complement component C7

P07357 Complement component C8 alpha chain

P07358 Complement component C8 beta chain

P07360 Complement component C8 gamma chain

P02748 Complement component C9

P00746 Complement factor D

P08603 Complement factor H

Q03591 Complement factor H-related protein 1

P36980 Complement factor H-related protein 2

P05156 Complement factor I

P08185 Corticosteroid-binding globulin

Q16610 Extracellular matrix protein 1

P02671 Fibrinogen alpha chain

P02675 Fibrinogen beta chain

P02679 Fibrinogen gamma chain

P02751 Fibronectin

P09104 Gamma-enolase

142

P22352 Glutathione peroxidase 3

P69905 Hemoglobin subunit alpha

P68871 Hemoglobin subunit beta

P02042 Hemoglobin subunit delta

P05546 Heparin cofactor 2

P04196 Histidine-rich glycoprotein

P16403 Histone H1.2

P22692 Insulin-like growth factor-binding protein 4

P24592 Insulin-like growth factor-binding protein 6

Q16270 Insulin-like growth factor-binding protein 7

P35858 Insulin-like growth factor-binding protein complex acid labile subunit

P19827 Inter-alpha-trypsin inhibitor heavy chain H1

Q14624 Inter-alpha-trypsin inhibitor heavy chain H4

P29622 Kallistatin

P35527 Keratin, type I cytoskeletal 9

P04264 Keratin, type II cytoskeletal 1

P01042 Kininogen-1

P02750 Leucine-rich alpha-2-glycoprotein

P51884 Lumican

P61626 Lysozyme C

P07333 Macrophage colony-stimulating factor 1 receptor

P01033 Metalloproteinase inhibitor 1

P20774 Mimecan

P02144 Myoglobin

Q96PD5 N-acetylmuramoyl-L-alanine amidase

O15394 Neural cell adhesion molecule 2

O00533 Neural cell adhesion molecule L1-like protein

P55058 Phospholipid transfer protein

P03952 Plasma kallikrein

P05155 Plasma protease C1 inhibitor

P05154 Plasma serine protease inhibitor

P00747 Plasminogen

Q15113 Procollagen C-endopeptidase enhancer 1

P41222 Prostaglandin-H2 D-isomerase

P02760 Protein AMBP

Q99497 Protein deglycase DJ-1

P06702 Protein S100-A9

P00734 Prothrombin

P14618 Pyruvate kinase PKM

P02753 Retinol-binding protein 4

P07998 Ribonuclease pancreatic

Q86VB7 Scavenger receptor cysteine-rich type 1 protein M130

P35542 Serum amyloid A-4 protein

P27169 Serum paraoxonase/arylesterase 1

O00391 Sulfhydryl oxidase 1

143

P22105 Tenascin-X

P05452 Tetranectin

P05543 Thyroxine-binding globulin

Q15582 Transforming growth factor-beta-induced protein ig-h3

P02766 Transthyretin

P60174 Triosephosphate isomerase

P02774 Vitamin D-binding protein

P04004 Vitronectin

P54289 Voltage-dependent calcium channel subunit alpha-2/delta-1

P25311 Zinc-alpha-2-glycoprotein

P63267 Actin, gamma-enteric smooth muscle

P05067 Amyloid beta A4 protein

P51693 Amyloid-like protein 1

O43505 Beta-1,4-glucuronyltransferase 1

P80723 Brain acid soluble protein 1

P07711 Cathepsin L1

P13987 CD59 glycoprotein

Q8TCZ2 CD99 antigen-like protein 2

Q8N126 Cell adhesion molecule 3

Q8NFZ8 Cell adhesion molecule 4

P10645 Chromogranin-A

P12109 Collagen alpha-1 chain

Q12860 Contactin-1

Q9P0K1 Disintegrin and metalloproteinase domain-containing protein 22

Q13822 Ectonucleotide pyrophosphatase/phosphodiesterase family member 2

O94919 Endonuclease domain-containing 1 protein

P54764 Ephrin type-A receptor 4

P61916 Epididymal secretory protein E1

Q96KK5 Histone H2A type 1-H

P01876 Ig alpha-1 chain C region

P01857 Ig gamma-1 chain C region

P01859 Ig gamma-2 chain C region

P01860 Ig gamma-3 chain C region

P01861 Ig gamma-4 chain C region

P01764 Ig heavy chain V-III region 23

P01766 Ig heavy chain V-III region BRO

P01767 Ig heavy chain V-III region BUT

P01768 Ig heavy chain V-III region CAM

P01781 Ig heavy chain V-III region GAL

P01765 Ig heavy chain V-III region TIL

P01834 Ig kappa chain C region

P01593 Ig kappa chain V-I region AG

P01597 Ig kappa chain V-I region DEE

P01598 Ig kappa chain V-I region EU

P01613 Ig kappa chain V-I region Ni

144

P01606 Ig kappa chain V-I region OU

P01617 Ig kappa chain V-II region TEW

P01619 Ig kappa chain V-III region B6

P04207 Ig kappa chain V-III region CLL

P01620 Ig kappa chain V-III region SIE

P04433 Ig kappa chain V-III region VG

P04434 Ig kappa chain V-III region VH

P01625 Ig kappa chain V-IV region Len

P80748 Ig lambda chain V-III region LOI

P01714 Ig lambda chain V-III region SH

P0CG04 Ig lambda-1 chain C regions

P0CG05 Ig lambda-2 chain C regions

P0CG06 Ig lambda-3 chain C regions

Q9Y6R7 IgGFc-binding protein

B9A064 Immunoglobulin lambda-like polypeptide 5

P01344 Insulin-like growth factor II

Q92876 Kallikrein-6

O94772 Lymphocyte antigen 6H

P04156 Major prion protein

P41271 Neuroblastoma suppressor of tumorigenicity 1

P05408 Neuroendocrine protein 7B2

Q92823 Neuronal cell adhesion molecule

O95502 Neuronal pentraxin receptor

O15240 Neurosecretory protein VGF

Q02818 Nucleobindin-1

P10451 Osteopontin

Q96FE7 Phosphoinositide-3-kinase-interacting protein 1

Q9H3G5 Probable serine carboxypeptidase CPVL

Q9UHG2 ProSAAS

Q92520 Protein FAM3C

Q99435 Protein kinase C-binding protein NELL2

P05060 Secretogranin-1

P13521 Secretogranin-2

O75326 Semaphorin-7A

Q5TFQ8 Signal-regulatory protein beta-1 isoform 3

P04216 Thy-1 membrane glycoprotein

P13611 Versican core protein

Q8TAG5 V-set and transmembrane domain-containing protein 2A

Q8TEU8 WAP, Kazal, immunoglobulin, Kunitz and NTR domain-containing protein 2

Healthy CSF Proteins Only

P11021 78 kDa glucose-regulated protein

P24593 Insulin-like growth factor-binding protein 5

Q08431 Lactadherin

P04180 Phosphatidylcholine-sterol acyltransferase

145

Q06481 Amyloid-like protein 2

Q16620 BDNF/NT-3 growth factors receptor

P11362 Fibroblast growth factor receptor 1

P22304 Iduronate 2-sulfatase

Q86UX2 Inter-alpha-trypsin inhibitor heavy chain H5

Q96KG7 Multiple epidermal growth factor-like domains protein 10

P23471 Receptor-type tyrosine-protein phosphatase zeta

Q9NPR2 Semaphorin-4B

O14773 Tripeptidyl-peptidase 1

P30530 Tyrosine-protein kinase receptor UFO

Q99969 Retinoic acid receptor responder protein 2

P26992 Ciliary neurotrophic factor receptor subunit alpha

O95967 EGF-containing fibulin-like extracellular matrix protein 2

P21802 Fibroblast growth factor receptor 2

P98095 Fibulin-2

Q8NBJ4 Golgi membrane protein 1

P21246 Pleiotrophin

O15031 Plexin-B2

P51888 Prolargin

O60883 Prosaposin receptor GPR37L1

O75711 Scrapie-responsive protein 1

Q9Y646 Carboxypeptidase Q

Q01459 Di-N-acetylchitobiase

P40189 Interleukin-6 receptor subunit beta

Q92859 Neogenin

Q6UX71 Plexin domain-containing protein 2

P23470 Receptor-type tyrosine-protein phosphatase gamma

O60241 Adhesion G protein-coupled receptor B2

P49641 Alpha-mannosidase 2x

P55283 Cadherin-4

Q9BY67 Cell adhesion molecule 1

Q9Y287 Integral membrane protein 2B

Q9HCB6 Spondin-1

P08253 72 kDa type IV collagenase

P27797 Calreticulin

P02461 Collagen alpha-1 chain

P12111 Collagen alpha-3 chain

P14625 Endoplasmin

Q9UBQ6 Exostosin-like 2

P14314 Glucosidase 2 subunit beta

O75144 ICOS ligand

Mannosyl-oligosaccharide 1,2-alpha-mannosidase IA

P16035 Metalloproteinase inhibitor 2

Q7Z7M0 Multiple epidermal growth factor-like domains protein 8

P32004 Neural cell adhesion molecule L1

146

P14543 Nidogen-1

P23515 Oligodendrocyte-myelin glycoprotein

Q6UXB8 Peptidase inhibitor 16

P23284 Peptidyl-prolyl cis-trans isomerase B

Q96S96 Phosphatidylethanolamine-binding protein 4

Q96NZ9 Proline-rich acidic protein 1

Q9NYQ8 Protocadherin Fat 2

P23468 Receptor-type tyrosine-protein phosphatase delta

Q13332 Receptor-type tyrosine-protein phosphatase S

P34096 Ribonuclease 4

Q9Y6N7 Roundabout homolog 1

Q8WZ42 Titin

Q24JP5 Transmembrane protein 132A

Q9BRK5 45 kDa calcium-binding protein

O94910 Adhesion G protein-coupled receptor L1

O00468 Agrin

P07686 Beta-hexosaminidase subunit beta

Q9BQT9 Calsyntenin-3

Q8N3J6 Cell adhesion molecule 2

Q99674 Cell growth regulator with EF hand domain protein 1

Q6UW01 Cerebellin-3

Q16568 Cocaine- and amphetamine-regulated transcript protein

P39060 Collagen alpha-1 chain

P08174 Complement decay-accelerating factor

Q02246 Contactin-2

Q9C0A0 Contactin-associated protein-like 4

P07585 Decorin

P09417 Dihydropteridine reductase

P52799 Ephrin-B2

O94769 Extracellular matrix protein 2

Q8IWU5 Extracellular sulfatase Sulf-2

Q9UBX5 Fibulin-5

P14207 Folate receptor beta

Q6MZW2 Follistatin-related protein 4

O00451 GDNF family receptor alpha-2

P48058 Glutamate receptor 4

Q16769 Glutaminyl-peptide cyclotransferase

Q9Y2T3 Guanine deaminase

Q8IZP7 Heparan-sulfate 6-O-sulfotransferase 3

P18136 Ig kappa chain V-III region HIC

P01622 Ig kappa chain V-III region Ti

Q969P0 Immunoglobulin superfamily member 8

Q9NX62 Inositol monophosphatase 3

Q9UMF0 Intercellular adhesion molecule 5

O43291 Kunitz-type protease inhibitor 2

147

Q8N2S1 Latent-transforming growth factor beta-binding protein 4

Q9NT99 Leucine-rich repeat-containing protein 4B

P42785 Lysosomal Pro-X carboxypeptidase

P09603 Macrophage colony-stimulating factor 1

P22897 Macrophage mannose receptor 1

P55083 Microfibril-associated glycoprotein 4

Q16653 Myelin-oligodendrocyte glycoprotein

Q9NY97 N-acetyllactosaminide beta-1,3-N-acetylglucosaminyltransferase 2

Q9ULB1 Neurexin-1

Q9P2S2 Neurexin-2

Q9Y4C0 Neurexin-3

Q9NPD7 Neuritin

O94856 Neurofascin

Q7Z3B1 Neuronal growth regulator 1

Q15818 Neuronal pentraxin-1

P47972 Neuronal pentraxin-2

Q5BLP8 Neuropeptide-like protein C4orf48

Q99574 Neuroserpin

Q14112 Nidogen-2

Q14982 Opioid-binding protein/cell adhesion molecule

Q99983 Osteomodulin

P19021 Peptidyl-glycine alpha-amidating monooxygenase

P01127 Platelet-derived growth factor subunit B

Q9NZ53 Podocalyxin-like protein 2

P01210 Proenkephalin-A

Q5FWE3 Proline-rich transmembrane protein 3

P01303 Pro-neuropeptide Y

O15354 Prosaposin receptor GPR37

O60888 Protein CutA

P48745 Protein NOV homolog

Q8WZA1 Protein O-linked-mannose beta-1,2-N-acetylglucosaminyltransferase 1

Q9Y5F6 Protocadherin gamma-C5

C9JVW0 Putative transmembrane protein INAFM1

Q92932 Receptor-type tyrosine-protein phosphatase N2

Q16849 Receptor-type tyrosine-protein phosphatase-like N

P78509 Reelin

O75787 Renin receptor

Q9BZR6 Reticulon-4 receptor

Q6NW40 RGM domain family member B

Q93091 Ribonuclease K6

Q9BYH1 Seizure 6-like protein

Q6UXD5 Seizure 6-like protein 2

Q53EL9 Seizure protein 6 homolog

Q96PX8 SLIT and NTRK-like protein 1

Q8WVQ1 Soluble calcium-activated nucleotidase 1

148

Q9H4F8 SPARC-related modular calcium-binding protein 1

O60279 Sushi domain-containing protein 5

Q08629 Testican-1

Q92563 Testican-2

O43493 Trans-Golgi network integral membrane protein 2

O75509 Tumor necrosis factor receptor superfamily member 21

Q6UX73 UPF0764 protein C16orf89

Q9UPU3 VPS10 domain-containing receptor SorCS3

A6NLU5 V-set and transmembrane domain-containing protein 2B

Q15904 V-type proton ATPase subunit S1

Q9ULF5 Zinc transporter ZIP10

Table 3.4: TBI and Control CSF proteomes

Analytical mass spectrometry, by LC-MS/MS on LTQ-Orbitrap and Q-Exactive Orbitrap

mass spectrometers was conducted on CSF from 19 severe and moderate TBI patients

and 9 Control donors. Proteins were identified by MASCOT database searching

(SwissProt, Homo sapiens, ≥2 unique peptides, 95% peptide confidence) to arrive at

cumulative conservative TBI and healthy CSF protein lists using same conditions. These

CSF proteomes served as clinical correlates to determine the signature of a previously

identified astrocytic trauma-release proteome. Proteins from the mouse trauma release

proteome found in TBI CSF are highlighted in yellow (32). Astrocyte enriched proteins are

highlighted in blue (42). Proteins from both categories of trauma release and astrocyte

enrichment are highlighted in green. Italics indicate proteins present in plasma proteomes

(43, 44).

149

% Dead Total

GFAP ALDOC BLBP PEA15

%

Leaky

-0.0402 0.337 0.759 0.499 0.785 r s (Spearman)

0.809 0.155 < 0.001 0.0338 < 0.001 p value

38 19 20 18 20 Number of observations

% Dead

0.719 0.452 0.543 0.405

< 0.001 0.044 0.0194 0.0754

19 20 18 20

Total

GFAP

0.595 0.493 0.456

< 0.001 < 0.001 < 0.001

54 42 54

ALDOC

0.398 0.681

0.0084 < 0.001

43 55

BLBP

0.499

< 0.001

43

Table 3.5: Spearman correlations between culture trauma fluid biomarkers and

astrocyte cell fates

The p-values for Spearman correlations of ranks are shown for culture-released total

GFAP (full size band and all fragments), ALDOC, BLBP and PEA15 as well as their

correlation to rates of cell wounding (leaky membranes) and cell death. Numbers of

culture observations for control and all post-stretching data are included.

150

151

152

Table 3.6: TBI patient data, clinical samples and conducted experimental analyses

(A) Figure IDs are roman italic numbers that refer to signals of control subject CSF and

blood samples shown in Figures 3.5 and 3.8. Gender and age are listed for 17 healthy

subjects who donated CSF, obtained by lumbar drain, blood or both. Sample

measurements and replicates are listed for immunoblotting (IB), MRM-MS and proteome

analyses (LC-MS/MS). (B) Italicized figure IDs list severe TBI patients whose CSF,

obtained by ventriculostomy or/and blood samples are shown in Figures 3.5 and 3.8 with

gender and age. Injury cause including motor vehicle accidents (MVA) and gunshot

wound (GSW) is listed. Post-resuscitation GCS scores, survival and computed

tomography (CT) findings are given. CT scan reports include presence of intracerebral

hemorrhage (ICH) including one or more findings of contusion (Cnts), subdural hematoma

(SDH), subarachnoid hemorrhage (SAH) or intraventricular hemorrhage (IVH). Further,

153

presence of epidural hematoma (EDH), diffuse Axonal Injury (DAI), ischemia (Is) and

edema or midline shift (Edm, mdls) are reported. Post-injury days are given for CSF,

plasma and serum samples averaging multiple same-day samples for analyses. Italicized

figure IDs indicate mild TBI patients whose serum IB data are shown in Figure 3.8F.

Gender, age, injury cause, GCS score and CT normal (CT-) or abnormal CT (CT+) is

listed with scan findings as well as post-injury hrs are given for serum samples.

154

GFAP ALDOC BLBP GS PEA15 APOB PTGDS

Minimum OD 0.001 0.017 0.005 0.000 0.002 0.001 0.00039

Maximum OD 6.629 1.391 3.660 0.982 4.173 0.845 1.116

Orders of magnitude 4.1 1.9 2.9 3.4 3.3 3.0 3.5

# of observations 54 102 48 51 44 51 40

Table 3.7: Dynamic ranges of biomarker levels in CSF

Listed are minimum and maximum optical density readings for astrocyte injury biomarkers

GFAP, ALDOC, BLBP, GS and PEA15, as well as bleeding indicator APOB and CSF

standard PTGDS. Resulting signal ranges are given in orders of magnitude and number

of observation are listed (italic).

155

Marker Detection Limit

Interquartile Concentration Range

Immunoblot MRM

Immunoblot MRM Blood CSF CSF

ALDOC 0.2-1ng 58pg 1ng/mL - 13.3 ng/mL 600ng/mL - 1.3μg/mL 361ng/mL-1.5μg/mL

BLBP ~50pg 1pg 0ng/mL - 20ng/mL 2.3ng/mL - 20ng/mL 3.1ng/mL-14.4ng/mL

GFAP 8-40pg 1pg 267pg/mL - 20ng/mL 2.7ng/mL - 253ng/mL 86ng/mL-544ng/mL

Table 3.8: Concentrations and detection limits of ALDOC, BLBP and GFAP in TBI

CSF and blood

Pure protein dilution series in 0.5 % serum albumin were used to calibrate immunoblot

densitometry signals and determine their approximate detection limits in TBI CSF and

blood. Known amounts of heavy isotope labeled biomarker-specific peptides were used

as standards to determine MRM-MS interquartile ranges in TBI CSF.

156

Variable by Variable Spearman

corr. p

value n =

observations

GFAP total GFAP large

BDPs 0.9877

< 0.0001

64 Very strong

ALDOC total ALDOC 40 kD 0.9833 <

0.0001 121

APOB GFAP small

BDPs 0.898

< 0.001 42

S100β GFAP small

BDPs 0.87

< 0.001 54

APOB S100β 0.847 0 44

PEA15 BLBP 0.8054 <.0001

* 46

GFAP total GFAP small

BDPs 0.757

< 0.001 64 Strong

S100β GFAP total 0.7391 <.0001

* 54 APOB GS 0.726 0 44

BLBP ALDOC total 0.6816 <.0001

* 56

PEA15 S100β 0.6772 <.0001

* 43

GS AldoC total 0.6724 <.0001

* 53 APOB BLBP 0.638 0 44

GS BLBP 0.603 <.0001

* 49 APOB ALDOC total 0.602 0 46

BLBP GFAP small

BDPs 0.59

< 0.001 54 Moderate

BLBP S100β 0.5833 <.0001

* 51

GS S100β 0.5826 <.0001

* 46

PEA15 GFAP total 0.5755 <.0001

* 47

GS GFAP small

BDPs 0.573

< 0.001 51

PEA15 GFAP small

BDPs 0.572

< 0.001 47

PEA15 ALDOC total 0.5589 <.0001

* 49

GS ALDOC 38kD

BDP 0.549 0.0009

33 APOB GFAP total 0.541 0.0002 42

PEA15 GS 0.5334 <.0001

* 49

BLBP ALDOC 38kD

BDP 0.532 0.0003

41

157

BLBP GFAP total 0.5149 <.0001

* 54 APOB PEA15 0.506 0.0002 48

ALDOC total ALDOC 38kD

BPD 0.506

< 0.001 59

ALDOC total GFAP small

BDPs 0.477

< 0.001 61

ALDOC total S100β 0.4503 0.0005

* 56

ALDOC total GFAP total 0.3927 0.0017

* 61 Weak

GS GFAP total 0.3275 0.0190

* 51 P<0.05

PEA15 ALDOC 38kD

BDP 0.309 0.0749

34 Very weak

APOB ALDOC 38kD

BDP 0.261 0.1353

34 PTGDS ALDOC total 0.017 0.893 61

PTGDS ALDOC 38kD

BDP -0.024 0.8779

42 ALDOC 38kD

BDP GFAP small

BDPs -0.029 0.8628

39

S100β ALDOC 38kD

BDP -0.04 0.808

39 None PTGDS GS -0.13 0.3641 59 PTGDS APOB -0.183 0.2344 44 PTGDS BLBP -0.198 0.1473 57

GFAP total ALDOC 38kD

BDP -0.201 0.221

39

PTGDS GFAP small

BDPs -0.251 0.055

59

PTGDS PEA15 -0.307 0.0356 47 P<0.05 PTGDS S100β -0.314 0.0248 54

PTGDS GFAP total -0.446 0.0004 58 Moderate

Table 3.9: Biomarker panel correlations from CSF of severe TBI patients

Spearman rank correlation coefficients are given for all pairs of new and known astroglial

neurotrauma biomarkers and CSF standards with their p-values and number of CSF

samples analyzed. Coefficients >0.8-0.99 are considered very strong, 0.6-0.8 strong,

moderate >0.4-0.6, weak <0.4 and divergent if coefficients were <-0.3.

158

3.7 SUPPLEMENTAL FIGURES

S3.1: The flow chart illustrates the AID biomarker selection strategy

Flow chart shows steps used to arrive at this astroglial biomarker panel. First, TBI and

control CSF proteomes both generated by same LC-MS/MS settings were compiled and

compared against each other (Table 3.4). 59 significantly trauma-changed proteins were

previously identified by 2D gel analysis of culture medium (CM) from stretched astrocytes,

the majority of which was present in the CSF proteomes (32). Then, astrocyte enriched

proteins, by larger than 2-fold, were selected among the CSF and the trauma model

proteome lists (42). From the resulting 14 candidates, those proteins present in healthy

donor plasma were removed, including coactosin-like protein 1, heat shock cognate

71kDa protein, vinculin, apolipoprotein E, clusterin and lactate dehydrogenase B (43, 44).

Proteins with dominant expression outside the central nervous system (CNS) were also

excluded: transgelin, F-box only protein 2 and N, N-dimethyl arginine dimethyl

159

aminohydrolase 1 (45). Resulting astroglial neurotrauma biomarker candidates were

ALDOC, GS, BLBP and PEA15, all with predominant expression in the CNS, with some

presence outside of the CNS for GS and PEA15 (45).

160

S3.2: Human astrocyte cell leak and cell death populations show trends over time

with combined severities

Shown are arithmetic means of percent (A) leaky (membrane wounded) and (B) dead

astrocytes at various timepoints post-stretching, combining different pressure pulses.

Percent leaky cells were elevated at 30min against control and percent dead cells were

elevated by 2 days against control and 30min post-injury levels using Kruskal-Wallis

ANOVA on ranks (p<0.001). One-day post-injury cell death rates nearly reached

significance versus controls (p=0.052) in n=8-13 donors.

161

S3.3: Culture trauma temporal fluid profiles of full size GFAP plus large BDPs and

small GFAP BDPs are different

(A) Mean fluid levels of full size GFAP (50 kD) and larger proteolytic breakdown products

(large BDPs: 47, 42, 37 kD) were significantly elevated over controls at all times post-

stretching (asterisks), showed severity difference at 5h (black dot) and their release

increased significantly between 30min and 1-2 days post-injury (triangles). (B) Small

GFAP fragments (25, 20 and 18kD BDPs) were only released significantly more than

controls by 1 and 2 days post-injury (asterisks). Levels also differed significantly between

timepoints (triangles).

162

S3.4: Peptide mapping of glial fibrillary acidic protein break-down products

Glial fibrillary acidic protein (GFAP) break-down products (BDPs) were

immunoprecipitated from the conditioned medium (CM) and whole cell lysates (WCL) of

stretch injured astrocytes. Immunoprecipitated proteins were separated by SDS-PAGE

and stained with Sypro Ruby. Bands corresponding to the intact 49 kDa GFAP and 42,

38, 25, and 20 kDa BDPs were excised and in-gel digested with trypsin. LC-MS/MS

peptide mapping identified a common core region starting from alanine residue 71 for all

BDPs. Lower molecular weight BDPs are generated from additional C-terminal

cleavages.

163

S3.5: BLBP and GFAP are co-expressed in human astrocytes

A population of control human astrocytes show robust GFAP (white) and BLBP (green)

expression (A, B). 30min post-stretching, BLBP signals were depleted. The same cells

retained GFAP, but with altered distribution showing filament-disassembled or focal

presence in process endings (C, D). Membrane-wounded cells have PI-positive nuclei

(red).

164

S3.6: TBI patient CSF trajectories are shown separately for full size plus large

fragments and for small GFAP BDPs

Plots show CSF levels in Control and TBI patients of normalized GFAP optical densities

(A) for “upper” signal range (50kD and large BDPs <50-37kD) and (B) for “lower” GFAP

bands (BDPs sizes between 18-25kD). All severe TBI patients had robust elevation in

upper GFAP levels upper bands on injury day followed by decreases on later post-injury

days (red asterisks). Smaller sized GFAP BDPs had variable signal levels and means

decreased between one and 4-5 post-injury days. (A) Large GFAP signals (50-37kD)

were elevated on all TBI days versus Crl (p<0.06) and declined over time as indicated in

red (p <0.05, repeated measures ANOVA, see Methods). (B) Small GFAP BDPs (25-

18kD) were elevated in TBI versus Crl (p < 0.03) and declined between first post-injury

day later post-injury days (p< 0.05).

165

S3.7: TBI patient CSF trajectories for astroglial injury-defined biomarkers

measured by MRM-MS

Astroglial injury-defined biomarkers GFAP, ALDOC, BLBP, and GS were measured in

TBI patient CSF by MRM-MS. (A) GFAP was elevated on all TBI days compared to control

(grey *, p <0.01). (B) ALDOC was elevated on all TBI days compared to control (grey *, p

<0.01). (C) BLBP was elevated on all TBI days compared to control (grey *, p <0.05). (D)

166

GS was elevated on all TBI days compared to control (grey *, p <0.05).

167

S3.8: Temporal CSF trends for PTGDS, PEA15 and small GFAP BDPs differ between

TBI survivors and non-survivors

Explorative trend lines plot geometric mean levels of (A) PTGDS, (B) PEA15 and (C)

small GFAP fragments in Controls (black), TBI survivors (red) and non-survivors (blue)

with lower and upper bound error bars (95% confidence interval). CSF post-injury

trajectories of GFAP lower BDPs are significantly elevated in non-survivors versus

survivors by 28-fold on the first and 388-fold on the third post-injury day. PEA15 means

are elevated over up to three orders of magnitude in non-survivors versus survivors. Mean

PTGDS levels decrease more in survivors than non-survivors, and levels recover

gradually for survivors, resulting in significantly higher means on the third post-injury day

compared to non-survivors of TBI. Statistical test was multiple measures ANOVA, mixed

model, with non-constant intraclass variance (139).

168

S3.9: Temporal CSF trends for GFAP, ALDOC, BLBP, and GS differ between TBI

survivors and non-survivors

Explorative trend lines plot geometric mean levels of CSF biomarker concentrations

between TBI survivors and non-survivors. Survivor concentration profiles over time was

shown in grey, blue, pink, and orange for (A) GFAP, (B) ALDOC, (C) BLBP, and (D) GS

respectively. Non-survivor traces are displayed in red dashed lines. Patient n is listed by

169

color under each injury day post-TBI. SEM around the geometric mean are displayed by

the error bars.

170

S3.10: Immunological and mass spectrometry measurements show comparable

CSF profiles of a severe TBI patient

Shown are temporal profiles of (A) GFAP, (B) ALDOC, (C) GS and (D) BLBP in

longitudinal CSF samples of a severe TBI patient ‘1’ every 6h post-TBI. Biomarker levels

were measured using immunoblot densitometry (continued lines, optical densities, left y-

axes) and MRM-MS (dashed lines, ng/ml, right y-axes).

171

S3.11: Appearance of AID biomarkers in CSF and serum are different

Presence of GFAP, ALDOC, BLBP and PEA15 at 3 and 34h (first day post-injury) in CSF

and serum of a severe TBI patient (patient 1, Table 3.6). Temporal profile of same

172

markers in concurrent CSF and serum samples of same patient. AID markers were

elevated in serum acutely post-TBI, prior to GFAP elevation.

173

S3.12: Immunoblots shows distinct presence of AID biomarkers on injury day

among 15 mild TBI patients

Composite shows immunoblots of pure proteins, depleted sera from control (Crl) and 15

mild TBI patients (clinical data see Table 3.6) between 1-31h post-concussion for GFAP

25 kD BDP, ALDOC, BLBP and PEA15. Absence (-) or presence (+) of CT findings in

those patients are given. Exposure times match those shown for severe TBI patients

(Figure 3.8A).

174

3.8 REFERENCES

1. J. L. Wheble, D. K. Menon, TBI-the most complex disease in the most complex

organ: the CENTER-TBI trial-a commentary. Journal of the Royal Army Medical

Corps 162, 87-89 (2016); published online EpubApr (10.1136/jramc-2015-

000472).

2. B. Roozenbeek, A. I. Maas, D. K. Menon, Changing patterns in the epidemiology

of traumatic brain injury. Nature reviews. Neurology 9, 231-236 (2013); published

online EpubApr (10.1038/nrneurol.2013.22).

3. S. B. Rosenbaum, M. L. Lipton, Embracing chaos: the scope and importance of

clinical and pathological heterogeneity in mTBI. Brain imaging and behavior 6, 255-

282 (2012); published online EpubJun (10.1007/s11682-012-9162-7).

4. M. E. Brogan, J. J. Provencio, Spectrum of catastrophic brain injury: coma and

related disorders of consciousness. J Crit Care 29, 679-682 (2014); published

online EpubAug (10.1016/j.jcrc.2014.04.014).

5. A. Buki, N. Kovacs, E. Czeiter, K. Schmid, R. P. Berger, F. Kobeissy, D. Italiano,

R. L. Hayes, F. C. Tortella, E. Mezosi, A. Schwarcz, A. Toth, O. Nemes, S.

Mondello, Minor and repetitive head injury. Advances and technical standards in

neurosurgery 42, 147-192 (2015)10.1007/978-3-319-09066-5_8).

6. K. M. Helmick, C. A. Spells, S. Z. Malik, C. A. Davies, D. W. Marion, S. R. Hinds,

Traumatic brain injury in the US military: epidemiology and key clinical and

research programs. Brain imaging and behavior 9, 358-366 (2015); published

online EpubSep (10.1007/s11682-015-9399-z).

175

7. R. A. Stern, D. O. Riley, D. H. Daneshvar, C. J. Nowinski, R. C. Cantu, A. C.

McKee, Long-term consequences of repetitive brain trauma: chronic traumatic

encephalopathy. PM R 3, S460-467 (2011); published online EpubOct

(10.1016/j.pmrj.2011.08.008).

8. G. L. Sternbach, The Glasgow coma scale. J Emerg Med 19, 67-71 (2000).

9. E. W. Steyerberg, N. Mushkudiani, P. Perel, I. Butcher, J. Lu, G. S. McHugh, G.

D. Murray, A. Marmarou, I. Roberts, J. D. Habbema, A. I. Maas, Predicting

outcome after traumatic brain injury: development and international validation of

prognostic scores based on admission characteristics. PLoS medicine 5, e165;

discussion e165 (2008); published online EpubAug 5

(10.1371/journal.pmed.0050165).

10. N. A. Mushkudiani, C. W. Hukkelhoven, A. V. Hernandez, G. D. Murray, S. C. Choi,

A. I. Maas, E. W. Steyerberg, A systematic review finds methodological

improvements necessary for prognostic models in determining traumatic brain

injury outcomes. Journal of clinical epidemiology 61, 331-343 (2008); published

online EpubApr (10.1016/j.jclinepi.2007.06.011).

11. M. Bergsneider, D. A. Hovda, S. M. Lee, D. F. Kelly, D. L. McArthur, P. M. Vespa,

J. H. Lee, S. C. Huang, N. A. Martin, M. E. Phelps, D. P. Becker, Dissociation of

cerebral glucose metabolism and level of consciousness during the period of

metabolic depression following human traumatic brain injury. Journal of

neurotrauma 17, 389-401 (2000); published online EpubMay

(10.1089/neu.2000.17.389).

176

12. T. B. Meier, P. S. Bellgowan, R. Singh, R. Kuplicki, D. W. Polanski, A. R. Mayer,

Recovery of cerebral blood flow following sports-related concussion. JAMA

neurology 72, 530-538 (2015); published online EpubMay

(10.1001/jamaneurol.2014.4778).

13. O. Farkas, J. Lifshitz, J. T. Povlishock, Mechanoporation induced by diffuse

traumatic brain injury: an irreversible or reversible response to injury? The Journal

of neuroscience : the official journal of the Society for Neuroscience 26, 3130-3140

(2006).

14. K. A. Barbee, Mechanical cell injury. Ann N Y Acad Sci 1066, 67-84 (2005);

published online EpubDec (10.1196/annals.1363.006).

15. J. T. Povlishock, E. H. Pettus, Traumatically induced axonal damage: evidence for

enduring changes in axolemmal permeability with associated cytoskeletal change.

Acta neurochirurgica. Supplement 66, 81-86 (1996).

16. J. A. Colombo, A. Yanez, S. J. Lipina, Interlaminar astroglial processes in the

cerebral cortex of non human primates: response to injury. J Hirnforsch 38, 503-

512 (1997).

17. D. H. Smith, K. Uryu, K. E. Saatman, J. Q. Trojanowski, T. K. McIntosh, Protein

accumulation in traumatic brain injury. Neuromolecular medicine 4, 59-72

(2003)10.1385/NMM:4:1-2:59).

18. A. Buki, R. Siman, J. Q. Trojanowski, J. T. Povlishock, The role of calpain-mediated

spectrin proteolysis in traumatically induced axonal injury. Journal of

neuropathology and experimental neurology 58, 365-375 (1999).

177

19. M. A. Hemphill, S. Dauth, C. J. Yu, B. E. Dabiri, K. K. Parker, Traumatic brain injury

and the neuronal microenvironment: a potential role for neuropathological

mechanotransduction. Neuron 85, 1177-1192 (2015); published online EpubMar

18 (10.1016/j.neuron.2015.02.041).

20. S. Mondello, U. Muller, A. Jeromin, J. Streeter, R. L. Hayes, K. K. Wang, Blood-

based diagnostics of traumatic brain injuries. Expert Review of Molecular

Diagnostics 11, 65-78 (2011).

21. A. Petzold, Glial fibrillary acidic protein is a body fluid biomarker for glial pathology

in human disease. Brain research 1600, 17-31 (2015); published online EpubMar

10 (10.1016/j.brainres.2014.12.027).

22. P. K. Dash, J. Zhao, G. Hergenroeder, A. N. Moore, Biomarkers for the diagnosis,

prognosis, and evaluation of treatment efficacy for traumatic brain injury.

Neurotherapeutics : the journal of the American Society for Experimental

NeuroTherapeutics 7, 100-114 (2010).

23. S. H. Chou, C. S. Robertson, M. Participants in the International Multi-disciplinary

Consensus Conference on the Multimodality, Monitoring biomarkers of cellular

injury and death in acute brain injury. Neurocritical care 21 Suppl 2, S187-214

(2014); published online EpubDec (10.1007/s12028-014-0039-z).

24. A. M. Boutte, C. Yao, F. Kobeissy, X. C. May Lu, Z. Zhang, K. K. Wang, K. Schmid,

F. C. Tortella, J. R. Dave, Proteomic analysis and brain-specific systems biology

in a rodent model of penetrating ballistic-like brain injury. Electrophoresis 33, 3693-

3704 (2012); published online EpubDec (10.1002/elps.201200196).

178

25. J. M. Lubieniecka, F. Streijger, J. H. Lee, N. Stoynov, J. Liu, R. Mottus, T. Pfeifer,

B. K. Kwon, J. R. Coorssen, L. J. Foster, T. A. Grigliatti, W. Tetzlaff, Biomarkers

for severity of spinal cord injury in the cerebrospinal fluid of rats. PloS one 6,

e19247 (2011)10.1371/journal.pone.0019247).

26. S. Roche, A. Gabelle, S. Lehmann, Clinical proteomics of the cerebrospinal fluid:

Towards the discovery of new biomarkers. Proteomics Clin Appl 2, 428-436

(2008); published online EpubMar (10.1002/prca.200780040).

27. M. O. Sjodin, J. Bergquist, M. Wetterhall, Mining ventricular cerebrospinal fluid

from patients with traumatic brain injury using hexapeptide ligand libraries to

search for trauma biomarkers. Journal of chromatography. B, Analytical

technologies in the biomedical and life sciences 878, 2003-2012 (2010).

28. J. Hanrieder, M. Wetterhall, P. Enblad, L. Hillered, J. Bergquist, Temporally

resolved differential proteomic analysis of human ventricular CSF for monitoring

traumatic brain injury biomarker candidates. Journal of neuroscience methods

177, 469-478 (2009).

29. P. N. Lizhnyak, A. K. Ottens, Proteomics: in pursuit of effective traumatic brain

injury therapeutics. Expert review of proteomics 12, 75-82 (2015); published online

EpubFeb (10.1586/14789450.2015.1000869).

30. G. Poste, Bring on the biomarkers. Nature 469, 156-157 (2011).

31. G. Poste, Biospecimens, biomarkers, and burgeoning data: the imperative for

more rigorous research standards. Trends in molecular medicine 18, 717-722

(2012); published online EpubDec (10.1016/j.molmed.2012.09.003).

179

32. J. Levine, E. Kwon, M. Sondej, P. Paez, G. Czerwieniec, Y. Ao, M. V. Sofroniew,

J. A. Loo, I. B. Wanner, Traumatically injured astrocytes release a proteomic

signature modulated by STAT3 dependent cell survival. Glia 64, 668-694 (2016).

33. V. Muoio, P. B. Persson, M. M. Sendeski, The neurovascular unit - concept review.

Acta Physiol (Oxf) 210, 790-798 (2014); published online EpubApr

(10.1111/apha.12250).

34. P. J. Magistretti, Neuron-glia metabolic coupling and plasticity. Exp Physiol 96,

407-410 (2011).

35. L. Hertz, Bioenergetics of cerebral ischemia: a cellular perspective.

Neuropharmacology 55, 289-309 (2008); published online EpubSep

(10.1016/j.neuropharm.2008.05.023).

36. P. Mamczur, B. Borsuk, J. Paszko, Z. Sas, J. Mozrzymas, J. R. Wisniewski, A.

Gizak, D. Rakus, Astrocyte-neuron crosstalk regulates the expression and

subcellular localization of carbohydrate metabolism enzymes. Glia 63, 328-340

(2015); published online EpubFeb (10.1002/glia.22753).

37. D. Lovatt, U. Sonnewald, H. S. Waagepetersen, A. Schousboe, W. He, J. H. Lin,

X. Han, T. Takano, S. Wang, F. J. Sim, S. A. Goldman, M. Nedergaard, The

transcriptome and metabolic gene signature of protoplasmic astrocytes in the adult

murine cortex. The Journal of neuroscience : the official journal of the Society for

Neuroscience 27, 12255-12266 (2007); published online EpubNov 7

(10.1523/JNEUROSCI.3404-07.2007).

38. D. P. Pelvig, H. Pakkenberg, A. K. Stark, B. Pakkenberg, Neocortical glial cell

numbers in human brains. Neurobiology of aging 29, 1754-1762 (2008).

180

39. I. B. Wanner, M. A. Anderson, B. Song, J. Levine, A. Fernandez, Z. Gray-

Thompson, Y. Ao, M. V. Sofroniew, Glial scar borders are formed by newly

proliferated, elongated astrocytes that interact to corral inflammatory and fibrotic

cells via STAT3-dependent mechanisms after spinal cord injury. The Journal of

neuroscience : the official journal of the Society for Neuroscience 33, 12870-12886

(2013); published online EpubJul 31 (10.1523/JNEUROSCI.2121-13.2013).

40. D. R. Li, T. Ishikawa, L. Quan, D. Zhao, T. Michiue, B. L. Zhu, H. J. Wang, H.

Maeda, Morphological analysis of astrocytes in the hippocampus in mechanical

asphyxiation. Leg Med (Tokyo) 12, 63-67 (2010); published online EpubMar

(10.1016/j.legalmed.2009.11.005).

41. F. A. Azevedo, L. R. Carvalho, L. T. Grinberg, J. M. Farfel, R. E. Ferretti, R. E.

Leite, W. Jacob Filho, R. Lent, S. Herculano-Houzel, Equal numbers of neuronal

and nonneuronal cells make the human brain an isometrically scaled-up primate

brain. The Journal of comparative neurology 513, 532-541 (2009).

42. J. D. Cahoy, B. Emery, A. Kaushal, L. C. Foo, J. L. Zamanian, K. S.

Christopherson, Y. Xing, J. L. Lubischer, P. A. Krieg, S. A. Krupenko, W. J.

Thompson, B. A. Barres, A transcriptome database for astrocytes, neurons, and

oligodendrocytes: a new resource for understanding brain development and

function. The Journal of neuroscience : the official journal of the Society for

Neuroscience 28, 264-278 (2008).

43. G. S. Omenn, D. J. States, M. Adamski, T. W. Blackwell, R. Menon, H. Hermjakob,

R. Apweiler, B. B. Haab, R. J. Simpson, J. S. Eddes, E. A. Kapp, R. L. Moritz, D.

W. Chan, A. J. Rai, A. Admon, R. Aebersold, J. Eng, W. S. Hancock, S. A. Hefta,

181

H. Meyer, Y. K. Paik, J. S. Yoo, P. Ping, J. Pounds, J. Adkins, X. Qian, R. Wang,

V. Wasinger, C. Y. Wu, X. Zhao, R. Zeng, A. Archakov, A. Tsugita, I. Beer, A.

Pandey, M. Pisano, P. Andrews, H. Tammen, D. W. Speicher, S. M. Hanash,

Overview of the HUPO Plasma Proteome Project: results from the pilot phase with

35 collaborating laboratories and multiple analytical groups, generating a core

dataset of 3020 proteins and a publicly-available database. Proteomics 5, 3226-

3245 (2005).

44. S. Schenk, G. J. Schoenhals, G. de Souza, M. Mann, A high confidence, manually

validated human blood plasma protein reference set. BMC Med Genomics 1, 41

(2008).

45. M. Kapushesky, T. Adamusiak, T. Burdett, A. Culhane, A. Farne, A. Filippov, E.

Holloway, A. Klebanov, N. Kryvych, N. Kurbatova, P. Kurnosov, J. Malone, O.

Melnichuk, R. Petryszak, N. Pultsin, G. Rustici, A. Tikhonov, R. S. Travillian, E.

Williams, A. Zorin, H. Parkinson, A. Brazma, Gene Expression Atlas update--a

value-added database of microarray and sequencing-based functional genomics

experiments. Nucleic acids research 40, D1077-1081 (2012); published online

EpubJan (10.1093/nar/gkr913).

46. H. Haimoto, K. Kato, Highly Sensitive Enzyme-Immunoassay for Human-Brain

Aldolase-C. Clin Chim Acta 154, 203-212 (1986); published online EpubFeb 15

(Doi 10.1016/0009-8981(86)90032-X).

47. E. F. Ellis, J. S. McKinney, K. A. Willoughby, S. Liang, J. T. Povlishock, A new

model for rapid stretch-induced injury of cells in culture: characterization of the

model using astrocytes. Journal of neurotrauma 12, 325-339 (1995).

182

48. I. B. Wanner, An in vitro trauma model to study rodent and human astrocyte

reactivity. Methods in molecular biology 814, 189-219 (2012)10.1007/978-1-

61779-452-0_14).

49. J. Strotmann, I. Wanner, J. Krieger, K. Raming, H. Breer, Expression of odorant

receptors in spatially restricted subsets of chemosensory neurones. Neuroreport

3, 1053-1056 (1992).

50. I. B. Wanner, M. Deik, M. Torres, A. R. Rosendahl, J. T. Neary, V. P. Lemmon, J.

L. Bixby, A new in vitro model of the glial scar inhibits axon growth. Glia 56, 1691-

1709 (2008).

51. D. A. Hinkle, S. A. Baldwin, S. W. Scheff, P. M. Wise, GFAP and S100beta

expression in the cortex and hippocampus in response to mild cortical contusion.

Journal of neurotrauma 14, 729-738 (1997).

52. Z. Zhang, J. S. Zoltewicz, S. Mondello, K. J. Newsom, Z. Yang, B. Yang, F.

Kobeissy, J. Guingab, O. Glushakova, S. Robicsek, S. Heaton, A. Buki, J. Hannay,

M. S. Gold, R. Rubenstein, X. C. Lu, J. R. Dave, K. Schmid, F. Tortella, C. S.

Robertson, K. K. Wang, Human traumatic brain injury induces autoantibody

response against glial fibrillary acidic protein and its breakdown products. PLoS

One 9, e92698 (2014)10.1371/journal.pone.0092698).

53. K. Watanabe, Y. Urade, M. Mader, C. Murphy, O. Hayaishi, Identification of beta-

trace as prostaglandin D synthase. Biochemical and biophysical research

communications 203, 1110-1116 (1994); published online EpubSep 15

(10.1006/bbrc.1994.2297).

183

54. J. Clausen, Proteins in normal cerebrospinal fluid not found in serum. Proc Soc

Exp Biol Med 107, 170-172 (1961).

55. L. R. Fabrigar, D. T. Wegener, Exploratory factor analysis. Understanding statistics

(Oxford University Press, Oxford ; New York, 2012), pp. viii, 159 p.

56. L. Breiman, Classification and regression trees. The Wadsworth

statistics/probability series (Wadsworth International Group, Belmont, Calif.,

1984), pp. x, 358 p.

57. H. Zetterberg, K. Blennow, Fluid markers of traumatic brain injury. Molecular and

cellular neurosciences 66, 99-102 (2015); published online EpubMay

(10.1016/j.mcn.2015.02.003).

58. M. Bergsneider, D. A. Hovda, D. L. McArthur, M. Etchepare, S. C. Huang, N.

Sehati, P. Satz, M. E. Phelps, D. P. Becker, Metabolic recovery following human

traumatic brain injury based on FDG-PET: time course and relationship to

neurological disability. The Journal of head trauma rehabilitation 16, 135-148

(2001).

59. A. R. Mayer, P. S. Bellgowan, F. M. Hanlon, Functional magnetic resonance

imaging of mild traumatic brain injury. Neurosci Biobehav Rev 49, 8-18 (2015);

published online EpubFeb (10.1016/j.neubiorev.2014.11.016).

60. P. McCrory, W. H. Meeuwisse, M. Aubry, B. Cantu, J. Dvorak, R. J. Echemendia,

L. Engebretsen, K. Johnston, J. S. Kutcher, M. Raftery, A. Sills, B. W. Benson, G.

A. Davis, R. G. Ellenbogen, K. Guskiewicz, S. A. Herring, G. L. Iverson, B. D.

Jordan, J. Kissick, M. McCrea, A. S. McIntosh, D. Maddocks, M. Makdissi, L.

Purcell, M. Putukian, K. Schneider, C. H. Tator, M. Turner, Consensus statement

184

on concussion in sport: the 4th International Conference on Concussion in Sport

held in Zurich, November 2012. British journal of sports medicine 47, 250-258

(2013); published online EpubApr (10.1136/bjsports-2013-092313).

61. A. M. Boutte, Y. Deng-Bryant, D. Johnson, F. C. Tortella, J. R. Dave, D. A. Shear,

K. E. Schmid, Serum Glial Fibrillary Acidic Protein Predicts Tissue Glial Fibrillary

Acidic Protein Break-Down Products and Therapeutic Efficacy after Penetrating

Ballistic-Like Brain Injury. Journal of neurotrauma 33, 147-156 (2016); published

online EpubJan 1 (10.1089/neu.2014.3672).

62. S. Mondello, D. A. Shear, H. M. Bramlett, C. E. Dixon, K. E. Schmid, W. D. Dietrich,

K. K. Wang, R. L. Hayes, O. Glushakova, M. Catania, S. P. Richieri, J. T.

Povlishock, F. C. Tortella, P. M. Kochanek, Insight into Pre-Clinical Models of

Traumatic Brain Injury Using Circulating Brain Damage Biomarkers: Operation

Brain Trauma Therapy. Journal of neurotrauma 33, 595-605 (2016); published

online EpubMar 15 (10.1089/neu.2015.4132).

63. J. S. Zoltewicz, S. Mondello, B. Yang, K. J. Newsom, F. Kobeissy, C. Yao, X. C.

Lu, J. R. Dave, D. A. Shear, K. Schmid, V. Rivera, T. Cram, J. Seaney, Z. Zhang,

K. K. Wang, R. L. Hayes, F. C. Tortella, Biomarkers track damage after graded

injury severity in a rat model of penetrating brain injury. Journal of neurotrauma 30,

1161-1169 (2013); published online EpubJul 1 (10.1089/neu.2012.2762).

64. M. J. Whalen, T. Dalkara, Z. You, J. Qiu, D. Bermpohl, N. Mehta, B. Suter, P. G.

Bhide, E. H. Lo, M. Ericsson, M. A. Moskowitz, Acute plasmalemma permeability

and protracted clearance of injured cells after controlled cortical impact in mice.

Journal of cerebral blood flow and metabolism : official journal of the International

185

Society of Cerebral Blood Flow and Metabolism 28, 490-505 (2008); published

online EpubMar (10.1038/sj.jcbfm.9600544).

65. L. Papa, G. M. Brophy, R. D. Welch, L. M. Lewis, C. F. Braga, C. N. Tan, N. J.

Ameli, M. A. Lopez, C. A. Haeussler, D. I. Mendez Giordano, S. Silvestri, P.

Giordano, K. D. Weber, C. Hill-Pryor, D. C. Hack, Time Course and Diagnostic

Accuracy of Glial and Neuronal Blood Biomarkers GFAP and UCH-L1 in a Large

Cohort of Trauma Patients With and Without Mild Traumatic Brain Injury. JAMA

neurology 73, 551-560 (2016); published online EpubMay 1

(10.1001/jamaneurol.2016.0039).

66. W. Carr, A. M. Yarnell, R. Ong, T. Walilko, G. H. Kamimori, U. da Silva, R. M.

McCarron, M. L. LoPresti, Ubiquitin carboxy-terminal hydrolase-l1 as a serum

neurotrauma biomarker for exposure to occupational low-level blast. Frontiers in

neurology 6, 49 (2015)10.3389/fneur.2015.00049).

67. P. Shahim, T. Linemann, D. Inekci, M. A. Karsdal, K. Blennow, Y. Tegner, H.

Zetterberg, K. Henriksen, Serum Tau Fragments Predict Return to Play in

Concussed Professional Ice Hockey Players. Journal of neurotrauma, (2016);

published online EpubMay 2 (10.1089/neu.2014.3741).

68. B. A. Plog, M. Nedergaard, Why have we not yet developed a simple blood test for

TBI? Expert review of neurotherapeutics 15, 465-468 (2015); published online

EpubMay (10.1586/14737175.2015.1031112).

69. M. Latterich, J. E. Schnitzer, Streamlining biomarker discovery. Nature

biotechnology 29, 600-602 (2011); published online EpubJul (10.1038/nbt.1917).

186

70. F. G. Strathmann, S. Schulte, K. Goerl, D. J. Petron, Blood-based biomarkers for

traumatic brain injury: Evaluation of research approaches, available methods and

potential utility from the clinician and clinical laboratory perspectives. Clinical

biochemistry, (2014); published online EpubJan 31

(10.1016/j.clinbiochem.2014.01.028).

71. T. A. Addona, X. Shi, H. Keshishian, D. R. Mani, M. Burgess, M. A. Gillette, K. R.

Clauser, D. Shen, G. D. Lewis, L. A. Farrell, M. A. Fifer, M. S. Sabatine, R. E.

Gerszten, S. A. Carr, A pipeline that integrates the discovery and verification of

plasma protein biomarkers reveals candidate markers for cardiovascular disease.

Nature biotechnology 29, 635-643 (2011).

72. M. A. Gillette, S. A. Carr, Quantitative analysis of peptides and proteins in

biomedicine by targeted mass spectrometry. Nature methods 10, 28-34 (2013);

published online EpubJan (10.1038/nmeth.2309).

73. J. Lemoine, T. Fortin, A. Salvador, A. Jaffuel, J. P. Charrier, G. Choquet-

Kastylevsky, The current status of clinical proteomics and the use of MRM and

MRM(3) for biomarker validation. Expert review of molecular diagnostics 12, 333-

342 (2012); published online EpubMay (10.1586/erm.12.32).

74. Y. B. Lee, S. Du, H. Rhim, E. B. Lee, G. J. Markelonis, T. H. Oh, Rapid increase

in immunoreactivity to GFAP in astrocytes in vitro induced by acidic pH is mediated

by calcium influx and calpain I. Brain research 864, 220-229 (2000).

75. S. Du, A. Rubin, S. Klepper, C. Barrett, Y. C. Kim, H. W. Rhim, E. B. Lee, C. W.

Park, G. J. Markelonis, T. H. Oh, Calcium influx and activation of calpain I mediate

187

acute reactive gliosis in injured spinal cord. Experimental neurology 157, 96-105

(1999); published online EpubMay (10.1006/exnr.1999.7041).

76. R. E. White, D. M. McTigue, L. B. Jakeman, Regional heterogeneity in astrocyte

responses following contusive spinal cord injury in mice. The Journal of

comparative neurology 518, 1370-1390 (2010); published online EpubApr 15

(10.1002/cne.22282).

77. N. C. Reich, STAT3 revs up the powerhouse. Science signaling 2, pe61

(2009)10.1126/scisignal.290pe61).

78. J. Wegrzyn, R. Potla, Y. J. Chwae, N. B. Sepuri, Q. Zhang, T. Koeck, M. Derecka,

K. Szczepanek, M. Szelag, A. Gornicka, A. Moh, S. Moghaddas, Q. Chen, S.

Bobbili, J. Cichy, J. Dulak, D. P. Baker, A. Wolfman, D. Stuehr, M. O. Hassan, X.

Y. Fu, N. Avadhani, J. I. Drake, P. Fawcett, E. J. Lesnefsky, A. C. Larner, Function

of mitochondrial Stat3 in cellular respiration. Science 323, 793-797 (2009);

published online EpubFeb 6 (10.1126/science.1164551).

79. D. Kilinc, G. Gallo, K. A. Barbee, Mechanical membrane injury induces axonal

beading through localized activation of calpain. Experimental neurology 219, 553-

561 (2009); published online EpubOct (10.1016/j.expneurol.2009.07.014).

80. J. Sword, T. Masuda, D. Croom, S. A. Kirov, Evolution of neuronal and astroglial

disruption in the peri-contusional cortex of mice revealed by in vivo two-photon

imaging. Brain : a journal of neurology 136, 1446-1461 (2013); published online

EpubMay (10.1093/brain/awt026).

81. J. J. Bazarian, J. Zhong, B. Blyth, T. Zhu, V. Kavcic, D. Peterson, Diffusion tensor

imaging detects clinically important axonal damage after mild traumatic brain

188

injury: a pilot study. Journal of neurotrauma 24, 1447-1459 (2007); published

online EpubSep (10.1089/neu.2007.0241).

82. Kirov, II, A. Tal, J. S. Babb, J. Reaume, T. Bushnik, T. A. Ashman, S. Flanagan,

R. I. Grossman, O. Gonen, Proton MR spectroscopy correlates diffuse axonal

abnormalities with post-concussive symptoms in mild traumatic brain injury.

Journal of neurotrauma 30, 1200-1204 (2013); published online EpubJul 1

(10.1089/neu.2012.2696).

83. A. D. Lafrenaye, M. J. McGinn, J. T. Povlishock, Increased intracranial pressure

after diffuse traumatic brain injury exacerbates neuronal somatic membrane

poration but not axonal injury: evidence for primary intracranial pressure-induced

neuronal perturbation. Journal of cerebral blood flow and metabolism : official

journal of the International Society of Cerebral Blood Flow and Metabolism 32,

1919-1932 (2012); published online EpubOct (10.1038/jcbfm.2012.95).

84. K. Sakai, T. Fukuda, K. Iwadate, Beading of the astrocytic processes

(clasmatodendrosis) following head trauma is associated with protein degradation

pathways. Brain injury : [BI] 27, 1692-1697

(2013)10.3109/02699052.2013.837198).

85. J. A. Colombo, S. Gayol, A. Yanez, P. Marco, Immunocytochemical and electron

microscope observations on astroglial interlaminar processes in the primate

neocortex. Journal of neuroscience research 48, 352-357 (1997); published online

EpubMay 15 (

86. N. A. Oberheim, X. Wang, S. Goldman, M. Nedergaard, Astrocytic complexity

distinguishes the human brain. Trends in neurosciences 29, 547-553 (2006).

189

87. J. R. Gerstner, W. M. Vanderheyden, T. LaVaute, C. J. Westmark, L. Rouhana, A.

I. Pack, M. Wickens, C. F. Landry, Time of day regulates subcellular trafficking,

tripartite synaptic localization, and polyadenylation of the astrocytic Fabp7 mRNA.

The Journal of neuroscience : the official journal of the Society for Neuroscience

32, 1383-1394 (2012); published online EpubJan 25 (10.1523/JNEUROSCI.3228-

11.2012).

88. A. Schousboe, S. Scafidi, L. K. Bak, H. S. Waagepetersen, M. C. McKenna,

Glutamate metabolism in the brain focusing on astrocytes. Advances in

neurobiology 11, 13-30 (2014)10.1007/978-3-319-08894-5_2).

89. A. Eckert, B. C. Bock, K. E. Tagscherer, T. L. Haas, K. Grund, J. Sykora, C. Herold-

Mende, V. Ehemann, M. Hollstein, H. Chneiweiss, O. D. Wiestler, H. Walczak, W.

Roth, The PEA-15/PED protein protects glioblastoma cells from glucose

deprivation-induced apoptosis via the ERK/MAP kinase pathway. Oncogene 27,

1155-1166 (2008); published online EpubFeb 14 (10.1038/sj.onc.1210732).

90. P. Mergenthaler, A. Kahl, A. Kamitz, V. van Laak, K. Stohlmann, S. Thomsen, H.

Klawitter, I. Przesdzing, L. Neeb, D. Freyer, J. Priller, T. J. Collins, D. Megow, U.

Dirnagl, D. W. Andrews, A. Meisel, Mitochondrial hexokinase II (HKII) and

phosphoprotein enriched in astrocytes (PEA15) form a molecular switch governing

cellular fate depending on the metabolic state. Proceedings of the National

Academy of Sciences of the United States of America 109, 1518-1523 (2012);

published online EpubJan 31 (10.1073/pnas.1108225109).

91. P. J. Magistretti, Neuron-glia metabolic coupling and plasticity. J Exp Biol 209,

2304-2311 (2006); published online EpubJun (10.1242/jeb.02208).

190

92. N. R. Stein, D. L. McArthur, M. Etchepare, P. M. Vespa, Early cerebral metabolic

crisis after TBI influences outcome despite adequate hemodynamic resuscitation.

Neurocritical care 17, 49-57 (2012); published online EpubAug (10.1007/s12028-

012-9708-y).

93. P. Vespa, M. Bergsneider, N. Hattori, H. M. Wu, S. C. Huang, N. A. Martin, T. C.

Glenn, D. L. McArthur, D. A. Hovda, Metabolic crisis without brain ischemia is

common after traumatic brain injury: a combined microdialysis and positron

emission tomography study. Journal of cerebral blood flow and metabolism :

official journal of the International Society of Cerebral Blood Flow and Metabolism

25, 763-774 (2005); published online EpubJun (10.1038/sj.jcbfm.9600073).

94. B. L. Bartnik, S. M. Lee, D. A. Hovda, R. L. Sutton, The fate of glucose during the

period of decreased metabolism after fluid percussion injury: a 13C NMR study.

Journal of neurotrauma 24, 1079-1092 (2007)

95. D. A. Hovda, A. Yoshino, T. Kawamata, Y. Katayama, D. P. Becker, Diffuse

prolonged depression of cerebral oxidative metabolism following concussive brain

injury in the rat: a cytochrome oxidase histochemistry study. Brain research 567,

1-10 (1991).

96. B. L. Bartnik-Olson, U. Oyoyo, D. A. Hovda, R. L. Sutton, Astrocyte oxidative

metabolism and metabolite trafficking after fluid percussion brain injury in adult

rats. Journal of neurotrauma 27, 2191-2202 (2010); published online EpubDec

(10.1089/neu.2010.1508).

191

97. R. Bullock, W. L. Maxwell, D. I. Graham, G. M. Teasdale, J. H. Adams, Glial

swelling following human cerebral contusion: an ultrastructural study. Journal of

neurology, neurosurgery, and psychiatry 54, 427-434 (1991).

98. N. Hattori, S. C. Huang, H. M. Wu, W. Liao, T. C. Glenn, P. M. Vespa, M. E. Phelps,

D. A. Hovda, M. Bergsneider, PET investigation of post-traumatic cerebral blood

volume and blood flow. Acta neurochirurgica. Supplement 86, 49-52 (2003).

99. R. L. Sutton, D. A. Hovda, P. D. Adelson, E. C. Benzel, D. P. Becker, Metabolic

changes following cortical contusion: relationships to edema and morphological

changes. Acta neurochirurgica. Supplementum 60, 446-448 (1994).

100. X. Zhao, A. Ahram, R. F. Berman, J. P. Muizelaar, B. G. Lyeth, Early loss of

astrocytes after experimental traumatic brain injury. Glia 44, 140-152 (2003);

published online EpubNov (10.1002/glia.10283).

101. J. M. Lytle, J. R. Wrathall, Glial cell loss, proliferation and replacement in the

contused murine spinal cord. The European journal of neuroscience 25, 1711-

1724 (2007); published online EpubMar (10.1111/j.1460-9568.2007.05390.x).

102. D. R. Li, F. Zhang, Y. Wang, X. H. Tan, D. F. Qiao, H. J. Wang, T. Michiue, H.

Maeda, Quantitative analysis of GFAP- and S100 protein-immunopositive

astrocytes to investigate the severity of traumatic brain injury. Leg Med (Tokyo) 14,

84-92 (2012); published online EpubMar (10.1016/j.legalmed.2011.12.007).

103. V. Di Pietro, A. M. Amorini, G. Lazzarino, K. M. Yakoub, S. D'Urso, G. Lazzarino,

A. Belli, S100B and Glial Fibrillary Acidic Protein as Indexes to Monitor Damage

Severity in an In Vitro Model of Traumatic Brain Injury. Neurochemical research

40, 991-999 (2015); published online EpubMay (10.1007/s11064-015-1554-9).

192

104. P. E. Mouser, E. Head, K. H. Ha, T. T. Rohn, Caspase-mediated cleavage of glial

fibrillary acidic protein within degenerating astrocytes of the Alzheimer's disease

brain. The American journal of pathology 168, 936-946 (2006); published online

EpubMar (10.2353/ajpath.2006.050798).

105. T. T. Rohn, L. W. Catlin, W. W. Poon, Caspase-cleaved glial fibrillary acidic protein

within cerebellar white matter of the Alzheimer's disease brain. International

journal of clinical and experimental pathology 6, 41-48 (2013).

106. M. H. Chen, T. L. Hagemann, R. A. Quinlan, A. Messing, M. D. Perng, Caspase

cleavage of GFAP produces an assembly-compromised proteolytic fragment that

promotes filament aggregation. ASN neuro 5, e00125

(2013)10.1042/AN20130032).

107. R. B. Borgens, P. Liu-Snyder, Understanding secondary injury. The Quarterly

review of biology 87, 89-127 (2012).

108. D. G. Jones, Stability and storage characteristics of enzymes in sheep blood.

Research in veterinary science 38, 307-311 (1985).

109. D. G. Jones, Stability and storage characteristics of enzymes in cattle blood.

Research in veterinary science 38, 301-306 (1985).

110. S. Pontremoli, E. Melloni, M. Michetti, F. Salamino, B. Sparatore, B. L. Horecker,

Limited proteolysis of liver aldolase and fructose 1,6-bisphosphatase by lysosomal

proteinases: effect on complex formation. Proceedings of the National Academy

of Sciences of the United States of America 79, 2451-2454 (1982).

111. M. K. Offermann, J. F. Chlebowski, J. S. Bond, Action of cathepsin D on fructose-

1,6-bisphosphate aldolase. The Biochemical journal 211, 529-534 (1983).

193

112. M. F. Hopgood, S. E. Knowles, F. J. Ballard, Proteolysis of N-ethylmaleimide-

modified aldolase loaded into erythrocyte ghosts: prevention by inhibitors of

calpain. The Biochemical journal 259, 237-242 (1989).

113. J. S. Zoltewicz, D. Scharf, B. Yang, A. Chawla, K. J. Newsom, L. Fang,

Characterization of Antibodies that Detect Human GFAP after Traumatic Brain

Injury. Biomark Insights 7, 71-79 (2012)10.4137/BMI.S9873).

114. K. Fujita, T. Kato, M. Yamauchi, M. Ando, M. Honda, Y. Nagata, Increases in

fragmented glial fibrillary acidic protein levels in the spinal cords of patients with

amyotrophic lateral sclerosis. Neurochemical research 23, 169-174 (1998).

115. A. Martinez, M. Carmona, M. Portero-Otin, A. Naudi, R. Pamplona, I. Ferrer, Type-

dependent oxidative damage in frontotemporal lobar degeneration: cortical

astrocytes are targets of oxidative damage. Journal of neuropathology and

experimental neurology 67, 1122-1136 (2008); published online EpubDec

(10.1097/NEN.0b013e31818e06f3).

116. B. A. Plog, M. L. Dashnaw, E. Hitomi, W. Peng, Y. Liao, N. Lou, R. Deane, M.

Nedergaard, Biomarkers of traumatic injury are transported from brain to blood via

the glymphatic system. The Journal of neuroscience : the official journal of the

Society for Neuroscience 35, 518-526 (2015); published online EpubJan 14

(10.1523/JNEUROSCI.3742-14.2015).

117. M. M. Pelsers, T. Hanhoff, D. Van der Voort, B. Arts, M. Peters, R. Ponds, A. Honig,

W. Rudzinski, F. Spener, J. R. de Kruijk, A. Twijnstra, W. T. Hermens, P. P.

Menheere, J. F. Glatz, Brain- and heart-type fatty acid-binding proteins in the brain:

194

tissue distribution and clinical utility. Clinical chemistry 50, 1568-1575 (2004);

published online EpubSep (10.1373/clinchem.2003.030361).

118. T. M. Mathiisen, K. P. Lehre, N. C. Danbolt, O. P. Ottersen, The perivascular

astroglial sheath provides a complete covering of the brain microvessels: an

electron microscopic 3D reconstruction. Glia 58, 1094-1103 (2010).

119. M. Nuriya, T. Shinotsuka, M. Yasui, Diffusion properties of molecules at the blood-

brain interface: potential contributions of astrocyte endfeet to diffusion barrier

functions. Cerebral cortex 23, 2118-2126 (2013); published online EpubSep

(10.1093/cercor/bhs198).

120. C. L. Willis, L. Leach, G. J. Clarke, C. C. Nolan, D. E. Ray, Reversible disruption

of tight junction complexes in the rat blood-brain barrier, following transitory focal

astrocyte loss. Glia 48, 1-13 (2004); published online EpubOct

(10.1002/glia.20049).

121. M. M. Saw, J. Chamberlain, M. Barr, M. P. Morgan, J. R. Burnett, K. M. Ho,

Differential disruption of blood-brain barrier in severe traumatic brain injury.

Neurocritical care 20, 209-216 (2014); published online EpubApr

(10.1007/s12028-013-9933-z).

122. A. F. Logsdon, B. P. Lucke-Wold, R. C. Turner, J. D. Huber, C. L. Rosen, J. W.

Simpkins, Role of Microvascular Disruption in Brain Damage from Traumatic Brain

Injury. Compr Physiol 5, 1147-1160 (2015); published online EpubJul 1

(10.1002/cphy.c140057).

123. R. R. Hicks, D. H. Smith, D. H. Lowenstein, R. Saint Marie, T. K. McIntosh, Mild

experimental brain injury in the rat induces cognitive deficits associated with

195

regional neuronal loss in the hippocampus. Journal of neurotrauma 10, 405-414

(1993).

124. A. Korn, H. Golan, I. Melamed, R. Pascual-Marqui, A. Friedman, Focal cortical

dysfunction and blood-brain barrier disruption in patients with Postconcussion

syndrome. J Clin Neurophysiol 22, 1-9 (2005).

125. A. K. Shetty, V. Mishra, M. Kodali, B. Hattiangady, Blood brain barrier dysfunction

and delayed neurological deficits in mild traumatic brain injury induced by blast

shock waves. Front Cell Neurosci 8, 232 (2014)10.3389/fncel.2014.00232).

126. D. Vajtr, O. Benada, J. Kukacka, R. Prusa, L. Houstava, P. Toupalik, R. Kizek,

Correlation of ultrastructural changes of endothelial cells and astrocytes occurring

during blood brain barrier damage after traumatic brain injury with biochemical

markers of BBB leakage and inflammatory response. Physiological research /

Academia Scientiarum Bohemoslovaca 58, 263-268 (2009).

127. N. Marchi, J. J. Bazarian, V. Puvenna, M. Janigro, C. Ghosh, J. Zhong, T. Zhu, E.

Blackman, D. Stewart, J. Ellis, R. Butler, D. Janigro, Consequences of repeated

blood-brain barrier disruption in football players. PloS one 8, e56805

(2013)10.1371/journal.pone.0056805).

128. N. Marchi, M. Cavaglia, V. Fazio, S. Bhudia, K. Hallene, D. Janigro, Peripheral

markers of blood-brain barrier damage. Clin Chim Acta 342, 1-12 (2004); published

online EpubApr (10.1016/j.cccn.2003.12.008).

129. V. J. Willson, R. J. Thompson, Human brain aldolase C4 isoenzyme: purification,

radioimmunoassay, and distribution in human tissues. Annals of clinical

biochemistry 17, 114-121 (1980).

196

130. N. D. Silverberg, A. J. Gardner, J. R. Brubacher, W. J. Panenka, J. J. Li, G. L.

Iverson, Systematic review of multivariable prognostic models for mild traumatic

brain injury. Journal of neurotrauma 32, 517-526 (2015); published online EpubApr

15 (10.1089/neu.2014.3600).

131. B. K. Kwon, F. Streijger, N. Fallah, V. K. Noonan, L. M. Belanger, L. Ritchie, S. J.

Paquette, T. Ailon, M. C. Boyd, J. Street, C. G. Fisher, M. F. Dvorak, Cerebrospinal

Fluid Biomarkers To Stratify Injury Severity and Predict Outcome in Human

Traumatic Spinal Cord Injury. Journal of neurotrauma, (2016); published online

EpubAug 15 (10.1089/neu.2016.4435).

132. J. E. Buonora, A. M. Yarnell, R. C. Lazarus, M. Mousseau, L. L. Latour, S. B. Rizoli,

A. J. Baker, S. G. Rhind, R. Diaz-Arrastia, G. P. Mueller, Multivariate analysis of

traumatic brain injury: development of an assessment score. Front Neurol 6, 68

(2015)10.3389/fneur.2015.00068).

133. D. L. Bohac, J. F. Malec, A. M. Moessner, Factor analysis of the Mayo-Portland

Adaptability Inventory: Structure and validity. Brain Injury 11, 469-482 (1997);

published online EpubJul (Doi 10.1080/713802185).

134. J. DeJong, J. Donders, A confirmatory factor analysis of the California Verbal

Learning Test--Second Edition (CVLT-II) in a traumatic brain injury sample.

Assessment 16, 328-336 (2009); published online EpubDec

(10.1177/1073191109336989).

135. M. Asaka, T. Kimura, S. Nishikawa, M. Saitoh, T. Miyazaki, T. Takatori, E. Alpert,

Serum aldolase isozyme levels in patients with cerebrovascular diseases. The

American journal of the medical sciences 300, 291-295 (1990).

197

136. G. T. Manley, R. Diaz-Arrastia, M. Brophy, D. Engel, C. Goodman, K. Gwinn, T. D.

Veenstra, G. Ling, A. K. Ottens, F. Tortella, R. L. Hayes, Common data elements

for traumatic brain injury: recommendations from the biospecimens and

biomarkers working group. Archives of physical medicine and rehabilitation 91,

1667-1672 (2010); published online EpubNov (10.1016/j.apmr.2010.05.018).

137. G. Haralambie, Serum Aldolase Isoenzymes in Athletes at Rest and after Long-

Lasting Exercise. Int J Sports Med 2, 31-36 (1981)DOI 10.1055/s-2008-1034581).

138. O. Ruzgar, A. K. Bilge, Z. Bugra, S. Umman, E. Yilmaz, B. Ozben, B. Umman, M.

Meric, The use of human heart-type fatty acid-binding protein as an early

diagnostic biochemical marker of myocardial necrosis in patients with acute

coronary syndrome, and its comparison with troponin-T and creatine kinase-

myocardial band. Heart and vessels 21, 309-314 (2006); published online

EpubSep (10.1007/s00380-006-0908-2).

139. M. J. Crowder, D. J. Hand, in Monographs on statistics and applied probability.

(Chapman and Hall, London ; New York, 1990), chap. 1-3, pp. 1-59.

198

CHAPTER 4: ASSESSMENT OF ASTROGLIAL INJURY DEFINED BIOMARKERS

IN SPINAL CORD INJURY

4.1 INTRODUCTION

The long term debilitating effects of CNS injury in the form of head or spinal cord

trauma is a major occupational concern for military personnel. High quality care and

symptom mitigation for cases of severe neurotrauma starts with rapid, safe, and effective

field transportation to emergency medical care centers (1, 2). Presently, there are

reported clinical cases of spinal cord injury (SCI) patients’ conditions worsening as a

result of transport to hospitals (3-5). Field care providers have reported on the severe

pain experienced by casualties of SCI and traumatic brain injury (TBI) patients during

bumpy and high vibrational ground and air transport. The present standard of care

dictates the immobilization of SCI patients prior to transport, a procedure that may delay

time to treatment. Currently, the interactions of patient immobilization, dynamic transport

environment, and recovery and outcome are not clearly understood. It is believed that

SCI and TBI patients may be especially sensitive to repeated vibrational shock resulting

from vehicle transport (2, 6) Because the extent of patient recovery is heavily dependent

on early treatment (hours post-injury), a better understanding regarding transport effects

is needed to better manage SCI casualties (7, 8).

In order to accurately assess whether the effects of vehicle transport exacerbate

patient outcome, accurate diagnostics tools are needed to quantitatively assess and

monitor injury state from the initial point of care to the medical treatment center to post-

treatment patient outcome. This requires further preclinical research into the dynamics of

199

vehicular shock on pathology to better optimize transport protocols for improved patient

outcome. Biomarkers capable of detecting the presence of injury that also relate to the

severity of injury represent a minimally invasive, quantitative assessment of disease

progression. To this end, we applied our astroglial injury derived (AID) protein biomarkers,

initially identified for head injury, to the study of the effects of ground medical vehicle

evacuation in a recoverable spinal cord injury animal model. While exact disease

pathologies between head and spine injury differ, the high concentration of astroglia in

the spinal cord make the application of our AID biomarkers to SCI assessment relevant

as all astrocytes are believed to react to injury through a process of reactive astrogliosis

with characteristic upregulation of GFAP expression and development of star like

morphology (9). Consequently, biomechanical trauma induced plasmalemmal

permeability and cell death should result in similar populations of astroglial protein release

in a SCI model. What this study will uncover is whether these markers are (1) specific to

SCI, (2) whether AID marker concentrations are capable of stratifying injury severity, and

(3) whether transport post-SCI affects biofluid concentrations of our AID biomarkers.

Additionally, the occurrence of traumatic head trauma has been documented to

occur concomitantly with spinal cord injury as a result of both classes of injuries resulting

from high kinetic accidents such as falls and traffic accidents (10). SCIs and TBIs are the

naturally occurring secondary injuries from the indirect forces of the initial trauma.

Cervical SCI, for example, occurs from the indirect forces to the spine from initial head

injury (11). Because TBI patients suffer from cognitive and emotional deficits (e.g.

attention deficits, inability to concentrate, memory loss, irritability, and impulse control),

rehabilitation of patients suffering from both TBI concurrent with SCI is typically results in

200

poor patient outcome (12-14). The co-occurrence of TBI with SCI is estimated at

approximately 40-60% (15, 16), making the need for better diagnostics tools essential as

patients suffering from both conditions will require modified rehabilitative approaches.

The application of astroglial injury markers to SCI may set the foundation for better

diagnostics of concomitant injury. Should AID biomarkers demonstrate SCI utility, future

studies in dual TBI/SCI injury models may uncover distinct concentration profiles that

indiscriminately identifies the presence of both injuries, allowing physicians to adjust

treatment modalities to optimize patient care and recovery. In this chapter, we examine

the application of 3 previously described (Chapter 3) biomarkers, glial fibrillary acidic

protein (GFAP), aldolase C (ALDOC), and brain lipid binding protein (BLBP), for astroglial

injury.

201

4.2 RESULTS

A total of 21 yucatan swine specimens were injured using a modified weight drop

injury mechanism onto an exposed spinal cord (17). These animals were divided into 3

experimental groups – uninjured (sham) animals (n=7), SCI injured animals without

vehicular transport (7), and SCI injured animals subjected to vehicular transport.

Cerebrospinal fluid (CSF) was extracted by lumbar puncture for each at animal within all

three experimental groups at the following 5 time-points – baseline (pre-SCI), 15-30m

post-SCI, 2-3h post-SCI, 2d post-SCI, and 7d post-SCI (Figure 4.1). Animals in our

vehicle transport cohort were subjected to ground transport under experimentally

controlled vibrational forces along the Aberdeen Test Center track (Figure 4.2) in New

Mexico. Transported animals were exposed to vehicle speeds ranging from 5 mph to 25

mph with respect to the extent of vibrational forces with a maximum exposure time not

exceeding 1 hour.

AID biomarkers are sensitively detected in CSF and specific to SCI

Significantly elevated (p ≤ 0.02) AID biomarker concentrations for GFAP, ALDOC,

and BLBP were observed acutely after SCI injury compared to pre-injury baseline levels

(Figure 4.3). Geometric means of GFAP CSF concentrations were measured at 25.9

ng/mL (± 23.9) and 15.1 ng/mL (± 31.3) 20 minutes and 2.7 hours after SCI respectively

before dropping off to baseline levels by 2 days post SCI. Similar observations were made

for ALDOC, displaying acute concentrations of 14.5 ng/mL (± 38.3) and 1.59 ng/mL (±

38.7) before dropping out in the post-acute period. BLBP levels also exhibited an early

concentration drop off with concentrations of 13.9 ng/mL (± 34.0) at 20 minutes and 6.3

202

ng/mL (± 27.9) at 2.7 hours. Natural log transformed geometric means were calculated

due to the large concentration differences observed between injury and non-injury

conditions. Unlike arithmetic mean, geometric means tend to dampen the effects of

outliers. The large standard errors (SE) measured from aggregative concentration values

at each time-point can be explained by the large spread in concentration for GFAP (0-

3484.9 ng/mL), ALDOC (0-9397.0 ng/mL), and BLBP (0-2132.8 ng/mL). This variance is

further assessed in later sections through evaluation of single animal biomarker profiles

and differences in pathophysiological injury responses. GS was also evaluated as part of

our AID SCI panel but was too weakly detected by PRM-MS for meaningful interpretation

(Figure 4.14).

High CSF biomarker concentration variance observed within SCI injured animals

Examination of individual animal CSF concentrations post-injury revealed distinct

temporal profiles within our SCI swine cohort (Figure 4.4). As a whole, GFAP, ALDOC,

and BLBP concentrations were observed to rise hyper-acutely (within 20 minutes) after

surgery and weight drop before returning to base line levels by 2 days. 10 and 9 swine

specimens maintained elevated GFAP and BLBP concentrations through the post-

transport time-point (2.7h) while 1 animal displayed elimination of GFAP and BLBP

respectively within the same time frame. 3 and 4 animals displayed no observable GFAP

and BLBP readings (Figure 4.4A, C). ALDOC levels were similarly maintained in 7 of 14

specimens through the two acute post-SCI time-points with concentrations dropping out

for 4 animals by 2.7h. ALDOC was not observed in 3 of 14 animals (Figure 4.4A). These

203

animal specific differences in biomarker CSF concentration highlight inter-specimen

heterogeneity.

Weight-drop SCI varies in injury severity and outcome

Severity of pathophysiological damage was assessed at the impact site of

surgically excised spinal cords from specimens sacrificed at 7 days. Considerable

physical injury and molecular pathophysiology was observed at the impact site between

animals in the SCI cohort. This was displayed in the varying degrees of bleeding and

hemorrhage present at the injury cavity (Figure 4.5A). Various amounts of astroglial white

matter damage, visualized by immunohistological staining, were observed around the

injury site as shown by GFAP beading representing glial fiber fragmentation (Figure 4.5B).

Consistent with visual observations of differential lesion severities, total injury, measured

by the combined expansion of tissue loss and white matter fragmentation, confirmed a

range of injury severities between animals (Figure 4.5C, Table 4.1). Injury severity was

further assessed by each individual animal’s ability to recovery ambulation after surgery

rated by the Porcine Thoracic Injury Behavioral Scale (PTIBS) (17). Compared to baseline

mobility scores, all animals exhibited diminished ambulation following SCI (Table 4.1).

Exceptions to this trend were observed for animals exhibiting minor tissue loss cavitation

and white matter beading. Overall, a negative correlation (Spearman r.s. = -0.885, p-value

<0.001) was observed between SCI cavity length and recoverable mobility (Figure 4.6).

Biomarker levels and trajectories document trauma severity by the presence and size of

an injury lesion

204

Immunohistological assessment revealed differential responses to SCI injury,

resulting in a range of ambulatory recoveries. This physiological response difference is

believed to explain the large standard deviations in acute CSF biomarker readings

presented in Figure 4.3. To further investigate the relationship between trauma physiology

and proximal fluid concentration, CSF concentrations of GFAP, ALDOC, and BLBP were

distinguished between lesion positive (n=9) and negative (n=3) animals. Figure 4.7

demonstrates a distinct concentration difference between animals with measurable injury

cavities at 7 days compared to those without. Biomarker concentration values were

natural log transformed to calculate the geometric mean. This minimizes the skewing

effects of non-normally distributed data that results from both injury response and

individual specimen heterogeneity. GFAP, ALDOC, BLBP levels remained elevated in

lesion positive animals for up to 2.7 hours after injury in biofluid compared to animals

exhibiting minimal tissue loss with correspondingly high mobility 7 days after injury. T-

tests (one-tailed) between cavity +/- animal showed highly significant (p-values < 0.01)

differences in proximal fluid concentrations for GFAP, ALDOC, and BLBP 0.3h after

injury. Additionally, 2.7h hour differences were also significant for biomarkers GFAP and

ALDOC but not BLBP (Figure 4.8).

Next, we examined the correlation between GFAP, ALDOC, and BLBP

concentrations with cavity length. Strong (r.s. = 0.773, p-value < 0.001) to very strong (r.s.

= 0.991 and 0.829, p-values < 0.001) spearman correlations were observed for BLBP,

GFAP, and ALDOC respectively (Figure 4.9). Having demonstrated a relationship

between physiological injury and biomarker concentrations, further stratification of injury

severity was evaluated based on a combination of tissue loss diameter and radiation of

205

astroglial damage visualized by GFAP immunohistochemical staining around the injury

site. Total injury was classified based on the total diameter of tissue loss and astroglial

disintegration around the impact site (Table 4.1). However, lower correlation was obtained

between this classification metric and biofluid concentration of our markers (Table 4.2).

Strong negative correlation was observed between 7 day PTIBS scores and AID

biomarker CSF concentrations for GFAP and ALDOC with weaker association for BLBP

(Figure 4.10). Based on these findings, further SCI segregation was performed based on

cavity size and 7 day ambulation scores. Using these two criteria, 14 SCI were further

stratified into mild (n=5), moderate (n=6), and severe SCI (n=3) injury groups. This more

in-depth injury classification uncovered additional differences between animals based on

AID biomarker concentrations (Figure 4.11). Statistical analysis highlighted the strongest

distinction between both severe and mild (p-value < 0.001) and severe and moderate (p-

value < 0.01) SCI for ALDOC at 0.3h post-injury (Figure 4.12). Significant distinctions

were also observed for GFAP and BLBP at 0.3h between the three different injury groups.

Additionally, BLBP was observed to be significantly different at 2.7h post injury as well

(Figure 4.12).

Effect of transportation inconclusive

The assessment of transportation related effects on biofluid biomarker

concentrations was inconclusive. Natural log transformed means for each marker over

time did not show apparent increases in concentration or immediate alternations in kinetic

profiles (Figure 4.13) when segregated (n=7 for each group) by whether ground

transportation effects were experienced. It was noted that the profile of GFAP showed a

206

slower reduction in slope from 20m to 2.7h after injury. The effects of transportation on

the rate of CSF concentration changes was subsequently evaluated but no significance

(p-value < 0.05) was established between animals (Table 4.3). The lowest p-values (0.14-

0.15) were observed for GFAP between 20m and 2.7h and ALDOC between 20m and 7

days.

207

4.3 DISCUSSION

The contusion SCI model used in this study produced less severe injuries to those

reported in the literature using the same weight drop contusion apparatus (17, 18). In

comparison to these studies where injured animals never recovered ambulation, half of

the animals investigated in this study experienced recoverable injury along with higher

mean PTIBS scores. Despite precise optimization of injury settings, histopathology

revealed a range of injury severities at the site of impact. While our measured injury

response was less consistent compared to previous groups, this injury response

heterogeneity allowed a graded scale of severity, based on both lesion presence, size,

diffuse astroglial damage expansion around the site of injury, and functional recovery, to

be established and assessed in relation to the sensitivity and prognostic utility of our

astroglial injury derived (AID) biomarker panel of GFAP, ALDOC, and BLBP.

Rapid identification of GFAP, ALDOC, and BLBP in SCI but not healthy animal

CSF demonstrated the successful application of AID biomarkers, developed originally for

traumatic head injury, to the study of spinal cord contusion-like injuries. Our results

highlighted the utility of AID biomarkers to not only identify the presence of SCI but also

provide quantitative metrics that associate with pathophysiological and functional

observations. In the acute post-injury phase (20m – 2.7h), GFAP, ALDOC, and BLBP

displayed a graded CSF concentration response when animals were segregated based

on presence of a 7 day lesion cavity as well as the combined expansion of tissue loss

and astroglial fiber damage. Analysis of the relationship between tissue damage and

behavioral locomotion at 7 days also revealed a strong association. Taken together, these

findings suggest that robust and immediate elevation of GFAP, ALDOC, and BLBP can

208

successfully identify the severity of SCI and also predict the degree of functional recovery

a week after injury.

Acute phase detection of surrogate signals of injury offers the promise of improved

trauma management under hostile environments where decisions with long-lasting health

implications need to be made quickly. Graded concentration signals may equip medical

personnel with better diagnostic and prognostic tools to triage wounded soldiers in

combat. In addition to severe injury, our AID biomarkers demonstrated the capacity to

evaluate mild forms of injury where minor or no tissue loss was experienced. A sensitive

measure for mild SCI has some applications for rapid on-field diagnostics that can be

used to evaluate athletes immediately after injury to allow proper resting of individuals

who have undergone a neurotraumatic event.

209

4.4 METHODS

Spinal Cord Injury

Spinal cord injury (SCI) was generated using an existing contusion injury model as

described by Lee et al. (17). In brief, this procedure involves a surgical laminectomy of

the thoracolumbar spine, followed by a spinal cord contusion that is caused by a weight

(50g) drop onto the exposed cord from 10 cm. The incision is closed, without fixation of

the modified spinal vertebrae, leaving a possibly unstable spinal column. A consistent

weight strike is critical to obtaining consistent injury severities across subjects. This

procedure was performed by our collaborators at the Department of Defense.

CSF sample preparation

Protease inhibitors bestatin (40 micromoles per liter [µM]), pepstatin (1 microgram

per milliliter [µg/ml]) and phosphoramidon (10 micromolar [µM]) and EDTA (1 millimolar

[mM]) were added to swine CSF samples followed by delipidation (centrifugation 10 min

at 16,060 x g). Protein concentration was determined using Pierce 660 assay and a Tris-

bovine serum albumin (BSA) dilution series.

Quantitative histopathology

Histopathological analyses were done 1 wk post-injury. Coronal (longitudinal) free

floating 60 micrometer (µm) vibratome sections were prepared for each spinal segment

containing the injury site, stained with Sudan Black to quench tissue autofluorescence,

and permeabilized using a 0.5% Triton X100 solution. Nonspecific protein binding was

blocked and a rabbit (rb) anti-GFAP (Dako) antibody was incubated overnight, followed

210

by detection using an AlexaFluor 488 donkey anti-rb secondary antibody. Bleeding was

detected using a goat Cy3-conjugated anti-swine immunoglobulin (SwIgG) stain on

repeated sections of each injured and sham animal.

Spinal tissue atrophy: rostro-caudal lesion expansion and white matter fiber damage

(clasmatodendrosis)

Longitudinal sections were collected ~0.5 mm from the dorsal surface until past

the lesion, approximately over a depth of 3.5 to 4 mm (~48 sections/cord). As a proxy for

spinal tissue atrophy, the average rostro-caudal length of the cavity was determined from

measurements in 2 to 4 sections, covering a depth of >1 mm. All available sections of

animals without lesion were examined to confirm lesion absence. Adjacent spinal

segments were sectioned and stained if the injury expanded beyond the edge of the initial

trimmed injury segment.

The abundance of astroglial process injury was used as proxy for white matter fiber

damage. Glial fibrillary acidic protein (GFAP) staining was used to identify white matter

and to quantify clasmatodendrosis (19-21). White matter was identified by uniquely

organized, highly aligned, and brightly stained GFAP fibers.

Quantitation of Biomarkers in CSF using parrallel reaction monitoring- mass spectrometry

Synthetic standard peptides designed with stable isotope labeled arginine (6C13

14H 4N15 2O) and lysine (6C13 14H 2N15 2O) were purchased (Thermo Scientific)

corresponding to our surrogate biomarker peptides. Peptide standards were prepared in

5% acetonitrile in water at a concentration of 5pmol/µL. Heavy peptide standards were

211

spiked into CSF samples to concentrations of 25 fmol/µL. CSF samples are then reduced,

alkylated, and digested as described previously (Chapter 3).

Digested CSF peptides are dried by vacuum centrifugation and reconstituted in

0.1% formic acid, 3% acetonitrile in water. Samples are desalted using an on-line C18

trap column prior to LC-MS/MS analysis. Peptides were separated on a 5%-35% gradient

of mobile phase B (0.1% formic acid in acetonitrile) over 40 minutes on a C18 PepMap

(Thermo) reversed phase HPLC column. Samples were analyzed by a parallel-reaction-

monitoring (PRM-MS) workflow on a Q-Exactive Orbitrap MS operating in targeted-MS2

mode with an inclusion list of precursor peptide ions (Table 4.4) for MS2 analysis with the

following parameters: resolution 17500, AGC target 2x105, maximum ion injection time

50ms, isolation window 3.0 Da, fixed first mass 100, and normalized collision energy

(NCE) 27.

Multiple-reaction-monitoring-mass spectrometry (MRM-MS) measured biomarker

peptide specific precursor-product ion transitions isolated for monitoring. These precursor

ions were fragmented by higher-energy collisional dissociation (HCD) into their

component ions. Biomarker abundance was calculated based on the area under the curve

(AUC) of precursor to product ion transitions of each biomarker specific peptide using

Skyline (MacCoss Lab). The 3 transitions were summed and ratios of endogenous

peptide to their heavy labeled counterparts were determined. Biomarker concentrations

were calculated based on each peptide’s ratio of endogenous peptide AUC over added

standard, heavy labeled peptide AUC, concentration of the labeled standard peptide,

protein molecular weight (MW), and a dimensional conversion factor according to the

following formula:

212

Endogenous protein concentration (ng/mL) = [endogenous/standard ratio × 50fmol heavy

standard × protein MW × 1/1000]/[0.02 x raw CSF volume].

Statistical analysis

Biomarker concentration associations with functional recovery and

immunohistology were performed by Spearman analysis using SIgmaplot. Determination

of significance between injury severity stratifications, transportation effects, and

development of tissue loss cavitation was performed by one-tailed t-test analysis using

Excel (Microsoft) and Graphpad (Prism).

213

4.5 FIGURES

Figure 4.1: Yucatan swine spinal cord injury (SCI) experimental design

21 Yucatan swine specimens were exposed to spinal cord contusion via a previously

developed injury model (17). Animals were divided into 3 groups of 7 representing

uninjured, SCI injured with vehicle transport, and SCI injured without transport cohorts.

SCI injured animals were first subjected to surgical laminectomy of the thoracolumbar

spine to allow for a 10 cm weight drop onto the exposed spinal cord to induce contusion.

Following injury, the incision is closed without fixation of the spinal vertebrae. All 7

uninjured animals were vehicle transported. CSF samples were collected for biofluid

analysis at a pre-SCI baseline time-point, a 15-30m post-SCI acute time-point, a 2-3h

post-SCI acute time-point representing the post-transport condition, and 2 post-acute

time-points at 2d and 7d post-SCI.

214

Figure 4.2: Test track layout and pass sequence for transportation effect

assessment

The figure above displays the vehicle route used to evaluate effects of field transport.

Routes labeled 1-5 represent areas of the test track with different levels of surface

unevenness that SCI animals were driven over to assess the impact of vibrational forces.

215

Figure 4.3: AID biomarkers GFAP, ALDOC, and BLBP acutely elevated after SCI

Comparison of cerebrospinal fluid (CSF) levels of glial fibrillary acidic protein (GFAP, A),

aldolase C (ALDOC, B), and brain lipid binding protein (BLBP, C) in Yucatan swine at

pre- spinal cord injury (SCI) baseline (Bl) versus acute (20m, 2.7h) and post-acute (2d,

7d) time-points after SCI. For GFAP, ALDOC, and BLBP, CSF concentrations were

predominantly elevated within the first 24h of injury compared to baseline levels. Average

CSF collection times are displayed on the x-axis with geometric means of concentration

values (ng/mL ± SD) on the y-axis.

216

Figure 4.4: Individual temporal concentration profiles demonstrate animal specific

biomarker responses to SCI

AID biomarker concentrations for glial fibrillary acidic protein (GFAP, A), aldolase C

(ALDOC, B), and brain lipid binding protein (BLBP, C) were plotted with respect to time

pre- and post-spinal cord injury (SCI) from individual Yucatan swine cerebrospinal fluid.

While GFAP, ALDOC, and BLBP concentrations are all elevated post-SCI, distinct

differences (log10 y-axis) in protein concentrations (ng/mL) are observed between

217

animals. Concentration values of 0 were adjusted to 0.1 to accommodate the log10 y-axis

scaling. Concentration values from 14 different animals are represented at each time-

point.

218

Figure 4.5: Heterogeneous injury response observed in spinal cord injury (SCI)

cohort

10 cm weight drop induced SCI contusion resulted in heterogeneous injury response at

the site of impact (A). Tissue loss at the injury site was quantified by the diameter of lesion

with bruises and hemorrhage. Astroglial damage was measured by visualization of white

matter glial fibrillary acidic protein (GFAP) staining around the site of injury. Glial fiber

disintegration (B) represents reversible, diffuse white matter injury. Total injury (C) was

measured by the combination of both lesion cavity diameter (red) and expansion of white

matter fragmentation (grey).

219

Figure 4.6: SCI-related cavity length correlates negatively to animal recovery

A spearman correlation of r.s. = -0.885 (p <0.001) was observed for spinal cord injury site

cavitation, measured by tissue loss, and ambulatory recovery at 7 days, measured by the

Porcine Thoracic Injury Behavioral Scale (PTIBS). PTIBS is graded from 1-10, with higher

values representing higher recovered mobility. Data from this biplot indicates that high

tissue (7 days) loss associates with poor recovery of walking at 7 days.

Recovery (PTIBS)

2 4 6 8 10

Cavit

y L

en

gth

(m

m)

0.1

1

10

100

Crawl Walk

rs = -0.885

p < 0.001

220

Figure 4.7: AID biomarkers display different concentration and temporal dynamics

between animals with varying degrees of injury in cerebrospinal fluid

14 SCI injured animals were separated into cavity negative (-, yellow) and positive (+,

red) injury groups based on extent of tissue damage. 7 uninjured (sham) pigs were also

analyzed as the control group (grey). Geometric means of PRM-MS CSF concentrations

for glial fibrillary acidic protein (GFAP, A), aldolase C (ALDOC, B), and brain lipid binding

protein (BLBP, C) were plotted for 5 experimental time points (baseline (Bl), 20m, 2.7h,

221

2d, and 7d post-SCI) with 69% confidence intervals on a log10 y-scale. Concentration

values of 0 were changed to 0.01 to accommodate log scaling for graphic visualization.

222

Figure 4.8: Animals that develop an injury lesion exhibit significantly higher CSF

biomarker concentrations acutely after injury

14 SCI injured animals were separated into cavity negative (-, yellow) and positive (+,

red) injury groups based on extent of tissue damage. 7 uninjured (sham) pigs were also

analyzed as the control group (grey). Geometric means of PRM-MS CSF concentrations

for glial fibrillary acidic protein (GFAP, A), aldolase C (ALDOC, B), and brain lipid binding

protein (BLBP, C) were plotted for 5 experimental time points (baseline (Bl), 20m, 2.7h,

2d, and 7d post-SCI) with 69% confidence intervals on a log10 y-scale. Concentration

223

values of 0 were changed to 0.01 to accommodate log scaling for graphic visualization.

A one-tailed, t-test was used to evaluate mean differences in biomarker concentrations

between cavity + and – animals. GFAP and ALDOC levels were determined to be

statistically higher up to 2.7h after injury while BLBP displayed quicker clearance kinetics

and was only significantly different from the lesion negative group at 20m post-SCI.

224

Figure 4.9: CSF concentrations of GFAP, ALDOC, and BLBP associate positively

with extent of tissue loss measured at 7 days

Biplots show strong negative Spearman correlation between CSF concentrations of

GFAP (A), ALDOC (B), and BLBP (C) and 7 day injury site cavitation measured by tissue

loss.

225

Figure 4.10: GFAP, ALDOC, and BLBP may be predictive of functional recovery in

spinal cord injury

Biplots show strong negative Spearman correlation between CSF concentrations of

GFAP (A), ALDOC (B), and BLBP (C) and ambulatory recovery graded by the Porcine

Thoracic Injury Behavioral Scale (PTIBS).

226

Figure 4.11: AID biomarkers demonstrate differences in CSF concentration profiles

over time between injury severity assessed by cavity formation and 7 day

ambulatory recovery

14 SCI injured animals were separated into mild (yellow, n=5), moderate (orange, n=6),

and severe (red, n=3) injury groups based on extent of tissue damage and recovery of

ambulation at 7 days (Table 4.1). 7 uninjured (sham) pigs were also analyzed as the

control group (grey). Geometric means of PRM-MS CSF concentrations for glial fibrillary

acidic protein (GFAP, A), aldolase C (ALDOC, B), and brain lipid binding protein (BLBP,

C) were plotted for 5 experimental time points (baseline (Bl), 20m, 2.7h, 2d, and 7d post-

227

SCI) with 69% confidence intervals on a log10 y-scale. Concentration values of 0 were

changed to 0.01 to accommodate log scaling for graphic visualization.

228

Figure 4.12: AID biomarkers may be capable of distinguishing between varying

levels of injury severity in the hyper acute post-injury period.

14 SCI injured animals were separated into mild (yellow ,n=5), moderate (orange, n=6),

and severe (red, n=3) injury groups based on extent of tissue damage and recovery of

ambulation at 7 days (Table 4.1). 7 uninjured (sham) pigs were also analyzed as the

control group (grey). Geometric means of PRM-MS CSF concentrations for glial fibrillary

acidic protein (GFAP, A), aldolase C (ALDOC, B), and brain lipid binding protein (BLBP,

C) were plotted for 5 experimental time points (baseline (Bl), 20m, 2.7h, 2d, and 7d post-

229

SCI) with 69% confidence intervals on a log10 y-scale. Concentration values of 0 were

changed to 0.01 to accommodate log scaling for graphic visualization.

230

Figure 4.13: Vibrational forces from ground transport did not significantly impact

biofluid concentrations of AID biomarkers

21 animals were separated into sham (grey, n=7), SCI only (orange, n=7), and SCI +

transportation (blue, n=7) experimental groups. Geometric means of PRM-MS CSF

concentrations for glial fibrillary acidic protein (GFAP, A), aldolase C (ALDOC, B), and

brain lipid binding protein (BLBP, C) were plotted for 5 experimental time points (baseline

(Bl), 20m, 2.7h, 2d, and 7d post-SCI) with 69% confidence intervals on a log10 y-scale.

231

Concentration values of 0 were changed to 0.01 to accommodate log scaling for graphic

visualization.

232

Figure 4.14: GS was weakly detected in pig SCI CSF samples

(A) Line and (B) bar graph representations of GS CSF concentration in sham injured and

SCI (separated into cavity negative (yellow) and positive (red)) swine. Natural log

transformed means are plotted with corresponding 69% confidence intervals. However,

GS was poorly detected in CSF by MRM-MS, resulting in too few values for adequate

analysis.

233

4.6 TABLES

Animal

PTIBS

(7day)

Total-Injury

(mm)

Cavity

(mm)

0.3h CSF

GFAP

(ng/mL)

0.3h CSF

ALDOC

(ng/mL)

0.3h CSF

BLBP

(ng/mL)

43-090 9.8 47.6 0.0 0.0 0.0 1.1

46-091 8.4 8.8 6.8 24.1 33.7 0.0

42-131 9.9 0.0 0.0 0.0 0.0 0.0

47-094 9.9 5.6 0.0 0.0 0.0 0.0

46-030 4.5 8.8 7.2 64.4 9.0 67.2

42-115 9.8 6.2 0.9 17.7 35.8 26.2

42-101 3 13.4 9.9 75.6 1893.5 0.0

43-031 9.3 0.0 0.0 0.0 2.3 37.4

43-082 4.2 11.2 7.9 57.0 1.4 202.7

42-017 9.3 9.4 8.3 85.7 1.7 95.0

42-127 3.8 11.7 10.2 355.0 40.6 289.2

42-068 1.9 27.9 18.0 401.7 717.7 323.8

45-157 3.4 32.0 30.8 1729.4 668.5 389.6

42-132 3.1 46.0 36.0 2584.9 9397.0 2132.8

Table 4.1: Spinal cord impact site injury severity measured by astroglial beading

and tissue loss cavity size

Extent of pathophysiological damage following weight drop spinal cord injury (SCI) was

quantified by physical and histological examination of excised spinal cord from animals

sacrificed after the 7 day time-point. Tissue loss at 7 days was quantified by the diameter

234

of the lesion cavity and extent of clasmatodendrosis was quantified by the distance

astroglial beading measured from the site of injury visualized by immunostaining. Total

injury was measured by combination of both these parameters. Ambulatory recovery at 7

days is presented as a Porcine Thoracic Injury Behavioral Scale (PTIBS) score. PTIBS

is graded from 1-10, with higher values representing higher recovered mobility. Animals

highlighted in green, yellow, and red represent our classification of mild, moderate, and

severe SCI respectively.

235

Spearman Correlations

Biomaker Cavity Length

(mm) PTIBS Total Injury Expansion

(mm)

GFAP r.s. 0.991 -0.847 0.647

p *** *** *

ALDOC r.s. 0.824 -0.848 0.484

p *** *** ns

BLBP r.s. 0.773 -0.622 0.560

p *** * *

Table 4.2: Spearman correlations for biofluid biomarker concentrations to

immunohistology and functional recovery

The table lists Spearman correlations (r.s.) between biomarkers GFAP, ALDOC, BLBP

and corresponding histopathological (tissue loss diameter and total injury expansion

including tissue cavity and diffusion of astroglial fiber damage) and functional

observations (mobility at 7 days post-injury was assessed using the Porcine Thoracic

Injury Behavioral Scale (PTIBS))

236

SCI SCI+T

Biomarker Slope [ng/(mL*h)] Mean (ng/(mL*h)) Mean (ng/(mL*h)) p-value

GFAP m (20m-2.7h) -74.9 55.1 0.14

m (20m-7d) -2.7 -2.0 0.40

ALDOC m (20m-2.7h) -42.0 193.4 0.36

m (20m-7d) -1.5 -8.5 0.15

BLBP m (20m-2.7h) -51.1 -34.7 0.34

m (20m-7d) -1.4 -2.1 0.48

Table 4.3: t-test of AID biomarker slope changes between transported and un-

transported animals

Effects of transport on CSF concentration of astroglial injury-defined biomarkers were

measured by assessing the rate of changes within the hyper-active post-injury time-points

(20m, 2.7h) and the hyper-acute and post-acute post-injury time-points (20m, 2d). Rate

of change was defined by change in biomarker concentration over time in hours.

237

Name Peptide Sequence Measured MRM Transition

GFAP ALAAELNQLR(Heavy) 554.821 (2+) --> 924.514 (1+, y8)

554.821 (2+) --> 853.477 (1+, y7)

554.821 (2+) --> 782.439 (1+, y6)

ALAAELNQLR(Light) 549.816 (2+) --> 914.505 (1+, y8)

549.816 (2+) --> 843.468 (1+, y8)

549.816 (2+) --> 722.431 (1+, y8)

LADVYQAELR (Heavy) 594.758 (2+) --> 1003.508 (1+, y8)

594.758 (2+) --> 789.413 (1+, y6)

594.758 (2+) --> 626.350 (1+, y5)

LADVYQAELR (Light) 589.314 (2+) --> 993.500 (1+, y8)

589.314 (2+) --> 779.405 (1+, y6)

589.314 (2+) --> 616.341 (1+, y5)

ALDOC TPSALAILENANVLAR (Heavy) 831.974 (2+) --> 1193.688 (1+ y11)

831.974 (2+) --> 1122.651 (1+ y10)

831.974 (2+) --> 1009.566 (1+ y9)

TPSALAILENANVLAR (Light) 826.970 (2+) --> 1183.679 (1+, y11)

826.970 (2+) --> 1112.642 (1+, y10)

826.970 (2+) --> 999.558 (1+, y9)

GS DIVEAHYR (Heavy) 506.758 (2+) --> 784.398 (1+, y6)

506.758 (2+) --> 685.329 (1+, y5)

506.758 (2+) --> 556.287 (1+, y4)

DIVEAHYR (Light) 501.753 (2+) --> 774.389 (1+, y6)

501.753 (2+) --> 675.321 (1+, y5)

501.753 (2+) --> 546.278 (1+, y4)

BLBP ALGVGFATR (Heavy) 451.260 (2+) --> 717.392 (1+, y7)

= FABP7 451.260 (2+) --> 660.370 (1+, y6)

451.260 (2+) --> 561.302 (1+, y5)

ALGVGFATR (Light) 446.256 (2+) --> 707.384 (1+, y7)

238

446.256 (2+) --> 650.362 (1+, y6)

446.256 (2+) --> 551.294 (1+, y5)

Table 4.4: PRM-MS precursor ion inclusion list and measured transitions

The above table lists PRM-MS transitions monitored for quantitative mass spectrometry

analysis of biomarkers GFAP, ALDOC, BLBP, and GS in Yucatan swine SCI CSF.

239

4.7 REFERENCES

1. M. Hauswald, A re-conceptualisation of acute spinal care. Emergency medicine

journal : EMJ 30, 720-723 (2013); published online EpubSep (10.1136/emermed-

2012-201847).

2. N. Theodore, B. Aarabi, S. S. Dhall, D. E. Gelb, R. J. Hurlbert, C. J. Rozzelle, T.

C. Ryken, B. C. Walters, M. N. Hadley, Transportation of patients with acute

traumatic cervical spine injuries. Neurosurgery 72 Suppl 2, 35-39 (2013); published

online EpubMar (10.1227/NEU.0b013e318276edc5).

3. E. R. Haut, B. T. Kalish, D. T. Efron, A. H. Haider, K. A. Stevens, A. N. Kieninger,

E. E. Cornwell, 3rd, D. C. Chang, Spine immobilization in penetrating trauma: more

harm than good? The Journal of trauma 68, 115-120; discussion 120-111 (2010);

published online EpubJan (10.1097/TA.0b013e3181c9ee58).

4. J. S. Harrop, G. M. Ghobrial, R. Chitale, K. Krespan, L. Odorizzi, T. Fried, M.

Maltenfort, M. Cohen, A. Vaccaro, Evaluating initial spine trauma response: injury

time to trauma center in PA, USA. Journal of clinical neuroscience : official journal

of the Neurosurgical Society of Australasia 21, 1725-1729 (2014); published online

EpubOct (10.1016/j.jocn.2014.03.011).

5. A. E. Ropper, M. T. Neal, N. Theodore, Acute management of traumatic cervical

spinal cord injury. Practical neurology 15, 266-272 (2015); published online

EpubAug (10.1136/practneurol-2015-001094).

6. M. N. Hadley, Cervical Spine Immobilization before Admission to the Hospital.

Neurosurgery 50, S7-S17 (2002).

240

7. H. J. Hachen, Emergency transportation in the event of acute spinal cord lesion.

Paraplegia 12, 33-37 (1974); published online EpubMay (10.1038/sc.1974.6).

8. G. A. Zach, W. Seiler, P. Dollfus, Treatment results of spinal cord injuries in the

Swiss Parplegic Centre of Basle. Paraplegia 14, 58-65 (1976); published online

EpubMay (10.1038/sc.1976.9).

9. A. E. Kerstetter, R. H. Miller, Isolation and culture of spinal cord astrocytes.

Methods in molecular biology 814, 93-104 (2012)10.1007/978-1-61779-452-0_7).

10. A. Tolonen, J. Turkka, O. Salonen, E. Ahoniemi, H. Alaranta, Traumatic brain injury

is under-diagnosed in patients with spinal cord injury. Journal of rehabilitation

medicine 39, 622-626 (2007); published online EpubOct (10.2340/16501977-

0101).

11. A. Mahmoud, S. Rengachary, R. Zafonte, Biomechanics of associated spine

injuries in head-injured patients. Topics in Spinal Cord Injury Rehabilitation 5, 41-

46 (1999).

12. E. Roth, G. Davidoff, P. Thomas, R. Doljanac, M. Dijkers, S. Berent, J. Morris, G.

Yarkony, A controlled study of neuropsychological deficits in acute spinal cord

injury patients. Paraplegia 27, 480-489 (1989); published online EpubDec

(10.1038/sc.1989.75).

13. J. N. K. Hsiang, T. Yueng, A. L. M. Yu, W. S. Poon, High-risk mild head injury.

Journal of Neurosurger 87, 234-238 (1997).

14. M. R. Garnett, A. M. Blamire, B. Rajagopalan, P. Styles, T. A. D. Cadoux-Hudson,

Evidence for cellular damage in normal-appearing white matter correlates with

241

injury severity in patients following traumatic brain injury: A magnetic resonance

spectroscopy study. Brain 123, 1403-1409 (2000).

15. G. Davidoff, E. Roth, J. Morris, J. Bleiberg, P. R. Meyer, Assessment of closed

head injury in trauma-related spinal cord injury. Paraplegia 24, 97-104 (1986).

16. G. Davidoff, P. Thomas, M. Johnson, S. Berent, M. Dijkers, R. Doljanac, Closed

head injury in acute traumatic spinal cord injury: incidence and risk factors.

Archives of physical medicine and rehabilitation 69, 869-872 (1988).

17. J. H. Lee, C. F. Jones, E. B. Okon, L. Anderson, S. Tigchelaar, P. Kooner, T.

Godbey, B. Chua, G. Gray, R. Hildebrandt, P. Cripton, W. Tetzlaff, B. K. Kwon, A

novel porcine model of traumatic thoracic spinal cord injury. Journal of

neurotrauma 30, 142-159 (2013); published online EpubFeb 1

(10.1089/neu.2012.2386).

18. F. Streijger, J. H. Lee, N. Manouchehri, A. D. Melnyk, J. Chak, S. Tigchelaar, K.

So, E. B. Okon, S. Jiang, R. Kinsler, K. Barazanji, P. A. Cripton, B. K. Kwon,

Responses of the Acutely Injured Spinal Cord to Vibration that Simulates Transport

in Helicopters or Mine-Resistant Ambush-Protected Vehicles. Journal of

neurotrauma, (2016); published online EpubJul 5 (10.1089/neu.2016.4456).

19. J. A. Colombo, A. Yanez, S. J. Lipina, Interlaminar astroglial processes in the

cerebral cortex of non human primates: response to injury. J Hirnforsch 38, (1997).

20. U. Ito, Y. Hakamata, E. Kawakami, K. Oyanagi, Degeneration of astrocytic

processes and their mitochondria in cerebral cortical regions peripheral to the

cortical infarction heterogeneity of their disintegration is closely associated with

242

disseminated selective neuronal necrosis and maturation of injury. Stroke 40,

2173-2181 (2009).

21. M. G. Salter, R. Fern, The mechanisms of acute ischemic injury in the cell

processes of developing white matter astrocytes. Journal of Cerebral Blood Flow

& Metabolism 28, 588-601 (2008).

243

CHAPTER 5: CHARACTERIZING THE PREFERENTIAL RELEASE OF PROTEIN

SUBPOPULATIONS BY INJURED ASTROCYTES

5.1 INTRODUCTION

Traumatic central nervous system (CNS) injury is caused by direct physical

damage to the brain or spinal cord resulting diffuse axonal damage in addition to

immediate hemorrhage and contusion at the site of injury. Traumatic brain injury (TBI) is

perhaps the most common form of CNS damage that affects more than 57 million

hospitalizations globally. Over 5 million people are estimated to live with TBI-related

disabilities and is the most common cause of disability in individuals under 30 (1, 2).

Traumatic brain injury is of increasing concern to military personnel, emergency

responders, and athletes with the leading causes from blast injury, violence, and falls.

Despite the immense health and financial costs associated with TBI, current

evaluation of injury is limited by the insensitivity of standard neurocognitive assessments

such as the Glasgow Coma Scale (GCS). These tests measure a patient’s level of

consciousness and cognitive function and rely solely on verbal communication, motor

function, and memory related responses. At best, improper assessment results from

deficiencies in clinical expertise and at worst, these test may be subject to motivational

confounds from business pressures. Better diagnostic measures, in the form of an easy

to administer and decipher biofluid biomarker assay offer the potential of unambiguous

identification of injury with the promise of quantitative and standardized severity

categorization.

244

Despite advances in proteomic screening methods, no established biomarker for

TBI is currently used clinically in the US. Proteomic screening of injured CNS tissue and

patient biofluids have identified a wealth of potential biomarkers for neurotrauma. The

challenge, however, lies in the interpretation of these long candidate lists due to the

complexity of events at and around a dynamically changing injury site and variations

between trauma models (3-5). Tissue derived protein signals are products of a changing

composition of viable, injured, and dead cells as well as infiltrating non-neural cells, all of

which complicate the interpretation of proteomic studies (6, 7).

Due to the confounding complexity of clinical TBI and clinic-resembling animal

injury models, we propose a targeted proteomic screen using a well-characterized in vitro

cell-based trauma model as a starting point for TBI marker candidate identification (8-12).

This will limit protein changes to those directly related to an acute mechanical trauma by

applying an abrupt pressure pulse inflicting shear forces and deformation onto cortical

brain cells in a reproducible fashion at various severities. We identified robust cellular

release patterns that correlate with cell injury and cell death and apply a suitable selection

strategy that builds on our previous work in clinical samples toward the ultimate goal of a

blood based diagnostic.

245

5.2 RESULTS

Astrocyte cultures derived from 3 different human fetal cerebral neocortex

donations were subjected to biomechanical trauma from nitrogen pressure pulses to

evaluate the impact of injury on released and intracellular proteomes. Additionally, the

effect of injury severity was assessed using either mild or severe stretch parameters

defined by the application of 50 ms of nitrogen gas flow at 2.6-4.0 or 4.4-5.3 psi

respectively. The effect of injury on astrocyte release proteomes and intracellular

proteomes were assessed by collection and trypsin digestion of conditioned medium (CM)

and whole cell lysates (WCL). Relative quantitation between injured and un-stretched

astrocytes was performed using isobaric labeling with TMT 6-plex mass tags (Figure 5.1)

and LC-MS/MS. This approach allows for the simultaneous comparison of proteomes

before and after treatment based on the ratios of the relative intensities of the differential

reporter ions cleaved from the isobaric mass tags during higher-energy collisional

dissociation (HCD). Because precursor co-isolation negatively impacts reporter ion

quantification, trypsin digested samples were subjected to strong cation exchange (SCX)

pre-fractionation. Pre-fractionation reduces the occurrence of precursor co-elution

through improved chromatographic separation to improve the accuracy of quantitative

results (13-15). Additionally, peptides with co-isolation percentages higher than 50% were

also excluded from TMT ratio calculations (Methods).

Stretched astrocytes release different populations of injury-related proteins

TMT ratios for CM proteins released after exposure to mild or severe pressure

stretch conditions were averaged to determine relative fold changes to control conditions

246

(un-stretched). Intensities from replicate analyses were evaluated by paired, 1-tailed t-

test to determine statistically significant (p < 0.05) quantitative observations. A fold

change (FC) of greater than 2 compared to control was selected for biological

significance. These statistical and biological boundaries were selected to identify released

proteins of interest from our injury model and visualized in the volcano plots presented in

Figure 5.2. For each time-point evaluated, severe stretching resulted in more abundant

(FC>3 and above) release of injury-related proteins compared to mild stretch conditions.

Difference in differentially released proteins were most apparent between severe and mild

stretching at 5 and 24 hours (Figure 5.3). At 48 hours, minimal change in CM protein

abundances were observed between mild and severe injury groups.

Distinct size profiles observed for injury released proteins in conditioned medium

Cellular membrane permeability is a hallmark of diffuse axonal injury in traumatic

brain injury (TBI) (16, 17). This phenomenon has also been demonstrated by our group

following biomechanical injury in vitro (Chapter 3). Building upon this, we examined the

relationship between released protein molecular weights (MW) and mechanoporated

astrocytes using our trauma model. Using the released protein selection criteria described

above, the MW distributions of CM proteins with at least a 2-fold increase relative to

control were compared. Figure 5.4A displays median MWs of differentially released

(FC>2) proteins for all experimental conditions along with 1.5 interquartile ranges.

Comparison of stretch severities at 5 and 24 hours denote lower median MWs and tighter

size distributions in elevated CM proteins for astrocytes stretched with 4.0 psi or lower.

No real difference is observed at 48 hours between mild and severe injury groups,

247

consistent with minimal changes in release abundances examined previously. Looking

deeper at the effect on stretching on the various sub-populations of differentially released

proteins, an inverse relationship between CM abundance (measured by FC) and mean

MWs is observed (Figure 5.4B, Table 5.1) in the mildly stretched cohort. At both 5 and 24

hours, the mean MW of proteins with 3-fold CM increase are measured at 36.6 ± 26.67

kDa and 15.7 ± 7.95 kDa compared to 57.96 ± 74.5 kDa and 45.87 ± 58.4 kDa

respectively. These observations are suggestive of the preferential release of lower MW

proteins from injured astrocytes. Overall, released protein MWs rise with time after injury

for all stretch conditions that likely relate to temporal changes in cell death dynamics.

Intracellular protein expression dynamics relatively unchanged following injury

Astrocyte whole cell lysates (WCL) corresponding to post-injury CM fluid samples

were also harvested at 5, 24, and 48 hours post-stretch injury. Given prior evidence

suggestive of protein leakage from injured cells, a corresponding change in intracellular

protein concentrations was expected from cell lysates. However, in contrast to the

significant changes to CM protein concentrations, little deviation from baseline protein

levels was measured from TMT labeled WCL peptides. Even at more modest expression

deviations of ± 1.25-fold relative to control, very few WCL proteins were statistically or

biologically altered in our stretch trauma model (Figure 5.5).

Select proteins display corresponding expression and release trends

Looking further at the WCL proteins displaying differential expression (Table 5.2),

thymosin beta-4, thymosin beta-10, and 14 kDa phosphohistidine phosphatase were

248

observed to exhibit a 25% reduction in intracellular protein concentration at 5 hours post

stretch injury. All 3 proteins also exhibited a correspondingly high increase (2<FC<4) in

CM protein concentration that is consistent with subpopulations of cells with membrane

irregularities as demonstrated in Chapter 3. Peptidyl-prolyl cis-trans isomerase FKBP10,

myosin regulatory light polypeptide 9, and reticulon-4 all showed a modest, but

statistically significant (~1.25-fold) increase from baseline levels. These proteins exhibited

around a 2-fold increase in CM concentration after injury with the exception of reticulon-

4 (FC ~ 1.25). The high MW of reticulon-4 is consistent with our hypothesis. At 24 hours,

thymosin beta proteins and transgelin continue to be decreased in WCL but highly

increased (FC>3) in CM. Adenylyl cyclase-associated protein 1 is decreased in WCL at

48h and very highly increased in CM. Ubiquitin-conjugating enzyme E2 N, calponin-3,

actin-related protein-2, and plasminogen activator inhibitor 1 are 1.25-fold elevated in

WCL with very high CM elevations. This population of proteins exhibiting matching

intracellular and released protein dynamics in response to injury may represent good

biomarkers for biomechanical neurotrauma.

Identification of potential astrocyte injury protein signatures with respect to time and

severity

A major goal of our astrocyte injury model is to identify highly abundant, ideally

central nervous system (CNS) enriched protein biomarkers sensitive and specific to

neurotraumatic injury. As part of this characterization, several stepwise comparisons were

performed in an attempt to identify both differentiating (with respect to time and injury

severity) and common trauma signatures. First, differentially released (FC>2) CM

249

proteins were compared at all time-points (5h, 24h, and 48h) with respect to our trauma

severity (Figure 5.6). This uncovered a common core of proteins between mild and severe

stretch released proteins as well as subgroups of proteins potentially specific to both

severity of injury and time after injury. Because a strict fold change cut off of 2 was

selected, these injury-time specific subpopulations were further curated manually to

eliminate proteins that were also observed with approximate (FC slightly less than 2)

abundance changes at other time-points (Table 5.3). A last round of selection was

performed to identify proteins observed from both severities of stretch injury and at time

points post-trauma (Figure 5.7).

Top neurotraumatic injury biomarker identification

Signature injury markers identified from our quantitative astrocyte trauma model

were further narrowed using the filtering strategy presented in Figure 5.8. As described

previously (Chapter 3), we established a human TBI cerebrospinal fluid (CSF)

traumatome that included proteins observed in TBI CSF only as well as proteins

measured in both 19 TBI and 9 healthy patients. Next, to reduce the contributions from

non-CNS specific organ systems and toward the development of a blood based assay,

proteins derived from blood (18, 19) were filtered. The final candidate list (Table 5.4) was

graded on the following criteria: (1) Observed in human TBI CSF traumatome

(preferentially TBI CSF only), (2) observed at multiple time-points in in vitro trauma model,

(3) low MW, (4) demonstrates relationship between WCL and CM concentrations in

response to injury, (5) not observed in blood proteome and (6) relative elevation across

all stretch trauma conditions.

250

5.3 DISCUSSION

Application of quantitative proteomics to a well-defined in vitro injury mode was

used to further characterize cellular changes with regard to both release and intracellular

proteomes following traumatic injury in astrocytes. Our findings contribute to the

continued elucidation of astrocyte response to injury with an emphasis on temporal

dynamics and reported plasma membrane compromise as they relate to the identification

of diagnostic protein signatures for traumatic CNS injury.

Mechanical injury may result in preferential release of lower molecular weight proteins

Pressure pulse (2.6 – 5.3 psi, 50 ms) induced stretch injury resulted in significant

changes to the release proteomes of astrocytes in culture. Differential release of proteins

was inferred from increase in protein abundances measured in conditioned media (CM)

compared to healthy un-stretched cells at three time-points from 5 to 48 hours after injury.

Plasmalemma damage is a documented cellular response that occurs early after injury

before the onset of other injury related sequelae (17, 20-22). Membrane irregularities

coupled with the wide spread occurrence of apoptotic and neurotic cell death (23-26) after

TBI are responsible for the changes to protein abundance in astrocyte CM. Both our

studies (Chapter 3) and published literature support the findings that the early sequelae

after neurotraumatic insult is dominated by changes to the cellular integrity. The non-

discriminant release of proteins from mechanoporated cells is most likely a function of

cellular protein molecular weight. This is supported by our in vitro study which identified

subpopulations of more differentially released proteins (FC>3) that occupy a much lower

distribution of molecular weights (MWs) compared to the entire population of differentially

251

released proteins (defined as FC>2). This preferential release of lower MW protein

species is most apparent under what we define as mild pressure stretching due to the

lower extent of early cell death as previously described (Chapter 3).

Further evidence for preferential release of lower MW species is presented in the

comparison of intracellular expression changes measured in WCL to corresponding

increases in CM concentrations. Reticulon-4, also known as Nog-66 receptor 4, was

observed to be 1.25-fold (p<0.05) elevated in whole cell lysates (WCLs) at 5 hours after

injury but only mildly elevated in CM (FC ~1.3). In contrast to smaller proteins observed

elevated in both WCL and CM at 5 hours, the modest increase in reticulon-4 CM

abundance despite elevated cellular expression is likely a consequence of its larger (102

kDa) size. In stark contrast, 3 proteins, thymosin beta-4, thymosin beta-10, and transgelin

(MW range 5-23 kDa) were observed to be highly elevated (average FC>3) in CM in the

acute time-point despite a decrease in intracellular concentrations. While it is unclear

whether increased injury induces increased expression of these proteins, this evidence

supports the notion that their low MW facilitates their cellular departure after trauma.

Mild trauma may generate comparable levels of cell death as severe trauma

Severe stretch trauma resulted in higher CM concentrations of released proteins.

This is believed to result from the complete release of cytosolic contents accompanying

increased cell death (27, 28). While differences in highly differentially released proteins

(FC>3) were most apparent between 5 and 24 hours, differences between mild and

severe trauma were minimized by 48 hours with near equal percentages of matching

proteins between injury conditions exhibiting 3-fold or higher concentration increases in

252

CM. This indirect evidence suggests that the extent of secondary sequelae related to cell

death processes may be comparable between mild and severe TBI, highlighting the

health risk of these invisible wounds. Additionally, from a diagnostic standpoint, this

finding points to the importance of time with respect to detection of injury as well as

biofluid marker concentrations as they relate to patient prognoses. Time-related increases

in cell death will greatly alter the effective concentrations of disease-associated proteins,

necessitating differential acceptance ranges depending on when diagnostics were

administered. It is important to note that what we have defined as mild and severe in our

in vitro trauma system may not correlate with observed pathophysiological manifestations

of clinical defined injury severity in patients.

New low molecular weight acute neurotraumatic injury signatures

Comparative analysis of release profiles after injury resulted in the identification of

subsets of protein signatures that may potentially aid in the elucidation of the underlying

molecular cascade of traumatic astrocyte injury. Stretching of astrocytes in vitro yielded

increased release of ribosomal protein subunits, heat shock proteins, components of the

ubiquitin-proteasome complex, and caspases associated with increases in protein

synthesis, stress response (29-31), injury-related abnormal protein degradation (32, 33),

and apoptosis (34). It is still unclear whether biomechanical trauma is responsible for the

systematic alteration of pathways associated with disease despite the presentation of a

host of molecular pathologies (35, 36). Consequently, it is possible that identified proteins

with increased release profiles may be purely associated with increased membrane

permeability. Because of this, preferential value was assigned to low MW proteins in our

253

selection of new neurotraumatic biomarkers. In addition to MW considerations, priority

was given based on representation within our previously established human TBI CSF

traumatome (Chapter 3) and proteins not normally present in blood circulation. However,

representation in the blood protein did not necessarily exclude candidate proteins given

the fact that well established TBI marker ubiquitin carboxyl-terminal hydrolase isozyme

L1 (UCHL1) (37) is naturally present in blood but with diagnostically relevant

concentrations after injury (38). Aldolase C (39) and protein 14-3-3 (40) were also

identified by our filtering strategy, further strengthening the validity of our approach as our

group has characterized these two candidates in clinical samples. Of particular interest

are new candidates from the thymosin family of proteins (5 kDa), 14 kDa phosphohistidine

phosphatase (14 kDa), and transgelin (23 kDa) that exhibited corresponding decreases

in intracellular protein concentrations after injury. This is strongly suggestive of their

preferential leak into surrounding fluid. Ezrin, an actin-related protein, was another

interesting hit from our screen that is reported to be present in high concentration

extracellularly after TBI. Ezrin localization has been observed on astrocyte lamellipodia

and around cellular debris suggesting dual roles in motility and immune recruitment after

injury (41, 42). Superoxide dismutase (SOD1) presents another biological relevant

candidate given the increases in brain reactive oxygen species (ROS) generation that

accompanies mitochondrial dysfunction after trauma. Increased SOD1 expression is

consistent with the neuroprotective process with previous studies demonstrating partial

ablation of ischemia related symptoms with SOD1 overexpression or supplementation

(43, 44).

254

Conclusions

The proteomic response to both injury-induced astrocyte plasmalemma

compromise and cell death have been evaluated in this work with strong implications for

preferential release of low MW species after injury. This was observed to be especially

apparent with regard to injury severity at acute time points within 24 hours of injury in vitro

that is believed to be related to the early onset of membrane compromise but prior to the

onset of secondary sequelae leading to widespread cell death. This early occurrence of

membrane compromise is of special interest due to its diagnostic implications. Low MW

biomarkers that are preferentially released as a result of cellular mechanoporation may

capture the development of early traumatic sequelae specific for TBI in the hyper-acute

post-injury period. Applications of this are of particular importance to healthcare for

athletes where a rapid diagnostic tool may prevent incorrect medical clearance of players

with diffuse axonal damage from a concussive injury. We have presented a manually

curated list of low MW candidates that are robustly detected not only in vitro but also in

clinical TBI patient CSF. Future verification in biological relevant injury systems will

determine the utility of these candidates and whether protein size should be a

consideration for neurotraumatic injury biomarkers.

255

5.4 METHODS

Fetal astrocyte culture and mechanical stretch injury

Primary human astrocytes were prepared as previously described (45). Donated

human fetal cerebral neocortex at 16-19 gestational weeks was de-identified, cleaned

and mechanically dissociated in calcium and magnesium-free Hank’s buffered saline

solution (HBSS) before filtering through 70 µm and 10 µm nylon meshes (Nitex) in culture

medium (DMEM-F12) with 10% fetal bovine serum (FBS). Astrocytes were separated

from neural progenitor cells by 30min centrifugation at 30,000xg (J6B Sorvall centrifuge,

rotor SA600) in a HBSS-buffered 33% Percoll gradient (Sigma). The top fraction was

washed and diluted in DMEM/F12, 10% FBS and cultured in T150 cell culture-treated

plastic flasks (Corning). Confluent cultures were shaken for 4 days at 200rpm on a shaker

in an incubator. Astrocytes were mechanically dissociated following brief treatment in

0.25% trypsin/EDTA, washed, collected by centrifugation at 400 x g in a clinical centrifuge

(IEC) and seeded onto collagen I-coated silastic membrane culture plates (6 well Bioflex)

at a density of ~ 135,000 human cells / 962mm2. Upon confluence, medium was replaced

by DMEM/F12 with 10% heat-inactivated horse serum (Atlanta Biol.) that was

subsequently stepwise reduced. Differentiated serum-free astrocytes in 2ml DMEM/F12

were stretch-injured using one mild (2.6-4.0psi) or one severe (4.4-5.3psi) 50ms nitrogen

pressure pulse with the CIC II pressure controller (Custom Design and Fabrication Inc.).

TMT isobaric labeling

Stretched and un-stretched fetal human astrocytes lysates (WCL) and their

conditioned media (CM) were labeled with isobaric TMT sixplex mass tags (Thermo).

256

Cultured astrocytes were stretched at various severities, described above and harvested

along with their surrounding culture media for analysis at various time-points. Astrocyte

WCLs were precipitated using cold acetone and then re-suspended in 100 µL of 100 mM

triethyl ammonium bicarbonate (TEAB), 0.1% deoxycholate. Samples were then reduced

and alkylated with TCEP and IAM for 1 hour (55°C) and 30 minutes (37°C) respectively.

2.5 µg of trypsin per 100 µg of protein added for overnight digestion at 37°C. CM samples

were measured by BCA and 100 µg of sample treated directly to reduction, alkylation,

and trypsin digestion.

TMT isobaric label reagent sets were used per manufacturer’s instructions. Prior

to use TMT label reagents were equilibrated to room temperature. 0.8 mg vials of each

label (m/z 126-131) were reconstituted in 41 µL of anhydrous acetonitrile (ACN) and

allowed to dissolve for 5 minutes with occasional vortexing. Entire aliquots of WCL or CM

were added directly to TMT reagent vials. Labeling reaction was carried out at room

temperature for 1 hour and then quenched with 8 µL of 5% hydroxylamine with 15 minute

incubation. Equal volumes of sample were then combined for fractionation and analysis.

Replicates from three separate experiments were labeled with TMT mass tags as

described in the table below (both WCL and CM). Samples were labeled according to

Table 5.5.

Offline SCX fractionation

TMT labeled samples were fractionated by C18/SCX spin-tips prepared in-house.

200 µL Eppendorf tips were packed (in order) with equal amounts of SCX and C18

packing (Empore). Spin-tips were conditioned sequentially with 100 µL of methanol, C18

257

elution buffer (80% ACN, 5% acetic acid (HOAc)), and loading buffer (3% ACN, 0.5%

HOAc), spinning down at 2K x g between solvents changes. 100 µL of SCX buffer (30%

ACN, 500 mM ammonium acetate, 0.5% HOAc) was added to condition SCX packing

following by 100 µL of loading buffer to re-equilibrate. Up to 50 µg of sample was then

added to the conditioned spin-tip with 100 µL loading buffer and spun down at 2K x g. 20

µL of C18 elution buffer was added to elute peptides onto SCX filter. Stepwise elution

with increasing ammonium acetate (25, 50, 100, 200, and 500 mM) in 30% ACN, 0.5%

HOAc was performed to fractionate samples. Fractionated samples were dried by

vacuum centrifugation and reconstituted with 3% ACN, 0.1% formic acid (FA) in water to

concentration of 0.5 mg/mL.

Protein identification and TMT quantification by nano-LC-MS/MS

Fractionated peptides were injected onto an Acclaim PepMap 100, 75 µm X 2cm

C18 (Thermo) trap column and EASY-Spray PepMap RSLC, C18, 2µm, 75 µM X 25 cm

analytical column (Thermo) attached to an EASY nLC 1000 (Proxeon). The flow rate of

the mobile phase was set to 300 nL/min. Peptides were separated with a 0.1% FA in

water (A) and 0.1% FA in ACN (B) mobile phase system as follows: 5-35% B over 90

minutes, 35-60% B over 30 minutes. Peptides were introduced from the nano-HPLC to

Q-Exactive (Thermo), an Orbitrap mass spectrometer, operating with a Top10 duty cycle

consisting of 1 full scan (70,000 resolution, AGC 1e6, 100 ms max IT, 200-2000 m/z)

followed by 10 consecutive data-dependent MS2 (HCD) acquisitions (17,500 resolution,

AGC 1e5, 100 ms max IT, 4 m/z isolation window, 100 m/z fixed first mass, and NCE 30).

258

A dynamic exclusion of 30 secs was applied and peptides with unassigned charge or

charge state 1 were excluded from MS2 analysis.

Raw data files were searched in Proteome Discoverer v1.4 (Thermo) configured

with MASCOT (Matrix Science) to identify and quantify proteins based on mass, peptide

spectral matches (PSM), and reporter ion intensities. Peptide mass data was matched

against the human SwissProt database with the following search parameters: Enzyme –

trypsin, 2 missed cleavages allowed, 10 ppm and 0.05 Da MS1 and MS2 mass

tolerances, static modifications – carbamidomethyl (C), and dynamic modifications –

TMT6plex (K, N-term), oxidation (M). Protein identifications were validated by searching

against a reverse sequence decoy database with a FDR of 0.05 and a minimum of 2

unique peptides. Common contaminant proteins were manually excised from protein ID

lists (46). Relative quantitation of TMT reported ions were performed off the most

confident centroid peak with 20 ppm mass tolerance. Precursor ions with high co-isolation

interference were excluded from ratio calculations in Proteome Discoverer based on the

following formula:

% Isolation Interference = 100 x [1-( 𝑝𝑟𝑒𝑐𝑢𝑟𝑠𝑜𝑟 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 𝑖𝑛 𝑖𝑠𝑜𝑙𝑎𝑡𝑖𝑜𝑛 𝑤𝑖𝑛𝑑𝑜𝑤

𝑡𝑜𝑡𝑎𝑙 𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 𝑖𝑛 𝑖𝑠𝑜𝑙𝑎𝑡𝑖𝑜𝑛 𝑤𝑖𝑛𝑑𝑜𝑤)].

Statistical analysis was performed using a combination of Excel (Microsoft) and Graph

Pad 7 (Prism).

259

5.5 FIGURES

Figure 5.1: Relative quantitation by TMT isobaric mass tags workflow

260

261

Figure 5.2: Astrocyte-released protein fold changes measured in conditioned

media at after injury

Conditioned media (CM) was collected pre-injury and at 5 (A), 24 (B), and 48 (C) hours

(h) post mild (left) or severe (right) stretch injury. Astrocyte released proteins from different

conditions were cleaved with trypsin and labeled with isobaric TMT mass tags. Dotted

vertical and horizontal lines designate boundaries for measured 2-fold CM concentrations

compared to control and statistical significance (p-value <0.05) of change calculated by

paired, 1-tailed t-test. Proteins with fold change (FC) >2, >3, and >4 are represented in

yellow, orange, and red respectively. Higher severity mechanical injury induces higher

subpopulations of proteins released with respect to FC.

262

Figure 5.3: Higher intensity pressure pulse stretching induces greater release of

injury-related proteins from astrocytes

The percentage of proteins with relative fold change (FC) increases in condition medium

(CM) relative to un-stretched cells are represented in bar graph format. FCs of >2, >3,

and >4 measured by TMT labeling are represented in yellow, orange, and red respectively

for CM collected at 5, 24, and 48 hours (h) following mild or severe pressure stretching.

CM Fold Change

5h M

ild

5h S

ever

e

24h M

ild

24h S

ever

e

48h M

ild

48h S

ever

e

0

20

40

60

80

100FC>2

FC>3

FC>4

% P

rote

ins

263

Figure 5.4: Stretch injury may induce preferential release of lower molecular weight

protein species

Molecular weight (MW) analysis of astrocyte-released proteins in conditioned media (CM)

at various 5, 24, and 48 hours (h) after mild (M) or severe (S) stretch injury. (A) Box

whisker plots displaying median MWs with Tukey (1.5 interquartile ranges) whiskers of 2-

fold differentially released astrocyte proteins in CM for each experimental group. From 5h

to 24h, higher stretch severity induces release proteins exhibiting a higher MW range with

little difference in protein sizes by 48 hours with regard to extent of injury. (B) Bar graphs

displaying mean MWs of proteins released with fold changes (FCs) of >2 and >3 at 5h,

24h, and 48h collection time-points post mild mechanical injury. Error bars display full

range of released protein MWs. A general trend is observed that possibly highlights the

preferential release of lower MW proteins in response to injury.

264

265

Figure 5.5: Little change observed in intracellular protein concentrations in

response to injury from astrocyte whole cell lysates

Whole cell lysate (WCL) was collected pre-injury and after mild (left) and severe (right)

stretch injury at 5 hours (h) (A), 24h (B), and 48h (C). Intracellular proteins were cleaved

with trypsin and labeled with isobaric TMT mass tags. Dotted vertical and horizontal lines

designate boundaries for measured fold changes (FC) of <0.5, 0<0.75, >1.25, and >2 (left

to right). WCL concentrations were compared to control and statistical significance (p-

value <0.05) of change calculated by paired, 1-tailed t-test. Proteins with FC <0.75 and

>1.25 are represented in light blue and yellow respectively. Minimal protein concentration

changes within cells were measured in response to injury.

266

Figure 5.6: Comparison of proteomic release profiles resulting from astrocytes

injured by mild and severe stretching

Comparative analysis of conditioned medium (CM) proteins with 2-fold elevation relative

to control after mild or severe stretch injury at 5, 24, and 48 hour (h) collection time-points

after injury. Distinct injury severity related protein profiles are observed at 5h (A), 24h (B),

and 48h (C).

267

Figure 5.7: A common subpopulation of injury released proteins may represent

strong candidates of neurotraumatic injury

Proteins observed in both mild and severe injury groups at each time-point (5h, 24h, and

48h, Figure 5.6) were compared against each other. The 32 proteins overlapping between

all three groups represent the most robustly observed injury released proteins in astrocyte

conditioned media. This list of 32 proteins were further filter to arrive at final candidate

lists.

268

Figure 5.8: Astrocyte injury related release biomarker filtering strategy

Differentially (FC>2) released injury proteins identified by relative quantitation with TMT

isobaric mass labels were filtered using the above scheme. Severity/Time specific

proteins were manually curated based on exclusivity to time or injury conditions. Proteins

were then compared to the UCLA human traumatic brain injury (TBI) cerebrospinal fluid

(CSF) proteome (Chapter 3) in an attempt to identify potentially robust in vivo candidates.

Finally a blood protein filter was applied based on published blood proteomes (18, 19).

Manual addition of select proteins was also added to our candidate lists (Table 5.3) as

described in the Results and Discussion sections.

269

5.6 TABLES

5h Mild 24h Mild 48h Mild

MW (kDa) FC>2 FC>3 FC>2 FC>3 FC>2 FC>3 Minimum 5.05 5.05 5.05 5.05 5.05 5.05

25% Percentile 22.6 17.93 18 9.086 27.97 26.79 Median 36.67 28.75 27.73 14.87 45.97 42.59

75% Percentile 59.81 47.5 47.14 23.06 72.72 70.39 Maximum 628.7 123.5 284.4 27.37 628.7 628.7

Mean 57.96 36.6 45.87 15.7 70.29 66.54 Std. Deviation 74.5 26.67 58.35 7.95 81.11 78.75

n 235 53 51 6 365 325

5h Severe 24h Severe 48h Severe MW (kDa) FC>2 FC>3 FC>2 FC>3 FC>2 FC>3 Minimum 5.05 5.05 5.05 5.05 5.05 5.05

25% Percentile 22.81 20.32 23.73 22.81 27.91 27.42 Median 38.57 31.33 40.4 39.4 45.97 44.72

75% Percentile 61.58 51.61 65.29 68.6 73.58 73.21 Maximum 628.7 628.7 468.5 468.5 628.7 628.7

Mean 57.37 52.11 61.79 65.27 70.72 68.53 Std. Deviation 73.34 78.23 66.98 75.62 81.52 78.53

n 236 125 199 129 361 340

Table 5.1: Differentially released CM protein molecular weight statistics

270

Differentially Expressed WCL Proteins (5h Mild)

Accession Description MW [kDa]

P63313 Thymosin beta-10 5.0 P62328 Thymosin beta-4 5.0 Q96AY3 Peptidyl-prolyl cis-trans isomerase FKBP10 64.2 Q9NQC3 Reticulon-4 129.9 P05121 Plasminogen activator inhibitor 1 45.0 Q9UDY4 DnaJ homolog subfamily B member 4 37.8 P24844 Myosin regulatory light polypeptide 9 19.8

Differentially Expressed WCL Proteins (5h Severe)

Accession Description MW [kDa]

Q9NRX4 14 kDa phosphohistidine phosphatase 13.8

Differentially Expressed WCL Proteins (24h Mild)

Accession Description MW [kDa]

P63313 Thymosin beta-10 5.0 P62328 Thymosin beta-4 5.0 Q01995 Transgelin 22.6 Q8NBS9 Thioredoxin domain-containing protein 5 47.6 P62266 40S ribosomal protein S23 15.8 Q7KZF4 Staphylococcal nuclease domain-containing protein 1 101.9 P50454 Serpin H1 46.4

Differentially Expressed WCL Proteins (24h Severe)

Accession Description MW [kDa]

P62328 Thymosin beta-4 5.0 Q01995 Transgelin 22.6 Q8NBS9 Thioredoxin domain-containing protein 5 47.6

Differentially Expressed WCL Proteins (48h Severe)

Accession Description MW (kDa)

P61088 Ubiquitin-conjugating enzyme E2 N 17.1 P05121 Plasminogen activator inhibitor 1 45.0 P35268 60S ribosomal protein L22 14.8 Q9Y2B0 Protein canopy homolog 2 20.6 P62424 60S ribosomal protein L7a 30.0

Differentially Expressed WCL Proteins (48h Severe)

Accession Description MW (kDa)

Q01518 Adenylyl cyclase-associated protein 1 51.9 P61088 Ubiquitin-conjugating enzyme E2 N 17.1 Q99439 Calponin-2 33.7 P61160 Actin-related protein 2 44.7 P05121 Plasminogen activator inhibitor 1 45.0 P35268 60S ribosomal protein L22 14.8 Q9Y2B0 Protein canopy homolog 2 20.6

271

Table 5.2: Differentially expressed whole cell lysate proteins at post-injury

conditions

Whole cell lysate proteins with expression differences at 5, 24, and 48 hours (h) after mild

or severe stretch injury. Proteins with a 25% reduction in intracellular concentrations are

displayed in italics. Remaining proteins represent intracellular increases of 25%. Bolded

proteins exhibited corresponding increases in time-point and stretch severity matched

conditioned medium.

272

Unique 5h Protein Signatures Accession Description 5hr

Mild 5h Severe MW(kDa)

P68402 Platelet-activating factor acetylhydrolase IB subunit

beta

2.7 25.6 P61247 40S ribosomal protein S3a 2.5 29.9 Q13643 Four and a half LIM domains

protein 3 2.2 31.2

P20930 Filaggrin 2.2 434.9 P15121 Aldose reductase 2.2 35.8 P62841 40S ribosomal protein S15 2.2 17.0 Q14315 Filamin-C 2.1 290.8 P29692 Elongation factor 1-delta 2.1 31.1 P35237 Serpin B6 2.1 42.6 P17174 Aspartate aminotransferase,

cytoplasmic 2.0 46.2

P05387 60S acidic ribosomal protein P2

2.0 11.7 Q9BWD1 Acetyl-CoA acetyltransferase,

cytosolic 4.8 41.3

O95782 AP-2 complex subunit alpha-1 3.4 107.5 P28300 Protein-lysine 6-oxidase 2.3 46.9

Unique 24h Protein Signatures

Accession Description 24h Mild

24h Severe MW(kDa) P12109 Collagen alpha-1(VI) chain 6.3 108.5 P24821 Tenascin 6.1 240.7 Q4ZHG4 Fibronectin type III domain-

containing protein 1 5.7 205.4

Q15121 Astrocytic phosphoprotein PEA-15

5.6 15.0 P30101 Protein disulfide-isomerase A3 4.5 56.7 Q9H4D0 Calsyntenin-2 4.5 106.9 P05997 Collagen alpha-2(V) chain 4.4 144.8 Q8NBS9 Thioredoxin domain-containing

protein 5 4.4 47.6

P39687 Acidic leucine-rich nuclear phosphoprotein 32 family

member A

4.3 28.6 P01008 Antithrombin-III 4.3 52.6 Q12805 EGF-containing fibulin-like

extracellular matrix protein 1 4.3 54.6

P07237 Protein disulfide-isomerase 4.2 57.1 Q92626 Peroxidasin homolog 4.2 165.2 P49327 Fatty acid synthase 4.0 273.3 P28838 Cytosol aminopeptidase 3.8 56.1 P35555 Fibrillin-1 3.8 312.0 P01033 Metalloproteinase inhibitor 1 3.8 23.2 Q969H8 UPF0556 protein C19orf10 3.8 18.8 P09972 Fructose-bisphosphate

aldolase C 3.8 39.4

Q02818 Nucleobindin-1 3.8 53.8 P08572 Collagen alpha-2(IV) chain 3.5 167.4 P18065 Insulin-like growth factor-

binding protein 2 3.4 34.8

P16035 Metalloproteinase inhibitor 2 3.4 24.4 P05204 Non-histone chromosomal

protein HMG-17 3.3 9.4

P07996 Thrombospondin-1 3.3 129.3 P98160 Basement membrane-specific

heparan sulfate proteoglycan core protein

3.3 468.5 O94985 Calsyntenin-1 3.3 109.7 Q9UI42 Carboxypeptidase A4 3.3 47.3 Q15063 Periostin 3.3 93.3 Q15582 Transforming growth factor-

beta-induced protein ig-h3 3.3 74.6

Q9UBP4 Dickkopf-related protein 3 3.2 38.4 P05121 Plasminogen activator inhibitor

1 3.2 45.0

P19827 Inter-alpha-trypsin inhibitor heavy chain H1

3.1 101.3 P01709 Ig lambda chain V-II region

MGC 3.0 11.6

P21810 Biglycan 3.0 41.6 Q14767 Latent-transforming growth

factor beta-binding protein 2 2.9 194.9

P12107 Collagen alpha-1(XI) chain 2.8 181.0

273

P23284 Peptidyl-prolyl cis-trans isomerase B

2.8 23.7 O14818 Proteasome subunit alpha

type-7 2.8 27.9

P24593 Insulin-like growth factor-binding protein 5

2.7 30.6 P02461 Collagen alpha-1(III) chain 2.5 138.5 Q08380 Galectin-3-binding protein 2.5 65.3 Q16270 Insulin-like growth factor-

binding protein 7 2.4 29.1

P62906 60S ribosomal protein L10a 2.4 24.8 Q14766 Latent-transforming growth

factor beta-binding protein 1 2.3 186.7

Q96HC4 PDZ and LIM domain protein 5 2.0 63.9

Unique 48h Protein Signatures Accession Description 48hr

Mild 48h Severe MW(kDa)

Q14697 Neutral alpha-glucosidase AB 3.8 106.8 O00410 Importin-5 7.9 123.5 Q01105 Protein SET 5.1 33.5 P26639 Threonine--tRNA ligase,

cytoplasmic 3.3 83.4

Table 5.3: Potential time and injury severity specific signatures

Unique released protein signatures identified from workflows presented in Figure 5.6 and

5.7. Results of comparative analysis were manually filtered to include for not only injury

related release proteins observed only under specified time and trauma levels but also

proteins that were measured in only low abundances (~2-fold less) at other time-points.

For 24 hour unique protein signatures, the presence of high 48 hour concentrations in CM

was ignored given increase in associated cell death resulting in high overall released

protein abundances.

274

Top Neurotraumatic Injury Biomarker Candidates

Accession Description 5hr M

5h S

24h M

24h S

48h M

48h S

MW (kDa)

P09493 Tropomyosin alpha-1 chain 2.5 3.1 2.1 6.8 5.3 7.6 32.7 P62328/ P63313

Thymosin beta-4 / Thymosin beta-10 4.4 5.0 3.5 5.4 8.4 11.9 5.0

P62937 Peptidyl-prolyl cis-trans isomerase A 3.5 3.8 2.0 2.5 5.1 7.1 18.0

P00441 Superoxide dismutase [Cu-Zn] 2.6 3.0 3.8 4.8 4.9 6.3 15.9

P09936 Ubiquitin carboxyl-terminal hydrolase

isozyme L1 3.7 3.9 2.6 3.8 4.8 6.7 24.8

P63104 14-3-3 protein zeta/delta 2.8 2.7 2.2 3.5 4.4 6.1 27.7

P09972 Fructose-bisphosphate aldolase C 2.4 2.4 1.9 3.8 3.6 4.9 39.4

Q01995 Transgelin 3.5 3.9 2.8 3.4 6.3 9.1 22.6

Q9NRX4 14 kDa phosphohistidine

phosphatase 2.4 2.3 3.8 3.1 3.6 5.1 13.8

P62158 Calmodulin 2.2 3.5 2.4 4.8 5.1 8.2 16.8

P07951 Tropomyosin beta chain 2.1 2.6 2.3 3.6 5.1 7.1 32.8

Q15121 Astrocytic phosphoprotein PEA-15 2.8 3.6 1.8 5.6 5.8 8.5 15.0

P15311 Ezrin 2.6 3.7 2.9 3.1 4.8 8.3 69.4

Table 5.4: Top protein biomarker candidates for neurotraumatic injury

List of top neurotraumatic injury markers displaying proteomic fold changes relative to

control in conditioned medium (CM) at 5, 24, and 48 hours (h) after mild (M) or severe (S)

stretch injury. This list was manually curated based on whole cell lysate (WCL) and CM

protein dynamics in a stretch injury astrocyte culture system and filtering strategy against

human traumatic brain injury (TBI) cerebrospinal fluid (CSF) proteomes and blood

proteomes (Figure 5.8). Proteins highlighted in red and orange represent proteins

observed in TBI CSF and both TBI and healthy CSF respectively. Italicized entries

represent proteins observed in the blood proteome (18, 19, 47). Bolded proteins represent

proteins with corresponding WCL and CM protein abundance changes in response to

stretch injury in vitro. Because many promising TBI biomarkers are also present normally

in blood (ubiquitin carboxyl-terminal hydrolase isozyme L1, fructose-bisphosphate

275

aldolase C), overlap with blood proteome lists did not exclude inclusion in our candidate

list.

276

Experiment 1 (Internal ID TMT3)

Replicate Injury Severity Time (h) TMT Label

A Un-stretched 0 126

B Un-stretched 0 127

A Mild 5 128

B Mild 5 129

A Severe 5 130

B Severe 5 131

Experiment 2 (Internal ID TMT4)

Replicate Injury Severity Time (h) TMT Label

A Un-stretched 0 126

B Un-stretched 0 127

A Mild 24 128

B Mild 24 129

A Severe 24 130

B Severe 24 131

Experiment 3 (Internal ID TMT5)

Replicate Injury Severity Time (h) TMT Label

A Un-stretched 0 126

A Mild 5 128

B Severe 5 129

A Mild 48 130

B Severe 48 131

Table 5.5: In vitro astrocyte WCL and CM TMT labeling table

277

Accession Description #

Peptides PSMs 5h M 5h S

MW [kDa]

P07437 Tubulin beta chain 21 285 6.5 9.9 49.6

Q71U36 Tubulin alpha-1A chain 22 231 6.0 7.8 50.1

O60664 Perilipin-3 2 2 4.9 6.5 47.0

P40261 Nicotinamide N-

methyltransferase 8 41 4.2 6.1 29.6

Q9NTK5 Obg-like ATPase 1 3 6 3.5 5.8 44.7

P06703 Protein S100-A6 3 142 4.5 5.5 10.2

O00410 Importin-5 8 14 4.1 5.4 123.5

P04406 Glyceraldehyde-3-phosphate

dehydrogenase 20 1264 4.2 5.4 36.0

P30044 Peroxiredoxin-5, mitochondrial 4 10 3.7 5.1 22.1

P62328 Thymosin beta-4 4 14 4.4 5.0 5.0

P62857 40S ribosomal protein S28 2 2 3.9 5.0 7.8

P61970 Nuclear transport factor 2 4 19 3.7 5.0 14.5

P78417 Glutathione S-transferase

omega-1 5 9 3.6 5.0 27.5

P04792 Heat shock protein beta-1 14 46 3.1 4.9 22.8

P58546 Myotrophin 3 6 3.5 4.9 12.9

O75874 Isocitrate dehydrogenase

[NADP] cytoplasmic 10 35 3.4 4.8 46.6

P31946 14-3-3 protein beta/alpha 17 288 3.6 4.8 28.1

P54687 Branched-chain-amino-acid aminotransferase, cytosolic

3 3 3.4 4.8 42.9

Q9BWD1 Acetyl-CoA acetyltransferase,

cytosolic 2 15 4.8 41.3

Q9HC38 Glyoxalase domain-containing

protein 4 2 3 3.4 4.8 34.8

P62701 40S ribosomal protein S4, X

isoform 3 6 3.3 4.6 29.6

P68036 Ubiquitin-conjugating enzyme E2

L3 6 10 3.2 4.6 17.9

P20962 Parathymosin 2 5 2.7 4.6 11.5

Q04917 14-3-3 protein eta 9 180 3.2 4.6 28.2

Q96C90 Protein phosphatase 1 regulatory

subunit 14B 2 2 4.5 15.9

Q9UGI8 Testin 3 4 3.5 4.5 48.0

P55786 Puromycin-sensitive

aminopeptidase 16 28 3.1 4.4 103.2

P30085 UMP-CMP kinase 6 31 3.4 4.4 22.2

P12277 Creatine kinase B-type 4 15 2.9 4.4 42.6

P30041 Peroxiredoxin-6 6 31 2.8 4.3 25.0

P30153

Serine/threonine-protein phosphatase 2A 65 kDa

regulatory subunit A alpha isoform

8 19 3.1 4.3 65.3

P08729 Keratin, type II cytoskeletal 7 14 48 3.0 4.2 51.4

278

Q14847 LIM and SH3 domain protein 1 9 16 2.9 4.1 29.7

Q15819 Ubiquitin-conjugating enzyme E2

variant 2 2 8 3.2 4.1 16.4

P05783 Keratin, type I cytoskeletal 18 9 26 2.9 4.1 48.0

P47756 F-actin-capping protein subunit

beta 6 20 3.2 4.1 31.3

Q13642 Four and a half LIM domains

protein 1 11 45 3.1 4.1 36.2

P17655 Calpain-2 catalytic subunit 6 13 3.3 4.0 79.9

P27348 14-3-3 protein theta 17 216 3.2 4.0 27.7

P68363 Tubulin alpha-1B chain 22 234 3.2 4.0 50.1

P46821 Microtubule-associated protein

1B 12 19 2.9 3.9 270.5

Q15843 NEDD8 2 2 2.9 3.9 9.1

P39019 40S ribosomal protein S19 4 6 2.8 3.9 16.1

P02511 Alpha-crystallin B chain 5 29 2.5 3.9 20.1

Q01995 Transgelin 21 1730 3.5 3.9 22.6

P09936 Ubiquitin carboxyl-terminal

hydrolase isozyme L1 15 437 3.7 3.9 24.8

Q15746 Myosin light chain kinase,

smooth muscle 2 3 2.9 3.9 210.6

Q53FA7 Quinone oxidoreductase PIG3 2 2 2.8 3.9 35.5

P20618 Proteasome subunit beta type-1 4 10 2.9 3.8 26.5

P18085 ADP-ribosylation factor 4 3 8 2.7 3.8 20.5

P62937 Peptidyl-prolyl cis-trans

isomerase A 19 333 3.5 3.8 18.0

P61204 ADP-ribosylation factor 3 4 10 3.0 3.8 20.6

P14550 Alcohol dehydrogenase

[NADP(+)] 9 27 3.0 3.8 36.5

Q7KZF4 Staphylococcal nuclease

domain-containing protein 1 6 15 2.8 3.8 101.9

Q14019 Coactosin-like protein 7 20 3.0 3.8 15.9

Q9Y570 Protein phosphatase

methylesterase 1 2 6 2.9 3.7 42.3

O95394 Phosphoacetylglucosamine

mutase 5 14 2.9 3.7 59.8

P14324 Farnesyl pyrophosphate

synthase 3 3 3.0 3.7 48.2

Q9H4A4 Aminopeptidase B 4 9 2.9 3.7 72.5

P52209 6-phosphogluconate

dehydrogenase, decarboxylating 15 46 2.8 3.7 53.1

P13693 Translationally-controlled tumor

protein 6 54 3.3 3.7 19.6

P15311 Ezrin 22 134 2.6 3.7 69.4

Q15121 Astrocytic phosphoprotein PEA-

15 5 102 2.8 3.6 15.0

P34932 Heat shock 70 kDa protein 4 9 16 2.9 3.6 94.3

P23528 Cofilin-1 15 439 3.0 3.6 18.5

279

P22314 Ubiquitin-like modifier-activating

enzyme 1 15 29 3.7 3.6 117.8

P61160 Actin-related protein 2 8 37 2.6 3.6 44.7

Q15404 Ras suppressor protein 1 7 17 2.9 3.6 31.5

P28066 Proteasome subunit alpha type-5 2 2 2.9 3.5 26.4

P61960 Ubiquitin-fold modifier 1 2 2 2.8 3.5 9.1

Q15942 Zyxin 6 10 2.7 3.5 61.2

Q12765 Secernin-1 4 7 2.7 3.5 46.4

P62979 Ubiquitin-40S ribosomal protein

S27a 11 140 2.6 3.5 18.0

P22392 Nucleoside diphosphate kinase B 8 90 3.0 3.5 17.3

P60981 Destrin 10 207 2.9 3.5 18.5

P60900 Proteasome subunit alpha type-6 3 3 2.6 3.5 27.4

P62158 Calmodulin 3 10 2.2 3.5 16.8

P61978 Heterogeneous nuclear

ribonucleoprotein K 2 2 2.4 3.5 50.9

Q01469 Fatty acid-binding protein,

epidermal 3 8 2.2 3.5 15.2

P52565 Rho GDP-dissociation inhibitor 1 3 10 3.3 3.4 23.2

Q14974 Importin subunit beta-1 12 23 2.6 3.4 97.1

Q99584 Protein S100-A13 3 5 2.4 3.4 11.5

P63208 S-phase kinase-associated

protein 1 3 3 2.7 3.4 18.6

P10599 Thioredoxin 7 28 2.7 3.4 11.7

Q96FW1 Ubiquitin thioesterase OTUB1 3 5 2.7 3.4 31.3

O95782 AP-2 complex subunit alpha-1 2 2 3.4 107.5

Q06830 Peroxiredoxin-1 10 38 2.7 3.4 22.1

P46940 Ras GTPase-activating-like

protein IQGAP1 21 54 2.7 3.4 189.1

P12955 Xaa-Pro dipeptidase 2 2 2.5 3.4 54.5

Q9NVA2 Septin-11 4 8 2.5 3.4 49.4

P53396 ATP-citrate synthase 10 20 2.8 3.4 120.8

Q8WUM4 Programmed cell death 6-

interacting protein 3 3 2.2 3.3 96.0

P11766 Alcohol dehydrogenase class-3 3 3 2.7 3.3 39.7

P36871 Phosphoglucomutase-1 11 34 2.7 3.3 61.4

P37837 Transaldolase 5 16 2.4 3.3 37.5

O75368 SH3 domain-binding glutamic

acid-rich-like protein 5 13 2.8 3.3 12.8

Q07955 Serine/arginine-rich splicing

factor 1 2 32 2.2 3.3 27.7

P06733 Alpha-enolase 29 1290 3.0 3.3 47.1

Q99497 Protein DJ-1 9 75 2.7 3.3 19.9

P68371 Tubulin beta-4B chain 19 255 2.5 3.3 49.8

Q16851 UTP--glucose-1-phosphate

uridylyltransferase 5 11 2.5 3.3 56.9

280

P14174 Macrophage migration inhibitory

factor 4 49 2.9 3.3 12.5

P29966 Myristoylated alanine-rich C-

kinase substrate 4 4 2.6 3.2 31.5

P32119 Peroxiredoxin-2 5 15 2.3 3.2 21.9

P08107 Heat shock 70 kDa protein 1A/1B 9 20 2.4 3.2 70.0

Q01813 ATP-dependent 6-

phosphofructokinase, platelet type

3 4 2.4 3.2 85.5

Q09666 Neuroblast differentiation-associated protein AHNAK

9 31 2.4 3.2 628.7

P00558 Phosphoglycerate kinase 1 28 267 2.7 3.2 44.6

P21266 Glutathione S-transferase Mu 3 9 31 2.9 3.2 26.5

P62241 40S ribosomal protein S8 3 8 2.5 3.2 24.2

Q01518 Adenylyl cyclase-associated

protein 1 17 74 2.5 3.2 51.9

Q9ULV4 Coronin-1C 8 26 2.5 3.1 53.2

P67936 Tropomyosin alpha-4 chain 16 130 2.7 3.1 28.5

P37802 Transgelin-2 15 255 2.7 3.1 22.4

P14618 Pyruvate kinase PKM 37 1061 2.8 3.1 57.9

P09382 Galectin-1 9 217 3.1 3.1 14.7

P25786 Proteasome subunit alpha type-1 5 8 2.2 3.1 29.5

P09493 Tropomyosin alpha-1 chain 15 126 2.5 3.1 32.7

P21291 Cysteine and glycine-rich protein

1 11 94 2.6 3.1 20.6

P09211 Glutathione S-transferase P 11 135 2.9 3.1 23.3

Q14204 Cytoplasmic dynein 1 heavy

chain 1 9 12 2.8 3.1 532.1

Q9H299 SH3 domain-binding glutamic

acid-rich-like protein 3 5 29 3.3 3.0 10.4

Q16527 Cysteine and glycine-rich protein

2 6 19 2.5 3.0 20.9

P13639 Elongation factor 2 21 92 2.6 3.0 95.3

P09960 Leukotriene A-4 hydrolase 14 35 2.5 3.0 69.2

Q9Y696 Chloride intracellular channel

protein 4 16 251 3.6 3.0 28.8

Q16555 Dihydropyrimidinase-related

protein 2 8 13 2.4 3.0 62.3

Q96CX2 BTB/POZ domain-containing

protein KCTD12 3 5 2.3 3.0 35.7

P26641 Elongation factor 1-gamma 8 17 2.6 3.0 50.1

Q92820 Gamma-glutamyl hydrolase 4 11 2.6 3.0 35.9

P00441 Superoxide dismutase [Cu-Zn] 8 65 2.6 3.0 15.9

P06744 Glucose-6-phosphate isomerase 17 127 2.6 3.0 63.1

Q14192 Four and a half LIM domains

protein 2 7 50 3.8 3.0 32.2

P07195 L-lactate dehydrogenase B chain 16 157 2.5 3.0 36.6

281

Q04446 1,4-alpha-glucan-branching

enzyme 5 6 2.5 2.9 80.4

Q9Y617 Phosphoserine aminotransferase 14 138 2.6 2.9 40.4

O75083 WD repeat-containing protein 1 27 126 2.6 2.9 66.2

P07737 Profilin-1 10 284 2.7 2.9 15.0

P80723 Brain acid soluble protein 1 5 7 2.4 2.9 22.7

P26038 Moesin 31 292 2.6 2.9 67.8

Q05682 Caldesmon 16 95 2.8 2.9 93.2

P68032 Actin, alpha cardiac muscle 1 20 1041 2.7 2.9 42.0

P11216 Glycogen phosphorylase, brain

form 4 5 2.4 2.8 96.6

O00299 Chloride intracellular channel

protein 1 10 34 2.5 2.8 26.9

P26022 Pentraxin-related protein PTX3 9 34 2.2 2.8 41.9

P00338 L-lactate dehydrogenase A chain 29 452 2.7 2.8 36.7

P62258 14-3-3 protein epsilon 15 226 2.2 2.8 29.2

P12814 Alpha-actinin-1 57 1326 2.5 2.8 103.0

P07602 Prosaposin 8 71 2.4 2.8 58.1

P00966 Argininosuccinate synthase 13 64 2.6 2.8 46.5

P50395 Rab GDP dissociation inhibitor

beta 21 235 2.4 2.8 50.6

P26639 Threonine--tRNA ligase,

cytoplasmic 6 8 2.2 2.8 83.4

P62826 GTP-binding nuclear protein Ran 7 25 2.3 2.8 24.4

Q16658 Fascin 16 99 3.5 2.8 54.5

P63104 14-3-3 protein zeta/delta 20 358 2.8 2.7 27.7

P60842 Eukaryotic initiation factor 4A-I 11 30 3.1 2.7 46.1

P18669 Phosphoglycerate mutase 1 16 273 2.5 2.7 28.8

P42771 Cyclin-dependent kinase inhibitor

2A, isoforms 1/2/3 2 2 2.1 2.7 16.5

P13645 Keratin, type I cytoskeletal 10 9 12 2.1 2.7 58.8

P21980 Protein-glutamine gamma-

glutamyltransferase 2 6 14 3.0 2.7 77.3

P68104 Elongation factor 1-alpha 1 15 144 2.4 2.7 50.1

P48163 NADP-dependent malic enzyme 4 5 2.7 64.1

P11142 Heat shock cognate 71 kDa

protein 30 304 2.6 2.7 70.9

Q9UBG0 C-type mannose receptor 2 7 11 2.4 2.7 166.6

P07858 Cathepsin B 3 5 2.2 2.7 37.8

P18206 Vinculin 62 559 2.5 2.7 123.7

P04075 Fructose-bisphosphate aldolase

A 21 412 2.4 2.7 39.4

O15144 Actin-related protein 2/3 complex

subunit 2 11 31 2.1 2.7 34.3

P60709 Actin, cytoplasmic 1 22 1650 2.4 2.6 41.7

Q9Y3B8 Oligoribonuclease, mitochondrial 6 11 2.2 2.6 26.8

282

P13797 Plastin-3 20 95 2.3 2.6 70.8

P09651 Heterogeneous nuclear

ribonucleoprotein A1 6 43 2.9 2.6 38.7

P51911 Calponin-1 7 12 2.2 2.6 33.1

P31949 Protein S100-A11 5 68 2.4 2.6 11.7

P60174 Triosephosphate isomerase 16 525 2.4 2.6 30.8

P07951 Tropomyosin beta chain 16 133 2.1 2.6 32.8

P00568 Adenylate kinase isoenzyme 1 6 17 2.6 2.6 21.6

Q14195 Dihydropyrimidinase-related

protein 3 17 64 2.3 2.6 61.9

Q9NZU5 LIM and cysteine-rich domains

protein 1 9 16 2.0 2.6 40.8

P30086 Phosphatidylethanolamine-

binding protein 1 11 66 2.6 2.5 21.0

Q96AY3 Peptidyl-prolyl cis-trans

isomerase FKBP10 6 16 2.0 2.5 64.2

P61088 Ubiquitin-conjugating enzyme E2

N 5 12 2.1 2.5 17.1

Q92499 ATP-dependent RNA helicase

DDX1 2 2 3.7 2.5 82.4

P61158 Actin-related protein 3 9 31 2.3 2.5 47.3

P21333 Filamin-A 107 1119 2.5 2.5 280.6

P49720 Proteasome subunit beta type-3 3 3 2.2 2.5 22.9

Q14738 Serine/threonine-protein phosphatase 2A 56 kDa

regulatory subunit delta isoform 4 7 2.9 2.5 69.9

P62942 Peptidyl-prolyl cis-trans

isomerase FKBP1A 4 16 2.3 2.5 11.9

P61981 14-3-3 protein gamma 18 297 2.3 2.5 28.3

O43707 Alpha-actinin-4 55 782 2.2 2.5 104.8

P63241 Eukaryotic translation initiation

factor 5A-1 3 229 2.3 2.5 16.8

Q14103 Heterogeneous nuclear

ribonucleoprotein D0 6 10 2.0 2.4 38.4

P40925 Malate dehydrogenase,

cytoplasmic 12 61 2.5 2.4 36.4

Q8NBS9 Thioredoxin domain-containing

protein 5 7 8 2.0 2.4 47.6

P19022 Cadherin-2 6 10 1.8 2.4 99.7

P09972 Fructose-bisphosphate aldolase

C 8 90 2.4 2.4 39.4

O14498 Immunoglobulin superfamily

containing leucine-rich repeat protein

6 20 2.1 2.4 46.0

Q9Y490 Talin-1 37 90 2.3 2.3 269.6

P04080 Cystatin-B 4 73 2.9 2.3 11.1

Q9BRA2 Thioredoxin domain-containing

protein 17 5 13 2.4 2.3 13.9

O43852 Calumenin 13 38 2.0 2.3 37.1

283

P28300 Protein-lysine 6-oxidase 2 4 2.3 46.9

P05388 60S acidic ribosomal protein P0 4 11 2.4 2.3 34.3

Q9NRX4 14 kDa phosphohistidine

phosphatase 7 23 2.4 2.3 13.8

P24534 Elongation factor 1-beta 5 10 2.5 2.3 24.7

P35052 Glypican-1 7 10 1.9 2.3 61.6

P08670 Vimentin 36 735 2.0 2.3 53.6

Q86VP6 Cullin-associated NEDD8-

dissociated protein 1 19 46 2.3 2.3 136.3

P41250 Glycine--tRNA ligase 16 97 2.4 2.3 83.1

P62873 Guanine nucleotide-binding

protein G(I)/G(S)/G(T) subunit beta-1

2 2 1.9 2.2 37.4

P07686 Beta-hexosaminidase subunit

beta 5 8 2.0 2.2 63.1

Q9NY33 Dipeptidyl peptidase 3 7 15 2.0 2.2 82.5

P98095 Fibulin-2 4 8 2.0 2.2 126.5

P08758 Annexin A5 13 81 2.2 2.2 35.9

Q15417 Calponin-3 7 13 1.9 2.2 36.4

P14314 Glucosidase 2 subunit beta 3 5 1.7 2.2 59.4

P27797 Calreticulin 13 53 1.9 2.2 48.1

Q6UVK1 Chondroitin sulfate proteoglycan

4 10 20 2.1 2.2 250.4

Q15149 Plectin 22 44 1.9 2.2 531.5

O43854 EGF-like repeat and discoidin I-like domain-containing protein 3

5 9 2.0 2.2 53.7

Q13219 Pappalysin-1 21 49 2.1 2.2 180.9

P02462 Collagen alpha-1(IV) chain 18 86 2.0 2.1 160.5

P29401 Transketolase 16 94 2.1 2.1 67.8

O95084 Serine protease 23 3 4 1.8 2.1 43.0

P51397 Death-associated protein 1 3 5 3.6 2.1 11.2

P52926 High mobility group protein

HMGI-C 3 3 2.0 2.1 11.8

Q8IUX7 Adipocyte enhancer-binding

protein 1 8 13 1.7 2.1 130.8

Q00610 Clathrin heavy chain 1 34 106 2.1 2.1 191.5

P25787 Proteasome subunit alpha type-2 7 24 2.0 2.1 25.9

P23381 Tryptophan--tRNA ligase,

cytoplasmic 11 38 2.0 2.1 53.1

P60660 Myosin light polypeptide 6 6 24 2.0 2.1 16.9

P48637 Glutathione synthetase 6 19 2.3 2.1 52.4

P05452 Tetranectin 5 15 2.0 2.1 22.5

P07339 Cathepsin D 13 68 1.9 2.0 44.5

Q86UX2 Inter-alpha-trypsin inhibitor heavy

chain H5 2 18 2.0 104.5

P28074 Proteasome subunit beta type-5 4 11 1.9 2.0 28.5

284

Q15084 Protein disulfide-isomerase A6 4 6 1.8 2.0 48.1

Q9BY76 Angiopoietin-related protein 4 3 7 1.7 2.0 45.2

P08238 Heat shock protein HSP 90-beta 25 201 2.0 2.0 83.2

Q92743 Serine protease HTRA1 11 84 1.8 2.0 51.3

P27658 Collagen alpha-1(VIII) chain 13 45 1.8 1.9 73.3

P27816 Microtubule-associated protein 4 10 33 2.2 1.9 120.9

P31150 Rab GDP dissociation inhibitor

alpha 14 167 2.0 1.9 50.6

P15502 Elastin 6 18 1.9 1.9 68.4

P35579 Myosin-9 55 232 2.4 1.9 226.4

P22626 Heterogeneous nuclear

ribonucleoproteins A2/B1 9 27 2.0 1.9 37.4

P29279 Connective tissue growth factor 4 8 1.9 1.9 38.1

Q16610 Extracellular matrix protein 1 10 27 1.6 1.8 60.6

P28799 Granulins 5 12 1.8 1.8 63.5

Q92626 Peroxidasin homolog 23 65 1.8 1.8 165.2

O60565 Gremlin-1 2 2 2.3 1.8 20.7

P07900 Heat shock protein HSP 90-

alpha 24 225 2.0 1.8 84.6

P06865 Beta-hexosaminidase subunit

alpha 5 11 1.7 1.8 60.7

O75369 Filamin-B 43 218 2.0 1.8 278.0

P35442 Thrombospondin-2 26 116 1.8 1.8 129.9

P07585 Decorin 14 96 1.7 1.8 39.7

P10915 Hyaluronan and proteoglycan

link protein 1 15 127 1.9 1.8 40.1

P16870 Carboxypeptidase E 9 19 1.8 1.8 53.1

P55209 Nucleosome assembly protein 1-

like 1 3 5 1.8 45.3

P06396 Gelsolin 29 188 1.9 1.7 85.6

P07355 Annexin A2 17 61 1.7 1.7 38.6

P14625 Endoplasmin 14 69 1.8 1.7 92.4

Q92688 Acidic leucine-rich nuclear phosphoprotein 32 family

member B 4 13 1.6 1.7 28.8

P35556 Fibrillin-2 10 25 1.6 1.7 314.6

P30101 Protein disulfide-isomerase A3 15 59 1.9 1.7 56.7

P24593 Insulin-like growth factor-binding

protein 5 9 48 1.5 1.7 30.6

Q9NRN5 Olfactomedin-like protein 3 6 12 1.8 1.7 46.0

P12110 Collagen alpha-2(VI) chain 13 34 1.6 1.7 108.5

P08572 Collagen alpha-2(IV) chain 43 144 1.6 1.6 167.4

P12107 Collagen alpha-1(XI) chain 15 49 1.6 1.6 181.0

Q96HF1 Secreted frizzled-related protein

2 9 22 1.6 1.6 33.5

Q15063 Periostin 38 427 1.7 1.6 93.3

285

P36578 60S ribosomal protein L4 4 8 2.0 1.6 47.7

P12111 Collagen alpha-3(VI) chain 45 124 1.7 1.6 343.5

P08603 Complement factor H 11 20 1.6 1.6 139.0

O76061 Stanniocalcin-2 6 78 1.5 1.5 33.2

Q76M96 Coiled-coil domain-containing

protein 80 16 69 1.7 1.5 108.1

P02545 Prelamin-A/C 8 42 1.8 1.5 74.1

P61769 Beta-2-microglobulin 4 41 1.6 1.5 13.7

O00391 Sulfhydryl oxidase 1 20 123 1.7 1.5 82.5

P08476 Inhibin beta A chain 16 35 1.6 1.5 47.4

Q99715 Collagen alpha-1(XII) chain 59 175 1.5 1.5 332.9

Q9UBP4 Dickkopf-related protein 3 8 34 1.5 1.4 38.4

P01033 Metalloproteinase inhibitor 1 8 236 1.6 1.4 23.2

Q9UI42 Carboxypeptidase A4 10 29 1.4 1.4 47.3

Q9UBX5 Fibulin-5 5 12 1.5 1.4 50.1

P07237 Protein disulfide-isomerase 16 95 1.6 1.4 57.1

P07942 Laminin subunit beta-1 17 42 1.5 1.4 197.9

Q16777 Histone H2A type 2-C 3 25 1.4 1.4 14.0

P10909 Clusterin 10 91 1.3 52.5

P18065 Insulin-like growth factor-binding

protein 2 15 97 1.6 1.3 34.8

P29692 Elongation factor 1-delta 4 9 2.1 1.3 31.1

P13611 Versican core protein 16 87 1.5 1.3 372.6

Q15582 Transforming growth factor-beta-

induced protein ig-h3 28 676 1.5 1.3 74.6

P12109 Collagen alpha-1(VI) chain 34 285 1.5 1.3 108.5

Q9NQC3 Reticulon-4 2 2 1.5 1.2 129.9

P62633 Cellular nucleic acid-binding

protein 3 10 1.9 1.2 19.4

O15540 Fatty acid-binding protein, brain 3 34 1.5 1.2 14.9

P00749 Urokinase-type plasminogen

activator 4 20 1.3 0.8 48.5

P01011 Alpha-1-antichymotrypsin 2 3 0.8 47.6

P02790 Hemopexin 2 3 1.2 0.8 51.6

Q6NXT2 Histone H3.3C 2 5 0.7 0.7 15.2

P01024 Complement C3 5 40 0.7 187.0

P01709 Ig lambda chain V-II region MGC 2 15 0.7 11.6

P02787 Serotransferrin 3 30 0.6 77.0

P00738 Haptoglobin 6 227 0.6 45.2

Q13907 Isopentenyl-diphosphate Delta-

isomerase 1 2 4 26.3

P07814 Bifunctional glutamate/proline--

tRNA ligase 2 4 170.5

O14818 Proteasome subunit alpha type-7 4 5 27.9

286

Q06210 Glutamine--fructose-6-phosphate aminotransferase [isomerizing] 1

3 5 78.8

Q01105 Protein SET 2 2 33.5

P42574 Caspase-3 2 3 31.6

O75711 Scrapie-responsive protein 1 2 2 11.1

P62269 40S ribosomal protein S18 2 3 17.7

Q14011 Cold-inducible RNA-binding

protein 2 2 18.6

Q96FQ6 Protein S100-A16 3 7 11.8

O95678 Keratin, type II cytoskeletal 75 4 8 59.5

O95865 N(G),N(G)-dimethylarginine dimethylaminohydrolase 2

3 8 29.6

Q96KP4 Cytosolic non-specific

dipeptidase 3 4 52.8

Q9BQE3 Tubulin alpha-1C chain 19 216 49.9

P09104 Gamma-enolase 7 558 47.2

Q15181 Inorganic pyrophosphatase 4 6 32.6

O00469 Procollagen-lysine,2-

oxoglutarate 5-dioxygenase 2 6 6 84.6

P62081 40S ribosomal protein S7 3 4 22.1

Q13509 Tubulin beta-3 chain 15 180 50.4

Q01082 Spectrin beta chain, non-

erythrocytic 1 5 5 274.4

P10768 S-formylglutathione hydrolase 3 4 31.4

P04264 Keratin, type II cytoskeletal 1 14 88 66.0

O43776 Asparagine--tRNA ligase,

cytoplasmic 4 7 62.9

Q13308 Inactive tyrosine-protein kinase 7 7 12 118.3

P62273 40S ribosomal protein S29 2 2 6.7

Q05707 Collagen alpha-1(XIV) chain 2 2 193.4

P24844 Myosin regulatory light

polypeptide 9 6 20 19.8

O95965 Integrin beta-like protein 1 4 7 53.9

P59998 Actin-related protein 2/3 complex

subunit 4 3 3 19.7

P13667 Protein disulfide-isomerase A4 2 3 72.9

Q6IBS0 Twinfilin-2 2 2 39.5

P46781 40S ribosomal protein S9 2 2 22.6

P35527 Keratin, type I cytoskeletal 9 7 130 62.0

P00491 Purine nucleoside phosphorylase 2 4 32.1

P49368 T-complex protein 1 subunit

gamma 2 2 60.5

P06737 Glycogen phosphorylase, liver

form 4 4 97.1

Q16363 Laminin subunit alpha-4 3 7 202.4

Q13200 26S proteasome non-ATPase

regulatory subunit 2 4 7 100.1

287

P67809 Nuclease-sensitive element-

binding protein 1 4 8 35.9

P55060 Exportin-2 2 2 110.3

Q9P1F3 Costars family protein ABRACL 2 2 9.1

P63244 Guanine nucleotide-binding protein subunit beta-2-like 1

2 2 35.1

P01034 Cystatin-C 8 51 15.8

P28482 Mitogen-activated protein kinase

1 2 6 41.4

P46783 40S ribosomal protein S10 2 4 18.9

P36222 Chitinase-3-like protein 1 4 4 42.6

P35908 Keratin, type II cytoskeletal 2

epidermal 11 64 65.4

Q86YZ3 Hornerin 12 20 282.2

Q08629 Testican-1 4 4 49.1

P55083 Microfibril-associated

glycoprotein 4 5 105 28.6

O60814 Histone H2B type 1-K 2 3 13.9

P11021 78 kDa glucose-regulated protein 14 77 1.8 72.3

P30043 Flavin reductase (NADPH) 2 2 2.3 22.1

Q13813 Spectrin alpha chain, non-

erythrocytic 1 8 19 1.8 284.4

Q14315 Filamin-C 26 205 2.1 290.8

P17174 Aspartate aminotransferase,

cytoplasmic 6 10 2.0 46.2

P35237 Serpin B6 6 12 2.1 42.6

P04083 Annexin A1 7 51 1.9 38.7

P05387 60S acidic ribosomal protein P2 3 4 2.0 11.7

P13489 Ribonuclease inhibitor 7 12 2.0 49.9

P11047 Laminin subunit gamma-1 13 35 1.6 177.5

O94760 N(G),N(G)-dimethylarginine dimethylaminohydrolase 1

5 12 1.5 31.1

P61247 40S ribosomal protein S3a 2 3 2.5 29.9

P28070 Proteasome subunit beta type-4 4 11 1.7 29.2

Q96D15 Reticulocalbin-3 2 3 1.6 37.5

Q08431 Lactadherin 4 18 1.5 43.1

Q7Z304 MAM domain-containing protein

2 16 47 1.7 77.5

P50454 Serpin H1 17 94 1.5 46.4

P02795 Metallothionein-2 3 21 3.0 6.0

P23284 Peptidyl-prolyl cis-trans

isomerase B 8 123 1.5 23.7

P68402 Platelet-activating factor

acetylhydrolase IB subunit beta 3 23 2.7 25.6

P55072 Transitional endoplasmic

reticulum ATPase 4 4 2.5 89.3

P15018 Leukemia inhibitory factor 2 24 1.8 22.0

288

P36873 Serine/threonine-protein

phosphatase PP1-gamma catalytic subunit

2 4 1.6 37.0

P51858 Hepatoma-derived growth factor 2 4 1.9 26.8

P24043 Laminin subunit alpha-2 11 20 1.7 343.7

P20908 Collagen alpha-1(V) chain 30 127 1.5 183.4

Q13643 Four and a half LIM domains

protein 3 4 8 2.2 31.2

P07093 Glia-derived nexin 14 121 1.6 44.0

P62841 40S ribosomal protein S15 2 2 2.2 17.0

Q02818 Nucleobindin-1 13 52 1.5 53.8

P21810 Biglycan 21 674 1.4 41.6

P02751 Fibronectin 103 5607 1.5 262.5

Q9Y4K0 Lysyl oxidase homolog 2 10 46 1.4 86.7

Q14697 Neutral alpha-glucosidase AB 4 6 2.2 106.8

P24821 Tenascin 48 370 1.4 240.7

Q02809 Procollagen-lysine,2-

oxoglutarate 5-dioxygenase 1 23 122 1.4 83.5

Q14767 Latent-transforming growth factor

beta-binding protein 2 27 127 1.5 194.9

Q9P2E9 Ribosome-binding protein 1 3 6 1.5 152.4

Q9BWS9 Chitinase domain-containing

protein 1 3 13 1.4 44.9

Q9Y240 C-type lectin domain family 11

member A 2 2 1.8 35.7

P15121 Aldose reductase 4 11 2.2 35.8

Q14766 Latent-transforming growth factor

beta-binding protein 1 14 34 1.5 186.7

Q14118 Dystroglycan 4 8 1.7 97.4

P16035 Metalloproteinase inhibitor 2 11 39 1.5 24.4

P98160 Basement membrane-specific heparan sulfate proteoglycan

core protein 30 146 1.4 468.5

P02461 Collagen alpha-1(III) chain 44 179 1.5 138.5

P02765 Alpha-2-HS-glycoprotein 2 3 0.5 39.3

P35555 Fibrillin-1 76 522 1.5 312.0

P20930 Filaggrin 5 6 2.2 434.9

P01023 Alpha-2-macroglobulin 22 200 1.1 163.2

P62906 60S ribosomal protein L10a 2 4 1.6 24.8

Q4ZHG4 Fibronectin type III domain-

containing protein 1 11 22 1.5 205.4

P07996 Thrombospondin-1 46 387 1.4 129.3

O94985 Calsyntenin-1 8 26 1.3 109.7

O00468 Agrin 15 44 1.6 217.1

P41222 Prostaglandin-H2 D-isomerase 2 2 1.4 21.0

P14136 Glial fibrillary acidic protein 8 33 1.3 49.8

289

P06753 Tropomyosin alpha-3 chain 10 79 2.6 32.9

Q9UJ70 N-acetyl-D-glucosamine kinase 5 9 1.8 37.4

P16070 CD44 antigen 2 15 1.5 81.5

P46777 60S ribosomal protein L5 3 6 1.8 34.3

Q12841 Follistatin-related protein 1 22 718 1.4 35.0

P46108 Adapter molecule crk 2 2 1.7 33.8

Q9H4D0 Calsyntenin-2 10 22 1.3 106.9

P17936 Insulin-like growth factor-binding

protein 3 11 42 1.6 31.7

Q12805 EGF-containing fibulin-like

extracellular matrix protein 1 17 89 1.3 54.6

Q16270 Insulin-like growth factor-binding

protein 7 21 1187 1.3 29.1

P51884 Lumican 12 92 1.2 38.4

Q08380 Galectin-3-binding protein 21 137 1.3 65.3

P05121 Plasminogen activator inhibitor 1 24 1641 1.3 45.0

Q10471 Polypeptide N-

acetylgalactosaminyltransferase 2

9 46 1.3 64.7

P02452 Collagen alpha-1(I) chain 86 1945 1.3 138.9

P05997 Collagen alpha-2(V) chain 37 117 1.4 144.8

P61916 Epididymal secretory protein E1 5 14 1.3 16.6

P08123 Collagen alpha-2(I) chain 74 1563 1.4 129.2

P02768 Serum albumin 16 381 1.3 69.3

P08253 72 kDa type IV collagenase 25 131 1.2 73.8

P09486 SPARC 20 3614 1.3 34.6

O95967 EGF-containing fibulin-like

extracellular matrix protein 2 4 18 1.3 49.4

Table 5.6: Differentially released conditioned medium proteins at 5 hours

Astrocyte-released proteins measured in condition media from TMT experiments

comparing protein abundance between mild and severe stretched cells in conditioned

media collected 5 hours post-injury.

290

Accession Description #

Peptides PSMs 24h M 24h S

MW (kDa)

P61158 Actin-related protein 3 9 12 1.4 7.5 47.3

P22626 Heterogeneous nuclear

ribonucleoproteins A2/B1 10 72 2.2 7.2 37.4

P09493 Tropomyosin alpha-1 chain 15 198 2.1 6.8 32.7

P12109 Collagen alpha-1(VI) chain 13 50 1.3 6.3 108.5

P40925 Malate dehydrogenase,

cytoplasmic 11 87 2.3 6.2 36.4

P24821 Tenascin 10 13 6.1 240.7

P31946 14-3-3 protein beta/alpha 16 199 1.8 5.9 28.1

Q4ZHG4 Fibronectin type III domain-

containing protein 1 5 11 5.7 205.4

Q15121 Astrocytic phosphoprotein PEA-

15 5 10 1.8 5.6 15.0

P62328 Thymosin beta-4 4 22 3.5 5.4 5.0

O75369 Filamin-B 22 63 5.3 278.0

P29401 Transketolase 14 74 2.1 5.2 67.8

P61163 Alpha-centractin 4 34 2.1 4.9 42.6

P62158 Calmodulin 4 31 2.4 4.8 16.8

P00441 Superoxide dismutase [Cu-Zn] 3 40 3.8 4.8 15.9

P62258 14-3-3 protein epsilon 13 215 1.7 4.7 29.2

P00558 Phosphoglycerate kinase 1 19 189 1.9 4.7 44.6

P46821 Microtubule-associated protein

1B 5 8 4.6 270.5

P30101 Protein disulfide-isomerase A3 15 46 1.6 4.5 56.7

Q9H4D0 Calsyntenin-2 4 5 4.5 106.9

P05997 Collagen alpha-2(V) chain 12 22 4.4 144.8

P61981 14-3-3 protein gamma 15 225 1.9 4.4 28.3

Q8NBS9 Thioredoxin domain-containing

protein 5 5 6 1.3 4.4 47.6

P39687 Acidic leucine-rich nuclear phosphoprotein 32 family

member A 2 2 1.5 4.3 28.6

P01008 Antithrombin-III 7 19 0.8 4.3 52.6

P12814 Alpha-actinin-1 56 1056 1.9 4.3 103.0

Q12805 EGF-containing fibulin-like

extracellular matrix protein 1 9 43 4.3 54.6

P13797 Plastin-3 16 29 1.7 4.3 70.8

P21291 Cysteine and glycine-rich protein

1 9 142 2.1 4.3 20.6

P26038 Moesin 29 297 2.1 4.3 67.8

P07237 Protein disulfide-isomerase 15 50 1.6 4.2 57.1

Q92626 Peroxidasin homolog 3 3 4.2 165.2

P01024 Complement C3 5 20 4.1 187.0

Q15056 Eukaryotic translation initiation

factor 4H 2 2 3.1 4.0 27.4

291

P17174 Aspartate aminotransferase,

cytoplasmic 4 4 1.8 4.0 46.2

P37802 Transgelin-2 14 170 1.7 4.0 22.4

P49327 Fatty acid synthase 2 2 4.0 273.3

O75083 WD repeat-containing protein 1 10 42 1.8 3.9 66.2

Q9BVA1 Tubulin beta-2B chain 15 116 2.1 3.9 49.9

Q14847 LIM and SH3 domain protein 1 5 25 2.2 3.9 29.7

P09382 Galectin-1 6 119 2.0 3.9 14.7

Q96C90 Protein phosphatase 1 regulatory

subunit 14B 2 3 3.9 15.9

P51397 Death-associated protein 1 2 3 2.9 3.9 11.2

P68036 Ubiquitin-conjugating enzyme E2

L3 5 15 1.7 3.8 17.9

P28838 Cytosol aminopeptidase 2 2 3.8 56.1

P52565 Rho GDP-dissociation inhibitor 1 3 18 2.3 3.8 23.2

P35555 Fibrillin-1 23 86 1.3 3.8 312.0

P62942 Peptidyl-prolyl cis-trans

isomerase FKBP1A 3 12 2.2 3.8 11.9

P0CG48 Polyubiquitin-C 3 23 2.4 3.8 77.0

P01033 Metalloproteinase inhibitor 1 7 158 1.5 3.8 23.2

Q969H8 UPF0556 protein C19orf10 2 2 3.8 18.8

P09936 Ubiquitin carboxyl-terminal

hydrolase isozyme L1 14 241 2.6 3.8 24.8

Q9BRA2 Thioredoxin domain-containing

protein 17 2 4 3.8 13.9

P09972 Fructose-bisphosphate aldolase

C 7 81 1.9 3.8 39.4

P23396 40S ribosomal protein S3 4 5 3.8 26.7

P04083 Annexin A1 5 15 1.3 3.8 38.7

Q02818 Nucleobindin-1 4 5 3.8 53.8

P07900 Heat shock protein HSP 90-

alpha 21 146 1.8 3.7 84.6

P06396 Gelsolin 19 113 1.3 3.7 85.6

O43707 Alpha-actinin-4 43 681 1.6 3.7 104.8

Q14192 Four and a half LIM domains

protein 2 6 24 1.9 3.7 32.2

P26022 Pentraxin-related protein PTX3 3 6 3.7 41.9

P07951 Tropomyosin beta chain 17 204 2.3 3.6 32.8

P10909 Clusterin 8 85 1.2 3.6 52.5

P08238 Heat shock protein HSP 90-beta 17 109 1.6 3.5 83.2

P63104 14-3-3 protein zeta/delta 20 328 2.2 3.5 27.7

P08572 Collagen alpha-2(IV) chain 14 57 3.5 167.4

P27816 Microtubule-associated protein 4 7 12 1.7 3.5 120.9

P31150 Rab GDP dissociation inhibitor

alpha 14 51 1.5 3.5 50.6

292

Q9H299 SH3 domain-binding glutamic

acid-rich-like protein 3 5 41 3.3 3.5 10.4

P35579 Myosin-9 21 27 2.3 3.4 226.4

Q53FA7 Quinone oxidoreductase PIG3 2 2 3.4 35.5

P62826 GTP-binding nuclear protein Ran 4 6 1.8 3.4 24.4

P07737 Profilin-1 9 172 1.9 3.4 15.0

P35237 Serpin B6 4 5 1.4 3.4 42.6

P18065 Insulin-like growth factor-binding

protein 2 11 103 1.3 3.4 34.8

P06703 Protein S100-A6 3 26 2.3 3.4 10.2

Q01995 Transgelin 19 989 2.8 3.4 22.6

P60174 Triosephosphate isomerase 15 305 2.1 3.4 30.8

P16035 Metalloproteinase inhibitor 2 4 11 3.4 24.4

Q71U36 Tubulin alpha-1A chain 15 163 3.4 50.1

P04406 Glyceraldehyde-3-phosphate

dehydrogenase 14 369 1.8 3.4 36.0

P60709 Actin, cytoplasmic 1 19 1242 1.8 3.4 41.7

P04075 Fructose-bisphosphate aldolase

A 18 368 2.1 3.4 39.4

P30085 UMP-CMP kinase 5 26 1.7 3.4 22.2

P10599 Thioredoxin 3 20 2.0 3.4 11.7

P30086 Phosphatidylethanolamine-

binding protein 1 10 118 2.2 3.3 21.0

P05204 Non-histone chromosomal

protein HMG-17 2 2 3.3 9.4

O75368 SH3 domain-binding glutamic

acid-rich-like protein 4 7 2.1 3.3 12.8

P07996 Thrombospondin-1 33 247 3.3 129.3

P98160 Basement membrane-specific heparan sulfate proteoglycan

core protein 11 23 3.3 468.5

P19105 Myosin regulatory light chain 12A 3 6 1.4 3.3 19.8

O94985 Calsyntenin-1 2 2 3.3 109.7

Q9UI42 Carboxypeptidase A4 7 46 3.3 47.3

P21333 Filamin-A 84 791 1.8 3.3 280.6

P30153

Serine/threonine-protein phosphatase 2A 65 kDa

regulatory subunit A alpha isoform

2 2 3.3 65.3

P13693 Translationally-controlled tumor

protein 3 42 1.9 3.3 19.6

Q15063 Periostin 15 80 1.2 3.3 93.3

Q14315 Filamin-C 20 70 1.5 3.3 290.8

Q15582 Transforming growth factor-beta-

induced protein ig-h3 13 49 1.2 3.3 74.6

Q9NY33 Dipeptidyl peptidase 3 4 4 3.2 82.5

Q15417 Calponin-3 4 7 1.9 3.2 36.4

293

P21266 Glutathione S-transferase Mu 3 4 12 2.2 3.2 26.5

Q9UBP4 Dickkopf-related protein 3 6 14 3.2 38.4

P05121 Plasminogen activator inhibitor 1 15 430 3.2 45.0

P32119 Peroxiredoxin-2 6 51 1.8 3.2 21.9

Q92820 Gamma-glutamyl hydrolase 3 3 3.2 35.9

P07195 L-lactate dehydrogenase B chain 13 193 1.8 3.2 36.6

P49720 Proteasome subunit beta type-3 3 5 3.2 22.9

P09211 Glutathione S-transferase P 6 64 2.3 3.2 23.3

P68371 Tubulin beta-4B chain 14 86 1.6 3.1 49.8

P68032 Actin, alpha cardiac muscle 1 17 856 2.0 3.1 42.0

P06733 Alpha-enolase 22 526 2.4 3.1 47.1

Q9Y696 Chloride intracellular channel

protein 4 11 89 1.8 3.1 28.8

Q9Y617 Phosphoserine aminotransferase 12 60 1.5 3.1 40.4

P18669 Phosphoglycerate mutase 1 15 149 2.0 3.1 28.8

P19827 Inter-alpha-trypsin inhibitor heavy

chain H1 2 3 3.1 101.3

P80723 Brain acid soluble protein 1 2 2 3.1 22.7

P15311 Ezrin 23 179 2.9 3.1 69.4

P30046 D-dopachrome decarboxylase 4 9 1.7 3.1 12.7

Q9NRX4 14 kDa phosphohistidine

phosphatase 6 34 3.8 3.1 13.8

P01709 Ig lambda chain V-II region MGC 2 6 3.0 11.6

Q13219 Pappalysin-1 4 4 3.0 180.9

P23528 Cofilin-1 13 253 2.5 3.0 18.5

P02795 Metallothionein-2 3 5 2.0 3.0 6.0

P31949 Protein S100-A11 5 65 1.9 3.0 11.7

P52209 6-phosphogluconate

dehydrogenase, decarboxylating 2 3 3.0 53.1

P21810 Biglycan 15 174 1.2 3.0 41.6

P14618 Pyruvate kinase PKM 34 636 1.9 3.0 57.9

P18206 Vinculin 64 551 1.9 3.0 123.7

P28066 Proteasome subunit alpha type-5 3 9 1.6 3.0 26.4

Q14103 Heterogeneous nuclear

ribonucleoprotein D0 4 7 3.0 38.4

P61970 Nuclear transport factor 2 2 2 1.5 3.0 14.5

P04792 Heat shock protein beta-1 13 60 1.9 2.9 22.8

Q562R1 Beta-actin-like protein 2 8 435 2.9 42.0

P15531 Nucleoside diphosphate kinase A 5 51 2.9 17.1

Q14767 Latent-transforming growth factor

beta-binding protein 2 11 36 2.9 194.9

O00299 Chloride intracellular channel

protein 1 6 12 1.8 2.9 26.9

Q16658 Fascin 11 29 1.6 2.9 54.5

294

Q96T49 Protein phosphatase 1 regulatory

inhibitor subunit 16B 2 2 2.9 63.5

P12107 Collagen alpha-1(XI) chain 3 12 2.8 181.0

P50395 Rab GDP dissociation inhibitor

beta 18 100 1.5 2.8 50.6

Q13642 Four and a half LIM domains

protein 1 9 35 1.7 2.8 36.2

P08107 Heat shock 70 kDa protein 1A/1B 7 14 1.6 2.8 70.0

Q06830 Peroxiredoxin-1 9 135 2.3 2.8 22.1

P04080 Cystatin-B 3 83 1.6 2.8 11.1

P23284 Peptidyl-prolyl cis-trans

isomerase B 7 27 1.3 2.8 23.7

O14818 Proteasome subunit alpha type-7 4 4 1.8 2.8 27.9

P37837 Transaldolase 5 6 1.8 2.8 37.5

P28482 Mitogen-activated protein kinase

1 2 2 2.8 41.4

P48637 Glutathione synthetase 3 3 2.7 52.4

Q16881 Thioredoxin reductase 1,

cytoplasmic 3 3 2.7 70.9

P07355 Annexin A2 12 53 1.8 2.7 38.6

P60660 Myosin light polypeptide 6 4 9 1.6 2.7 16.9

P40261 Nicotinamide N-

methyltransferase 4 13 1.9 2.7 29.6

P61088 Ubiquitin-conjugating enzyme E2

N 4 12 1.8 2.7 17.1

P22314 Ubiquitin-like modifier-activating

enzyme 1 5 5 2.7 117.8

Q16527 Cysteine and glycine-rich protein

2 5 14 1.6 2.7 20.9

P24593 Insulin-like growth factor-binding

protein 5 8 70 2.7 30.6

P48163 NADP-dependent malic enzyme 3 3 2.6 64.1

P07339 Cathepsin D 13 77 2.6 44.5

Q9UBR2 Cathepsin Z 2 2 2.6 33.8

O75874 Isocitrate dehydrogenase

[NADP] cytoplasmic 2 2 2.6 46.6

P16152 Carbonyl reductase [NADPH] 1 3 6 2.0 2.6 30.4

P68363 Tubulin alpha-1B chain 15 163 2.6 50.1

P25786 Proteasome subunit alpha type-1 6 16 2.0 2.6 29.5

Q99733 Nucleosome assembly protein 1-

like 4 2 2 2.5 42.8

P02461 Collagen alpha-1(III) chain 22 47 2.5 138.5

Q32P51 Heterogeneous nuclear

ribonucleoprotein A1-like 2 3 7 2.3 2.5 34.2

P27348 14-3-3 protein theta 15 158 1.7 2.5 27.7

P41250 Glycine--tRNA ligase 2 3 2.5 83.1

Q00610 Clathrin heavy chain 1 2 2 2.5 191.5

295

Q01518 Adenylyl cyclase-associated

protein 1 12 38 1.9 2.5 51.9

P61160 Actin-related protein 2 6 36 1.8 2.5 44.7

P51911 Calponin-1 4 5 2.5 33.1

Q08380 Galectin-3-binding protein 12 61 0.8 2.5 65.3

P62937 Peptidyl-prolyl cis-trans

isomerase A 14 239 2.0 2.5 18.0

P15289 Arylsulfatase A 2 2 2.4 53.6

P63167 Dynein light chain 1, cytoplasmic 2 2 2.4 10.4

Q16270 Insulin-like growth factor-binding

protein 7 17 904 2.4 29.1

P14174 Macrophage migration inhibitory

factor 3 8 2.1 2.4 12.5

P62906 60S ribosomal protein L10a 4 5 1.7 2.4 24.8

P30044 Peroxiredoxin-5, mitochondrial 2 3 2.4 22.1

Q9NTK5 Obg-like ATPase 1 2 2 2.4 44.7

O15145 Actin-related protein 2/3 complex

subunit 3 2 2 2.4 20.5

P46940 Ras GTPase-activating-like

protein IQGAP1 16 19 2.3 189.1

Q14766 Latent-transforming growth factor

beta-binding protein 1 2 5 2.3 186.7

P02462 Collagen alpha-1(IV) chain 6 27 2.3 160.5

Q9Y3B8 Oligoribonuclease, mitochondrial 2 3 2.3 26.8

P24534 Elongation factor 1-beta 5 11 2.0 2.3 24.7

P13667 Protein disulfide-isomerase A4 2 2 2.3 72.9

P00966 Argininosuccinate synthase 3 4 1.7 2.2 46.5

P14625 Endoplasmin 9 13 2.2 92.4

P01009 Alpha-1-antitrypsin 3 75 2.2 46.7

P27797 Calreticulin 10 106 1.5 2.1 48.1

Q9NZU5 LIM and cysteine-rich domains

protein 1 4 9 1.8 2.0 40.8

P14324 Farnesyl pyrophosphate

synthase 3 11 2.2 2.0 48.2

Q96HC4 PDZ and LIM domain protein 5 2 2 2.0 63.9

P14314 Glucosidase 2 subunit beta 3 5 1.5 2.0 59.4

Q76M96 Coiled-coil domain-containing

protein 80 5 6 1.9 108.1

P06753 Tropomyosin alpha-3 chain 8 87 1.9 32.9

P20742 Pregnancy zone protein 4 26 0.8 1.9 163.8

P00568 Adenylate kinase isoenzyme 1 5 9 3.2 1.9 21.6

P0C0L4 Complement C4-A 4 9 1.9 192.7

P02751 Fibronectin 72 1126 1.9 262.5

O00151 PDZ and LIM domain protein 1 2 2 1.9 36.0

P61812 Transforming growth factor beta-

2 3 6 1.8 47.7

296

Q9NR12 PDZ and LIM domain protein 7 2 2 1.8 49.8

P08253 72 kDa type IV collagenase 14 42 1.8 73.8

Q14118 Dystroglycan 3 3 1.8 97.4

P30043 Flavin reductase (NADPH) 2 2 1.8 22.1

Q15084 Protein disulfide-isomerase A6 5 7 1.8 48.1

P55786 Puromycin-sensitive

aminopeptidase 5 8 1.8 103.2

Q13813 Spectrin alpha chain, non-

erythrocytic 1 9 11 2.1 1.8 284.4

O15144 Actin-related protein 2/3 complex

subunit 2 4 9 2.0 1.8 34.3

Q9Y281 Cofilin-2 6 139 1.6 1.7 18.7

P07093 Glia-derived nexin 14 62 1.7 44.0

Q02809 Procollagen-lysine,2-

oxoglutarate 5-dioxygenase 1 4 4 1.7 83.5

Q12905 Interleukin enhancer-binding

factor 2 3 3 1.7 43.0

P35241 Radixin 9 50 1.7 68.5

P23142 Fibulin-1 2 2 1.7 77.2

Q99538 Legumain 3 4 1.7 49.4

P00338 L-lactate dehydrogenase A chain 18 397 1.9 1.7 36.7

P10915 Hyaluronan and proteoglycan

link protein 1 7 36 1.6 40.1

P28070 Proteasome subunit beta type-4 4 8 1.6 1.6 29.2

Q99439 Calponin-2 2 5 1.6 33.7

P07437 Tubulin beta chain 14 133 1.8 1.6 49.6

P35908 Keratin, type II cytoskeletal 2

epidermal 5 16 1.6 65.4

Q14204 Cytoplasmic dynein 1 heavy

chain 1 3 3 1.6 532.1

P05388 60S acidic ribosomal protein P0 4 6 1.6 34.3

P11021 78 kDa glucose-regulated protein 12 72 1.4 1.6 72.3

P02545 Prelamin-A/C 8 26 1.7 1.5 74.1

P02788 Lactotransferrin 2 13 1.5 78.1

O95336 6-phosphogluconolactonase 4 6 1.5 27.5

O15511 Actin-related protein 2/3 complex

subunit 5 2 2 1.5 16.3

P11142 Heat shock cognate 71 kDa

protein 18 76 1.8 1.5 70.9

O76061 Stanniocalcin-2 3 13 1.5 33.2

P02511 Alpha-crystallin B chain 5 76 1.9 1.5 20.1

O43852 Calumenin 4 5 1.5 37.1

Q16181 Septin-7 2 4 1.4 50.6

P50454 Serpin H1 10 32 1.5 1.4 46.4

P60900 Proteasome subunit alpha type-6 2 3 1.4 27.4

297

P52926 High mobility group protein

HMGI-C 2 2 1.4 11.8

P68104 Elongation factor 1-alpha 1 10 47 1.9 1.4 50.1

P22392 Nucleoside diphosphate kinase B 6 83 2.2 1.4 17.3

Q16853 Membrane primary amine

oxidase 2 2 1.4 84.6

P29279 Connective tissue growth factor 4 6 1.4 38.1

P36871 Phosphoglucomutase-1 6 19 1.6 1.4 61.4

P25787 Proteasome subunit alpha type-2 2 2 1.4 25.9

Q99627 COP9 signalosome complex

subunit 8 2 2 1.4 23.2

Q15181 Inorganic pyrophosphatase 3 3 1.4 32.6

P01023 Alpha-2-macroglobulin 10 134 1.4 163.2

P13639 Elongation factor 2 12 25 2.3 1.3 95.3

P63241 Eukaryotic translation initiation

factor 5A-1 4 23 2.0 1.3 16.8

P08670 Vimentin 34 520 1.8 1.3 53.6

P67809 Nuclease-sensitive element-

binding protein 1 5 11 1.7 1.3 35.9

P09960 Leukotriene A-4 hydrolase 7 11 2.1 1.3 69.2

Q05682 Caldesmon 23 189 2.3 1.3 93.2

P15121 Aldose reductase 3 5 1.3 35.8

Q99497 Protein DJ-1 7 53 1.9 1.3 19.9

P17936 Insulin-like growth factor-binding

protein 3 9 33 1.3 1.3 31.7

P01034 Cystatin-C 4 11 1.2 15.8

Q14019 Coactosin-like protein 6 42 2.4 1.2 15.9

P60981 Destrin 4 17 2.1 1.2 18.5

P61769 Beta-2-microglobulin 2 14 1.2 13.7

P67936 Tropomyosin alpha-4 chain 16 152 2.5 1.2 28.5

Q14974 Importin subunit beta-1 3 3 1.1 97.1

Q8NCW5 NAD(P)H-hydrate epimerase 2 3 1.1 31.7

Q99584 Protein S100-A13 4 6 1.8 1.1 11.5

Q07955 Serine/arginine-rich splicing

factor 1 3 3 27.7

Q7Z304 MAM domain-containing protein

2 10 26 77.5

P02774 Vitamin D-binding protein 2 20 52.9

P13645 Keratin, type I cytoskeletal 10 5 11 58.8

Q01105 Protein SET 2 2 33.5

P20618 Proteasome subunit beta type-1 2 3 26.5

Q13185 Chromobox protein homolog 3 2 2 20.8

P11766 Alcohol dehydrogenase class-3 2 2 39.7

P08123 Collagen alpha-2(I) chain 54 715 129.2

P35052 Glypican-1 5 5 61.6

298

P07942 Laminin subunit beta-1 3 3 197.9

Q15019 Septin-2 3 3 41.5

Q14195 Dihydropyrimidinase-related

protein 3 4 5 61.9

P35442 Thrombospondin-2 8 18 129.9

P08729 Keratin, type II cytoskeletal 7 8 17 51.4

Q15746 Myosin light chain kinase,

smooth muscle 3 4 210.6

P30041 Peroxiredoxin-6 3 3 25.0

P60983 Glia maturation factor beta 2 4 16.7

P35527 Keratin, type I cytoskeletal 9 10 97 62.0

P34932 Heat shock 70 kDa protein 4 3 3 94.3

P09104 Gamma-enolase 6 115 47.2

Q9ULV4 Coronin-1C 6 6 53.2

P49590 Probable histidine--tRNA ligase,

mitochondrial 2 2 56.9

P27658 Collagen alpha-1(VIII) chain 4 6 73.3

P00738 Haptoglobin 7 160 45.2

Q9BRF8 Serine/threonine-protein phosphatase CPPED1

2 2 35.5

Q15149 Plectin 5 5 531.5

Q16555 Dihydropyrimidinase-related

protein 2 3 3 62.3

Q16610 Extracellular matrix protein 1 8 17 60.6

P09486 SPARC 16 837 34.6

P28074 Proteasome subunit beta type-5 2 3 28.5

Q99715 Collagen alpha-1(XII) chain 8 10 332.9

P07585 Decorin 4 5 39.7

P25789 Proteasome subunit alpha type-4 3 6 29.5

Q10471 Polypeptide N-

acetylgalactosaminyltransferase 2

2 2 64.7

Q14393 Growth arrest-specific protein 6 2 2 79.6

Q14011 Cold-inducible RNA-binding

protein 2 2 18.6

P04264 Keratin, type II cytoskeletal 1 16 91 66.0

P02452 Collagen alpha-1(I) chain 69 1061 138.9

Q96KP4 Cytosolic non-specific

dipeptidase 2 2 52.8

Q96HF1 Secreted frizzled-related protein

2 2 2 33.5

O00391 Sulfhydryl oxidase 1 9 26 82.5

Q12841 Follistatin-related protein 1 21 442 35.0

P08476 Inhibin beta A chain 2 2 47.4

Q9Y4K0 Lysyl oxidase homolog 2 3 3 86.7

P61586 Transforming protein RhoA 2 2 21.8

299

P51884 Lumican 11 88 38.4

Q9HC38 Glyoxalase domain-containing

protein 4 3 3 34.8

P01860 Ig gamma-3 chain C region 2 2 41.3

P20908 Collagen alpha-1(V) chain 8 20 183.4

Q9H4A4 Aminopeptidase B 3 4 72.5

P46777 60S ribosomal protein L5 3 3 34.3

Q86VP6 Cullin-associated NEDD8-

dissociated protein 1 2 2 136.3

O00468 Agrin 5 5 217.1

P63010 AP-2 complex subunit beta 2 2 104.5

P08758 Annexin A5 8 12 1.8 35.9

P47756 F-actin-capping protein subunit

beta 2 3 31.3

P07602 Prosaposin 9 19 1.3 58.1

P26639 Threonine--tRNA ligase,

cytoplasmic 2 2 1.9 83.4

Q12906 Interleukin enhancer-binding

factor 3 3 3 95.3

P62241 40S ribosomal protein S8 2 6 24.2

P60842 Eukaryotic initiation factor 4A-I 6 14 1.8 46.1

O95865 N(G),N(G)-dimethylarginine dimethylaminohydrolase 2

5 5 1.9 29.6

P81605 Dermcidin 2 13 11.3

P06744 Glucose-6-phosphate isomerase 12 60 1.6 63.1

Q16851 UTP--glucose-1-phosphate

uridylyltransferase 6 9 1.6 56.9

Q9BUF5 Tubulin beta-6 chain 9 56 49.8

Q9Y490 Talin-1 18 21 2.2 269.6

Table 5.7: Differentially released conditioned medium proteins at 24 hours

Astrocyte-released proteins measured in condition media from TMT experiments

comparing protein abundance between mild and severe stretched cells in conditioned

media collected 24 hours post-injury.

300

Accession Description #

Peptides PSMs 48h M 48h S

MW (kDa)

P62328 Thymosin beta-4 4 14 8.4 11.9 5.0

P07437 Tubulin beta chain 20 225 6.5 11.6 49.6

O60664 Perilipin-3 2 2 7.4 11.5 47.0

P20962 Parathymosin 2 5 5.4 11.5 11.5

P68032 Actin, alpha cardiac muscle 1 20 619 5.9 10.4 42.0

Q01995 Transgelin 20 1047 6.3 9.1 22.6

Q15121 Astrocytic phosphoprotein PEA-

15 5 102 5.8 8.5 15.0

Q71U36 Tubulin alpha-1A chain 22 173 5.7 8.4 50.1

P15311 Ezrin 20 95 4.8 8.3 69.4

P37802 Transgelin-2 15 206 6.0 8.3 22.4

P22392 Nucleoside diphosphate kinase B 7 62 5.3 8.2 17.3

P62979 Ubiquitin-40S ribosomal protein

S27a 11 140 5.1 8.2 18.0

P62158 Calmodulin 3 10 5.1 8.2 16.8

P05388 60S acidic ribosomal protein P0 4 9 5.6 8.2 34.3

P60709 Actin, cytoplasmic 1 22 1101 5.3 8.1 41.7

Q9H299 SH3 domain-binding glutamic

acid-rich-like protein 3 5 15 5.3 8.1 10.4

O00410 Importin-5 8 14 7.9 123.5

P62857 40S ribosomal protein S28 2 2 4.5 7.9 7.8

P13693 Translationally-controlled tumor

protein 6 37 5.7 7.8 19.6

P23528 Cofilin-1 15 332 5.4 7.7 18.5

P09493 Tropomyosin alpha-1 chain 15 109 5.3 7.6 32.7

P05783 Keratin, type I cytoskeletal 18 9 26 5.2 7.5 48.0

P30044 Peroxiredoxin-5, mitochondrial 4 10 5.1 7.4 22.1

P08670 Vimentin 34 567 5.2 7.3 53.6

P04406 Glyceraldehyde-3-phosphate

dehydrogenase 20 907 4.2 7.3 36.0

P47756 F-actin-capping protein subunit

beta 6 20 4.9 7.3 31.3

P67936 Tropomyosin alpha-4 chain 15 108 5.2 7.2 28.5

P13797 Plastin-3 20 73 4.6 7.2 70.8

P50395 Rab GDP dissociation inhibitor

beta 21 173 4.5 7.2 50.6

Q06830 Peroxiredoxin-1 9 36 4.8 7.2 22.1

P07951 Tropomyosin beta chain 16 112 5.1 7.1 32.8

P62937 Peptidyl-prolyl cis-trans

isomerase A 19 265 5.1 7.1 18.0

P42574 Caspase-3 2 3 4.6 7.0 31.6

P07195 L-lactate dehydrogenase B chain 16 115 4.7 6.9 36.6

P31946 14-3-3 protein beta/alpha 16 170 5.1 6.9 28.1

301

Q14847 LIM and SH3 domain protein 1 9 16 4.8 6.9 29.7

P00568 Adenylate kinase isoenzyme 1 6 14 5.5 6.9 21.6

P60981 Destrin 10 178 4.9 6.9 18.5

P08729 Keratin, type II cytoskeletal 7 14 48 5.0 6.8 51.4

P04792 Heat shock protein beta-1 14 46 4.8 6.8 22.8

P14174 Macrophage migration inhibitory

factor 4 33 4.4 6.8 12.5

P26641 Elongation factor 1-gamma 8 17 4.7 6.8 50.1

P06703 Protein S100-A6 3 142 5.1 6.7 10.2

Q05682 Caldesmon 15 88 4.9 6.7 93.2

P09936 Ubiquitin carboxyl-terminal

hydrolase isozyme L1 15 263 4.8 6.7 24.8

O75874 Isocitrate dehydrogenase

[NADP] cytoplasmic 10 35 4.6 6.6 46.6

P10768 S-formylglutathione hydrolase 3 4 5.0 6.6 31.4

Q9HC38 Glyoxalase domain-containing

protein 4 2 3 4.4 6.6 34.8

P26038 Moesin 31 201 4.6 6.6 67.8

Q14019 Coactosin-like protein 7 20 4.8 6.6 15.9

Q96C90 Protein phosphatase 1 regulatory

subunit 14B 2 2 6.6 15.9

P30041 Peroxiredoxin-6 6 31 4.4 6.5 25.0

P13639 Elongation factor 2 21 79 4.7 6.5 95.3

P09211 Glutathione S-transferase P 11 90 4.5 6.5 23.3

P00338 L-lactate dehydrogenase A chain 29 412 4.4 6.5 36.7

P30085 UMP-CMP kinase 6 31 4.7 6.5 22.2

P13489 Ribonuclease inhibitor 7 12 4.8 6.5 49.9

Q9UGI8 Testin 3 4 5.1 6.4 48.0

P07737 Profilin-1 9 148 4.6 6.4 15.0

P20618 Proteasome subunit beta type-1 4 10 4.7 6.4 26.5

P51911 Calponin-1 7 12 4.4 6.4 33.1

Q15819 Ubiquitin-conjugating enzyme E2

variant 2 2 8 4.6 6.4 16.4

P00558 Phosphoglycerate kinase 1 28 229 5.0 6.4 44.6

Q15843 NEDD8 2 2 4.5 6.4 9.1

P61204 ADP-ribosylation factor 3 4 10 3.9 6.4 20.6

Q99497 Protein DJ-1 9 55 4.4 6.4 19.9

P00441 Superoxide dismutase [Cu-Zn] 8 58 4.9 6.3 15.9

P62701 40S ribosomal protein S4, X

isoform 3 6 4.5 6.3 29.6

Q9Y617 Phosphoserine aminotransferase 14 74 4.5 6.3 40.4

P54687 Branched-chain-amino-acid aminotransferase, cytosolic

3 3 4.2 6.3 42.9

P68036 Ubiquitin-conjugating enzyme E2

L3 6 10 4.6 6.2 17.9

302

Q09666 Neuroblast differentiation-associated protein AHNAK

9 31 4.7 6.2 628.7

P58546 Myotrophin 3 6 4.9 6.2 12.9

P18206 Vinculin 59 371 4.5 6.2 123.7

P46821 Microtubule-associated protein

1B 12 19 4.5 6.2 270.5

P78417 Glutathione S-transferase

omega-1 5 9 4.6 6.2 27.5

P27348 14-3-3 protein theta 17 107 4.6 6.1 27.7

P68104 Elongation factor 1-alpha 1 15 136 4.0 6.1 50.1

P21266 Glutathione S-transferase Mu 3 8 24 4.8 6.1 26.5

P80723 Brain acid soluble protein 1 5 7 5.1 6.1 22.7

O75711 Scrapie-responsive protein 1 2 2 5.5 6.1 11.1

P63104 14-3-3 protein zeta/delta 19 209 4.4 6.1 27.7

P51858 Hepatoma-derived growth factor 2 4 4.1 6.0 26.8

Q04917 14-3-3 protein eta 9 73 4.5 6.0 28.2

P30153

Serine/threonine-protein phosphatase 2A 65 kDa

regulatory subunit A alpha isoform

8 19 4.1 6.0 65.3

P18085 ADP-ribosylation factor 4 3 8 4.0 6.0 20.5

Q9BVA1 Tubulin beta-2B chain 17 204 3.9 6.0 49.9

P61981 14-3-3 protein gamma 18 187 4.3 5.9 28.3

P40261 Nicotinamide N-

methyltransferase 8 41 4.3 5.9 29.6

P14324 Farnesyl pyrophosphate

synthase 3 3 4.5 5.9 48.2

P60842 Eukaryotic initiation factor 4A-I 9 25 4.5 5.9 46.1

P09382 Galectin-1 9 167 4.5 5.9 14.7

P01033 Metalloproteinase inhibitor 1 8 193 5.1 5.9 23.2

P06733 Alpha-enolase 29 1110 4.5 5.9 47.1

P18669 Phosphoglycerate mutase 1 16 178 4.3 5.9 28.8

P68363 Tubulin alpha-1B chain 22 176 4.3 5.9 50.1

Q53FA7 Quinone oxidoreductase PIG3 2 2 4.1 5.9 35.5

P55786 Puromycin-sensitive

aminopeptidase 16 28 4.0 5.9 103.2

P40925 Malate dehydrogenase,

cytoplasmic 11 36 4.1 5.9 36.4

P61970 Nuclear transport factor 2 4 19 4.5 5.8 14.5

Q16658 Fascin 15 57 4.7 5.8 54.5

Q9Y570 Protein phosphatase

methylesterase 1 2 6 4.3 5.8 42.3

P62258 14-3-3 protein epsilon 15 127 4.3 5.8 29.2

Q14195 Dihydropyrimidinase-related

protein 3 16 38 4.5 5.8 61.9

Q86VP6 Cullin-associated NEDD8-

dissociated protein 1 18 38 4.3 5.8 136.3

303

P61158 Actin-related protein 3 9 31 4.1 5.8 47.3

P12814 Alpha-actinin-1 56 921 4.0 5.7 103.0

P06744 Glucose-6-phosphate isomerase 16 102 4.5 5.7 63.1

P04075 Fructose-bisphosphate aldolase

A 20 357 4.1 5.7 39.4

Q9BQE3 Tubulin alpha-1C chain 19 158 4.5 5.7 49.9

P63208 S-phase kinase-associated

protein 1 3 3 4.4 5.7 18.6

Q15404 Ras suppressor protein 1 7 17 4.2 5.7 31.5

P21333 Filamin-A 104 619 4.3 5.7 280.6

P30101 Protein disulfide-isomerase A3 15 56 3.8 5.7 56.7

P17655 Calpain-2 catalytic subunit 6 13 4.9 5.7 79.9

O75368 SH3 domain-binding glutamic

acid-rich-like protein 5 13 4.4 5.6 12.8

Q15942 Zyxin 6 10 3.8 5.6 61.2

O95394 Phosphoacetylglucosamine

mutase 5 14 4.0 5.6 59.8

P28066 Proteasome subunit alpha type-5 2 2 4.5 5.6 26.4

O95782 AP-2 complex subunit alpha-1 2 2 4.4 5.6 107.5

Q9BRA2 Thioredoxin domain-containing

protein 17 5 13 4.6 5.6 13.9

P25786 Proteasome subunit alpha type-1 5 8 3.5 5.6 29.5

Q9ULV4 Coronin-1C 8 26 4.4 5.6 53.2

P29966 Myristoylated alanine-rich C-

kinase substrate 4 4 4.1 5.6 31.5

O43707 Alpha-actinin-4 54 525 4.0 5.5 104.8

Q9NTK5 Obg-like ATPase 1 3 6 3.7 5.5 44.7

P62942 Peptidyl-prolyl cis-trans

isomerase FKBP1A 2 11 4.3 5.5 11.9

P61088 Ubiquitin-conjugating enzyme E2

N 5 12 3.7 5.5 17.1

P36871 Phosphoglucomutase-1 11 34 3.9 5.5 61.4

P61978 Heterogeneous nuclear

ribonucleoprotein K 2 2 4.0 5.5 50.9

P52926 High mobility group protein

HMGI-C 3 3 4.5 5.5 11.8

P32119 Peroxiredoxin-2 5 15 4.0 5.5 21.9

Q9UBG0 C-type mannose receptor 2 7 11 4.4 5.4 166.6

P14625 Endoplasmin 14 69 3.7 5.4 92.4

P23284 Peptidyl-prolyl cis-trans

isomerase B 8 97 3.8 5.4 23.7

Q13642 Four and a half LIM domains

protein 1 11 45 4.6 5.4 36.2

P07602 Prosaposin 8 71 4.0 5.4 58.1

P60174 Triosephosphate isomerase 15 390 3.9 5.4 30.8

P26022 Pentraxin-related protein PTX3 9 34 4.3 5.4 41.9

P60660 Myosin light polypeptide 6 6 24 4.3 5.4 16.9

304

Q15746 Myosin light chain kinase,

smooth muscle 2 3 4.5 5.4 210.6

P35237 Serpin B6 4 7 4.0 5.3 42.6

P10599 Thioredoxin 7 28 4.0 5.3 11.7

P23381 Tryptophan--tRNA ligase,

cytoplasmic 11 38 3.8 5.3 53.1

P61769 Beta-2-microglobulin 4 35 5.1 5.3 13.7

P22626 Heterogeneous nuclear

ribonucleoproteins A2/B1 7 21 3.6 5.3 37.4

P16035 Metalloproteinase inhibitor 2 7 20 4.4 5.3 24.4

P15121 Aldose reductase 3 5 4.3 5.3 35.8

P21291 Cysteine and glycine-rich protein

1 11 56 4.3 5.3 20.6

O00299 Chloride intracellular channel

protein 1 10 34 4.3 5.2 26.9

P14550 Alcohol dehydrogenase

[NADP(+)] 9 27 4.0 5.2 36.5

O75083 WD repeat-containing protein 1 26 76 4.1 5.2 66.2

Q01469 Fatty acid-binding protein,

epidermal 3 8 3.5 5.2 15.2

P22314 Ubiquitin-like modifier-activating

enzyme 1 15 29 3.8 5.2 117.8

P61960 Ubiquitin-fold modifier 1 2 2 4.3 5.2 9.1

P53396 ATP-citrate synthase 10 20 3.8 5.2 120.8

P11142 Heat shock cognate 71 kDa

protein 30 230 3.7 5.1 70.9

Q16851 UTP--glucose-1-phosphate

uridylyltransferase 5 11 3.6 5.1 56.9

Q9Y490 Talin-1 36 84 3.9 5.1 269.6

Q9NRX4 14 kDa phosphohistidine

phosphatase 5 10 3.6 5.1 13.8

P52565 Rho GDP-dissociation inhibitor 1 3 10 4.7 5.1 23.2

Q01105 Protein SET 2 2 5.1 33.5

P68371 Tubulin beta-4B chain 18 194 3.6 5.1 49.8

Q96FW1 Ubiquitin thioesterase OTUB1 3 5 3.7 5.1 31.3

P27797 Calreticulin 13 53 3.6 5.1 48.1

P27816 Microtubule-associated protein 4 8 17 3.6 5.1 120.9

P31949 Protein S100-A11 5 66 4.3 5.1 11.7

Q92743 Serine protease HTRA1 11 84 4.6 5.1 51.3

P30086 Phosphatidylethanolamine-

binding protein 1 11 43 3.8 5.1 21.0

O15144 Actin-related protein 2/3 complex

subunit 2 10 25 3.7 5.1 34.3

P14618 Pyruvate kinase PKM 37 818 3.9 5.1 57.9

P24534 Elongation factor 1-beta 4 8 3.5 5.0 24.7

Q15417 Calponin-3 7 13 3.8 5.0 36.4

P05387 60S acidic ribosomal protein P2 3 4 3.6 5.0 11.7

305

Q14974 Importin subunit beta-1 12 23 4.1 5.0 97.1

Q04446 1,4-alpha-glucan-branching

enzyme 3 4 4.4 5.0 80.4

Q99584 Protein S100-A13 3 5 3.8 5.0 11.5

Q01518 Adenylyl cyclase-associated

protein 1 14 50 3.7 5.0 51.9

Q9BWD1 Acetyl-CoA acetyltransferase,

cytosolic 2 15 3.7 5.0 41.3

P09651 Heterogeneous nuclear

ribonucleoprotein A1 5 14 3.6 5.0 38.7

P14314 Glucosidase 2 subunit beta 3 5 3.6 5.0 59.4

Q92820 Gamma-glutamyl hydrolase 4 11 4.1 5.0 35.9

Q12765 Secernin-1 4 7 3.8 5.0 46.4

P09972 Fructose-bisphosphate aldolase

C 8 54 3.6 4.9 39.4

P52209 6-phosphogluconate

dehydrogenase, decarboxylating 15 46 3.9 4.9 53.1

P98095 Fibulin-2 4 8 4.0 4.9 126.5

O43852 Calumenin 13 38 4.0 4.9 37.1

Q7KZF4 Staphylococcal nuclease

domain-containing protein 1 6 15 3.7 4.9 101.9

P12109 Collagen alpha-1(VI) chain 34 285 4.5 4.9 108.5

Q9NVA2 Septin-11 4 8 4.0 4.9 49.4

O43854 EGF-like repeat and discoidin I-like domain-containing protein 3

5 9 4.4 4.9 53.7

P07858 Cathepsin B 3 5 3.8 4.9 37.8

P04080 Cystatin-B 4 54 4.0 4.9 11.1

P25787 Proteasome subunit alpha type-2 6 14 3.7 4.9 25.9

Q14738 Serine/threonine-protein phosphatase 2A 56 kDa

regulatory subunit delta isoform 4 5 3.5 4.9 69.9

P62906 60S ribosomal protein L10a 2 4 3.5 4.9 24.8

O76061 Stanniocalcin-2 6 78 5.1 4.9 33.2

P28300 Protein-lysine 6-oxidase 2 4 4.0 4.9 46.9

Q9H4A4 Aminopeptidase B 4 9 3.9 4.8 72.5

P63241 Eukaryotic translation initiation

factor 5A-1 3 216 3.6 4.8 16.8

P29401 Transketolase 15 51 3.6 4.8 67.8

Q13813 Spectrin alpha chain, non-

erythrocytic 1 6 9 3.7 4.8 284.4

P11216 Glycogen phosphorylase, brain

form 4 5 3.9 4.8 96.6

P41250 Glycine--tRNA ligase 16 95 3.6 4.8 83.1

P12277 Creatine kinase B-type 4 15 3.0 4.8 42.6

P37837 Transaldolase 5 16 3.4 4.8 37.5

Q14315 Filamin-C 23 119 3.9 4.8 290.8

P60900 Proteasome subunit alpha type-6 3 3 3.5 4.8 27.4

306

Q00610 Clathrin heavy chain 1 30 73 3.8 4.8 191.5

P12955 Xaa-Pro dipeptidase 2 2 3.4 4.8 54.5

O60565 Gremlin-1 2 2 4.4 4.8 20.7

P02511 Alpha-crystallin B chain 5 29 3.5 4.7 20.1

P62241 40S ribosomal protein S8 3 8 3.6 4.7 24.2

P34932 Heat shock 70 kDa protein 4 9 16 3.5 4.7 94.3

P02545 Prelamin-A/C 7 11 3.7 4.7 74.1

P11021 78 kDa glucose-regulated protein 14 70 3.4 4.7 72.3

P08238 Heat shock protein HSP 90-beta 23 124 3.7 4.7 83.2

P46940 Ras GTPase-activating-like

protein IQGAP1 21 54 3.6 4.7 189.1

Q8NBS9 Thioredoxin domain-containing

protein 5 7 8 3.5 4.6 47.6

Q14204 Cytoplasmic dynein 1 heavy

chain 1 9 12 3.8 4.6 532.1

Q16555 Dihydropyrimidinase-related

protein 2 8 13 3.5 4.6 62.3

P28074 Proteasome subunit beta type-5 3 9 3.5 4.6 28.5

P35579 Myosin-9 54 223 3.8 4.6 226.4

Q9Y696 Chloride intracellular channel

protein 4 15 184 4.0 4.6 28.8

Q96CX2 BTB/POZ domain-containing

protein KCTD12 3 5 4.0 4.6 35.7

Q02818 Nucleobindin-1 13 46 4.6 4.6 53.8

Q15084 Protein disulfide-isomerase A6 4 6 3.2 4.6 48.1

Q01813 ATP-dependent 6-

phosphofructokinase, platelet type

3 4 3.1 4.5 85.5

O00391 Sulfhydryl oxidase 1 19 116 4.5 4.5 82.5

P00966 Argininosuccinate synthase 11 26 3.4 4.5 46.5

Q15149 Plectin 22 44 3.6 4.5 531.5

P61160 Actin-related protein 2 8 37 3.6 4.5 44.7

P31150 Rab GDP dissociation inhibitor

alpha 14 129 3.6 4.5 50.6

P55072 Transitional endoplasmic

reticulum ATPase 2 2 3.1 4.4 89.3

P12110 Collagen alpha-2(VI) chain 13 34 4.2 4.4 108.5

P20908 Collagen alpha-1(V) chain 30 116 4.0 4.4 183.4

P07237 Protein disulfide-isomerase 16 77 3.5 4.4 57.1

Q9NZU5 LIM and cysteine-rich domains

protein 1 7 14 3.3 4.4 40.8

P39019 40S ribosomal protein S19 4 6 3.2 4.4 16.1

Q8WUM4 Programmed cell death 6-

interacting protein 3 3 3.7 4.4 96.0

P02462 Collagen alpha-1(IV) chain 18 86 3.7 4.3 160.5

307

O14498 Immunoglobulin superfamily

containing leucine-rich repeat protein

6 20 3.7 4.3 46.0

P21980 Protein-glutamine gamma-

glutamyltransferase 2 6 14 3.7 4.3 77.3

P09960 Leukotriene A-4 hydrolase 13 30 3.3 4.3 69.2

Q14103 Heterogeneous nuclear

ribonucleoprotein D0 4 8 3.4 4.3 38.4

P12111 Collagen alpha-3(VI) chain 45 124 3.9 4.3 343.5

Q96AY3 Peptidyl-prolyl cis-trans

isomerase FKBP10 6 16 3.4 4.3 64.2

Q07955 Serine/arginine-rich splicing

factor 1 2 32 3.4 4.3 27.7

Q14767 Latent-transforming growth factor

beta-binding protein 2 26 82 4.0 4.3 194.9

P07355 Annexin A2 16 54 3.5 4.3 38.6

P08107 Heat shock 70 kDa protein 1A/1B 9 20 3.3 4.2 70.0

O75369 Filamin-B 36 125 3.5 4.2 278.0

P11766 Alcohol dehydrogenase class-3 3 3 3.3 4.2 39.7

P42771 Cyclin-dependent kinase inhibitor

2A, isoforms 1/2/3 2 2 4.1 4.2 16.5

P06396 Gelsolin 29 160 3.4 4.2 85.6

O95965 Integrin beta-like protein 1 4 7 3.7 4.2 53.9

P39687 Acidic leucine-rich nuclear phosphoprotein 32 family

member A 3 7 3.4 4.1 28.6

P07900 Heat shock protein HSP 90-

alpha 24 149 3.4 4.1 84.6

Q13219 Pappalysin-1 21 49 3.8 4.1 180.9

P04083 Annexin A1 7 35 3.4 4.1 38.7

P13611 Versican core protein 16 54 3.2 4.1 372.6

P50454 Serpin H1 17 67 2.9 4.1 46.4

P19022 Cadherin-2 6 10 3.2 4.0 99.7

Q9BY76 Angiopoietin-related protein 4 3 7 2.9 4.0 45.2

P10915 Hyaluronan and proteoglycan

link protein 1 15 127 3.9 4.0 40.1

Q14192 Four and a half LIM domains

protein 2 6 17 3.5 4.0 32.2

P07093 Glia-derived nexin 13 100 3.7 4.0 44.0

P05452 Tetranectin 5 15 3.8 4.0 22.5

Q96D15 Reticulocalbin-3 2 3 3.3 4.0 37.5

P49720 Proteasome subunit beta type-3 3 3 3.3 4.0 22.9

P07585 Decorin 14 96 3.9 4.0 39.7

P35442 Thrombospondin-2 26 113 3.8 4.0 129.9

Q9NY33 Dipeptidyl peptidase 3 5 11 3.3 4.0 82.5

P18065 Insulin-like growth factor-binding

protein 2 15 97 3.9 3.9 34.8

308

O94760 N(G),N(G)-dimethylarginine dimethylaminohydrolase 1

4 8 3.1 3.9 31.1

Q76M96 Coiled-coil domain-containing

protein 80 16 69 3.8 3.9 108.1

P17174 Aspartate aminotransferase,

cytoplasmic 6 10 3.3 3.9 46.2

P24043 Laminin subunit alpha-2 11 20 3.7 3.9 343.7

Q92626 Peroxidasin homolog 23 65 3.5 3.9 165.2

P35555 Fibrillin-1 73 446 3.2 3.9 312.0

Q16527 Cysteine and glycine-rich protein

2 5 17 3.2 3.9 20.9

Q9Y3B8 Oligoribonuclease, mitochondrial 6 11 3.3 3.9 26.8

Q8IUX7 Adipocyte enhancer-binding

protein 1 8 13 3.5 3.9 130.8

Q9NRN5 Olfactomedin-like protein 3 6 12 4.1 3.9 46.0

P01034 Cystatin-C 8 51 3.3 3.8 15.8

P07686 Beta-hexosaminidase subunit

beta 5 8 3.1 3.8 63.1

Q9UBP4 Dickkopf-related protein 3 8 34 3.6 3.8 38.4

O95084 Serine protease 23 3 4 3.5 3.8 43.0

Q9UI42 Carboxypeptidase A4 9 27 4.0 3.8 47.3

P11047 Laminin subunit gamma-1 13 35 3.0 3.8 177.5

P30043 Flavin reductase (NADPH) 2 2 3.5 3.8 22.1

Q14766 Latent-transforming growth factor

beta-binding protein 1 14 34 3.5 3.7 186.7

P48163 NADP-dependent malic enzyme 4 5 3.2 3.7 64.1

Q4ZHG4 Fibronectin type III domain-

containing protein 1 11 17 2.9 3.7 205.4

P08758 Annexin A5 9 24 3.1 3.7 35.9

P29279 Connective tissue growth factor 4 8 3.3 3.6 38.1

Q6UVK1 Chondroitin sulfate proteoglycan

4 10 20 3.7 3.6 250.4

Q15063 Periostin 38 425 3.5 3.6 93.3

P05997 Collagen alpha-2(V) chain 36 110 3.6 3.5 144.8

P35556 Fibrillin-2 10 25 3.2 3.5 314.6

P16870 Carboxypeptidase E 9 19 3.1 3.5 53.1

P08123 Collagen alpha-2(I) chain 74 1436 3.7 3.5 129.2

P35052 Glypican-1 7 10 2.8 3.5 61.6

P07339 Cathepsin D 13 68 3.4 3.5 44.5

P41222 Prostaglandin-H2 D-isomerase 2 2 3.2 3.5 21.0

P21810 Biglycan 21 599 3.2 3.4 41.6

P02751 Fibronectin 102 4800 3.6 3.4 262.5

P08603 Complement factor H 11 20 3.1 3.4 139.0

Q9H4D0 Calsyntenin-2 10 20 3.1 3.4 106.9

P62826 GTP-binding nuclear protein Ran 7 25 2.7 3.4 24.4

309

P27658 Collagen alpha-1(VIII) chain 13 45 3.2 3.4 73.3

P26639 Threonine--tRNA ligase,

cytoplasmic 6 8 3.3 83.4

Q15582 Transforming growth factor-beta-

induced protein ig-h3 28 647 3.2 3.3 74.6

Q7Z304 MAM domain-containing protein

2 16 47 3.2 3.3 77.5

Q02809 Procollagen-lysine,2-

oxoglutarate 5-dioxygenase 1 22 117 3.8 3.3 83.5

O00468 Agrin 14 29 2.9 3.3 217.1

P15502 Elastin 6 18 3.5 3.3 68.4

P02461 Collagen alpha-1(III) chain 44 177 3.3 3.3 138.5

P24593 Insulin-like growth factor-binding

protein 5 9 48 3.3 3.3 30.6

P04264 Keratin, type II cytoskeletal 1 13 42 2.6 3.3 66.0

P13667 Protein disulfide-isomerase A4 2 3 2.8 3.3 72.9

P08572 Collagen alpha-2(IV) chain 43 144 3.0 3.3 167.4

P17936 Insulin-like growth factor-binding

protein 3 11 38 3.2 3.3 31.7

Q12841 Follistatin-related protein 1 22 590 3.5 3.2 35.0

P13645 Keratin, type I cytoskeletal 10 9 12 2.6 3.2 58.8

P28799 Granulins 5 12 3.0 3.2 63.5

Q16610 Extracellular matrix protein 1 10 27 3.0 3.2 60.6

O94985 Calsyntenin-1 7 24 2.8 3.2 109.7

Q08431 Lactadherin 4 18 2.7 3.1 43.1

P55083 Microfibril-associated

glycoprotein 4 4 52 3.5 3.1 28.6

Q9Y4K0 Lysyl oxidase homolog 2 9 38 2.7 3.1 86.7

Q16270 Insulin-like growth factor-binding

protein 7 21 879 2.9 3.1 29.1

P02452 Collagen alpha-1(I) chain 86 1758 3.2 3.1 138.9

P06865 Beta-hexosaminidase subunit

alpha 5 11 2.5 3.1 60.7

P12107 Collagen alpha-1(XI) chain 15 47 3.0 3.1 181.0

P05121 Plasminogen activator inhibitor 1 24 1059 3.1 3.1 45.0

Q9UBX5 Fibulin-5 5 12 2.6 3.0 50.1

Q14118 Dystroglycan 4 8 3.1 3.0 97.4

P10909 Clusterin 9 86 2.9 3.0 52.5

P07996 Thrombospondin-1 46 364 3.8 3.0 129.3

Q96HF1 Secreted frizzled-related protein

2 9 22 3.0 3.0 33.5

P09486 SPARC 20 3097 3.2 3.0 34.6

Q16363 Laminin subunit alpha-4 3 7 2.4 3.0 202.4

Q16777 Histone H2A type 2-C 2 2 2.6 2.9 14.0

P08476 Inhibin beta A chain 16 35 3.0 2.9 47.4

310

Q9BWS9 Chitinase domain-containing

protein 1 3 13 2.2 2.9 44.9

Q10471 Polypeptide N-

acetylgalactosaminyltransferase 2

6 16 2.7 2.9 64.7

Q99715 Collagen alpha-1(XII) chain 59 175 2.8 2.9 332.9

P36873 Serine/threonine-protein

phosphatase PP1-gamma catalytic subunit

2 4 2.4 2.9 37.0

P07942 Laminin subunit beta-1 17 42 2.8 2.8 197.9

P55209 Nucleosome assembly protein 1-

like 1 3 5 2.3 2.8 45.3

P35908 Keratin, type II cytoskeletal 2

epidermal 9 15 2.3 2.8 65.4

O95678 Keratin, type II cytoskeletal 75 4 8 2.1 2.7 59.5

P08253 72 kDa type IV collagenase 23 74 2.5 2.7 73.8

P35527 Keratin, type I cytoskeletal 9 7 38 2.1 2.6 62.0

Q08380 Galectin-3-binding protein 21 128 2.8 2.6 65.3

Q12805 EGF-containing fibulin-like

extracellular matrix protein 1 17 75 2.7 2.6 54.6

P24821 Tenascin 35 150 2.9 2.6 240.7

P98160 Basement membrane-specific heparan sulfate proteoglycan

core protein 23 99 2.3 2.5 468.5

O95967 EGF-containing fibulin-like

extracellular matrix protein 2 3 6 2.5 2.5 49.4

P51884 Lumican 12 75 2.3 2.3 38.4

P01023 Alpha-2-macroglobulin 11 13 1.6 2.0 163.2

P02768 Serum albumin 15 81 1.7 1.9 69.3

P02765 Alpha-2-HS-glycoprotein 2 3 1.9 39.3

Q06210 Glutamine--fructose-6-phosphate aminotransferase [isomerizing] 1

3 5 78.8

Q86UX2 Inter-alpha-trypsin inhibitor heavy

chain H5 2 18 104.5

P48637 Glutathione synthetase 5 13 52.4

Q9P2E9 Ribosome-binding protein 1 3 6 152.4

O00469 Procollagen-lysine,2-

oxoglutarate 5-dioxygenase 2 6 6 84.6

Q9Y240 C-type lectin domain family 11

member A 2 2 35.7

Q96FQ6 Protein S100-A16 3 7 11.8

P28070 Proteasome subunit beta type-4 4 11 29.2

P07814 Bifunctional glutamate/proline--

tRNA ligase 2 4 170.5

P62081 40S ribosomal protein S7 3 4 22.1

P62269 40S ribosomal protein S18 2 3 17.7

P68402 Platelet-activating factor

acetylhydrolase IB subunit beta 3 11 25.6

Q13308 Inactive tyrosine-protein kinase 7 7 12 118.3

311

Q13907 Isopentenyl-diphosphate Delta-

isomerase 1 2 4 26.3

P46781 40S ribosomal protein S9 2 2 22.6

Q13509 Tubulin beta-3 chain 14 169 50.4

Q05707 Collagen alpha-1(XIV) chain 2 2 193.4

P06737 Glycogen phosphorylase, liver

form 4 4 97.1

P67809 Nuclease-sensitive element-

binding protein 1 4 8 35.9

P62873 Guanine nucleotide-binding

protein G(I)/G(S)/G(T) subunit beta-1

2 2 37.4

Q13200 26S proteasome non-ATPase

regulatory subunit 2 4 7 100.1

P55060 Exportin-2 2 2 110.3

P00491 Purine nucleoside phosphorylase 2 4 32.1

Q6IBS0 Twinfilin-2 2 2 39.5

Q9P1F3 Costars family protein ABRACL 2 2 9.1

Q01082 Spectrin beta chain, non-

erythrocytic 1 3 3 274.4

P63244 Guanine nucleotide-binding protein subunit beta-2-like 1

2 2 35.1

P24844 Myosin regulatory light

polypeptide 9 6 20 19.8

P02795 Metallothionein-2 3 10 6.0

Q15181 Inorganic pyrophosphatase 4 6 3.2 32.6

O95865 N(G),N(G)-dimethylarginine dimethylaminohydrolase 2

3 8 3.0 29.6

Q14697 Neutral alpha-glucosidase AB 4 6 3.8 106.8

Q96KP4 Cytosolic non-specific

dipeptidase 3 4 3.6 52.8

P59998 Actin-related protein 2/3 complex

subunit 4 3 3 4.1 19.7

O14818 Proteasome subunit alpha type-7 4 5 3.4 27.9

O43776 Asparagine--tRNA ligase,

cytoplasmic 4 7 4.0 62.9

P09104 Gamma-enolase 7 539 3.4 47.2

Table 5.8: Differentially released conditioned medium proteins at 48 hours

Astrocyte-released proteins measured in condition media from TMT experiments

comparing protein abundance between mild and severe stretched cells in conditioned

media collected 48 hours post-injury.

312

5.7 REFERENCES

1. S. C. Choi, R. Bullock, Design and statistical issues in multicenter trials of severe

head injury. Neurological research 23, 190-192 (2001); published online EpubMar-

Apr (10.1179/016164101101198325).

2. J. A. Langlois, W. Rutland-Brown, M. M. Wald, The Epidemiology and Impact of

Traumatic Brain Injury. Journal of Head Trauma Rehabilitation 21, 375-378 (2006).

3. G. Franz, R. Beer, A. Kampfl, K. Engelhardt, E. Schmutzhard, H. Ulmer, F.

Deisenhammer, Amyloid beta 1–42 and tau in cerebrospinal fluid after severe

traumatic brain injury. Neurology 60, (2003).

4. N. Shiiya, T. Kunihara, T. Miyatake, K. Matsuzaki, K. Yasuda, Tau protein in the

cerebrospinal fluid is a marker of brain injury after aortic surgery. Ann Thorac Surg

77, (2004).

5. P. P. Tsitsopoulos, N. Marklund, Amyloid-beta peptides and tau protein as

biomarkers in cerebrospinal and interstitial fluid following traumatic brain injury: a

review of experimental and clinical studies. Front Neurol 4, (2013).

6. I. B. Wanner, M. A. Anderson, B. Song, J. Levine, A. Fernandez, Z. Gray-

Thompson, Y. Ao, M. V. Sofroniew, Glial scar borders are formed by newly

proliferated, elongated astrocytes that interact to corral inflammatory and fibrotic

cells via STAT3-dependent mechanisms after spinal cord injury. J Neurosci 33,

(2013).

7. J. E. Burda, M. V. Sofroniew, Reactive gliosis and the multicellular response to

CNS damage and disease. Neuron 81, (2014).

313

8. I. B. Wanner, An in vitro trauma model to study rodent and human astrocyte

reactivity. Methods in molecular biology 814, (2012).

9. I. B. Wanner, M. Deik, M. Torres, A. R. Rosendahl, J. T. Neary, V. P. Lemmon, J.

L. Bixby, A new in vitro model of the glial scar inhibits axon growth. Glia 56, (2008).

10. M. Sondej, P. Doran, J. A. Loo, I. Wanner, in Sample preparation in biological mass

spectrometry, A. Ivanov, A. Lazarev, Eds. (Springer, Dordrecht, 2011).

11. E. F. Ellis, J. S. McKinney, K. A. Willoughby, S. Liang, J. T. Povlishock, A new

model for rapid stretch-induced injury of cells in culture: characterization of the

model using astrocytes. Journal of neurotrauma 12, (1995).

12. E. F. Ellis, K. A. Willoughby, S. A. Sparks, T. Chen, S100B protein is released from

rat neonatal neurons, astrocytes, and microglia by in vitro trauma and anti-S100

increases trauma-induced delayed neuronal injury and negates the protective

effect of exogenous S100B on neurons. J Neurochem 101, (2007).

13. A. Sandberg, R. M. Branca, J. Lehtio, J. Forshed, Quantitative accuracy in mass

spectrometry based proteomics of complex samples: the impact of labeling and

precursor interference. Journal of proteomics 96, 133-144 (2014); published online

EpubJan 16 (10.1016/j.jprot.2013.10.035).

14. H. Wang, S. Alvarez, L. M. Hicks, Comprehensive comparison of iTRAQ and label-

free LC-based quantitative proteomics approaches using two Chlamydomonas

reinhardtii strains of interest for biofuels engineering. Journal of proteome research

11, 487-501 (2012); published online EpubJan 1 (10.1021/pr2008225).

314

15. M. Bantscheff, M. Boesche, D. Eberhard, T. Matthieson, G. Sweetman, B. Kuster,

Robust and Sensitive iTRAQ Quantification on an LTQ Orbitrap Mass

Spectrometer. Molecular & Cellular Proteomics 7, 1702-1713 (2008)10.1074/).

16. E. H. Pettus, C. W. Christman, M. L. Giebel, J. T. Povlishock, Traumatically

Induced Altered Membrane Permeability: Its Relationship to Traumatically Induced

Reactive Axonal Change. Journal of Neurotrauma 11, 507-522 (1994).

17. O. Farkas, J. Lifshitz, J. T. Povlishock, Mechanoporation Induced by Diffuse

Traumatic Brain Injury: An Irreversible or Reversible Response to Injury? The

Journal of Neuroscience 25, 3130-3140 (2006).

18. S. Schenk, G. J. Schoenhals, G. de Souza, M. Mann, A high confidence, manually

validated human blood plasma protein reference set. BMC medical genomics 1,

41 (2008); published online EpubSep 15 (10.1186/1755-8794-1-41).

19. V. Nanjappa, J. K. Thomas, A. Marimuthu, B. Muthusamy, A. Radhakrishnan, R.

Sharma, A. Ahmad Khan, L. Balakrishnan, N. A. Sahasrabuddhe, S. Kumar, B. N.

Jhaveri, K. V. Sheth, R. Kumar Khatana, P. G. Shaw, S. M. Srikanth, P. P. Mathur,

S. Shankar, D. Nagaraja, R. Christopher, S. Mathivanan, R. Raju, R. Sirdeshmukh,

A. Chatterjee, R. J. Simpson, H. C. Harsha, A. Pandey, T. S. Prasad, Plasma

Proteome Database as a resource for proteomics research: 2014 update. Nucleic

acids research 42, D959-965 (2014); published online EpubJan

(10.1093/nar/gkt1251).

20. M. J. Whalen, T. Dalkara, Z. You, J. Qiu, D. Bermpohl, N. Mehta, B. Suter, P. G.

Bhide, E. H. Lo, M. Ericsson, M. A. Moskowitz, Acute plasmalemma permeability

and protracted clearance of injured cells after controlled cortical impact in mice.

315

Journal of cerebral blood flow and metabolism : official journal of the International

Society of Cerebral Blood Flow and Metabolism 28, 490-505 (2008); published

online EpubMar (10.1038/sj.jcbfm.9600544).

21. R. H. Singleton, J. T. Povlishock, Identification and characterization of

heterogeneous neuronal injury and death in regions of diffuse brain injury:

evidence for multiple independent injury phenotypes. The Journal of neuroscience

: the official journal of the Society for Neuroscience 24, 3543-3553 (2004);

published online EpubApr 7 (10.1523/JNEUROSCI.5048-03.2004).

22. M. C. LaPlaca, G. R. Prado, Neural mechanobiology and neuronal vulnerability to

traumatic loading. Journal of Biomechanics 43, 71-78 (2010).

23. R. S. B. Clark, P. M. Kochanek, S. C. Watkins, M. Chen, C. E. Dixon, N. A.

Seidberg, J. Melick, J. E. Loeffert, P. D. Nathaniel, K. L. Jin, S. H. Graham,

Caspase-3 Mediated Neuronal Death AfterTraumatic Brain Injury in Rats. Journal

of Neurochemistry 74, 740-753 (2000).

24. A. Degterev, Z. Huang, M. Boyce, Y. Li, P. Jagtap, N. Mizushima, G. D. Cuny, T.

J. Mitchison, M. A. Moskowitz, J. Yuan, Chemical inhibitor of nonapoptotic cell

death with therapeutic potential for ischemic brain injury. Nature chemical biology

1, 112-119 (2005); published online EpubJul (10.1038/nchembio711).

25. R. S. Clark, P. M. Kochanek, P. D. Adelson, M. J. Bell, J. A. Carcillo, M. Chen, S.

R. Wisniewski, K. Janesko, M. J. Whalen, S. H. Graham, Increases in bcl-2 protein

in cerebrospinal fluid and evidence for programmed cell death in infants and

children after severe traumatic brain injury. The Journal of pediatrics 137, 197-204

(2000); published online EpubAug (10.1067/mpd.2000.106903).

316

26. A. K. Ottens, L. Bustamante, E. C. Golden, C. Yao, R. L. Hayes, K. K. Wang, F. C.

Tortella, J. R. Dave, Neuroproteomics: a biochemical means to discriminate the

extent and modality of brain injury. J Neurotrauma 27, 1837-1852 (2010);

published online EpubOct (10.1089/neu.2010.1374).

27. K. E. Saatman, K. J. F. Feeko, R. L. Pape, R. Raghupathi, Differential Behavioral

and Histopathological Responses to Graded Cortical Impact Injury in Mice. Journal

of Neurotrauma 23, 1241-1253 (2006).

28. J. M. Spaethling, D. M. Geddes-Klein, W. J. Miller, C. R. von Reyn, P. Singh, M.

Mesfin, S. J. Bernstein, D. F. Meaney, Linking impact to cellular and molecular

sequelae of CNS injury: Modeling in vivo complexity with in vitro simplicity. 161,

27-39 (2007)10.1016/s0079-6123(06)61003-0).

29. J. Y. Kim, N. Kim, Z. Zheng, J. E. Lee, M. A. Yenari, The 70 kDa heat shock protein

protects against experimental traumatic brain injury. Neurobiology of disease 58,

289-295 (2013); published online EpubOct (10.1016/j.nbd.2013.06.012).

30. J. Y. Kim, M. A. Yenari, The immune modulating properties of the heat shock

proteins after brain injury. Anatomy & cell biology 46, 1-7 (2013); published online

EpubMar (10.5115/acb.2013.46.1.1).

31. D. L. Feinstein, E. Galea, D. A. Aquino, G. C. Li, H. Xu, D. J. Reis, Heat Shock

Protein 70 Suppresses Astroglial-inducible Nitric-oxide Synthase Expression by

Decreasing NFkB Activation. The Journal of Biological Chemistry 271, 17724-

17732 (1996).

32. X. Yao, J. Liu, J. T. McCabe, Ubiquitin and ubiquitin-conjugated protein expression

in the rat cerebral cortex and hippocampus following traumatic brain injury (TBI).

317

Brain research 1182, 116-122 (2007); published online EpubNov 28

(10.1016/j.brainres.2007.08.076).

33. M. Majetschak, D. R. King, U. Krehmeier, L. T. Busby, C. Thome, S. Vajkoczy, K.

G. Proctor, Ubiquitin immunoreactivity in cerebrospinal fluid after traumatic brain

injury: Clinical and experimental findings. Critical Care Medicine 33, 1589-1594

(2005)10.1097/01.ccm.0000169883.41245.23).

34. K. Dikranian, R. Cohen, C. Mac Donald, Y. Pan, D. Brakefield, P. Bayly, A.

Parsadanian, Mild traumatic brain injury to the infant mouse causes robust white

matter axonal degeneration which precedes apoptotic death of cortical and

thalamic neurons. Experimental neurology 211, 551-560 (2008); published online

EpubJun (10.1016/j.expneurol.2008.03.012).

35. P. N. Lizhnyak, A. K. Ottens, Proteomics: in pursuit of effective traumatic brain

injury therapeutics. Expert review of proteomics 12, 75-82 (2015); published online

EpubFeb (10.1586/14789450.2015.1000869).

36. G. Barkhoudarian, D. A. Hovda, C. C. Giza, The molecular pathophysiology of

concussive brain injury. Clinics in sports medicine 30, 33-48, vii-iii (2011);

published online EpubJan (10.1016/j.csm.2010.09.001).

37. L. Papa, L. Akinyi, M. C. Liu, J. A. Pineda, J. J. Tepas, 3rd, M. W. Oli, W. Zheng,

G. Robinson, S. A. Robicsek, A. Gabrielli, S. C. Heaton, H. J. Hannay, J. A.

Demery, G. M. Brophy, J. Layon, C. S. Robertson, R. L. Hayes, K. K. Wang,

Ubiquitin C-terminal hydrolase is a novel biomarker in humans for severe traumatic

brain injury. Crit Care Med 38, 138-144 (2010); published online EpubJan

(10.1097/CCM.0b013e3181b788ab).

318

38. S. Mondello, A. Linnet, A. Buki, S. Robiscek, A. Gabrielli, J. Tepas, L. Papa, G. M.

Brophy, F. Tortella, R. L. Hayes, K. K. Wang, Clinical Utility of Serum Levels of

Ubiquitin C-Terminal Hydrolase as a Biomarker for Severe Traumatic Brain Injury.

Neurosurgery 70, 666-675 (2011)10.1227/NEU.0b013e318236a809).

39. E. P. Thelin, D. Just, A. Frostell, A. Haggmark-Manberg, M. Risling, M. Svensson,

P. Nilsson, B. M. Bellander, Protein profiling in serum after traumatic brain injury

in rats reveals potential injury markers. Behavioural brain research, (2016);

published online EpubAug 31 (10.1016/j.bbr.2016.08.058).

40. R. Siman, T. K. McIntosh, K. M. Soltesz, Z. Chen, R. W. Neumar, V. L. Roberts,

Proteins released from degenerating neurons are surrogate markers for acute

brain damage. Neurobiology of disease 16, 311-320 (2004); published online

EpubJul (10.1016/j.nbd.2004.03.016).

41. C. Loov, A. G. Nadadhur, L. Hillered, F. Clausen, A. Erlandsson, Extracellular

ezrin: a novel biomarker for traumatic brain injury. J Neurotrauma 32, 244-251

(2015); published online EpubFeb 15 (10.1089/neu.2014.3517).

42. C. Loov, G. Shevchenko, A. Geeyarpuram Nadadhur, F. Clausen, L. Hillered, M.

Wetterhall, A. Erlandsson, Identification of injury specific proteins in a cell culture

model of traumatic brain injury. PLoS One 8, e55983

(2013)10.1371/journal.pone.0055983).

43. M. Fujimura, T. Tominaga, P. H. Chan, Neuroprotective effect of an antioxidant in

ischemic brain injury. Neurocritical care 2, 59-66 (2005).

44. H. Endo, C. Nito, H. Kamada, F. Yu, P. H. Chan, Reduction in oxidative stress by

superoxide dismutase overexpression attenuates acute brain injury after

319

subarachnoid hemorrhage via activation of Akt/glycogen synthase kinase-3β

survival signaling. Journal of Cerebral Blood Flow & Metabolism 27, 975-982

(2007).

45. I. B. Wanner, An in vitro trauma model to study rodent and human astrocyte

reactivity. Methods in molecular biology 814, 189-219 (2012)10.1007/978-1-

61779-452-0_14).

46. K. Hodge, S. T. Have, L. Hutton, A. I. Lamond, Cleaning up the masses: exclusion

lists to reduce contamination with HPLC-MS/MS. Journal of proteomics 88, 92-103

(2013); published online EpubAug 2 (10.1016/j.jprot.2013.02.023).

47. G. S. Omenn, D. J. States, M. Adamski, T. W. Blackwell, R. Menon, H. Hermjakob,

R. Apweiler, B. B. Haab, R. J. Simpson, J. S. Eddes, E. A. Kapp, R. L. Moritz, D.

W. Chan, A. J. Rai, A. Admon, R. Aebersold, J. Eng, W. S. Hancock, S. A. Hefta,

H. Meyer, Y. K. Paik, J. S. Yoo, P. Ping, J. Pounds, J. Adkins, X. Qian, R. Wang,

V. Wasinger, C. Y. Wu, X. Zhao, R. Zeng, A. Archakov, A. Tsugita, I. Beer, A.

Pandey, M. Pisano, P. Andrews, H. Tammen, D. W. Speicher, S. M. Hanash,

Overview of the HUPO Plasma Proteome Project: results from the pilot phase with

35 collaborating laboratories and multiple analytical groups, generating a core

dataset of 3020 proteins and a publicly-available database. Proteomics 5, 3226-

3245 (2005); published online EpubAug (10.1002/pmic.200500358).

320

CHAPTER 6: FUTURE DIRECTIONS FOR SPINAL CORD AND HEAD TRAUMA

6.1 INTRODUCTION

Swine is an important biomedical model for the study of human diseases given its

similarities with the human genome. This has allowed researchers the ability to generate

transgenic models for the study of specific human diseases. Additionally, comparison of

predicted porcine sequences to predicted human orthologues has not only demonstrated

high primary sequence identity but also similarities in disease related amino acid point

mutations (1). Here we examine the differences in global proteomic changes between

healthy and spinal cord injured (SCI) Yucatan swine with the goal of identifying new

candidate markers for SCI. Top candidates were selected based on CNS enrichment and

overlap between candidates from our astrocyte injury model.

Additionally, we also evaluated the effects of pre-analytical factors that may

influence both the qualitative and quantitative analyses of biofluid proteomes (2).

Cerebrospinal fluid (CSF), a filtrate of plasma, is the most proximal fluid to the central

nervous system (CNS). As such, CSF is perhaps the most valuable biofluid source to

monitor and identify differential protein signatures in neurological trauma and diseases. It

has been documented in the literature that even small degrees of blood contamination

can have large manifestations in protein compositions measured between control and

healthy states (3, 4). We evaluated the issue of blood contamination through the

development of hemolysis blood protein assay.

321

6.2 RESULTS

Blood contamination of CSF and hemolysis analysis

Blood contamination critically alters the protein composition of a CSF sample, as

blood protein concentrations exceeds that of CSF by a factor of 200 to 1 (3). In our present

study of Yucatan swine SCI CSF, our team identified significant, persistent blood

contamination. The effects of blood contamination were observed by our collaborators in

their SDS-PAGE separations of specimen CSF. Ponceau S staining confirmed the

presence of blood in CSF, illustrated by the ~60kDa sized blood-derived albumin signal

in samples (Figure 6.1). The presence of high blood protein signal manifested itself in our

shotgun CSF analysis in the form of relatively low protein IDs. To establish an

approximate but quantitative assessment of blood contamination, we employed spectral

counting (5, 6), a label-free mass spectrometry based quantitative proteomics analysis.

This method calculated a normalized spectral abundance factor (NSAF) based on the

number of peptide MS/MS spectra used in protein identification as a measure of its

abundance. Observed spectral counts are normalized based on protein size to account

for smaller proteins having fewer peptides identified compared to large proteins. Based

on previous reports of blood contamination, hemoglobin was chosen as a surrogate

marker for hemolysis (7, 8). It should be noted that under normal physiological conditions,

red blood cells are unable to cross the blood spinal cord barrier (BSCB). And while BSCB

compromise has been documented to occur rapidly after SCI (9, 10), high blood

contamination was also observed in both baseline CSF and sham uninjured animals as

well. This data suggested that not all cases of blood contamination were injury related

and that inconsistent CSF collection procedures were also an issue.

322

Figure 6.1 demonstrates an example sample comparison of qualitative Ponceau S

staining observations with our label-free MS quantitation. For pig 43-031, high relative

abundances of hemoglobin and albumin associated with both low protein identifications

within the run and a thick blood-derived albumin band on the gel. Upon completion of the

entire SCI CSF shotgun dataset, we found that lower protein IDs correlated better with

increased albumin signal than total hemoglobin content, albeit both correlations were

poor. Using the data from Table 6.1, elevated albumin content of greater than 10%

generally correlated to reduced protein IDs with a Spearman correlation coefficient

of -0.517 compared to -0.329 for total hemoglobin signal. As a result of these findings,

albumin content, in addition to hemoglobin content will be evaluated moving forward for

a blood contamination assay. Depletion procedures using cibacron blue and commercial

top 12 blood protein spin columns were also assessed for swine biofluids. This, however,

caused complete removal or marked reduction of all biomarker signals and is thus not an

option for improving IDs from proteomic screenings of contaminated swine CSF samples.

Establishing a pig spinal cord injury cerebrospinal fluid proteome

Injured animals 43-031, 42-115, 42-068, 43-082, and 42-127 were used in our

identification of a spinal cord injury-related proteome or “traumatome.” Exclusion of the

remaining 9 SCI and 7 sham uninjured animals was based on the results of our

hemoglobin blood contamination assay (Table 6.1). Triplicate analyses of CSF from

baseline and SCI time-points 20m, 2.7h, 2d, and 7d were performed using a top-10 data-

dependent workflow. A total of 413 proteins were identified from 5 baseline CSF samples.

462 and 537 proteins were identified for the acute SCI (20m and 2.7h) and all SCI time-

323

points, respectively. Proteins were identified from database searching against a Sus

scrofa (pig) reference proteome (UP00008227) consisting of 26,101 proteins transcribed

from a reference genome established by the Swine Genome Sequencing Consortium (11,

12). However, in contrast to the well curate SwissProt human reference proteome, a large

portion of the Sus scrofa reference proteome consisted of uncharacterized proteins,

offering little insight or information into protein level differences between SCI and healthy

CSF. To circumvent this road block, uncharacterized pig protein IDs were converted to

ENSEMBL gene IDs to identify human orthologues and their corresponding gene

products. This resulted in a total 340, 385 and 435 proteins successfully identified from

baseline CSF, acute SCI CSF, and all SCI CSF.

The 340 proteins identified from baseline samples were used as our reference

healthy CSF proteome. This was compared with proteins identified in our acute SCI time-

points. This window acutely post-injury was chosen based on concerns over the rapid

onset of proteolysis after injury as well as protein clearance into the blood as early as

overnight after injury (13). Comparative analysis (Figure 6.2) revealed 100 proteins

specific to SCI CSF acutely after injury that constitutes an acute SCI traumatome (Table

6.4) The 285 overlapping proteins from both conditions is shown in Table 6.5.

Identification of new spinal cord injury biomarkers

Proteins within our SCI traumatome were then evaluated for candidate biomarkers

based on the criteria of central nervous system tissue enrichment. This was performed

with the aid of the Human Protein Atlas (14, 15), which presents a map of the human

tissue proteome compiled using a combination of quantitative transcriptomics from tissue

324

and organs and microarray immunohistochemistry-based protein profiling on tissue.

Furthermore, despite shotgun data hindered by blood protein contamination, proteomic

profiles from additional SCI samples 46-030, 47-094, 42-132 and sham animals 46-149,

47-050, 47-051, 47-052, 47-018, and 46-101 were additionally considered in narrowing

our brain enriched candidate list. A total of 12 SCI specific-brain enriched candidates were

identified in 8 SCI samples (Table 6.2). Several of these proteins were also observed in

6 uninjured samples. While their presence in control CSF may potentially lower their

specificity, it does not necessarily exclude their diagnostic value. Additionally, the fact that

these were identified despite high blood contamination and low proteomic depth may be

a beneficial quality when considering biomarker robustness and sensitivity. Of these 12

proteins, lumican, carboxypeptidase E, and glial fibrillary acidic protein (GPAP) were also

observed to be preferentially released by injured astrocytes.

325

6.3 DISCUSSION

Improved CSF extraction and blood contamination assay needed

Vasculature damage and breakdown of the blood spinal-cord barrier is a

documented consequence of spinal cord injury in both human patients and animal models

(9, 10, 16, 17). This pathology complicates analysis of proximal fluid proteomics such as

CSF in identifying changes in protein compositions as a result of injury as the

concentration of protein in CSF is very low (0.2-0.5%) compared to blood. As a result,

minor blood contamination during the collection of CSF may be highly consequential to

the observed protein profiles. While the presence of blood signatures may be expected

for animals who had experienced a severe SCI as characterized in Chapter 4, high levels

of blood protein were also observed in baseline and un-injured animal samples. This led

to the suspicion of issues with CSF sample collection prior to MS analysis.

A label-free quantitative assay utilizing the number of MS2 acquisitions as a

surrogate measure of protein abundance within a run was used to assess all shotgun

analysis of SCI CSF. Red blood cells (RBC) are unable to cross an intact BSCB. However,

constituent RBC proteins released by hemolysis are clear identifiers of blood

contamination in sample. This combined with previous studies (7, 8) led us to choose

hemoglobin percentage per sample as a metric for adequate CSF extraction. While initial

results showed promising results with strong associations to qualitative staining results

and protein IDs, later samples encountered lower spearman associations (-0.329) with

proteomic depth. Additionally, hemoglobin was not detected in some samples. This

observation could be related to the stability of hemoglobin. Hemoglobin is not normally

present in CSF and thus could be more susceptible to the degradative processes

326

compared to native CSF proteins (3). It is also possible that our added protease cocktail

did not confer protection or that degradation occurred prior to addition. These unknown

factors represent additional pre-analytical considerations to be evaluated in future

animals.

From our data, albumin content was found to associate more strongly with the

number of identified CSF proteins (Spearman r.s. -0.517). This may prove to be a better

measure of sample purity to be further evaluated. Additional blood specific, abundant

proteins such as catalase, peroxiredoxin, and carbonic anhydrase I (2, 7) may also be

evaluated in our assay.

Lumican and carboxypeptidase E are two interesting SCI biomarker candidates

While identification of completely CNS-specific proteins has proved challenging,

we were able to identify a group of proteins in our swine SCI traumatome that are CNS

enriched with the aid of the Human Protein Tissue Atlas. Inferences of organ specificity

in pigs was made based on high similarities between domestic pigs and humans in terms

of anatomy, physiology, and genetics (1, 18, 19). From this CNS-enriched, list, new

candidate protein lumican (F1SQ09) was identified only in severely injured animals along

with known biomarker GFAP. It should be noted that low-to-no GFAP levels were

measured in many of our mild-moderately injured SCI cohort. Co-identification of GFAP

and lumican by less sensitive untargeted MS experiments suggest both sensitivity and

selectivity as a candidate diagnostic. Lumican is a 40 kDa keratin sulfate proteoglycan

that regulates collagen fibril assembly that is commonly associated with scarring as a

result of injury. While this evidence suggests injury specificity, lumican, despite being

327

more highly expressed in the brain, is ubiquitously distributed throughout the

mesenchymal tissue (20). How lumican protein levels change after injury and whether

these measures can be distinguished from co-morbidities associated with CNS injury will

need to be assessed.

Carboxypeptidase E (CPE) represents the second of our top SCI (and potentially

TBI) new biomarker candidates. CPE is responsible for processing neuropeptides

involved in regulating CNS responses to stimuli and stresses. Previous reports in the

literature have demonstrated that CPE has a neuroprotective effect in the CNS and that

disruption of CPE function by changes in calcium dynamics leads to greater adverse

effects (21, 22). CPE has been shown to reduce ER stress and apoptosis in models of

diabetes through prohormone processing (23). While previously regarded as a

housekeeping enzyme, these findings demonstrate the potential for CPE to be

differentially expressed or regulated in response to stress.

After culling through our top candidate lists from SCI and astroglial injury (Chapter

5), we have arrived at 12 priority proteins (14 kDa phosphohistidine phosphatase,

calmodulin, CPE, ezrin, lumican, peptidyl-prolyl cis-trans isomerase A, superoxide

dismutase, transgelin, tropomyosin alpha-1, tropomyosin beta chain, tymosin beta-3,

tymosin beta-10) for further verification in our readily available SCI injury model. Skyline

was used to identify top peptide candidates from spectral libraries (Peptide Atlas) that are

unique to a pig protein background. An inclusion list for these top peptides in included in

Table 6.3.

328

6.4 CONCLUDING REMARKS

A major hurdle to the development of diagnostic tools and therapeutic agents for

neurotraumatic head and spine injury lies in our inadequate characterization of the

disease state. As discussed in previous chapters, traumatic brain injury (TBI)

classification through clinical point of care neurocognitive assessments are insensitive

and post-hoc multivariate classifications schemes lack standardizations between

research and clinical groups. So despite the large number of TBI and spinal cord injury

(SCI) biomarker studies, evidence of their clinical utility has thus far been underwhelming.

Perhaps the biggest hurdle to the overarching biomarker goal has been inadequately

defined injury models on a cellular level with special focus on protein levels changes that

are most relevant to disease pathology. Achievement of a more nuanced understanding

of proteomic changes related to injury sequelae requires development in the following

areas. First, an effort must be made to collect more complete clinical data that includes

multiple time-matched Glasgow coma scale (GCS) scores, ICPs for severe patients,

imaging data, long-term functional recovery, and whether any medications were

administered after injury. Clinical cerebrospinal fluid (CSF) samples are already difficult

to acquire as evidence by our limited cohort of TBI patient CSF samples and incomplete

patient information further hinders our ability to categorize patients for analysis.

Secondly, and most challengingly, the field must strive to establish a more

objective molecular fingerprint of TBI. Our research has focused on the identification of a

proteins that are discriminate of TBI and robustly detectable after injury. Our verification

studies have narrowed its focus to proteins with enriched astroglial contributions in the

central nervous system (CNS) with less emphasis on their relation to the molecular

329

pathologies of neurotrauma. While, our markers have demonstrated the ability to define

injury, we observe a wide range of concentration responses in proximal fluid from patient

to patient. This variance in our data arises from either injury response heterogeneity

(individuals develop different pathophysiological responses to similar injury forces) or

from the differing severities of trauma within our patient cohort. We can abate the

influences of these sources of variance by more quantitatively defining the extent of injury

through specific molecular markers of post-traumatic processes such as mitochondrial

dysfunction, cellular ionic imbalance, increased glutamate levels, cell death, and

membrane compromise. Our markers have started along this path by examining

extracellular concentrations of proteins as they relate to cell wounding. However, because

the neurotraumatic disease state arises not from singular pathway irregularities but a

multitude of dysfunction, additional markers are needed to adequately define individual

mechanisms associated with initial injury. Markers related to increased levels of oxidative

stress, cell death, decrease in cellular integrity, and immunity will help us to better

characterize both cell based and animal injury research models. And while many of these

markers may lack CNS specificity, they are none the less beneficial in this context to

establishing a continuum of injury for graded fluid biomarker assessment. So in our efforts

to identify clinically relevant biomarkers, we may need to first take a step back and

establish better metrics to define the molecular taxonomy of CNS injury as they relate to

both TBI and SCI.

The other hand of TBI management is therapeutic interventions. Drug

development in TBI is straddled with variables related to population heterogeneity.

Further limiting the success of research studies is the lack of mechanistic measures of

330

efficacy (and also safety) as described above. The importance of establishing molecular

signatures extends beyond its associated diagnostic potential but also increases the

value of preclinical research into new therapies by establishing more sensitive and

hopefully standardized criteria for outcome measures. On the clinical end, this may

ensure more accurate enrollment of and assessment of clinical trials by allowing clinicians

to isolate only patients of a specific severity or patients with specific pathway related

marker elevations. When considering the study of therapeutic effects of new treatment

modalities, time is another critical factor for neurotraumatic studies that requires further

investigation. Temporal proteomic profiling allows for characterization of the evolution of

biochemical processes that mechanically injured cells experience. Although this is a very

resource and analysis intensive process, it is the only way to accurately capture the

dynamic events of TBI. Protein level trends visualized in the form of rate of clearance or

accumulation may also possess more utility than concentration alone when considering

how individual responses to injury may shift the timing of disease related sequences. This

can be missed from a static picture of disease. We have started some work in this area

by examining changes in preferentially released proteins from stretched astrocytes with

relation to time and characterizing our astroglial injury biomarkers up to week after injury

in clinical TBI patient samples. Still, additional proteomic work examining this dynamic

window will tell a better story of injury as it relates to protein expression, release,

degradation, and clearance.

Finally, it is important to address animal injury models. While many models exist

in the literature (24), we should focus on those that produce injury as reproducibly as

possible. In our swine SCI experiments, we observed variable levels of injury from our

331

weight drop contusion model. While contusion models may best mimic the type of injury

patients may experience in everyday life, they may not be the most effective at

establishing reproducible severities of injury that are needed for research models.

Focusing on our pig SCI work, we may consider the use of a clip compression model (25,

26). This model provides a combination compression-contusion type injury using a

procedure that involves a laminectomy of the spine. Following surgery, a clip is closed at

a specific force around the spinal cord to produce an acute injury and then left to

compress the cord. This produces a combinatory compression, contusion type injury that

has been shown to produce graded responses. The simplicity of this design seems more

conducive to reproducibility compared to weight drop models where small deviations to

the impact site have large physiological consequences. Additionally, this technique can

be used to occlude blood flow to induce ischemic events for study. These type of

compression forces may also more closely mimic the type of compression forces

experienced in blast related TBI and SCI. While observations of injury response

heterogeneity are important to the field, consistent trauma models are more beneficial to

the goal of establishing defining molecular indicators of injury.

Great strides have been made in this field of neurotrauma study. However, the

limited progress toward a clinical useable marker in the last decade necessitates the need

to reevaluate our initial approaches. Cell and animal based injury models offer a

controlled and less confounded platform for comparative proteomic analysis but still

suffers from an incomplete molecular signature. A shift in focus to the characterization

distinct markers for metabolic, homeostatic, signaling, and regenerative abnormalities

332

after injury as they relate to cell death or functional recovery may address present issues

with reproducibility and variability of experimental models and results.

333

6.5 METHODS

Shotgun proteomics of swine cerebrospinal fluid

Volumes of swine CSF corresponding to 25 µg of protein measured by BCA protein

assay were reduced, alkylated, and digested with trypsin for bottom-up proteomic

analysis as previously described. 25 µg of digested protein was targeted for sample

preparation. Digested samples were desalted online using a C18 reversed phase trap

column before peptide separation on a C18 reversed phase column. Eluting peptides

were analyzed on a Q-Exactive Orbitrap mass spectrometer operating in Top10 data-

dependent acquisition as previously described. Duplicate to triplicate analyses of each

sample were searched using Proteome Discoverer 1.4 software configured with MASCOT

against a sus scrofa (pig) reference proteome (UP00008227) consisting of 26,101

proteins transcribed from a reference genome by the Swine Genome Sequencing

Consortium (11, 12). Proteins identified by only one unique peptide were not considered.

A subset of uncharacterized proteins were identified by homology to human gene

products using Ensembl Biomart (27, 28) web-based conversion tools.

Quantitative label-free proteomics

Spectral counting, a form of label-free quantitation, was used to calculate a

normalized spectral abundance factor (NSAF) (5, 6) value for each protein. NSAF values

were used to assess blood contamination/hemolysis through quantitation of hemoglobin

and albumin levels in CSF samples. NSAF was calculated for each protein k by the

following equation: NSAFk = [(SpC/L)k ]/ [Σ (SpC/L)] where SpC is the number of spectral

334

counts (or number of MS/MS spectra) for protein k divided by the protein’s length (L),

divided by the sum of all spectral counts (SpC/L) for all proteins in identified in the sample.

Blood depletion by cibacron blue and Top12 spin columns

50 µL of bloody CSF samples were depleted of α1-acid glycoprotein, α1-

antitrypsin, α2-macroglubulin, albumin, apolipoprotein A-I/II, fibrinogen, haptoglobin, IgA,

IgG, IgM, transferrin using Top 12 abundant protein depletion spin columns (PierceTM).

CSF was added directly to the resin slurry in the column and incubated with occasional

(every 5 min) gentle end-over-end mixing for 60 minutes at room temperature. Depleted

CSF was eluted off the column by centrifugation at 1000 x g for 2 minutes.

Alternatively, cibacron blue affinity depletion was assessed using a PierceTM

albumin depletion kit. 50 µL of CSF was diluted in 1:1 in binding/wash buffer to reduce

salt concentration for proper albumin binding. 400 µL of slurry was added to each spin

column and equilibrated per manufacturer’s instructions. 200 µL of diluted CSF was

applied was applied to the resin and allowed to incubate for 5 minutes at room

temperature. Samples was centrifuged at 12,000 x g for 1 minute and flow through

reapplied for maximal albumin binding. Unbound proteins were released with addition of

50 µL of 25mM Tris, 25mM NaCl; pH 7.5 with centrifugation at 12,000 x g for 1 minute.

This elution was repeated 3 more times with sequential flow-throughs combined and dried

by vacuum centrifugation prior to analysis.

335

6.6 FIGURES

Figure 6.1: High hemoglobin and albumin content observed in CSF corresponding

to low numbers of protein IDs

Hemolysis marker hemoglobin and albumin were measured in swine CSF samples for

animal 43-031 (p031) by spectral counting (left). Ponceau S staining of 30 µL CSF by

SDS-PAGE is shown on the right. Presence of blood protein is indicated by the ~60 kDa

blood derived albumin band designated by the red arrows. Increased blood protein signal

by Ponceau S corresponds to blood protein abundances measured by MS and accounts

for the low protein identifications (IDs) compared to samples without high blood protein

measurements by MS and Ponceau staining. Time points BL, Ac, pT, 2d, 7d correspond

to baseline, 15-30min post-SCI, 2-3h post-SCI, 2 days and 7 days post-SCI.

336

Figure 6.2: Comparison of baseline and acute spinal cord injury cerebrospinal fluid

samples identifies a trauma specific proteome

Baseline and acute (20 minutes – 2.7 hours) post spinal cord injury (SCI) cerebrospinal

(CSF) samples from animals 43-031, 42-115, 42-068, 43-082, and 42-127 analyzed by a

Top 10 data-dependent acquisition workflow. Baseline proteins and SCI proteins were

compared to identify 100 proteins specific to injury, termed the SCI traumatome.

337

6.7 TABLES

Condition Animal Time point % Hb (A+B) % Albumin %Albumin+%Hb #IDs

SCI 47-094 Baseline 2.7% 17.3% 20.0% 61 SCI 47-094 2.7 hr 13.2% 17.2% 30.5% 40 SCI 47-094 7 d 11.8% 11.1% 22.9% 33 SCI 42-068 Baseline 2.9% 10.0% 12.8% 155 SCI 42-068 20 min 4.4% 7.5% 11.9% 255 SCI 42-068 2.7 hr 8.5% 6.4% 14.9% 266 SCI 42-068 2 d 6.6% 10.6% 17.2% 68 SCI 42-068 7 d 7.5% 8.7% 16.3% 206 SCI 43-082 Baseline 19.8% 25.1% 44.9% 57 SCI 43-082 20 min 8.8% 26.6% 35.5% 183 SCI 43-082 2.7 hr 8.5% 20.2% 28.8% 184 SCI 43-082 2 d 31.3% 15.3% 46.6% 94 SCI 43-082 7 d 0.9% 18.0% 18.9% 123 SCI 42-127 Baseline 5.6% 8.5% 14.1% 193 SCI 42-127 20 min 3.2% 8.8% 12.0% 214 SCI 42-127 2.7 hr 12.7% 7.3% 20.0% 192 SCI 42-127 2 d 6.2% 9.3% 15.5% 200 SCI 42-127 7 d 7.7% 6.5% 14.2% 273 SCI 43-031 Baseline 14.6% 11.1% 25.7% 42 SCI 43-031 20 min 5.3% 8.7% 14.0% 265 SCI 43-031 2.7 hr 5.4% 9.9% 15.3% 260 SCI 43-031 2 d 18.1% 12.2% 30.3% 71 SCI 43-031 7 d 2.1% 8.9% 11.0% 234

SCI+ 43-090 20 min 4.9% 7.1% 12.0% 35 SCI+ 43-090 2.7 hr 18.3% 10.6% 28.9% 65 SCI+ 43-090 2 d 21.8% 13.0% 34.8% 35 SCI+ 42-101 2 d 24.9% 22.4% 47.3% 25 SCI+ 46-030 Baseline 1.5% 12.9% 14.4% 47 SCI+ 46-030 20 min 2.7% 17.3% 20.0% 28 SCI+ 46-030 2 d 11.2% 17.8% 29.0% 14 SCI+ 46-030 7 d 13.9% 11.0% 24.9% 26 SCI+ 42-132 Baseline 16.2% 15.5% 31.6% 60 SCI+ 42-132 20 min 8.4% 15.0% 23.4% 62 SCI+ 42-132 2 d 7.1% 16.6% 23.8% 46 SCI+ 42-132 7 d 9.7% 11.0% 20.6% 48 SCI+ 46-091 Baseline 28.1% 15.6% 43.7% 17 SCI+ 46-091 2.7 hr 12.5% 19.1% 31.5% 17 SCI+ 46-091 2 d 15.0% 15.7% 30.7% 24 SCI+ 46-091 7 d 12.2% 21.3% 33.5% 17 SCI+ 42-115 Baseline 15.5% 8.5% 24.0% 350 SCI+ 42-115 20 min 32.8% 5.0% 37.7% 229 SCI+ 42-115 2.7 hr 4.9% 12.8% 17.8% 282 SCI+ 42-115 2 d 10.0% 8.4% 18.4% 225 SCI+ 42-115 7 d 10.7% 6.3% 17.0% 238 SCI+ 42-131 2.7 hr 23.9% 29.9% 53.9% 10 SCI+ 42-131 2 d 42.2% 34.7% 76.9% 7

SHAM 47-018 20 min 57.3% 5.4% 62.7% 46 SHAM 47-018 2 d 4.4% 14.9% 19.3% 17 SHAM 47-018 7 d 36.2% 11.0% 47.2% 30 SHAM 47-050 Baseline 22.8% 14.1% 36.9% 71 SHAM 47-050 2 d 42.3% 25.9% 68.2% 50 SHAM 47-050 7 d 3.3% 14.4% 17.7% 30

338

SHAM 47-051 Baseline 8.5% 15.0% 23.5% 18 SHAM 47-051 20 min 5.2% 17.7% 22.9% 47 SHAM 47-051 7 d 27.7% 14.3% 42.0% 38 SHAM 47-052 Baseline 1.4% 12.6% 14.0% 70 SHAM 47-052 20 min 1.6% 16.1% 17.7% 55 SHAM 47-052 7 d 19.7% 17.4% 37.1% 68 SHAM 46-101 20 min 61.5% 8.0% 69.5% 25 SHAM 46-101 2.7 hr 2.8% 10.4% 13.2% 15 SHAM 46-101 2 d 47.4% 20.5% 67.9% 19 SHAM 46-101 7 d 43.3% 14.2% 57.5% 20 SHAM 42-121 2.7 hr 18.5% 14.7% 33.2% 53 SHAM 46-149 Baseline 13.3% 21.6% 34.9% 66 SHAM 46-149 20 min 20.5% 25.2% 45.7% 30 SHAM 46-149 2.7 hr 22.2% 30.7% 52.9% 34 SHAM 46-149 7 d 19.6% 21.8% 41.4% 38

Table 6.1: Hemoglobin and albumin content in spinal cord injury and sham injured

animals

Hemoglobin and albumin content measured by spectral counting are displayed in the

table above. The number of protein identifications (IDs) are displayed in the rightmost

column.

339

Table 6.2: Top spinal cord injury related biomarker proteins

The table above presents spinal cord injury (SCI) specific, CNS enriched candidates

identified in SCI and sham injured animal samples. Black boxes denote presence in

cerebrospinal fluid (CSF). Animal IDs were abbreviated for space. Specimens 46-030,

43-090, 42-127, 43-082, 47-094, 42-068, 42-132, 46-149, 47-050, 47-051, 47-052, 47-

018, and 46-101 are abbreviated as 30, 90, 127, 82, 94, 68, 132, 149, 50, 51, 52, 18, and

101 respectively. Lumican, glial fibrillary acidic protein, and carboxypeptidase E were also

identified as preferentially released from injured astrocytes.

340

Protein Peptide m/z

14 kDa phosphohistidine phosphatase

WAEYHADIYDK 705.8199

Calmodulin EAFSLFDK 478.7398

Calmodulin DGNGYISAAELR 633.3097

Carboxypeptidase E SGSAHEYSSSPDDAIFQSLAR 1113.0089

Carboxypeptidase E TYWEDNK 478.2114

Carboxypeptidase E SNAQGIDLNR 544.2782

Carboxypeptidase E FPPEETLK 480.7555

Carboxypeptidase E DGDYWR 406.1721

Ezrin LFFLQVK 447.7760

Ezrin SGYLSSER 449.7169

Ezrin IQVWHAEHR 588.3071

Ezrin IGFPWSEIR 552.7955

Ezrin APDFVFYAPR 591.8007

Ezrin SQEQLAAELAEYTAK 826.4123

Ezrin EDEVEEWQHR 678.7944

Ezrin QLLTLSSELSQAR 723.4016

Ezrin IGFPWSEIR 552.7955

Ezrin FVIKPIDK 480.2999

Ezrin APDFVFYAPR 591.8007

Ezrin ALQLEEER 494.2589

Ezrin IALLEEAR 457.7689

Lumican NNQIDHIDEK 613.2940

Lumican SLEDLQLTHNK 649.3410

Lumican EDAVSAAFK 469.2349

Lumican FNALQYLR 512.7824

Lumican ILGPLSYSK 489.2869

Peptidyl-prolyl cis-trans isomerase A FEDENFILK 577.7900

Peptidyl-prolyl cis-trans isomerase A VSFELFADK 528.2740

Peptidyl-prolyl cis-trans isomerase A FDDENFILK 570.7822

Peptidyl-prolyl cis-trans isomerase A TEWLDGK 424.7111

Peptidyl-prolyl cis-trans isomerase A HVVFGK 343.7028

Superoxide dismutase [Cu-Zn] GDGPVQGIINFEQK 751.3859

Superoxide dismutase [Cu-Zn] HVGDLGNVTADK 613.3122

Transgelin YDEELEER 541.7355

Transgelin LGFQVWLK 495.7922

Transgelin NGVILSK 365.7265

Transgelin LVNSLYPDGSKPVK 758.9221

Transgelin AAEDYGVIK 483.2506

Transgelin LGFQVWLK 495.7922

Transgelin LVNSLYPDGSKPVK 758.9221

Transgelin EFTESQLQEGK 648.3093

341

Tropomyosin alpha-1 chain LVIIESDLER 593.8375

Tropomyosin alpha-1 chain SIDDLEDELYAQK 769.8647

Tropomyosin alpha-1 chain ATDAEADVASLNR 666.8231

Tropomyosin alpha-1 chain IQLVEEELDR 622.3301

Tropomyosin alpha-1 chain LATALQK 372.7343

Tropomyosin alpha-1 chain LVIIESDLER 593.8375

Tropomyosin alpha-1 chain SIDDLEDELYAQK 769.8647

Tropomyosin beta chain QLEEEQQALQK 672.3437

Tropomyosin beta chain TIDDLEDEVYAQK 769.8647

Tymosin beta-4 PDMAEIEK 466.7233

Tymosin beta-10 PDMGEIASFDK 605.2764

Table 6.3: Selected PRM-MS peptides for top spinal cord and astroglial injury

derived biomarkers

Top candidates preferentially released from injured astrocytes and CNS enriched proteins

from our spinal cord injury (SCI) traumatome are displayed in the table above. Top peptide

observations from online spectral libraries were selected and filtered for PRM-MS

compatibility.

342

Accession Description

F1SDR7 14-3-3 protein beta/alpha

I3LLI8 14-3-3 protein epsilon

F1SA98 14-3-3 protein theta

F1RQQ8 Alpha-1,4 glucan phosphorylase

I3LLP2 Alpha-amylase 1

D0G0C7 Antioxidant protein 1 homolog

F2Z5E2 Antithrombin-III

Q29248 Apolipoprotein A-I

F1SCV9 Apolipoprotein B-100

K7GN63 Apolipoprotein D

A4D7T6 Brain-type fatty acid-binding protein

I3L5X9 Calsyntenin-1

A1XF98 Cartilage acidic protein 1

P15175 Cathelin

K7GLE2 CD44 antigen

B3F0B7 Cellular retinoic acid binding protein 1

I3VKE6 Ceruloplasmin

I3LD22 Cochlin

P10668 Cofilin-1

Q1HNM7 Collagen alpha-1

Q8HYS4 Collagen type 5 alpha 1

Q69DK9 Complement C1qC

Q69DL3 Complement C1r

F1SBS4 Complement C3

F1RQW2 Complement C4-A

A0SEH0 Complement component C6

F1SMJ6 Complement component C9

F1S133 Complement factor I

F1S3P6 Connective tissue growth factor

F1RJ76 C-reactive protein

Q29594 Creatine kinase B-type

Q9GJX2 Diazepam binding inhibitor

G9F6X9 Dihydropyrimidinase-like 2

Q0R678 DJ-1 protein

O97788 Fatty acid-binding protein, adipocyte

I3LQR9 Fibrinogen alpha chain

K7GSU8 Fibroblast growth factor receptor 2

I3L5W3 Ficolin-2

K7GS06 Four and a half LIM domains protein 1

I3LCN1 Gamma-enolase

P20305 Gelsolin

343

F1RR02 Glial fibrillary acidic protein

P80031 Glutathione S-transferase P

A0SNU7 Glyceraldehyde-3-phosphate dehydrogenase

F1RFQ7 GTP-binding nuclear protein Ran

F1S9Q3 Heat shock cognate 71 kDa protein

O02705 Heat shock protein HSP 90-alpha

F1RGX4 Hemoglobin subunit theta-1

K7GLP2 Hemoglobin subunit theta-1

L8B0R9 IgG heavy chain

L8B0U1 IgG heavy chain

L8B0V6 IgG heavy chain

I3LU56 Inducible T-cell co-stimulator ligand

B3TFF0 Insulin-like growth factor 2

P79263 Inter-alpha-trypsin inhibitor heavy chain H4

I3L697 Intercellular adhesion molecule 5

I3LLY8 Keratin, type II cytoskeletal 79

F1SD69 Legumain

P12068 Lysozyme C

F1RL77 Macrophage colony-stimulating factor 1 receptor

K7GPG1 Metalloproteinase inhibitor 1

I3LQ45 Metallothionein-3

F1RTN3 Moesin

F1SKJ1 Myosin-9

F1SRZ3 Neuroendocrine protein 7B2

P14287 Osteopontin

K7GKJ8 Phosphoglycerate kinase

D0G784 Phosphoglycerate kinase

K7GNI9 Phosphoglycerate kinase 1

F1S8Y5 Phosphoglycerate mutase 1

P01304 Pro-neuropeptide Y

F1RII4 Protein deglycase DJ-1

I3LBK0 Protein IGKV2-28

I3L5R6 Protein S100

I3L893 Rab GDP dissociation inhibitor alpha

I3LGK3 Ribonuclease pancreatic

P81405 Saposin-B-Val

A4H2R5 Secreted protein, acidic, cysteine-rich

F1ST01 Selenium-binding protein 1

I3LC80 Semaphorin-7A

I3LQF4 Semaphorin-7A

F1S9C0 Serum amyloid A protein

F1SFA1 Serum paraoxonase/arylesterase 1

344

K7GL06 Signal-regulatory protein beta-1

I3LG87 Somatostatin

Z4YP82 SPARC

F1RQB3 SPARC

I3LM94 SPARC-related modular calcium-binding protein 1

Q9TTB8 Tissue inhibitor of metalloproteinase-2

Q1PC32 Triosephosphate isomerase

F2Z5T5 Tubulin alpha-1A chain

F1SR80 Tubulin alpha-1A chain

Q2XVP4 Tubulin alpha-1B chain

F1SHC1 Tubulin alpha-1C chain

Q767L7 Tubulin beta chain

F2Z5B2 Tubulin beta-2B chain

F2Z571 Tubulin beta-4B chain

Q6SEG5 Ubiquitin carboxyl-terminal hydrolase isozyme L1

I3LCZ6 Uncharacterized protein

P02543 Vimentin

Table 6.4: Proteins in our Yucatan swine spinal cord injury cerebrospinal fluid

traumatome

345

Accession Description

F1RJF7 45 kDa calcium-binding protein

F1RF11 72 kDa type IV collagenase

F1RS36 78 kDa glucose-regulated protein

F1SHP1 A disintegrin and metalloproteinase with thrombospondin motifs 1

F1RM86 ADAM DEC1

P00571 Adenylate kinase isoenzyme 1

F1RUM1 Afamin

I3LGD9 Agrin

Q29014 Alpha-1 acid glycoprotein

Q19PY1 Alpha-1,4 glucan phosphorylase

F1SCC7 Alpha-1-antichymotrypsin

F1SCD0 Alpha-1-antichymotrypsin

F1SCC6 Alpha-1-antichymotrypsin

Q9GMA9 Alpha-1-antichymotrypsin 2

Q9GMA8 Alpha-1-antichymotrypsin 3

I3L818 Alpha-2-antiplasmin

K7GQ48 Alpha-2-macroglobulin

F1SLX2 Alpha-2-macroglobulin

K9J6H8 Alpha-2-macroglobulin

F1S573 Alpha-amylase

A0A0B8RW31 Amyloid beta -like protein 1

Q2XQA0 Amyloid beta A4 protein

F1S6E8 Amyloid-like protein 2

K7GPQ7 Angiotensinogen

Q7M364 Antithrombin III

Q19AZ5 Antithrombin protein

A0A0F6TNY5 APOB

A0A0C3SG01 Apolipoprotein A-I

F1S1A9 Apolipoprotein A-II

P27917 Apolipoprotein C-III

F1SQX9 Apolipoprotein D

I3LLD8

Basement membrane-specific heparan sulfate proteoglycan core protein

F1RUS9 Beta-1,4-glucuronyltransferase 1

I3LGN5 Beta-2-glycoprotein 1

Q07717 Beta-2-microglobulin

A5PF00 B-factor, properdin

F1SRL9 Brain acid soluble protein 1

F1RP38 Brevican core protein

F1S0J2 C4b-binding protein alpha chain

F1S0J3 C4b-binding protein beta chain

K7GT48 Cadherin-2

346

P28491 Calreticulin

F1RIG4 Calsyntenin-1

Q5S1S4 Carbonic anhydrase 3

F1RK01 Carboxypeptidase B2

A1IU54 Carboxypeptidase E

F1S8V7 Carboxypeptidase N catalytic chain

I3LF89 Carboxypeptidase N subunit 2

B6VNT8 Cardiac muscle alpha actin 1

I3LPI4 Cartilage acidic protein 1

A1XF97 Cartilage acidic protein 1

A1E295 Cathepsin B

P00795 Cathepsin D

Q5MJE5 Cathepsin D protein

Q28944 Cathepsin L1

F1RN76 CD5 antigen-like

O62680 CD59 glycoprotein

F1RMV8 Cell adhesion molecule 4

K7GQB8 Ceruloplasmin

B0LUW3 Chemerin

P04404 Chromogranin-A

Q29549 Clusterin

P16293 Coagulation factor IX

I3LGM9 Coagulation factor XII

O97507 Coagulation factor XII

Q9BDP9 Cocaine- and amphetamine-regulated transcript protein

F1RYI8 Collagen alpha-1 chain

F1S021 Collagen alpha-1 chain

I3LS72 Collagen alpha-1 chain

I3LBZ1 Collagen alpha-1 chain

F1SFA7 Collagen alpha-2 chain

I3LUR7 Collagen alpha-3 chain

F1STZ4 Complement C1q subcomponent subunit A

F1STZ3 Complement C1q subcomponent subunit C

Q69DL4 Complement C1qB

F1SLV6 Complement C1r subcomponent

Q69DK8 Complement C1s subcomponent

Q69DL2 Complement C2

I3LTB8 Complement C3

P01025 Complement C3

A5PF02 Complement component 2

A5A8W8 Complement component 4A

F1SMI8 Complement component C6

347

Q9TUQ3 Complement component C7

F1S788 Complement component C8 alpha chain

F1S790 Complement component C8 beta chain

A0SEG9 Complement component C9

F1RQW6 Complement factor B

P51779 Complement factor D

K7GPW1 Complement factor I

Q8MI72 Complement regulator factor H

K7GK71 Contactin-1

K7GL63 Contactin-1

F1SCF1 Corticosteroid-binding globulin

B5A562 C-reactive protein

Q5XLD3 Creatine kinase M-type

F1RRU7 C-type mannose receptor 2

F1RU34 Cystatin-B

F1S5H0 Cytokine-like protein 1

Q29243 Dystroglycan

F1S280 Ectonucleotide pyrophosphatase/phosphodiesterase family member 2

F8SIP2 EGF-containing fibulin-like extracellular matrix protein 1

F1RU22 EGF-containing fibulin-like extracellular matrix protein 2

I3LNM9 Endogenous retrovirus group V member 1 Env polyprotein

A0A0B8RSY9 Enolase 1

B5M6R3 Ephrin receptor A4

O97763 Epididymal secretory protein E1

I3LC64 Extracellular matrix protein 1

A0A0B8RTR5 Extracellular matrix protein 1

F1SFI6 Fetuin-B

Q9TV36 Fibrillin-1

Q28936 Fibrinogen A-alpha-chain

P14460 Fibrinogen alpha chain

F1RX36 Fibrinogen alpha chain

I3L651 Fibrinogen beta chain

F1RX35 Fibrinogen gamma chain

Q8MIP7 Fibroleukin

F1S6B5 Fibromodulin

F1SS24 Fibronectin

F1SPG5 Fibulin-2

F1SD87 Fibulin-5

Q29041 Ficolin-2

I3LQH7 Flavin reductase

U3GT97 Fstl1

Q6J267 Galectin

348

F1RVN0 Glutathione S-transferase P

P00355 Glyceraldehyde-3-phosphate dehydrogenase

A5GFT7 GNAS complex locus

F1S4I1 Golgi membrane protein 1

Q8SPS7 Haptoglobin

F1SA70 Heat shock-related 70 kDa protein 2

P01965 Hemoglobin subunit alpha

P02067 Hemoglobin subunit beta

P50828 Hemopexin

F1RKY2 Heparin cofactor 2

F1S8N1 Hepatocyte growth factor activator

F1SFI5 Histidine-rich glycoprotein

F1STC5 Ig kappa chain C region

P01846 Ig lambda chain C region

K7ZRK0 IgA heavy chian constant region

L8B0S2 IgG heavy chain

L8B0U3 IgG heavy chain

L8B0U8 IgG heavy chain

L8B0V2 IgG heavy chain

L8B0W0 IgG heavy chain

L8B0X5 IgG heavy chain

L8B180 IgG heavy chain

K7ZPU8 IgG heavy chian constant region

K7ZJP7 IgM heavy chain constant region

F1RUQ0 Immunoglobulin J chain

F1SIE1 Immunoglobulin superfamily containing leucine-rich repeat protein

F1RJW5 Immunoglobulin superfamily member 8

Q29545 Inhibitor of carbonic anhydrase

A6ZIC9 Insulin-like growth factor binding protein 6

C7EDN1 Insulin-like growth factor binding protein 7

P24853 Insulin-like growth factor-binding protein 2

E9KYT3 Insulin-like growth factor-binding protein 4

F1RVH7 Insulin-like growth factor-binding protein 7

Q8MJI5 Insulin-like-growth factor 2 preproprotein

F1RP09 Integrin beta-like protein 1

F1SH96 Inter-alpha-trypsin inhibitor heavy chain H1

Q29052 Inter-alpha-trypsin inhibitor heavy chain H1

O02668 Inter-alpha-trypsin inhibitor heavy chain H2

F1SH94 Inter-alpha-trypsin inhibitor heavy chain H3

F1RUM0 Inter-alpha-trypsin inhibitor heavy chain H5

P20305-2 Isoform 2 of Gelsolin

A5A758 Keratin 1

349

I3LDS3 Keratin, type I cytoskeletal 10

I3LNT6 Keratin, type II cytoskeletal 1b

F1SFI4 Kininogen-1

F1S663 Laminin subunit gamma-1

M3V7X9 Lectin, galactoside-binding, soluble, 3 binding protein

F1S7K2 Leucine-rich alpha-2-glycoprotein

I3LEZ3 Limbic system-associated membrane protein

F1SQ09 Lumican

F1SEY1 Lysosomal alpha-mannosidase

P12067 Lysozyme C-1

K9IVS4 Macrophage colony-stimulating factor 1 receptor

B6DSR1 Major prion protein

I3L9T6 Mimecan

A0MWC5 Monocyte differentiation antigen CD14

F1RPU6 Myelin protein zero-like protein 1

P02189 Myoglobin

K7GR86 Neural cell adhesion molecule 1

K7GMV4 Neural cell adhesion molecule 1

I3LUG8 Neural cell adhesion molecule 2

F1SFM2 Neural cell adhesion molecule L1-like protein

F1RQP6 Neurexin-2-beta

I3LKM2 Neuroblastoma suppressor of tumorigenicity 1

I3LRR9 Neurofascin

F1SAE8 Neuronal cell adhesion molecule

B9TRX1 Neuronal growth regulator 1

F1SNX9 Neuronal pentraxin receptor

F1RZA8 Neuronal pentraxin-1

F1S6D0 Neurotrimin

F1RRX1 Neutrophil gelatinase-associated lipocalin

F1RIP6 Nucleobindin-1

F1RJ55 Oligodendrocyte-myelin glycoprotein

I3LUM4 Out at first protein homolog

A4US67 Paraoxonase

F1RVS9 Peptidase inhibitor 16

F1RN59 Peptidyl-glycine alpha-amidating monooxygenase

P62936 Peptidyl-prolyl cis-trans isomerase A

P52552 Peroxiredoxin-2

Q9TSX9 Peroxiredoxin-6

F1RKG8 Phosphatidylethanolamine-binding protein 1

F1S814 Phosphoglucomutase-1

Q7SIB7 Phosphoglycerate kinase 1

B5KJG2 Phosphoglycerate mutase 2

350

F1RPC1 Phosphoinositide-3-kinase-interacting protein 1

Q0PM28 Pigment epithelium-derived factor

F1S715 Plasma alpha-L-fucosidase

F1RZN7 Plasma kallikrein

Q8WMN7 Plasma phospholipid transfer protein

F1SJW8 Plasma protease C1 inhibitor

P06867 Plasminogen

F1SB81 Plasminogen

I3LEE6 Procollagen C-endopeptidase enhancer 1

I3LEB3 ProSAAS

F1SMK6 Prosaposin receptor GPR37

E3VVJ2 Prosaposin variant 2

Q29095 Prostaglandin-H2 D-isomerase

P04366 Protein AMBP

F1SJF9 Protein FAM3C

F1STC2 Protein IGKV2D-40

A0A075B7I6 Protein IGLV3-27

A0A075B7H9 Protein IGLV8-61

A0A075B7H6 Protein IGLV8-61

A0A075B7J0 Protein IGLV8-61

A0A075B7I9 Protein IGLV8-61

A0A075B7I5 Protein IGLV8-61

F1SGY4 Protein kinase C-binding protein NELL2

F1S279 Protein NOV homolog

Q29094 Protein S

B3STX9 Prothrombin

A0A0B8S031 Pyruvate kinase

F1SLX4 Receptor-type tyrosine-protein phosphatase zeta

F1SC80 Retinol-binding protein 4

I3LDZ2 Ribonuclease 4

P00671 Ribonuclease pancreatic

Q7M329 Ribonuclease T2

I3LIJ2 Scrapie-responsive protein 1

B2DCZ8 Secreted frizzled-related protein 4

A6N9J9 Secreted phosphoprotein 1

Q9GLG4 Secretogranin-1

Q5FZP5 Secretogranin-2

F1RYP7 Secretogranin-3

F1RG83 Seizure 6-like protein

I3LBX3 Seizure 6-like protein 2

K9IVC4 Semaphorin-7A isoform 1 preproprotein

B3CL06 Serotransferrin

351

P09571 Serotransferrin

F1S9B8 Serum amyloid A-4 protein

A3RIE0 SPARCL-1

Q95ME5 Superoxide dismutase [Cu-Zn]

P04178 Superoxide dismutase [Cu-Zn]

Q007T6 Superoxide dismutase [Cu-Zn]

A8D737 T-cadherin

F1SRC8 Tetranectin

P82460 Thioredoxin

F1S981 Thrombospondin type-1 domain-containing protein 7B

F1RF28 Thrombospondin-4

K7GL43 Thy-1 membrane glycoprotein

B7TJ02 Thymosin beta 4 X-linked

C0JPM4 Tissue inhibitor of metalloproteases-2

O11780 Transforming growth factor-beta-induced protein ig-h3

P50390 Transthyretin

P00761 Trypsin

Q9TT86 Type I collagen alpha1

Q1T7A9 Type VI collagen alpha-1 chain

G9F6X7

Tyrosine 3-monooxygenase tryptophan 5-monooxygenase activation protein

I3LN42 Vitamin D-binding protein

I3LSF4 Vitamin K-dependent protein S

I3LQM5 vitamin K-dependent protein S

P48819 Vitronectin

O77773 Voltage-dependent calcium channel subunit alpha-2/delta-1

F1SF08 V-set and transmembrane domain-containing protein 2A

K9IWA3 V-type proton ATPase subunit S1

F1RTB4

WAP, Kazal, immunoglobulin, Kunitz and NTR domain-containing protein 2

F1RNP2 Zinc-alpha-2-glycoprotein

Table 6.5: Proteins common to baseline and acute spinal cord injury cerebrospinal

fluid

352

6.8 REFERENCES

1. F. Meurens, A. Summerfield, H. Nauwynck, L. Saif, V. Gerdts, The pig: a model

for human infectious diseases. Trends in microbiology 20, 50-57 (2012); published

online EpubJan (10.1016/j.tim.2011.11.002).

2. E. Aasebo, J. A. Opsahl, Y. Bjorlykke, K. M. Myhr, A. C. Kroksveen, F. S. Berven,

Effects of blood contamination and the rostro-caudal gradient on the human

cerebrospinal fluid proteome. PloS one 9, e90429

(2014)10.1371/journal.pone.0090429).

3. F. S. Berven, A. C. Kroksveen, M. Berle, T. Rajalahti, K. Flikka, R. Arneberg, K. M.

Myhr, C. Vedeler, O. M. Kvalheim, R. J. Ulvik, Pre-analytical influence on the low

molecular weight cerebrospinal fluid proteome. Proteomics. Clinical applications

1, 699-711 (2007); published online EpubJul (10.1002/prca.200700126).

4. A. H. Simonsen, J. M. Bahl, P. B. Danborg, V. Lindstrom, S. O. Larsen, A. Grubb,

N. H. Heegaard, G. Waldemar, Pre-analytical factors influencing the stability of

cerebrospinal fluid proteins. Journal of neuroscience methods 215, 234-240

(2013); published online EpubMay 15 (10.1016/j.jneumeth.2013.03.011).

5. B. Zybailov, M. K. Coleman, L. Florens, M. P. Washburn, Correlation of Relative

Abundance Ratios Derived from Peptide Ion Chromatograms and Spectrum

Counting for Quantitative Proteomic Analysis Using Stable Isotope Labeling.

Analytical Chemistry 77, 6218-6224 (2005).

6. B. Zybailov, A. L. Mosley, M. E. Sardiu, M. K. Coleman, L. Florens, M. P.

Washburn, Statistical Analysis of Membrane Proteome Expression Changes in

Saccharomyces cerevisiae. Journal of Proteome Research 5, 2339-2347 (2006).

353

7. J.-S. You, V. Gelfanova, M. D. Knierman, F. A. Witzmann, M. Wang, J. E. Hale,

The impact of blood contamination on the proteome of cerebrospinal fluid.

Proteomics 5, 290-296 (2005)10.1002/pmic.200400889).

8. K. Laks, T. Kirsipuu, T. Dmitrijeva, A. Salumets, P. Palumaa, Assessment of Blood

Contamination in Biological Fluids Using MALDI-TOF MS. The protein journal 35,

171-176 (2016); published online EpubJun (10.1007/s10930-016-9657-y).

9. P. G. Popovich, P. J. Horner, B. B. Mullin, B. T. Stokes, A Quantitative Spatial

Analysis of the Blood–Spinal Cord Barrier I. Permeability Changes after

Experimental Spinal Contusion Injury. Experimental Neurology 142, 258-275

(1996).

10. V. Bartanusz, D. Jezova, B. Alajajian, M. Digicaylioglu, The blood-spinal cord

barrier: morphology and clinical implications. Annals of neurology 70, 194-206

(2011); published online EpubAug (10.1002/ana.22421).

11. A. L. Archibald, L. Bolund, C. Churcher, M. Fredholm, M. A. Groenen, B. Harlizius,

K.-T. Lee, D. Milan, J. Rogers, M. F. Rothschild, Pig genome sequence-analysis

and publication strategy. BMC genomics 11, 1 (2010).

12. L. B. Schook, J. E. Beever, J. Rogers, S. Humphray, A. Archibald, P. Chardon, D.

Milan, G. Rohrer, K. Eversole, Swine Genome Sequencing Consortium (SGSC): a

strategic roadmap for sequencing the pig genome. Comparative and functional

genomics 6, 251-255 (2005)10.1002/cfg.479).

13. B. A. Plog, M. L. Dashnaw, E. Hitomi, W. Peng, Y. Liao, N. Lou, R. Deane, M.

Nedergaard, Biomarkers of traumatic injury are transported from brain to blood via

the glymphatic system. The Journal of neuroscience : the official journal of the

354

Society for Neuroscience 35, 518-526 (2015); published online EpubJan 14

(10.1523/JNEUROSCI.3742-14.2015).

14. M. Uhlén, E. Björling, C. Agaton, C. A.-K. Szigyarto, B. Amini, E. Andersen, A.-C.

Andersson, P. Angelidou, A. Asplund, C. Asplund, A human protein atlas for

normal and cancer tissues based on antibody proteomics. Molecular & Cellular

Proteomics 4, 1920-1932 (2005).

15. M. Uhlen, L. Fagerberg, B. M. Hallstrom, C. Lindskog, P. Oksvold, A. Mardinoglu,

A. Sivertsson, C. Kampf, E. Sjostedt, A. Asplund, I. Olsson, K. Edlund, E.

Lundberg, S. Navani, C. A. Szigyarto, J. Odeberg, D. Djureinovic, J. O. Takanen,

S. Hober, T. Alm, P. H. Edqvist, H. Berling, H. Tegel, J. Mulder, J. Rockberg, P.

Nilsson, J. M. Schwenk, M. Hamsten, K. von Feilitzen, M. Forsberg, L. Persson, F.

Johansson, M. Zwahlen, G. von Heijne, J. Nielsen, F. Ponten, Proteomics. Tissue-

based map of the human proteome. Science 347, 1260419 (2015); published

online EpubJan 23 (10.1126/science.1260419).

16. L. J. Noble, J. R. Wrathall, Blood-Spinal Cord Barrier Disruption Proximal to a

Spinal Cord Transection in the Rat: Time Course and Pathways Associated with

Protein Leakage. Experimental Neurology 99, 567-578 (1988).

17. J. T. Maikos, D. I. Shreiber, Immediate damage to the blood-spinal cord barrier

due to mechanical trauma. Journal of neurotrauma 24, 492-507 (2007); published

online EpubMar (10.1089/neu.2006.0149).

18. T. P. Sullivan, W. H. Eaglstein, S. C. Davis, P. Mertz, The pig as a model for human

wound healing. Wound Repair and Regeneration 9, 66-76 (2001).

355

19. Z. Ibrahim, J. Busch, M. Awwad, R. Wagner, K. Wells, D. K. Cooper, Selected

physiologic compatibilities and incompatibilities between human and porcine organ

systems. Xenotransplantation 13, 488-499 (2006); published online EpubNov

(10.1111/j.1399-3089.2006.00346.x).

20. S. Chakravarti, Functions of lumican and fibromodulin: lessons from knockout

mice. Glycoconjugate journal 19, 287-293 (2002).

21. A. Zhou, M. Minami, X. Zhu, S. Bae, J. Minthorne, J. Lan, Z. G. Xiong, R. P. Simon,

Altered biosynthesis of neuropeptide processing enzyme carboxypeptidase E after

brain ischemia: molecular mechanism and implication. Journal of cerebral blood

flow and metabolism : official journal of the International Society of Cerebral Blood

Flow and Metabolism 24, 612-622 (2004); published online EpubJun

(10.1097/01.WCB.0000118959.03453.17).

22. A. Chen, L. J. Xiong, Y. Tong, M. Mao, The neuroprotective roles of BDNF in

hypoxic ischemic brain injury. Biomedical reports 1, 167-176 (2013); published

online EpubMar (10.3892/br.2012.48).

23. K. D. Jeffrey, E. U. Alejandro, D. S. Luciani, T. B. Kalynyak, X. Hu, H. Li, Y. Lin, R.

R. Townsend, K. S. Polonsky, J. D. Johnson, Carboxypeptidase E mediates

palmitate-induced beta-cell ER stress and apoptosis. Proceedings of the National

Academy of Sciences of the United States of America 105, 8452-8457 (2008);

published online EpubJun 17 (10.1073/pnas.0711232105).

24. T. Cheriyan, D. J. Ryan, J. H. Weinreb, J. Cheriyan, J. C. Paul, V. Lafage, T.

Kirsch, T. J. Errico, Spinal cord injury models: a review. Spinal cord 52, 588-595

(2014); published online EpubAug (10.1038/sc.2014.91).

356

25. M. Joshi, M. G. Fehlings, Development and characterization of a novel, graded

model of clip compressive spinal cord injury in the mouse: Part 1. Clip design,

behavioral outcomes, and histopathology. Journal of neurotrauma 19, 175-190

(2002).

26. J. A. Zivin, U. DeGirolami, Spinal cord infarction: a highly reproducible stroke

model. Stroke 11, 200-202 (1980).

27. B. L. Aken, S. Ayling, D. Barrell, L. Clarke, V. Curwen, S. Fairley, J. Fernandez

Banet, K. Billis, C. Garcia Giron, T. Hourlier, K. Howe, A. Kahari, F. Kokocinski, F.

J. Martin, D. N. Murphy, R. Nag, M. Ruffier, M. Schuster, Y. A. Tang, J. H. Vogel,

S. White, A. Zadissa, P. Flicek, S. M. Searle, The Ensembl gene annotation

system. Database : the journal of biological databases and curation 2016,

(2016)10.1093/database/baw093).

28. R. J. Kinsella, A. Kahari, S. Haider, J. Zamora, G. Proctor, G. Spudich, J. Almeida-

King, D. Staines, P. Derwent, A. Kerhornou, P. Kersey, P. Flicek, Ensembl

BioMarts: a hub for data retrieval across taxonomic space. Database : the journal

of biological databases and curation 2011, bar030

(2011)10.1093/database/bar030).