Proteomics is not only a biomarker discovery tool

Daniel Martins-de-Souza

Max Planck Institute for Psychiatry, Munich, Germany

Received: May 14, 2009

Accepted: June 25, 2009

The relatively young science of proteomics has been extensively used to identify biomarkers.

However, a detailed and careful interpretation of proteomics data can also provide a clear

picture of integrated biochemical systems, which can lead to a better comprehension of

pathological processes. For example, the proteome analysis of human brain tissue from

patients with schizophrenia, bipolar disorder, or multiple sclerosis compared with healthy

controls has identified differentially expressed proteins that may not only be potential

biomarkers but may also provide information that may increase the comprehension of these

neurological disorders. Thus, proteomics is not only a biomarker discovery tool but can also

identify potential players of relevance for diseases.

Keywords:

Brain / Biomarker / Neuroscience

Proteomics is a young science whose name arose from the

term ‘‘proteome’’ [1, 2]. Proteomics was originally used to

identify a complete set of expressed proteins. Nowadays, it is

a much more complex science and covers the characteriza-

tion of these proteins sets, for example protein–protein

interactions, protein structure, protein function and activity,

and protein translocations. Despite the wide array of

features covered by proteomics, its most widespread use is

to identify differentially expressed proteins of a cell, tissue or

organism under distinct situations (e.g. pathological states).

Although it is a relatively young science, proteomics has

been widely used in research: a PubMed search (performed

on June 25th 2009) using the keyword ‘‘proteomics’’ iden-

tified 20 917 articles. We can therefore define proteomics as

a young science that has revealed a world of answers.

However there is a universe of questions

Proteomics has been extensively used as a biomarker

discovery tool in the investigation of pathological states.

Several proteomics articles have prematurely claimed to

have identified disease biomarkers; however, to establish a

differentially expressed protein as a biomarker, validation

experiments in a larger and diverse set of samples are

necessary. Furthermore, diseases probably do not have a

single biomarker [3], but most likely a set of biomarkers.

And these sets of biomarkers might overlap in different

disorders, making the task of identifying biomarkers more

complex.

Despite these difficulties, the data generated by proteo-

mics biomarker research contributes considerably to

expanding its world of answers: the identification of the set

of differentially expressed proteins facilitates deeper inves-

tigation into possible direct or indirect protein interactions,

which might lead to an explanation for and comprehension

of pathological states independent of the identification of

biomarkers. Furthermore, this data also provides a founda-

tion for further research into the characterization of specific

disease-related proteins.

Neurodegenerative and mental disorders are problems of

vast importance for public health, from the patient’s welfare

to health treatment investments. Thus, it is important to

clarify the molecular pathogenesis of disorders such as

schizophrenia (SCZ), bipolar disorder (BPD), and multiple

sclerosis. SCZ is a chronic, debilitating, psychotic mental

disorder that affects about 1% of the world population and is

characterized by a range of positive and negative symptoms

[4]. BPD, or manic depression, is a mood disorder char-

acterized by episodes of mania (extremely elevated mood,

energy, unusual thought patterns, and sometimes psycho-

sis) and depression that can affect 1–5% of the general

Proteomics is not only a biomarker

discovery tool

Abbreviations: BPD, bipolar disorder; SCZ, schizophrenia

Correspondence: Dr. Daniel Martins-de-Souza, Max Planck

Institute for Psychiatry, Kraepelinstr. 2, 80804 Munich, Germany

E-mail: [email protected]

Fax: 149-89-30622-200

& 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.clinical.proteomics-journal.com

1136 Proteomics Clin. Appl. 2009, 3, 1136–1139DOI 10.1002/prca.200900096

population [5]. Multiple sclerosis is an autoimmune condi-

tion that is more common in females than in males. In

multiple sclerosis, the immune system attacks the central

nervous system, which results in nervous system demyeli-

nation that negatively affects communication between the

brain and spinal cord [6, 7]. These three disorders are most

likely multifactorial; the causes are not completely under-

stood but are known to include strong genetic components,

neurodevelopmental aspects, and environmental factors.

The lack of a molecular diagnosis for SCZ and BPD and

of specific biomarkers for the different phases of multiple

sclerosis has stimulated researchers to study these

proteomes and look for protein biomarkers in body fluids

such as plasma and cerebrospinal fluid and in human brain

tissue. Although most of the studies cited here were

conducted using the traditional combination of 2-DE – to

separate the proteins – and MS – to identify the proteins

[8–18] –, some studies were conducted using shotgun MS

approaches [19, 20], and some have used both methods [21].

The combination 2-DE/MS can easily separate and detect

hundreds of proteins through a gel-based method, using a

low-cost platform. These proteins can be extracted directly

from the gel matrixes and digested for identification with

MS. However, there is a bias towards highly expressed

proteins, while low expressed proteins and hydrophobic

proteins – both of which can be important players in disease

mechanisms – are not well detected [22]. These 2-DE/MS

drawbacks can be partially solved by using shotgun MS

methods [23] on which instrumentation and specific algo-

rithms to data interpretation can be limiting factors.

The proteome studies of SCZ, BPD, and multiple

sclerosis using 2-DE/MS or shotgun MS have identified

many differentially expressed proteins that not only serve as

potential biomarkers but could also be useful in the eluci-

dation of the disease mechanisms. The great advantage of

proteome studies is that within the overall pathobiology

picture they are able to identify the role players involved in

disorders, shedding light on pathogenic mechanisms.

Most of the SCZ, BPD, and multiple sclerosis proteome

reports have found alterations in proteins involved in brain

energy metabolism. The brain has a high energy demand,

because of all the functions it performs, and 95% of its energy

is produced in mitochondria. Brain tissue therefore has a

high mitochondrial content. The mitochondrial energy

regulatory processes are quite sensitive because they have an

interlaced and complex web of interaction, which might

logically make us assume that alterations in the concentration

of mitochondrial enzymes would result in an altered energy

production, affecting the whole mitochondrial metabolism.

SCZ proteome studies performed in brain regions such

as the dorsolateral prefrontal cortex, anterior temporal lobe,

anterior cingulate cortex, Wernicke’s area, and corpus

callosum as well as subproteome studies such as performed

in layer 2 of the insular cortex mainly have shown the

differential expression of enzymes in energy pathways such

as glycolysis and the Krebs Cycle [8–15, 19, 20]. BPD studies

have shown in the dorsolateral prefrontal cortex and anterior

cingulate cortex brain regions altered concentrations of a

number of ATP subunits (which would compromise the

oxidative phosphorylation process) and changes in anti-

oxidant enzymes and proteins involved in ubiquination

processes [10, 14], whilst proteome reports of multiple

sclerosis have shown in human cerebral endothelial cells

and multiple sclerosis cerebral lesions disturbed oxidative

phosphorylation [16, 17] and differential expression of

antioxidant enzymes in cerebrospinal fluid [18]. Although

previous researches have already investigated the involve-

ment of energy metabolism in SCZ, BPD, and multiple

sclerosis, questions such as whether these alterations cause

or are consequences of the diseases have not been answered.

Comparative proteomics data could be helpful in answering

these questions, since they show the differential expression

of the players in the energy processes.

Many of the differentially expressed enzymes found in

proteome analyses of SCZ brain tissue are related to glycolysis.

A Kyoto Encyclopedia of Genes and Genomes (KEGG)

analysis [24] (http://www.genome.jp/kegg) showed that a

number of enzymes of the glycolysis pathway – such as

hexokinase, triose phosphate isomerase, glyceraldehyde phos-

phate dehydrogenase, phosphoglyceromutase, enolase, and

pyruvate kinase – are differentially expressed (Fig. 1). These

alterations will certainly compromise glucose metabolism, the

key to energy generation in the cell. These findings can lead to

several explanations for the causes and consequences of SCZ.

Moreover, if the differential expression of these glycolysis

enzymes are validated, glycolysis metabolites might also

represent therapeutic and pharmacologic targets. Other path-

ways have been described through proteome analysis of SCZ

brain tissue, such as the dysfunction of the immune system,

oligodendrocyte metabolism, and calcium homeostasis.

Only some of the energy metabolism data and of the other

pathways compromised in SCZ are mentioned above. However,

a closer examination of the validated differentially expressed

proteins identified in proteome studies can reveal several other

pathways and processes. Thus, proteome data are a huge source

of hypotheses and explanations for disease processes.

It is important to note that, for several disorders, proteome

studies have identified proteins that have previously never

been related to these disorders. For example, our group has

shown in SCZ brain tissue the differential expression of

phosphatidylethanolamine-binding protein 1, aggregan core

protein, and hyaluronan and proteoglycan link protein 2,

Proteomics Clin. Appl. 2009, 3, 1136–1139 1137


none of which has ever been related to SCZ before [20]; we

also identified proteins that have no defined biological func-

tion, such as CGI-38 brain-specific protein and hypothetical

protein MGC29649 [19]. These proteins might play an

important role in SCZ pathogenesis that is as yet unknown.

Further analyses are needed to validate this hypothesis.

To summarize, I believe that data generated by proteo-

mics should be examined more closely since proteomics is

not only a biomarker discovery tool. The lists of differentially

expressed proteins contain much more information than

just potential biomarkers, especially for disorders such as

SCZ, BPD, and multiple sclerosis, which are not completely

understood. Moreover, these differentially expressed

proteins might be targets for therapeutic studies and may

potentially lead to the discovery of new drugs.

The author specially thanks Prof. Chris W. Turck for all hissupport of my research at the Max Planck Institute of Psychiatry,in Munich, Germany, and the Max Planck Gesellschaft for thesupport of my projects. I thank Dr. Giuseppina Maccarrone for

Figure 1. Glucose metabolism path-

way. The black boxes show the

differentially expressed enzymes in

the glycolysis pathway in SCZ human

prefrontal cortex revealed by proteo-

mics studies (Illustration by KEGG –

http://www.genome.jp/kegg).

1138 D. Martins-de-Souza Proteomics Clin. Appl. 2009, 3, 1136–1139


the MS support, Stefan Reckow and Philipp Gormanns for thebioinformatics support as well as Maria Lebar, Dr. Jeeva Vara-darajulu, Christiane Rewerts, Michaela Filiou, ChristianWebhofer, Larysa Teplytska, and Yaoyang Zhang for the scien-tific discussions. I also thank Jacquie Klesing for Englishlanguage editing of the manuscript.

The author has declared no conflict of interest.

References

[1] Wilkins, M. R., Sanchez, J. C., Gooley, A. A., Appel, R. D.

et al., Progress with proteome projects: why all proteins

expressed by a genome should be identified and how to do

it. Biotechnol. Genet. Eng. Rev. 1996, 13, 19–50.

[2] Wilkins, M. R., Pasquali, C., Appel, R. D., Ou, K. et al., From

proteins to proteomes: large scale protein identification by

two-dimensional electrophoresis and amino acid analysis.

Biotechnology (N Y), 1996, 14, 61–65.

[3] Mischak, H., Apweiler, R., Banks, R. E., Conaway, M. et al.,

Clinical proteomics: a need to define the field and to begin

to set adequate standards. Proteomics Clin. Appl. 2007, 1,

148–156.

[4] Freedman, R., Schizophrenia. N. Engl. J. Med. 2003, 349,

1738–1749.

[5] Martinowich, K., Schloesser, R. J., Manji, H. K., Bipolar

disorder: from genes to behavior pathways. J. Clin. Invest.

2009, 119, 726–736.

[6] Compston, A., Coles, A., Multiple sclerosis. Lancet 2002,

359, 1221–1231.

[7] Compston, A., Coles, A., Multiple sclerosis. Lancet 2008,

372, 1502–1517.

[8] Prabakaran, S., Swatton, J. E., Ryan, M. M., Huffaker, S. J.,

et al., Mitochondrial dysfunction in schizophrenia: evidence

for compromised brain metabolism and oxidative stress.

Mol. Psychiatry 2004, 9, 684–697.

[9] Clark, D., Dedova, I., Cordwell, S., Matsumoto, I., A

proteome analysis of the anterior cingulate cortex gray

matter in schizophrenia. Mol. Psychiatry 2006, 11, 459–470.

[10] Beasley, C. L., Pennington, K., Behan, A., Wait, R. et al.,

Proteomic analysis of the anterior cingulate cortex in the

major psychiatric disorders: evidence for disease-asso-

ciated changes. Proteomics. 2006, 6, 3414–3425.

[11] Sivagnanasundaram, S., Crossett, B., Dedova, I., Cordwell, S.,

Matsumoto, I., Abnormal pathways in the genu of the corpus

callosum in schizophrenia pathogenesis: a proteome study.

Proteomics Clin. Appl. 2007, 1, 1291–1305.

[12] Martins-de-Souza, D., Gattaz, W. F., Schmitt, A., Maccar-

rone, G. et al., Proteomic analysis of dorsolateral pre-

frontal cortex indicates the involvement of cytoskeleton,

oligodendrocyte, energy metabolism and new potential

markers in schizophrenia. J. Psychiatr. Res. 2009, 43,

978–986.

[13] Martins-de-Souza, D., Gattaz, W. F., Schmitt, A., Novello,

J. C. et al., Proteome analysis of schizophrenia patients

Wernicke’s area reveals an energy metabolism dysregula-

tion. BMC Psychiatry 2009, 9, 17.

[14] Pennington, K., Beasley, C. L., Dicker, P., Fagan, A. et al.,

Prominent synaptic and metabolic abnormalities revealed

by proteomic analysis of the dorsolateral prefrontal cortex

in schizophrenia and bipolar disorder. Mol. Psychiatry 2008,

13, 1102–1117.

[15] Pennington, K., Dicker, P., Dunn, M. J., Cotter, D. R.,

Proteomic analysis reveals protein changes within layer 2 of

the insular cortex in schizophrenia. Proteomics 2008, 8,

5097–5107.

[16] Alexander, J. S., Minagar, A., Harper, M., Robinson-Jack-

son, S. et al., Proteomic analysis of human cerebral endo-

thelial cells activated by multiple sclerosis serum and

IFNbeta-1b. J. Mol. Neurosci. 2007, 32, 169–178.

[17] Satoh, J., Tabunoki, H., Yamamura, T., Molecular network

of the comprehensive multiple sclerosis brain-lesion

proteome. Mult. Scler. 2009, 15, 531–541.

[18] Hammack, B. N., Fung, K. Y., Hunsucker, S. W.,

Duncan, M. W. et al., Proteomic analysis of multiple

sclerosis cerebrospinal fluid. Mult. Scler. 2004, 10,

245–260.

[19] Martins-de-Souza, D., Gattaz, W. F., Schmitt, A., Rewerts, C.

et al., Prefrontal cortex shotgun proteome analysis reveals

altered calcium homeostasis and immune system imbal-

ance in schizophrenia. Eur. Arch. Psychiatr. Clin. Neurosci.

2009, 259, 151–163.

[20] Martins-de-Souza, D., Gattaz, W. F., Schmitt, A., Rewerts, C.

et al., Alterations in oligodendrocyte proteins, calcium

homeostasis and new potential markers in schizophrenia

anterior temporal lobe are revealed by shotgun proteome

analysis. J. Neural Transm. 2009, 116, 275–289.

[21] Behan, A. T., Byrne, C., Dunn, M. J., Cagney, G., Cotter,

D. R., Proteomic analysis of membrane microdomain-

associated proteins in the dorsolateral prefrontal cortex in

schizophrenia and bipolar disorder reveals alterations in

LAMP, STXBP1 and BASP1 protein expression. Mol.

Psychiatry 2009, 14, 601–613.

[22] Gygi, S. P., Corthals, G. L., Zhang, Y., Rochon, Y. et al.,

Evaluation of two-dimensional gel electrophoresis-based

proteome analysis technology. Proc. Natl. Acad. Sci. USA.

2000, 97, 9390–9395.

[23] Link, A. J., Eng, J., Schieltz, D. M., Carmack, E. et al., Direct

analysis of protein complexes using mass spectrometry.

Nat. Biotechnol. 1999, 17, 676–682.

[24] Kanehisa, M., Goto, S., KEGG: kyoto encyclopedia of genes

and genomes. Nucleic Acids Res. 2000, 28, 27–30.


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Proteomics is not only a biomarker discovery tool