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“LIPOMICS”
David C. White, MD, PhD, [email protected], 865-974-8001Current team: Peacock. A. D., C. Lytle, Y-J. Chang, Y-D. Gan, J. Cantu, K. Salone, L. Kline, J. Bownas, S. Pfiffner, R Thomas
Collaborators in the last 48 Months:A my, Penny S., Univ. Nevada (Las Vegas); Appelgate, Bruce, UTK; Balkwill, David L., Florida State Univ.; Bienkowski, Paul R., UTK; Bjornstad, B.N., DOE PNNL; Boone, David R., Univ. Portland (Oregon); Brockman, Fred J., DOE PNNL; Coleman, Max L., Univ. Reading (UK); Colwell, Fredrick S., DOE INNEL; Curtis, Peter S., Univ. Michigan; Davis, Wayne T., UTK; DeFlaun, Mary F., Envirogen; Dever, Molly, UTK; Eagenhouse, Robert, USGS, Reston; Fayer, Ronald, USDA (Beltsville); Flemming, Hans-Kurt, Univ. of Druisberg (Germany); Fredrickson, James K., DOE , PNNL; Geesey, Gill G., Montana State Univ.; Ghiorse, William C., Cornell, Univ.; Griffin, Tim, Golder Associates; Griffiths, Robert. P., Univ. Oregon; Gsell, T.C., DOE PNNL; Guezennec, Jon. G.,IFMER (Brest, France); Haldeman, Dana S., Univ. Nevada (Las Vegas); Heitzer, Armin, ABB Consulting (Zurich Switzerland); Hersman, Larry E., DOE Los Alamos; Holben, William E., Univ., Montana; Kaneshiro, Edna S., Univ. Cincinnati; Kieft, Thomas L., New Mexico State Univ.; Kjelleberg, Stephan, Univ. New South Wales (Australia); Krumholtz, Lee R., Univ. Oklahoma; Larsson, Lennart, Univ. Lund (Sweden); Lehman, Robert M., DOE INEEL; Li, S-M., DOE PNNL; Little, Brenda, Naval Research Lab. Stennis; Lovell, Charles R., Univ. South Carolina; McDonald, E.V., DOE PNNL; McKinley, James P., DOE PNNL; Murphy, Ellen M., DOE PNNL; Nichols, Peter. D., CSIRO (Hobart, Taz); Nierzwicki-Bauer, S.A., Rensselaer Polytec. Inst.; Nold, Steven C., Montana State Univ.; Norby, Robert J., DOE ORNL; O'Neill, Eugena G., DOE ORNL; O'Neill, Robert V., DOE ORNL: Onstott, T.C., Princeton Univ.; Palumbo, Anthony V., DOE ORNL; Pfiffner, Susan M., DOE ORNL; Phelps, Tommy J., DOE ORNL; Pregitzer, K.S., Michigan Univ.; Randlett, D.L., DOE INEEL; Rawson, Sally, A., DOE INNEL; Ringelberg, David B., US Army Corps of Engineers Watershed Experiment Station; Rogers, Rob, DOE, INEEL; Russell, Bert, Golder Associates; Sayler, Gary S., UTK; Schmitt, Jurgen, University of Druisberg (Germany); Stevens, Todd O., DOE PNNL; Suflita, Joseph M., Univ., Oklahoma; Sutton, Sue D., Miami Univ. (Ohio); Venosa, Albert. D., USEPA (Cincinnati); Whitaker Kylen W., Microbial Insights, Inc.; Wobber, Frank J. DOE (Germantown); Wolfram, James W. , DOE INEEL; Zac, Donald R., Univ., Michigan; Zogg, G. P., Univ. Michigan. Associated post doctoral, and student advisees of White in last 5 yearsAlmeida, J.S., Univ. Lisbon, Portugal; Angell, Peter, Canadian Atomic Energy Commission; Burkhalter, Robert S., UTK; Chen, George, Vapor Technologies, Inc., Co.; Kehrmeyer, Stacy, DOE LLNL; Lou, Jung. S., US Patent Office; Macnaughton, Sarah, J., UTK; Nivens, David E., UTK; Palmer, Robert J., UTK; Phiefer, Charles B., Celmar MD; Pinkart, Holly C., Univ. Central Washington; Rice, James F., UTK; Smith, Carol A., UTK; Sonesson, Anders, Univ. Lund Sweden; Stephen, John R., UTK; Tunlid, Anders, Univ. Lund Sweden; Webb, Oren. F., DOE ORNL; Zinn, Manfred, Harvard.
“LIPOMICS”
Inception: 1972 U. Kentucky Med Center Biochemistry of membrane bound electron transport system including lipids ( GC) Florida State Univ. Marine & Estuarine Lab microbial ecology PLFA of detrital biofilms Note shifts in membrane lipids with growth conditions in monocultures
Fungus Heaven & Hell otherwise ignored as “too difficult and chemical”.
Myron Sasser at Delaware carefully grew plant and then clinical isolates with rigidly standardized conditions, extracted, did acid hydrolysis, methylated and identified on capillary GC. HP developed pattern recognition algorithm for 4 major peaks and he developed a large library (10,000 strains) now founded MIDI (0M for HP) international company.
Myron says DC got famous Myron got Rich
1991 Andrew B. White founded Microbial Insights, Inc to do PLFA & DNA in environmental matrices commercially 1999 sold
Microbial Insights, Inc.
“LIPOMICS”
Inception: MIDI 1. Requires isolate grown under standard conditions 2. Economical Not need MS to identify analytes can do analyses $30/sample
and make money. 3. Now Automated Quick ~identify in 30 min4. Specific tells E. coli from Salmonella if isolate grown under standard conditions 5. Unknown organisms have been a disaster
miss 99.9% of the cells in a soil or sediment often the dominants6. Excellent way to quickly tell if new isolates are identical PLFA 1. Much more specific Extract lipid the fractionate on silicic acid column into
neutral lipids, Phospholipids, and residue lipids requiring hydrolysis before extraction LPS, spores etc.
2. Mild alkaline methanolysis vs acid hydrolysis Transesterify only Esters (need mild acid to find Plasmalogen vinyl ethers)
3. Identify analytes with MS vs adding pig fat to the sample4. Requires days, expensive equipment, compulsive analysts $300/sample
“LIPOMICS”
Development:~ Effectiveness methods, resources & tools limited
Establish interpretation in environmental samples with 8000 species/g
1. Add a microbe and recover it 13C labeled or with distinctive lipids [Sphingomonas]
2. Manipulate and detect expected responses Anaerobic Aerobic Aerobic Anaerobic Sulfate [SRB] & DSR genes Aerobic Anaerobic Nitrate nifS, nifX, noxE genes Aerobic Anaerobic + Acetate & Fe(III), U (III) Geobacter 3OH 21, rDNA Aerobic Anaerobic + Hydrogen + molybdate Methanogens (ether lipids)
3. Manipulate with toxins, pH, antibiotics Fungus heaven vs Fungus Hell, hydrocarbons, pesticides, or PCB expected response
4. Add specific predators protozoa, amphipods, bacteriophage specific disappearance
5. Correspondence of rDNA and signature lipids derived from isolates
“LIPOMICS”
Current Status: [a[pplication limited by, analytical skill, equipmentCost, time & arcane literature for intrepretation
Most comprehensive, rapid, quantitative, measure of in-situ microbial communities Combines phenotypic and genotypic responses “Cathedral from a brick”
1. Viable & Total Microbial Biomass, Community Composition, Physiological Status2. Rhizosphere & defining forest biodiversity 3. Waste treatment effectiveness monitoring4. Validating source of deep subsurface microbiota 5. Defining food sources & effectiveness of utilization (with 13C “) 6. Monitoring bioremediation effectiveness & defensible treatment endpoints 7, Multi-species toxicological assessment8. Ultrasensitive detection of biomarkers forward contamination of spacecraft 9. Quantitatively defining soil quality and effects of tilth10. Monitoring carbon sequestration in soils11. Rapid detection of biocontamination & antigenic immune potentiators in indoor air12. Rapid detection and monitoring of contamination in drinking water biofilms13. Detecting pathogens in microbial consortia & food14. Defining food source effectiveness [Triglyceride/sterol or PLFA]15. Defining disturbance artifacts in soils and sediments [PHA/PLFA] 16. Lipid extraction purifies DNA for PCR
Phospholipid Fatty Acid [PLFA] Biomarker Analysis = Single most quantitative, comprehensive insight into in-situ microbial community
Why not Universally utilized?
1. Requires 8 hr extraction with ultrapure solvents [emulsions]. 2. Ultra clean glassware [incinerated 450oC]. 3. Fractionation of Polar Lipids4. Derivatization [transesterification] 5. GC/MS analysis ~ picomole detection ~ 104 cells LOD 6. Arcane Interpretation [Scattered Literature] 7. 3-4 Days and ~ $250
Signature Lipid Biomarker Analysis
“LIPOMICS”
Future: Automated sequential extraction tandem MS detection of Lipid Biomarkers DNA / mRNA with arrays coupled data bases & GPS map
20 min? Analysis of microbial contamination & insight into infectivity Ft. Johnson Seminar
Clinical & Veterinary Monitor Airports Buses, Ports to data base
CBW Defense Food Safety, Indoor Air vs adult Asthsma & Sick Building Syndrome
Monitor exhaled breath (capture in silicone bottle) GC/TOFMS Monitor bioremediation, use in-situ microbial community define end points
~ multispecies, multi trophic levels Monitor effects of GMO plants Drugs, hormones, endocrine disrupters, antibiotics are most often hydrophobic as
they interact with the membranes of cells. collect biofilms (act as solid phase extractor) analyze with HPLC/ES/MS/MS
Urban watershed monitoring & Toilet to Tap
“LIPOMICS”
Tools:
Thou shall know structure & concentration of each analyte
Progress (equipment) for speed, specificity, selectivity and sensitivity)Extraction1. Extraction high pressure/temperature faster more complete2. Supercritical CO2 pressure becomes gas directly into MS inlet3. Sequential saves time & effortChromatography1. GC high pressure , 0.1 mm controlled flow, > resolution & faster 2. SFC not much used3. HPLC smaller diameter, Chiral, 4. CZE high resolution, requires charge, presently difficult Detection (lipids generally lack chromophores) 1. NMR insensitive, expensive, 2. Laser fluorescence not as specific but incredibly sensitive3. Light scattering cheep & nonspecific 4. Mass Spectrometry
IonizationElectron impact 70 eV known structure catalogue but inefficient
Electrospray the dream but needs charged analyte ~ 100%
“LIPOMICS”
Tools:
Thou shall know structure & concentration of each analyte
Mass Spectrometry Ionization EI Electron impact 70 eV known structure catalogue but inefficient
ES Electrospray the dream but needs charged analyte ~100% APCI less sensitive not require charge Photometric APCI potential mild “booster” + light
SIMS to map Phospholipids have that charge Detection Quadrupole slow and good to 3000 m/z MS/MS sensitive chemical noise MRM ITMS (MS)n sensitive . Exploring
TOFMS Speed increases scans sensitivity & resolution, m/z 200K Q/TOF Sequence on the fly but 650K
FTMS mass resolution to 0.0000001 , large capacity in trap, expensive, difficult require superconducting magnet & often not working
Data Analysis Jonas Almeida comprehensiveness of ANN ~ PLFA, Neutral Lipids, rDNA functional genes, activity measures Biolog (samples “weeds”)
ESI (cone voltage) Q-1 CAD Q-3
ESI/MS/MS
PE-Sciex API 365 HPLC/ESI/MS/MS Functional Sept 29, 2000
Expanded Lipid Analysis Greatly Increase Specificity ~Electrospray Ionization ( Cone voltage between skimmer and inlet ) In-Source Collision-induced dissociation (CID)
Tandem Mass Spectrometry Scan Q-1 CID* Q-3 DifferenceProduct ion Fix Vary VaryPrecursor ion Vary Fix VaryNeutral loss Vary Vary FixNeutral gain Vary Vary Fix
MRM Fix Fix Fix(Multiple Reaction Monitoring)
*Collision-induced dissociation (CID) is a reaction region between quadrupoles
Lipid Biomarker Analysis
Tandem Mass Spectrometers
CEBJPL
Ion trap MSn (Tandem in Time)Smaller, Least Expensive, >Sensitive (full scan)
Quadrupole/TOF> Mass Range, > Resolution
MS/CAD/MS (Tandem in Space)1. True Parent Ion Scan to Product Ion Scan2. True Neutral Loss Scan 3. Generate Neutral Gain Scan4. More Quantitative 5. > Sensitivity for MRM6. > Dynamic Range
LIPIDS
Lipids1. Defined by process as Cellular components
extracted from by organic solvents
2. Diverse Chemical Structure characterized by hydrophobic properties
3. Relatively small molecules compared to Biopolymers [molecular weights < 2000]
4. Not with properties of the Biopolymer macromolecules
Polysaccharides, Nucleic Acids, Proteins
LIPIDS
PROBLEM IN Assessing the microbes : 1. The largest and most critical biomass on Earth is essentially invisible
Earth did well (Geochemical Cycles maintaining disequilibrium) for 3 billion years without multicellular eukaryotes
2. Methods Limited Classical plate counts miss 99.9%, NPN need to grow and be isolated from matrices into single cells, VBNC common
3. Morphology not define function Direct counts need .> 104 to detect matricides often fluorescent
4. Live as multispecies biofilms with interactions and communication
5. Disturbance artifact ~live like coiled spring waiting for nutrient
LIPIDS
A Solution look for biomarkers : 1. Not persist with death of cells
ATP. DNA, RNA, Enzymes, Uronic acid polymers, Cell walls, neutral lipids (petroleum) , lignin, KDO, Muramic Acid all found outside of cells and persist
POLAR LIPIDS ~ Metabolically Labile not found in petroleum
2. Universally present in the same ~ amount /cell ~pmol in 2-6 x 104 cells size of E. coli
3. Structurally diverse enough to provide insight into composition Bacteria make ~ 1000 Fatty acids, eukaryotes (except plant seeds)
~ 100; Diverse structures-- rings, branches, amides, ethers, . . .
4. Present at measurable quantities & be Readily determined
HPLC/ES/MS/MS, ~ 10-16 moles/L GC/MS, ~ 10-9 moles/L GC/TOFMS ? 10--12 moles/L ??
LIPIDS
Intact lipid membrane a necessary but not sufficient criteria of life [ON Earth]
1. Cannot have a functional cell without an intact lipid membrane Phospholipid Diglyceride evidence of cell lysis
deeper in the subsurface the > the diglyceride to phospholipid ratio
2. Intact membrane ~ Lipids form micelles in water [not living]
Micelles do not show orderly reproduction & evolution Micelles do not have porins and show transport
Micelles do not maintain disequilibrium > Donnan Equilibrium Usually not all the same size & do not move
Why is the lipid composition so exact in each species of bacteria when
enzymes requiring lipids for function can be relatively nonspecific?
LIPID Biomarker Analysis
1. Intact Membranes essential for Earth-based life
2. Membranes contain Phospholipids
3. Phospholipids have a rapid turnover from endogenous phospholipases .
4. Sufficiently complex to provide biomarkers for viable biomass, community composition, nutritional/physiological status
5. Analysis with extraction provides concentration & purification
6. Structure identifiable by Electrospray Ionization Mass Spectrometry at attomoles/uL (near single bacterial cell)7. Surface localization, high concentration ideal for organic
SIMS mapping localization
VIABLE NON-VIABLE
O O || ||
H2COC H2COC
| |C O CH C O CH
| |
H2 C O P O CH2CN+ H3
||
|
O
O-
||O
H2 C O H
||O
Polar lipid, ~ PLFA
Neutral lipid, ~DGFA
phospholipase
cell death
Membrane Liability (turnover)
Bacterial Phospholipid ester linked fatty acids
Monoenoic
cis trans
cyclopropylOH, = position
-CH2
CH=CH
CH2- -CH2
CH=CHCH2-
Isomerconformation
CH3(CH2)XCH=CHCH2CH(CH2)YCOOH 0H CH2
-CH2CHCHCH2-
CEBMicrobial Insights, Inc.
JPL
Bacterial Phospholipid ester-linked fatty acids
iso
anteiso
RCH2CHCH3
CH3 RCH2CHCH2CH3|CH3
mid-chain
RCH2CHCH2CH2R’|CH3
Methyl Branching
CEBMicrobial Insights, Inc. JPL
Biofilm Community Composition
Detect viable microbes & Cell-fragment biomarkers : Legionella pneumophila, Francisella tularensis,
Coxellia burnetii, Dienococcus, PLFA oocysts of Cryptosporidium parvum, Fungal spores PLFAActinomycetes Me-br PLFA Mycobacteria Mycocerosic acids, (species and drug resistance)Sphingomonas paucimobilis Sphingolipids Pseudomonas Ornithine lipidsEnterics LPS fragmentsClostridia PlasmalogensBacterial spores Dipicolinic acid Arthropod Frass PLFA, SterolsHuman desquamata PLFA, Sterols
Fungi PLFA, Sterols Algae Sterols, PLFA, Pigments
In-situ Microbial Community Assessment
What do you want to know? Characterization of the microbial community: 1. Viable and Total biomass ( < 0.1% culturable &
VBNC ) 2. Community Composition
General + proportions of clades Specific organisms (? Pathogens)
Functional groups [Signature Lipids]-Specific Strains [PCR-DGGE]
3. Physiological/Nutritional Status ~ Evidence forAlmeida Manifesto Cathedral from a brick
4 Metabolic Activities (Genes +Enzymes + Action)Consequences of Activities = Gene frequency & Phenotypic Responses vs the Disturbance Artifact
5.Community Interactions & Communications
Microniche Properties from Lipids
1. Aerobic microniche/high redox potential.~ high respiratory benzoquinone/PLFA ratio, high proportions of Actinomycetes, and low levels of i15:0/a15:0 (< 0.1) characteristic of Gram-positive Micrococci type bacteria, Sphinganine from Sphingomonas 2. Anaerobic microniches ~high plasmalogen/PLFA ratios (plasmalogens are characteristic Clostridia), the isoprenoid ether lipids of the methanogenic Archae.
3. Microeukaryote predation ~ high proportions of phospholipid polyenoic fatty acids in phosphatidylcholine (PC) and cardiolipin (CL). Decrease Viable biomass (total PLFA) 4. Cell lysis ~ high diglyceride/PLFA ratio.
Signature Lipid Biomarker Analysis
Microniche Properties from Lipids
5. Microniches with carbon & terminal electron acceptors with limiting N or Trace growth factors ~ high ( > 0.2) poly β-hydroxyalkonate (PHA)/PLFA ratios
6. Microniches with suboptimal growth conditions (low water activity, nutrients or trace components) ~ high ( > 1) cyclopropane to monoenoic fatty acid ratios in the PG and PE, as well as greater ratios of cardiolipin (CL) to PG ratios.
7. Inadequate bioavailable phosphate ~ high lipid ornithine levels
8. Low pH ~ high lysyl esters of phosphatidyl glycerol (PG) in Gram-positive Micrococci.
9. Toxic exposure ~ high Trans/Cis monoenoic PLFA
Signature Lipid Biomarker Analysis
Capillary GC PLFA 20m x 0.1mm i.d. x 0.1m film thickness, 0.3 ml/min flow rateQuadrupole MS 41-450 m/z scan, 1.84 scan/sec ~av. Peak = 6 sec /sec 11 scans. TOFMS 6 sec = 280,000 scans resolution & sensitivity ~ 50 times greater
6.00 8.00 10.00 12.00 14.00 16.00 18.00 20.00 22.00 24.00 26.00 28.00 30.000
200000
400000
600000
800000
1000000
1200000
1400000
1600000
1800000
2000000
2200000
2400000
2600000
2800000
3000000
3200000
3400000
3600000
3800000
4000000
4200000
4400000
4600000
4800000
Time-->
Abundance
TIC: SERDP2.D
EI off during solvent elution
Details of GC/MS tracing showing deconvolution of PLFA
LIPIDS –DATA ANAYSIS
Problem: PLFA Analysis is like comparing spectraFew replications but huge data load/sample
1. Classic Statistics likes replications of simple data ~ group data in rational clusters
2. Do replications then test the variance between them perform ANOVA Assumes variables are independent and form a normal distribution
3. Do a Tukeys post hoc test for more stringent test of significant difference to control better for chance in large replications
4. Assume Linear Relationships and display graphically with:
Hierarchical Cluster AnalysisPrincipal components Analysis PCA
Essentially a huge correlation matrix
Scatterplot
Uranium vs Mid-Chain Branched Saturated PLFA
Uranium
Mid
-Ch
ain
Bra
nc
he
d P
LF
A
608
610615
617
624626
826
828
853
857
4
8
12
16
20
24
28
0 500 1000 1500 2000 2500 3000 3500 4000
October
-1
2
-1
1
August
-1
1
-1
PCA 2 Analysis of Forest Community Soil total PLFAP
CA
1
PCA Analysis Sugar Maple-Basswood Black Oak- White Oak Sugar Maple- Red Oak
LIPIDS-DATA ANALYSIS
Problem: PLFA Analysis is like comparing spectraFew replications but huge data load/sample
5. Assume non-Linear Relationship
ANN Use data for training to generate a Artificial neural network using nodes for interactions. If relatively few nodes are required easier to
interpret
Predictability is the test and with “training” gets better and better but must test for ‘OVERTRAINING” ie memorization
Perform a sensitivity analysis ~ components contribute most to predictability
Now map on a surface to explore spatial and temporal interactions
ANN Analysis of CR impacted Soil Microbial Communities
1. Cannelton Tannery Superfund Site, 75 Acres on the Saint Marie River near Sault St. Marie, Upper Peninsula, MI
2. Contaminated with Cr+3 and other heavy metals between1900-1958 by the Northwestern Leather Co.
3. Cr+3 background ~10-50 mg/Kg to 200,000 mg/Kg.
4. Contained between ~107-109/g dry wt. viable biomass by PLFA; no correlation with [Cr] (P>0.05)
5. PLFA biomass correlated (P<001) with TOM &TOC but not with viable counts (P=0.5)
-CEB
0 400ft
N
C4
B5 B7 B9
C8 C10
D9 D11
C16
D17E16 E18
G14
H15 H17 H19 H21I20 I22
J19J21
K22K20
L21M20
J23
N21O22
P23
Q24
N23
O24
P25
U26T27
K28
TANNERY
G18
Q26
RemovedBeach
Grass Pond
Woodland
Swampy/Cattails
Wooded Wetland
Grassy Wetland
Running Water
A
Cannelton Tannery Superfund Site
100,001-300,000
75,001-100,000
50,001-75,000
25,001-50,000
10,001-25,000
7,001-10,000
5,001-7,000
3,001-5,000
2,001-3,000
1,001-2,000
501-1,000
101-500
51-100
1-50
ND
Cr+3 Concentrations Site map
Total Biomass (~108 cells)
Chromium
Bio
mas
s (n
mol
e PL
FA g
-1)
-200
20406080
100120140
1 2 3 4 5
Biomarkers for Sulfate/metal reducing bacteria
NABIR
Chromium
Sulf
ate/
met
al r
educ
ers
(mol
e%)
1
2
3
4
5
6
1 2 3 4 5
“Stress” biomarkers
NABIR
Chromium
18:1
w7t
/18:
1w7c
-0.02
0.02
0.06
0.10
0.14
0.18
1 2 3 4 5
Met
abol
i c
stre
ss
Principal components analysis~ associated with wetlands, eukaryote biomarkers and bacterial stress markers
Factor Loadings, Factor 1 vs. Factor 2
Rotation: Unrotated
Extraction: Principal components
Factor 1
Fac
tor
2
WETLAND
CI140
C140
CI151A
CI151B
CI151W11
CI151CCI150
CA150
C151
C150
CBR150A
CBR150B
CI161
CBR150C
C162
CI160
C161W11C
C161W7C
C161W7T
C161W5C
C160
CBR160
CI171W8
C10ME160
C11ME160
C12ME160
CI170
CA170
CCY170A
CCY170B
C170
CBR170A
CBR170B
C182A
C182W6
C183W3
C181W9C
C181W7C
C181W7T
C181W5CC180
CBR181
C10ME180
C12ME180
CCY190
C204W6
C205W3
C203W6
C201W9C
C200C210
C220
C230
C240
PH
%TOM
BIOMASS
CR__MG_K VIABLE_C
%TOC
P
K
CA
MG
-1.0
-0.6
-0.2
0.2
0.6
1.0
-1.0 -0.6 -0.2 0.2 0.6 1.0 1.4
Eukaryote PLFA
Summary: Biomass
• Biomass (bacterial abundance): ~ 6 x 107 to 109 •• cells gram-1. No correlation between [Cr] and
total biomass (P>0.05)
• Viable cell counts were between 1-3 orders of magnitude lower than bacterial abundance from PLFA
• Biomass (PLFA) correlated positively with both TOM and TOC (P<0.001)
Summary: community composition/physiological status
• Significant shifts in PLFA profiles with [Cr]
• [10me16:0] (sulfate/metal reducers) peaked at 103 mg kg-1 Cr
• No clear pattern was determined between bacterial sequence identity (from PCR/DGGE) and increasing [Cr]
• Bacterial Stress markers (18:17t/18:17c) increased at the higher [Cr]
• PCA - association between [Cr] and wetlands, biomarkers for eukaryotes and “stress”. Needs a different analysis.
regression
training
testing
Pred
ictiv
e er
ror
cross-validated error
Stop !
classificationvector
Inputprofile hidden layer
Schematic architecture of a three layer
feedforward network used to associate
microbial community typing profiles
(MCT) with classification vectors.
Symbols correspond to neuronal nodes
Generalization is assured
by cross-validation
ANN are universal predictors
Capable oflearning from examples
1 10 100 1000 10000 100000 1E+006
Observed Cr3+ concentration (mg Kg-1)
1
10
100
1000
10000
100000
1E+006
Predicted Cr3+ concentration (mg Kg-1)
training setvalidation setregressionidentity
slope = 1.09
R2 = 0.98
Good Predictive Accuracy at > 100 mg Cr+3 /Kg
C181W
9C
CI1
70
C181W
7C
C10M
E180
CA
170
CI1
51W
11C
I151A
C161W
5C
CI1
50
C201W
9C
C161W
11C
C10M
E160
CB
R181
CA
150
CI1
60
%TO
MC
160
CC
Y170B
C170
C150
C203W
6C
AC
210
PH
C12M
E160
C161W
7T
C183W
3%
TO
CC
I171W
8C
BR
150B
CB
R170B
C181W
7T
C182A
CB
R170A
BIO
MA
SS
C151 P
CI1
51B
WE
TLA
ND
CB
R150A
CC
Y190
MG
CI1
40
C180
C161W
7C
C230
CB
R160 K
C11
ME
160
C205W
3C
12M
E180
C200
CC
Y170A
CI1
51C
C182W
6C
140
C220
CI1
61
C162
C240
CB
R150C
C204W
6V
IAB
LE
_C
C181W
5C
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
Rel
ativ
e se
nsiti
vity
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
110%
Cum
mulative sensitivity
Sensitivity analysis ranks the inputs by importance in predicting [Cr+3]PLFA have a significant larger predictive value than environment parameters (marked with arrows).
PLFA profiles are a can be used as a general purpose biosensorgeneral purpose biosensor
Biological systems are so complex that prediction of function from the composition of system components is inversely proportional to the distance to the function itself
OR
It’s hard to see the forest for the trees!
One cannot easily predict if a brick (DNA) will be used to build a cathedral or a prison but the structure of the windows will tell.
BUT Cellular membranes are in contact with the environment and the intracellular space. So
Cellular membranes are in contact with the environment and the int PLFA is an ideal sensor of the environmental composition and the biological response, e.g. degree of contamination by a pollutant and its bioremediation.
Cellular membranes are in contact with the environment and the intracellular space.
ANN Analysis of CR impacted Soil Microbial Communities
SENSITIVITY (from ANN) 20% of the variables accounted for 50% of the predictive of Cr+3
concentrationOf these 20 %:
18:1w9c (6.6%) Eukaryote (Fungal) correlated with 18:26 (P<0.02)
10Me 16:0 (2.5%) correlated with i17:0 (4.8), 16:1 11c (2.9), i15:0 (3.1) (P<0.001). Thus all are most likely indicative of SRBs or MRBs.
18:17c (4.6%) = Gram negative bacteria
10Me 18:0 (4.3%) (Actinomycetes)
-CEBNABIR
ANN Analysis of CR impacted Soil Microbial Communities
CONCLUSIONS:1. Non-Linear ANN >> predictor than Linear PCA (principal Components Analysis)
2. No Direct Correlation (P>0.05) Cr+3 with Biomass (PLFA), Positive correlation between biomass (PLFA) and TOC,TOM
3. ANN: Sensitivity to Cr+3 Correlates with Microeukaryotes (Fungi)18:19c, and SRB/Metal reducers (i15:0, i 17:0, 16:1w11, and 10Me 16:0)
4. SRB & Metal reducers peaked 10,000 mg/Kg Cr+3
5. PLFA of stress > trans/cis monoenoic, > aliphatic saturated with > Cr+3
-CEBNABIR
“LIPOMICS”
Future: Automated sequential extraction tandem MS detection of Lipid Biomarkers DNA / mRNA with arrays coupled data bases & GPS map
20 min? Analysis of microbial contamination & insight into infectivity Ft. Johnson Seminar
Clinical & Veterinary Monitor Airports Buses, Ports to data base
CBW Defense Food Safety, Indoor Air vs adult Asthsma & Sick Building Syndrome
Monitor exhaled breath (capture in silicone bottle) GC/TOFMS Monitor bioremediation, use in-situ microbial community define end points
~ multispecies, multi trophic levels Monitor effects of GMO plants Drugs, hormones, endocrine disrupters, antibiotics are most often hydrophobic as
they interact with the membranes of cells. collect biofilms (act as solid phase extractor) analyze with HPLC/ES/MS/MS
Urban watershed monitoring & Toilet to Tap
Sequential Extraction & HPLC/ESI/MS analysis ~ 1-2 hrs
Concentration/Recovery
Extraction SFE/ESE
SeparationHPLC/in-line
Fractionation
DetectionHPLC/ESI/MS(CAD)MS
or HPLC/ESI/IT(MS)n
CEBMicrobial Insights, Inc.
Lipid Biomarker Analysis
Sequential High Pressure/Temperature Extraction (~ 1 Hour)
Supercritical CO2 + Methanol enhancer Neutral Lipids, (Sterols, Diglycerides, Ubiquinones)
Lyses Cells Facilitates DNA Recovery (for off-line analysis
2. Polar solvent Extraction Phospholipids CID detect negative ions
Plasmalogens
Archeal Ethers 3). In-situ Derivatize & Extract Supercritical CO2 + Methanol
enhancer 2,6 Dipicolinic acid Bacterial Spores
Amide-Linked Hydroxy Fatty acids [Gram-negative LPS]
Three Fractions for HPLC/ESI/MS/MS Analysis
Microbial Insights, Inc.
CEB
Supercritical Fluid Extraction (SFECO2 + Methanol Enhancer)for Neutral Lipids
Liquid Gas1. vs. liquids greater solute diffusivityless solute viscositydensity varies with pressure
2. Fractionate with sequential addition of modifiers3. Effective in situ derivatization4. Less toxic than solvents 5. Fast 20 min vs. 8 hrs with solvents6. Potential for automation7. Compatible with ES/MS/MS & IT(MS)n
8. Generate micellar emulsions + water + surfactants9. SFCO2 becomes a gas < 1070 psi10. Low Temperature Possible ~ 390C
*Macnaughton, S. J., T. L. Jenkins, M. H. Wimpee, M. R. Cormier, and D. C. White. 1997. Rapid extraction of lipid biomarkers from pure culture and environmental samples using pressurized accelerated hot solvent extraction. J. Microbial Methods 31: 19-27(1997)
Feasibility of “Flash” Extraction
ASE vs B&D solvent extraction*
Bacteria = B&D, no distortionFungal Spores = 2 x B&D Bacterial Spores = 3 x B&D Eukaryotic = 3 x polyenoic FA
[2 cycles 80oC, 1200 psi, 20 min] vs B&D = 8 -14 Hours
CEBMicrobial Insights, Inc.
Problem: Rapid Detection/Identification of Microbes
Propose a Sequential High Pressure/Temperature Extractor Delivers Three Analytes to HPLC/ESI/MS/MS
CO2
Pump
N2 blowdownAutosampler
HPLC/ES/MS/MS
Fraction Collector
Spe-ed SFE-4 NL
PL
LPS
MeOHMeOHCHCl3PO4
-
Expand the Lipid Biomarker Analysis
1. Increase speed and recovery of extraction “Flash”
2. Include new lipids responsive to physiological status HPLC (not need derivatization)
Respiratory quinone ~ redox & terminal electron acceptorDiglyceride ~ cell lysisArchea ~ methanogensLipid ornithine ~ bioavailable phosphateLysyl-phosphatidyl glycerol ~ low pHPoly beta-hydroxy alkanoate ~ unbalanced growth
3. Increased Sensitivity and Specificity ESI/MS/MS
Signature Lipid Biomarker Analysis
Lyophilized Soil Fractions, Pipe Biofilm
SFECO2 1. Neutral Lipids
UQ isoprenologues
Derivatize –N-methyl pyridyl Diglycerides Sterols Ergostrerol Cholesterol
ESE Chloroform.methanol
2. Polar Lipids
Transesterify
PLFA
CG/MS
Intact Lipids
HPLC/ES/MS/MS
Phospholipids PG, PE, PC, Cl, & sn1 sn2 FAAmino Acid PGOrnithine lipidArchea ether lipidsPlamalogens
PHAThansesterify & Derivatize N-methyl pyridyl
3. In-situ acidolysis in SFECO2
2,6 DPA (Spores)
LPS-Lipid A OH FA
+Q1: 119 MCA scans from 0928002.wiff Max. 8.7e8 cps.
600 610 620 630 640 650 660 670 680 690 700 710 720 730 740 750 760 770 780 790 800m/z, amu
0.0
5.0e7
1.0e8
1.5e8
2.0e8
2.5e8
3.0e8
3.5e8
4.0e8
4.5e8
5.0e8
5.5e8
6.0e8
6.5e8
7.0e8
7.5e8
8.0e8
8.5e8
In
te
ns
ity
, c
ps
693.7
694.6
679.7
635.5680.6
653.8 696.7 725.7617.5 707.6637.5
O O
CH2CH3
H
CH3
H
H O
HO
CH3
CH3
CH2OHH
OO
HO
H
CH3
H3C
H3CHC CH3
OH3C
CO
ONa
C36H61NaO11Exact Mass: 692.41
Mol. Wt.: 692.85
+Product (693.8): 119 MCA scans from 0929001.wiff Max. 4.9e7 cps.
400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700m/z, amu
0.0
5.0e6
1.0e7
1.5e7
2.0e7
2.5e7
3.0e7
3.5e7
4.0e7
4.5e7
4.9e7
In
te
ns
ity
, c
ps
693.2
675.4
461.4
479.3501.2 695.2581.5443.6 599.3 657.7
Monensin Q1 scan
693.7
675.4
461.3
Q6
Q7Q10
O
O
H3OC
H3OC
CH3
]H
n
197 m/z
Respiratory Benzoquinone (UQ)
Gram-negative Bacteria with Oxygen as terminal acceptor LOQ = 580 femtomole/ul, LOD = 200 femtomole/ul ~ 104 E. coli
CH2O C
O
CH2(CH2)13CH3
CH2OH
CHO C
O
CH2(CH2)13CH3N
CH3
F+
CH3
SO3
N
CH3
O
N
CH3
CH2O C
O
CH2(CH2)13CH3
CHO C
O
CH2(CH2)13CH3
OCH
CH2O C
O
CH2(CH2)13CH3
CH2
CHO C
O
CH2(CH2)13CH3
Pyridinium Derivative of 1, 2 Dipalmitin
C41H73NO5+
Exact Mass: 659.55
Mol. Wt.: 660.02
C6H7NOExact Mass: 109.05
Mol. Wt.: 109.13
neutral loss
C35H67O4+
Exact Mass: 551.50
Mol. Wt.: 551.90
[M+92]+
[M+92-109]+
M = mass of original Diglyceride
LOD ~100 attomoles/ uL
HPLC/ESI/MS
CEB
• Enhanced Sensitivity• Less Sample
Preparation• Increased Structural
Information• Fragmentation highly
specific i.e. no proton donor/acceptor fragmentation processes occurring
CH2
HC O C
O
R1
CH2OC
O
R2
OP
O
O
OX
Parent product ion MS/MS of synthetic PG Q-1 1ppm PG scan m/z 110-990 (M –H) -
Sn1 16:0, Sn2 18:2
Q-3 product ion scan of m/z 747 scanned m/z 110-990 Note 50X > sensitivity
SIM additional 5x > sensitivity ~ 250X
“LIPOMICS”
Tools:
Thou shall know structure & concentration of each analyte
Progress (equipment) for speed, specificity, selectivity and sensitivity)Extraction1. Extraction high pressure/temperature faster more complete2. Supercritical CO2 pressure becomes gas directly into MS inlet3. Sequential saves time & effortChromatography1. GC high pressure , 0.1 mm controlled flow, > resolution & faster 2. SFC not much used3. HPLC smaller diameter, Chiral, 4. CZE high resolution, requires charge, presently difficult Detection (lipids generally lack chromophores) 1. NMR insensitive, expensive, 2. Laser fluorescence not as specific but incredibly sensitive3. Light scattering cheep & nonspecific 4. Mass Spectrometry
IonizationElectron impact 70 eV known structure catalogue but inefficient
Electrospray the dream but needs charged analyte ~ 100%
Petroleum Bioremediation of soils at KwajaleinNutrient Amendment and Ex Situ Composting vs Control Showed:1. VIABLE BIOMASS (PLFA)2. SHIFT PROPORTIONS: Gram + , Gram - (Terminal branched PLFA, :: Monoenoic, normal PLFA )3. Cyclo17:0/16:17c :: Cyclo19:0/18:17c (Stress)4. = 16:17t/16:7c (Toxicity), [often ] 5. 16:9c/16:17c (Decreased Aerobic Desaturase) 6. % 10Me16:0 & Br17:1 PLFA (Sulfate-reducing bacteria) 7. % 10Me18:0 (Actinomycetes) 8. = PROTOZOA, FUNGI + (Polyenoic PLFA) [ often ]In other studies also usually see: 1. PHA/PLFA (Decreased Unbalanced Growth)2. RATIO BENZOQUINONE/NAPHTHOQUINONE
(Increased Aerobic Metabolism)
DEGREE OF SHIFT IN SIGNATURE LIPID BIOMARKERS PROPORTIONAL TO DEGRADATION
1. 104-106 cells/cm2 vs ~ 103-104 /Liter 2. Integrates Over Time3. Pathogen trap & nurture
(including Cryptosporidum oocysts) 4. Serves as a built in solid phase extractor for
hydrophobic drugs, hormones, bioactive agents5. Convenient to recover & analyze for biomarkers Its not in the water but the slime on the pipe
Biofilms not pelagic in the fluid
Sampling Drinking Water-- Collect Biofilms on Coupons
1. Add from continuous culture vessels:Pseudomonas Spp.Acetovorax spp.Bacillus spp.
2. Seed with trace surrogate/pathogen E. coli (GFP), Mycobacterium pflei (GFP), Legionella bosmanii , Sphingomonas
In the Drinking Water Biofilm
Reproducibly Generate a Drinking Water Biofilm:
Microbial Insights, Inc.
CEB
Tap Water Biofilm ~ 600 L in 3 weeks on 200 cm2 stainless steel beads
Microbial Insights, Inc.
CEB
Tap Water Biofilm ~ 600 L in 3 weeks on 200 cm2 stainless steel beads
1. Biomass = 2,85 pmoles PLFA ~ 2,8 x 107 2. Largely Gram - heterotrophs
monoenoic PLFA derivativesCyclopropane (Stationary Phase) No trans PLFA (little toxicity)
3. Gram + aerobes Terminally branched saturated PLFAi17:0/a17:0 = 0.7
4. No actinomycetes, Mycobacteria (10 Me 18:0)5. No microeukaryotes (polyenoic PLFA)6. No Cryptosporidium Cholesterol7. No Legionella (2,3 di OH i14:) UQ-138. No Sphingomonas (sphanganine-uronic acid)9. Pseudomonas >>> Enterics (LPS 3 0H 10, 12:0 >> 30H 14:0)10. Chlorine toxicity = oxirane & dioic PLFA
Biofilm Test System
Rapid Detection of Bacterial Spores & LPS OH Fatty Acids in Complex Matrices
From the lipid-extracted residue, Acid methanolysis & Extract: Strong Acid methanolysis SPORE Biomarker
1. Detect 2,6 dipicolinate with HPLC/ES/MS/MS 1 hour and 100% yield vs Pasteurize& Plate ---- 3 days and ~ 20% viable Weak acid methanolysis ( 1% HAc, 100oC, 30 min.)
2. Detect 3-OH Fatty Acids Ester-linked to Lipid A in LPS of Gram-negative Bacteria with HPLC/ES/MS/MS or GC/MS
Enterics & Pathogens 3OH 14:0Pseudomonad's 3OH 10:0 & 3OH 12:0 (Should Dog Drink from Toilet Bowl?)
OOP
OO
OH O HN
O
OHO
OHN O
P O
OH
O
O
OH
OO
OO
O
O
O
OH
C93H174N2O24P22-
Exact Mass: 1765.19
Mol. Wt.: 1766.32
1414
14
14
1412
Gram-negative Bacteria lipid-extracted residue, hydrolize [1% Acetic acid, 30 min, 100oC], extract = Lipid A
E. Coli Lipid A MS/MS 3 OH 14:0, 14:0 as negative ions
Acid sensitive bond
{to KDO]
Lipid A
Lipid A from E. coliFatty acids liberated by acid hydrolysis followed by
acid–catalyzed (trans) esterification
14:03OH 14:0
3OH 14:0 TMS
phthalatesiloxane
GC/MS of Methyl esters
-Q1: 49 MCA scans from 1004001.wiff Max. 1.6e8 cps.
100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700m/z, amu
0.0
1.0e7
2.0e7
3.0e7
4.0e7
5.0e7
6.0e7
7.0e7
8.0e7
9.0e7
1.0e8
1.1e8
1.2e8
1.3e8
1.4e8
1.5e8
1.6e8
Inte
ns
ity, c
ps
227.8
243.9
177.6
367.4
199.6
396.0
424.2 1280.7586.6 1099.0872.5
284.7 1508.4451.9509.5 751.4 1054.7768.9 854.2255.9 691.0 1325.3339.8 480.2 1262.9978.1162.8 708.9 1491.0208.7 1718.9795.3551.4 836.7 1205.7118.8 921.3 1463.01064.1
Electrospray Mass Spectrum of Lipid A Standard from E. coli
14:0 m/z 227OH 14:0 m/z 243
14:0 and 3 0H 14:0 are clearly detectible as negative ions
WQ1 669 524 94
LIPID A:
Pseudomonas 3 0H 12:0 & 3 0H 10:0 (water organism) Enteric & Pathogens 30H 14:0 (fecal potential pathogen)
Toilet bowl biofilms: High flush vs Low flush rate Higher monoenoic, lower cyclopropane PLFA ~ Gram-negative more actively growing bacteria
mol% ratios of 72 (30)*/19 (4) of 3 0H 10 +12/ 3 OH 14:0 LPS fatty acids
Human feces 7 (0.6)/19 (4) 3 0H 10 +12/ 3 OH 14:0 in human feces [*mean(SD)].
Pet safety if access to processed non-potable water.
Toxicity Biomarkers
Hypochlorite, peroxide exposure induces:
1. Formation of oxirane (epoxy) fatty acids from phospholipid ester-linked unsaturated fatty acids
2. Oxirane fatty acid formation correlates with inability to culture in rescue media. Viability?
3. Oxirane fatty acid formation correlates with cell lysis indicated by diglyceride formation and
loss of phospholipids.
Compounds not readily ionized, that contain a hydroxy groupcan be derivatized to their methylpyridyl ether
Cl
Cl
O
OH
Cl N
CH3
F
CH3
SO3
+
Triclosan
2-flour-1-methylpyridinium-toluenesulfonate
Cl
Cl
O
O
Cl
NH3C
CH2Cl2
TEA
+Q1: 181 MCA scans from 0927001.wiff Max. 1.3e9 cps.
60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500m/z, amu
0.0
1.0e8
2.0e8
3.0e8
4.0e8
5.0e8
6.0e8
7.0e8
8.0e8
9.0e8
1.0e9
1.1e9
1.2e9
1.3e9
In
te
ns
ity
, c
ps
101.8
380.3
124.2
384.374.2
81.3 110.3
58.480.9
375.7
116.3397.775.2 165.486.4
Triclosan (Pyridinium derivative) Q1scan
+Product (380.3): 181 MCA scans from 0927003.wiff Max. 9.3e6 cps.
60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500m/z, amu
0.0
5.0e5
1.0e6
1.5e6
2.0e6
2.5e6
3.0e6
3.5e6
4.0e6
4.5e6
5.0e6
5.5e6
6.0e6
6.5e6
7.0e6
7.5e6
8.0e6
8.5e6
9.0e69.3e6
In
te
ns
ity
, c
ps
218.1
236.1
93.2219.1
380.2125.1204.2141.0110.079.1 237.0112.1
Cl
Cl
O
O
Cl
NH3C
C18H13Cl3NO2+
Exact Mass: 380.00
Mol. Wt.: 381.66
Product ion scan
380.3
218.1
+Q1: 0.573 to 1.962 min from 0928001.wiff Max. 8.1e6 cps.
260 280 300 320 340 360 380 400 420 440 460 480 500 520 540 560 580 600m/z, amu
0.0
5.0e5
1.0e6
1.5e6
2.0e6
2.5e6
3.0e6
3.5e6
4.0e6
4.5e6
5.0e6
5.5e6
6.0e6
6.5e6
7.0e6
7.5e6
8.0e6
In
te
ns
ity
, c
ps
475.7
476.8
507.6492.0281.7 416.0 447.7253.7 312.7
+Product (475.7): 119 MCA scans from 0928003.wiff Max. 8.5e7 cps.
60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500m/z, amu
0.0
5.0e6
1.0e7
1.5e7
2.0e7
2.5e7
3.0e7
3.5e7
4.0e7
4.5e7
5.0e7
5.5e7
6.0e7
6.5e7
7.0e7
7.5e7
8.0e7
8.5e7
In
te
ns
ity
, c
ps
100.1
99.2
475.458.1
311.4283.4
299.4377.1163.4 329.470.0 285.3
Sildenafil (Viagra) Q1 scan
S
O
CH3
N
N
H3C
O
ON
HN
O
N
N
CH3
CH2CH2CH3
C22H30N6O4SExact Mass: 474.20
Mol. Wt.: 474.58
Product ion scan
475.4
100.1
WQ1 669 524 94
Goal:
Provide a Rapid (minutes) Quantitative Automated Analytical System that can analyze coupons from water systems to:
1).) Monitor for Chlorine-resistant pathogens [Legionella, Mycobacteria], Spores
2). Provide indicators for specific tests (Sterols for Cryptosporidium, LPS OH-FA for enteric bacteria
3). Monitor hydrophobic drugs & bioactive molecules
Establish Monitored Reprocessed Waste Water
as safer than the wild type
The CH vs 13C- Problem H = 1.007825 12-C = 12.00000 13-C = 13.003345So the differentiate CH from 13-C must differentiate 13.0034 from 13.0078 requites High resolution Mass Spectrometry
Solution: 13C Label to saturation by growth with 13C so avoid CH problem a). Recover polar lipids (Extraction & Concentration) unique biomarker b). HPLC/ESI/MS/MS ~ attomolar sensitivity c) . Detect unique masses of PLFA for specific P-lipids
Detection of 13C grown bacteria
Solution: Use a polar lipid biomarker:
a) Total lipids can be extracted & concentrated from large sample environmental samples.
b) polar lipids can be purified
c) specific intact polar lipid can be purified with HPLC
d) polar lipids excellent for HPLC/eletrospray ionization [~ 100% vs < 1% for electron impact with GC/MS]
Problem: detect 13-C grown bacteria
Extract lipids, HPLC/ESI/MS/MS analysis of phospholipids detect specific PLFA as negative ions PLFA 12C Per 13C 16:1 253 269 same as 12C 17:0
16:0 255 271 Unusual 12C 17:0 (269) + 2 13C cy17:0 267 284 12C 18:0 (283) + 13C
18:1 281 299 12C 20:6 , 12C 19:0 with 2 13C 19:1 295 314 12C 21:5 (315), 12C 21:6 (313)
Detection of specific per 13C-labeled bacteria added to soils
13C bacteria added
No 13C bacteria added
1 Part 13C DA001 Spiked into 10 Parts of Soil Sample
PE from soil with 13C added
PE from soil with 13C added
Detection of Shrimp Gut Microbes
1. Recover DNA from Hind and Mid gut 2. Amplify with PCR using rDNA eubacterial primers3. Separate Amplicons with Denaturating
Gel Gradient Electrophoresis (DGGE)4. Isolate Bands, 5. Sequence and match with rDNA database 6. Phylogenetic analysis
Figure 1. DGGE analysis bacterial community in water and shrimp gut samples. Amplified 16S rDNAs were separated on a gradient of 20% to 65% denaturant.
Wat
er 8
31
Wat
er 8
17
Sta
ndar
d
For
e gu
t
Hin
d gu
t
Water changed composition between Aug 17 & 31st, much > diversity than shrimp gut, Fore gut less diverse than Hind gut.
Major bands have been RecoveredFor sequencing& Phylogenetic analysis
(Vib
rio)
Gra
m p
osit
ive
Mycobacteria
Propioni-bacterium
Marine α-proteobacteria
δ-proteobacteria
γ-pr
oteo
bact
eriu
m
BCF group
Green alga
Figure 2. Neighbor-joining analysis of 16S sequences from excised DGGE bands, relationships with reference organisms downloaded from RDP.
= Foregut,
= Hindgut,
= Water
Microbial Community in Water (W), Fore Gut (F), Hind Gut (H)
W F H W F H W F H W F H W F H
0%
20%
40%
60%
80%
100%
8020
1
8020
1F
8020
1H
8030
1
8030
1F
8030
1H
8100
1
8100
1F
8100
1H
8230
1
8230
1F
8230
1H
8310
1
8310
1F
8310
1H
Monos
Bmonos
TBSats
MBSats
NSats
Microbial Viable Biomass: Water (W), Fore Gut (F), Hind Gut (H)
W F H W F H W F H W F H W F H
Biomass PLFA
1.00E+00
1.00E+01
1.00E+02
1.00E+03
1.00E+04
1.00E+05
1.00E+06
1.00E+07
1.00E+08
80201
80201F
80201H
80301
80301F
80301H
81001
81001F
81001H
82301
82301F
82301H
83101
83101F
83101H
pm
ol/g
Note Log scale
Microbial Viable Biomass: Food, Flock, Water, Fore, Gut Hind Gut
0
10
20
30
40
50
60
70
80
90
100
Food Flock Water 8/31 Foregut 8/31 Hindgut 8/31
mo
l%
Poly
Mono
Bmono
Tbsat
MBSat
Nsat
Shrimp In Mariculture Water & Gut Microbial Community
Over one month of aquiculture: • Water microbial biomass increases somewhat• Algal and Microeukaryotes decrease • Desulfobacter increase Desulfovibrio slight decrease • Gram-negative bacteria increase then decrease • Water microbial composition relatively constant gets
more anaerobic? SRB? Not important in Gut• Fore Gut & Hind gut same viable biomass• Gut Community very different from water• DGGE shows Fore and Hind Gut differences & much
less diverse community• Gut 2-order of magnitude > viable microbial biomass
than water • Gut and Water different PLFA from Shrimp food
Feed per-13-C labeled bacteria, Algae, microeukaryotes to shrimp:
1. Determine Triglyceride Fatty acids to Phospholipid
fatty acids in muscle, hepatopancreas, gut etc. using HPLC/ES/MS/MS [Lithiated TG (positive ions) & PG with detection of negative ions)]
2. This gives evidence for both incorporation and nutritional status into the Shrimp
3. Can differentiate between bacteria PE, PG vs the eukaryotes with Ceramides and PC with HPLC/ES/MS/MS
Detection of specific per 13C-labeled bacteria, Algae, etc. in Shrimp
Problem: Rapid Non-invasive Detection of Infection or Metabolic stress for Emergency room Triage
Human Breath sample GC/MS
Problem: Detecting Indoor Air Biocontamination
Collect particulates on a tape with vortex flow collector
In lab process tape Lyse cells PCR DGGE or use hybridization chip for :Bacteria, Fungi and spores Immune potentiators ~ LPS, Fungal Antigens, dust mites, cat dander, cockroach frass
Adult Asthmas
Microbial Insights, Inc.
CEB
Biomarkers for Confined Space Air Biocontaminant Monitoring:
1. Viable Biomass (all cells with an intact membrane) PLFA2. Detect Recently Lysed (diglyceride fatty acids)3. Community Composition4. Nutritional/Physiological status (Infectivity & Toxin production)5. Evidence for Toxicity (trans/cis PLFA)6. Detect Specific Microbes Mycobacteria, Legionella, Francisella,
some Aspergillis, complementary with gene probes and PCR7. Detection of Allergens: pollens, danders, spores, arthropod frass8. Detection of immune potentiators (bacterial endotoxin)9. Detection of mycotoxins10. Independent of “culturability”11. Independent of sample source (tiles, covers, carpet, air filters)12. + Proteins & Nucleic Acids ~ detect virus
