Transcript Document

“LIPOMICS”
David C. White, MD, PhD, [email protected], 865-974-8001
Current 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 years
Almeida, 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 min
4. 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 dominants
6. 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 sample
4. 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, equipment
Cost, 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 Status
2. Rhizosphere & defining forest biodiversity
3. Waste treatment effectiveness monitoring
4. 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 assessment
8. Ultrasensitive detection of biomarkers forward contamination of spacecraft
9. Quantitatively defining soil quality and effects of tilth
10. Monitoring carbon sequestration in soils
11. Rapid detection of biocontamination & antigenic immune potentiators in indoor air
12. Rapid detection and monitoring of contamination in drinking water biofilms
13. Detecting pathogens in microbial consortia & food
14. 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
Signature Lipid Biomarker Analysis
Phospholipid Fatty Acid [PLFA] Biomarker Analysis =
Single most quantitative, comprehensive insight into insitu 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 Lipids
4. Derivatization [transesterification]
5. GC/MS analysis ~ picomole detection ~ 104 cells LOD
6. Arcane Interpretation [Scattered Literature]
7. 3-4 Days and ~ $250
“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)
Extraction
1. Extraction high pressure/temperature faster more complete
2. Supercritical CO2 pressure becomes gas directly into MS inlet
3. Sequential saves time & effort
Chromatography
1. GC high pressure , 0.1 mm controlled flow, > resolution & faster
2. SFC not much used
3. 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 sensitive
3. Light scattering cheep & nonspecific
4. Mass Spectrometry
Ionization
Electron 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
ESI/MS/MS
CAD
Q-3
PE-Sciex API 365 HPLC/ESI/MS/MS Functional Sept 29, 2000
Lipid Biomarker Analysis
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
Difference
Product ion
Fix
Vary
Vary
Precursor ion
Vary
Fix
Vary
Neutral loss
Vary
Vary
Fix
Neutral gain
Vary
Vary
Fix
MRM
Fix
Fix
Fix
(Multiple Reaction Monitoring)
*Collision-induced dissociation (CID) is a reaction region
between quadrupoles
Tandem Mass Spectrometers
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 Scan
2. True Neutral Loss Scan
3. Generate Neutral Gain Scan
4. More Quantitative
5. > Sensitivity for MRM
6. > Dynamic Range
JPL
CEB
LIPIDS
Lipids
1. 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
Membrane Liability (turnover)
VIABLE
NON-VIABLE
O
O
||
||
H2COC
O H2COC
O
phospholipase
|| |
|| |
cell death
C O CH
C O CH
O
|
| ||
H2 C O H
H2 C O P O CH2CN+ H3
|
Neutral lipid, ~DGFA
OPolar lipid, ~ PLFA
Bacterial Phospholipid ester linked fatty acids
-CH2
CH2-
CH=CH
cis
Monoenoic
-CH2
Isomer
conformation
CH3(CH2)XCH=CHCH2CH(CH2)YCOOH
0H
OH, = position
Microbial
Insights, Inc.
JPL
CH=CH
CH2trans
-CH2CHCHCH2CH2
cyclopropyl
CEB
Bacterial Phospholipid ester-linked fatty
acids
CH3
RCH2CH
CH3
iso
RCH2CHCH2CH3
|
CH3
anteiso
Methyl Branching
Microbial
Insights, Inc.
RCH2CHCH2CH2R’
|
CH3
mid-chain
JPL
CEB
Biofilm Community Composition
Detect viable microbes & Cell-fragment biomarkers :
Legionella pneumophila, Francisella tularensis,
Coxellia burnetii, Dienococcus, PLFA
oocysts of Cryptosporidium parvum, Fungal spores PLFA
Actinomycetes Me-br PLFA
Mycobacteria Mycocerosic acids, (species and drug resistance)
Sphingomonas paucimobilis Sphingolipids
Pseudomonas Ornithine lipids
Enterics LPS fragments
Clostridia Plasmalogens
Bacterial spores Dipicolinic acid
Arthropod Frass PLFA, Sterols
Human 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 for
Almeida 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
Signature Lipid Biomarker Analysis
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
Capillary GC PLFA 20m x 0.1mm i.d. x 0.1m film thickness, 0.3 ml/min flow rate
Quadrupole 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
TIC: SERDP2.D

EI off during
solvent elution
6.00
8.00 10.0012.00 14.0016.0018.0020.0022.0024.0026.0028.0030.00
Details of GC/MS tracing showing deconvolution of PLFA
LIPIDS –DATA ANAYSIS
Problem: PLFA Analysis is like comparing spectra
Few 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 Analysis
Principal components Analysis PCA
Essentially a huge correlation matrix
S c a tte r plot
U r a nium v s Mid-C ha in B r a nc he d S a tur a te d P LFA
28
608
826
Mid-Chain Branched PLFA
24
20
615
624
626
610
617
16
857
12
828
8
853
4
0
500
1000
1500
2000
U r a nium
2500
3000
3500
4000
PCA 2 Analysis of Forest Community Soil total PLFA
October
2
-1
PCA Analysis
Sugar MapleBasswood
Black OakWhite Oak
Sugar MapleRed Oak
1
-1
PCA 1
August
1
-1
-1
LIPIDS-DATA ANALYSIS
Problem: PLFA Analysis is like comparing spectra
Few 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
U26
T27
Wooded Wetland
Grassy Wetland
Q24 Q26
P23 P25
Swampy/Cattails
O22 O24
N21
N23
Running Water
Woodland
M20
L21
Grass
Pond
K20 K22
J19
J21 J23
I20 I22
Beach
Removed
H15 H17
H19 H21
G18
G14
D9
C8
C4
B5
B7
D11
C10
K28
N
E16 E18
D17
C16
0
400ft
B9
TANNERY
Cannelton Tannery Superfund Site
A
ND
1-50
51-100
101-500
501-1,000
1,001-2,000
2,001-3,000
3,001-5,000
5,001-7,000
7,001-10,000
10,001-25,000
25,001-50,000
50,001-75,000
75,001-100,000
100,001-300,000
Cr+3 Concentrations Site map
-1
Biomass (nmole PLFA g )
Total Biomass (~108 cells)
140
120
100
80
60
40
20
0
-20
1
2
3
Chromium
4
5
Sulfate/metal reducers (mole%)
Biomarkers for Sulfate/metal reducing bacteria
NABIR
6
5
4
3
2
1
1
2
3
Chromium
4
5
“Stress” biomarkers
18:1w7t/18:1w7c
0.18
0.14
0.10
0.06
0.02
-0.02
1
2
3
Chromium
NABIR
4
5
Fact or Loadings, Fact or 1 vs. Fact or 2
Rot at ion: Unr ot at ed
Ext r act ion: Pr incipal component s
1. 0
C1 6 1 W 7 C
C1 8 2 A
CCY 1 7 0 A
% TO M
0. 6
% TO
C
PH
CI 1 4 0
Fact or 2
VI ABL E_ C
CR_ _ M G _ K
C1 6 1 W 7 T
W E T L A ND
CA 1 5 0
C1 4 0
C1 8 1 W 7 T
C1 6 2
- 1. 0
- 1. 0
K
CCY 1 7 0 B
CI 1 7 1 W 8
CB R1 8 1
CB R1 5 0 C
CCY 1 9 C
0I 151C
C1 8 1 W 5 C
CB R1 6 0
P
C2 0 5 W 3
CI 1 5 1 B
M E1 6 0
C A 1C7B0R 1C 71 02 A
C1 8 0
C1 7 0
- 0. 6
CB R1 5 0 B
C1 0 M E 1 6 0 C1 8 1 W
C 1 76 C
1W 5C
CC
I I1 15 51 1AW 1 1
C2 0 1 W 9 C
C1 6 0
- 0. 2
C1 6 1 W 1 1 C
C1 1 M E 1 6 0
BI O M ASS
M G
C1 5 1
0. 2
CA
C2 4 0
C2 2 0
C2 0 0
C1 8 3 W 3
C1 5 0
CI 1 6 1
C1 C
0 IM1 6
E0
180
C 2C3C
1 01
88
21
WW6 9 C
Eukaryote
PLFA
- 0. 6
C 2 0C41W
2 M6 E 1 8 0
C2 0 3 W 6
CB R1 5 0 A
C2 1 0
C IC 1
I 5
10
70
CB R1 7 0 B
- 0. 2
0. 2
0. 6
1. 0
Fact or 1
Principal components analysis~ associated with wetlands,
eukaryote biomarkers and bacterial stress markers
1.
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 (18:17t/18:17c) increased at
the higher [Cr]
• PCA - association between [Cr] and wetlands, biomarkers
for eukaryotes and “stress”. Needs a different analysis.
ANN are universal predictors
Schematic architecture of a three layer
feedforward network used to associate
microbial community typing profiles
(MCT) with classification vectors.
Generalization is assured
by cross-validation
Stop !
int
sig erpo
na
l + latin
no g
ise
Capable of
learning from examples
int
erp
ola
tin
g
h id d e n la y e r
Predictive error
In p u t
p ro file
Symbols correspond to neuronal nodes
sig
na
l
c la ssific a tio n
v e c to r
testing
cross-validated error
training
regression
Good Predictive Accuracy at > 100 mg Cr+3 /Kg
Predicted Cr3+ concentration (mg Kg-1)
1E+006
slope = 1.09
100000
R2 = 0.98
10000
1000
100
training set
validation set
regression
identity
10
1
1
10
100
1000
10000
Observed Cr3+ concentration (mg Kg-1)
100000
1E+006
7.0%
6.0%
90%
5.0%
80%
70%
4.0%
60%
3.0%
50%
40%
2.0%
30%
1.0%
20%
Cummulative sensitivity
0.0%
C181W9C
CI170
C181W7C
C10ME180
CA170
CI151W11
CI151A
C161W5C
CI150
C201W9C
C161W11C
C10ME160
CBR181
CA150
CI160
%TOM
C160
CCY170B
C170
C150
C203W6
CA
C210
PH
C12ME160
C161W7T
C183W3
%TOC
CI171W8
CBR150B
CBR170B
C181W7T
C182A
CBR170A
BIOMASS
C151
P
CI151B
WETLAND
CBR150A
CCY190
MG
CI140
C180
C161W7C
C230
CBR160
K
C11ME160
C205W3
C12ME180
C200
CCY170A
CI151C
C182W6
C140
C220
CI161
C162
C240
CBR150C
C204W6
VIABLE_C
C181W5C
Relative 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).
110%
100%
10%
0%
PLFA profiles are a can be used as a general 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
the intracellular
space.with
So the
Cellularand
membranes
are in contact
environment and the intracellular space.
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.
ANN Analysis of CR impacted Soil Microbial
Communities
SENSITIVITY (from ANN)
20% of the variables accounted for 50% of the predictive of Cr+3
concentration
Of these 20 %:
18:1w9c (6.6%) Eukaryote (Fungal) correlated with 18:26
(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.
18:17c (4.6%) = Gram negative bacteria
10Me 18:0 (4.3%) (Actinomycetes)
NABIR
-CEB
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)18:19c, 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
NABIR
-CEB
“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 Fractionation
SFE/ESE
Separation
Detection
HPLC/in-line
HPLC/ESI/MS(CAD)MS
or
HPLC/ESI/IT(MS)n
CEB
Microbial
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
Supercritical Fluid Extraction (SFECO2 + Methanol Enhancer)
for Neutral Lipids
Liquid
Gas
1. vs. liquids
greater solute diffusivity
less solute viscosity
density varies with pressure
2. Fractionate with sequential addition of modifiers
3. Effective in situ derivatization
4. Less toxic than solvents
5. Fast 20 min vs. 8 hrs with solvents
6. Potential for automation
7. Compatible with ES/MS/MS & IT(MS)n
8. Generate micellar emulsions + water + surfactants
9. SFCO2 becomes a gas < 1070 psi
10. Low Temperature Possible ~ 390C
Microbial
Insights, Inc.
CEB
Feasibility of “Flash” Extraction
ASE vs B&D solvent extraction*
Bacteria = B&D, no distortion
Fungal 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
*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)
Microbial
Insights, Inc.
CEB
Problem: Rapid Detection/Identification of Microbes
Propose a Sequential High Pressure/Temperature
Extractor Delivers Three Analytes to HPLC/ESI/MS/MS
MeOH
MeOH
CHCl3
PO 4-
Pump
CO2
Spe-ed SFE-4
NL
PL
LPS
Fraction Collector
N2 blowdown
Auto
sampler
HPLC/ES/MS/MS
Signature Lipid Biomarker Analysis
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 acceptor
Diglyceride ~ cell lysis
Archea ~ methanogens
Lipid ornithine ~ bioavailable phosphate
Lysyl-phosphatidyl glycerol ~ low pH
Poly beta-hydroxy alkanoate ~ unbalanced growth
3. Increased Sensitivity and Specificity ESI/MS/MS
Lyophilized Soil Fractions, Pipe Biofilm
1. Neutral Lipids
SFECO2
UQ isoprenologues
ESE Chloroform.methanol
Derivatize –N-methyl pyridyl
Diglycerides
Sterols
Ergostrerol
Cholesterol
2. Polar Lipids
Transesterify
Intact Lipids
PLFA
CG/MS
Phospholipids
PG, PE, PC, Cl,
& sn1 sn2 FA
Amino Acid PG
Ornithine lipid
Archea ether lipids
Plamalogens
3. In-situ acidolysis in SFECO2
PHA
Thansesterify &
Derivatize
N-methyl pyridyl
2,6 DPA (Spores)
LPS-Lipid A OH FA
HPLC/ES/MS/MS
Monensin Q1 scan
+Q1: 119 MCA scans from 0928002.wiff
Max. 8.7e8 cps.
693.7
693.7
HO
8.5e8
CH3
CH3
8.0e8
CH3
7.5e8
H
CH2CH3
7.0e8
CH3
H3C
6.5e8
694.6
In te n s ity , c p s
6.0e8
5.5e8
O
OH3C
O
H
O
O
H
H
O
CH2OH
5.0e8
HO
H
4.5e8
4.0e8
3.5e8
CH3
H3CHC
3.0e8
O
C
2.5e8
C36H61NaO11
Exact Mass: 692.41
Mol. Wt.: 692.85
2.0e8
ONa
1.5e8
635.5
1.0e8
5.0e7
0.0
600
679.7
610
620
680.6
653.8
617.5
696.7
637.5
630
640
650
660
670
680
+Product (693.8): 119 MCA scans from 0929001.wiff
690
707.6
700
710
m/z, amu
725.7
720
730
740
750
760
770
780
790
800
Max. 4.9e7 cps.
693.2
4.9e7
4.5e7
4.0e7
In te n s ity , c p s
3.5e7
3.0e7
2.5e7
2.0e7
675.4
675.4
1.5e7
1.0e7
461.3
461.4
5.0e6
479.3
443.6
0.0
400
420
440
460
480
501.2
500
581.5
520
540
560
m/z, amu
580
599.3
600
695.2
657.7
620
640
660
680
700
Respiratory Benzoquinone (UQ)
Gram-negative Bacteria with Oxygen as terminal acceptor
LOQ = 580 femtomole/ul, LOD = 200 femtomole/ul ~ 104 E. coli
Q7
Q6
Q10
O
H3 O C
CH3
H3 O C
O
197 m/z
H
]n
Pyridinium Derivative of 1, 2 Dipalmitin
SO3
O
CH2O C
CH2(CH2)13CH3
O
C
CH2(CH2)13CH3
CHO
+
F
N
CH3
CH3
[M+92-109]+
CH2OH
O
CH2O C
CH2(CH2)13CH3
O
C
CH2(CH2)13CH3
CHO
C6H7NO
Exact Mass: 109.05
Mol. Wt.: 109.13
neutral loss
C41H73NO5+
Exact Mass: 659.55
Mol. Wt.: 660.02
OCH
N
CH3
O
N
CH3
M = mass of original
Diglyceride
O
CH2O C
CH2(CH2)13CH3
O
C
CH2(CH2)13CH3
CHO
CH2
C35H67O4+
Exact Mass: 551.50
Mol. Wt.: 551.90
LOD ~100 attomoles/ uL
[M+92]+
HPLC/ESI/MS
• Enhanced Sensitivity
• Less Sample
Preparation
• Increased Structural
Information
• Fragmentation highly
specific i.e. no proton
donor/acceptor
fragmentation
processes occurring
O
X O
P
O
CH2
O
O
HC O C R1
O
R2
C O CH2
CEB
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)
Extraction
1. Extraction high pressure/temperature faster more complete
2. Supercritical CO2 pressure becomes gas directly into MS inlet
3. Sequential saves time & effort
Chromatography
1. GC high pressure , 0.1 mm controlled flow, > resolution & faster
2. SFC not much used
3. 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 sensitive
3. Light scattering cheep & nonspecific
4. Mass Spectrometry
Ionization
Electron impact 70 eV known structure catalogue but inefficient
Electrospray the dream but needs charged analyte ~ 100%
Petroleum Bioremediation of soils at Kwajalein
Nutrient 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
Sampling Drinking Water-- Collect Biofilms on Coupons
Biofilms not pelagic in the fluid
1. 104-106 cells/cm2 vs ~ 103-104 /Liter
2. Integrates Over Time
3. Pathogen trap & nurture
(including Cryptosporidum oocysts)
4. Serves as a built in solid phase extractor for
hydrophobic drugs, hormones, bioactive agents
5. Convenient to recover & analyze for biomarkers
Its not in the water but the slime on the pipe
In the Drinking Water Biofilm
Reproducibly Generate a Drinking Water Biofilm:
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
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 derivatives
Cyclopropane (Stationary Phase)
No trans PLFA (little toxicity)
3. Gram + aerobes
Terminally branched saturated PLFA
i17:0/a17:0 = 0.7
4. No actinomycetes, Mycobacteria (10 Me 18:0)
5. No microeukaryotes (polyenoic PLFA)
6. No Cryptosporidium Cholesterol
7. No Legionella
(2,3 di OH i14:) UQ-13
8. No Sphingomonas (sphanganine-uronic acid)
9. Pseudomonas >>> Enterics (LPS 3 0H 10, 12:0 >> 30H 14:0)
10. Chlorine toxicity = oxirane & dioic PLFA
Microbial
Insights, Inc.
CEB
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 14:0
Pseudomonad's 3OH 10:0 & 3OH 12:0
(Should Dog Drink from Toilet Bowl?)
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
O
{to KDO]
Lipid A
O
O
O P O
OH
O
HO
O
HN
O
O
O
O
C93H174N2O24P22-

O
HN
O
OH
O
O
Exact Mass: 1765.19
Mol. Wt.: 1766.32


14
14
14
14
12
14
O
P O
O
OH
O
OH
Lipid A from E. coli
Fatty acids liberated by acid hydrolysis followed by
acid–catalyzed (trans) esterification
3OH 14:0 TMS
GC/MS of
Methyl esters
14:0
3OH 14:0
phthalate
siloxane
Electrospray Mass Spectrum of Lipid A Standard from E. coli
-Q1: 49 MCA scans from 1004001.wiff
Max. 1.6e8 cps.
227.8
1.6e8
1.5e8
243.9
14:0 m/z 227
OH 14:0 m/z 243
1.4e8
177.6
1.3e8
1.2e8
1.1e8
In te n s ity , c p s
367.4
14:0 and 3 0H 14:0 are clearly
detectible as negative ions
1.0e8
199.6
9.0e7
8.0e7
7.0e7
396.0
6.0e7
5.0e7
424.2
4.0e7
586.6
3.0e7
284.7
2.0e7
451.9
255.9
162.8
1.0e7
339.8
480.2
208.7
118.8
0.0
1280.7
1099.0
872.5
100
200
300
400
500
509.5
1508.4
751.4768.9 854.2
691.0
708.9
795.3 836.7
551.4
600
700
800
1054.7
978.1
1262.9
921.3 1064.1
1205.7
900
1000
m/z, amu
1100
1200
1325.3
1491.0
1463.0
1300
1400
1500
1718.9
1600
1700
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 group
can be derivatized to their methylpyridyl ether
OH
Cl
SO3
O
+
N
Cl
Cl
Triclosan
CH3
CH2Cl2
H3C
N
O
O
Cl
CH3
2-flour-1-methylpyridinium
-toluenesulfonate
TEA
Cl
F
Cl
Triclosan (Pyridinium derivative) Q1scan
+Q1: 181 MCA scans from 0927001.wiff
Max. 1.3e9 cps.
101.8
1.3e9
H3C
N
1.2e9
1.1e9
380.3
380.3
1.0e9
Cl
O
O
8.0e8
7.0e8
6.0e8
124.2
Cl
5.0e8
Cl
384.3
74.2
4.0e8
3.0e8
81.3
58.4
110.3
C18H13Cl3NO2+
80.9
Exact Mass: 380.00
2.0e8
75.2
0.0
60
86.4
80
116.3
100
375.7
Mol. Wt.: 381.66
1.0e8
397.7
165.4
120
140
160
180
200
220
240
260
280
300
m/z, amu
320
340
360
380
400
420
440
+Product (380.3): 181 MCA scans from 0927003.wiff
460
480
500
Max. 9.3e6 cps.
218.1
218.1
9.3e6
9.0e6
Product ion scan
8.5e6
8.0e6
7.5e6
7.0e6
6.5e6
In te n s ity , c p s
In te n s ity , c p s
9.0e8
6.0e6
5.5e6
5.0e6
4.5e6
4.0e6
3.5e6
3.0e6
2.5e6
2.0e6
236.1
1.5e6
93.2
1.0e6
0.0
219.1
125.1
5.0e5
79.1110.0
60
80
100
141.0
237.0
112.1
120
140
380.2
204.2
160
180
200
220
240
260
280
300
m/z, amu
320
340
360
380
400
420
440
460
480
500
Sildenafil (Viagra) Q1 scan
+Q1: 0.573 to 1.962 min from 0928001.wiff
8.0e6
CH3
N
7.0e6
N
HN
O
6.5e6
N
6.0e6
N
S
5.5e6
In te n s ity , c p s
475.7
H3C
7.5e6
Max. 8.1e6 cps.
475.4
O
N
5.0e6
476.8
O
CH2CH2CH3
4.5e6
4.0e6
O
3.5e6
3.0e6
2.5e6
CH3
2.0e6
C22H30N6O4S
Exact Mass: 474.20
Mol. Wt.: 474.58
1.5e6
1.0e6
5.0e5
281.7
253.7
0.0
260
280
300
320
340
360
380
400
+Product (475.7): 119 MCA scans from 0928003.wiff
100.1
507.6
447.7
420
440
m/z, amu
460
480
500
520
540
560
580
600
Max. 8.5e7 cps.
Product ion scan
100.1
8.5e7
492.0
416.0
312.7
8.0e7
7.5e7
7.0e7
6.5e7
In te n s ity , c p s
6.0e7
5.5e7
99.2
5.0e7
4.5e7
4.0e7
3.5e7
3.0e7
475.4
58.1
2.5e7
311.4
2.0e7
283.4
1.5e7
1.0e7
299.4
5.0e6
163.4
70.0
0.0
60
80
100
120
140
160
285.3
180
200
220
240
260
280
300
m/z, amu
377.1
329.4
320
340
360
380
400
420
440
460
480
500
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
Detection of 13C grown bacteria
The CH vs 13C- Problem
H = 1.007825
12-C = 12.00000
13-C = 13.003345
So 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
Problem: detect 13-C 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]
Detection of specific per 13C-labeled bacteria added to soils
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
cy17:0 267
18:1 281
19:1 295
271 Unusual 12C 17:0 (269) + 2 13C
284 12C 18:0 (283) + 13C
299
314
13C
12C
20:6 , 12C 19:0 with 2 13C
12C 21:5 (315), 12C 21:6 (313)
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 primers
3. Separate Amplicons with Denaturating
Gel Gradient Electrophoresis
(DGGE)
4. Isolate Bands,
5. Sequence and match with rDNA
database
6. Phylogenetic analysis
Standard
Fore gut
Water 817
Water 831
Hind gut
Major bands
have been
Recovered
For sequencing
& 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.
Water changed composition between Aug 17 & 31st, much >
diversity than shrimp gut, Fore gut less diverse than Hind gut.
= Foregut,
= Hindgut,
Mycobacteria
Propioni
-bacterium
Gram positive
joining analysis of 16S
sequences from
excised DGGE bands,
relationships with
reference organisms
downloaded from
RDP.
(Vibrio)
γ-proteobacterium
Figure 2. Neighbor-
Marine αproteobacteria
δ-proteobacteria
BCF group
= Water
Green alga
Microbial Community in Water (W), Fore Gut (F), Hind Gut (H)
100%
80%
60%
Monos
Bmonos
TBSats
MBSats
NSats
40%
20%
W F H W F H
W F H
83101H
83101F
83101
82301H
82301F
82301
81001H
81001F
81001
80301H
80301F
80301
80201H
80201F
80201
0%
W F H W F H
Microbial Viable Biomass: Water (W), Fore Gut (F), Hind Gut (H)
Biomass PLFA
Note Log scale
1.00E+08
1.00E+07
1.00E+06
1.00E+04
1.00E+03
1.00E+02
1.00E+01
W F H W F H
W F H
83101H
83101F
83101
82301H
82301F
82301
81001H
81001F
81001
80301H
80301F
80301
80201H
80201F
1.00E+00
80201
pmol/g
1.00E+05
W F H W F H
Microbial Viable Biomass: Food, Flock, Water, Fore, Gut Hind Gut
100
90
80
70
60
Poly
mol%
Mono
Bmono
50
Tbsat
MBSat
Nsat
40
30
20
10
0
Food
Flock
Water 8/31
Foregut 8/31
Hindgut 8/31
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
Detection of specific per 13C-labeled bacteria, Algae, etc. in Shrimp
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
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
Biomarkers for Confined Space Air Biocontaminant Monitoring:
1.
2.
3.
4.
5.
6.
Viable Biomass (all cells with an intact membrane) PLFA
Detect Recently Lysed (diglyceride fatty acids)
Community Composition
Nutritional/Physiological status (Infectivity & Toxin production)
Evidence for Toxicity (trans/cis PLFA)
Detect Specific Microbes Mycobacteria, Legionella, Francisella,
some Aspergillis, complementary with gene probes and PCR
7. Detection of Allergens: pollens, danders, spores, arthropod frass
8. Detection of immune potentiators (bacterial endotoxin)
9. Detection of mycotoxins
10. Independent of “culturability”
11. Independent of sample source (tiles, covers, carpet, air filters)
12. + Proteins & Nucleic Acids ~ detect virus
Microbial
Insights, Inc.
CEB