1
LIPOMICS
David C. White, MD, PhD, milipids_at_aol.com,
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 MonthsA 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.

2
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.

3
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
• 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.
• 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

4
LIPOMICS

• Development
• Effectiveness methods, resources tools
  limited
• Establish interpretation in environmental samples
  with 8000 species/g
• 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

5
LIPOMICS

• Current Status application 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
• 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

6
Signature Lipid Biomarker Analysis

• Phospholipid Fatty Acid PLFA Biomarker
  Analysis Single most quantitative,
  comprehensive insight into in-situ microbial
  community
• Why not Universally utilized?
• Requires 8 hr extraction with ultrapure solvents
  emulsions.
• Ultra clean glassware incinerated 450oC.
• Fractionation of Polar Lipids
• Derivatization transesterification
• 5. GC/MS analysis picomole detection 104
  cells LOD
• 6. Arcane Interpretation Scattered Literature
• 7. 3-4 Days and 250

7
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

8
LIPOMICS

• Tools
• Thou shall know structure concentration of
  each analyte
• Progress ?(equipment) for speed, specificity,
  selectivity and sensitivity)
• Extraction
• Extraction high pressure/temperature faster more
  complete
• Supercritical CO2 ?pressure becomes gas
  directly into MS inlet
• Sequential saves time effort
• Chromatography
• GC high pressure , 0.1 mm controlled flow, gt
  resolution faster
• SFC not much used
• HPLC smaller diameter, Chiral,
• CZE high resolution, requires charge, presently
  difficult
• Detection (lipids generally lack chromophores)
• NMR insensitive, expensive,
• Laser fluorescence not as specific but
  incredibly sensitive
• Light scattering cheep nonspecific
• Mass Spectrometry
• Ionization
• Electron impact 70 eV known structure
  catalogue but inefficient

9
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)

10
ESI (cone voltage)
Q-1
CAD
Q-3
ESI/MS/MS
11
PE-Sciex API 365 HPLC/ESI/MS/MS Functional Sept
29, 2000
12
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

13
Tandem Mass Spectrometers
Ion trap MSn (Tandem in Time) Smaller, Least
Expensive, gtSensitive (full scan)
Quadrupole/TOF gt Mass Range, gt 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. gt Sensitivity for MRM 6. gt
Dynamic Range
JPL
CEB
14
LIPIDS

• Lipids
• Defined by process as Cellular components
  extracted from by organic solvents
• Diverse Chemical Structure characterized by
  hydrophobic properties
• Relatively small molecules compared to
  Biopolymers molecular weights lt 2000
• Not with properties of the Biopolymer
  macromolecules
• Polysaccharides, Nucleic Acids, Proteins

15
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 .gt 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


16
LIPIDS

• A Solution ? look for biomarkers
• 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 ??

17
LIPIDS

• Intact lipid membrane a necessary but not
  sufficient criteria of life ON Earth
• Cannot have a functional cell without an intact
  lipid membrane Phospholipid ? Diglyceride
  evidence of cell lysis
• deeper in the subsurface the gt 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 gt
  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?

18
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
19
Membrane Liability (turnover)
VIABLE
NON-VIABLE
O
O


H2COC
H2COC
O
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
O-
Polar lipid, PLFA
20
Bacterial Phospholipid ester linked fatty acids
Monoenoic
-CH2
CH2-
-CH2
CHCH
CHCH
Isomer conformation
CH2-
trans
cis
-CH2CHCHCH2-
CH3(CH2)XCHCHCH2CH(CH2)YCOOH 0H
CH2
cyclopropyl
OH, position
JPL
CEB
Microbial Insights, Inc.
21
Bacterial Phospholipid ester-linked fatty acids
CH3
RCH2CH
CH3
RCH2CHCH2CH3
iso

CH3
anteiso
RCH2CHCH2CH2R

CH3
Methyl Branching
mid-chain
JPL
CEB
Microbial Insights, Inc.
22
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
23
In-situ Microbial Community Assessment
What do you want to know? Characterization of
the microbial community 1. Viable and Total
biomass ( lt 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
24
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
i150/a150 (lt 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.

25
Signature Lipid Biomarker Analysis
Microniche Properties from Lipids 5.
Microniches with carbon terminal electron
acceptors with limiting N or Trace growth factors
high ( gt 0.2) poly ß-hydroxyalkonate
(PHA)/PLFA ratios 6. Microniches with
suboptimal growth conditions (low water activity,
nutrients or trace components) high ( gt 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

26
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
?
EI off during solvent elution
27
Details of GC/MS tracing showing deconvolution of
PLFA
28
LIPIDS DATA ANAYSIS

• Problem PLFA Analysis is like comparing spectra
• Few replications but huge data load/sample
• Classic Statistics likes replications of simple
  data
• group data in rational clusters
• 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

29
(No Transcript)
30
PCA 2 Analysis of Forest Community Soil total
PLFA
PCA 1
31
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


32
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 Cr3 and other heavy metals
   between1900-1958 by the Northwestern Leather Co.
3. Cr3 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
   (Pgt0.05)
5. PLFA biomass correlated (Plt001) with TOM TOC but
   not with viable counts (P0.5)

-CEB
33
Cannelton Tannery Superfund Site
34
Cr3 Concentrations Site map
35
Total Biomass (108 cells)
36
Biomarkers for Sulfate/metal reducing bacteria
37
Stress biomarkers
Metabolic stress
38
Eukaryote PLFA
Principal components analysis associated with
wetlands, eukaryote biomarkers and bacterial
stress markers
39
Summary Biomass

• Biomass (bacterial abundance) 6 x 107 to 109
• cells gram-1. No correlation between Cr and
  total biomass (Pgt0.05)
• 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 (Plt0.001)

40
Summary community composition/physiological
status

• Significant shifts in PLFA profiles with Cr
• 10me160 (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 (181?7t/181?7c)
  increased at the higher Cr
• PCA - association between Cr and wetlands,
  biomarkers for eukaryotes and stress. Needs a
  different analysis.

41
ANN are universal predictors
Schematic architecture of a three layer
feedforward network used to associate microbial
community typing profiles (MCT) with
classification vectors. Symbols correspond to
neuronal nodes
Capable of learning from examples
Generalization is assured by cross-validation
42
Good Predictive Accuracy at gt 100 mg Cr3 /Kg
43
Sensitivity analysis ranks the inputs by
importance in predicting Cr3 PLFA have a
significant larger predictive value than
environment parameters (marked with arrows).
PLFA profiles are a can be used as a general
purpose biosensor
44
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 Its 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.
45
ANN Analysis of CR impacted Soil Microbial
Communities
SENSITIVITY (from ANN) 20 of the variables
accounted for 50 of the predictive of Cr3
concentration Of these 20 181w9c (6.6)
Eukaryote (Fungal) correlated with 182?6
(Plt0.02) 10Me 160 (2.5) correlated with i170
(4.8), 161 ?11c (2.9), i150 (3.1) (Plt0.001).
Thus all are most likely indicative of SRBs or
MRBs. 181?7c (4.6) Gram negative
bacteria 10Me 180 (4.3) (Actinomycetes)
-CEB
NABIR
46
ANN Analysis of CR impacted Soil Microbial
Communities
CONCLUSIONS 1. Non-Linear ANN gtgt predictor than
Linear PCA (principal Components Analysis) 2. No
Direct Correlation (Pgt0.05) Cr3 with Biomass
(PLFA), Positive correlation between biomass
(PLFA) and TOC,TOM 3. ANN Sensitivity to Cr3
Correlates with Microeukaryotes (Fungi)181?9c,
and SRB/Metal reducers (i150, i 170, 161w11,
and 10Me 160) 4. SRB Metal reducers peaked
10,000 mg/Kg Cr3 5. PLFA of stress gt trans/cis
monoenoic, gt aliphatic saturated with gt Cr3
-CEB
NABIR
47
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

48
Sequential Extraction HPLC/ESI/MS analysis
1-2 hrs
Extraction SFE/ESE
Concentration/ Recovery
Fractionation

Separation HPLC/in-line
Detection HPLC/ESI/MS(CAD)MS or HPLC/ESI/IT(MS)n
CEB
Microbial Insights, Inc.
49
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
50
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 lt
1070 psi 10. Low Temperature Possible 390C
CEB
Microbial Insights, Inc.
51
Feasibility of Flash Extraction
ASE vs BD solvent extraction Bacteria BD,
no distortion Fungal Spores 2 x BD Bacterial
Spores 3 x BD Eukaryotic 3 x polyenoic
FA 2 cycles 80oC, 1200 psi, 20 min vs BD
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)
CEB
Microbial Insights, Inc.
52
Problem Rapid Detection/Identification of
Microbes
Propose a Sequential High Pressure/Temperature
Extractor Delivers Three Analytes to
HPLC/ESI/MS/MS
53
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

54
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 PLFA
Intact Lipids
Phospholipids PG, PE, PC, Cl, sn1
sn2 FA Amino Acid PG Ornithine lipid Archea ether
lipids Plamalogens
3. In-situ acidolysis in SFECO2
CG/MS
PHA Thansesterify Derivatize N-methyl
pyridyl
2,6 DPA (Spores)
LPS-Lipid A OH FA
HPLC/ES/MS/MS
55
Monensin Q1 scan
693.7
675.4
461.3
56
Respiratory Benzoquinone (UQ)
Gram-negative Bacteria with Oxygen as terminal
acceptor LOQ 580 femtomole/ul, LOD 200
femtomole/ul 104 E. coli
Q7
Q10
Q6
197 m/z
57
Pyridinium Derivative of 1, 2 Dipalmitin
M92-109
M mass of original Diglyceride LOD 100
attomoles/ uL
M92
58
HPLC/ESI/MS

• Enhanced Sensitivity
• Less Sample Preparation
• Increased Structural Information
• Fragmentation highly specific i.e. no proton
  donor/acceptor fragmentation processes occurring

CEB
59
Parent product ion MS/MS of synthetic PG
Q-1 1ppm PG scan m/z 110-990
(M H) -
Sn1 160, Sn2 182
Q-3 product ion scan of m/z 747 scanned m/z
110-990 Note 50X gt sensitivity
SIM additional 5x gt sensitivity 250X
60
LIPOMICS

• Tools
• Thou shall know structure concentration of
  each analyte
• Progress ?(equipment) for speed, specificity,
  selectivity and sensitivity)
• Extraction
• Extraction high pressure/temperature faster more
  complete
• Supercritical CO2 ?pressure becomes gas
  directly into MS inlet
• Sequential saves time effort
• Chromatography
• GC high pressure , 0.1 mm controlled flow, gt
  resolution faster
• SFC not much used
• HPLC smaller diameter, Chiral,
• CZE high resolution, requires charge, presently
  difficult
• Detection (lipids generally lack chromophores)
• NMR insensitive, expensive,
• Laser fluorescence not as specific but
  incredibly sensitive
• Light scattering cheep nonspecific
• Mass Spectrometry
• Ionization
• Electron impact 70 eV known structure
  catalogue but inefficient

61
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. ? Cyclo170/1617c
? Cyclo190/1817c (Stress) 4.
1617t/167c (Toxicity), often ?   5. ?
169c/1617c (Decreased Aerobic Desaturase) 6.
? 10Me160 Br171 PLFA (Sulfate-reducing
bacteria) 7. ? 10Me180 (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
62
Sampling Drinking Water-- Collect Biofilms on
Coupons
Biofilms not pelagic in the fluid

• 104-106 cells/cm2 vs 103-104 /Liter
• Integrates Over Time
• 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

63
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
64
Tap Water Biofilm 600 L in 3 weeks on 200 cm2
stainless steel beads
CEB
Microbial Insights, Inc.
65
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 i170/a170 0.7 4. No actinomycetes,
Mycobacteria (10 Me 180) 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 gtgtgt
Enterics (LPS 3 0H 10, 120 gtgt 30H 140) 10.
Chlorine toxicity oxirane dioic PLFA
CEB
Microbial Insights, Inc.
66
Biofilm Test System
67
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
• 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 140
• Pseudomonad's 3OH 100 3OH 120
• (Should Dog Drink from Toilet
  Bowl?)

68
Gram-negative Bacteria ? lipid-extracted
residue, ? hydrolize 1 Acetic acid, 30 min,
100oC, ? extract Lipid A
E. Coli Lipid A ? MS/MS ? 3 OH 140, 140 as
negative ions
? Acid sensitive bond
to KDO
Lipid A
?
?
?
14
14
14
14
12
14
69
Lipid A from E. coli Fatty acids liberated by
acid hydrolysis followed by acidcatalyzed
(trans) esterification
3OH 140 TMS
GC/MS of Methyl esters
3OH 140
140
phthalate siloxane
70
Electrospray Mass Spectrum of Lipid A Standard
from E. coli
140 m/z 227 OH 140 m/z 243
140 and 3 0H 140 are clearly detectible as
negative ions
71
WQ1 669 524 94
LIPID A     Pseudomonas 3 0H 120 3 0H 100
(water organism) Enteric Pathogens 30H 140
(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 140 LPS fatty acids Human feces 7
(0.6)/19 (4) 3 0H 10 12/ 3 OH 140 in human
feces mean(SD). Pet
safety if access to processed non-potable
water.
72
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.

73
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74
Compounds not readily ionized, that contain a
hydroxy group can be derivatized to their
methylpyridyl ether
Triclosan
CH2Cl2
2-flour-1-methylpyridinium ?-toluenesulfonate
TEA
75
Triclosan (Pyridinium derivative) Q1scan
380.3
218.1
Product ion scan
76
Sildenafil (Viagra) Q1 scan
475.4
100.1
Product ion scan
77
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
78
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
79
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 lt 1 for electron impact with
GC/MS

80
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
161 253 269 same as
12C 170 160 255 271 Unusual
12C 170 (269) 2 13C ? cy170 267
284 12C 180 (283) 13C 181 281
299 12C 206 , 12C 190 with 2 13C
? 191 295 314 12C 215 (315),
12C 216 (313)
?
13C bacteria added
?
No 13C bacteria added
81
1 Part 13C DA001 Spiked into 10 Parts of Soil
Sample
PE from soil with 13C added
?
?
PE from soil with 13C added
82
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
83
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 gt diversity than shrimp gut, Fore gut less
diverse than Hind gut.
84
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85
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
86
Microbial Viable Biomass Water (W), Fore Gut
(F), Hind Gut (H)
Note Log scale

W F H W F H W F H
W F H W F H
87
Microbial Viable Biomass Food, Flock, Water,
Fore, Gut Hind Gut


88
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 gt viable microbial
  biomass than water
• Gut and Water different PLFA from Shrimp food

89
Detection of specific per 13C-labeled bacteria,
Algae, etc. in Shrimp

• Feed per-13-C labeled bacteria, Algae,
  microeukaryotes to shrimp
• 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)
• 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

90
Problem Rapid Non-invasive Detection of
Infection or Metabolic stress for Emergency room
Triage

Human Breath sample GC/MS
91
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
92
Biomarkers for Confined Space Air Biocontaminant
Monitoring
1. Viable Biomass (all cells with an intact
membrane) PLFA 2. Detect Recently Lysed
(diglyceride fatty acids) 3. Community
Composition 4. 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 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
CEB
Microbial Insights, Inc.