Analysis of Lipid Metabolites =...
Transcript of Analysis of Lipid Metabolites =...
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Analysis of Lipid Metabolites
= Lipidomics
Pentti Somerharju
Transmed 11.10.2012
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Outline of the talk• What is Lipidomics? • Where is lipidomics needed?• Lipid classes and their functions• Why mass-spectrometric analysis?• Targeted or nontargeted analysis?• How to improve selectivity of detection? • Data analysis and interpretation• Dynamic lipidomics (study on lipid metabolism)• Glycerophospholipid homeostasis
– Regulation of synthesis– Regulation of degradation– Coordination by superlattice formation?
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Lipidome is part of the metabolome
DIET
Functional Lipidomics = How other molecules affect the lipidome and vice versa
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Where is lipidomics needed?Biology
Functions of lipids?Regulation lipid composition of membranes?
ClinicsSearch of diagnostic/predictive markersSearch of drugs targeting lipid disorders
IndustryModification of fats and oilsQuality control
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Significance of lipidomic data?
Very similar changes in lipidome when P53 or ApoE is knocked out
=>Changes in lipidome can be nonspecific!
”False biomarkers” also when the number of lipids analyzed exceeds the number of samples (patients)
Many confounding factors: diet, gut microbiota, physical activity, age, genetic background etc
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Targeted analysis• When you know what you are looking for
(e.g. studies on lipid homeostasis)• Selective detection (MS/MS or LC-SRM)
Nontargeted analysis• When you do not know…(search for disease markers/biomarkers)• Nonselective detection (LC-MS)
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Mammalian Lipid classes and their main Functions
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Glycerophospholipids
>10 classes (PC, PE, PS, PI, PA etc)
Each class consists of numerous species due to different fatty acid combinations
=> Thousands of different species possible!
Functions:Main structural components of membranesSecond messengers in signal transductionRegulators of membrane traffickingetc
O
OO
O
HCH2C CH2
O
PO
O
ON
H
H
H
H
HHH
H
HH
HH
H
Phosphatidylcholine
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Apolar (neutral) lipids
Fatty acidsStructural componets of other lipidsEnergy source/storagePrecursors of eicosanoids etc
Acylglycerols (TG etc)Fatty acid storage and transportHundreads of species possible
Cholesteryl estersStorage forms of cholesterol
O
O
O
O
HCH2C CH2
O
O
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Lactosylceramide
O
O
HNHC
CH2
O
C OH
CH
O
H
H
OH
H
O
H
H
OH
O
OH
H
H
OH
H
O
HO HH
OHGlygosphingolipids
Tens of different classes (head groups)Many different fatty acid=> Hundreads of possible species
FunctionsStructural component of membranesCell-cell regognitionSignal transduction
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Other lipid classes
Sterols (cholesterol etc)Structural components of membranesPrecursor or steroid hormones
Eicosanoids (prostagandins etc)Signaling
Prenol lipids Membrane anchors in some proteins
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A mammalian cell may contain thousands of differerent lipid species!
The biological challenge: Why?
Each lipid species has a specific function?
No..most lipids act in an ensemble!
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The Analytical challenge
How to quantify so many species with so different properties and present at sodifferent concentrations?
....with Mass Spectrometry!
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Advantages of mass-spectrometry
Conventional analysis (PL)
1. Lipid extraction 2. Separation of lipid classes
by TLC or HPLC3. Separation of molecular
species by HPLC4. Treatment by phospholipase A25. Analysis of fatty acids by GC 6. Data processing
Slow (several days)Low sensitivityPlenty of manual work
MS-analysis
1. Lipid extraction 2. MS/MS or LC-MS analysis3. Data processing
Fast (even less than 1 hour)Very high sensitivityCan be automated
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Which ionization mode?
Electrospray (ESI)– Does not cause fragmentation – Compatible with on-line LC
Matrix-assisted laser desorption (MALDI)– Used less due to e.g. suppression effects
=> All lipids not detected
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Electrospray ionization
Competition for charge => Suppression effects!
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Which mass analyser?
Triple quadrupole“Workhorse”of lipidomicsAllows precursor and neutral-loss scanning
= Lipid class-specific detectionModestly (?) priced
Fourier transform or OrbitrapVery high mass resolution and accuracy Allow detailed analysis of lipid structureVery expensive!
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Direct MS scanning (triple quadrupole)
Scanning
MS1 MS2Collision cell Detector
HeLa in C/M/2P 1:2:4 + 0.5mM NH4Ac
0
100
%
773.5659053
747.5251805
745.4742906
714.5022583
699.4021692685.77
18151672.4613010 701.45
8823
742.5320205
722.5014447 740.48
8765
748.5433056 761.53
29032
788.5334705
775.5415077
861.5429995790.51
22346818.5417591792.56
10744 833.459706 859.62
5838
863.6518468 885.53
14726
864.548600
887.589637
788.48
MS- spectrum of HeLa cell lipid extract
> More resolution/selectivity needed!
PIs
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Means to improve resolution
• MS/MS (tandem MS)
• LC-MS (with SRM)
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Lipid class -specific scanningPhospholipid class consist of species with the same polar head-group but different fatty acid combination
Phospholipid class Specific scanPhosphatidylcholines Precursors of +184Phosphatidylinositols Precursors of -241Phosphatidylethanolamines Neutral-loss of 141Phosphatidylserines Neutral-loss of 87
O
O O
O
CH
CH2
H2C O P
O
O
O X
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Precursor ion scanning
Requires a characteristic, charged product ion
PC => Diglyceride + phosphocholine (+184)
FragmentationScanning Static (+184)
Quadrupole 1mass ”filter”
Quadrupole 2mass ”filter”
Collision cell(Helium or Argon)
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Precursors of +184 => PC + SM
-Alkaline hydrolysis can be used to remove PCs
SM-16:0
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Neutral-loss scanning
Scanning Fragmentation Scanning
Qaudrupole 1 Quadrupole 2Collision cell
Mass interval = 141
..when the characteristic fragment is uncharged
PE => Diglyceride (+) + phosphoethanolamine (141)
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Selective detection of PE, PS and PI
• Martinilta kuva MS- ja muut
HeLa-cells +CTRL-siRNA + D9Chol+D4-EA+D3-Ser +HA
m/z690 700 710 720 730 740 750 760 770 780 790 800 810 820 830 840 850 860 870 880 890 900 910
%
0
100
%
0
100
%
0
100101208 Allstar 4h 9 1 (2.514) Sb (1,40.00 ); Sm (SG, 10x0.50); Sm (SG, 10x0.50) Neutral Loss 141ES+
6.66e5744.4665870
718.4288039
716.3212939
690.364887
712.328025
742.4208502
738.3120092
768.4552013
766.4485530
745.4295373
746.4221707
762.381579
800.5476617
790.4306640769.4
260924788.4
161459770.4125344
786.461259
801.5236480
802.564973 822.5
56773814.327630
101208 Allstar 4h 3 1 (3.019) Sb (1,40.00 ); Sm (SG, 10x0.50) Neutral Loss 87ES- 4.08e5788.2
407952
786.2221173
760.2156295
758.226686708.8
22382704.28871
732.315724
746.210512
761.284866 774.2
36017
842.3270773
789.2195176
810.297039790.2
54227 808.222345
834.174231812.2;44228
843.3108831
844.227259 884.1
16260896.214557
908.211330
101208 Allstar 4h 5 1 (3.123) Sb (1,40.00 ); Sm (SG, 10x0.50) Parents of 241ES- 5.18e5837.4
517529
809.4212029
795.423218
810.385502 823.3
63587835.430933
838.4212711
865.4165355
861.455440851.4
36455
866.467757 885.4
64017
PE
PS
PI
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Analysis of Sphingolipids
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Sphingosine Ceramide Lactosylceramide Ganglioside Sulfatide
O
O
HNHC
CH2
O
C O
CH
O
H
H
OH
H
O
H
H
OH
O
OH
H
H
OH
H
O
HO HH
OH
O
O
HNHC
CH2
O
C OH
CH
O
H
H
OH
H
O
H
H
OH
O
O
H
H
OH
H
O
HO HH
HO
O
COOH
O
HO
OH
AcHN
OH
HO
O
O
HNHC
CH2
O
C OH
CH
O
H
H
O
H
O
H
H
OH
H3SO3-
OOH
O
COOH
O
HO
OH
AcHN
OH
HO
H3SO3-
OO
HNHC
CH2
C OH
CH
OH
HHNHC
CH2
C OH
CH
OH
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Ceramide and Neutral Glycosphingolipids
- Precursors of sphingosine (m/z +264)
Ceramides
Glucosylceramides
24:1
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Sulfatides
- Precursors of sulfate (m/z -97)
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Quantification is not simple because intensity depends on:
Lipid head-group structureAcyl chain lengthAcyl chain unsaturationIons present (adduct formation)Detergent and other impurities (suppression)Solvent composition and instrument settings
=> Internal standards necessary!
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Data analysis software is essential
LIMSALIMSA does:
Peak picking and fitting
Peak overlap correction
Peak assignment (database of >3000 lipids)
Quantification using internal standards
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PAUSE
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Dynamic Lipidomics: Analysis of lipidMetabolism
BiosynthesisDegradation
Precursors:
– Choline, ethanolamine, glycerol, fatty acids,– Sphingosine, monosaccharides etc
– 2H or 13C -labeled
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Selective detection of headgroup-labeled PCs
D9-PC > Diglyceride + D9-Phosphocholine (+193)
7 0 0 7 5 0 8 0 0 8 5 0
Inte
nsity
m / z
P I + 1 8 4P I + 1 9 3
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LC-MS/MS with selective reaction monitoring
TIC
767 => 193 (D9-PC)
758 => 184 (PC)
Time1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00 13.00 14.00 15.00 16.00 17.00 18.00 19.00
%
0
100
1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00 13.00 14.00 15.00 16.00 17.00 18.00 19.00
%
0
100
1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00 13.00 14.00 15.00 16.00 17.00 18.00 19.00
%
0
100
767.5 > 193 (d9-PC-34:2)5.46 e6
758.5 > 184 (PC-34:2)9.66 e6
TIC
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Selective detection of other labeled GPLs
D6-PI = Precursors of -247
D4-PE = Neutral loss of 145
D3-PS = Neutral loss of 90
Specific labeling is easy to determine
All precursors can be present simultaneously!
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How a cell maintains the phospholipid homeostatis of its membranes?
• Biosynthesis• Remodeling• Degradation• Trafficking
How are these coordinated?
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Biosynthesis
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Glycerol-3-P DHAP Choline
Ethanolamine
1-acyl-G-3-P 1-acyl-DHAP
PA
CDP-DG
DAG
PI
Choline-PCDP-choline
PC
CT
PE
PS
PSS1
CDP-Ethanolamine
Ethanolamine-P
PG-P
PG
CL
PSS2
+ Inositol
Biosynthesis of Glycerophospholipids
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Our studies on GPL biosynthesis
PROTOCOL
Label cellular GPLs using a mix of D9-choline, D4-ethanolamine, D3-serine and D6-inositol
Load a GPL to cells using m -CD
Incubate and extract lipids
Quantify the labeled and unlabeled GPLs by MS using HG-specific scans
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Introduction of GPLs to cells using Cyclodextrin
Purpose:
1. Introduction of exogenous labeled GPLs to cells
2. Perturbation of GPL homeostasis
=> Concentration of a GPL can be encreased by 30 - 400% without compromizing cell viability (Kainu et al. J. Lipid Res. 2010)
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PI loading inhibits PI synthesis
0 5 10 15 20 250,0
0,3
0,6
0,9
labl
eled
/ un
labe
led
Time (h)
CTRL PI-vesicles
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0 5 10 15 20 250,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
labl
eled
/ un
labe
led
Time (h)
CTRL PE-vesicles
PE loading inhibits PE synthesis
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0 5 10 15 20 250,0
0,3
0,6
0,9
1,2
labl
eled
/unl
abel
ed
Time (h)
CTRL PS-vesicles
PS loading blocks PS synthesis
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0 5 10 15 20 250,0
0,3
0,6
0,9
1,2
labl
eled
/ un
labe
led
Time (h)
CTRL PC-vesicles
PC loading inhibits PC synthesis
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Product inhibition is specific!
But the details of the mechanism remains unknown..
Reversal on biosynthesis??
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Degradation
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A-type phospholipases (PLAs) are important players in GPL homeostasis because:
Boosting of the biosynthesis of a phospholipid class does not increase its cellular content
-E.g. over-expression of cytidyl transferase in HeLa cells did not increase tha amount of PC significantly (Baburina & Jackowski 1999)
…but the concentration of the deacylation product (glycerophospholine) was greatly increased
-Analogous data have been obtained for PE and PS
Which PLAs are involved?
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Protocol to identify the homeostatic PLAs1. Determine which PLAs expressed in HeLa cells
Ca2+-independent PLAsiPLA-betaiPLA-gammaiPLA2-deltaiPLA2-epsiloniPLA2-zetaiPLA2-eta
+ several cPLAs and sPLAs (not considered homeostatic)
2. Knock-down each iPLA in turn using RNAi
3. Determine effects on phospholipid turnover using labeled precursors and MS-analysis
4. Purify the implicated iPLAs and determine specificity and regulation in vitro (and in vivo..)
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PC turnover is 50% slower in iPLAknock-down cells
0 5 10 15 20 250.0
0.2
0.4
0.6
0.8
1.0
T 1/2 = 12.5h
CTRL siRNA iBeta siRNA
D9-
labe
led
/ Unl
abel
ed P
C
Chase (h)
T 1/2 = 18.7h
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Also PE and PS turnover is decreased in iPLA -knock down cells
Similar results for iPLA-delta and -gamma
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Open questions:
How do the homeostatic PLAs sense the ”proper” composition, i.e. how they are regulated?
How biosynthesis and degardation are coordinated?
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Burke JE , Dennis EA (2009) J. Lipid Res. 50:S237-S242
Does iPLA- activity depend on substrate efflux propensity?
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MS-based assay of PLA specificity
Protocol
•Mix the phospholipids (>100 different allowed)•Make vesicles •Add a phospholipase•Incubate and take samples at intervals •Extract lipids and analyze by MS
=> High throughput=> No matrix ambiquiety!
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Effects of acyl chain length and unsaturation on hydrolysis by PLA
Mixture of 27 PC species:
- Acyl Chain lenght = 6 - 22 carbons
- Double bonds = 0 - 12 per molecule
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STD = Shingomyelin
0 10 20 300,00
0,25
0,50
0,75
1,00
0
100
7 00 800 9000
100
B
[spe
cies
]/T0
T im e (m in)
E G F C 20 C 21 C 25
S TD
0 m inA
Rel
ativ
e In
tesi
ty (%
)
S TD 3 m in
m /z
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Activity of iPLA- deceases with increasing substrate hydrophobicity
26 28 30 32 34 36 38 40 42 440,0
0,5
1,0
1,5
2,0
2,5
3,0
3,5
Rel
ativ
e ra
te o
f hyd
roly
sis
Total acyl chain carbons
PC-mix Hydrolysis decreases strongly with acyl chain length
Hydrolysis increases with increasing unsaturation
Substarate efflux is rate-limiting?
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Efflux propensity can be determined from the rate of interbilayer translocation
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1E-7
1E-5
1E-3
0,1
10
24 26 28 30 32 34 36 38 40 42 44 46
0,01
0,1
1
A
Tran
sfer
rate
(h-1)
0 1 2 4 6 8 12
B
Rel
ativ
e hy
drol
ysis
rate
Total acyl chain carbon number
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How could efflux regulate homeostatic PLAs in vivo?
Superlattice Model predics that efflux propensity (chemical activity) increases abruptly
at ”critical” compositions
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=> Only a limited number of “allowed” compositions !
Superlattice model
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Xguest = 1/(a2 + a * b + b2)
Two-component bilyers
0.154 0. 25 0.50
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Superlattices are dynamic minimum-energy structures
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Regular distribution represent the lowest free energy state of the bilayer because it:
1. Allows optimal packing of different lipids in the bilayer
2. Minimizes the proximity of charged lipids
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Optimimal packing of lipids with complementary shapes
Suboptimal packing Optimal packing
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Minimal charge-charge repulsion
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Excess of lipid A => Enhanced efflux (fugality)
=> Hydrolysis of by a homeostatic PLA
Regulation of homeostatic phospholipases by Superlattice formation?
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Excess of a phospholipid A => Increased chemical activity of A
=> Increased feed-back inhibition of the synthetic enzyme
Regulation of biosynthetic enzymes by Superlattice formation
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SL-based regulation of synthesis and degradation
- Simple mechanism that minimises compositional fluctuations
Synthesis PC Degradation Synthesis PE Degradation
PC-Regulator PE- Regulator
PS-Regulator PI- Regulator
Synthesis PS Degradation Synthesis PI Degradation
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Conclusions
Heavy-isotope –labeled precursors combined with Mass spectrometry is a superior tool to study GPL metabolism
Feed-back -inhibition by the end product seems to regulate the biosynthesis of major GPLs
Substrate efflux propensity could regulate the activity of homeostatic PLAs
Superlattice formation could coordinate synthesis and degradation by modulating the chemical activity/efflux propensity of GPLs