Analysis of Lipid Metabolites = Lipidomics Pentti Somerharju Transmed 11.10.2012
utline of the talk What is Lipidomics? Where is lipidomics needed? Lipid classes and their functions Why mass-spectrometric analysis? Targeted or nontargeted analysis? ow 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?
Lipidome is part of the metabolome DIET Functional Lipidomics = ow other molecules affect the lipidome and vice versa
Where is lipidomics needed? Biology Functions of lipids? Regulation lipid composition of membranes? Clinics Search of diagnostic/predictive markers Search of drugs targeting lipid disorders Industry Modification of fats and oils Quality control
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
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)
Mammalian Lipid classes and their main Functions
Glycerophospholipids 2 C C C 2 P N >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 membranes Second messengers in signal transduction Regulators of membrane trafficking etc Phosphatidylcholine
Apolar (neutral) lipids C 2 C C 2 Fatty acids Structural componets of other lipids Energy source/storage Precursors of eicosanoids etc Acylglycerols (TG etc) Fatty acid storage and transport undreads of species possible Cholesteryl esters Storage forms of cholesterol
Glygosphingolipids C 2 C C C N Tens of different classes (head groups) Many different fatty acid => undreads of possible species Functions Structural component of membranes Cell-cell regognition Signal transduction Lactosylceramide
ther lipid classes Sterols (cholesterol etc) Structural components of membranes Precursor or steroid hormones Eicosanoids (prostagandins etc) Signaling Prenol lipids Membrane anchors in some proteins
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!
The Analytical challenge ow to quantify so many species with so different properties and present at so different concentrations?...with Mass Spectrometry!
Advantages of mass-spectrometry Conventional analysis (PL) 1. Lipid extraction 2. Separation of lipid classes by TLC or PLC 3. Separation of molecular species by PLC 4. Treatment by phospholipase A2 5. Analysis of fatty acids by GC 6. Data processing Slow (several days) Low sensitivity Plenty of manual work MS-analysis 1. Lipid extraction 2. MS/MS or LC-MS analysis 3. Data processing Fast (even less than 1 hour) Very high sensitivity Can be automated
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
Electrospray ionization Competition for charge => Suppression effects!
Which mass analyser? Triple quadrupole Workhorse of lipidomics Allows precursor and neutral-loss scanning = Lipid class-specific detection Modestly (?) priced Fourier transform or rbitrap Very high mass resolution and accuracy Allow detailed analysis of lipid structure Very expensive!
Direct MS scanning (triple quadrupole) Scanning MS1 Collision cell MS2 Detector MS- spectrum of ela cell lipid extract ela in C/M/2P 1:2:4 + 0.5mM N4Ac 100 % 0 672.46 13010 685.77 18151 699.40 21692 701.45 8823 714.50 22583 745.47 42906 742.53 20205 722.50 14447 740.48 8765 747.52 51805 748.54 33056 761.53 29032 773.56 59053 775.54 15077 788.53 34705 788.48 790.51 22346 818.54 792.56 17591 10744 833.45 9706 859.62 5838 861.54 29995 > More resolution/selectivity needed! PIs 863.65 18468 864.54 8600 885.53 14726 887.58 9637
Means to improve resolution MS/MS (tandem MS) LC-MS (with SRM)
Lipid class -specific scanning Phospholipid class consist of species with the same polar head-group but different fatty acid combination C 2 C 2 C P X Phospholipid class Specific scan Phosphatidylcholines Precursors of +184 Phosphatidylinositols Precursors of -241 Phosphatidylethanolamines Neutral-loss of 141 Phosphatidylserines Neutral-loss of 87
Precursor ion scanning Requires a characteristic, charged product ion PC => Diglyceride + phosphocholine (+184) Scanning Fragmentation Static (+184) Quadrupole 1 mass filter Collision cell (elium or Argon) Quadrupole 2 mass filter
Precursors of +184 => PC + SM SM-16:0 -Alkaline hydrolysis can be used to remove PCs
Neutral-loss scanning..when the characteristic fragment is uncharged PE => Diglyceride (+) + phosphoethanolamine (141) Mass interval = 141 Scanning Fragmentation Scanning Qaudrupole 1 Collision cell Quadrupole 2
Selective detection of PE, PS and PI ela-cells +CTRL-siRNA + D9Chol+D4-EA+D3-Ser +A 101208 Allstar 4h 9 1 (2.514) Sb (1,40.00 ); Sm (SG, 10x0.50); Sm (SG, 10x0.50) Neutral Loss 141ES+ 744.4 100 665870 6.66e5 % 0 690.3 64887 716.3 212939 712.3 28025 718.4 288039 738.3 120092 742.4 208502 745.4 295373 746.4 221707 766.4 485530 762.3 81579 768.4 552013 769.4 260924 788.4 770.4 161459 125344 786.4 61259 790.4 306640 800.5 476617 801.5 236480 802.5 64973 822.5 814.3 56773 27630 PE 101208 Allstar 4h 3 1 (3.019) Sb (1,40.00 ); Sm (SG, 10x0.50) Neutral Loss 87ES- 788.2 100 407952 4.08e5 % 0 704.2 708.8 8871 22382 732.3 15724 758.2 746.2 26686 10512 760.2 156295 761.2 84866 774.2 36017 786.2 221173 789.2 195176 810.2 790.2 97039 54227 808.2 812.2;44228 22345 834.1 74231 842.3 270773 843.3 108831 PS 844.2 27259 884.1 16260 896.2 14557 908.2 11330 101208 Allstar 4h 5 1 (3.123) Sb (1,40.00 ); Sm (SG, 10x0.50) Parents of 241ES- 837.4 100 517529 5.18e5 PI 809.4 212029 Martinilta kuva MS- ja muut % 0 795.4 23218 810.3 85502 823.3 63587 835.4 30933 690 700 710 720 730 740 750 760 770 780 790 800 810 820 830 840 850 860 870 880 890 900 910 838.4 212711 851.4 36455 861.4 55440 865.4 165355 866.4 67757 885.4 64017 m/z
Analysis of Sphingolipids
C AcN C 3 S 3 - C 2 C N C C C 2 C N C C C 2 C N C C AcN C 2 C C C N C 2 C N C C 3 S 3 - Sphingosine Ceramide Lactosylceramide Ganglioside Sulfatide
Ceramide and Neutral Glycosphingolipids - Precursors of sphingosine (m/z +264) Glucosylceramides Ceramides 24:1
Sulfatides - Precursors of sulfate (m/z -97)
Quantification is not simple because intensity depends on: Lipid head-group structure Acyl chain length Acyl chain unsaturation Ions present (adduct formation) Detergent and other impurities (suppression) Solvent composition and instrument settings => Internal standards necessary!
Data analysis software is essential LIMSA LIMSA does: Peak picking and fitting Peak overlap correction Peak assignment (database of >3000 lipids) Quantification using internal standards
PAUSE
Dynamic Lipidomics: Analysis of lipid Metabolism Biosynthesis Degradation Precursors: Choline, ethanolamine, glycerol, fatty acids, Sphingosine, monosaccharides etc 2 or 13 C -labeled
Selective detection of headgroup-labeled PCs D 9 -PC > Diglyceride + D 9 -Phosphocholine (+193) PI +184 PI +193 Intensity 700 750 800 850 m /z
LC-MS/MS with selective reaction monitoring 100 TIC TIC % % 0 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 19.00 100 767 => 193 (D 9 -PC) 767.5 > 193 (d9-pc-34:2) 5.46 e6 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 19.00 758 => 184 (PC) 758.5 > 184 (PC-34:2) 9.66 e6 0 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 Time
Selective detection of other labeled GPLs D 6 -PI = Precursors of -247 D 4 -PE = Neutral loss of 145 D 3 -PS = Neutral loss of 90 Specific labeling is easy to determine All precursors can be present simultaneously!
ow a cell maintains the phospholipid homeostatis of its membranes? Biosynthesis Remodeling Degradation Trafficking ow are these coordinated?
Biosynthesis
Biosynthesis of Glycerophospholipids Glycerol-3-P DAP Choline 1-acyl-G-3-P 1-acyl-DAP CDP-choline CT Choline-P PA PC PSS1 PG-P CDP-DG + Inositol DAG PE PSS2 PS PG PI CDP-Ethanolamine CL Ethanolamine-P Ethanolamine
ur studies on GPL biosynthesis PRTCL Label cellular GPLs using a mix of D 9 -choline, D 4 -ethanolamine, D 3 -serine and D 6 -inositol Load a GPL to cells using m -CD Incubate and extract lipids Quantify the labeled and unlabeled GPLs by MS using G-specific scans
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)
PI loading inhibits PI synthesis 0,9 CTRL PI-vesicles lableled / unlabeled 0,6 0,3 0,0 0 5 10 15 20 25 Time (h)
PE loading inhibits PE synthesis 0,8 0,7 0,6 CTRL PE-vesicles lableled / unlabeled 0,5 0,4 0,3 0,2 0,1 0,0 0 5 10 15 20 25 Time (h)
PS loading blocks PS synthesis 1,2 CTRL PS-vesicles lableled/unlabeled 0,9 0,6 0,3 0,0 0 5 10 15 20 25 Time (h)
PC loading inhibits PC synthesis 1,2 CTRL PC-vesicles lableled / unlabeled 0,9 0,6 0,3 0,0 0 5 10 15 20 25 Time (h)
Product inhibition is specific! But the details of the mechanism remains unknown.. Reversal on biosynthesis??
Degradation
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 ela 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?
Protocol to identify the homeostatic PLAs 1. Determine which PLAs expressed in ela cells Ca2+-independent PLAs ipla-beta ipla-gamma ipla 2 -delta ipla 2 -epsilon ipla 2 -zeta ipla 2 -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..)
PC turnover is 50% slower in ipla knock-down cells D9-labeled / Unlabeled PC 1.0 0.8 0.6 0.4 0.2 T 1/2 = 12.5h T 1/2 = 18.7h CTRL sirna ibeta sirna 0.0 0 5 10 15 20 25 Chase (h)
Also PE and PS turnover is decreased in ipla -knock down cells Similar results for ipla-delta and -gamma
pen questions: ow do the homeostatic PLAs sense the proper composition, i.e. how they are regulated? ow biosynthesis and degardation are coordinated?
Does ipla- activity depend on substrate efflux propensity? Burke JE, Dennis EA (2009) J. Lipid Res. 50:S237-S242
MS-based assay of PLA specificity => igh throughput => No matrix ambiquiety! Protocol Mix the phospholipids (>100 different allowed) Make vesicles Add a phospholipase Incubate and take samples at intervals Extract lipids and analyze by MS
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
100 A STD 0 min STD = Shingomyelin Relative Intesity (%) 0 100 STD 3 min 0 700 800 900 m/z [species]/t 0 1,00 0,75 0,50 0,25 B E G F C20 C21 C25 0,00 0 10 20 30 Time (min)
Activity of ipla- deceases with increasing substrate hydrophobicity 3,5 3,0 PC-mix ydrolysis decreases strongly with acyl chain length Relative rate of hydrolysis 2,5 2,0 1,5 1,0 0,5 0,0 26 28 30 32 34 36 38 40 42 44 Total acyl chain carbons ydrolysis increases with increasing unsaturation Substarate efflux is ratelimiting?
Efflux propensity can be determined from the rate of interbilayer translocation
Transfer rate (h -1 ) 10 0,1 1E-3 1E-5 A 0 1 2 4 6 8 12 Relative hydrolysis rate 1E-7 1 0,1 B 0,01 24 26 28 30 32 34 36 38 40 42 44 46 Total acyl chain carbon number
ow could efflux regulate homeostatic PLAs in vivo? Superlattice Model predics that efflux propensity (chemical activity) increases abruptly at critical compositions
Superlattice model => nly a limited number of allowed compositions!
Two-component bilyers X guest = 1/(a 2 + a * b + b 2 ) 0.154 0. 25 0.50
Superlattices are dynamic minimum-energy structures
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
ptimimal packing of lipids with complementary shapes Suboptimal packing ptimal packing
Minimal charge-charge repulsion
Regulation of homeostatic phospholipases by Superlattice formation? Excess of lipid A => Enhanced efflux (fugality) => ydrolysis of by a homeostatic PLA
Regulation of biosynthetic enzymes by Superlattice formation Excess of a phospholipid A => Increased chemical activity of A => Increased feed-back inhibition of the synthetic enzyme
SL-based regulation of synthesis and degradation Synthesis PC Degradation Synthesis PE Degradation PC-Regulator PE- Regulator PS-Regulator PI- Regulator Synthesis PS Degradation Synthesis PI Degradation - Simple mechanism that minimises compositional fluctuations
Conclusions eavy-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