Computational Lipidomics and Lipid Bioinformatics: Filling in the Blanks

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Copyright 2016 The Author(s). Published by Journal of Integrative Bioinformatics. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License (http://creativecommons.org/licenses/by-nc-nd/3.0/). . h iioisWorkflow Lipidomics The their spectra, 2.1 mass of analysis the sample to a spectrometry from mass lipids interpretation. using extracting and from integration lipidome lipidomics the of measuring overview detailed and a provides Insights section Functional This to Sample a From Lipidomics: 2 a high- of involving entities. quantification workflows lipid and molecular lipid-analytical identification of in plethora high-throughput progress allowing rapid spectrometers to by field mass created demand needed resolution was urgently high that an a is gap creating research the lipidomics cells of close computational mammalian models. space Hence, investigating lipid combinatorial aid. computational when the computational creating for lost reduces (16:1 for is greatly acid advantageous advantage is also palmitoleic this which This However, the yeast e.g. in in [16]. acids, species desaturating cis-9), fatty lipid of (18:1 possible unsaturated capable acid of such only oleic acids amount is and fatty limited and cis-9) of a (18:0) in set acid in research limited stearic resulting a lipid and position only for (16:0) synthesizes used and acid [15], widely metabolic palmitic advantages cell’s been as many a has by its limited that to be organism due may eukaryotes an of Yeast, composed are positions. capabilities. they carbon lipids biosynthetic Cellular of various sets entities. positions at lipid the molecular bond attached and theoretical lipidomes double in groups explosion hydroxyl types, combinatorial a as bond observe we such double Therefore, have modifications bonds, acyl can other fatty double acid many many fatty atoms, single or and carbon A one [14]. of contain structure numbers can molecular they varying class-specific lipid since a diversity to structural attached high moieties a display lipids Single changes composition between progression. nomencla- relationship disease (lipid the and 13] highlights profiles 12, This lipid 11, 2.2). in [10, section NASH in and explained NAFL is for (diHETrE) markers ture acids potential as dihydroxyeicosatrienoic identified and be could (HETE) acids hydroxyeicosatetraenoic fatty eicosapen- certain as non-alcoholic such (20:5 states acids fatty acid progression specific taenoic (NASH), different steatohepatitis two non-alcoholic and the (NAFL) liver comprises which (NAFLD), four and disease carbons 20 with acid fatty Journal of IntegrativeBioinformatics,13(1):299,2016 Journal doi:10.2390/biecoll-jib-2016-299 mc nlss h ubr niete”r a”bxidct h ifrn rnir ncompu- in review. frontiers this different throughout the and indicate section integrated this box of in lab” context presented ”Dry the as the in lipidomics inside interpretation tational an numbers to The sample analyses. a from omics workflow lipidomics The 1: Figure ω 3 n ooaeani cd(22:6 acid docosahexaenoic and -3) dul od.Freape nnnachlcftyliver fatty non-alcoholic in example, For bonds. cis-double ω 3 swl secsnissc as such eicosanoids as well as -3) http://journal.imbio.de/ ∆-9 2

Copyright 2016 The Author(s). Published by Journal of Integrative Bioinformatics. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License (http://creativecommons.org/licenses/by-nc-nd/3.0/). olpdmc aata nert uhfidnswt te mc aaa ela ltom for platforms as well dedicated as methods data Developing omics other link. with suitable findings a such be integrate that to interactions data appears enzymatic lipidomics and proteomics between to physical that ex- relation direct lipid so tight display dedicated lipids often Proteins require their with often and function. lipids properties and but biochemical structure 26]; distinct molecular [25, their of includes proteomics omics This because and heterogeneous pertise, genomics fields. combining omics surfaced e.g. insights other already sets, from lipid-mediated have data sets solutions of data which incorporation with for the challenges together and various context, analysis biology functional systems is a step into last and third The start-to- high-throughput a facilitating routine. interfaces stan- lipidomics formulate standardized end to and communities routines procedures biochemistry evaluation and operating analytics statistical dard lipid sophisticated with of collaboration the development also the but quality only for not is checks data including This lipid statistical lipidomics analysis. high-quality at produce functional to for ending automatically ready and fully sets formulate operate extraction could to lipid counteract solutions implemented software at likely were control, beginning would procedures workflow operating methods standardization, standard of universal routine lack if therefore a However, a assur- and to and nature. set-specific Due critical assessment data its scheme. quality mostly quantification for next is and the post-analysis step identification assembled, statistical this the been a of has part lipidome is as a workflow and ance lipidomics characterized a been have in set step data a in outcomes lipids mixed After very with Cur- solutions applicable). results. in-house (where comparable non-standardized, workflow hardly by as and entire solved results the often storing throughout is and standards this saving rently, of for formulation formats data the suitable as detec- and well peak nomenclature of lipid details to technical heuristics from large-scale, ranges tion from task lipids This of experiments. quantification of lipidomics and development high-throughput identification the is automatic realm the lipidomics for the solutions and in software challenges the more computational Therefore, become current of have 1) heterogeneous. data-sets Figure and (see derived fragmented, attention, complex, of comprehensive, center more the lipid into molecular lipidome-scale moving party With third analysis analysis. with for- species functional processing data and further suitable statistical immediate a allow subsequent in to for compositions standardized software lipid be should found format about This report comprehensive mat. libraries a spectral saving annotating to as and required reading well is unifying, as it organizing, etc., of modes, experiments capable and ionization lipid support ranges, identified large-scale software specific scan have In be points, a [24]. time of must quantification types, addition accurate extract sample careful several for sample The including crucial is the [23]. standards spectrometry in lipid mass lipids of via set step, done mostly extraction ionization is the electrospray which during After [20] quantified efficiency acetylation ionization 22]. or varying [21, of [19] because sulfation (ESI) more applied as various be from such to derivatization lipids have re- of chemical may citations, spectrum further 40000 broader classes, over even lipid and Dyer an known and 50000 for Bligh over probing by with and when techniques However, [17] applied spectively. al. commonly et procedures most Folch The extraction by the Multiple proposed are 1. sample. methods [18] a Figure the from which in lipids of shown of published extraction been as have the steps is lipidomics necessary in several step comprises first workflow lipidomics typical A Journal of IntegrativeBioinformatics,13(1):299,2016 Journal doi:10.2390/biecoll-jib-2016-299 h eodfrontier second the seFgr )i computational in 1) Figure (see http://journal.imbio.de/ rtfrontier first 3

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Copyright 2016 The Author(s). Published by Journal of Integrative Bioinformatics. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License (http://creativecommons.org/licenses/by-nc-nd/3.0/). eetcmrssol e rgetseta oee,lpdmc ehooyfcsson in- focuses of technology set lipidomics However, data the fragment-spectra. when can few fragment-spectra process a particular inference only of The comprises inspection [57]. terest manual as sphingolipids such by yeast [50] performed and phosphatidylethanolamine classes be 56], [53], lipid [55, phosphatidylcholine many phosphatidylinositol particular for [52], a [54], explored acid of well phosphatidic existence been the [51], for have ceramides be evidence pathways should molecular contain Fragmentation species as can lipid serve lipid. molecular also that of fragments they inference multiple since rep- However, specific on ion which that based glycerol. product highlights 184.073321 the same This of of the fragment m/z phosphocholine. position show typical a an sn-3 may has A the precursors mode from (Sphingomyelin) ion [49]. dissociated SM a positive that details by in phosphocholine molecular molecule the e.g. unique PC resents isolated, reveal a are then from > ions in- derives may (n First, fragmented that MSn ratio be 48]. of m/z case 47, can fragment’s the [46, ions A in spectrometer details, and mass filter, structural a mass more of multi-pole cell acquire collision to the and side power resolving gain To analysis MSn and dissociation Fragment 2.5 heterogeneous dimension. independent, time several additional and from an analysis and lipidome computational spectra complete subsequent mass a acquisition sensitivity for reassembling spectrum challenge possible in and bigger simplicity control best a of quality ensures imposes cost the it strategy accurate Additionally, at and This achieved speed. complete is this producing 45]. However, fragmentation 44, profiles. and prior lipidome 43, extract separation the 42, chromatographic in spectrometry 23, utilizing present mass by [41, species tandem lipid analysis of chromatography MSMS separation liquid to optimal a achieve to to system introduced (LC-MSMS) are Af- extracts protocols. extraction the class-optimized terwards, lipid and category- lipid employs lipidomics Targeted lipidomics Targeted chromatographic 2.4 of lack the to due sensitivity of protocols. cost extraction the optimized at and iso- profiles but separation or lipidome manner 37:1 spectral PE In high-throughput sufficient 34:1, produces a 16:1/18:0. (PC lipidomics in well PC untargeted as and essence, isomeric analyses In 16:0/18:1 be functional PC isomers). may for topic classes e.g. lipid information isomers, across of in molecules piece lipid result critical addition, compositions particular a acid of often fatty incorporation the is many since since molecules limitation lipid major to a into lead imposes acids compositions. may this fatty analysis lipidome which lipid Isomeric distorted resolved of to be case eventually the compounds. cannot In and molecules chemical quantification isobaric matching and as to identification well values erroneous as m/z intact isomers) their Detecting positional map spectrum. (e.g. mass to common allows the molecules mass creating ESI lipid transform (ordinate) (mass-over-charge) an Fourier intensity m/z by their or and with ionized (abscissa) (ITMS) reported are ratio and trap detected molecules ion are lipid high-resolution ions Finally, lipidomics a (FTMS). spectrometer shotgun into injected In directly 40]. and 39, source 38, 37, [36, lipidomics Journal of IntegrativeBioinformatics,13(1):299,2016 Journal doi:10.2390/biecoll-jib-2016-299 usqetyfragmented subsequently 1) http://journal.imbio.de/ n − 1 times. 6

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Copyright 2016 The Author(s). Published by Journal of Integrative Bioinformatics. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License (http://creativecommons.org/licenses/by-nc-nd/3.0/). ti iie opopoii dnicto ny hshlpd r eeal ipei structure in simple generally are but Phospholipids analysis only. MS/MS identification for phospholipid solution to available support limited freely also is these a Lipotype it is of and [77] All LipidSearch Greazy However, mea- separation. are molecule. extracts. chromatographic lipidomes lipid lipid where biological single framework since from a lipidomics process directly for (shotgun) inference sured untargeted specific the the or in within unique challenge operate not tools combinatorial are frag- a observable fragments is on observed this based many mentioned, inference briefly computational As through from only species spectra ments. lipid far fragmentation molecular [76] so annotating of Lipotype and field, library identifying and the of a [75] for capable in LipidSearch are configuration advancement They [66], ability. revolutionary acyl ALEX123 this a possess fatty as quite such particular frag- is products a unique available this of commercially Since with inference level molecule. allow species lipid that molecular each evidence on solutions structural spectra lipidome-scale as fragmentation high-throughput, ments from automatic towards lipids advance conducted. identify be to that can necessity experiment a the (molec- of Hereafter, is set data There data reassembled. this entire are From the compositions of lipidome reported. analysis before functional finally inferred and is statistical first ions are fragment lipid species corresponding detected lipid of their ”Untar- ular) set as subsection The isomers, well required. of of as are methods kinds ions collection correction all a sophisticated (see fragments- and on challenge lipidomics”) of a geted based quantification proposes accurate identification species attention, While molecular some inferred received re- has sub-classes. 16:0/18:1) spectra which PC and fragment species measured classes lipid e.g. each lipid for lipid, patterns between each fragmentation differ containing on database configuration fragment acyl lipid a fatty species quires lipid the molecular provides their lipids. by (this resolved theoretical composition lipidomes lipid of of quantification intact possibly set of and compositions. specific Identification quantification sum a their and by for identification annotated the accordingly library are allows spectral which data multiple molecules acquired patterns. spectral to produces isotope the environments values using commonly scan both by m/z This In resolved to peak be is may mapping compound. way which analytes by underlying Another isobaric followed the and and isomeric is identify with ON), detection to matches Concord, peak entry ALEX123 SCIEX, a database [73], (AB Usually, ALEX a LipidView are of those capable Scientific), [74]. are Among Fisher LipidXplorer that (Thermo frontier). decade last (first LipidSearch the spectra [66], over MS developed full been annotating have solutions quickly software of number A Quantification and Identification Lipid Computer-assisted 3.2 Journal of IntegrativeBioinformatics,13(1):299,2016 Journal doi:10.2390/biecoll-jib-2016-299 ercfrlpdm oooy[2.Ti oooymti a uniysystematic quantify can metric homology This [72]. homology lipidome for metric A • [71]. pathways metabolic into data lipidomics integrating for framework computational A • ifrne nlpdm opstos ec,i sa al praht iioemeta- lipidome to approach early an is it Hence, analyses. compositions. lipidome in differences on based reactions catabolic metabolism. and lipid biosynthetic investigate of considerations. to discovery thermodynamic used and be reconstruction can allows and It NICELips named was framework This http://journal.imbio.de/ 8

Copyright 2016 The Author(s). Published by Journal of Integrative Bioinformatics. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License (http://creativecommons.org/licenses/by-nc-nd/3.0/). eeeprmnal aiae.Atidsuyb Sch by study which third enzymes involved A between couplings validated. functional experimentally metabolism. for were acid evidence arachidonic provided in involved approach cyclooxygenase pathways This the mediated for (LOX) ODEs lipoxygenase Subse- on and based 5-triphosphate. (COX) model adenosine kinetic with comprehensive receptor a developed purinergic A marrow- they P2X7 quently, Kdo2-Lipid bone the with mouse of 4 stimulation receptor in Toll-like and network the analyzed (KLA) of metabolic [82] stimulation eicosanoid upon al. (BMDM) the et macrophages of Kihara derived changes discovery. changes flux drug elucidated dynamic for model and information This temporal provided [81]. and for al. treatment model et differential Yang drug A ordinary by upon on [80]. developed been based al. has leukocytes et (ODEs) polymorphic Rao equations human by in proposed metabolism been for acid method has general arachidonic networks rather biochemical A of dynamics. temporal various modeling by including kinetic paradigm systems analysis metabolic the integrating extend fruit- of also provided way but a already omics provide has only modeling not computational that that contributions advancement ful valuable a therefore individual con- is on It and based difficult often far is so proteomics is and integration transcriptomics solutions. genomics, Data over with mining entirely. data data missing lipid and are necting meta-analyses particular sets perform the data that for published platforms only molecules multiple Online done lipid usually set. unresolved is data frag- several mining lipid by to Data investigated as mapped quantification. well be accurate as to maintaining isotopomeres peak while by single deconvolution a peak allowing quantitative patterns pack- for mentation analysis e.g. lipidomics-specific needed, and are spectrometry direct ages mass using Similarly, analyses spectrometry [79]. mass adduct of ionization characteristic Here, chemical a [78]. exploited, data are oxi- HPLC-MS patterns lipid, high-mass-accuracy formation in ion of biomarkers R identification oxylipin the putative and published and lipid, al. dized annotation et for high-throughput Collins sufficient recently for Very is LOBSTAHS developed. R package been providing as yet packages not such have lipid-specific software routines comprehensive statistical standardized statisti- assessments, regular mining, statistical While data standard conduct integration. conducting (semi-)automatically data can or these informa- analysis, of of the levels cal none Partially, various but with complexity frontiers). sets and third data tion pre-processed and systemic provide (second tools a system software form soft- biological aforementioned to conversions, and underlying interpretation unit the Algorithms and and format of prediction includes analysis: understanding modeling, that analysis, lipid data of and for amounts processing created. vast support statistical mining rapidly computational of more capable in be are gap that will ware samples next biological the of- various reveals more from applied This lipidomes being complex software of quantification data and ten, identification lipid (semi-)automatic With Landscape Omics the Linking Bioinformatics: Lipid 3.3 an remains currently Overall, spectra complex. fragment more on vastly based be problem. lipidomes to unresolved molecular fragments of observed on quantification inference based exact causing species space combinatorial lipid the molecular expanding greatly three of acids of fatty consist (TAGs) four Triacylglycerols of cardiolipins acids. and fatty two or (lysophospholipids) one containing Journal of IntegrativeBioinformatics,13(1):299,2016 Journal doi:10.2390/biecoll-jib-2016-299 thl ta.[3 oesacompre- a models [83] al. et utzhold ¨ http://journal.imbio.de/ 9

Copyright 2016 The Author(s). Published by Journal of Integrative Bioinformatics. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License (http://creativecommons.org/licenses/by-nc-nd/3.0/). h etfieyas hswl otiueipratisgt notevrosrlso iisin lipids of roles various the into insights important over pathways. studies contribute omics-driven metabolic will in and This aspect networks regular biological a years. become five will next analysis with lipid the and that infrastuc- disease expected bioinformatics is and and it health workflows ture, standardized in solutions, investigations computational bioinformatics lipid-mediated of the provision of in the attention increase gained recently an is just With infrastructure has accessible and community. freely lipidomics and for publicly missing the largely this with far However, databases so lipid branches. dedicated these few fuels a has greatly of infrastructure which exception This fields omics datasets. other available for publicly implemented on been com- analyses support to meta and up and quality built large-scale gradually set enable and be data must and collaborations infrastructure high a bioinformatics interdisciplinary ensure critical to to close parallel, In sample procedures requires parability. operating the This standard from to needed. protocols agreement is analytical common in- set and workflows data biochemical of processed development as readily the well Then, as determining computational modeling. research quantitative cluding biomedical in accu- enabling aspect sufficiently and important be an fates is may metabolic moieties approximations high-throughput acyl but fatty far the quantification So Knowing exact rate. of lipidomes. capable complete lipid not for molecular of are data quantification solutions fragment exact the on is based lipidomics of compositions core species the at problems major the of One general. in research omics-driven also but itself field are: the are frontiers strategies advance three computational These to which only in not lipidomics needed in urgently frontiers major three describes review This Conclusion and Summary 4 data. multi-omics discrete in from approaches metabolism models modeling eicosanoid continuous computational and for of dynamic, efficacy model comprehensive, the and kinetic create demonstrate metabolism to a studies eicosanoid these [84] of of network integrated All al. an desat- et signaling. on as Gupta based developed such by was properties study cells their macrophage fourth the and a on species cat- conclusions In derive lipid the to of uration. in allows distributions mutations it potential location-dependent and changes, and precursors fatty parameter different time- the various of of Additionally, provision effects with or the time. species enzymes analyze over alyzing lipid to headgroups all used multiple be of and can dynamics desaturation model the of of follow instead degrees approach to different stochastic allowed acids, object-oriented approach an This using metabolism ODEs. lipid yeast of part hensive Journal of IntegrativeBioinformatics,13(1):299,2016 Journal doi:10.2390/biecoll-jib-2016-299 .Tidfote:fntoa nlss aamnn n nerto nomlioissettings multi-omics into integration and data-mining analysis, functional frontier: Third 3. comprehensive of maintenance and control quality for routines statistical frontier: Second 2. fragment including libraries spectral mass heterogeneous large, of analysis frontier: First 1. iioisdtst n tr-oedstandardization start-to-end and datasets lipidomics quantification and identification for spectra http://journal.imbio.de/ 10

Copyright 2016 The Author(s). Published by Journal of Integrative Bioinformatics. This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License (http://creativecommons.org/licenses/by-nc-nd/3.0/). hswr a upre yteBB-uddpormLSM(K 031L0034). (FKZ LiSyM program BMBF-funded the by supported was work This Acknowledgements 5 Journal of IntegrativeBioinformatics,13(1):299,2016 Journal doi:10.2390/biecoll-jib-2016-299 http://journal.imbio.de/ 11

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