漆小泉植物代謝組學(xué)及其應(yīng)用課件

CenterforSignalTransduction&Metabolomics漆小泉中國(guó)科學(xué)院植物研究所植物分子生理重點(diǎn)實(shí)驗(yàn)室植物代謝組學(xué)及其應(yīng)用CenterforSignalTransductionCenterforSignalTransduction&MetabolomicsCenterforSignalTransductionAlkaloidsAminesCyanogenicglycosidesGlucosinolatesMonoterpenesSesquiterpenesNon-proteinaminoacidsDiterpenesTriterpenes,steroidsFlavonoidsPolyketidesPolyacetylenes,fattyacids,waxes植物合成二十多萬種不同的化合物

（主要為次生代謝物）AlkaloidsAminesCyanogenicglycCenterforSignalTransduction&Metabolomics植物次生代謝物合成的模式BackboneEarlystepFinalproductP450s,GTs,etcCenterforSignalTransductionCenterforSignalTransduction&Metabolomics植物產(chǎn)生次生代謝物適應(yīng)不良自然環(huán)境PollinationandseeddispersalScentsColoursFlavoursChemicaldefencePestsPathogensAllelopathyCenterforSignalTransductionCenterforSignalTransduction&Metabolomics次生代謝與植物發(fā)育有著不可分割的聯(lián)系SpearmintGlandulartrichomesThymeTrichomes–monoterpenesandsesquiterpenesLemonSecretorycavityCenterforSignalTransductionCenterforSignalTransduction&Metabolomics次生代謝物是很多中藥的主要成分TraditionalChineseMedicinesNaturalproductsasdrugsCenterforSignalTransduction人體需要的特殊營(yíng)養(yǎng)物質(zhì)主要來源于植物異黃酮(植物雌激素)維生素E葉酸b-胡蘿卜素人體需要的特殊營(yíng)養(yǎng)物質(zhì)主要來源于植物異黃酮維生素E葉酸bCenterforSignalTransduction&Metabolomics維生素A缺乏導(dǎo)致眼睛疾病。我國(guó)兒童VA缺乏率達(dá)9.3%維生素C缺乏導(dǎo)致壞血病維生素缺乏導(dǎo)致人體多種疾病的發(fā)生維生素E缺乏導(dǎo)致皮膚病、早衰等葉酸缺乏導(dǎo)致新生兒神經(jīng)系統(tǒng)疾病、貧血等CenterforSignalTransduction生長(zhǎng)發(fā)育適應(yīng)不良環(huán)境人體必需營(yíng)養(yǎng)成分藥用化合物b-胡蘿卜素GA1BrStrigolactone植物萜類代謝物具有重要功能和作用生長(zhǎng)發(fā)育適應(yīng)不良環(huán)境人體必需營(yíng)養(yǎng)成分藥用化合物b-胡蘿卜素GCenterforSignalTransduction&MetabolomicsFromWink,M.Phytochemistry64:3(2003)QuinolizidinealkaloidsSteroidalalkaloidsMono-andsesqui-terpenes次生代謝多樣性的特點(diǎn)——特定的種屬合成并積累特異的代謝物CenterforSignalTransductionArabidopsis–ModelplantCropsYeast-ModelunicellulareukaryoteChineseherbsandotherplantspecies(>250,000)Referencespecies基因組、代謝組學(xué)研究加快代謝途徑的解析Arabidopsis–CropsYeast-ChinCenterforSignalTransduction&MetabolomicsMetabolomicsisanew“omics”.Thenamemetabolomicswascoinedinthe1990s:ThefirstpaperusingthewordmetabolomeisOliver,S.G.,Winson,M.K.,Kell,D.B.&Baganz,F.(1998).Systematicfunctionalanalysisoftheyeastgenome.TrendsBiotechnol.16(9):373-8Thenameisgiventothevarietyoftechniquesusedtorecognisepatternsinthechemicalspresentinbiologicalsamplesinordertodeciphertheirsignificance.

代謝組學(xué)（Metabolomics）CenterforSignalTransductionCenterforSignalTransduction&MetabolomicsMetabolomicsistheanalysisofthetotalpopulationofmetabolitesinagivensample,cellortissueandtheintegrationofthedatainthecontextoffunctionalgenomicsgenomicstranscriptomicsproteomicsmetabolomicsCombiningallthe“omics”datawillprovideaclearerunderstandingofcellbiology.代謝組學(xué)（Metabolomics）CenterforSignalTransductionTraitsDNARNAProteinsMetabolitesNH2OHOOHOHOHOHOHOOHPhenomicsGenomicsTranscriptomicsProteomics

Metabolomics

從組學(xué)到分子系統(tǒng)生物學(xué)From漆小泉等《植物代謝組學(xué)-方法與應(yīng)用》2011年TraitsDNARNAProteinsMetaboliteCenterforSignalTransduction&MetabolomicsMetabolomicsresearchisparticularlyimportantintheplantfieldbecausecollectivelyproduceahugevarietyofchemicalcompounds,farmorethananimalsandevenmicroorganism.Otherareasofimpact:environmentalgenomicsfoodquality,diagnosticsinterorganismsignallingbioactivecompounds植物代謝組學(xué)更具有挑戰(zhàn)性CenterforSignalTransductionCenterforSignalTransduction&MetabolomicsNosinglemethodcanbeusedtodetectthewholepopulationofmetabolitesThechoiceofinitialextractionsolventimmediatelylimitstheclassesofmetabolitesextractedbaseduponpolarityNospectroscopymethod,currentlyavailable,isideallysuitedtothedetectionofeverymetabolite.AvarietyofanalyticalmethodsshouldbeusedandthedataintegratedinordertogaininformationonasmanymetabolitesaspossibleDatagainedfromavarietyofmethodsneedstobeintegrated.Metabolomics:theneedtointegratedatafromseveralplatformstoincreasecoverageMetaboliteidentityHighthroughputGlobalmetabolitefingerprintFT-IRFT-NMRESI-MSFT-MSCE-MSGC-MSLC-MSCenterforSignalTransductionCenterforSignalTransduction&MetabolomicsMassSpectrometryEquipmentAvailableinTheNationalCentreforPlantandMicrobialMetabolomics,UKThermoFinniganLCQLC-MSWatersGC-MSAccuratemassGC-MSAgilentGC-MSThermoFinniganGCQGC-MSThermoFinniganMaT95XPWatersQ-TOFWatersMaldi-TOFLecoPegasusTOFFastScanningGC-MS(boughtunderMeT-RO)FromMikeBealeCenterforSignalTransductionCenterforSignalTransduction&Metabolomics600MHzNMRMagnetBACSsamplechangerBrukerEsquire3000MassspectrometerBNMIinterfaceDADdetectorHPLCSparkIISPEUnit2XFOXYFractionCollectors600MHzHyphenatedLC-SPE-NMR-MSsystemTheNationalCentreforPlantandMicrobialMetabolomics,UKFromMikeBealeCenterforSignalTransductionGC-TOF/MSUPLC-Q-TOF/MSGC-QQQ/MS段禮新博士(Res.Assistant)漆小泉(TeamLeader)薛震(Tech.Assistant)韓彬博士(Tech.Assistant)樣品前處理數(shù)據(jù)分析IB-CASaimstoestablishanadvanceplantmetabolomicsplatformandnetworkinChinaGC-TOF/MSUPLC-Q-TOF/MSGC-QQQ/M海量質(zhì)譜數(shù)據(jù)分析軟件的開發(fā)開發(fā)了Pmass質(zhì)譜分析軟件，用分布式并行的方式加速了GC-MS數(shù)據(jù)的峰對(duì)齊和定量速度。使用目前廣泛使用的GC-MS數(shù)據(jù)分析軟件XCMS處理1200個(gè)樣本需要時(shí)近90天，而使用Pmass軟件只需2-3天。(楊輝華，任洪軍，李靈巧，段禮新，郭拓，杜玲玲，漆小泉（2013）基于Sector/Sphere平臺(tái)的GC-MS多樣本并行對(duì)齊算法實(shí)現(xiàn).《計(jì)算機(jī)應(yīng)用》33(1):inPress,與桂林電子科技大學(xué)的合作研究）水稻代謝物化合物庫及質(zhì)譜數(shù)據(jù)庫的建立通用化合物純品：230種植物甾醇及萜醇：116種質(zhì)譜數(shù)據(jù)庫：230+116+>200=>900種IB-CASaimstoestablishanadvanceplantmetabolomicsplatformandnetworkinChina海量質(zhì)譜數(shù)據(jù)分析軟件的開發(fā)水稻代謝物化合物庫及質(zhì)譜數(shù)據(jù)庫的建Rawdataprocession(MStopeaks)Biologicalannotation(pathwayandnetwork)Multivariateanalysis

(findfeatureandmarkers)DeconvolutionDataformatconvertAlignmentQuantitativeSmoothingDenoisngPeakdetectionCalibrationbyISandweightPCA:principlecomponentsanalysisPLS-DA:partialleastsquares-discriminateanalysis

OPLS-DA:OrthogonalPLS-DAHCA:hierarchicalclusteranalysis

CA:correlationanalysisANOVA:analysisofvarianceT-testPeakidentificationPathwayanalysisMetabolicnetworkIntegratewithother“–omic”dataNormalizationMetabolomicsdataanalysisworkflowRawdataprocessionBDatapre-processionTOF/MSRawdataDeconvolutionAlignmentDatamatrixQuantitativeDatacalibrationDatapre-processionTOF/MSRawdMultivariateanalysisNon-supervisedSupervisedPCAHCAPLS-DAOPLS-DAPrincipleofPCAScoreplotLoadingplotvMultivariateanalysisNon-superPathwayandnetworkanalysisThemainmetabolicmapMetabolicnetworkPathwayandnetworkanalysisThMetabolomicsdatabasesMetaCyc:genes,proteins,compounds,reactionandpathways.Compounds(MassandNMR)databaseMetabolomicsdatabasesMetaCyc:漆小泉植物代謝組學(xué)及其應(yīng)用ppt課件植物代謝組學(xué)的應(yīng)用基因功能解析代謝途徑及代謝網(wǎng)絡(luò)調(diào)控機(jī)理植物與生物逆境和非生物逆境互作研究作物的產(chǎn)量作物營(yíng)養(yǎng)成分及品質(zhì)等基因功能解析植物代謝組學(xué)的應(yīng)用鑒別標(biāo)識(shí)化合物代謝途徑的構(gòu)建代謝調(diào)控網(wǎng)絡(luò)的構(gòu)建鑒別標(biāo)識(shí)化合物CenterforSignalTransduction&Metabolomics中藥種類繁多，資源豐富，來源復(fù)雜，品種混雜嚴(yán)重。2000年版藥典收載的534種中藥材，即有143種為多基源(二基源以上),占收載總數(shù)的27%。中藥材基源品種的真?zhèn)?，關(guān)系到該味中藥的確切療效和療效的重現(xiàn)性，進(jìn)而直接影響到中藥制劑的質(zhì)量，是實(shí)現(xiàn)中藥現(xiàn)代化的首要問題。長(zhǎng)期的醫(yī)療實(shí)踐發(fā)現(xiàn)即使是同種藥材，由于產(chǎn)地不同、野生與栽培以及生長(zhǎng)年限不同都表現(xiàn)出質(zhì)量和療效上的差異，這些問題為中藥材鑒別方法提出了新的挑戰(zhàn)。中藥黃芪的鑒別CenterforSignalTransduction中藥黃芪的鑒別材料選取膜莢黃芪：Astragalusmembranaceus(Fisch.)Bunge蒙古黃芪：Astragalusmemeranaceus(Fisch.)Bge.var.mongholicus(Bge.)Hsiao肖培根等（1965）認(rèn)為蒙古黃芪是膜莢黃芪的變種。CenterforSignalTransduction&Metabolomics分析手段ＤＮＡ分子標(biāo)記技術(shù)：ＡＦＬＰ代謝組學(xué)分析技術(shù)：ＧＣ-ＴＯＦ/MS編號(hào)品種地理位置種植方式１膜莢黃芪吉林四平種植２膜莢黃芪吉林通化種植３膜莢黃芪甘肅魏源野生４膜莢黃芪甘肅漳縣野生５蒙古黃芪甘肅隴西種植６蒙古黃芪山西渾源種植７蒙古黃芪甘肅漳縣種植８蒙古黃芪山西應(yīng)縣野生材料來源中藥黃芪的鑒別材料選取CenterforSignalTCenterforSignalTransduction&MetabolomicsClusterofgenetic（Ａ）andmetabolicfingerprinting（Ｂ）蒙古黃芪膜莢黃芪（甘肅）膜莢黃芪（吉林）蒙古黃芪膜莢黃芪中藥黃芪的鑒別CenterforSignalTransductionCenterforSignalTransduction&Metabolomics中藥黃芪的鑒別OPLS分析CenterforSignalTransductionCenterforSignalTransduction&Metabolomics區(qū)分膜莢黃芪和蒙古黃芪標(biāo)識(shí)物的選取(OPLS)V-plotLoadingplot中藥黃芪的鑒別CenterforSignalTransduction初步鑒定出21個(gè)標(biāo)識(shí)物indexrt(min)VIPnamechinesenameMW-UROC-AUCp24927.37211.41691Galactose半乳糖3.70E-099.97E-01p5410.18121.94196Malonicacid丙二酸2.88E-091.00E+00p39239.08291.68164Maltose麥芽糖2.30E-089.70E-01p11215.22621.49712L-ThreonineL-蘇氨酸

6.72E-079.18E-01o70344.05671.59318Stigmasterol豆甾醇7.38E-089.53E-01p38238.48291.99751Sucrose蔗糖7.70E-099.55E-01o57435.01171.337081-Monohexadecanoylglycerol1-單棕櫚酸甘油酯6.90E-058.35E-01o34320.76921.72885Azelaicacid壬二酸1.30E-070.944444o42825.82331.91289Hexadecanoicacid棕櫚酸(16酸)5.42E-080.955729o47228.80921.53552linoleicacid亞油酸3.49E-068.90E-01o10910.29671.60082OctanoicaciD辛酸（8碳酸）6.04E-070.920139p17721.05291.43659L-GlutamicacidL-谷氨酸6.95E-068.78E-01o10810.27581.46575Ethanolamine乙醇胺1.14E-060.909722o47428.92831.479789-(Z)-Octadecenoicacid9-順-十八烯酸1.04E-070.947917o23314.88581.414414-Amino-Butanoicacidγ－氨基丁酸4.87E-070.923611o33319.97671.64515L-Glycerol-3-phosphate3-磷酸-甘油酯3.70E-090.996528o15711.99671.6474L-Homoserine高絲氨酸2.91E-080.967014p22825.41541.54198Ornithine瓜氨酸2.03E-079.38E-01o12410.85751.34682L-Proline脯氨酸2.30E-080.970486p23025.54461.81697Citricacid檸檬酸1.42E-089.77E-01o29317.91921.49245L-Asparagine天冬酰胺3.70E-090.996528中藥黃芪的鑒別初步鑒定出21個(gè)標(biāo)識(shí)物indexrt(min)VIPnameCenterforSignalTransduction&Metabolomics產(chǎn)地和種植方式的差別（ＰＣＡ）膜莢黃芪：產(chǎn)地和種植方式疊加效果蒙古黃芪：產(chǎn)地因素>種植方式因素中藥黃芪的鑒別Duan

etal(2012)MolPlant5:376

CenterforSignalTransductionCenterforSignalTransduction&Metabolomics代謝途徑分析代謝物含量比值較高代謝物含量比值較低膜莢黃芪/蒙古黃芪萜類,甾類,胡蘿卜素芳香化合物,木質(zhì)素,黃酮膽堿,喹啉脂肪簇氨基酸生物堿Glc1PUDP-Glc多糖脂肪酸多胺生物堿中藥黃芪的鑒別Duan

etal(2012)MolPlant5:376

CenterforSignalTransductionCenterforSignalTransduction&Metabolomics代謝組學(xué)技術(shù)能夠快速區(qū)分兩種黃芪，能綜合反映生長(zhǎng)環(huán)境與基因相互作用的影響。利用OPLS模型鑒定了21個(gè)代謝差異物質(zhì)。由于只使用了GC-TOF/MS分析平臺(tái)，不能檢測(cè)到黃芪的有效成分，如黃酮類、三萜苷類、多糖類等代謝物。應(yīng)使用多平臺(tái)整合分析手段。中藥黃芪的鑒別CenterforSignalTransduction.DiterpenequinonesknownastanshinonesandphenolicacidderivativessuchassalvianolicacidarethemainbioactivecomponentsofS.miltiorrhiza.

Morethan100compoundshaveisolatedfromDanshen,whilehalfofthemarediterpenes.Theyhavebeenfoundtohaveavarietyofpharmaceuticalactivities,includingantibacterial,antiinflammatory,andanticancerproperties.解析丹參二萜代謝途徑.DiterpenequinonesknownastCPS1istheonlyclassIIdiTPSInvolvedinTanshinonesBiosyntheticPathwayinRootCui

etal,unpublisheddata解析丹參二萜代謝途徑CPS1istheonlyclassIIdiTPQuantificationoffiveknownmajorcompoundsinCKand

CPS1-RNAi

linesCui

etal,unpublisheddata解析丹參二萜代謝途徑QuantificationoffiveknownmIdentificationofmetabolitesdownstreamofSmCPS1catalyticstepintanshinonespathwayCui

etal,unpublisheddata解析丹參二萜代謝途徑Identificationofmetabolites84383CKirSmCPS1DataanalyzedbyMPP(hairyroot)解析丹參二萜代謝途徑84383CKDataanalyzedbyMPP(h漆小泉植物代謝組學(xué)及其應(yīng)用ppt課件5x10012+ESIEIC(371.2345)???Frag=130.0Vck49.d5x10012+ESIEIC(325.2526)???Frag=130.0Vcps13.d???????(??)1.522.533.544.555.566.577.588.599.51010.51111.51212.51313.5145x1000.511.522.53+???(9.264??)ck49.d313.1810371.2344780.5551227.1076647.3355??????(m/z)751001251501752002252502753003253503754004254504755005255505756006256506757007257507758008258508759009259509755x1000.511.522.53+???(9.264??)ck49.d313.1810335.1631371.2344330.2075358.2389299.0688277.0866??????(m/z)2702752802852902953003053103153203253303353403453503553603653703753803853903955x10012+ESIEIC(371.2345)???6x1000.250.50.751+ESIEIC(277.0868)9.108min

116x1000.250.50.751+ESIEIC(281.1178)8.545min

116x1000.250.50.751+ESIEIC(279.1027)

Frag=130.0VPK-1-10-plant.d11min12345678910111213148.178min9.712minTrijuarone1,2-dihydrotanshinonedihydrotanshinoneItanshinoneI6x1000.250.50.751+ESIEIC(277.6x1000.20.40.60.81+277.0871(9.108min)299.0683277.0871575.1483335.16266x1000.250.50.7511.251.51.75+279.1016(9.737min)301.0836279.1016579.1776261.0917233.0965324.1598205.10126x1000.250.50.7511.251.5+281.1174(8.545min)583.2088303.0993235.1118263.1070207.1173m/z)1802002202402602803003203403603804004204404604805005205405605806006202M+Na2M+Na2M+NaM+HM+HM+H6x1000.20.40.60.81+277.08716x1000.20.40.60.81+277.0871(9.108min)299.0683277.0871335.1626322.1445249.09166x1000.250.50.7511.251.51.75+279.1016(9.737min)301.0836279.1016261.0917233.0965324.1598205.1012190.07766x1000.250.50.7511.251.5+281.1174(8.545min)303.0993281.1174235.1118263.1070207.1173192.0930319.0725m/z)180190200210220230240250260270280290300310320330340350360M+HM+NaM+HM+NaM+NaM+HM-H2OM-H2O-18.0099-27.9952-27.9953-15.0236-CH3-CO-CO-18.0104-27.9952-27.9945-15.0243-CH3-CO-CO-18.0099M-H2O6x1000.20.40.60.81+277.0871(9SmCPS1wastheonlyclassIIditerpenecyclaseinvovledintanshinonesbiosynthesisintherootofDanshen.PeridermistheplacewheretanshinoneswerebiosynthesizedandaccumulatedTanshinonebiosynthesisisacomplexnetworkandit

islikelythattanshinoneIcanbesynthesizedthroughdifferentpathways解析丹參二萜代謝途徑解析丹參二萜代謝途徑漆小泉植物代謝組學(xué)及其應(yīng)用ppt課件CenterforSignalTransduction&MetabolomicsApplicationsofMetabolomicsanalysisNatureGenetics(2006)Volume38number7July,P842-849CenterforSignalTransductionCenterforSignalTransduction&MetabolomicsApplicationsofMetabolomicsanalysisKeurentjesetal.200614ArabidopsisecotypesandRILsUntargetedmetabolomicsanalysis(LC-QTOFMS)CenterforSignalTransductionCenterforSignalTransduction&MetabolomicsApplicationsofMetabolomicsanalysisKeurentjesetal.2006CenterforSignalTransductionCenterforSignalTransduction&MetabolomicsKeurentjesetal.2006FrequencydistributionofthenumberofsignificantQTLsdetectedateachmarkerpositionatfoursignificancelevel.NumberofmassesdetectedintheRIpopulationanditsparents.NotdetectedintheparentsDetectedinbothparentsCenterforSignalTransductionCenterforSignalTransduction&MetabolomicsKeurentjesetal.2006ApplicationsofMetabolomicsanalysisBeforesidechainmodificationAftersidechainmodificationCenterforSignalTransductionCenterforSignalTransduction&MetabolomicsApplicationsofMetabolomicsanalysisKeurentjesetal.2006Largegeneticvariationsformasspeaksinthe14accessions(only13.4%ofmasspeakswaredetectedincommoninall14accession)75%ofthe2000masspeakscanbeexplainedbyQTLsintheRIpopulation.Many(one-third)metabolitesareproducedasaresultoftherecombinationofthegenomesofthetwoparents,sincetheyareabsentinbothparents.CenterforSignalTransductionConclusionInsummary,authorsintegratedhigh-throughputmetabolomicsandgenotypingdatafromalargepopulationcohortforelucidatingthebiochemicalidentitiesofunknownmetabolites.Tothisend,authorsappliedmetabolomicsgenome-wideassociationstudiesandGaussiangraphicalmodelinginordertolinktheseunknownmetaboliteswithknownmetabolicclassesandbiologicalprocesses.Itistobenotedthatthemethoddoesnotspecificallyrequiregenotypingdata.Evenmetabolomicsmeasurementsalone,analyzedthroughtheGGMs,mayprovidesufficientinformationfortheclassificationandevenpreciseidentityprediction.Onelimitationofthisapproachistherequirementforassociationswithfunctionallydescribedlociorknownmetabolites.ConclusionInsummary,authors水稻種子低溫萌發(fā)的代謝組分析DNAmarkers珍汕97x明恢63120RILsSSDPhenotypesMetabolitespQTLsmQTLsMetabolicnetwork/pathwayscontrolledbyQTLsco-locationcorrelation15℃,

10days15℃,

10days水稻種子低溫萌發(fā)的代謝組分析DNAmarkers珍汕97萌發(fā)率分布圖RIL15

MH63RIL78ZS97RIL163水稻種子低溫萌發(fā)的代謝組分析萌發(fā)率分布圖RIL15水稻種子低溫萌發(fā)的代謝組分析珍汕97x明恢63

RI群體的高密度SNP連鎖圖譜Xieetal(2010)PNAS107:10578Yu

et

al

(2011)PloSOne

6:e175951839

markers水稻種子低溫萌發(fā)的代謝組分析珍汕97x明恢63RI群體的高Bin690Bin6923.1Bin6951.9Bin7002.8Bin7021.3Bin72313.3Bin73012.7Bin74311.2Bin76011.9Bin77010.9Bin78710.6Bin79216.8RM264.1Bin8083.7Bin8187.4RM311.7Chr5胚根QTLs胚芽鞘QTLs水稻種子低溫萌發(fā)的代謝組分析低溫萌發(fā)速率QTL的定位Bin690Bin6923.1Bin6951.9Bin700C161Bin1410.7Bin2615.0Bin4413.5Bin5216.6Bin6616.7Bin10317.9Bin11914.7Bin13015.2Bin13210.2G39310.5Bin1626.9C23402.1C866.8Bin19419.9Bin19911.7Bin21712.8Bin690Bin6923.1Bin6951.9Bin7002.8Bin7021.3Bin72313.3Bin73012.7Bin74311.2Bin76011.9Bin77010.9Bin78710.6Bin79216.8RM264.1Bin8083.7Bin8187.4RM311.7Bin1391Bin139914.7Bin14139.3Bin142211.0Bin143017.8Bin14619.6Bin147610.3Bin14889.4G40018.9Bin14951.2Bin15032.7Bin15062.9Bin15074.7G1819.7TEL37.3低溫胚根長(zhǎng)度QTLs低溫胚芽鞘長(zhǎng)度QTLs常溫胚根長(zhǎng)度QTLs常溫胚芽鞘長(zhǎng)度QTLs水稻種子低溫萌發(fā)的代謝組分析種子萌發(fā)速率QTLsChr1Chr5Chr11C161Bin1410.7Bin2615.0Bin4413.0hour2days4days6daysendospermendospermendospermendospermembryoembryoembryoembryoZS97MH63ZS97MH63ZS97MH63ZS97MH63MH63MH63水稻種子低溫萌發(fā)的代謝組分析親本低溫萌期間代謝譜變化0hour2days4days6daysendosp取樣時(shí)間：萌發(fā)后第4天

萌發(fā)溫度：15oCendospermembryoZS97MH63MH63100

RILs

x4EmbryoEndospermPolarfractionNon-polarfractionPolarfractionNon-polarfraction1,600samplesGC-Tof/MS水稻種子低溫萌發(fā)的代謝組分析代謝組分析方法取樣時(shí)間：萌發(fā)后第4天endospermembryoZS9InternalstandardcorrectionQualitycontrolmQTLanalysisforRILsGC-MSofsamplesXCMSIonfeatureAbundancefilteringMetaboliteinteractionnetworkColocalizationanalysisMultiplestatisticanalysisAnovaanalysisCandidategenepredictionMolecularmechanism水稻種子低溫萌發(fā)的代謝組分析遺傳代謝組學(xué)分析流程InternalstandardcorrectionmQChr1Chr2Chr3Chr4Chr5Chr6Chr7Chr8Chr9Chr10Chr11Chr12GeneticPosition(cM)Ionfeatures紅色代表碎片離子豐度在明恢63中高于珍汕97，綠色相反。水稻種子低溫萌發(fā)的代謝組分析50-5Lod定位mQTLs—1417個(gè)QTLs(碎片離子豐度)32300Xieetal,unpublisheddataChr1Chr2Chr3Ch鑒定出4個(gè)數(shù)量遺傳位點(diǎn)(QTLs)控制種子低溫和常溫萌發(fā)速率。在低溫(15℃)萌發(fā)第4天時(shí)，珍汕97和明恢63之間的代謝譜出現(xiàn)了明顯的變化。鑒定出1417個(gè)離子碎片數(shù)量性狀位點(diǎn)。其中，與種子萌發(fā)速率表型共定位的有405個(gè)。在控制種子淀粉成分位點(diǎn)出現(xiàn)了大量的mQTLs。去卷積分析這些離子碎片將有助于解析這些位點(diǎn)在種子低溫萌發(fā)速率及營(yíng)養(yǎng)成分的代謝調(diào)控機(jī)制及建立代謝調(diào)控網(wǎng)絡(luò)。水稻種子低溫萌發(fā)的代謝組分析鑒定出4個(gè)數(shù)量遺傳位點(diǎn)(QTLs)控制種子低溫和常溫萌發(fā)速率MethodsMetabolomicsanalysisGaussiangraphicalmodeling(GGM)Genome-wideassociationanalysis(GWAS)IdentificationofunknownmetabolitesbasedonGGMandGWASMethodsMetabolomicsanalysisIGGMsarebasedonpartialcorrelationcoefficients,thatispairwisePearsoncorrelationcoefficientsconditionedagainstthecorrelationwithallothermetabolites.Krumsieketal.BMCSystemBiology.2011,5IdentificationofunknownmetabolitesbasedonGGMandGWASGGMsarebasedonpartialcorrIdentificationof