Featured Publications
Proteotype coevolution and quantitative diversity across 11 mammalian species
Ba Q, Hei Y, Dighe A, Li W, Maziarz J, Pak I, Wang S, Wagner GP, Liu Y. Proteotype coevolution and quantitative diversity across 11 mammalian species. Science Advances 2022, 8: eabn0756. PMID: 36083897, PMCID: PMC9462687, DOI: 10.1126/sciadv.abn0756.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsBiological EvolutionGene Expression ProfilingMammalsProteomeProteomicsTranscriptomeConceptsMammalian speciesRNA metabolic processesCommon mammalian speciesUbiquitin-proteasome systemEvolutionary profilingMammalian lineagesProteomic methodsProtein degradationProtein abundanceGene expressionProtein expression levelsHigh interspeciesMetabolic processesCovariation analysisFunctional roleNucleotide levelExpression levelsQuantitative diversityCoevolutionMammalsSpeciesRemarkable variationExpressionTranscriptomeBiological variabilityGlobal and Site-Specific Effect of Phosphorylation on Protein Turnover
Wu C, Ba Q, Lu D, Li W, Salovska B, Hou P, Mueller T, Rosenberger G, Gao E, Di Y, Zhou H, Fornasiero EF, Liu Y. Global and Site-Specific Effect of Phosphorylation on Protein Turnover. Developmental Cell 2020, 56: 111-124.e6. PMID: 33238149, PMCID: PMC7855865, DOI: 10.1016/j.devcel.2020.10.025.Peer-Reviewed Original ResearchConceptsProtein turnoverProtein lifetimeCyclin-dependent kinase substrateStable isotope-labeled amino acidsSite-specific phosphorylationPulse-labeling approachIsotope-labeled amino acidsMass spectrometry-based methodCell fitnessKinase substratePhosphorylation sitesPhosphorylated sitesProteomic methodsCell signalingSpectrometry-based methodsLive cellsAmino acidsPhosphositesRich resourceDisease biologyLabeling approachPhosphorylationModification typesGlutamic acidTurnoverMulti-omic measurements of heterogeneity in HeLa cells across laboratories
Liu Y, Mi Y, Mueller T, Kreibich S, Williams EG, Van Drogen A, Borel C, Frank M, Germain PL, Bludau I, Mehnert M, Seifert M, Emmenlauer M, Sorg I, Bezrukov F, Bena FS, Zhou H, Dehio C, Testa G, Saez-Rodriguez J, Antonarakis SE, Hardt WD, Aebersold R. Multi-omic measurements of heterogeneity in HeLa cells across laboratories. Nature Biotechnology 2019, 37: 314-322. PMID: 30778230, DOI: 10.1038/s41587-019-0037-y.Peer-Reviewed Original ResearchConceptsCell linesGenome-wide copy numberMulti-omic measurementsHuman cultured cellsProtein turnover ratesPhenotypic responsesGenomic variabilityDifferent cell linesHeLa variantsSpecific cell linesCopy numberHeLa cellsCultured cellsHeLa cell linePhenotypic variabilityProgressive divergenceTurnover rateCellsBiological variationTechnical variationUniform conditionsTranscriptomeProteomeSuccessive passagesLines
2024
PTMoreR-enabled cross-species PTM mapping and comparative phosphoproteomics across mammals
Wang S, Di Y, Yang Y, Salovska B, Li W, Hu L, Yin J, Shao W, Zhou D, Cheng J, Liu D, Yang H, Liu Y. PTMoreR-enabled cross-species PTM mapping and comparative phosphoproteomics across mammals. Cell Reports Methods 2024, 4: 100859. PMID: 39255793, PMCID: PMC11440062, DOI: 10.1016/j.crmeth.2024.100859.Peer-Reviewed Original ResearchConceptsP-siteSurrounding amino acid sequenceKinase-substrate networkQuantitative phosphoproteomic analysisFunctional enrichment analysisPhosphoproteomic resultsKinase motifsComparative phosphoproteomicsPTM sitesPhosphorylation eventsPhosphoproteomic analysisProteomic analysisEnrichment analysisMammalian speciesSpeciesEvolutionary anglePhosphoproteomeMotifEnvironmental factorsNon-human speciesPTMProteomicsKinaseMammalsProteinNetwork-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis
Rosenberger G, Li W, Turunen M, He J, Subramaniam P, Pampou S, Griffin A, Karan C, Kerwin P, Murray D, Honig B, Liu Y, Califano A. Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis. Nature Communications 2024, 15: 3909. PMID: 38724493, PMCID: PMC11082183, DOI: 10.1038/s41467-024-47957-3.Peer-Reviewed Original ResearchConceptsMechanism of cell responseResistance mechanismsSignaling pathway responsesDrug resistance mechanismsEnzyme/substrate interactionsAdaptive resistance mechanismsNetwork rewiringPhosphorylation stateSignaling pathway activationDrug perturbationsProteomic technologiesSignaling crosstalkPathway responsesInhibitor designPathway activationCancer drug resistance mechanismsCell adaptive responsesAdaptive responsePhosphatase activityNetwork-based methodologyRewiringTherapeutic efficacyPhosphoproteome coverageCell responsesControl medium
2022
Toward a hypothesis‐free understanding of how phosphorylation dynamically impacts protein turnover
Li W, Salovska B, Fornasiero E, Liu Y. Toward a hypothesis‐free understanding of how phosphorylation dynamically impacts protein turnover. Proteomics 2022, 23: e2100387. PMID: 36422574, PMCID: PMC10964180, DOI: 10.1002/pmic.202100387.Peer-Reviewed Original ResearchConceptsPost-translational modificationsProtein turnoverDynamic stable isotope labelingCell starvationStable isotope labelingData-independent acquisition mass spectrometryAcquisition mass spectrometryProteome levelTurnover diversityPhosphoproteomic datasetsPhosphorylation stoichiometryMetabolic labelingIsotope labelingMass spectrometryPhosphorylationAmino acidsCell culturesBiological perspectiveStarvationTurnoverTurnover measurementsRecent studiesSILACProteoformsPeptidoforms
2021
BoxCarmax: A High-Selectivity Data-Independent Acquisition Mass Spectrometry Method for the Analysis of Protein Turnover and Complex Samples
Salovska B, Li W, Di Y, Liu Y. BoxCarmax: A High-Selectivity Data-Independent Acquisition Mass Spectrometry Method for the Analysis of Protein Turnover and Complex Samples. Analytical Chemistry 2021, 93: 3103-3111. PMID: 33533601, PMCID: PMC8959401, DOI: 10.1021/acs.analchem.0c04293.Peer-Reviewed Original ResearchConceptsData-independent acquisitionProtein turnoverDIA mass spectrometryStable isotope labelingValuable biological insightsRelative protein quantificationSerum starvation stressIsotopic labeling approachSILAC experimentsStarvation stressConventional DIA methodGas-phase separation strategyBiological insightsDegradation regulationIsotope labelingCultured cellsAmino acidsDIA-MSProtein quantificationLabeling approachPeptide pairsCell culturesBiological investigationsMultiplexed acquisitionComplex samples
2020
Germ‐free and microbiota‐associated mice yield small intestinal epithelial organoids with equivalent and robust transcriptome/proteome expression phenotypes
Hausmann A, Russo G, Grossmann J, Zünd M, Schwank G, Aebersold R, Liu Y, Sellin ME, Hardt W. Germ‐free and microbiota‐associated mice yield small intestinal epithelial organoids with equivalent and robust transcriptome/proteome expression phenotypes. Cellular Microbiology 2020, 22: e13191. PMID: 32068945, PMCID: PMC7317401, DOI: 10.1111/cmi.13191.Peer-Reviewed Original Research
2019
motifeR: An Integrated Web Software for Identification and Visualization of Protein Posttranslational Modification Motifs
Wang S, Cai Y, Cheng J, Li W, Liu Y, Yang H. motifeR: An Integrated Web Software for Identification and Visualization of Protein Posttranslational Modification Motifs. Proteomics 2019, 19: e1900245. PMID: 31622013, DOI: 10.1002/pmic.201900245.Peer-Reviewed Original ResearchConceptsUser-friendly web toolWeb softwarePublic datasetsBioinformatics backgroundLarge datasetsWeb toolMotif discoveryOptional featuresDatasetPresentation of motivesExponential growthLocation probabilitySoftwareKinase-substrate relationsModification sitesProtein post-translational modificationsPost-translational modificationsUsabilityUsersToolToolkitNetworkModification motifsPhosphoproteomic datasetsSite enrichmentBreast Cancer Classification Based on Proteotypes Obtained by SWATH Mass Spectrometry
Bouchal P, Schubert OT, Faktor J, Capkova L, Imrichova H, Zoufalova K, Paralova V, Hrstka R, Liu Y, Ebhardt HA, Budinska E, Nenutil R, Aebersold R. Breast Cancer Classification Based on Proteotypes Obtained by SWATH Mass Spectrometry. Cell Reports 2019, 28: 832-843.e7. PMID: 31315058, PMCID: PMC6656695, DOI: 10.1016/j.celrep.2019.06.046.Peer-Reviewed Original Research
2018
Similarities and Differences of Blood N-Glycoproteins in Five Solid Carcinomas at Localized Clinical Stage Analyzed by SWATH-MS
Sajic T, Liu Y, Arvaniti E, Surinova S, Williams EG, Schiess R, Hüttenhain R, Sethi A, Pan S, Brentnall TA, Chen R, Blattmann P, Friedrich B, Niméus E, Malander S, Omlin A, Gillessen S, Claassen M, Aebersold R. Similarities and Differences of Blood N-Glycoproteins in Five Solid Carcinomas at Localized Clinical Stage Analyzed by SWATH-MS. Cell Reports 2018, 23: 2819-2831.e5. PMID: 29847809, DOI: 10.1016/j.celrep.2018.04.114.Peer-Reviewed Original ResearchConceptsN-glycoproteinsAvailable proteomic dataSWATH mass spectrometryImmense clinical interestCancer-type specificProteomic changesProteomic dataN-glycositesProteomic workflowParticular carcinomaProtein compositionClinical stageMetastatic stageBlood samplesPatient plasmaSolid carcinomasEarly cancer detectionSWATH-MSTumor tissueSystemic responseCarcinomaProteinEarly detectionClinical interestCancer
2017
Systematic proteome and proteostasis profiling in human Trisomy 21 fibroblast cells
Liu Y, Borel C, Li L, Müller T, Williams EG, Germain PL, Buljan M, Sajic T, Boersema PJ, Shao W, Faini M, Testa G, Beyer A, Antonarakis SE, Aebersold R. Systematic proteome and proteostasis profiling in human Trisomy 21 fibroblast cells. Nature Communications 2017, 8: 1212. PMID: 29089484, PMCID: PMC5663699, DOI: 10.1038/s41467-017-01422-6.Peer-Reviewed Original ResearchConceptsSWATH mass spectrometryPost-transcriptional effectsMitochondrial proteomeProteome remodelingProteomic resourceTranscriptomic dataProteomic dataProtein abundanceProtein turnoverEntire chromosome 21ProteomeProtein expressionPhenotypic manifestationsChromosome 21Fibroblast cellsStoichiometric complexSignificant downregulationMonozygotic twin pair discordantTwin pair discordantMass spectrometryMajor determinantChromosomesProteinPair discordantAbundanceMulti-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry
Collins BC, Hunter CL, Liu Y, Schilling B, Rosenberger G, Bader SL, Chan DW, Gibson BW, Gingras AC, Held JM, Hirayama-Kurogi M, Hou G, Krisp C, Larsen B, Lin L, Liu S, Molloy MP, Moritz RL, Ohtsuki S, Schlapbach R, Selevsek N, Thomas SN, Tzeng SC, Zhang H, Aebersold R. Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry. Nature Communications 2017, 8: 291. PMID: 28827567, PMCID: PMC5566333, DOI: 10.1038/s41467-017-00249-5.Peer-Reviewed Original ResearchImpact of Alternative Splicing on the Human Proteome
Liu Y, Gonzàlez-Porta M, Santos S, Brazma A, Marioni JC, Aebersold R, Venkitaraman AR, Wickramasinghe VO. Impact of Alternative Splicing on the Human Proteome. Cell Reports 2017, 20: 1229-1241. PMID: 28768205, PMCID: PMC5554779, DOI: 10.1016/j.celrep.2017.07.025.Peer-Reviewed Original ResearchConceptsProteomic diversityAlternative splicingAlternative splicing eventsDifferential transcript usageIntron retentionSplicing eventsHuman transcriptomeHuman proteomeTranscript usageRNA sequencingProtein abundanceTranscript levelsHuman diseasesProteomeSWATH-MSSplicingQuantitative snapshotIntegrative approachCritical determinantDiversityTranscriptomeSequencingAbundanceMRNAQuantitative mannerA Class of Environmental and Endogenous Toxins Induces BRCA2 Haploinsufficiency and Genome Instability
Tan SLW, Chadha S, Liu Y, Gabasova E, Perera D, Ahmed K, Constantinou S, Renaudin X, Lee M, Aebersold R, Venkitaraman AR. A Class of Environmental and Endogenous Toxins Induces BRCA2 Haploinsufficiency and Genome Instability. Cell 2017, 169: 1105-1118.e15. PMID: 28575672, PMCID: PMC5457488, DOI: 10.1016/j.cell.2017.05.010.Peer-Reviewed Original ResearchConceptsBRCA2 haploinsufficiencyChromosomal aberrationsBRCA2 mutation carriersDNA replication forksReplication fork instabilityStructural chromosomal aberrationsRNA-DNA hybridsMutation carriersFork instabilityBRCA2 expressionEndogenous toxinsHeterozygous mutationsGenome instabilityReplication forksCellular proteinsDNA replicationCancer susceptibilityProteasomal degradationUbiquitous environmental toxinSingle copySpontaneous mutagenesisMechanisms of toxicityTransient exposureEnvironmental toxinsTumor suppressor
2014
A repository of assays to quantify 10,000 human proteins by SWATH-MS
Rosenberger G, Koh CC, Guo T, Röst HL, Kouvonen P, Collins BC, Heusel M, Liu Y, Caron E, Vichalkovski A, Faini M, Schubert OT, Faridi P, Ebhardt HA, Matondo M, Lam H, Bader SL, Campbell DS, Deutsch EW, Moritz RL, Tate S, Aebersold R. A repository of assays to quantify 10,000 human proteins by SWATH-MS. Scientific Data 2014, 1: 140031. PMID: 25977788, PMCID: PMC4322573, DOI: 10.1038/sdata.2014.31.Peer-Reviewed Original Research
2013
Mass spectrometric protein maps for biomarker discovery and clinical research
Liu Y, Hüttenhain R, Collins B, Aebersold R. Mass spectrometric protein maps for biomarker discovery and clinical research. Expert Review Of Molecular Diagnostics 2013, 13: 811-825. PMID: 24138574, PMCID: PMC3833812, DOI: 10.1586/14737159.2013.845089.Peer-Reviewed Original Research
2011
A high-quality secretome of A549 cells aided the discovery of C4b-binding protein as a novel serum biomarker for non-small cell lung cancer
Luo X, Liu Y, Wang R, Hu H, Zeng R, Chen H. A high-quality secretome of A549 cells aided the discovery of C4b-binding protein as a novel serum biomarker for non-small cell lung cancer. Journal Of Proteomics 2011, 74: 528-538. PMID: 21262398, DOI: 10.1016/j.jprot.2011.01.011.Peer-Reviewed Original ResearchConceptsNon-small cell lung cancerCellular proteomeCell lung cancerCancer secretomeLung cancerOne-dimensional gel electrophoresisA549 cellsBiomarker discoveryProteomic dataGene expressionSecretory proteinsIntracellular contaminationNovel promising biomarkerNovel serum biomarkersEnzyme-linked immunosorbent assaySecretomeProteinSerum proteomic dataClinical stagingProteomeSerum biomarkersGel electrophoresisC4BP levelsPromising biomarkerImmunosorbent assay
2010
Proteomic mining in the dysplastic liver of WHV/c‐myc mice – insights and indicators for early hepatocarcinogenesis
Liu Y, Li C, Xing Z, Yuan X, Wu Y, Xu M, Tu K, Li Q, Wu C, Zhao M, Zeng R. Proteomic mining in the dysplastic liver of WHV/c‐myc mice – insights and indicators for early hepatocarcinogenesis. The FEBS Journal 2010, 277: 4039-4053. PMID: 20807235, DOI: 10.1111/j.1742-4658.2010.07795.x.Peer-Reviewed Original ResearchConceptsHepatocellular carcinomaMouse modelFK506-binding protein 4C-myc miceTransgenic mouse modelEarly HCC diagnosisAsymptomatic processC-myc-induced hepatocarcinogenesisDysplastic liversFerritin heavy chainHCC diagnosisTissue microarrayImmunohistochemical analysisStage IIITumor onsetPutative biomarkersSerum samplesWestern blottingEarly detectionControl casesEarly hepatocarcinogenesisProtein 4Cancer initiationProtein expression profilesDysplastic stage