2025
Epigenomic pathways from racism to preterm birth: secondary analysis of the Nulliparous Pregnancy Outcomes Study: monitoring Mothers-to-be (nuMoM2b) cohort study in the USA to examine how DNA methylation mediates the relationship between multilevel racism and preterm birth in black women: a study protocol
Barcelona V, Ray M, Zhao Y, Samari G, Wu H, Reho P, McNeil R, Reddy U. Epigenomic pathways from racism to preterm birth: secondary analysis of the Nulliparous Pregnancy Outcomes Study: monitoring Mothers-to-be (nuMoM2b) cohort study in the USA to examine how DNA methylation mediates the relationship between multilevel racism and preterm birth in black women: a study protocol. BMJ Open 2025, 15: e091801. PMID: 40037666, PMCID: PMC11881185, DOI: 10.1136/bmjopen-2024-091801.Peer-Reviewed Original ResearchConceptsNulliparous Pregnancy Outcomes StudyMonitoring Mothers-to-BeMothers-to-beBlack womenPregnancy Outcomes StudyParticipants' electronic health recordsPreterm birthSecondary analysisCohort studyGeocoded participant addressesSecondary analysis of dataStudy protocolElectronic health recordsStructural racism measuresUniversity Institutional Review BoardEffects of individual-Black pregnant womenOutcome studiesRacial residential segregationProspective cohort studyPregnancy-related morbidityParticipant's addressHealth recordsAdverse pregnancy outcomesWhite women
2024
Multi-omics profiling of DNA methylation and gene expression alterations in human cocaine use disorder
Zillich E, Belschner H, Avetyan D, Andrade-Brito D, Martínez-Magaña J, Frank J, Mechawar N, Turecki G, Cabana-Domínguez J, Fernàndez-Castillo N, Cormand B, Montalvo-Ortiz J, Nöthen M, Hansson A, Rietschel M, Spanagel R, Witt S, Zillich L. Multi-omics profiling of DNA methylation and gene expression alterations in human cocaine use disorder. Translational Psychiatry 2024, 14: 428. PMID: 39384764, PMCID: PMC11464785, DOI: 10.1038/s41398-024-03139-9.Peer-Reviewed Original ResearchConceptsCocaine use disorderUse disorderAlternative splicingHuman prefrontal cortexProfiling of DNA methylationBrodmann area 9Differential alternative splicingDeregulated biological processesPostmortem brain tissueMulti-omics approachCocaine intakeMulti-omics studiesPrefrontal cortexBrain alterationsMulti-omics profilingGene expression alterationsArea 9Fatty acid metabolismReceptor-targeting drugsSpliced transcriptsEpigenome-wideDNA methylationNeuronal morphogenesisAS changesDrug repositioning analysisA cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders
Lee A, Ayers L, Kosicki M, Chan W, Fozo L, Pratt B, Collins T, Zhao B, Rose M, Sanchis-Juan A, Fu J, Wong I, Zhao X, Tenney A, Lee C, Laricchia K, Barry B, Bradford V, Jurgens J, England E, Lek M, MacArthur D, Lee E, Talkowski M, Brand H, Pennacchio L, Engle E. A cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders. Nature Communications 2024, 15: 8268. PMID: 39333082, PMCID: PMC11436875, DOI: 10.1038/s41467-024-52463-7.Peer-Reviewed Original ResearchConceptsNon-coding variantsCranial motor neuronsMendelian disordersIn vivo transgenic assayPredictor of enhancer activityCis-regulatory elementsMulti-omic frameworkWhole-genome sequencingEnhanced activityVariant discoveryGenome sequenceChromatin accessibilityPutative enhancersHistone modificationsRegulatory elementsGene expression assaysGene predictionTransgenic assaysEpigenomic profilingMendelian casesExpression assaysMutational enhancementCongenital cranial dysinnervation disordersCell typesFunctional impactIdentifying Depression Through Machine Learning Analysis of Omics Data: Scoping Review
Taylor B, Hobensack M, de Rivera S, Zhao Y, Creber R, Cato K. Identifying Depression Through Machine Learning Analysis of Omics Data: Scoping Review. JMIR Nursing 2024, 7: e54810. PMID: 39028994, PMCID: PMC11297379, DOI: 10.2196/54810.Peer-Reviewed Original ResearchConceptsProvision of mental health careCritical Appraisal Checklist for Analytical Cross-Sectional StudiesJoanna Briggs Institute Critical Appraisal Checklist for Analytical Cross-Sectional StudiesIncreased risk of depressionShortage of providersMental health careRisk of depressionAnalytical cross-sectional studyDiagnosis of depressionCross-sectional studySymptoms of depressionSelf-reported symptomsNursing workforceIdentifying depressionPRISMA-ScRScoping reviewHealth careCritical appraisalMental disordersIncreased riskNursesAssess individualsClinical interviewDepressionScreening processSANTO: a coarse-to-fine alignment and stitching method for spatial omics
Li H, Lin Y, He W, Han W, Xu X, Xu C, Gao E, Zhao H, Gao X. SANTO: a coarse-to-fine alignment and stitching method for spatial omics. Nature Communications 2024, 15: 6048. PMID: 39025895, PMCID: PMC11258319, DOI: 10.1038/s41467-024-50308-x.Peer-Reviewed Original ResearchDetecting small cell transformation in patients with advanced EGFR mutant lung adenocarcinoma through epigenomic cfDNA profiling
Zarif T, Meador C, Qiu X, Seo J, Davidsohn M, Savignano H, Lakshminarayanan G, McClure H, Canniff J, Fortunato B, Li R, Banwait M, Semaan K, Eid M, Long H, Hung Y, Mahadevan N, Barbie D, Oser M, Piotrowska Z, Choueiri T, Baca S, Hata A, Freedman M, Berchuck J. Detecting small cell transformation in patients with advanced EGFR mutant lung adenocarcinoma through epigenomic cfDNA profiling. Clinical Cancer Research 2024, 30: 3798-3811. PMID: 38912901, PMCID: PMC11369616, DOI: 10.1158/1078-0432.ccr-24-0466.Peer-Reviewed Original ResearchConceptsEGFR mutant lung adenocarcinomaSmall cell lung cancerSmall cell transformationLung cancer patient-derived xenograftPatient-derived xenograftsLung adenocarcinomaEGFR mutantsChIP-seqEpigenomic featuresMeDIP-seqImmunoprecipitation sequencingCell transformationHistological transformation to small cell lung cancerTransformation to small cell lung cancerMethylated DNA immunoprecipitation sequencingTransposase-accessible chromatin sequencingH3K27ac ChIP-seqMechanisms of treatment resistanceChromatin immunoprecipitation sequencingHistone modification H3K27acMutant lung adenocarcinomaCell lung cancerChromatin accessibilityChromatin sequencingEpigenomic landscapeGenetic drivers of heterogeneity in type 2 diabetes pathophysiology
Suzuki K, Hatzikotoulas K, Southam L, Taylor H, Yin X, Lorenz K, Mandla R, Huerta-Chagoya A, Melloni G, Kanoni S, Rayner N, Bocher O, Arruda A, Sonehara K, Namba S, Lee S, Preuss M, Petty L, Schroeder P, Vanderwerff B, Kals M, Bragg F, Lin K, Guo X, Zhang W, Yao J, Kim Y, Graff M, Takeuchi F, Nano J, Lamri A, Nakatochi M, Moon S, Scott R, Cook J, Lee J, Pan I, Taliun D, Parra E, Chai J, Bielak L, Tabara Y, Hai Y, Thorleifsson G, Grarup N, Sofer T, Wuttke M, Sarnowski C, Gieger C, Nousome D, Trompet S, Kwak S, Long J, Sun M, Tong L, Chen W, Nongmaithem S, Noordam R, Lim V, Tam C, Joo Y, Chen C, Raffield L, Prins B, Nicolas A, Yanek L, Chen G, Brody J, Kabagambe E, An P, Xiang A, Choi H, Cade B, Tan J, Broadaway K, Williamson A, Kamali Z, Cui J, Thangam M, Adair L, Adeyemo A, Aguilar-Salinas C, Ahluwalia T, Anand S, Bertoni A, Bork-Jensen J, Brandslund I, Buchanan T, Burant C, Butterworth A, Canouil M, Chan J, Chang L, Chee M, Chen J, Chen S, Chen Y, Chen Z, Chuang L, Cushman M, Danesh J, Das S, de Silva H, Dedoussis G, Dimitrov L, Doumatey A, Du S, Duan Q, Eckardt K, Emery L, Evans D, Evans M, Fischer K, Floyd J, Ford I, Franco O, Frayling T, Freedman B, Genter P, Gerstein H, Giedraitis V, González-Villalpando C, González-Villalpando M, Gordon-Larsen P, Gross M, Guare L, Hackinger S, Hakaste L, Han S, Hattersley A, Herder C, Horikoshi M, Howard A, Hsueh W, Huang M, Huang W, Hung Y, Hwang M, Hwu C, Ichihara S, Ikram M, Ingelsson M, Islam M, Isono M, Jang H, Jasmine F, Jiang G, Jonas J, Jørgensen T, Kamanu F, Kandeel F, Kasturiratne A, Katsuya T, Kaur V, Kawaguchi T, Keaton J, Kho A, Khor C, Kibriya M, Kim D, Kronenberg F, Kuusisto J, Läll K, Lange L, Lee K, Lee M, Lee N, Leong A, Li L, Li Y, Li-Gao R, Ligthart S, Lindgren C, Linneberg A, Liu C, Liu J, Locke A, Louie T, Luan J, Luk A, Luo X, Lv J, Lynch J, Lyssenko V, Maeda S, Mamakou V, Mansuri S, Matsuda K, Meitinger T, Melander O, Metspalu A, Mo H, Morris A, Moura F, Nadler J, Nalls M, Nayak U, Ntalla I, Okada Y, Orozco L, Patel S, Patil S, Pei P, Pereira M, Peters A, Pirie F, Polikowsky H, Porneala B, Prasad G, Rasmussen-Torvik L, Reiner A, Roden M, Rohde R, Roll K, Sabanayagam C, Sandow K, Sankareswaran A, Sattar N, Schönherr S, Shahriar M, Shen B, Shi J, Shin D, Shojima N, Smith J, So W, Stančáková A, Steinthorsdottir V, Stilp A, Strauch K, Taylor K, Thorand B, Thorsteinsdottir U, Tomlinson B, Tran T, Tsai F, Tuomilehto J, Tusie-Luna T, Udler M, Valladares-Salgado A, van Dam R, van Klinken J, Varma R, Wacher-Rodarte N, Wheeler E, Wickremasinghe A, van Dijk K, Witte D, Yajnik C, Yamamoto K, Yamamoto K, Yoon K, Yu C, Yuan J, Yusuf S, Zawistowski M, Zhang L, Zheng W, Raffel L, Igase M, Ipp E, Redline S, Cho Y, Lind L, Province M, Fornage M, Hanis C, Ingelsson E, Zonderman A, Psaty B, Wang Y, Rotimi C, Becker D, Matsuda F, Liu Y, Yokota M, Kardia S, Peyser P, Pankow J, Engert J, Bonnefond A, Froguel P, Wilson J, Sheu W, Wu J, Hayes M, Ma R, Wong T, Mook-Kanamori D, Tuomi T, Chandak G, Collins F, Bharadwaj D, Paré G, Sale M, Ahsan H, Motala A, Shu X, Park K, Jukema J, Cruz M, Chen Y, Rich S, McKean-Cowdin R, Grallert H, Cheng C, Ghanbari M, Tai E, Dupuis J, Kato N, Laakso M, Köttgen A, Koh W, Bowden D, Palmer C, Kooner J, Kooperberg C, Liu S, North K, Saleheen D, Hansen T, Pedersen O, Wareham N, Lee J, Kim B, Millwood I, Walters R, Stefansson K, Ahlqvist E, Goodarzi M, Mohlke K, Langenberg C, Haiman C, Loos R, Florez J, Rader D, Ritchie M, Zöllner S, Mägi R, Marston N, Ruff C, van Heel D, Finer S, Denny J, Yamauchi T, Kadowaki T, Chambers J, Ng M, Sim X, Below J, Tsao P, Chang K, McCarthy M, Meigs J, Mahajan A, Spracklen C, Mercader J, Boehnke M, Rotter J, Vujkovic M, Voight B, Morris A, Zeggini E. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature 2024, 627: 347-357. PMID: 38374256, PMCID: PMC10937372, DOI: 10.1038/s41586-024-07019-6.Peer-Reviewed Original ResearchMeSH KeywordsAdipocytesChromatinCoronary Artery DiseaseDiabetes Mellitus, Type 2Diabetic NephropathiesDisease ProgressionEndothelial CellsEnteroendocrine CellsEpigenomicsGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansIslets of LangerhansMultifactorial InheritancePeripheral Arterial DiseaseSingle-Cell AnalysisConceptsGenome-wide association study dataIndividuals of diverse ancestryRegions of open chromatinAncestry groupsGenome-wide significanceSingle-cell epigenomicsCases of T2DT2D signalsAssociation signalsObesity-related processesOpen chromatinDiverse ancestryTrait associationsDrivers of heterogeneityGenetic driversProgression of T2DEnteroendocrine cellsType 2 diabetes pathophysiologyGenetic contributionMolecular mechanismsPolygenic scoresAncestryLociPancreatic isletsStudy dataIntegrative omics analysis reveals epigenomic and transcriptomic signatures underlying brain structural deficits in major depressive disorder
Zheng J, Womer F, Tang L, Guo H, Zhang X, Tang Y, Wang F. Integrative omics analysis reveals epigenomic and transcriptomic signatures underlying brain structural deficits in major depressive disorder. Translational Psychiatry 2024, 14: 17. PMID: 38195555, PMCID: PMC10776753, DOI: 10.1038/s41398-023-02724-8.Peer-Reviewed Original ResearchConceptsGray matter volumeBrain structural deficitsFrontal cortexGMV changesStructural deficitsDecreased GMVGray matter volume abnormalitiesInferior frontal cortexAnterior cingulate cortexAllen Human Brain AtlasDifferentially methylated CpG positionsGray matter abnormalitiesHuman Brain AtlasRegionally specific correlationsDepressive disorderCingulate cortexMatter volumeMorphological deficitsMDDBetween-group differencesCortex regionsCortexSynaptic transmission processesDeficitsHealthy controls
2023
The epigenetic evolution of glioma is determined by the IDH1 mutation status and treatment regimen
Malta T, Sabedot T, Morosini N, Datta I, Garofano L, Vallentgoed W, Varn F, Aldape K, D'Angelo F, Bakas S, Barnholtz-Sloan J, Gan H, Hasanain M, Hau A, Johnson K, Cazacu S, deCarvalho A, Khasraw M, Kocakavuk E, Kouwenhoven M, Migliozzi S, Niclou S, Niers J, Ormond D, Paek S, Reifenberger G, Smitt P, Smits M, Stead L, van den Bent M, Van Meir E, Walenkamp A, Weiss T, Weller M, Westerman B, Ylstra B, Wesseling P, Lasorella A, French P, Poisson L, Woehrer A, Lowman A, deCarvalho A, Castro A, Transou A, Brodbelt A, Hau A, Lasorella A, Golebiewska A, Walenkamp A, Molinaro A, Iavarone A, Ismail A, Westerman B, Ylstra B, Bock C, Ormond D, Brat D, Kocakavuk E, Van Meir E, Barthel F, Varn F, D'Angelo F, Finocchiaro G, Rao G, Zadeh G, Reifenberger G, ngNg H, Kim H, Noushmehr H, Miletic H, Gan H, Datta I, Rock J, Snyder J, Huse J, Connelly J, Barnholtz-Sloan J, Niers J, deGroot J, Akdemir K, Kannan K, Ligon K, Aldape K, Bulsara K, Johnson K, Alfaro K, Poisson L, Garofano L, Stead L, Nasrallah M, Smits M, van den Bent M, Kouwenhoven M, Weller M, Hasanain M, Khasraw M, Gould P, Smitt P, LaViolette P, Tatman P, Wesseling P, French P, Beroukhim R, Verhaak R, Migliozzi S, Niclou S, Bakas S, Kalkanis S, Paek S, Short S, Ghazaleh T, Malta T, Sabedot T, Weiss T, Walbert T, Baid U, Vallentgoed W, Yung W, Verhaak R, Iavarone A, Noushmehr H. The epigenetic evolution of glioma is determined by the IDH1 mutation status and treatment regimen. Cancer Research 2023, 84: 741-756. PMID: 38117484, PMCID: PMC10911804, DOI: 10.1158/0008-5472.can-23-2093.Peer-Reviewed Original ResearchConceptsIDHmut gliomasIDHmut tumorsIDHwt gliomasResponse to therapeutic pressureGenes associated with tumor progressionT cell infiltrationLevels of global methylationIDH1 mutation statusAssociated with survivalDNA methylationLoss of DNA methylationGenome-wide DNA methylationRecurrent tumorsIncreased neoangiogenesisMutation statusIDH-wildtypeTherapy resistanceHistological progressionTherapeutic pressureLevel of genome-wide DNA methylationTumor microenvironmentT cellsTreatment regimenTumor adaptationIDH-mutantCancer Evolution: A Multifaceted Affair
Ciriello G, Magnani L, Aitken S, Akkari L, Behjati S, Hanahan D, Landau D, Lopez-Bigas N, Lupiáñez D, Marine J, Martin-Villalba A, Natoli G, Obenauf A, Oricchio E, Scaffidi P, Sottoriva A, Swarbrick A, Tonon G, Vanharanta S, Zuber J. Cancer Evolution: A Multifaceted Affair. Cancer Discovery 2023, 14: of1-of13. PMID: 38047596, PMCID: PMC10784746, DOI: 10.1158/2159-8290.cd-23-0530.Peer-Reviewed Original ResearchConceptsEvolutionary mechanismsMultiple evolutionary mechanismsCancer evolutionHeritable genetic changesTumor evolutionImprove personalized medicineEpigenetic reprogrammingGenetic changesTumor microenvironmentGenetic instabilityCancer hallmarksEvolutionary toolkitNongenetic mechanismsCancer cellsPersonalized medicineBiomarker discoveryTumor cellsTumor progressionTumorComprehensive characterizationMolecular modificationsCancerCellsChromatinReprogrammingEpigenetic markers and therapeutic targets for metastasis
Kravitz C, Yan Q, Nguyen D. Epigenetic markers and therapeutic targets for metastasis. Cancer And Metastasis Reviews 2023, 42: 427-443. PMID: 37286865, PMCID: PMC10595046, DOI: 10.1007/s10555-023-10109-y.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsEpigenomic alterationsLineage integrityTherapeutic targetEpigenetic markersCancer cellsGenetic aberrationsCurrent knowledgeHuman tumorsMalignant cell cloneTumor progressionDNANumber of discoveriesCell clonesDisseminated diseaseCertain organsPrimary tumorTherapeutic responseMetastatic cancerEpigenomeChromatinHistonesLiquid biopsyAlterationsClonesTargetSEACells infers transcriptional and epigenomic cellular states from single-cell genomics data
Persad S, Choo Z, Dien C, Sohail N, Masilionis I, Chaligné R, Nawy T, Brown C, Sharma R, Pe’er I, Setty M, Pe’er D. SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data. Nature Biotechnology 2023, 41: 1746-1757. PMID: 36973557, PMCID: PMC10713451, DOI: 10.1038/s41587-023-01716-9.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsCD4-Positive T-LymphocytesChromatinEpigenomicsGenomicsHumansSingle-Cell AnalysisConceptsCell statesTransposase-accessible chromatinSingle-cell sequencing dataSingle-cell dataDiscrete cell typesChromatin landscapeSequence dataGenomic dataExpression dynamicsAssociated with disease onsetCritical regulatorsGene scoreT cell differentiationCD4 T cell differentiationCell typesHematopoietic differentiationMetaCellChromatinCell clustersActive stateDifferentiationATACGenomeCellsRNASpatial epigenome–transcriptome co-profiling of mammalian tissues
Zhang D, Deng Y, Kukanja P, Agirre E, Bartosovic M, Dong M, Ma C, Ma S, Su G, Bao S, Liu Y, Xiao Y, Rosoklija G, Dwork A, Mann J, Leong K, Boldrini M, Wang L, Haeussler M, Raphael B, Kluger Y, Castelo-Branco G, Fan R. Spatial epigenome–transcriptome co-profiling of mammalian tissues. Nature 2023, 616: 113-122. PMID: 36922587, PMCID: PMC10076218, DOI: 10.1038/s41586-023-05795-1.Peer-Reviewed Original ResearchConceptsGene expressionSingle-cell resolutionChromatin accessibilityJoint profilingHistone modificationsGene regulationCellular statesEpigenetic mechanismsCentral dogmaSpatial transcriptomeTranscriptional phenotypeCell statesOmics informationSpatial transcriptomicsEpigenetic primingMammalian tissuesEpigenomeMolecular biologyTissue architectureCell dynamicsMechanistic relationshipDifferential rolesNew insightsMouse brainProfilingComprehensive molecular phenotyping of ARID1A-deficient gastric cancer reveals pervasive epigenomic reprogramming and therapeutic opportunities
Xu C, Huang K, Law J, Chua J, Sheng T, Flores N, Pizzi M, Okabe A, Tan A, Zhu F, Kumar V, Lu X, Benitez A, Lian B, Ma H, Ho S, Ramnarayanan K, Anene-Nzelu C, Razavi-Mohseni M, Ghani S, Tay S, Ong X, Lee M, Guo Y, Ashktorab H, Smoot D, Li S, Skanderup A, Beer M, Foo R, Wong J, Sanghvi K, Yong W, Sundar R, Kaneda A, Prabhakar S, Mazur P, Ajani J, Yeoh K, So J, Tan P. Comprehensive molecular phenotyping of ARID1A-deficient gastric cancer reveals pervasive epigenomic reprogramming and therapeutic opportunities. Gut 2023, 72: 1651-1663. PMID: 36918265, DOI: 10.1136/gutjnl-2022-328332.Peer-Reviewed Original ResearchConceptsGastric cancerMolecular subtypesPromoter activityMutational signaturesProinflammatory tumor microenvironmentTumor microenvironmental changesMutated driver genesSingle-cell transcriptome profilingCTCF occupancyGC molecular subtypesChromatin profilingDistal enhancerRegulatory networksEpigenetic landscapeBRD4 bindingEpigenomic reprogrammingEpigenomic levelsTumor-intrinsicTumor inflammationTumor microenvironmentTherapeutic vulnerabilitiesTranscriptome profilingDriver genesNFkB inhibitorGene expressionCurrent landscape of translational and clinical research in myelodysplastic syndromes/neoplasms (MDS): Proceedings from the 1st International Workshop on MDS (iwMDS) Of the International Consortium for MDS (icMDS)
Bewersdorf J, Xie Z, Bejar R, Borate U, Boultwood J, Brunner A, Buckstein R, Carraway H, Churpek J, Daver N, Porta M, DeZern A, Fenaux P, Figueroa M, Gore S, Griffiths E, Halene S, Hasserjian R, Hourigan C, Kim T, Komrokji R, Kuchroo V, List A, Loghavi S, Majeti R, Odenike O, Patnaik M, Platzbecker U, Roboz G, Sallman D, Santini V, Sanz G, Sekeres M, Stahl M, Starczynowski D, Steensma D, Taylor J, Abdel-Wahab O, Xu M, Savona M, Wei A, Zeidan A. Current landscape of translational and clinical research in myelodysplastic syndromes/neoplasms (MDS): Proceedings from the 1st International Workshop on MDS (iwMDS) Of the International Consortium for MDS (icMDS). Blood Reviews 2023, 60: 101072. PMID: 36934059, DOI: 10.1016/j.blre.2023.101072.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsImmune checkpoint inhibitorsSpecific molecular alterationsNovel animal modelInnate immune systemCheckpoint inhibitorsImmune dysregulationMDS patientsClinical trialsNovel therapiesTherapeutic strategiesAnimal modelsGermline predispositionImmune systemMolecular alterationsClinical researchInternational ConsortiumNeoplasmsClinical workGenetic landscapeInternational WorkshopPatientsCurrent landscapePathogenesisTherapyDiseaseThe Cutting Edge of Epigenetic Clocks: In Search of Mechanisms Linking Aging and Mental Health
Harvanek Z, Boks M, Vinkers C, Higgins-Chen A. The Cutting Edge of Epigenetic Clocks: In Search of Mechanisms Linking Aging and Mental Health. Biological Psychiatry 2023, 94: 694-705. PMID: 36764569, PMCID: PMC10409884, DOI: 10.1016/j.biopsych.2023.02.001.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsEpigenomic charting and functional annotation of risk loci in renal cell carcinoma
Nassar A, Abou Alaiwi S, Baca S, Adib E, Corona R, Seo J, Fonseca M, Spisak S, El Zarif T, Tisza V, Braun D, Du H, He M, Flaifel A, Alchoueiry M, Denize T, Matar S, Acosta A, Shukla S, Hou Y, Steinharter J, Bouchard G, Berchuck J, O’Connor E, Bell C, Nuzzo P, Mary Lee G, Signoretti S, Hirsch M, Pomerantz M, Henske E, Gusev A, Lawrenson K, Choueiri T, Kwiatkowski D, Freedman M. Epigenomic charting and functional annotation of risk loci in renal cell carcinoma. Nature Communications 2023, 14: 346. PMID: 36681680, PMCID: PMC9867739, DOI: 10.1038/s41467-023-35833-5.Peer-Reviewed Original ResearchConceptsMaster transcription factorChIP-seqATAC-seq dataH3K27ac ChIP-seqCcRCC cell linesEpigenomic atlasATAC-seqFunctional annotationTranscriptional landscapePrimary human samplesTranscription factorsRNA-seqRisk lociTranscriptional upregulationSNP arrayRisk SNPsETS-1EpigenomeCell linesFOXI1Renal cell carcinomaEPAS1RCC histologic subtypesHuman samplesBHLHE41
2022
Dissecting the epigenomic differences between smoking and nicotine dependence in a veteran cohort
Nagamatsu S, Pietrzak R, Xu K, Krystal J, Gelernter J, Montalvo‐Ortiz J. Dissecting the epigenomic differences between smoking and nicotine dependence in a veteran cohort. Addiction Biology 2022, 28: e13259. PMID: 36577721, DOI: 10.1111/adb.13259.Peer-Reviewed Original ResearchConceptsSmoking statusNicotine dependenceVeteran cohortNon-current smokersSerious public health issueNovel treatment strategiesPublic health issueUS military veteransEpigenome-wide association studiesCurrent smokersTreatment strategiesFagerström TestNicotine addictionSmokingHealth issuesRole of epigeneticsMilitary veteransMethylationEPIC BeadChip arraySmokersContinuous variablesF2RL3 geneCohortBiomarkersBeadChip arrayPrevious findingsUnified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer’s disease progression and heterogeneity
Iturria-Medina Y, Adewale Q, Khan A, Ducharme S, Rosa-Neto P, O'Donnell K, Petyuk V, Gauthier S, De Jager P, Breitner J, Bennett D. Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer’s disease progression and heterogeneity. Science Advances 2022, 8: eabo6764. PMID: 36399579, PMCID: PMC9674284, DOI: 10.1126/sciadv.abo6764.Peer-Reviewed Original ResearchConceptsMolecular taxonomy of ADAlzheimer's diseaseMolecular dataAlzheimer's disease progressionGenetic variationDNA methylationPattern of alterationsAD dementia progressionAD variantsCell typesPostmortem brainsHeterogeneous disorderBiological domainSpatial pattern of alterationAlzheimerClinical heterogeneityTaxonomyAD subtypesMetabolomic profilesSpatial patternsMolecular indicesAdvanced machine learning analysisDisease progressionRNADNAPersonal airborne chemical exposure and epigenetic ageing biomarkers in healthy Chinese elderly individuals: Evidence from mixture approaches
Shi W, Gao X, Cao Y, Chen Y, Cui Q, Deng F, Yang B, Lin E, Fang J, Li T, Tang S, Godri Pollitt K, Shi X. Personal airborne chemical exposure and epigenetic ageing biomarkers in healthy Chinese elderly individuals: Evidence from mixture approaches. Environment International 2022, 170: 107614. PMID: 36375280, DOI: 10.1016/j.envint.2022.107614.Peer-Reviewed Original ResearchConceptsEpigenetic ageing biomarkersPolycyclic aromatic hydrocarbonsOrganic chemical contaminantsVolatile organic compoundsElderly individualsChemical exposureAge-related adverse effectsBayesian kernel machine regression (BKMR) modelsOrganic compoundsAging biomarkersChinese elderly individualsOrganic chemicalsHealthy elderly peopleLinear mixed-effects regression modelsHealthy elderly individualsAirborne chemical exposuresQuantile sum (WQS) regressionPassive samplersNitroaromaticsMixed effects regression modelsEpigenetic biomarkersChemical contaminantsPersonal chemical exposureChlorinated hydrocarbonsChemical mixtures
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