2024
Single-cell genomics and regulatory networks for 388 human brains
Emani P, Liu J, Clarke D, Jensen M, Warrell J, Gupta C, Meng R, Lee C, Xu S, Dursun C, Lou S, Chen Y, Chu Z, Galeev T, Hwang A, Li Y, Ni P, Zhou X, Bakken T, Bendl J, Bicks L, Chatterjee T, Cheng L, Cheng Y, Dai Y, Duan Z, Flaherty M, Fullard J, Gancz M, Garrido-Martín D, Gaynor-Gillett S, Grundman J, Hawken N, Henry E, Hoffman G, Huang A, Jiang Y, Jin T, Jorstad N, Kawaguchi R, Khullar S, Liu J, Liu J, Liu S, Ma S, Margolis M, Mazariegos S, Moore J, Moran J, Nguyen E, Phalke N, Pjanic M, Pratt H, Quintero D, Rajagopalan A, Riesenmy T, Shedd N, Shi M, Spector M, Terwilliger R, Travaglini K, Wamsley B, Wang G, Xia Y, Xiao S, Yang A, Zheng S, Gandal M, Lee D, Lein E, Roussos P, Sestan N, Weng Z, White K, Won H, Girgenti M, Zhang J, Wang D, Geschwind D, Gerstein M, Akbarian S, Abyzov A, Ahituv N, Arasappan D, Almagro Armenteros J, Beliveau B, Berretta S, Bharadwaj R, Bhattacharya A, Brennand K, Capauto D, Champagne F, Chatzinakos C, Chen H, Cheng L, Chess A, Chien J, Clement A, Collado-Torres L, Cooper G, Crawford G, Dai R, Daskalakis N, Davila-Velderrain J, Deep-Soboslay A, Deng C, DiPietro C, Dracheva S, Drusinsky S, Duong D, Eagles N, Edelstein J, Galani K, Girdhar K, Goes F, Greenleaf W, Guo H, Guo Q, Hadas Y, Hallmayer J, Han X, Haroutunian V, He C, Hicks S, Ho M, Ho L, Huang Y, Huuki-Myers L, Hyde T, Iatrou A, Inoue F, Jajoo A, Jiang L, Jin P, Jops C, Jourdon A, Kellis M, Kleinman J, Kleopoulos S, Kozlenkov A, Kriegstein A, Kundaje A, Kundu S, Li J, Li M, Lin X, Liu S, Liu C, Loupe J, Lu D, Ma L, Mariani J, Martinowich K, Maynard K, Myers R, Micallef C, Mikhailova T, Ming G, Mohammadi S, Monte E, Montgomery K, Mukamel E, Nairn A, Nemeroff C, Norton S, Nowakowski T, Omberg L, Page S, Park S, Patowary A, Pattni R, Pertea G, Peters M, Pinto D, Pochareddy S, Pollard K, Pollen A, Przytycki P, Purmann C, Qin Z, Qu P, Raj T, Reach S, Reimonn T, Ressler K, Ross D, Rozowsky J, Ruth M, Ruzicka W, Sanders S, Schneider J, Scuderi S, Sebra R, Seyfried N, Shao Z, Shieh A, Shin J, Skarica M, Snijders C, Song H, State M, Stein J, Steyert M, Subburaju S, Sudhof T, Snyder M, Tao R, Therrien K, Tsai L, Urban A, Vaccarino F, van Bakel H, Vo D, Voloudakis G, Wang T, Wang S, Wang Y, Wei Y, Weimer A, Weinberger D, Wen C, Whalen S, Willsey A, Wong W, Wu H, Wu F, Wuchty S, Wylie D, Yap C, Zeng B, Zhang P, Zhang C, Zhang B, Zhang Y, Ziffra R, Zeier Z, Zintel T. Single-cell genomics and regulatory networks for 388 human brains. Science 2024, 384: eadi5199. PMID: 38781369, PMCID: PMC11365579, DOI: 10.1126/science.adi5199.Peer-Reviewed Original ResearchMeSH KeywordsAgingBrainCell CommunicationChromatinGene Regulatory NetworksGenomicsHumansMental DisordersPrefrontal CortexQuantitative Trait LociSingle-Cell AnalysisConceptsSingle-cell genomicsSingle-cell expression quantitative trait locusExpression quantitative trait lociDrug targetsQuantitative trait lociPopulation-level variationSingle-cell expressionCell typesDisease-risk genesTrait lociGene familyRegulatory networksGene expressionCell-typeMultiomics datasetsSingle-nucleiGenomeGenesCellular changesHeterogeneous tissuesExpressionCellsChromatinLociMultiomicsCross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain
Wen C, Margolis M, Dai R, Zhang P, Przytycki P, Vo D, Bhattacharya A, Matoba N, Tang M, Jiao C, Kim M, Tsai E, Hoh C, Aygün N, Walker R, Chatzinakos C, Clarke D, Pratt H, Peters M, Gerstein M, Daskalakis N, Weng Z, Jaffe A, Kleinman J, Hyde T, Weinberger D, Bray N, Sestan N, Geschwind D, Roeder K, Gusev A, Pasaniuc B, Stein J, Love M, Pollard K, Liu C, Gandal M, Akbarian S, Abyzov A, Ahituv N, Arasappan D, Almagro Armenteros J, Beliveau B, Bendl J, Berretta S, Bharadwaj R, Bicks L, Brennand K, Capauto D, Champagne F, Chatterjee T, Chatzinakos C, Chen Y, Chen H, Cheng Y, Cheng L, Chess A, Chien J, Chu Z, Clement A, Collado-Torres L, Cooper G, Crawford G, Davila-Velderrain J, Deep-Soboslay A, Deng C, DiPietro C, Dracheva S, Drusinsky S, Duan Z, Duong D, Dursun C, Eagles N, Edelstein J, Emani P, Fullard J, Galani K, Galeev T, Gaynor S, Girdhar K, Goes F, Greenleaf W, Grundman J, Guo H, Guo Q, Gupta C, Hadas Y, Hallmayer J, Han X, Haroutunian V, Hawken N, He C, Henry E, Hicks S, Ho M, Ho L, Hoffman G, Huang Y, Huuki-Myers L, Hwang A, Iatrou A, Inoue F, Jajoo A, Jensen M, Jiang L, Jin P, Jin T, Jops C, Jourdon A, Kawaguchi R, Kellis M, Kleopoulos S, Kozlenkov A, Kriegstein A, Kundaje A, Kundu S, Lee C, Lee D, Li J, Li M, Lin X, Liu S, Liu J, Liu J, Liu S, Lou S, Loupe J, Lu D, Ma S, Ma L, Mariani J, Martinowich K, Maynard K, Mazariegos S, Meng R, Myers R, Micallef C, Mikhailova T, Ming G, Mohammadi S, Monte E, Montgomery K, Moore J, Moran J, Mukamel E, Nairn A, Nemeroff C, Ni P, Norton S, Nowakowski T, Omberg L, Page S, Park S, Patowary A, Pattni R, Pertea G, Phalke N, Pinto D, Pjanic M, Pochareddy S, Pollen A, Purmann C, Qin Z, Qu P, Quintero D, Raj T, Rajagopalan A, Reach S, Reimonn T, Ressler K, Ross D, Roussos P, Rozowsky J, Ruth M, Ruzicka W, Sanders S, Schneider J, Scuderi S, Sebra R, Seyfried N, Shao Z, Shedd N, Shieh A, Shin J, Skarica M, Snijders C, Song H, State M, Steyert M, Subburaju S, Sudhof T, Snyder M, Tao R, Therrien K, Tsai L, Urban A, Vaccarino F, van Bakel H, Voloudakis G, Wamsley B, Wang T, Wang S, Wang D, Wang Y, Warrell J, Wei Y, Weimer A, Whalen S, White K, Willsey A, Won H, Wong W, Wu H, Wu F, Wuchty S, Wylie D, Xu S, Yap C, Zeng B, Zhang C, Zhang B, Zhang J, Zhang Y, Zhou X, Ziffra R, Zeier Z, Zintel T. Cross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain. Science 2024, 384: eadh0829. PMID: 38781368, DOI: 10.1126/science.adh0829.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesGenome-wide association study lociSplicing quantitative trait lociQuantitative trait lociSplicing regulationCross-ancestryTrait lociAssociation studiesRegulatory elementsCellular contextHuman brainTranscriptome regulationCoexpression networkRisk genesAutism spectrum disorderGenesCellular heterogeneityComprehensive landscapeSpectrum disorderIsoformsSplicingIncreased cellular heterogeneityLociNeuronal maturationRegulation
2020
Transformative Network Modeling of Multi-omics Data Reveals Detailed Circuits, Key Regulators, and Potential Therapeutics for Alzheimer’s Disease
Wang M, Li A, Sekiya M, Beckmann ND, Quan X, Schrode N, Fernando MB, Yu A, Zhu L, Cao J, Lyu L, Horgusluoglu E, Wang Q, Guo L, Wang YS, Neff R, Song WM, Wang E, Shen Q, Zhou X, Ming C, Ho SM, Vatansever S, Kaniskan HÜ, Jin J, Zhou MM, Ando K, Ho L, Slesinger PA, Yue Z, Zhu J, Katsel P, Gandy S, Ehrlich ME, Fossati V, Noggle S, Cai D, Haroutunian V, Iijima KM, Schadt E, Brennand KJ, Zhang B. Transformative Network Modeling of Multi-omics Data Reveals Detailed Circuits, Key Regulators, and Potential Therapeutics for Alzheimer’s Disease. Neuron 2020, 109: 257-272.e14. PMID: 33238137, PMCID: PMC7855384, DOI: 10.1016/j.neuron.2020.11.002.Peer-Reviewed Original ResearchConceptsLate-onset Alzheimer's diseaseAlzheimer's diseaseKey regulatorPluripotent stem cell-derived neuronsRNAi-based knockdownStem cell-derived neuronsNovel therapeutic targetNext-generation therapeutic agentsCell-derived neuronsKey brain regionsIntegrative network analysisMulti-omics dataComplex molecular interactionsMulti-omics profilingNCH-51Neuronal impairmentGene subnetworksDisease-related processesCortical areasTherapeutic targetDrosophila modelNeuropathological phenotypeBrain regionsTherapeutic agentsMolecular mechanisms
2019
Differential transcriptional response following glucocorticoid activation in cultured blood immune cells: a novel approach to PTSD biomarker development
Breen MS, Bierer LM, Daskalakis NP, Bader HN, Makotkine I, Chattopadhyay M, Xu C, Buxbaum Grice A, Tocheva AS, Flory JD, Buxbaum JD, Meaney MJ, Brennand K, Yehuda R. Differential transcriptional response following glucocorticoid activation in cultured blood immune cells: a novel approach to PTSD biomarker development. Translational Psychiatry 2019, 9: 201. PMID: 31434874, PMCID: PMC6704073, DOI: 10.1038/s41398-019-0539-x.Peer-Reviewed Original ResearchMeSH KeywordsAdultBiomarkersConstitutive Androstane ReceptorDexamethasoneDose-Response Relationship, DrugGene ExpressionGene Expression ProfilingGene Regulatory NetworksGlucocorticoidsHumansLeukocytes, MononuclearMaleMiddle AgedStress Disorders, Post-TraumaticTranscription, GeneticVeteransYoung AdultConceptsPeripheral blood mononuclear cellsPost-traumatic stress disorderGlucocorticoid signalingCultured peripheral blood mononuclear cellsBlood immune cellsBlood mononuclear cellsTranscriptional responseConcentrations of dexamethasoneDifferential transcriptional changesGenome-wide gene expression profilingCombat-exposed veteransStress-responsive pathwaysMolecular responseClinical manifestationsInflammatory cytokinesDynamic transcriptional responseMononuclear cellsApoptosis-related pathwaysImmune cellsBaseline differencesDifferential transcriptional responsesDifferential molecular responsesGlucocorticoid stimulationNovel markerReliable marker
2018
GJA1 (connexin43) is a key regulator of Alzheimer’s disease pathogenesis
Kajiwara Y, Wang E, Wang M, Sin WC, Brennand KJ, Schadt E, Naus CC, Buxbaum J, Zhang B. GJA1 (connexin43) is a key regulator of Alzheimer’s disease pathogenesis. Acta Neuropathologica Communications 2018, 6: 144. PMID: 30577786, PMCID: PMC6303945, DOI: 10.1186/s40478-018-0642-x.Peer-Reviewed Original ResearchConceptsPost-mortem Alzheimer's diseaseAlzheimer's diseaseTop key driverRNA sequencing analysisDisease pathogenesisProteomic datasetsKey regulatorNormal control brainsGJA1 expressionAlzheimer's disease (AD) pathogenesisApoE protein levelsPromising pharmacological targetSequencing analysisGJA1Wildtype astrocytesWildtype neuronsAβ metabolismAβ phagocytosisProtein levelsControl brainsAD pathogenesisAD amyloidPharmacological targetsAstrocytesCognitive function
2017
Common developmental genome deprogramming in schizophrenia — Role of Integrative Nuclear FGFR1 Signaling (INFS)
Narla S, Lee Y, Benson C, Sarder P, Brennand K, Stachowiak E, Stachowiak M. Common developmental genome deprogramming in schizophrenia — Role of Integrative Nuclear FGFR1 Signaling (INFS). Schizophrenia Research 2017, 185: 17-32. PMID: 28094170, PMCID: PMC5507209, DOI: 10.1016/j.schres.2016.12.012.Peer-Reviewed Original ResearchMeSH KeywordsAdultCell DifferentiationCells, CulturedFemaleGene Expression Regulation, DevelopmentalGene Regulatory NetworksGenomeGenomicsHumansInduced Pluripotent Stem CellsMaleMicroRNAsModels, BiologicalMutationReceptor, Fibroblast Growth Factor, Type 1Receptor, Notch1SchizophreniaSignal TransductionTranscriptomeYoung AdultConceptsMRNA networkMajor developmental pathwaysIntegrative nuclear FGFR1MiRNA-mRNA networkHuman gene promotersCommon developmental genomesMiRNA genesMiRNA transcriptomeGene networksUpregulated genesGene promoterNuclear FGFR1Genomic etiologyGene dysregulationDisease ontogenyNuclear formGlobal dysregulationDevelopmental pathwaysGenesNeuron formationDistinct pathwaysConcerted actionPotential therapeutic targetTranscriptomeGenome
2016
Activity-Dependent Changes in Gene Expression in Schizophrenia Human-Induced Pluripotent Stem Cell Neurons
Roussos P, Guennewig B, Kaczorowski D, Barry G, Brennand K. Activity-Dependent Changes in Gene Expression in Schizophrenia Human-Induced Pluripotent Stem Cell Neurons. JAMA Psychiatry 2016, 73: 1180-1188. PMID: 27732689, PMCID: PMC5437975, DOI: 10.1001/jamapsychiatry.2016.2575.Peer-Reviewed Original ResearchConceptsGene coexpression analysisActivity-dependent changesGene expressionCandidate genesCoexpression analysisSchizophrenia candidate genesSpecific molecular functionsGenome-wide profilingPluripotent stem cell-derived neuronsGene expression differencesSchizophrenia-associated variantsStem cell-derived neuronsDifferential expression analysisNeuronal activity-dependent changesHuman-induced pluripotent stem cell-derived neuronsCell-derived neuronsHuman-induced pluripotent stem cellsPluripotent stem cell neuronsPluripotent stem cellsCommon molecular pathwaysSchizophrenia risk genesMolecular functionsGene networksEtiopathogenesis of schizophreniaExpression analysisIntegrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimer’s disease
Wang M, Roussos P, McKenzie A, Zhou X, Kajiwara Y, Brennand K, De Luca G, Crary J, Casaccia P, Buxbaum J, Ehrlich M, Gandy S, Goate A, Katsel P, Schadt E, Haroutunian V, Zhang B. Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimer’s disease. Genome Medicine 2016, 8: 104. PMID: 27799057, PMCID: PMC5088659, DOI: 10.1186/s13073-016-0355-3.Peer-Reviewed Original ResearchConceptsGene expression changesCell type-specific marker genesExpression changesSingle-cell RNA-sequencing dataCo-expressed gene modulesLarge-scale gene expressionTranscriptomic network analysisCo-expression networkRNA-sequencing dataIntegrative network analysisNervous system developmentSelective regional vulnerabilityCritical molecular pathwaysActin cytoskeletonGenomic studiesGene modulesGenomic analysisGene expression abnormalitiesMarker genesMolecular basisGene expressionNetwork analysisMolecular mechanismsAxon guidanceMolecular pathways