2024
Single-cell multi-cohort dissection of the schizophrenia transcriptome
Ruzicka W, Mohammadi S, Fullard J, Davila-Velderrain J, Subburaju S, Tso D, Hourihan M, Jiang S, Lee H, Bendl J, Voloudakis G, Haroutunian V, Hoffman G, Roussos P, Kellis M, Akbarian S, Abyzov A, Ahituv N, Arasappan D, Almagro Armenteros J, Beliveau B, Berretta S, Bharadwaj R, Bhattacharya A, 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, Clarke D, Clement A, Collado-Torres L, Cooper G, Crawford G, Dai R, Daskalakis N, Deep-Soboslay A, Deng C, DiPietro C, Dracheva S, Drusinsky S, Duan Z, Duong D, Dursun C, Eagles N, Edelstein J, Emani P, Galani K, Galeev T, Gandal M, Gaynor S, Gerstein M, Geschwind D, Girdhar K, Goes F, Greenleaf W, Grundman J, Guo H, Guo Q, Gupta C, Hadas Y, Hallmayer J, Han X, Hawken N, He C, Henry E, Hicks S, Ho M, Ho L, Huang Y, Huuki-Myers L, Hwang A, Hyde T, Iatrou A, Inoue F, Jajoo A, Jensen M, Jiang L, Jin P, Jin T, Jops C, Jourdon A, Kawaguchi R, Kleinman J, 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 C, Liu S, Lou S, Loupe J, Lu D, Ma S, Ma L, Margolis M, Mariani J, Martinowich K, Maynard K, Mazariegos S, Meng R, Myers R, Micallef C, Mikhailova T, Ming G, 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, Peters M, Phalke N, Pinto D, Pjanic M, Pochareddy S, Pollard K, Pollen A, Pratt H, Przytycki P, Purmann C, Qin Z, Qu P, Quintero D, Raj T, Rajagopalan A, Reach S, Reimonn T, Ressler K, Ross D, Rozowsky J, Ruth M, Sanders S, Schneider J, Scuderi S, Sebra R, Sestan N, Seyfried N, Shao Z, Shedd N, Shieh A, Shin J, Skarica M, Snijders C, Song H, State M, Stein J, Steyert M, Sudhof T, Snyder M, Tao R, Therrien K, Tsai L, Urban A, Vaccarino F, van Bakel H, Vo D, Wamsley B, Wang T, Wang S, Wang D, Wang Y, Warrell J, Wei Y, Weimer A, Weinberger D, Wen C, Weng Z, 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 P, Zhang C, Zhang B, Zhang J, Zhang Y, Zhou X, Ziffra R, Zeier Z, Zintel T. Single-cell multi-cohort dissection of the schizophrenia transcriptome. Science 2024, 384: eadg5136. PMID: 38781388, DOI: 10.1126/science.adg5136.Peer-Reviewed Original ResearchConceptsGenetic risk factorsRisk factorsTranscriptional changesHeterogeneity of schizophreniaNeuronal cell statesSchizophrenia pathophysiologySingle-cell dissectionExcitatory neuronsEffective therapySchizophrenia transcriptomicsCortical cytoarchitectureSingle-cell atlasGenomic variantsCell groupsHuman prefrontal cortexMolecular pathwaysSchizophreniaTranscriptional alterationsTranscriptomic changesPrefrontal cortexCell statesAlterationsTherapyPathophysiologyDissection
2023
Contributions of circadian clock genes to cell survival in fibroblast models of lithium-responsive bipolar disorder
Mishra H, Wei H, Rohr K, Ko I, Nievergelt C, Maihofer A, Shilling P, Alda M, Berrettini W, Brennand K, Calabrese J, Coryell W, Frye M, Gage F, Gershon E, McInnis M, Nurnberger J, Oedegaard K, Zandi P, Kelsoe J, McCarthy M. Contributions of circadian clock genes to cell survival in fibroblast models of lithium-responsive bipolar disorder. European Neuropsychopharmacology 2023, 74: 1-14. PMID: 37126998, DOI: 10.1016/j.euroneuro.2023.04.009.Peer-Reviewed Original ResearchConceptsCell survival genesCircadian clockSurvival genesCell survivalCircadian clock genesCircadian rhythmGenetic variationClock genesKnockdown studiesCaspase activityCell deathMolecular pathwaysPrimary fibroblastsCellular modelGenesMouse fibroblastsFibroblast modelApoptosisStaurosporinePER1FibroblastsOpposite mannerLithium responsivenessDistinct patternsClock
2022
A translational genomics approach identifies IL10RB as the top candidate gene target for COVID-19 susceptibility
Voloudakis G, Vicari J, Venkatesh S, Hoffman G, Dobrindt K, Zhang W, Beckmann N, Higgins C, Argyriou S, Jiang S, Hoagland D, Gao L, Corvelo A, Cho K, Lee K, Bian J, Lee J, Iyengar S, Luoh S, Akbarian S, Striker R, Assimes T, Schadt E, Lynch J, Merad M, tenOever B, Charney A, Brennand K, Fullard J, Roussos P. A translational genomics approach identifies IL10RB as the top candidate gene target for COVID-19 susceptibility. Npj Genomic Medicine 2022, 7: 52. PMID: 36064543, PMCID: PMC9441828, DOI: 10.1038/s41525-022-00324-x.Peer-Reviewed Original ResearchCandidate gene targetsGene targetsTranslational genomics approachesHost susceptibilityGenomic approachesGenetic susceptibility variantsGenetic lociDruggable genesGene expressionMolecular pathwaysSusceptibility variantsCOVID-19 susceptibilityGenetic findingsApproach identifiesExpressionCOVID-19 patient bloodCritical next stepGenesLociOverexpressionTargetPathwaySusceptibilityIL10RBRecent efforts
2020
Massively parallel techniques for cataloguing the regulome of the human brain
Townsley KG, Brennand KJ, Huckins LM. Massively parallel techniques for cataloguing the regulome of the human brain. Nature Neuroscience 2020, 23: 1509-1521. PMID: 33199899, PMCID: PMC8018778, DOI: 10.1038/s41593-020-00740-1.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsRegulatory elementsTarget genesParallel reporter assaysPutative regulatory elementsNon-coding regionsDisease-associated lociSpecific expression patternsCandidate risk lociPluripotent stem cellsHigh-throughput assaysRelevant molecular pathwaysTranscriptional responseRegulatory architectureRisk lociExpression patternsReporter assaysComplex brain disordersMolecular pathwaysRegulomeStem cellsRisk architectureGenetic riskGenesLociGenetic diagnosisParsing the Functional Impact of Noncoding Genetic Variants in the Brain Epigenome
Powell SK, O'Shea C, Brennand KJ, Akbarian S. Parsing the Functional Impact of Noncoding Genetic Variants in the Brain Epigenome. Biological Psychiatry 2020, 89: 65-75. PMID: 33131715, PMCID: PMC7718420, DOI: 10.1016/j.biopsych.2020.06.033.Peer-Reviewed Original ResearchConceptsGenetic variantsDisease-associated genetic variationProtein-coding lociRisk-associated genetic variantsGene regulatory lociThousands of variantsFunctional impactRare genetic variantsEpigenomic mappingRegulatory lociBrain epigenomeGenetic variationDNA sequencesNoncoding variantsGene expressionIntegrative analysisEpigenomic architectureMolecular pathwaysPsychiatric geneticsFunctional readoutRisk variantsLociVariantsHighlight findingsEpigenome
2018
THC exposure of human iPSC neurons impacts genes associated with neuropsychiatric disorders
Guennewig B, Bitar M, Obiorah I, Hanks J, O’Brien E, Kaczorowski DC, Hurd YL, Roussos P, Brennand KJ, Barry G. THC exposure of human iPSC neurons impacts genes associated with neuropsychiatric disorders. Translational Psychiatry 2018, 8: 89. PMID: 29691375, PMCID: PMC5915454, DOI: 10.1038/s41398-018-0137-3.Peer-Reviewed Original ResearchConceptsHuman-induced pluripotent stem cellsPluripotent stem cellsHuman iPSC neuronsTranscriptional responseTranscriptomic analysisRNA transcriptomic analysisHuman neural cellsIPSC-neuronsMolecular pathwaysNeuropsychiatric disordersStem cellsNeural cellsDiagnosis-specific differencesGenesTHC exposureNeuronal depolarizationTHC administrationChronic exposureCannabis useNeuronsΔ9-tetrahydrocannabinolStrong associationSignificant alterationsCellsDynamic changes
2016
Integrative 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