2019
Gene set meta-analysis with Quantitative Set Analysis for Gene Expression (QuSAGE)
Meng H, Yaari G, Bolen CR, Avey S, Kleinstein SH. Gene set meta-analysis with Quantitative Set Analysis for Gene Expression (QuSAGE). PLOS Computational Biology 2019, 15: e1006899. PMID: 30939133, PMCID: PMC6461294, DOI: 10.1371/journal.pcbi.1006899.Peer-Reviewed Original ResearchInferred Allelic Variants of Immunoglobulin Receptor Genes: A System for Their Evaluation, Documentation, and Naming
Ohlin M, Scheepers C, Corcoran M, Lees WD, Busse CE, Bagnara D, Thörnqvist L, Bürckert JP, Jackson KJL, Ralph D, Schramm CA, Marthandan N, Breden F, Scott J, Matsen F, Greiff V, Yaari G, Kleinstein SH, Christley S, Sherkow JS, Kossida S, Lefranc MP, van Zelm MC, Watson CT, Collins AM. Inferred Allelic Variants of Immunoglobulin Receptor Genes: A System for Their Evaluation, Documentation, and Naming. Frontiers In Immunology 2019, 10: 435. PMID: 30936866, PMCID: PMC6431624, DOI: 10.3389/fimmu.2019.00435.Peer-Reviewed Original ResearchMeSH KeywordsAllelesBase SequenceDatabases, GeneticDatasets as TopicGene LibraryGenes, ImmunoglobulinGenetic VariationGerm-Line MutationHigh-Throughput Nucleotide SequencingHumansImmunoglobulin Heavy ChainsImmunoglobulin Variable RegionPolymerase Chain ReactionSequence AlignmentSequence Homology, Nucleic AcidTerminology as TopicV(D)J RecombinationVDJ ExonsConceptsGene databaseInternational ImMunoGeneTics information systemAdaptive immune receptor repertoire sequencingLymphocyte receptor genesAllelic variantsGermline genesReceptor geneAIRR CommunityVertebrate speciesGenetic variationIg diversityAIRR-seq dataJ genesIg genesAllelic sequencesGenesIGHV genesEffector moleculesUnprecedented insightsB-cell lineageBiological interpretationT cell receptorReference databaseGene variationRepertoire studies
2017
Polycomb Repressive Complex 2-Mediated Chromatin Repression Guides Effector CD8+ T Cell Terminal Differentiation and Loss of Multipotency
Gray SM, Amezquita RA, Guan T, Kleinstein SH, Kaech SM. Polycomb Repressive Complex 2-Mediated Chromatin Repression Guides Effector CD8+ T Cell Terminal Differentiation and Loss of Multipotency. Immunity 2017, 46: 596-608. PMID: 28410989, PMCID: PMC5457165, DOI: 10.1016/j.immuni.2017.03.012.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsCD8-Positive T-LymphocytesCell DifferentiationChromatinEnhancer of Zeste Homolog 2 ProteinFlow CytometryForkhead Box Protein O1Gene ExpressionHistonesImmunoblottingImmunologic MemoryLysineMethylationMice, Inbred C57BLMice, KnockoutMice, TransgenicModels, ImmunologicalMultipotent Stem CellsPolycomb Repressive Complex 2Reverse Transcriptase Polymerase Chain ReactionConceptsH3K27me3 depositionPolycomb repressive complex 2T cell terminal differentiationRepressive complex 2MP cellsLoss of multipotencyPro-survival genesCell terminal differentiationFate restrictionPermissive chromatinEpigenetic silencingMemory cell potentialDevelopmental plasticityCell developmentTerminal differentiationCell differentiationGenesPrecursor cellsFOXO1 expressionChromatinMemory precursor cellsMultipotencyCell maturationClonal expansionCells
2016
Recurrent genetic defects in classical Hodgkin lymphoma cell lines
Hudnall SD, Meng H, Lozovatsky L, Li P, Strout M, Kleinstein SH. Recurrent genetic defects in classical Hodgkin lymphoma cell lines. Leukemia & Lymphoma 2016, 57: 2890-2900. PMID: 27121023, DOI: 10.1080/10428194.2016.1177179.Peer-Reviewed Original ResearchConceptsMitosis-related genesSingle nucleotide variantsCHL cell linesCell linesRecurrent genetic defectsPathogenic single nucleotide variantsHL cell linesMitotic genesChromosome duplicationClassical Hodgkin lymphoma cell linesGenomic instabilityGenetic analysisWhole-exome sequencingNucleotide variantsGenesHodgkin's lymphoma cell linesLymphoma cell linesNumber variantsKaryotypic analysisGenetic defectsWealth of informationPoor growthVariantsDuplicationLines
2015
The transcription factors ZEB2 and T-bet cooperate to program cytotoxic T cell terminal differentiation in response to LCMV viral infection
Dominguez CX, Amezquita RA, Guan T, Marshall HD, Joshi NS, Kleinstein SH, Kaech SM. The transcription factors ZEB2 and T-bet cooperate to program cytotoxic T cell terminal differentiation in response to LCMV viral infection. Journal Of Experimental Medicine 2015, 212: 2041-2056. PMID: 26503446, PMCID: PMC4647261, DOI: 10.1084/jem.20150186.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsCD8-Positive T-LymphocytesCell DifferentiationCluster AnalysisFlow CytometryHomeodomain ProteinsHost-Pathogen InteractionsLectins, C-TypeLymphocytic ChoriomeningitisLymphocytic choriomeningitis virusMice, Inbred C57BLMice, KnockoutMice, TransgenicOligonucleotide Array Sequence AnalysisProtein BindingReceptors, ImmunologicRepressor ProteinsReverse Transcriptase Polymerase Chain ReactionT-Box Domain ProteinsT-Lymphocytes, CytotoxicTranscriptomeZinc Finger E-box Binding Homeobox 2ConceptsTerminal differentiationT cell terminal differentiationChromatin immunoprecipitation sequencingNovel genetic pathwaysTranscription factor ZEB2Cell terminal differentiationZeb2 functionImmunoprecipitation sequencingMemory cell potentialDifferentiation programGenetic pathwaysCytotoxic T lymphocyte differentiationTerminal effectorZEB2 mRNAPrecursor cellsCoordinated actionLymphocyte differentiationT lymphocyte differentiationMemory precursor cellsGenesT-betDifferentiationViral infectionZEB2CooperateInteractive Big Data Resource to Elucidate Human Immune Pathways and Diseases
Gorenshteyn D, Zaslavsky E, Fribourg M, Park CY, Wong AK, Tadych A, Hartmann BM, Albrecht RA, García-Sastre A, Kleinstein SH, Troyanskaya OG, Sealfon SC. Interactive Big Data Resource to Elucidate Human Immune Pathways and Diseases. Immunity 2015, 43: 605-614. PMID: 26362267, PMCID: PMC4753773, DOI: 10.1016/j.immuni.2015.08.014.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsBayes TheoremComputational BiologyGene Regulatory NetworksHost-Pathogen InteractionsHumansImmune SystemImmune System DiseasesInternetProtein Interaction MappingProtein Interaction MapsReproducibility of ResultsSignal TransductionSupport Vector MachineTranscriptomeVirus DiseasesConceptsPublic high-throughput dataGenome-scale experimentsDisease-associated genesHigh-throughput datasetsHigh-throughput dataData-driven hypothesesGenetic studiesImmune pathwaysGenesImmunological diseasesFunctional relationshipBiomedical research effortsImportant interactionsMolecular entitiesImmune systemHuman immune systemProteinExponential growthPathwayData resourcesBayesian integrationRelevant insightsGrowthCompendiumIdentification
2014
Dynamic expression profiling of type I and type III interferon‐stimulated hepatocytes reveals a stable hierarchy of gene expression
Bolen CR, Ding S, Robek MD, Kleinstein SH. Dynamic expression profiling of type I and type III interferon‐stimulated hepatocytes reveals a stable hierarchy of gene expression. Hepatology 2014, 59: 1262-1272. PMID: 23929627, PMCID: PMC3938553, DOI: 10.1002/hep.26657.Peer-Reviewed Original ResearchConceptsGene expressionExpression profilingIndividual interferonMicroarray-based gene expression profilingDynamic expression profilingGene expression profilingSimilar signaling cascadesPotential specific rolesPromoter analysisTranscriptional responseHuh7 hepatoma cellsGene inductionExpression hierarchySignaling cascadesIFN-α signalingAntiviral stateNegative feedback mechanismType IPrimary human hepatocytesHepatoma cellsISG inductionSpecific roleGenesInterferon-stimulated gene inductionSuperior clinical activity
2013
Multiple Transcription Factor Binding Sites Predict AID Targeting in Non-Ig Genes
Duke JL, Liu M, Yaari G, Khalil AM, Tomayko MM, Shlomchik MJ, Schatz DG, Kleinstein SH. Multiple Transcription Factor Binding Sites Predict AID Targeting in Non-Ig Genes. The Journal Of Immunology 2013, 190: 3878-3888. PMID: 23514741, PMCID: PMC3689293, DOI: 10.4049/jimmunol.1202547.Peer-Reviewed Original ResearchConceptsTranscription Factor Binding SitesAID-induced lesionsNon-Ig genesGenome instabilityTranscription factorsAberrant targetingSequence dataCertain genesGenesAID targetingGerminal center B cellsSomatic mutationsLikely targetBinding sitesAID targetsTargetingClassification tree modelMistargetingB cellsLociMechanismTargetMutationsSites
2011
Somatic hypermutation targeting is influenced by location within the immunoglobulin V region
Cohen RM, Kleinstein SH, Louzoun Y. Somatic hypermutation targeting is influenced by location within the immunoglobulin V region. Molecular Immunology 2011, 48: 1477-1483. PMID: 21592579, PMCID: PMC3109224, DOI: 10.1016/j.molimm.2011.04.002.Peer-Reviewed Original ResearchConceptsObserved mutation patternSpecific DNA motifsBiased codon usageImmunoglobulin V genesMutation accumulationGene positionCodon usageMutation patternsDNA motifsPositive selectionPosition-specific effectsImmunoglobulin V regionsNegative selectionB cellsMutationsMutation frequencyV geneGenesPeripheral B cellsSubstitution typeV regionsTargetingSpecific targetingCellsSequenceGene Expression Gradients along the Tonotopic Axis of the Chicken Auditory Epithelium
Frucht CS, Uduman M, Kleinstein SH, Santos-Sacchi J, Navaratnam DS. Gene Expression Gradients along the Tonotopic Axis of the Chicken Auditory Epithelium. Journal Of The Association For Research In Otolaryngology 2011, 12: 423-435. PMID: 21399991, PMCID: PMC3123449, DOI: 10.1007/s10162-011-0259-2.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsAnimals, NewbornCalcium ChannelsChickensCyclic AMP-Dependent Protein KinasesGene Expression ProfilingGene Expression Regulation, DevelopmentalHair Cells, AuditoryLarge-Conductance Calcium-Activated Potassium Channel alpha SubunitsMicroRNAsModels, AnimalOrgan of CortiProtein Kinase CSodium ChannelsConceptsGene expression gradientsAuditory epitheliumKinases protein kinase CChicken auditory epitheliumExpression gradientsAvian auditory epitheliumBasilar papillaFold-change cutoffActivity of kinasesIon channel genesProtein kinase CChicken basilar papillaGene setsGenetic basisEnrichment analysisExpression patternsGene expressionGeneSpring softwareChannel genesMechanism of inductionKinase CGenesMicroarray dataDifferential activityQuantitative PCR
2010
Gene Expression Analysis of Forskolin Treated Basilar Papillae Identifies MicroRNA181a as a Mediator of Proliferation
Frucht CS, Uduman M, Duke JL, Kleinstein SH, Santos-Sacchi J, Navaratnam DS. Gene Expression Analysis of Forskolin Treated Basilar Papillae Identifies MicroRNA181a as a Mediator of Proliferation. PLOS ONE 2010, 5: e11502. PMID: 20634979, PMCID: PMC2901389, DOI: 10.1371/journal.pone.0011502.Peer-Reviewed Original ResearchConceptsNew hair cellsAuditory epitheliumChicken auditory epitheliumHair cellsInner ear developmentHair cell regenerationGene expression analysisAuditory hair cellsEar developmentExpression analysisMyosin VIEnrichment analysisCycle controlGene expressionMolecular eventsSingle microRNAMediator of proliferationRelevant pathwaysFunctional experimentsPost-hatch chicksRegenerating tissueMammalsGenesBrdU incorporationCell regeneration
2008
Two levels of protection for the B cell genome during somatic hypermutation
Liu M, Duke JL, Richter DJ, Vinuesa CG, Goodnow CC, Kleinstein SH, Schatz DG. Two levels of protection for the B cell genome during somatic hypermutation. Nature 2008, 451: 841-845. PMID: 18273020, DOI: 10.1038/nature06547.Peer-Reviewed Original ResearchConceptsError-free DNA repairB cell genomeGenomic stabilityNumerous oncogenesDNA repairCell genomeBase excisionGenomeMismatch repairImmunoglobulin genesSomatic hypermutationWidespread mutationsHypermutationB-cell tumorsB-cell malignanciesHigh-affinity antibodiesB cellsGenesOncogeneLarge fractionDiversityVital roleMutationsEnzymeRepair