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
Solving Immunology?
Vodovotz Y, Xia A, Read EL, Bassaganya-Riera J, Hafler DA, Sontag E, Wang J, Tsang JS, Day JD, Kleinstein SH, Butte AJ, Altman MC, Hammond R, Sealfon SC. Solving Immunology? Trends In Immunology 2016, 38: 116-127. PMID: 27986392, PMCID: PMC5695553, DOI: 10.1016/j.it.2016.11.006.Peer-Reviewed Original Research
2015
The mutation patterns in B-cell immunoglobulin receptors reflect the influence of selection acting at multiple time-scales
Yaari G, Benichou JI, Vander Heiden J, Kleinstein SH, Louzoun Y. The mutation patterns in B-cell immunoglobulin receptors reflect the influence of selection acting at multiple time-scales. Philosophical Transactions Of The Royal Society B Biological Sciences 2015, 370: 20140242. PMID: 26194756, PMCID: PMC4528419, DOI: 10.1098/rstb.2014.0242.Peer-Reviewed Original ResearchMeSH KeywordsAntibody AffinityAntibody DiversityB-LymphocytesCell LineageClonal Selection, Antigen-MediatedComplementarity Determining RegionsGenes, ImmunoglobulinHumansImmunoglobulin Heavy ChainsImmunoglobulin Variable RegionModels, GeneticModels, ImmunologicalMutationReceptors, Antigen, B-CellSomatic Hypermutation, ImmunoglobulinTime FactorsConceptsLineage treesPositive selectionStrong selection pressureLong-term selectionInfluence of selectionGene familyVariable gene familiesComplementarity determining regionsClone membersMutation patternsSelection pressureB cell populationsImmunoglobulin genesB cellsFramework regionsSomatic hypermutationSomatic mutationsAffinity maturationMutationsClone sizeMaturation processLong trunkAffinity maturation processSignificant diversityMultiple roundsComparative analysis of anti-viral transcriptomics reveals novel effects of influenza immune antagonism
Thakar J, Hartmann BM, Marjanovic N, Sealfon SC, Kleinstein SH. Comparative analysis of anti-viral transcriptomics reveals novel effects of influenza immune antagonism. BMC Immunology 2015, 16: 46. PMID: 26272204, PMCID: PMC4536893, DOI: 10.1186/s12865-015-0107-y.Peer-Reviewed Original ResearchConceptsTranscription factor activityImmune antagonismExpression profilesGenome-wide expression profilesGenome-wide transcriptional profiling dataFactor activityGenome-wide transcriptional profilesTranscription factor SATB1DNA-binding sitesTranscriptional profiling dataHost-pathogen interactionsGene expression profilesISGF3 activityTranscriptional responseTranscription factorsTranscriptional profilesHost interactionsProfiling dataApplication of betaNovel effectMechanistic insightsInfected cellsInfluenza A virusesMechanistic differencesNewcastle disease virus
2014
B cells populating the multiple sclerosis brain mature in the draining cervical lymph nodes
Stern JN, Yaari G, Vander Heiden JA, Church G, Donahue WF, Hintzen RQ, Huttner AJ, Laman JD, Nagra RM, Nylander A, Pitt D, Ramanan S, Siddiqui BA, Vigneault F, Kleinstein SH, Hafler DA, O'Connor KC. B cells populating the multiple sclerosis brain mature in the draining cervical lymph nodes. Science Translational Medicine 2014, 6: 248ra107. PMID: 25100741, PMCID: PMC4388137, DOI: 10.1126/scitranslmed.3008879.Peer-Reviewed Original ResearchConceptsCervical lymph nodesCentral nervous systemB cellsCerebrospinal fluidLymph nodesMultiple sclerosisLymphoid tissueCNS of patientsCNS B cellsAntigen-experienced B cellsMultiple sclerosis brainSecondary lymphoid tissuesB cell compartmentB cell trafficB cell maturationImmunomodulatory therapyImmune infiltratesPeripheral bloodInflammatory diseasesLymphocyte transmigrationPeripheral tissuesNervous systemMembers of clonesCell maturationCell trafficIntegrating B Cell Lineage Information into Statistical Tests for Detecting Selection in Ig Sequences
Uduman M, Shlomchik MJ, Vigneault F, Church GM, Kleinstein SH. Integrating B Cell Lineage Information into Statistical Tests for Detecting Selection in Ig Sequences. The Journal Of Immunology 2014, 192: 867-874. PMID: 24376267, PMCID: PMC4363135, DOI: 10.4049/jimmunol.1301551.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsAntibody AffinityAntibody DiversityB-Lymphocyte SubsetsCell LineageClonal Selection, Antigen-MediatedComputer SimulationConfounding Factors, EpidemiologicGene Rearrangement, B-LymphocyteGenes, ImmunoglobulinHumansMiceModels, ImmunologicalModels, StatisticalROC CurveSequence Analysis, DNASomatic Hypermutation, ImmunoglobulinVDJ ExonsConceptsLineage treesHigh-throughput sequencing technologyLineage tree shapesCell lineage informationIg sequencesRatio of replacementTree-shape analysisStatistical frameworkSequence-based methodsBinomial statistical analysisExperimental data setsIndicators of selectionSequencing technologiesLineage informationSequencing depthNumber of generationsData setsHybrid methodVivo selectionSilent mutationsTree shapeStatistical testsSequenceShape analysisMutations
2003
Estimating Hypermutation Rates from Clonal Tree Data
Kleinstein SH, Louzoun Y, Shlomchik MJ. Estimating Hypermutation Rates from Clonal Tree Data. The Journal Of Immunology 2003, 171: 4639-4649. PMID: 14568938, DOI: 10.4049/jimmunol.171.9.4639.Peer-Reviewed Original ResearchWhy are there so few key mutant clones? The influence of stochastic selection and blocking on affinity maturation in the germinal center
Kleinstein SH, Singh JP. Why are there so few key mutant clones? The influence of stochastic selection and blocking on affinity maturation in the germinal center. International Immunology 2003, 15: 871-884. PMID: 12807826, DOI: 10.1093/intimm/dxg085.sgm.Peer-Reviewed Original Research
2001
Toward Quantitative Simulation of Germinal Center Dynamics: Biological and Modeling Insights from Experimental Validation
KLEINSTEIN S, SINGH J. Toward Quantitative Simulation of Germinal Center Dynamics: Biological and Modeling Insights from Experimental Validation. Journal Of Theoretical Biology 2001, 211: 253-275. PMID: 11444956, DOI: 10.1006/jtbi.2001.2344.Peer-Reviewed Original ResearchMeSH KeywordsAntibody AffinityBase SequenceB-LymphocytesGenes, ImmunoglobulinGerminal CenterHaptensHumansModels, ImmunologicalMolecular Sequence DataStochastic ProcessesConceptsCenter dynamicsParticular mathematical modelOrdinary differential equationsGerminal center dynamicsImmune system dynamicsDifferential equationsExperimental dataMathematical modelStochastic frameworkAverage dynamicsSpecific experimental dataDeterministic modelSystem dynamicsModel parametersPossible extensionsGeneral methodologyQuantitative simulationOpreaNew implementationDynamicsModeling insightsPerelsonCenter behaviorEquationsExperimental validation