2020
Alignment free identification of clones in B cell receptor repertoires
Lindenbaum O, Nouri N, Kluger Y, Kleinstein SH. Alignment free identification of clones in B cell receptor repertoires. Nucleic Acids Research 2020, 49: e21-e21. PMID: 33330933, PMCID: PMC7913774, DOI: 10.1093/nar/gkaa1160.Peer-Reviewed Original Research
2019
Phenotypic and Ig Repertoire Analyses Indicate a Common Origin of IgD−CD27− Double Negative B Cells in Healthy Individuals and Multiple Sclerosis Patients
Fraussen J, Marquez S, Takata K, Beckers L, Montes Diaz G, Zografou C, Van Wijmeersch B, Villar LM, O'Connor KC, Kleinstein SH, Somers V. Phenotypic and Ig Repertoire Analyses Indicate a Common Origin of IgD−CD27− Double Negative B Cells in Healthy Individuals and Multiple Sclerosis Patients. The Journal Of Immunology 2019, 203: 1650-1664. PMID: 31391234, PMCID: PMC6736705, DOI: 10.4049/jimmunol.1801236.Peer-Reviewed Original ResearchConceptsDN B cellsDouble-negative B cellsMultiple sclerosis patientsMS patientsNegative B cellsHealthy controlsClass-switched memoryB cellsAdaptive immune receptor repertoire sequencingSclerosis patientsRepertoire sequencingFrequency of CD95Naive B cellsUnique differentiation pathwayLow CD5Proinflammatory characteristicsImmune agingCD38 expressionHealthy individualsPatientsFlow cytometryLow mutation loadCD27Repertoire analysisMaturation stateInferred 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
2016
Individual heritable differences result in unique cell lymphocyte receptor repertoires of naïve and antigen-experienced cells
Rubelt F, Bolen CR, McGuire HM, Heiden J, Gadala-Maria D, Levin M, M. Euskirchen G, Mamedov MR, Swan GE, Dekker CL, Cowell LG, Kleinstein SH, Davis MM. Individual heritable differences result in unique cell lymphocyte receptor repertoires of naïve and antigen-experienced cells. Nature Communications 2016, 7: 11112. PMID: 27005435, PMCID: PMC5191574, DOI: 10.1038/ncomms11112.Peer-Reviewed Original ResearchConceptsChromosome-wide levelJ gene segmentsAntigen receptor repertoireHeritable mechanismsSingle chromosomeEpigenetic differencesHeritable differencesReceptor repertoireLymphocyte receptor repertoireGene segmentsAdaptive immune systemHeritable factorsRepertoireRelative usageAntigen-experienced cellsThymic selectionCellsImmune systemChromosomesSignificant variationCDR3 regionMonozygotic twinsRearrangementT lymphocyte subsets
2015
The mutation patterns in B-cell immunoglobulin receptors reflect the influence of selection acting at multiple time-scales
Yaari G, Benichou JI, 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 roundsChange-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data
Gupta NT, Vander Heiden JA, Uduman M, Gadala-Maria D, Yaari G, Kleinstein SH. Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. Bioinformatics 2015, 31: 3356-3358. PMID: 26069265, PMCID: PMC4793929, DOI: 10.1093/bioinformatics/btv359.Peer-Reviewed Original ResearchConceptsHigh-throughput sequencing technologyB cell immunoglobulinLarge-scale characterizationLineage treesSpecialized computational methodsSelection pressureSequencing technologiesSomatic diversityClonal populationsIg repertoireSomatic hypermutationIg sequencesDiversityNon-commercial useSuite of utilitiesRepertoire diversityGermlineComputational methodsAllelesHypermutationAutomated analysis of high-throughput B-cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles
Gadala-Maria D, Yaari G, Uduman M, Kleinstein SH. Automated analysis of high-throughput B-cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles. Proceedings Of The National Academy Of Sciences Of The United States Of America 2015, 112: e862-e870. PMID: 25675496, PMCID: PMC4345584, DOI: 10.1073/pnas.1417683112.Peer-Reviewed Original Research
2014
Integrating 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
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
2012
Identification of Core DNA Elements That Target Somatic Hypermutation
Kohler KM, McDonald JJ, Duke JL, Arakawa H, Tan S, Kleinstein SH, Buerstedde JM, Schatz DG. Identification of Core DNA Elements That Target Somatic Hypermutation. The Journal Of Immunology 2012, 189: 5314-5326. PMID: 23087403, PMCID: PMC3664039, DOI: 10.4049/jimmunol.1202082.Peer-Reviewed Original ResearchMeSH Keywords3' Flanking RegionAnimalsB-LymphocytesCells, CulturedChickensChromatin ImmunoprecipitationCytidine DeaminaseDNAEnhancer Elements, GeneticGenes, ImmunoglobulinGenetic LociImmunoassayImmunoglobulin Variable RegionMutationPhosphorylationRNA Polymerase IISerineSomatic Hypermutation, ImmunoglobulinTranscription, GeneticConceptsActivation-induced deaminaseDNA elementsSomatic hypermutationChicken DT40 B cellsIg lociChromatin immunoprecipitation experimentsDT40 B cellsRNA polymerase IISystematic deletion analysisL chain lociNon-Ig genesCore DNA elementSerine 5Epigenetic marksPolymerase IITranscriptional elongationMutational machineryDeletion analysisReporter cassetteImmunoprecipitation experimentsDeoxycytosine residuesIg genesDNA damageChain locusLociQuantifying selection in high-throughput Immunoglobulin sequencing data sets
Yaari G, Uduman M, Kleinstein SH. Quantifying selection in high-throughput Immunoglobulin sequencing data sets. Nucleic Acids Research 2012, 40: e134-e134. PMID: 22641856, PMCID: PMC3458526, DOI: 10.1093/nar/gks457.Peer-Reviewed Original ResearchConceptsQuantifying selectionDifferent selection pressuresHigh-throughput immunoglobulinSomatic hypermutationNext-generation sequencing dataDNA mutation patternsSomatic mutation patternsGroups of sequencesAntigen-driven selection processMutation patternsSequence dataSelection pressureSequencing dataB cell affinity maturationB-cell cancersNegative selection
2011
Detecting selection in immunoglobulin sequences
Uduman M, Yaari G, Hershberg U, Stern JA, Shlomchik MJ, Kleinstein SH. Detecting selection in immunoglobulin sequences. Nucleic Acids Research 2011, 39: w499-w504. PMID: 21665923, PMCID: PMC3125793, DOI: 10.1093/nar/gkr413.Peer-Reviewed Original ResearchSomatic 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 targetingCellsSequence
2008
Improved methods for detecting selection by mutation analysis of Ig V region sequences
Hershberg U, Uduman M, Shlomchik MJ, Kleinstein SH. Improved methods for detecting selection by mutation analysis of Ig V region sequences. International Immunology 2008, 20: 683-694. PMID: 18397909, DOI: 10.1093/intimm/dxn026.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