2023
The Plasma Cell Infiltrate Populating the Muscle Tissue of Patients with Inclusion Body Myositis Features Distinct B Cell Receptor Repertoire Properties
Jiang R, Roy B, Wu Q, Mohanty S, Nowak R, Shaw A, Kleinstein S, O’Connor K. The Plasma Cell Infiltrate Populating the Muscle Tissue of Patients with Inclusion Body Myositis Features Distinct B Cell Receptor Repertoire Properties. ImmunoHorizons 2023, 7: 310-322. PMID: 37171806, PMCID: PMC10579972, DOI: 10.4049/immunohorizons.2200078.Peer-Reviewed Original ResearchConceptsInclusion body myositisMemory B cellsCell infiltrateBody myositisB cellsIBM muscle biopsiesB-cell infiltratesPlasma cell infiltrateClass-switched IgGMuscle tissueAdaptive immune receptor repertoire sequencingHumoral responseHealthy controlsIgA isotypePlasma cellsCell repertoireMuscle biopsyInfiltratesDegenerative disordersDisease pathologyRepertoire sequencingSkeletal muscleDermatomyositisPolymyositisMyositis
2022
Reemergence of pathogenic, autoantibody-producing B cell clones in myasthenia gravis following B cell depletion therapy
Fichtner ML, Hoehn KB, Ford EE, Mane-Damas M, Oh S, Waters P, Payne AS, Smith ML, Watson CT, Losen M, Martinez-Martinez P, Nowak RJ, Kleinstein SH, O’Connor K. Reemergence of pathogenic, autoantibody-producing B cell clones in myasthenia gravis following B cell depletion therapy. Acta Neuropathologica Communications 2022, 10: 154. PMID: 36307868, PMCID: PMC9617453, DOI: 10.1186/s40478-022-01454-0.Peer-Reviewed Original ResearchConceptsB cell depletion therapyB cell clonesMuSK-MG patientsMyasthenia gravisB cellsMG patientsDepletion therapyCell clonesAutoantibody-producing B cellsMuscle-specific tyrosine kinaseComplete stable remissionB cell receptor repertoireCell receptor repertoireValuable candidate biomarkersB cell receptorMG relapseClinical relapseStable remissionDisease relapseAutoimmune disordersRelapsePatientsAcetylcholine receptorsCandidate biomarkersReceptor repertoire
2020
CD4+ follicular regulatory T cells optimize the influenza virus–specific B cell response
Lu Y, Jiang R, Freyn AW, Wang J, Strohmeier S, Lederer K, Locci M, Zhao H, Angeletti D, O’Connor K, Kleinstein SH, Nachbagauer R, Craft J. CD4+ follicular regulatory T cells optimize the influenza virus–specific B cell response. Journal Of Experimental Medicine 2020, 218: e20200547. PMID: 33326020, PMCID: PMC7748821, DOI: 10.1084/jem.20200547.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsAntibody FormationAntigensB-LymphocytesCD4 AntigensDisease Models, AnimalEpitopesForkhead Transcription FactorsGerminal CenterHumansImmunityImmunologic MemoryInfluenza, HumanInfluenzavirus BIntegrasesMice, Inbred C57BLOrthomyxoviridae InfectionsReceptors, Antigen, B-CellSpecies SpecificityT-Lymphocytes, RegulatoryVaccinationConceptsB cell responsesGerminal center B cell responsesFollicular regulatory T cellsRegulatory T cellsTfr cellsCell responsesT cellsViral challengeHumoral memoryVirus-specific B cell responsesAntigen-specific B cell responsesFollicular helper T cellsHA stalk regionHelper T cellsInfluenza virus infectionGerminal center developmentAntibody responsePlasma cellsVirus infectionImmunization modelAntibody productionBCR repertoireInfluenza virusRepeated exposureInfluenza virus glycoproteins
2019
Early B cell tolerance defects in neuromyelitis optica favour anti-AQP4 autoantibody production
Cotzomi E, Stathopoulos P, Lee CS, Ritchie AM, Soltys JN, Delmotte FR, Oe T, Sng J, Jiang R, K A, Vander Heiden JA, Kleinstein SH, Levy M, Bennett JL, Meffre E, O’Connor K. Early B cell tolerance defects in neuromyelitis optica favour anti-AQP4 autoantibody production. Brain 2019, 142: 1598-1615. PMID: 31056665, PMCID: PMC6536857, DOI: 10.1093/brain/awz106.Peer-Reviewed Original ResearchConceptsNeuromyelitis optica spectrum disorderB cell tolerance checkpointsNMOSD patientsNaïve B cellsAQP4 autoantibodiesTolerance checkpointsHealthy donorsB cellsEarly B cell tolerance checkpointsPeripheral B cell tolerance checkpointsMature naïve B cellsB cell tolerance defectsSeropositive NMOSD patientsOptica spectrum disorderRare autoimmune disorderNaïve B-cell compartmentB cell compartmentB cell populationsAquaporin-4 water channelsPathogenic autoantibodiesAutoantibody productionOptic nerveAutoimmune disordersSevere inflammationSpinal cord
2015
A model of somatic hypermutation targeting in mice based on high-throughput immunoglobulin sequencing data (TECH2P.910)
Cui A, Diniro R, Briggs A, Adams K, Vander Heiden J, O'Connor K, Vigneault F, Shlomchik M, Kleinstein S. A model of somatic hypermutation targeting in mice based on high-throughput immunoglobulin sequencing data (TECH2P.910). The Journal Of Immunology 2015, 194: 206.20-206.20. DOI: 10.4049/jimmunol.194.supp.206.20.Peer-Reviewed Original ResearchSimilar biological processesNon-functional sequencesObserved mutation patternDNA repair activityMutation patternsDNA motifsNext-generation sequencingHigh mutation frequencyUnselected mutationsSelection pressureSequencing dataBiological processesFunctional sequencesImmunoglobulin genesRepair activitySomatic hypermutation patternsSomatic hypermutationHypermutation patternsMutation frequencyIg sequencesT transitionSequenceHeavy chain transgenic miceMotifTransgenic mice
2014
pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires (TECH1P.863)
Vander Heiden J, Yaari G, Uduman M, Stern J, O’Connor K, Halfer D, Vigneault F, Kleinstein S. pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires (TECH1P.863). The Journal Of Immunology 2014, 192: 69.31-69.31. DOI: 10.4049/jimmunol.192.supp.69.31.Peer-Reviewed Original Research
2013
Models of Somatic Hypermutation Targeting and Substitution Based on Synonymous Mutations from High-Throughput Immunoglobulin Sequencing Data
Yaari G, Vander Heiden J, Uduman M, Gadala-Maria D, Gupta N, Stern JN, O’Connor K, Hafler DA, Laserson U, Vigneault F, Kleinstein SH. Models of Somatic Hypermutation Targeting and Substitution Based on Synonymous Mutations from High-Throughput Immunoglobulin Sequencing Data. Frontiers In Immunology 2013, 4: 358. PMID: 24298272, PMCID: PMC3828525, DOI: 10.3389/fimmu.2013.00358.Peer-Reviewed Original ResearchAccurate background modelSynonymous mutationsNon-coding regionsParticular codon usageNon-functional sequencesComputational analysis methodsObserved mutation patternExisting modelsBackground modelInfluence of selectionCodon usageSHM targetingBase compositionImproved modelSequencing dataNucleotide substitutionsAnalysis methodStatistical analysisFunctional sequencesMutation targetingB-cell cancersModelSomatic hypermutation patternsMutationsHypermutation patterns
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