Featured Publications
Noninvasive Analysis of the Sputum Transcriptome Discriminates Clinical Phenotypes of Asthma
Yan X, Chu JH, Gomez J, Koenigs M, Holm C, He X, Perez MF, Zhao H, Mane S, Martinez FD, Ober C, Nicolae DL, Barnes KC, London SJ, Gilliland F, Weiss ST, Raby BA, Cohn L, Chupp GL. Noninvasive Analysis of the Sputum Transcriptome Discriminates Clinical Phenotypes of Asthma. American Journal Of Respiratory And Critical Care Medicine 2015, 191: 1116-1125. PMID: 25763605, PMCID: PMC4451618, DOI: 10.1164/rccm.201408-1440oc.Peer-Reviewed Original ResearchConceptsHistory of intubationNitric oxide levelsOxide levelsClinical phenotypeMost subjectsHigher bronchodilator responseNormal lung functionBlood of patientsCohort of childrenLogistic regression analysisSputum gene expressionBlood of childrenAirway transcriptomeMilder asthmaPathophysiologic heterogeneityPrebronchodilator FEV1Steroid requirementsLung functionBronchodilator responseGene expressionPhenotype of diseaseAsthmaBlood samplesGene signatureIntubation
2022
Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19
Unterman A, Sumida TS, Nouri N, Yan X, Zhao AY, Gasque V, Schupp JC, Asashima H, Liu Y, Cosme C, Deng W, Chen M, Raredon MSB, Hoehn KB, Wang G, Wang Z, DeIuliis G, Ravindra NG, Li N, Castaldi C, Wong P, Fournier J, Bermejo S, Sharma L, Casanovas-Massana A, Vogels CBF, Wyllie AL, Grubaugh ND, Melillo A, Meng H, Stein Y, Minasyan M, Mohanty S, Ruff WE, Cohen I, Raddassi K, Niklason L, Ko A, Montgomery R, Farhadian S, Iwasaki A, Shaw A, van Dijk D, Zhao H, Kleinstein S, Hafler D, Kaminski N, Dela Cruz C. Single-cell multi-omics reveals dyssynchrony of the innate and adaptive immune system in progressive COVID-19. Nature Communications 2022, 13: 440. PMID: 35064122, PMCID: PMC8782894, DOI: 10.1038/s41467-021-27716-4.Peer-Reviewed Original ResearchMeSH KeywordsAdaptive ImmunityAgedAntibodies, Monoclonal, HumanizedCD4-Positive T-LymphocytesCD8-Positive T-LymphocytesCells, CulturedCOVID-19COVID-19 Drug TreatmentFemaleGene Expression ProfilingGene Expression RegulationHumansImmunity, InnateMaleReceptors, Antigen, B-CellReceptors, Antigen, T-CellRNA-SeqSARS-CoV-2Single-Cell AnalysisConceptsProgressive COVID-19B cell clonesSingle-cell analysisT cellsImmune responseMulti-omics single-cell analysisCOVID-19Cell clonesAdaptive immune interactionsSevere COVID-19Dynamic immune responsesGene expressionSARS-CoV-2 virusAdaptive immune systemSomatic hypermutation frequenciesCellular effectsProtein markersEffector CD8Immune signaturesProgressive diseaseHypermutation frequencyProgressive courseClassical monocytesClonesImmune interactionsLung Microenvironments and Disease Progression in Fibrotic Hypersensitivity Pneumonitis.
De Sadeleer LJ, McDonough JE, Schupp JC, Yan X, Vanstapel A, Van Herck A, Everaerts S, Geudens V, Sacreas A, Goos T, Aelbrecht C, Nawrot TS, Martens DS, Schols D, Claes S, Verschakelen JA, Verbeken EK, Ackermann M, Decottignies A, Mahieu M, Hackett TL, Hogg JC, Vanaudenaerde BM, Verleden SE, Kaminski N, Wuyts WA. Lung Microenvironments and Disease Progression in Fibrotic Hypersensitivity Pneumonitis. American Journal Of Respiratory And Critical Care Medicine 2022, 205: 60-74. PMID: 34724391, PMCID: PMC8865586, DOI: 10.1164/rccm.202103-0569oc.Peer-Reviewed Original ResearchConceptsFibrotic hypersensitivity pneumonitisIdiopathic pulmonary fibrosisHypersensitivity pneumonitisLung zonesMolecular traitsUnused donor lungsInterstitial lung diseaseLocal disease extentProgression of fibrosisSevere fibrosis groupGene co-expression network analysisCo-expression network analysisExplant lungsDonor lungsLung involvementEndothelial functionLung findingsDisease extentPulmonary fibrosisLung diseaseFibrosis groupLung microenvironmentClinical behaviorDisease progressionBAL samples
2021
Single-cell characterization of a model of poly I:C-stimulated peripheral blood mononuclear cells in severe asthma
Chen A, Diaz-Soto MP, Sanmamed MF, Adams T, Schupp JC, Gupta A, Britto C, Sauler M, Yan X, Liu Q, Nino G, Cruz CSD, Chupp GL, Gomez JL. Single-cell characterization of a model of poly I:C-stimulated peripheral blood mononuclear cells in severe asthma. Respiratory Research 2021, 22: 122. PMID: 33902571, PMCID: PMC8074196, DOI: 10.1186/s12931-021-01709-9.Peer-Reviewed Original ResearchConceptsPeripheral blood mononuclear cellsSevere asthmaEffector T cellsBlood mononuclear cellsT cellsHealthy controlsPoly IDendritic cellsMononuclear cellsUnstimulated peripheral blood mononuclear cellsInterferon responseTLR3 agonist poly IImpaired interferon responseMain cell subsetsNatural killer cellsPro-inflammatory profilePro-inflammatory pathwaysC stimulationCyTOF profilingHigh CD8Cell typesEffector cellsKiller cellsCell subsetsMain cell typesMicroRNA miR-24-3p reduces DNA damage responses, apoptosis, and susceptibility to chronic obstructive pulmonary disease
Nouws J, Wan F, Finnemore E, Roque W, Kim SJ, Bazan IS, Li CX, Sköld C, Dai Q, Yan X, Chioccioli M, Neumeister V, Britto CJ, Sweasy J, Bindra RS, Wheelock ÅM, Gomez JL, Kaminski N, Lee PJ, Sauler M. MicroRNA miR-24-3p reduces DNA damage responses, apoptosis, and susceptibility to chronic obstructive pulmonary disease. JCI Insight 2021, 6: e134218. PMID: 33290275, PMCID: PMC7934877, DOI: 10.1172/jci.insight.134218.Peer-Reviewed Original ResearchConceptsCellular stress responseStress responseHomology-directed DNA repairDNA damage responseProtein BRCA1Damage responseCellular stressDNA repairProtein BimCOPD lung tissueLung epithelial cellsCellular responsesExpression arraysEpithelial cell apoptosisDNA damageChronic obstructive pulmonary diseaseBRCA1 expressionCell apoptosisApoptosisEpithelial cellsCritical mechanismMicroRNAsRegulatorObstructive pulmonary diseaseIncreases Susceptibility
2020
Gene coexpression networks reveal novel molecular endotypes in alpha-1 antitrypsin deficiency
Chu JH, Zang W, Vukmirovic M, Yan X, Adams T, DeIuliis G, Hu B, Mihaljinec A, Schupp JC, Becich MJ, Hochheiser H, Gibson KF, Chen ES, Morris A, Leader JK, Wisniewski SR, Zhang Y, Sciurba FC, Collman RG, Sandhaus R, Herzog EL, Patterson KC, Sauler M, Strange C, Kaminski N. Gene coexpression networks reveal novel molecular endotypes in alpha-1 antitrypsin deficiency. Thorax 2020, 76: 134-143. PMID: 33303696, PMCID: PMC10794043, DOI: 10.1136/thoraxjnl-2019-214301.Peer-Reviewed Original ResearchConceptsWeighted gene co-expression network analysisAlpha-1 antitrypsin deficiencyGene modulesGene co-expression network analysisDifferential gene expression analysisCo-expression network analysisPeripheral blood mononuclear cellsGene expression patternsPBMC gene expression patternsGene coexpression networksAATD individualsGene expression profilesGene expression analysisBronchoalveolar lavageAugmentation therapyClinical variablesAntitrypsin deficiencyGene expression assaysRNA-seqCoexpression networkGene validationExpression analysisExpression assaysWGCNA modulesExpression patternsRegulation and characterization of tumor-infiltrating immune cells in breast cancer
Dai Q, Wu W, Amei A, Yan X, Lu L, Wang Z. Regulation and characterization of tumor-infiltrating immune cells in breast cancer. International Immunopharmacology 2020, 90: 107167. PMID: 33223469, PMCID: PMC7855363, DOI: 10.1016/j.intimp.2020.107167.Peer-Reviewed Original ResearchConceptsTumor-infiltrating immune cellsT cell activation statusImmune cellsCell activation statusT cell activationPatient survivalM2 macrophagesT cellsBreast cancerCell activationT cell peripheral toleranceTumor-infiltrating B cellsMultivariate Cox regression modelActivation statusBreast cancer patient survivalEffector T cellsT cell subsetsBreast cancer patientsImmune cell infiltrationAbundant plasma cellsCox regression modelKaplan-Meier survivalImmune cell typesMolecular pathwaysCancer patient survivalSingle-Cell Transcriptional Archetypes of Airway Inflammation in Cystic Fibrosis.
Schupp JC, Khanal S, Gomez JL, Sauler M, Adams TS, Chupp GL, Yan X, Poli S, Zhao Y, Montgomery RR, Rosas IO, Dela Cruz CS, Bruscia EM, Egan ME, Kaminski N, Britto CJ. Single-Cell Transcriptional Archetypes of Airway Inflammation in Cystic Fibrosis. American Journal Of Respiratory And Critical Care Medicine 2020, 202: 1419-1429. PMID: 32603604, PMCID: PMC7667912, DOI: 10.1164/rccm.202004-0991oc.Peer-Reviewed Original ResearchConceptsCF lung diseaseHealthy control subjectsImmune dysfunctionLung diseaseCystic fibrosisControl subjectsSputum cellsAbnormal chloride transportLung mononuclear phagocytesInnate immune dysfunctionDivergent clinical coursesImmune cell repertoireMonocyte-derived macrophagesCF monocytesAirway inflammationClinical courseProinflammatory featuresCell survival programInflammatory responseTissue injuryCell repertoireImmune functionTranscriptional profilesAlveolar macrophagesMononuclear phagocytesSevere respiratory viral infection induces procalcitonin in the absence of bacterial pneumonia
Gautam S, Cohen AJ, Stahl Y, Toro P, Young GM, Datta R, Yan X, Ristic NT, Bermejo SD, Sharma L, Restrepo M, Dela Cruz CS. Severe respiratory viral infection induces procalcitonin in the absence of bacterial pneumonia. Thorax 2020, 75: 974-981. PMID: 32826284, DOI: 10.1136/thoraxjnl-2020-214896.Peer-Reviewed Original ResearchConceptsPure viral infectionBacterial coinfectionViral infectionInfluenza infectionSevere respiratory viral infectionsAbility of procalcitoninRetrospective cohort studyViral respiratory infectionsRespiratory viral infectionsMarker of severityRespiratory viral illnessSevere viral infectionsSpecificity of procalcitoninCharacteristic curve analysisCellular modelHigher procalcitoninProcalcitonin expressionElevated procalcitoninCohort studyViral illnessRespiratory infectionsAntibiotic administrationBacterial pneumoniaSevere diseaseProcalcitoninApproaches for integrating heterogeneous RNA-seq data reveal cross-talk between microbes and genes in asthmatic patients
Spakowicz D, Lou S, Barron B, Gomez JL, Li T, Liu Q, Grant N, Yan X, Hoyd R, Weinstock G, Chupp GL, Gerstein M. Approaches for integrating heterogeneous RNA-seq data reveal cross-talk between microbes and genes in asthmatic patients. Genome Biology 2020, 21: 150. PMID: 32571363, PMCID: PMC7310008, DOI: 10.1186/s13059-020-02033-z.Peer-Reviewed Original ResearchA Network of Sputum MicroRNAs is Associated with Neutrophilic Airway Inflammation in Asthma
Gomez JL, Chen A, Diaz MP, Zirn N, Gupta A, Britto C, Sauler M, Yan X, Stewart E, Santerian K, Grant N, Liu Q, Fry R, Rager J, Cohn L, Alexis N, Chupp GL. A Network of Sputum MicroRNAs is Associated with Neutrophilic Airway Inflammation in Asthma. American Journal Of Respiratory And Critical Care Medicine 2020, 0: 51-64. PMID: 32255668, PMCID: PMC7328332, DOI: 10.1164/rccm.201912-2360oc.Peer-Reviewed Original ResearchConceptsEndoplasmic reticulum stressAirway inflammationNeutrophil countClinical featuresT-helper cell type 17Neutrophilic airway inflammationReticulum stressSputum of subjectsLung function impairmentHistory of hospitalizationNumber of neutrophilsPeripheral blood neutrophilsSputum of patientsMicroRNA expressionAsthma severityTh17 pathwayFunction impairmentAirway samplesMicroRNA networkBlood neutrophilsOzone exposureAsthmaSputumCellular sourceClinical phenotype
2019
Integrating multiomics longitudinal data to reconstruct networks underlying lung development
Ding J, Ahangari F, Espinoza CR, Chhabra D, Nicola T, Yan X, Lal CV, Hagood JS, Kaminski N, Bar-Joseph Z, Ambalavanan N. Integrating multiomics longitudinal data to reconstruct networks underlying lung development. American Journal Of Physiology - Lung Cellular And Molecular Physiology 2019, 317: l556-l568. PMID: 31432713, PMCID: PMC6879899, DOI: 10.1152/ajplung.00554.2018.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsAnimals, NewbornChildChild, PreschoolDNA MethylationEpigenesis, GeneticFemaleGene Expression ProfilingGene Expression Regulation, DevelopmentalGene Regulatory NetworksHigh-Throughput Nucleotide SequencingHumansImmunity, InnateInfantInfant, NewbornLungMaleMiceMice, Inbred C57BLMicroRNAsOrganogenesisProteomicsPulmonary AlveoliRNA, MessengerSingle-Cell AnalysisTranscriptomeConceptsSingle-cell RNA-seq dataLung developmentDynamic regulatory networksOmics data setsRNA-seq dataIndividual cell typesHuman lung developmentRegulatory networksDNA methylationLaser capture microdissectionEpigenetic changesExpression trajectoriesKey pathwaysCell typesActive pathwaysCapture microdissectionRegulatorKey eventsInnate immunityNew insightsSpecific key eventsPathwayComprehensive understandingProteomicsMethylation
2017
Characterisation of asthma subgroups associated with circulating YKL-40 levels
Gomez JL, Yan X, Holm CT, Grant N, Liu Q, Cohn L, Nezgovorova V, Meyers DA, Bleecker ER, Crisafi GM, Jarjour NN, Rogers L, Reibman J, Chupp GL. Characterisation of asthma subgroups associated with circulating YKL-40 levels. European Respiratory Journal 2017, 50: 1700800. PMID: 29025889, PMCID: PMC5967238, DOI: 10.1183/13993003.00800-2017.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAge of OnsetAirway ObstructionAsthmaChitinase-3-Like Protein 1Cluster AnalysisCross-Sectional StudiesDisease ProgressionFemaleGene Expression ProfilingHumansInflammationMaleMiddle AgedReproducibility of ResultsRespiratory SystemSeverity of Illness IndexSputumStatistics as TopicSymptom Flare UpConceptsSerum YKL-40 levelsYKL-40 levelsHigher serum YKL-40 levelsAirflow obstructionAsthma phenotypesElevated serum YKL-40 levelsSevere Asthma Research ProgramClinical asthma phenotypesExacerbation-prone asthmaIrreversible airway obstructionSevere airflow obstructionFrequent exacerbationsSevere exacerbationsAirway inflammationAirway obstructionAsthma exacerbationsAirway diseaseAsthma patientsAsthma severitySerum levelsInflammatory pathwaysAsthma subgroupsAdult onsetIdentification of individualsUnsupervised cluster analysisValidation of a 52-gene risk profile for outcome prediction in patients with idiopathic pulmonary fibrosis: an international, multicentre, cohort study
Herazo-Maya JD, Sun J, Molyneaux PL, Li Q, Villalba JA, Tzouvelekis A, Lynn H, Juan-Guardela BM, Risquez C, Osorio JC, Yan X, Michel G, Aurelien N, Lindell KO, Klesen MJ, Moffatt MF, Cookson WO, Zhang Y, Garcia JGN, Noth I, Prasse A, Bar-Joseph Z, Gibson KF, Zhao H, Herzog EL, Rosas IO, Maher TM, Kaminski N. Validation of a 52-gene risk profile for outcome prediction in patients with idiopathic pulmonary fibrosis: an international, multicentre, cohort study. The Lancet Respiratory Medicine 2017, 5: 857-868. PMID: 28942086, PMCID: PMC5677538, DOI: 10.1016/s2213-2600(17)30349-1.Peer-Reviewed Original ResearchMeSH KeywordsAgedCohort StudiesFemaleGene Expression ProfilingGenetic MarkersGenetic TestingHumansIdiopathic Pulmonary FibrosisLeukocytes, MononuclearLinear ModelsMaleMiddle AgedOligonucleotide Array Sequence AnalysisPrognosisProportional Hazards ModelsRisk AssessmentRisk FactorsTime FactorsVital CapacityConceptsIdiopathic pulmonary fibrosisTransplant-free survivalRisk profilePulmonary fibrosisAntifibrotic drugsPeripheral blood mononuclear cellsCox proportional hazards modelClinical prediction toolGroup of patientsBlood mononuclear cellsHigh-risk groupProportional hazards modelPulmonary Fibrosis FoundationPittsburgh cohortUntreated patientsCohort studyClinical courseIPF diagnosisBlood InstituteProspective studyVital capacityMononuclear cellsPeripheral bloodUS National InstitutesNational HeartPolysomnographic phenotypes and their cardiovascular implications in obstructive sleep apnoea
Zinchuk AV, Jeon S, Koo BB, Yan X, Bravata DM, Qin L, Selim BJ, Strohl KP, Redeker NS, Concato J, Yaggi HK. Polysomnographic phenotypes and their cardiovascular implications in obstructive sleep apnoea. Thorax 2017, 73: 472. PMID: 28935698, PMCID: PMC6693344, DOI: 10.1136/thoraxjnl-2017-210431.Peer-Reviewed Original ResearchMeSH KeywordsAcute Coronary SyndromeAgedCardiovascular DiseasesCluster AnalysisCross-Sectional StudiesFemaleHumansIschemic Attack, TransientLongitudinal StudiesMaleMiddle AgedMortalityPhenotypePolysomnographyProportional Hazards ModelsRisk AssessmentSeverity of Illness IndexSleep Apnea, ObstructiveStrokeConceptsObstructive sleep apnoeaCardiovascular outcomesPolysomnographic featuresSleep apnoeaPolysomnographic dataIncident transient ischemic attackUS Veteran cohortTransient ischemic attackAcute coronary syndromeAdverse cardiovascular outcomesPeriodic limb movementsOSA severity classificationPolysomnographic phenotypeCoronary syndromeIschemic attackCardiovascular implicationsOSA phenotypesOSA evaluationPathophysiological domainsPoor sleepMild/Patient clustersVeteran cohortSurvival analysisCombined outcome
2014
An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge
Brownstein CA, Beggs AH, Homer N, Merriman B, Yu TW, Flannery KC, DeChene ET, Towne MC, Savage SK, Price EN, Holm IA, Luquette LJ, Lyon E, Majzoub J, Neupert P, McCallie Jr D, Szolovits P, Willard HF, Mendelsohn NJ, Temme R, Finkel RS, Yum SW, Medne L, Sunyaev SR, Adzhubey I, Cassa CA, de Bakker P, Duzkale H, Dworzyński P, Fairbrother W, Francioli L, Funke BH, Giovanni MA, Handsaker RE, Lage K, Lebo MS, Lek M, Leshchiner I, MacArthur DG, McLaughlin HM, Murray MF, Pers TH, Polak PP, Raychaudhuri S, Rehm HL, Soemedi R, Stitziel NO, Vestecka S, Supper J, Gugenmus C, Klocke B, Hahn A, Schubach M, Menzel M, Biskup S, Freisinger P, Deng M, Braun M, Perner S, Smith R, Andorf JL, Huang J, Ryckman K, Sheffield VC, Stone EM, Bair T, Black-Ziegelbein EA, Braun TA, Darbro B, DeLuca AP, Kolbe DL, Scheetz TE, Shearer AE, Sompallae R, Wang K, Bassuk AG, Edens E, Mathews K, Moore SA, Shchelochkov OA, Trapane P, Bossler A, Campbell CA, Heusel JW, Kwitek A, Maga T, Panzer K, Wassink T, Van Daele D, Azaiez H, Booth K, Meyer N, Segal MM, Williams MS, Tromp G, White P, Corsmeier D, Fitzgerald-Butt S, Herman G, Lamb-Thrush D, McBride KL, Newsom D, Pierson CR, Rakowsky AT, Maver A, Lovrečić L, Palandačić A, Peterlin B, Torkamani A, Wedell A, Huss M, Alexeyenko A, Lindvall JM, Magnusson M, Nilsson D, Stranneheim H, Taylan F, Gilissen C, Hoischen A, van Bon B, Yntema H, Nelen M, Zhang W, Sager J, Zhang L, Blair K, Kural D, Cariaso M, Lennon GG, Javed A, Agrawal S, Ng PC, Sandhu KS, Krishna S, Veeramachaneni V, Isakov O, Halperin E, Friedman E, Shomron N, Glusman G, Roach JC, Caballero J, Cox HC, Mauldin D, Ament SA, Rowen L, Richards DR, Lucas F, Gonzalez-Garay ML, Caskey CT, Bai Y, Huang Y, Fang F, Zhang Y, Wang Z, Barrera J, Garcia-Lobo JM, González-Lamuño D, Llorca J, Rodriguez MC, Varela I, Reese MG, De La Vega FM, Kiruluta E, Cargill M, Hart RK, Sorenson JM, Lyon GJ, Stevenson DA, Bray BE, Moore BM, Eilbeck K, Yandell M, Zhao H, Hou L, Chen X, Yan X, Chen M, Li C, Yang C, Gunel M, Li P, Kong Y, Alexander AC, Albertyn ZI, Boycott KM, Bulman DE, Gordon P, Innes AM, Knoppers BM, Majewski J, Marshall CR, Parboosingh JS, Sawyer SL, Samuels ME, Schwartzentruber J, Kohane IS, Margulies DM. An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge. Genome Biology 2014, 15: r53. PMID: 24667040, PMCID: PMC4073084, DOI: 10.1186/gb-2014-15-3-r53.Peer-Reviewed Original Research