Featured Publications
SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data
Liu Y, Li N, Qi J, Xu G, Zhao J, Wang N, Huang X, Jiang W, Wei H, Justet A, Adams T, Homer R, Amei A, Rosas I, Kaminski N, Wang Z, Yan X. SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data. Genome Biology 2024, 25: 271. PMID: 39402626, PMCID: PMC11475911, DOI: 10.1186/s13059-024-03416-2.Peer-Reviewed Original ResearchDetecting differentially expressed genes by relative entropy
Yan X, Deng M, Fung K, Qian M. Detecting differentially expressed genes by relative entropy. Journal Of Theoretical Biology 2005, 234: 395-402. PMID: 15784273, DOI: 10.1016/j.jtbi.2004.11.039.Peer-Reviewed Original Research
2021
MicroRNA 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
Severe 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 diseaseProcalcitonin
2019
Transcriptional regulatory model of fibrosis progression in the human lung
McDonough JE, Ahangari F, Li Q, Jain S, Verleden SE, Herazo-Maya J, Vukmirovic M, DeIuliis G, Tzouvelekis A, Tanabe N, Chu F, Yan X, Verschakelen J, Homer RJ, Manatakis DV, Zhang J, Ding J, Maes K, De Sadeleer L, Vos R, Neyrinck A, Benos PV, Bar-Joseph Z, Tantin D, Hogg JC, Vanaudenaerde BM, Wuyts WA, Kaminski N. Transcriptional regulatory model of fibrosis progression in the human lung. JCI Insight 2019, 4 PMID: 31600171, PMCID: PMC6948862, DOI: 10.1172/jci.insight.131597.Peer-Reviewed Original ResearchConceptsIdiopathic pulmonary fibrosisAdvanced fibrosisAlveolar surface densityFibrosis progressionLung fibrosisHuman lungDynamic Regulatory Events MinerExtent of fibrosisIPF lungsPulmonary fibrosisControl lungsIPF tissueB lymphocytesFibrosisLungLinear mixed-effects modelsMixed-effects modelsGene expression changesSystems biology modelsDifferential gene expression analysisGene expression analysisProgressionGene expression networksRNA sequencingBiology modelsIntegrating 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
2018
Genomic Comparison Among Global Isolates of L. interrogans Serovars Copenhageni and Icterohaemorrhagiae Identified Natural Genetic Variation Caused by an Indel
Santos LA, Adhikarla H, Yan X, Wang Z, Fouts DE, Vinetz JM, Alcantara LCJ, Hartskeerl RA, Goris MGA, Picardeau M, Reis MG, Townsend JP, Zhao H, Ko AI, Wunder EA. Genomic Comparison Among Global Isolates of L. interrogans Serovars Copenhageni and Icterohaemorrhagiae Identified Natural Genetic Variation Caused by an Indel. Frontiers In Cellular And Infection Microbiology 2018, 8: 193. PMID: 29971217, PMCID: PMC6018220, DOI: 10.3389/fcimb.2018.00193.Peer-Reviewed Original ResearchConceptsSerovar CopenhageniPublic health relevanceSevere casesSerogroup IcterohaemorrhagiaeSerovar IcterohaemorrhagiaeWorldwide zoonosisHealth relevanceIcterohaemorrhagiaeCopenhageniVirulent strainHigh discriminatory powerSerovarsGlobal isolatesFrameshift mutationDiscriminatory powerFirst studyPathogenic speciesIsolates
2014
Signaling through the Adaptor Molecule MyD88 in CD4+ T Cells Is Required to Overcome Suppression by Regulatory T Cells
Schenten D, Nish SA, Yu S, Yan X, Lee HK, Brodsky I, Pasman L, Yordy B, Wunderlich FT, Brüning JC, Zhao H, Medzhitov R. Signaling through the Adaptor Molecule MyD88 in CD4+ T Cells Is Required to Overcome Suppression by Regulatory T Cells. Immunity 2014, 40: 78-90. PMID: 24439266, PMCID: PMC4445716, DOI: 10.1016/j.immuni.2013.10.023.Peer-Reviewed Original ResearchMeSH KeywordsAdaptive ImmunityAnimalsCells, CulturedImmunity, InnateImmunologic MemoryImmunosuppression TherapyInterleukin-1Interleukin-18MiceMice, Inbred C57BLMice, TransgenicMyeloid Differentiation Factor 88Organ SpecificityReceptors, Interleukin-1Signal TransductionTh1 CellsTh17 CellsT-Lymphocytes, RegulatoryConceptsToll-like receptorsTh1 cell responsesT cellsCell-specific ablationCell responsesIL-1Interleukin-1 receptor family memberT helper 17 (Th17) cell responsesTreg cell-mediated suppressionTreg cell-specific ablationT cell-specific ablationRegulatory T cellsT regulatory (Treg) cellsCell-mediated suppressionMemory T cellsAdaptor molecule MyD88Adaptive immune responsesIL-1 actsTreg cellsRegulatory cellsReceptor family membersMolecule MyD88Th1 cellsImmune responseMyD88T cell-intrinsic role of IL-6 signaling in primary and memory responses
Nish SA, Schenten D, Wunderlich FT, Pope SD, Gao Y, Hoshi N, Yu S, Yan X, Lee HK, Pasman L, Brodsky I, Yordy B, Zhao H, Brüning J, Medzhitov R. T cell-intrinsic role of IL-6 signaling in primary and memory responses. ELife 2014, 3: e01949. PMID: 24842874, PMCID: PMC4046568, DOI: 10.7554/elife.01949.Peer-Reviewed Original ResearchMeSH KeywordsAdaptive ImmunityAnimalsCD4-Positive T-LymphocytesCells, CulturedCoculture TechniquesDose-Response Relationship, DrugImmunity, InnateImmunizationImmunologic MemoryInterleukin-1betaInterleukin-6Interleukin-6 Receptor alpha SubunitMice, Inbred C57BLMice, KnockoutOvalbuminRecombinant ProteinsSignal TransductionTh1 CellsTh17 CellsT-Lymphocytes, RegulatoryConceptsIL-6T cellsIL-6 receptor α chainT cell memory formationT cell-intrinsic roleAbsence of TregsDepletion of TregsPrimary Th1 responseEffector T cellsT cell responsesFunctional memory cellsAdaptive immune responsesT cell-specific deletionInnate immune recognitionCell-intrinsic roleCell-specific deletionReceptor α chainTfh functionTh1 responseTh17 responsesIL-1βIL-2Immune responseTregsSuppressive effect
2008
Nonlinear cooperation of p53-ING1-induced bax expression and protein S-nitrosylation in GSNO-induced thymocyte apoptosis: a quantitative approach with cross-platform validation
Duan S, Wan L, Fu WJ, Pan H, Ding Q, Chen C, Han P, Zhu X, Du L, Liu H, Chen Y, Liu X, Yan X, Deng M, Qian M. Nonlinear cooperation of p53-ING1-induced bax expression and protein S-nitrosylation in GSNO-induced thymocyte apoptosis: a quantitative approach with cross-platform validation. Apoptosis 2008, 14: 236. PMID: 19082896, DOI: 10.1007/s10495-008-0288-4.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsApoptosisBcl-2-Associated X ProteinDexamethasoneGene DosageGene Expression RegulationInhibitor of Growth Protein 1Intracellular Signaling Peptides and ProteinsMiceModels, BiologicalNeural Networks, ComputerNonlinear DynamicsNuclear ProteinsOligonucleotide Array Sequence AnalysisProtein BindingReproducibility of ResultsReverse Transcriptase Polymerase Chain ReactionS-NitrosoglutathioneThymus GlandTumor Suppressor Protein p53Tumor Suppressor ProteinsProgressive recruitment of Runx2 to genomic targets despite decreasing expression during osteoblast differentiation
Pregizer S, Baniwal SK, Yan X, Borok Z, Frenkel B. Progressive recruitment of Runx2 to genomic targets despite decreasing expression during osteoblast differentiation. Journal Of Cellular Biochemistry 2008, 105: 965-970. PMID: 18821584, PMCID: PMC2591066, DOI: 10.1002/jcb.21900.Peer-Reviewed Original ResearchConceptsOC promoterGenomic targetsOsteoblast differentiationNovel genomic targetsChIP-chip analysisChromatin immunoprecipitation assaysDNA-binding abilityDNA-binding activityOsteoblast transcription factorSuch stringent controlTranscriptional controlMRNA levelsTranslational controlTranscription factorsImmunoprecipitation assaysMC3T3-E1 culturesRunx2 mRNA levelsOC mRNA levelsOsteoblast phenotypeRunx2 mRNAPromoterRunx2Protein amountOC expressionStringent control