2024
A cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders
Lee A, Ayers L, Kosicki M, Chan W, Fozo L, Pratt B, Collins T, Zhao B, Rose M, Sanchis-Juan A, Fu J, Wong I, Zhao X, Tenney A, Lee C, Laricchia K, Barry B, Bradford V, Jurgens J, England E, Lek M, MacArthur D, Lee E, Talkowski M, Brand H, Pennacchio L, Engle E. A cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders. Nature Communications 2024, 15: 8268. PMID: 39333082, PMCID: PMC11436875, DOI: 10.1038/s41467-024-52463-7.Peer-Reviewed Original ResearchConceptsNon-coding variantsCranial motor neuronsMendelian disordersIn vivo transgenic assayPredictor of enhancer activityCis-regulatory elementsMulti-omic frameworkWhole-genome sequencingEnhanced activityVariant discoveryGenome sequenceChromatin accessibilityPutative enhancersHistone modificationsRegulatory elementsGene expression assaysGene predictionTransgenic assaysEpigenomic profilingMendelian casesExpression assaysMutational enhancementCongenital cranial dysinnervation disordersCell typesFunctional impact
2022
A 6-mRNA host response classifier in whole blood predicts outcomes in COVID-19 and other acute viral infections
Buturovic L, Zheng H, Tang B, Lai K, Kuan W, Gillett M, Santram R, Shojaei M, Almansa R, Nieto J, Muñoz S, Herrero C, Antonakos N, Koufargyris P, Kontogiorgi M, Damoraki G, Liesenfeld O, Wacker J, Midic U, Luethy R, Rawling D, Remmel M, Coyle S, Liu Y, Rao A, Dermadi D, Toh J, Jones L, Donato M, Khatri P, Giamarellos-Bourboulis E, Sweeney T. A 6-mRNA host response classifier in whole blood predicts outcomes in COVID-19 and other acute viral infections. Scientific Reports 2022, 12: 889. PMID: 35042868, PMCID: PMC8766462, DOI: 10.1038/s41598-021-04509-9.Peer-Reviewed Original ResearchConceptsSeverity of viral infectionsViral infectionArea under curveViral illnessPredicting 30-day mortalitySevere respiratory failureAcute viral infectionTrained logistic regression classifierViral infected patientsViral infection settingsImprove patient managementReduce healthcare burdenUnmet medical needSeverity of COVID-19Respiratory failureRetrospective cohortInfected patientsClinical studiesPatient managementCross-validation areaHealthcare burdenGene expression assaysPatientsInfection settingsRisk assessment of COVID-19
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 patterns
2016
Risk Stratification of Newly Diagnosed Prostate Cancer with Genomic Platforms
Leapman M, Carroll P. Risk Stratification of Newly Diagnosed Prostate Cancer with Genomic Platforms. Urology Practice 2016, 4: 322-328. PMID: 37592678, DOI: 10.1016/j.urpr.2016.06.008.Peer-Reviewed Original Research
2013
D‐series resolvin attenuates vascular smooth muscle cell activation and neointimal hyperplasia following vascular injury
Miyahara T, Runge S, Chatterjee A, Chen M, Mottola G, Fitzgerald JM, Serhan CN, Conte MS. D‐series resolvin attenuates vascular smooth muscle cell activation and neointimal hyperplasia following vascular injury. The FASEB Journal 2013, 27: 2220-2232. PMID: 23407709, PMCID: PMC3659350, DOI: 10.1096/fj.12-225615.Peer-Reviewed Original ResearchConceptsD-series resolvinsVascular injuryLipid mediatorsNeointimal hyperplasiaBalloon-injured rabbit arteriesMonocyte adhesionVascular smooth muscle cell activationSuperoxide productionSmooth muscle cell activationSpecialized lipid mediatorsExpression of receptorsProinflammatory gene expressionDose-dependent inhibitionMuscle cell activationVascular smooth muscle cell phenotypeSmooth muscle cell phenotypeMuscle cell phenotypeArterial angioplastyLeukocyte recruitmentHuman VSMCsVSMC proliferationGene expression assaysRabbit arteriesVascular homeostasisRvD2
2010
Multi-targeted priming for genome-wide gene expression assays
Adomas AB, Lopez-Giraldez F, Clark TA, Wang Z, Townsend JP. Multi-targeted priming for genome-wide gene expression assays. BMC Genomics 2010, 11: 477. PMID: 20716356, PMCID: PMC3091673, DOI: 10.1186/1471-2164-11-477.Peer-Reviewed Original ResearchMeSH KeywordsDNA PrimersGene Expression ProfilingGene Expression Regulation, FungalGenes, FungalMetabolic Networks and PathwaysMyceliumNeurospora crassaNitrogenOligonucleotide Array Sequence AnalysisReproducibility of ResultsReverse Transcriptase Polymerase Chain ReactionReverse TranscriptionRNA, FungalRNA, MessengerSaccharomyces cerevisiaeSequence Analysis, RNAConceptsGene expressionGene expression assaysNeurospora crassaRibosomal RNAExpression assaysMost protein-coding genesEarly sexual developmentGenome-wide gene expressionTransfer RNA genesProtein-coding genesNitrogen starvation responseGenome of SaccharomycesPreponderance of genesGlobal gene expressionResponse of SaccharomycesCommon sequence motifsSexual developmentDetailed expression profilesReverse transcriptionRNA genesStarvation responseTag sequencingSequence motifsTransfer RNATranscriptomic assays
2000
Activation of human γ-globin gene expression via triplex-forming oligonucleotide (TFO)-directed mutations in the γ-globin gene 5′ flanking region
Xu X, Glazer P, Wang G. Activation of human γ-globin gene expression via triplex-forming oligonucleotide (TFO)-directed mutations in the γ-globin gene 5′ flanking region. Gene 2000, 242: 219-228. PMID: 10721715, DOI: 10.1016/s0378-1119(99)00522-3.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsBase SequenceBinding SitesCell LineDNADNA-Binding ProteinsGene Expression RegulationGlobinsHeLa CellsHost Cell Factor C1HumansK562 CellsMolecular Sequence DataMutagenesis, Site-DirectedMutationOctamer Transcription Factor-1OligonucleotidesProtein BindingRegulatory Sequences, Nucleic AcidTranscription FactorsTumor Cells, CulturedConceptsGamma-globin gene expressionGamma-globin geneGene expressionHuman γ-globin gene expressionVivo gene expression assaysΓ-globin gene expressionGenetic diseasesAgamma-globin geneMouse erythroleukemia cellsTarget gene expressionTarget siteBeta-globin disordersFetal hemoglobin (HPFH) conditionBeta-globin geneSingle base changeGene expression assaysProtein binding assaysTranscription factorsHuman normal fibroblast cellsDNA sequencing analysisCommon genetic diseaseFlanking regionsExpression assaysErythroleukemia cellsTriplex-forming oligonucleotides
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