2025
The application of irreversible genomic states to define and trace ancient cell type homologies
Simakov O, Wagner G. The application of irreversible genomic states to define and trace ancient cell type homologies. EvoDevo 2025, 16: 5. PMID: 40319312, PMCID: PMC12049793, DOI: 10.1186/s13227-025-00242-w.Peer-Reviewed Original ResearchImpact gene expressionGenomic stateCell typesGene expressionGene regulatory networksHomologous cell typesBranching animalsGenomic charactersRegulatory elementsRegulatory networksRegulatory signaturesMorphological traitsHomologyPhenotypic levelMolecular signaturesOntogenetic originGenesMorphological featuresNon-functionalCellsHypothesis articleGenomeExpressionTraitsTypeIntegration of metabolite and transcriptome profiles of cultivated and wild rice to unveil gene regulatory networks and key genes determining rice source and sink strength
Singh A, Mathan J, Dwivedi A, Rani R, Ranjan A. Integration of metabolite and transcriptome profiles of cultivated and wild rice to unveil gene regulatory networks and key genes determining rice source and sink strength. Functional & Integrative Genomics 2025, 25: 97. PMID: 40310586, DOI: 10.1007/s10142-025-01606-0.Peer-Reviewed Original ResearchConceptsWild rice accessionsWild riceCultivated varietiesSink strengthRice accessionsHub genesGene co-expression modulesPathways related to photosynthesisGene co-expression networksGene regulatory networksAmino acidsZinc finger proteinCo-expression modulesHigher photosynthesis rateCrop yield increasesCo-expression networkFatty acid metabolismFinger proteinRegulatory networksTranscriptional regulationTranscriptome comparisonFlag leavesPhotosynthesis ratePhotosynthetic leavesRice varietiesEmerging roles of transcriptional condensates as temporal signal integrators
Meyer K, Huang B, Weiner O. Emerging roles of transcriptional condensates as temporal signal integrators. Nature Reviews Genetics 2025, 1-12. PMID: 40240649, DOI: 10.1038/s41576-025-00837-y.Peer-Reviewed Original ResearchGene regulatory networksTranscriptional condensatesRegulatory networksControl cell physiologyTemporal signal integrationMethod to probeSignaling networksSignaling specificityCell physiologyGene activationTranscription factorsSignaling DynamicsBiophysical frameworkSignal adaptationTranscriptionGenesSignalDecoding mechanismSignal integrityFrequency of signalsPhysiologyMechanism
2024
Inferring Gene Regulatory Network Based on scATAC-seq Data with Gene Perturbation
Shao W, Liu Y, Zhang S, Chen S, Liu Q, Zhou J, Zeng W. Inferring Gene Regulatory Network Based on scATAC-seq Data with Gene Perturbation. 2024, 00: 451-456. DOI: 10.1109/bibm62325.2024.10822868.Peer-Reviewed Original ResearchGene regulatory networksGene regulatory network inferenceGene regulationScATAC-seqRegulatory networksConstruction of gene regulatory networksAccurate gene regulatory networksInferring gene regulatory networksSingle-cell ATAC-seqChromatin accessibility dataDynamic regulatory landscapeATAC-seqChromatin accessibilityGene perturbationsComplex biological systemsRegulatory relationshipsBiological processesGenesRegulatory landscapeRegulationIntricate interactionsBiological systemsChromatinPost-perturbationAccess dataTet2 Loss in Hematopoietic Stem Cells Triggers Chromatin Reorganization through DNA Methylation Shifts
Roy R, Pillai M, Boddu P. Tet2 Loss in Hematopoietic Stem Cells Triggers Chromatin Reorganization through DNA Methylation Shifts. Blood 2024, 144: 1812. DOI: 10.1182/blood-2024-211494.Peer-Reviewed Original ResearchTopologically associating domainsDisruption of TAD boundariesTopologically associating domains boundariesTAD boundariesMutant cellsChromatin organizationChromatin compartmentsDNA methylationLT-HSCsKO cellsEnhancer-promoterHigher-order chromatin organizationTet2 lossStudy of chromatin organizationGene expressionMethyl-seq dataHypermethylated differentially methylated regionsPaired-end readsWT cellsGene regulatory networksConversion of 5-methylcytosineDifferentially Methylated RegionsMyelodysplastic syndromeCompartment shiftsChanges to DNA methylationCell-specific gene networks and drivers in rheumatoid arthritis synovial tissues
Pelissier A, Laragione T, Gulko P, Martínez M. Cell-specific gene networks and drivers in rheumatoid arthritis synovial tissues. Frontiers In Immunology 2024, 15: 1428773. PMID: 39161769, PMCID: PMC11330812, DOI: 10.3389/fimmu.2024.1428773.Peer-Reviewed Original ResearchTranscription factorsNatural killer TPhenotypic differencesGene regulatory networksCo-regulatory networkRNA-seq databaseCell typesFibroblast-like synoviocytesRNA-seqRegulatory networksGene networksTF clustersMultiple cell typesB cellsCell regulationKiller TRheumatoid arthritis synovial tissuePhenotypic groupsRA pathogenesisRA synovial tissuePathway changesTissue genesGenesCompare network propertiesComputational approachRepresenting core gene expression activity relationships using the latent structure implicit in Bayesian networks
Gao J, Gerstein M. Representing core gene expression activity relationships using the latent structure implicit in Bayesian networks. Bioinformatics 2024, 40: btae463. PMID: 39051682, PMCID: PMC11316617, DOI: 10.1093/bioinformatics/btae463.Peer-Reviewed Original ResearchTranscriptional regulatory networksGene regulatory networksCo-expression networkGene expression activityChIP-seqGene conservationCluster genesSupplementary dataRegulatory networksBiological networksClearer clusteringCo-expressionExpression activityBioinformaticsGenesBiomedical studiesConservationExpressionClustersStochastic modeling of a gene regulatory network driving B cell development in germinal centers
Koshkin A, Herbach U, Martínez M, Gandrillon O, Crauste F. Stochastic modeling of a gene regulatory network driving B cell development in germinal centers. PLOS ONE 2024, 19: e0301022. PMID: 38547073, PMCID: PMC10977792, DOI: 10.1371/journal.pone.0301022.Peer-Reviewed Original ResearchConceptsGene regulatory network structureGene regulatory networksGene expression dataExpression dataB cell differentiationSingle-cellAssociated with cell developmentGC B cell differentiationStages of B-cell differentiationB cell developmentSelection of B cellsGene regulationRegulatory networksTranscriptome dataSystems biologyHigh-affinity antibodiesRegulatory mechanismsCell developmentGenesAdaptive immune systemMRNA distributionPlasmablast stageGerminal centersDifferentiationImmune system
2023
Single‐Cell Transcriptomics of Bone Marrow Stromal Cells in Diversity Outbred Mice: A Model for Population‐Level scRNA‐Seq Studies
Dillard L, Rosenow W, Calabrese G, Mesner L, Al‐Barghouthi B, Abood A, Farber E, Onengut‐Gumuscu S, Tommasini S, Horowitz M, Rosen C, Yao L, Qin L, Farber C. Single‐Cell Transcriptomics of Bone Marrow Stromal Cells in Diversity Outbred Mice: A Model for Population‐Level scRNA‐Seq Studies. Journal Of Bone And Mineral Research 2023, 38: 1350-1363. PMID: 37436066, PMCID: PMC10528806, DOI: 10.1002/jbmr.4882.Peer-Reviewed Original ResearchConceptsGene regulatory networksMesenchymal lineage cellsBone marrow-derived stromal cellsGenome-wide association studiesOsteocyte-like cellsLineage cellsOsteogenic conditionsTranscriptomic profilesSingle-cell RNA-seqCell typesTranscriptomic data setsGenetics of osteoporosisDisease-Associated VariantsSingle-cell levelMarrow-derived stromal cellsTranscriptomic perspectiveRegulatory networksCausal genesRNA-seqTranscriptomic dataScRNA-seqMesenchymal progenitorsAssociation studiesGenetic studiesLineage precursorsMicrotechnologies for single-cell and spatial multi-omics
Deng Y, Bai Z, Fan R. Microtechnologies for single-cell and spatial multi-omics. Nature Reviews Bioengineering 2023, 1: 769-784. DOI: 10.1038/s44222-023-00084-y.Peer-Reviewed Original ResearchSuch single-cell dataGene regulatory networksMulti-omics assaysSingle-cell dataMulti-omics studiesSingle cellsContext of tissuesGenome scaleRegulatory networksOmics informationGene expressionOmics assaysCellular profilingSubcellular levelIntact tissueCellsFunctional stateAssaysOmicsMicroarrayTissueProfilingRegulationExpressionMicrofluidic platformlinc-mipep and linc-wrb encode micropeptides that regulate chromatin accessibility in vertebrate-specific neural cells
Tornini V, Miao L, Lee H, Gerson T, Dube S, Schmidt V, Kroll F, Tang Y, Du K, Kuchroo M, Vejnar C, Bazzini A, Krishnaswamy S, Rihel J, Giraldez A. linc-mipep and linc-wrb encode micropeptides that regulate chromatin accessibility in vertebrate-specific neural cells. ELife 2023, 12: e82249. PMID: 37191016, PMCID: PMC10188112, DOI: 10.7554/elife.82249.Peer-Reviewed Original ResearchConceptsCell typesIntergenic non-coding RNAsChromatin architectural proteinCryptic open reading frameGene regulatory networksOpen reading frameNon-coding RNAsNew cell typesNeural cell typesBrain cell typesPutative lincRNAsVertebrate genomesArchitectural proteinsChromatin disruptionChromatin accessibilityRegulatory networksGenetic basisCell developmentMicropeptidesBrain cell developmentReceptor-mediated pathwaySystematic identificationLincRNAsNeural cellsCerebellar cellsThe Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks
Ben Guebila M, Wang T, Lopes-Ramos C, Fanfani V, Weighill D, Burkholz R, Schlauch D, Paulson J, Altenbuchinger M, Shutta K, Sonawane A, Lim J, Calderer G, van IJzendoorn D, Morgan D, Marin A, Chen C, Song Q, Saha E, DeMeo D, Padi M, Platig J, Kuijjer M, Glass K, Quackenbush J. The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks. Genome Biology 2023, 24: 45. PMID: 36894939, PMCID: PMC9999668, DOI: 10.1186/s13059-023-02877-1.Peer-Reviewed Original ResearchNew Horizons in Plant Photoperiodism
Gendron J, Staiger D. New Horizons in Plant Photoperiodism. Annual Review Of Plant Biology 2023, 74: 481-509. PMID: 36854481, PMCID: PMC11114106, DOI: 10.1146/annurev-arplant-070522-055628.Peer-Reviewed Original ResearchConceptsPlant photoperiodismRecent molecular genetic studiesGene regulatory networksMolecular genetic studiesPhotoperiodic floweringPlant speciesRegulatory networksDevelopmental processesMetabolic networksGenetic studiesPhotoperiodismSeasonal changesOrganismsPlantsPotential rolePhotoperiodClimate changeLatitudinal migrationTranscriptomicsFloweringSpeciesGrowthMigrationReproductionRice
2022
Membrane potential drives the exit from pluripotency and cell fate commitment via calcium and mTOR
Sempou E, Kostiuk V, Zhu J, Cecilia Guerra M, Tyan L, Hwang W, Camacho-Aguilar E, Caplan M, Zenisek D, Warmflash A, Owens N, Khokha M. Membrane potential drives the exit from pluripotency and cell fate commitment via calcium and mTOR. Nature Communications 2022, 13: 6681. PMID: 36335122, PMCID: PMC9637099, DOI: 10.1038/s41467-022-34363-w.Peer-Reviewed Original ResearchConceptsPluripotent cellsAdult tissue homeostasisCell fate commitmentDifferentiated cell fatesLeft-right patterningPluripotent embryonic cellsHuman embryonic stem cellsTemporal transcriptome analysisGene regulatory networksExpense of differentiationEmbryonic stem cellsGerm layer differentiationMembrane depolarizationFate commitmentPluripotent stateCell fateTranscriptome analysisRegulatory networksMyogenic lineageEmbryonic developmentTissue homeostasisDifferentiated fateEmbryonic cellsCandidate genesPluripotency
2021
Single-cell analysis of prostaglandin E2-induced human decidual cell in vitro differentiation: a minimal ancestral deciduogenic signal†
Stadtmauer DJ, Wagner G. Single-cell analysis of prostaglandin E2-induced human decidual cell in vitro differentiation: a minimal ancestral deciduogenic signal†. Biology Of Reproduction 2021, 106: 155-172. PMID: 34591094, PMCID: PMC8757638, DOI: 10.1093/biolre/ioab183.Peer-Reviewed Original ResearchMeSH KeywordsCell DifferentiationCell Line, TransformedCells, CulturedCyclic AMPCyclic AMP-Dependent Protein KinasesDeciduaDinoprostoneEndometriumFemaleFibroblastsGene ExpressionGenome-Wide Association StudyHumansMedroxyprogesterone AcetatePregnancyReceptors, Prostaglandin E, EP2 SubtypeSequence Analysis, RNASingle-Cell AnalysisConceptsPlacental mammalsCore gene regulatory networkCyclic AMP/protein kinase A (cAMP/PKA) pathwayProtein kinase A (PKA) pathwaySenescence-associated genesProgesterone-dependent activationGenome-wide studiesSingle-cell transcriptomicsGene regulatory networksProgesterone-dependent inductionMembrane-permeable cAMPKinase A PathwaySingle-cell analysisUse of PGE2Outgroup taxaCellular statesRegulatory networksPKA axisGene expressionDecidual genesPKA activationPGE2 receptor 2Progestin-dependent inductionA PathwayAdenylyl cyclase activationBayesian information sharing enhances detection of regulatory associations in rare cell types
Wu A, Peng J, Berger B, Cho H. Bayesian information sharing enhances detection of regulatory associations in rare cell types. Bioinformatics 2021, 37: i349-i357. PMID: 34252956, PMCID: PMC8275330, DOI: 10.1093/bioinformatics/btab269.Peer-Reviewed Original ResearchConceptsScRNA-seq datasetsRegulatory associationsCell typesRegulatory networksCell type-specific gene regulatory networksCell-type specific gene regulationSingle-cell RNA sequencing technologyCell-type specific networksBenchmark scRNA-seq datasetsDiverse cellular contextsGene regulatory network inference methodRNA sequencing technologyGene regulatory networksRare cell typesSingle-cell datasetsSpecific cell typesNetwork inference methodsDynamic biological processesTranscriptional statesGene regulationCellular contextNetwork inference algorithmsComplex rewiringBiological processesGene associationsDevo-Evo of Cell Types
Wagner G. Devo-Evo of Cell Types. 2021, 511-528. DOI: 10.1007/978-3-319-32979-6_153.Peer-Reviewed Original ResearchEvolutionary developmental biologyCell typesDevelopmental biologyCore gene regulatory networkAncestral cell typeCell type identityTranscription factor proteinsGene regulatory networksGene treesGene duplicationEvolutionary descentRegulatory complexRegulatory networksPopulation of cellsMolecular mechanismsFactor proteinOrganisms increasesEvolutionary perspectiveLineagesBiologyType identityMajor current challengeSpecific setTreesGenesMultiscale Modeling of Germinal Center Recapitulates the Temporal Transition From Memory B Cells to Plasma Cells Differentiation as Regulated by Antigen Affinity-Based Tfh Cell Help
Tejero E, Lashgari D, García-Valiente R, Gao X, Crauste F, Robert P, Meyer-Hermann M, Martínez M, van Ham S, Guikema J, Hoefsloot H, van Kampen A. Multiscale Modeling of Germinal Center Recapitulates the Temporal Transition From Memory B Cells to Plasma Cells Differentiation as Regulated by Antigen Affinity-Based Tfh Cell Help. Frontiers In Immunology 2021, 11: 620716. PMID: 33613551, PMCID: PMC7892951, DOI: 10.3389/fimmu.2020.620716.Peer-Reviewed Original ResearchMeSH KeywordsAsymmetric Cell DivisionB-LymphocytesCD40 AntigensCell LineageComputer SimulationGene Regulatory NetworksGerminal CenterHumansImmunologic MemoryInterferon Regulatory FactorsLymphopoiesisModels, ImmunologicalPlasma CellsPositive Regulatory Domain I-Binding Factor 1Proto-Oncogene Proteins c-bcl-6Signal TransductionT Follicular Helper CellsTime FactorsConceptsB cell to plasma cell differentiationAsymmetric divisionRegulatory interactions of transcription factorsPlasma cell differentiationInteraction of transcription factorsCore gene regulatory networkGene regulatory networksCell differentiationCell-fate decisionsTemporal switchB cell receptor affinityGerminal center reactionB cellsCD40 signaling pathwayRegulatory networksRegulatory interactionsTranscription factorsEffective immune protectionCenter reactionSignaling pathwayAdaptive immune systemT Follicular Helper CellsPlasma cell generationMemory B cellsMolecular modules
2020
FPGA Accelerated Analysis of Boolean Gene Regulatory Networks
Manica M, Polig R, Purandare M, Mathis R, Hagleitner C, Martínez M. FPGA Accelerated Analysis of Boolean Gene Regulatory Networks. IEEE Transactions On Computational Biology And Bioinformatics 2020, 17: 2141-2147. PMID: 31494553, DOI: 10.1109/tcbb.2019.2936836.Peer-Reviewed Original ResearchConceptsQualitative models of gene regulatory networksModels of gene regulatory networksAdvanced high-throughput technologiesGene regulatory networksHigh-throughput technologiesComplex molecular networkBoolean modelRegulatory networksBiological insightsT-cell large granular lymphocytic leukemiaMolecular networksAttractor detectionField Programmable Gate ArrayLarge granular lymphocytic leukemiaSoftware simulation toolGranular lymphocytic leukemiaSimulation toolPerformance improvementReconfigurable integrated circuitsNIMBus: a negative binomial regression based Integrative Method for mutation Burden Analysis
Zhang J, Liu J, McGillivray P, Yi C, Lochovsky L, Lee D, Gerstein M. NIMBus: a negative binomial regression based Integrative Method for mutation Burden Analysis. BMC Bioinformatics 2020, 21: 474. PMID: 33092526, PMCID: PMC7580035, DOI: 10.1186/s12859-020-03758-1.Peer-Reviewed Original ResearchConceptsDNase I hypersensitive sitesMutation rate heterogeneityDNA elementsCancer whole genome sequencesMutational hotspotsMutation burden analysisFunctional genomics dataNon-coding regionsGene regulatory networksWhole Genomes (PCAWG) projectWhole genome sequencesBackground mutation rateBurden analysisChromatin organizationReplication timingGenome sequenceRegulatory networksTranscription factorsHypersensitive sitesGenomic featuresRate heterogeneityGenome ProjectGenomic dataIntegrative methodGamma-Poisson mixture model
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