2025
Association of clinical manifestations and immune alterations with genetic variants of uncertain significance in patients concerned for inborn errors of immunity
Novotny S, Yoo N, Chen J, Granoth M, Kohli-Pamnani A, Hsu F, Rodenas M, Steele R, Kaman K, Soffer G, Price C, Kuster J, Kang I, Osmani L, Shin J. Association of clinical manifestations and immune alterations with genetic variants of uncertain significance in patients concerned for inborn errors of immunity. Clinical Immunology 2025, 277: 110513. PMID: 40354868, DOI: 10.1016/j.clim.2025.110513.Peer-Reviewed Original ResearchInborn errors of immunityErrors of immunityGenetic variantsInborn errorsAssociation of clinical manifestationsGene clusterGene functionStudy evaluated associationsImmune alterationsDiagnostic challengeClinical manifestationsImmunological profileGenetic testingVUSGenesImmunological characteristicsImmune functionLaboratory dataImmunityVariantsAssociationPatientsEmerging 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 signalsPhysiologyMechanismTrio exome sequencing in individuals with CAKUT identifies de novo variants in potential novel candidate genes in 19.62%
Merz L, Kolvenbach C, Wang C, Mertens N, Seltzsam S, Mansour B, Zheng B, Schneider S, Schierbaum L, Hölzel S, Salmanullah D, Pantel D, Kalkar G, Connaughton D, Mann N, Wilfred Wu C, Kause F, Nakayama M, Dai R, Schneider R, Buerger F, Nicolas-Frank C, Yousef K, Lemberg K, Saida K, Yu S, Elmubarak I, Franken G, Lomjansook K, Braun A, Bauer S, Rodig N, G Somers M, Traum A, Stein D, Daga A, Baum M, Daouk G, Awad H, Eid L, El Desoky S, Shalaby M, Kari J, Ooda S, Fathy H, Soliman N, Nabhan M, Abdelrahman S, Hilger A, Mane S, Ferguson M, Tasic V, Shril S, Hildebrandt F. Trio exome sequencing in individuals with CAKUT identifies de novo variants in potential novel candidate genes in 19.62%. Genetics In Medicine 2025, 101432. PMID: 40223730, DOI: 10.1016/j.gim.2025.101432.Peer-Reviewed Original ResearchCandidate genesExome sequencingDisease genesPotential novel candidate genesCandidate disease genesTrio-based exome sequencingDe novo variantsTrio exome sequencingDisease etiologyPathogenesis of CAKUTPotential novel causeTrio familiesTrio analysisMonogenic genesGenesNovel causeCHD1LSOX13VariantsTriosSequenceCongenital anomaliesHeterogeneous malformationUrinary tractCAKUTThe bridge-like lipid transport protein VPS13C/PARK23 mediates ER–lysosome contacts following lysosome damage
Wang X, Xu P, Bentley-DeSousa A, Hancock-Cerutti W, Cai S, Johnson B, Tonelli F, Shao L, Talaia G, Alessi D, Ferguson S, De Camilli P. The bridge-like lipid transport protein VPS13C/PARK23 mediates ER–lysosome contacts following lysosome damage. Nature Cell Biology 2025, 27: 776-789. PMID: 40211074, PMCID: PMC12081312, DOI: 10.1038/s41556-025-01653-6.Peer-Reviewed Original ResearchConceptsDisease genesResponse to lysosomal damageSurface of lysosomesER–lysosome contactsParkinson's disease genesDelivery to lysosomesLipid transport proteinsLysosomal damageVPS13 proteinsLysosomal surfaceDisease proteinsGenetic studiesDamaged lysosomesVPS13CLysosomal stressLipid transportLysosomesInhibited stateMembrane perturbationRab7Lysosomal dysfunctionProteinVps13LipidGenesScientific Advancements in Gene Therapies: Opportunities for Global Regulatory Convergence
Olaghere J, Williams D, Farrar J, Büning H, Calhoun C, Ho T, Inamdar M, Liu D, Makani J, Nyarko K, Ruiz S, Tisdale J, McCune J, Boadi E, for the FDA R. Scientific Advancements in Gene Therapies: Opportunities for Global Regulatory Convergence. Biomedicines 2025, 13: 758. PMID: 40149734, PMCID: PMC11940732, DOI: 10.3390/biomedicines13030758.Peer-Reviewed Original ResearchFood and Drug AdministrationGene therapySickle cell diseaseFDA-approved therapiesCost of therapyRegulatory convergenceGates FoundationCell diseaseTherapyDrug AdministrationMiddle-income countriesGlobal health advocatesModel disorderRegulatory gapsInternational regulatory bodiesReagan-Udall FoundationRegulated industriesGenesRegulatory bodiesMendelian non-syndromic and syndromic hearing loss genes contribute to presbycusis
Cornejo-Sanchez D, Bharadwaj T, Dong R, Wang G, Schrauwen I, DeWan A, Leal S. Mendelian non-syndromic and syndromic hearing loss genes contribute to presbycusis. European Journal Of Human Genetics 2025, 1-10. PMID: 40055553, DOI: 10.1038/s41431-025-01789-x.Peer-Reviewed Original ResearchRare-variantsHearing loss genesAssociated with HLNon-syndromicAssociation analysisHL geneHearing phenotypeUK BiobankMinor allele frequencyOlder adultsSensorineural disorderARHLEffect sizeWhite EuropeansAssociated with genesAge-relatedIn silico analysisAnalysis of variantsExome dataAssociationGenes i.Allele frequenciesHLGenesPresbycusisThe case-only design is a powerful approach to detect interactions but should be used with caution
Dong R, Wang G, DeWan A, Leal S. The case-only design is a powerful approach to detect interactions but should be used with caution. BMC Genomics 2025, 26: 222. PMID: 40050722, PMCID: PMC11884093, DOI: 10.1186/s12864-025-11318-1.Peer-Reviewed Original ResearchConceptsCase-only designRare disease assumptionType I error rateIncreased type I error ratesDisease prevalenceInvestigated type I errorComplex traitsInteraction termsInteraction effect sizesDetect interactionsCase-control designControlled type I error ratesSample sizeHigher disease prevalenceEffect sizeLow disease prevalenceType I errorPrevalenceExposure frequencyGenesType I andDesign studyEnvironmental factorsTraitsEnvironment interactionSynthetic Genetic Elements Enable Rapid Characterization of Inorganic Carbon Uptake Systems in Cupriavidus necator H16
Nakamura A, Fulk E, Johnson C, Isaacs F. Synthetic Genetic Elements Enable Rapid Characterization of Inorganic Carbon Uptake Systems in Cupriavidus necator H16. ACS Synthetic Biology 2025, 14: 943-953. PMID: 40048245, DOI: 10.1021/acssynbio.4c00869.Peer-Reviewed Original ResearchConceptsSynthetic genetic elementsExpression of heterologous pathwaysUptake systemCupriavidus necator H16Genome engineering technologiesHeterologous pathwaysHeterotrophic conditionsGenetic elementsChromosomal expressionTunable expressionInducible promoterGenetic engineering technologyModel microbesCarbon sourceGene expressionFacultative chemolithotrophsUptake pathwayH16PathwayLanding padExpressionGenomeGenesMicrobesChemolithotrophsRecessive genetic contribution to congenital heart disease in 5,424 probands
Dong W, Jin S, Sierant M, Lu Z, Li B, Lu Q, Morton S, Zhang J, López-Giráldez F, Nelson-Williams C, Knight J, Zhao H, Cao J, Mane S, Gruber P, Lek M, Goldmuntz E, Deanfield J, Giardini A, Mital S, Russell M, Gaynor J, Cnota J, Wagner M, Srivastava D, Bernstein D, Porter G, Newburger J, Roberts A, Yandell M, Yost H, Tristani-Firouzi M, Kim R, Seidman J, Chung W, Gelb B, Seidman C, Lifton R, Brueckner M. Recessive genetic contribution to congenital heart disease in 5,424 probands. Proceedings Of The National Academy Of Sciences Of The United States Of America 2025, 122: e2419992122. PMID: 40030011, PMCID: PMC11912448, DOI: 10.1073/pnas.2419992122.Peer-Reviewed Original ResearchConceptsRecessive genotypeCHD probandsCongenital heart diseaseAssociated with laterality defectsGene-based analysisAnalyzed whole-exome sequencingLeft-sided congenital heart diseaseWhole-exome sequencingCongenital heart disease phenotypeAshkenazi Jewish probandsOffspring of consanguineous unionsSingle-cell transcriptomicsCHD geneExome sequencingMouse notochordSecreted proteinsConsanguineous familyFounder variantGenesSignificant enrichmentLaterality phenotypesHeart diseaseProbandsAbnormal contractile functionConsanguineous unionsTNFRSF1A and NCF1 May Act as Hub Genes in Mastitis
Ekhtiyari M, Yousefi M, Samadian F, Ghaderi‐Zefrehei M, Neysi S, Shamsabadi J, Javanmard A, Shahriarpour H, Lesch B. TNFRSF1A and NCF1 May Act as Hub Genes in Mastitis. Veterinary Medicine And Science 2025, 11: e70278. PMID: 40028770, PMCID: PMC11875065, DOI: 10.1002/vms3.70278.Peer-Reviewed Original ResearchConceptsBovine mastitisHub genesMastitis susceptibilityExpression datasetsMolecular mechanismsMultiple expression datasetsProtein-protein interactionsCo-expression network analysisSusceptibility to bovine mastitisRegulatory networksTarget key genesKey genesDairy industryMolecular playersNetwork analysisMRNA-miRNACo-expressionGenesNCF1MastitisBacterial infectionsTherapeutic targetTNFRSF1AImmune responseSusceptibilityImplications of gene × environment interactions in post-traumatic stress disorder risk and treatment
Seah C, Sidamon-Eristoff A, Huckins L, Brennand K. Implications of gene × environment interactions in post-traumatic stress disorder risk and treatment. Journal Of Clinical Investigation 2025, 135: e185102. PMID: 40026250, PMCID: PMC11870735, DOI: 10.1172/jci185102.Peer-Reviewed Original ResearchConceptsPost-traumatic stress disorderGene x environment interactionsGenetic component of riskLimitations of genetic studiesTreating post-traumatic stress disorderExposure to traumatic stressPost-traumatic stress disorder riskInteraction of traumaGenetic screeningGenetic studiesGenetic componentEnvironment interactionMolecular mechanismsStress disorderPTSD riskTraumatic exposureTraumatic stressTraumatic experiencesDisorder riskGenetic factorsNovel therapeuticsBiological mechanismsGWASGeneral populationGenesSex‐Specific Association Between Polymorphisms in Estrogen Receptor Alpha Gene (ESR1) and Depression: A Genome‐Wide Association Study of All of Us and UK Biobank Data
Hu Y, Che M, Zhang H. Sex‐Specific Association Between Polymorphisms in Estrogen Receptor Alpha Gene (ESR1) and Depression: A Genome‐Wide Association Study of All of Us and UK Biobank Data. Genetic Epidemiology 2025, 49: e70004. PMID: 40007508, PMCID: PMC11924109, DOI: 10.1002/gepi.70004.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesSingle-nucleotide polymorphismsAssociation studiesAlpha geneEstrogen receptor alpha geneGenetic risk factorsRisk lociGenomic associationsMajor depressive disorderMDD phenotypesGenetic studiesGenetic associationRisk factors of MDDGenesESR1 geneUK BiobankESR1Participant genotypesPolymorphismSex-specificSex-specific associationsDepressive disorderRacial/ethnic disparitiesFindings lack consistencyLength of lifeA compendium of human gene functions derived from evolutionary modelling
Ramsey J, Siegele D, Chisholm R, Fey P, Giglio M, Nadendla S, Antonazzo G, Attrill H, Brown N, Garapati P, Marygold S, Ahmed S, Asanitthong P, Buitrago D, Erdol M, Gage M, Huang S, Kadhum M, Li K, Long M, Michalak A, Pesala A, Pritazahra A, Saverimuttu S, Su R, Xu Q, Lovering R, Blake J, Christie K, Corbani L, Dolan M, Ni L, Sitnikov D, Smith C, Lera-Ramirez M, Rutherford K, Wood V, D’Eustachio P, Demos W, De Pons J, Dwinell M, Hayman G, Kaldunski M, Kwitek A, Laulederkind S, Smith J, Tutaj M, Vedi M, Wang S, Engel S, Karra K, Miyasato S, Nash R, Skrzypek M, Weng S, Wong E, Achsel T, Andres-Alonso M, Bagni C, Bayés À, Biederer T, Brose N, Chua J, Coba M, Cornelisse L, de Juan-Sanz J, Goldschmidt H, Gundelfinger E, Huganir R, Imig C, Jahn R, Jung H, Kaeser P, Kim E, Koopmans F, Kreutz M, Lipstein N, MacGillavry H, McPherson P, O’Connor V, Pielot R, Ryan T, Sala C, Sheng M, Smalla K, Smit A, Toonen R, van Weering J, Verhage M, Verpelli C, Bakker E, Berardini T, Reiser L, Auchincloss A, Axelsen K, Argoud-Puy G, Blatter M, Boutet E, Breuza L, Bridge A, Casals-Casas C, Coudert E, Estreicher A, Famiglietti M, Gos A, Gruaz-Gumowski N, Hulo C, Hyka-Nouspikel N, Jungo F, Le Mercier P, Lieberherr D, Masson P, Morgat A, Pedruzzi I, Pourcel L, Poux S, Rivoire C, Sundaram S, Bowler-Barnett E, Bye-A-Jee H, Denny P, Ignatchenko A, Ishtiaq R, Lock A, Lussi Y, Magrane M, Martin M, Orchard S, Raposo P, Speretta E, Tyagi N, Warner K, Zaru R, Chan J, Diamantakis S, Raciti D, Fisher M, James-Zorn C, Ponferrada V, Zorn A, Ramachandran S, Ruzicka L, Westerfield M. A compendium of human gene functions derived from evolutionary modelling. Nature 2025, 1-9. PMID: 40011791, DOI: 10.1038/s41586-025-08592-0.Peer-Reviewed Original ResearchHuman gene functionHuman protein-coding genesProtein-coding genesGene functionFunctional repertoireGene Ontology enrichment analysisGene Ontology ConsortiumOntology enrichment analysisHuman genomeHuman genesEvolutionary timeEvolutionary originGenomic techniquesEvolutionary modeling approachExpert-curatedEnrichment analysisGenesRegulatory functionsFunctional characteristicsEvolutionary modelsBiomedical researchGenomeRepertoireOrganisms1,2Body of informationEmpowering genome-wide association studies via a visualizable test based on the regional association score
Jiang Y, Zhang H. Empowering genome-wide association studies via a visualizable test based on the regional association score. Proceedings Of The National Academy Of Sciences Of The United States Of America 2025, 122: e2419721122. PMID: 39999171, PMCID: PMC11892588, DOI: 10.1073/pnas.2419721122.Peer-Reviewed Original ResearchBatch correcting single-cell spatial transcriptomics count data with Crescendo improves visualization and detection of spatial gene patterns
Millard N, Chen J, Palshikar M, Pelka K, Spurrell M, Price C, He J, Hacohen N, Raychaudhuri S, Korsunsky I. Batch correcting single-cell spatial transcriptomics count data with Crescendo improves visualization and detection of spatial gene patterns. Genome Biology 2025, 26: 36. PMID: 40001084, PMCID: PMC11863647, DOI: 10.1186/s13059-025-03479-9.Peer-Reviewed Original ResearchConceptsBatch effectsVisualization of gene expression patternsSpatial gene patternsGene expression analysis of cellsGene expression patternsGene expression analysisGene expression levelsGene colocalizationAnalysis of cellsGene patternsTranscriptome analysisLigand-receptor interactionsExpression patternsSpatial transcriptomicsSpatial transcriptomic analysisExpression levelsGenesMultiple samplesSpatial patternsTranscriptomeColocalizationAnatomical contextPatternsCount dataIdentification of genes associated with testicular germ cell tumor susceptibility through a transcriptome-wide association study
Ugalde-Morales E, Wilf R, Pluta J, Ploner A, Fan M, Damra M, Aben K, Anson-Cartwright L, Chen C, Cortessis V, Daneshmand S, Ferlin A, Gamulin M, Gietema J, Gonzalez-Niera A, Grotmol T, Hamilton R, Harland M, Haugen T, Hauser R, Hildebrandt M, Karlsson R, Kiemeney L, Kim J, Lessel D, Lothe R, Loveday C, Chanock S, McGlynn K, Meijer C, Nead K, Nsengimana J, Popovic M, Rafnar T, Richiardi L, Rocca M, Schwartz S, Skotheim R, Stefansson K, Stewart D, Turnbull C, Vaughn D, Winge S, Zheng T, Monteiro A, Almstrup K, Kanetsky P, Nathanson K, Wiklund F, Consortium T. Identification of genes associated with testicular germ cell tumor susceptibility through a transcriptome-wide association study. American Journal Of Human Genetics 2025, 112: 630-643. PMID: 39999848, PMCID: PMC11947167, DOI: 10.1016/j.ajhg.2025.01.022.Peer-Reviewed Original ResearchConceptsTranscriptome-wide association studyGenome-wide association studiesAssociation studiesTesticular germ cell tumorsTranscriptome-wide association study signalsGenome-wide association study lociTesticular germ cell tumour susceptibilityTesticular germ cell tumor tissuesFine-mapping analysisGene-disease linksGonadal cell typesEvidence of colocalizationProtein levels accumulationExpression levelsTesticular germ cell tumour riskPrioritized genesFalse discovery rateNeighboring genesGene-diseaseRegulatory featuresGene associationsColocalization analysisProtein patternsGenesNormal testisNongenetic adaptation by collective migration
Vo L, Avgidis F, Mattingly H, Edmonds K, Burger I, Balasubramanian R, Shimizu T, Kazmierczak B, Emonet T. Nongenetic adaptation by collective migration. Proceedings Of The National Academy Of Sciences Of The United States Of America 2025, 122: e2423774122. PMID: 39970001, PMCID: PMC11874451, DOI: 10.1073/pnas.2423774122.Peer-Reviewed Original ResearchConceptsGene regulationCollective migrationPhenotype distributionPhenotypic compositionStress response pathwaysSwimming phenotypeCell populationsBacterial populationsStress responseAbundance distributionMultidimensional phenotypesGenetic mutationsPhenotypeDiverse environmentsEnvironmental conditionsGenesMutationsSwimming behaviorChanging environmentDoubling timeMigrating populationCellsRegulationMigrationAdaptationModeling thoracic aortic genetic variants in the zebrafish: useful for predicting clinical pathogenicity?
Prendergast A, Sheppard M, Famulski J, Nicoli S, Mukherjee S, Sips P, Elefteriades J. Modeling thoracic aortic genetic variants in the zebrafish: useful for predicting clinical pathogenicity? Frontiers In Cardiovascular Medicine 2025, 12: 1480407. PMID: 40066353, PMCID: PMC11892108, DOI: 10.3389/fcvm.2025.1480407.Peer-Reviewed Original ResearchPathogenicity of VUSProportion of variantsMedical genetic testingCausal genesPathogenicity assessmentClinical pathogensTested pathogensGenetic variantsCausative genesTAAD casesGenesGenetic defectsGenetic testingThoracic aortic aneurysmHeritable genetic defectsImpact cardiovascular morbidityPathogensVUSAortic aneurysmCardiovascular morbidityVariantsZebrafishTAADClinical applicationEnhance patient carePhysiologic mechanisms underlying polycystic kidney disease
Boletta A, Caplan M. Physiologic mechanisms underlying polycystic kidney disease. Physiological Reviews 2025 PMID: 39938884, DOI: 10.1152/physrev.00018.2024.Peer-Reviewed Original ResearchPrimary ciliaPolycystic kidney diseaseTrafficking of proteinsHuman ciliopathiesExtracellular signalsMultiple genesKidney diseaseProtein productionMolecular basisCell biologyMonogenic disordersCyst formationGenesRenal epithelial cellsProteinCiliaBiochemical informationApical surfaceEpithelial cellsFunctional expressionPhysiological propertiesWealth of informationPhysiological mechanismsCellsFibrocystinA Selective Review of Network Analysis Methods for Gene Expression Data
Li R, Yi H, Ma S. A Selective Review of Network Analysis Methods for Gene Expression Data. Methods In Molecular Biology 2025, 2880: 293-307. PMID: 39900765, DOI: 10.1007/978-1-0716-4276-4_14.Peer-Reviewed Original ResearchConceptsGene Expression DataGene expression networksExpression DataDownstream analysisExpression networksGene expressionBiological processesGenesMolecular mechanismsBiological implicationsHigh-throughput profiling techniquesBiological findingsGlobal viewComplex interactionsProfiling techniquesRegulation
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