2024
Connectivity as a universal predictor of tau spreading in typical and atypical Alzheimer’s disease
de Bruin H, Groot C, ADNI, Barthel H, Bischof G, Boellaard R, Brendel M, Cash D, Coath W, Day G, Dickerson B, Doering E, Drzezga A, van Dyck C, van Eimeren T, van der Flier W, Fredericks C, Fryer T, van de Giessen E, Gordon B, Graff‐Radford J, Hobbs D, Höglinger G, Hönig M, Irwin D, Jones P, Josephs K, Katsumi Y, Lee E, Levin J, Malpetti M, McGinnis S, Mecca A, Nasrallah I, O'Brien J, O'Dell R, Palleis C, Perneczky R, Phillips J, Pijnenburg Y, Putcha D, Rahmouni N, Rosa‐Neto P, Rowe J, Rullmann M, Sabri O, Saur D, Schildan A, Schott J, Schroeter M, Servaes S, Sintini I, Stevenson J, Therriault J, Touroutoglou A, Trainer A, Visser D, Weston P, Whitwell J, Wolk D, Franzmeier N, Ossenkoppele R. Connectivity as a universal predictor of tau spreading in typical and atypical Alzheimer’s disease. Alzheimer's & Dementia 2024, 20: e085869. PMCID: PMC11714601, DOI: 10.1002/alz.085869.Peer-Reviewed Original ResearchAlzheimer's diseaseTau spreadingProgression of Alzheimer's diseaseTau-PETFunctional proximityPosterior patterningAD variantsTauPersonalized medicineTau-PET standardized uptake value ratiosVariantsWidespread patternAtypical ADDominant patternAtypical Alzheimer's diseaseRegionNeurodegenerationPositive probabilityPatternsConnectivity as a universal predictor of tau spreading in typical and atypical Alzheimer’s disease
de Bruin H, Groot C, ADNI, Barthel H, Bischof G, Boellaard R, Brendel M, Cash D, Coath W, Day G, Dickerson B, Doering E, Drzezga A, van Dyck C, van Eimeren T, van der Flier W, Fredericks C, Fryer T, van de Giessen E, Gordon B, Graff‐Radford J, Hobbs D, Höglinger G, Hönig M, Irwin D, Jones P, Josephs K, Katsumi Y, Lee E, Levin J, Malpetti M, McGinnis S, Mecca A, Nasrallah I, O'Brien J, O'Dell R, Palleis C, Perneczky R, Phillips J, Pijnenburg Y, Putcha D, Rahmouni N, Rosa‐Neto P, Rowe J, Rullmann M, Sabri O, Saur D, Schildan A, Schott J, Schroeter M, Servaes S, Sintini I, Stevenson J, Therriault J, Touroutoglou A, Trainer A, Visser D, Weston P, Whitwell J, Wolk D, Franzmeier N, Ossenkoppele R. Connectivity as a universal predictor of tau spreading in typical and atypical Alzheimer’s disease. Alzheimer's & Dementia 2024, 20: e093663. PMCID: PMC11713789, DOI: 10.1002/alz.093663.Peer-Reviewed Original ResearchAlzheimer's diseaseTau spreadingProgression of Alzheimer's diseaseTau-PETFunctional proximityPosterior patterningAD variantsTauPersonalized medicineTau-PET standardized uptake value ratiosVariantsWidespread patternAtypical ADDominant patternAtypical Alzheimer's diseaseRegionNeurodegenerationPatternsLess-is-more: selecting transcription factor binding regions informative for motif inference
Xu J, Gao J, Ni P, Gerstein M. Less-is-more: selecting transcription factor binding regions informative for motif inference. Nucleic Acids Research 2024, 52: e20-e20. PMID: 38214231, PMCID: PMC10899791, DOI: 10.1093/nar/gkad1240.Peer-Reviewed Original ResearchConceptsChIP-seq signalsChIP-seqGenomic regionsMotif inferenceTranscription factorsTargeting motifTranscription factor binding regionsChIP-seq datasetsNon-specific interactionsC-scoreDNA motifsBinding regionMotifTranscriptionTF signalingAccurate inferenceStronger signalSignalDNARegionTargetInteraction
2023
Variants in JAZF1 are associated with asthma, type 2 diabetes, and height in the United Kingdom biobank population
DeWan A, Cahill M, Cornejo-Sanchez D, Li Y, Dong Z, Fabiha T, Sun H, Wang G, Leal S. Variants in JAZF1 are associated with asthma, type 2 diabetes, and height in the United Kingdom biobank population. Frontiers In Genetics 2023, 14: 1129389. PMID: 37377600, PMCID: PMC10291233, DOI: 10.3389/fgene.2023.1129389.Peer-Reviewed Original ResearchComplex traitsGenome-wide significant variantsFine-mapping analysisGenomic regionsMajor genetic componentAssociation analysisSusceptibility variantsGenetic componentSignificant variantsGenetic variantsSuggestive associationTraitsPhenotypeVariantsBiobank dataGenesNon-overlapping regionsRegionJAZF1Univariate association analysisType 2 diabetesHigh-throughput functional analysis of autism genes in zebrafish identifies convergence in dopaminergic and neuroimmune pathways
Mendes H, Neelakantan U, Liu Y, Fitzpatrick S, Chen T, Wu W, Pruitt A, Jin D, Jamadagni P, Carlson M, Lacadie C, Enriquez K, Li N, Zhao D, Ijaz S, Sakai C, Szi C, Rooney B, Ghosh M, Nwabudike I, Gorodezky A, Chowdhury S, Zaheer M, McLaughlin S, Fernandez J, Wu J, Eilbott J, Vander Wyk B, Rihel J, Papademetris X, Wang Z, Hoffman E. High-throughput functional analysis of autism genes in zebrafish identifies convergence in dopaminergic and neuroimmune pathways. Cell Reports 2023, 42: 112243. PMID: 36933215, PMCID: PMC10277173, DOI: 10.1016/j.celrep.2023.112243.Peer-Reviewed Original ResearchConceptsGene lossFunctional analysisHigh-throughput functional analysisZebrafish mutantsGene discoverySelect mutantsASD genesAutism genesKey pathwaysASD biologyBrain size differencesMutantsGenesSize differencesPathwayGlobal increaseRelevant mechanismsBiologyCentral challengeNeuroimmune dysfunctionRegionFunctionDiscoveryAutism spectrum disorder
2022
The complete sequence of a human genome
Nurk S, Koren S, Rhie A, Rautiainen M, Bzikadze AV, Mikheenko A, Vollger MR, Altemose N, Uralsky L, Gershman A, Aganezov S, Hoyt SJ, Diekhans M, Logsdon GA, Alonge M, Antonarakis SE, Borchers M, Bouffard GG, Brooks SY, Caldas GV, Chen NC, Cheng H, Chin CS, Chow W, de Lima LG, Dishuck PC, Durbin R, Dvorkina T, Fiddes IT, Formenti G, Fulton RS, Fungtammasan A, Garrison E, Grady PGS, Graves-Lindsay TA, Hall IM, Hansen NF, Hartley GA, Haukness M, Howe K, Hunkapiller MW, Jain C, Jain M, Jarvis ED, Kerpedjiev P, Kirsche M, Kolmogorov M, Korlach J, Kremitzki M, Li H, Maduro VV, Marschall T, McCartney AM, McDaniel J, Miller DE, Mullikin JC, Myers EW, Olson ND, Paten B, Peluso P, Pevzner PA, Porubsky D, Potapova T, Rogaev EI, Rosenfeld JA, Salzberg SL, Schneider VA, Sedlazeck FJ, Shafin K, Shew CJ, Shumate A, Sims Y, Smit AFA, Soto DC, Sović I, Storer JM, Streets A, Sullivan BA, Thibaud-Nissen F, Torrance J, Wagner J, Walenz BP, Wenger A, Wood JMD, Xiao C, Yan SM, Young AC, Zarate S, Surti U, McCoy RC, Dennis MY, Alexandrov IA, Gerton JL, O’Neill R, Timp W, Zook JM, Schatz MC, Eichler EE, Miga KH, Phillippy AM. The complete sequence of a human genome. Science 2022, 376: 44-53. PMID: 35357919, PMCID: PMC9186530, DOI: 10.1126/science.abj6987.Peer-Reviewed Original ResearchConceptsHuman genomeRecent segmental duplicationsHuman reference genomeProtein codingSegmental duplicationsGapless assemblyHeterochromatic regionsReference genomeGene predictionSatellite arraysComplete sequenceGenomeAcrocentric chromosomesPair sequenceBase pairsShort armFunctional studiesChromosomesSequenceComplex regionTelomeresDuplicationRegionAssemblyConsortium
2021
SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits
Zhang Y, Lu Q, Ye Y, Huang K, Liu W, Wu Y, Zhong X, Li B, Yu Z, Travers BG, Werling DM, Li JJ, Zhao H. SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits. Genome Biology 2021, 22: 262. PMID: 34493297, PMCID: PMC8422619, DOI: 10.1186/s13059-021-02478-w.Peer-Reviewed Original ResearchConceptsLocal genetic correlationsComplex traitsGenetic correlationsGenomic regionsLocal genetic correlation analysisGenome-wide association studiesLocal genomic regionsSpecific genomic regionsGenetic correlation analysisDistinct genetic signaturesGenetic similarityGenetic signaturesAssociation studiesTraitsSample overlapStatistical frameworkSummary statisticsDisequilibriumRegionAccurate estimationSimilarityGenome-wide association analyses of post-traumatic stress disorder and its symptom subdomains in the Million Veteran Program
Stein MB, Levey DF, Cheng Z, Wendt FR, Harrington K, Pathak GA, Cho K, Quaden R, Radhakrishnan K, Girgenti MJ, Ho YA, Posner D, Aslan M, Duman RS, Zhao H, Polimanti R, Concato J, Gelernter J. Genome-wide association analyses of post-traumatic stress disorder and its symptom subdomains in the Million Veteran Program. Nature Genetics 2021, 53: 174-184. PMID: 33510476, PMCID: PMC7972521, DOI: 10.1038/s41588-020-00767-x.Peer-Reviewed Original ResearchConceptsGenome-wide association analysisAssociation analysisMillion Veteran ProgramGenomic structural equation modelingSignificant lociGenetic varianceGene expressionDrug repositioning candidatesBiological coherenceVeteran ProgramMultiple testing correctionSymptom phenotypeLociRepositioning candidatesAfrican ancestryHeritabilityPhenotypeAncestryExpressionPTSD symptom factorsRegionSubdomainsEnrichment
2020
Coding functions of “noncoding” RNAs
Wei LH, Guo JU. Coding functions of “noncoding” RNAs. Science 2020, 367: 1074-1075. PMID: 32139529, DOI: 10.1126/science.aba6117.Commentaries, Editorials and LettersConceptsRNA regionsProtein-coding functionProtein-coding sequencesDistinct biological processesRNA sequencing studiesLong noncoding RNAPervasive transcriptionFunctional peptidesPervasive translationHuman genomeNoncoding RNAsTranslation eventsBiological processesSequencing studiesCell growthRNATranscriptomeGenomeTranscriptionLncRNAsPeptidesMicroproteinsTranslationSubsequent studiesRegion
2018
S-Palmitoylation Sorts Membrane Cargo for Anterograde Transport in the Golgi
Ernst AM, Syed SA, Zaki O, Bottanelli F, Zheng H, Hacke M, Xi Z, Rivera-Molina F, Graham M, Rebane AA, Björkholm P, Baddeley D, Toomre D, Pincet F, Rothman JE. S-Palmitoylation Sorts Membrane Cargo for Anterograde Transport in the Golgi. Developmental Cell 2018, 47: 479-493.e7. PMID: 30458139, PMCID: PMC6251505, DOI: 10.1016/j.devcel.2018.10.024.Peer-Reviewed Original ResearchConceptsS-palmitoylationAnterograde cargoAnterograde signalMembrane cargoCargo selectionTransmembrane domainMembrane proteinsGolgi membranesGolgiSpecific signalsMembrane interfaceModel systemCargoProteinRate of transportAnterograde transportVesiclesCisternaeCurved regionsMembraneTransportRegionSignalsDomainFluorescence
2017
The repeat region of cortactin is intrinsically disordered in solution
Li X, Tao Y, Murphy JW, Scherer AN, Lam TT, Marshall AG, Koleske AJ, Boggon TJ. The repeat region of cortactin is intrinsically disordered in solution. Scientific Reports 2017, 7: 16696. PMID: 29196701, PMCID: PMC5711941, DOI: 10.1038/s41598-017-16959-1.Peer-Reviewed Original ResearchConceptsCortactin repeatsRepeat regionActin filamentsHydrogen-deuterium exchange mass spectrometryAdjacent helical regionsMulti-domain proteinsExchange mass spectrometryExtensive biophysical analysisCircular dichroismHydrophobic core regionSmall-angle X-ray scatteringBiophysical analysisHelical regionCortactinRepeatsSimilar copiesUnfolded peptidesProteinMotifSize exclusion chromatographyMass spectrometryFilamentsExclusion chromatographyX-ray scatteringRegion
2016
Plasma treatment effect on angiogenesis in wound healing process evaluated in vivo using angiographic optical coherence tomography
Kim D, Park T, Jang S, You S, Oh W. Plasma treatment effect on angiogenesis in wound healing process evaluated in vivo using angiographic optical coherence tomography. Applied Physics Letters 2016, 109: 233701. DOI: 10.1063/1.4967375.Peer-Reviewed Original ResearchPlasma treatment effectAngiographic optical coherence tomographyAtmospheric pressure plasmaNon-thermal atmospheric pressure plasmaNon-thermal plasmaPressure plasmaOptical coherence tomographyPlasma treatmentCoherence tomographyPlasmaPlasma evaluationMouse earVascular area densityPhysicsDensityArea densityEarly daysImagingRegionRNA G-quadruplexes are globally unfolded in eukaryotic cells and depleted in bacteria
Guo J, Bartel D. RNA G-quadruplexes are globally unfolded in eukaryotic cells and depleted in bacteria. Science 2016, 353: aaf5371-aaf5371. PMID: 27708011, PMCID: PMC5367264, DOI: 10.1126/science.aaf5371.Peer-Reviewed Original ResearchConceptsRNA G-quadruplexesEukaryotic cellsG-quadruplexStable four-stranded structuresG-quadruplexes in vitroG-quadruplex-forming sequencesPosttranscriptional gene regulationG-quadruplex-forming regionsFour-stranded structuresBacterial transcriptomesRNA regionsGene regulationEscherichia coliImpaired translationRNABacteriaCellsIn vitroEukaryotesTranscriptomeSequenceMachineryRegionRegulation
2014
Putting together structures of epidermal growth factor receptors
Bessman NJ, Freed DM, Lemmon MA. Putting together structures of epidermal growth factor receptors. Current Opinion In Structural Biology 2014, 29: 95-101. PMID: 25460273, PMCID: PMC4268130, DOI: 10.1016/j.sbi.2014.10.002.Peer-Reviewed Original ResearchConceptsEpidermal growth factor receptorGrowth factor receptorIntact epidermal growth factor receptorChemical biology methodsNumerous crystal structuresFactor receptorTyrosine kinase domainVariety of inhibitorsKinase domainExtracellular regionMembrane environmentIntracellular regionBiology methodsIntact receptorReceptorsCancer therapyNext challengeCrystal structureMembraneActivationRegionInhibitorsDomain
2011
The RabGAP Proteins Gyp5p and Gyl1p Recruit the BAR Domain Protein Rvs167p for Polarized Exocytosis
Prigent M, Boy‐Marcotte E, Chesneau L, Gibson K, Dupré‐Crochet S, Tisserand H, Verbavatz J, Cuif M. The RabGAP Proteins Gyp5p and Gyl1p Recruit the BAR Domain Protein Rvs167p for Polarized Exocytosis. Traffic 2011, 12: 1084-1097. PMID: 21554509, DOI: 10.1111/j.1600-0854.2011.01218.x.Peer-Reviewed Original ResearchMeSH KeywordsExocytosisGlucan Endo-1,3-beta-D-GlucosidaseGTPase-Activating ProteinsMicrofilament ProteinsMutationNerve Tissue ProteinsProlineProtein BindingProtein Interaction Domains and Motifsrab GTP-Binding ProteinsSaccharomyces cerevisiaeSaccharomyces cerevisiae ProteinsSecretory Vesiclessrc Homology DomainsPropensity score‐based nonparametric test revealing genetic variants underlying bipolar disorder
Jiang Y, Zhang H. Propensity score‐based nonparametric test revealing genetic variants underlying bipolar disorder. Genetic Epidemiology 2011, 35: 125-132. PMID: 21254220, PMCID: PMC3077545, DOI: 10.1002/gepi.20558.Peer-Reviewed Original ResearchConceptsSingle nucleotide polymorphismsGenetic variantsWellcome Trust Case Control ConsortiumRPGRIP1L geneGenetic studiesAssociation analysisHaplotype blocksChromosome 16Nucleotide polymorphismsComplex diseasesGenesComplex disorderStrong signalUnreported regionsVariantsImportant roleStrong evidencePolymorphismBipolar disorderRegion
2010
Variants in several genomic regions associated with asperger disorder
Salyakina D, Ma D, Jaworski J, Konidari I, Whitehead P, Henson R, Martinez D, Robinson J, Sacharow S, Wright H, Abramson R, Gilbert J, Cuccaro M, Pericak‐Vance M. Variants in several genomic regions associated with asperger disorder. Autism Research 2010, 3: 303-310. PMID: 21182207, PMCID: PMC4435556, DOI: 10.1002/aur.158.Peer-Reviewed Original ResearchConceptsASP familiesWide association studyGenetic risk factorsGenomic regionsChromosomal regionsAssociation studiesAssociation resultsLinkage regionNovel regionLinkage areasGenetic heterogeneityCommon variationFamilyAssociation regionsDiscovery dataAutism spectrum disorderAsperger's disorderPhenotypeHomogenous subsetsRegionVariantsA genome-wide association study identifies pancreatic cancer susceptibility loci on chromosomes 13q22.1, 1q32.1 and 5p15.33
Petersen GM, Amundadottir L, Fuchs CS, Kraft P, Stolzenberg-Solomon RZ, Jacobs KB, Arslan AA, Bueno-de-Mesquita HB, Gallinger S, Gross M, Helzlsouer K, Holly EA, Jacobs EJ, Klein AP, LaCroix A, Li D, Mandelson MT, Olson SH, Risch HA, Zheng W, Albanes D, Bamlet WR, Berg CD, Boutron-Ruault MC, Buring JE, Bracci PM, Canzian F, Clipp S, Cotterchio M, de Andrade M, Duell EJ, Gaziano JM, Giovannucci EL, Goggins M, Hallmans G, Hankinson SE, Hassan M, Howard B, Hunter DJ, Hutchinson A, Jenab M, Kaaks R, Kooperberg C, Krogh V, Kurtz RC, Lynch SM, McWilliams RR, Mendelsohn JB, Michaud DS, Parikh H, Patel AV, Peeters PH, Rajkovic A, Riboli E, Rodriguez L, Seminara D, Shu XO, Thomas G, Tjønneland A, Tobias GS, Trichopoulos D, Van Den Eeden SK, Virtamo J, Wactawski-Wende J, Wang Z, Wolpin BM, Yu H, Yu K, Zeleniuch-Jacquotte A, Fraumeni JF, Hoover RN, Hartge P, Chanock SJ. A genome-wide association study identifies pancreatic cancer susceptibility loci on chromosomes 13q22.1, 1q32.1 and 5p15.33. Nature Genetics 2010, 42: 224-228. PMID: 20101243, PMCID: PMC2853179, DOI: 10.1038/ng.522.Peer-Reviewed Original Research
2008
Quantitative Trait Locus Analysis Identifies Rat Genomic Regions Related to Amphetamine-Induced Locomotion and Gαi3 Levels in Nucleus Accumbens
Potenza MN, Brodkin ES, Yang BZ, Birnbaum SG, Nestler EJ, Gelernter J. Quantitative Trait Locus Analysis Identifies Rat Genomic Regions Related to Amphetamine-Induced Locomotion and Gαi3 Levels in Nucleus Accumbens. Neuropsychopharmacology 2008, 33: 2735-2746. PMID: 18216777, PMCID: PMC2818767, DOI: 10.1038/sj.npp.1301667.Peer-Reviewed Original ResearchConceptsQuantitative trait lociRobust quantitative trait lociGenomic regionsChromosome 2Quantitative trait locus (QTL) analysisG protein levelsCommon genetic mechanismQTL patternsTrait lociRat genomic regionsGenetic mechanismsChromosome 3Locus analysisChromosome 13Genetic factorsGαi3LociAmphetamine-induced locomotionBetter understandingLocomotionRegionAnimal modelsSignificant implicationsLevelsNovelty-induced locomotion
2007
The intricate world of riboswitches
Coppins RL, Hall KB, Groisman EA. The intricate world of riboswitches. Current Opinion In Microbiology 2007, 10: 176-181. PMID: 17383225, PMCID: PMC1894890, DOI: 10.1016/j.mib.2007.03.006.Peer-Reviewed Original Research
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