grant

Gene regulation and social relationships across the life course in a nonhuman primate model

Organization DUKE UNIVERSITYLocation DURHAM, UNITED STATESPosted 30 Sept 2021Deadline 31 May 2027
NIHUS FederalResearch GrantFY202521+ years oldAddressAdultAdult HumanAffectAgingAleuritesAllelesAllelomorphsAnimal ModelAnimal Models and Related StudiesAnimalsBaboonsBacterial InfectionsBiologicalBiological MarkersBirthBloodBlood Reticuloendothelial SystemBlood SampleBlood specimenBody TissuesBrainBrain Nervous SystemCancersCardiac DiseasesCardiac DisordersCause of DeathCellular Immune FunctionChromatinClinicalComplexCouplingDNA-Binding ProteinsDataDietEcologic SystemsEcological SystemsEcosystemEncephalonEnvironmentEnvironmental FactorEnvironmental Risk FactorExerciseGene Action RegulationGene ExpressionGene Expression MonitoringGene Expression Pattern AnalysisGene Expression ProfilingGene Expression RegulationGene RegulationGene Regulation ProcessGene x Environment InteractionGenerationsGenesGeneticGenetic DiversityGenetic VariationGenomicsGenotypeGlucocorticoidsGoalsGrainGxE interactionHealthHeart DiseasesHistoryHumanImmune systemImmunomodulationIndividualInfantInfectionInnate Immune ResponseJointsKenyaLifeLife CycleLife Cycle StagesLigandsLinkLonelinessLongitudinal StudiesMalignant NeoplasmsMalignant TumorMapsMeasuresMediatingModelingModern ManMolecularMolecular FingerprintingMolecular ProfilingMorbidityMorbidity - disease rateNHP modelsPapioParturitionPathway interactionsPeripheralPhenotypePhysiologicPhysiologicalPopulationPredispositionRNA SeqRNA sequencingRNAseqRecording of previous eventsResearch DesignRiskRisk BehaviorsRisky BehaviorRoleSamplingSignal PathwaySilkSkinSocial BehaviorSocial EnvironmentSocial isolationSocial statusSocial supportStudy SubjectStudy TypeSurvey InstrumentSurveysSusceptibilityTestingTimeTissuesTranscript Expression AnalysesTranscript Expression AnalysisTungUnited StatesVariantVariationViralViral DiseasesVirus DiseasesWorkadulthoodaging associated diseaseaging associated disordersaging related diseaseaging related disordersanalyze gene expressionat risk behaviorbacteria infectionbacterial diseasebio-markersbiologicbiologic markerbiomarkercohortdeath riskdietsdisease associated with agingdisease of agingdisease riskdisorder of agingdisorder riskdisorders associated with agingdisorders related to agingenvironment effect on geneenvironmental riskexperiencefunctional genomicsgene environment interactiongene expression analysisgene expression assaygenome wide analysisgenome wide studiesgenome-wide analysisgenome-wide identificationgenomic datagenomic datasetgenomic signatureheart disorderhistorieshuman modelimmune functionimmune modulationimmune regulationimmunologic reactivity controlimmunomodulatoryimmunoregulationimmunoregulatoryinsightlife courselife spanlifespanlonelylong-term studylongitudinal outcome studiesmalignancymodel of animalmodel of humanmolecular profilemolecular signaturemortalitymortality riskneoplasm/cancernonhuman primate modelspathogenpathwayresponsesample collectionsexsocialsocial climatesocial contextsocial integrationsocial positionsocial relationshipssocial rolesocial standingsocial support networksociobehaviorsociobehavioralsocioenvironmentsocioenvironmentalspecimen collectionstudy designtranscriptional profilingtranscriptome sequencingtranscriptomic sequencingviral infectionvirus infectionvirus-induced disease
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Full Description

Project Summary
Social support and social integration are some of the most robust predictors of morbidity and mortality

identified to date. This relationship arises from increased susceptibility to several of the top causes of death in

the United States, including major diseases of aging such as heart disease and cancer. Recent studies

suggest that a signature of social relationships is also detectable in data on gene regulation, highlighting a

potential pathway through which social ties get “under the skin” to influence health. However, despite abundant

evidence that the health effects of social relationships begin early in life, no studies have related the full life

course trajectory of social relationships to data on gene regulation, or used these data to investigate why some

individuals appear more susceptible to social isolation than others.

The goal of this study is to address these gaps by linking fine-grained, longitudinal data on social

relationships to unbiased surveys of the molecular signature of social experience. To do so, we will leverage

an established model for social relationships and health in natural animal populations, the baboons of the

Amboseli ecosystem of Kenya. This population has been the subject of longitudinal study for up to 9

generations, revealing that social isolation predicts shortened lifespan in a manner highly analogous to

humans. We propose to link annual measures of social relationships, from birth through adulthood, with gene

expression and chromatin accessibility data collected both at baseline and following ex vivo challenge with

bacterial and viral mimics. This strategy will allow us to investigate the types of social relationships that matter

most, the timing of their effects on gene regulation, and their relevance for immune function, a primary

contributor to variation in health during aging.

Using these data, we will address three aims. First, we will characterize the gene regulatory signature of

variation in social relationships across the life course. We will investigate the relative roles of early life,

cumulative experience, and social relationships close to the time of biological sample collection, as well as the

relative importance of social relationship quantity versus quality. Second, we will assess whether individuals

vary in their sensitivity to social environments based on genotype, by identifying gene-social relationship

interactions that affect gene expression. Finally, we will test the consequences of social relationship-associated

gene regulation for immune defense and lifespan. In doing so, this work will shed important light on whether

gene regulatory signatures of social relationships are likely to be mechanistically implicated in the link between

social relationships and health, or instead serve as passive biomarkers. Together, our results will provide the

most comprehensive window into the functional genomic signature of social relationships available to date. By

revealing when, how, and for whom social relationships matter most, they will therefore address three

questions of outstanding importance to understanding the role of the social environment in human health.

Grant Number: 5R01AG075914-05
NIH Institute/Center: NIH

Principal Investigator: Susan Alberts

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