Emergent behavioral and transcriptional properties of pair bonds
Emergent behavioral and transcriptional properties of pair bonds
批准号:
10750878
负责人:
Liza Eden Brusman
金额:
$4.05万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-08-01 至 2025-07-31
关键词:
AddressAnimalsBackBehaviorBehavioralBioinformaticsBiologicalBrainBrain regionCellsComputational TechniqueComputer AnalysisDataData SetElementsEmotionalExhibitsExperimental DesignsFeedbackGene ExpressionGenetic TranscriptionGoalsGrainHealthHumanIndividualKnowledgeMachine LearningMapsMedialModelingMolecular BiologyNatureNeurobiologyNeurogliaNeuronsNeurosciencesNucleus AccumbensOrganizational ObjectivesOutcomeOxytocinOxytocin ReceptorPair BondPatternPersonal SatisfactionPersonsPlayPopulationPovertyPrefrontal CortexProcessPropertyResearch PersonnelResolutionRoleShapesSocial BehaviorSocial InteractionStructureTechniquesTestingTimeTissuesTrainingWell in selfWorkaffiliative behaviorbehavioral studycareercell typecomplex datadyadic interactionexperienceexperimental studylensmachine learning algorithmmachine learning pipelinememberneuralprairie volepreferencesingle nucleus RNA-sequencingskillssocialsuccesstranscriptome sequencingtranscriptomics
中文摘要
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英文摘要
Project Summary
Pair bonds play critical roles in the human experience and profoundly influence our physical and emotional
health. For a bond to be beneficial and the pair to accomplish shared goals, partners must work together
through reciprocal action where each person acts and the other responds in turn. Despite the dyadic nature of
pair bonds, the vast majority of studies on pair bonding focus on only one member of a pair, leaving us with an
impoverished view of the dynamics between partners that facilitate relationship success. To fill this knowledge
gap, I will use pair bonding prairie voles to examine the behavioral and biological basis of pair bonding in both
partners of bonded pairs. My preliminary data show that in prairie voles, partners organize their affiliative
behavior as bonds mature. In this proposal, I will use computational techniques to examine the discrete
elements that underlie organized intra-pair behavior at two levels of biological organization: behavior and
transcription. In my first Aim, I will leverage machine learning algorithms to decipher the within-animal and
between-animal behavioral sequences that partners exhibit when allowed to interact freely with each other.
This will reveal the behavioral components that contribute to intra-pair behavioral organization. To determine
the neuromolecular basis of this behavior, in my second Aim, I will use single nucleus RNA sequencing
(snRNA-seq) to map the transcriptional landscape of the prairie vole nucleus accumbens (NAc) and medial
prefrontal cortex (mPFC), two brain regions critical for pair bonding. snRNA-seq will enable me to identify
changes to discrete cellular populations upon bonding and will allow me to compare the similarity of
transcriptional landscapes between bonded partners, potentially revealing a role for transcriptional
convergence in the organization of intra-pair behavior. Together, these Aims will provide a new, dual-
individual lens through which we can understand pair bonding and will provide me with training in the
computational analysis of behavior and transcriptomics, invaluable skills for my career as an independent
researcher.
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