Integration of Experience-Induced Gene Expression and Circuit Functions
Integration of Experience-Induced Gene Expression and Circuit Functions
批准号:
9897551
负责人:
MEYER B. JACKSON
金额:
$40.37万
依托单位国家:
美国
项目类别:
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-04-01 至 2023-03-31
关键词:
AddressAffectAmygdaloid structureAttentionBasic ScienceBehaviorBehavioralBehavioral ParadigmBinding ProteinsBrainBrain regionCellsChromatinCognitionComplexComputer ModelsComputing MethodologiesDataDiseaseEnvironmentFeedbackFrequenciesGene ExpressionGene Expression ProfilingGenesGeneticGenetic RecombinationHippocampus (Brain)HybridsImageInformation NetworksInterneuron functionInterneuronsLeadLinkMachine LearningMediatingMethodologyMolecularMolecular ProbesNeuronal PlasticityNeuronsNeurosciencesParvalbuminsPathway AnalysisPharmacologyPhysiologyPlayPopulationPrefrontal CortexPropertyPyramidal CellsRabies virusRegulationRegulator GenesResearch PersonnelRoleRunningSensoryShapesShort-Term MemorySynapsesSystemSystems BiologyTechniquesTechnologyTestingUniversitiesWisconsinWorkelectrical propertyenvironmental enrichment for laboratory animalsexperienceexperimental studyfrontal lobeimmunoreactivityinnovationinterdisciplinary approachneural circuitneural networkneurodevelopmentneuronal circuitryneuronal excitabilityneurophysiologynovelnovel strategiespatch clamprelating to nervous systemresponsesensortranscriptometranslatomevoltage
中文摘要
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英文摘要
Multi-PI: Xinyu Zhao, Meyer Jackson, University of Wisconsin-Madison.
Title: Integration of Experience-Induced Gene Expression and Circuit Functions
Understanding the complex relationships between cells, gene networks, neural circuits, and behavior requires
techniques that can probe the molecular makeup of distinct types of neurons, evaluate their properties, and test
their roles in higher level functions. Genes expressed within specific populations of neurons determine their
electrical properties and these properties together with their synaptic connectivity collectively shape the electrical
activity of neural circuits. This is especially well illustrated by a population of neurons defined by expression of
the Ca2+ binding protein parvalbumin (PV). PV interneurons (PVIs) are sparsely distributed, fast-spiking cells that
provide feedback and feedforward inhibition to principal neurons. One of the most well-defined network functions
of PVIs is in the coordination of neuronal networks and their associated oscillations. PVIs entrain cortical
networks to drive gamma oscillations (30-100 Hz) and control their frequency and strength. PVI-mediated
gamma oscillations are known to have important roles in sensory processing, attention, working memory, and
cognition. However, the gene networks that control PVI functions and their impact on gamma oscillations remain
unclear. PVIs are readily modified by environmental conditions and experience. PV immunoreactivity increases
after exploration of a novel environment, rearing under environmental enrichment (EE), and voluntary running
(VR). These changes occur in brain regions associated with cognition, including hippocampus, prefrontal cortex,
and amygdala. The molecular mechanisms underlying PVI changes during behavioral adaptation remain
unknown. Although studies suggest that behavioral adaptions affect gamma oscillations, a role for PVIs in the
link between behavioral adaption and gamma oscillations has not been established. This application takes a
multidisciplinary approach to address the fundamental question of how PVIs contribute to behavioral adaptations.
Our overarching hypothesis is that changes in gene expression that modify the cellular properties of PVIs will
alter network oscillations, enabling PVIs to serve as a critical hub in behavioral adaptations. We will determine
whether behavioral adaptation mobilizes networks of genes in PVIs, and assess the contributions of these
networks to PVI physiology and gamma oscillations. This project combines the unique expertise of co-PIs Zhao
(genetic regulation of neurodevelopment) and Jackson (neurophysiology and neural circuits) and co-Is Roy
(system biology and machine learning) and Rosenberg (computational and system neuroscience). By integrating
experimental data with gene network analysis and computational modeling of multicellular networks, this work
will reveal how changes in molecular/cellular properties impact the emergent properties of neural circuits.
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