Gene Coexpression Network Regulating Repetitive Behavior under Nutritional Change.

营养变化下调节重复行为的基因共表达网络。

• 批准号: 10737180
• 负责人: Motoko Iwashita
• 金额: $ 40.35万
• 依托单位: UNIVERSITY OF HAWAII AT MANOA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-25 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10737180
• 关键词: Address; Algorithmic Analysis; Algorithms; Animal Genetics; Animal Model; Animals; Behavior; Behavioral; Biological Models; Blood Glucose; Brain; CRISPR/Cas technology; Carbohydrates; Chronic stress; Complex; Corpus striatum structure; Data; Data Analyses; Data Set; Development; Dietary Intervention; Dimensions; Disease; Disease model; Environment; Environmental Impact; Exhibits; Experimental Models; Exposure to; Fishes; Foundations; Future; Gene Expression; Genes; Genetic; Genetic Variation; Genome; Genomics; Goals; Heterogeneity; Human; Immediate-Early Genes; Inbred Mouse; Involuntary Movements; Ketone Bodies; Ketoses; Ketosis; Knowledge; Mammals; Maps; Mental disorders; Metabolic; Methods; Mexican; Modeling; Modernization; Molecular; Morphology; Mus; Nervous System Physiology; Neural Pathways; Neurons; Nutritional; Output; Pathway interactions; Patients; Periodicity; Physiological; Population; Population Heterogeneity; Property; Quantitative Trait Loci; Recording of previous events; Reducing diet; Regulator Genes; Research; Sensory; Serum; Signal Transduction; Social Behavior; Standard Model; Stereotyping; Stress; Surface; System; Techniques; Technology; Testing; Variant; Visualization; Wild Animals; behavior prediction; distraction; effective therapy; individual variation; innovation; insight; ketogenic diet; mating behavior; metabolomics; model organism; multidimensional data; neural; neuromechanism; null mutation; nutrition; open data; programs; repetitive behavior; response; social learning; success; teleost fish; transcriptome; transcriptomics

Stereotypic repetitive behaviors, which are thought to be an obstacle to complex task execution, including social behaviors and learning, are observed in mammalians and fish. Animals exposed to stress- associated environment frequently exhibit repetitive behaviors. Chronic stress is known to change the neurocircuit property and increase the blood glucose level. Accordingly, the low-carbohydrate ketogenic diet reduced repetitive behaviors in disorder model animals. However, there is a significant knowledge gap regarding how repetitive behaviors are particularly selected among other voluntary behaviors; is it based on neurocircuit and/or metabolic changes? Whether natural genetic variations promote an increase or decrease in repetitive behavior level is also poorly understood. Consequently, our central hypothesis is that, in an experimental system relevant to typical heterogeneity, nutritional ketosis reduces repetitive behavior by modifying the known dopaminergic and GABAergic signaling that choose the behavior modules (e.g., repetitive behavior, mating behavior, etc.). To provide the foundation to test this hypothesis, this project’s main objective is to identify the gene coexpression regulatory network and its hub genes that reduce repetitive behavior under ketosis. The Mexican teleost fish Astyanax mexicanus will be strategically chosen as an experimental model, which consists of cave-dwelling (cavefish) and surface-dwelling fish (surface fish). The cavefish display asocial behaviors and exhibit 1,839 of the shared directional gene expression changes seen in human disorders related to repetitive behavior. This project’s rationale is that the genetic and environmental impacts on repetitive behavior with the naturalistic heterogeneity are easy to study on our animal platform, yielding the basic knowledge for neuronal and cellular responses to ketosis associated with repetitive behavior. The research proposed in this application is innovative because it will use naturally heterogeneous populations whose genetic and behavioral conditions are similar to patients with psychiatric disorders. This project will also integrate omics data with the aid of an emerging clustering algorithm, topological data analysis (TDA). TDA is robust for noisy and sparse datasets while retaining individual variations that may be lost using typical dimension-reduction algorithms. This study is significant because it promises to provide the first insights into the genetic basis of the nutritional plasticity of repetitive behavior, which will foremost contribute to the future understanding of the neural and cellular responses governed by nutritional interventions. Furthermore, the knowledge derived from this R01 project will be applied to the murine system in the future to test if it is translational. The success of this test will support a conserved pathway among heterogeneous populations in our fish, and between fish and mammals, opening the door to human application of this knowledge.

刻板的重复行为被认为是执行复杂任务的障碍，