Unraveling Gene-Environment Interactions Shaping Metabolism: A Multi-Omics Analysis in Drosophila

点击翻译按钮获取中文摘要

Human diseases arise from complex interactions between genetics and the environment, and as our environment changes, genetic variations that were once beneficial can now contribute to diseases. The Western high-sugar diet has led to a global surge in metabolic diseases like diabetes and obesity, but our comprehension of the genetic-diet interactions in these conditions remains limited. Studying these interactions at the organismal level is challenging due to the absence of intermediate molecular phenotypes like transcriptome and metabolome data. To address this, our project aims to identify genetic variations linked to high-sugar diet-induced disease phenotypes in Drosophila, culminating in the construction of a comprehensive gene-transcript-metabolitephenotype network. Leveraging both outbred and inbred Drosophila populations and integrating molecular phenotypes, we seek to understand how these variations influence susceptibility to complex disease traits. Our preliminary data indicates that a high-sugar diet disrupts metabolic balance, revealing cryptic genetic variations that are hard to detect under normal conditions. To achieve our objectives, we first embark on exploring the phenome by inducing disease phenotypes in Drosophila populations through a high-sugar diet. This endeavor allows us to evaluate phenotypic variation and heritability, and identify correlations among metabolic, developmental, and mitochondrial traits. Next, we delve into unraveling the genome, employing a fine mapping strategy to uncover cryptic genetic variations under high-sugar dietary stress and gene-diet interactions across different sugar concentrations. Lastly, we center on deciphering the metabolome and transcriptome, employing a systems biology approach that integrates genetic variations with metabolomic and transcriptomic data. This integration will uncover the relationships between gene expression, metabolite profiles, and complex organismal traits in the context of diet-induced metabolic disorders. Overall, we expect to reveal novel gene-diet interactions and previously elusive pathway components, offering the potential to introduce new multi-omic strategies for investigating metabolic diseases. The successful execution of this research promises to propel precision medicine, enhance metabolic well-being, and deepen our core knowledge concerning the genetics underlying complex metabolic disorders.