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

人类疾病源于遗传和环境之间复杂的相互作用，随着我们环境的变化，曾经有益的遗传变异现在可能导致疾病。西方高糖饮食导致了全球糖尿病和肥胖等代谢疾病的激增，但我们对这些疾病中遗传-饮食相互作用的理解仍然有限。在生物体中研究这些相互作用 由于缺乏中间分子表型（如转录组和代谢组数据），因此水平具有挑战性。为了解决这个问题，我们的项目旨在确定与高糖饮食诱导的果蝇疾病表型相关的遗传变异，最终构建一个全面的基因-转录本-代谢表型 网络利用远交和近交果蝇种群和整合分子表型，我们试图了解这些变化如何影响复杂疾病性状的易感性。我们的初步数据表明，高糖饮食破坏了代谢平衡，揭示了在正常条件下难以检测到的神秘遗传变异。为了实现我们的目标，我们首先着手探索通过高糖饮食诱导果蝇种群的疾病表型的表型。这一奋进使我们能够评估表型变异和遗传力，并确定代谢，发育和线粒体性状之间的相关性。接下来，我们深入研究基因组，采用精细的作图策略来揭示高糖饮食压力下的神秘遗传变异以及基因-饮食相互作用。 不同的糖浓度最后，我们集中破译代谢组和转录组，采用系统生物学方法，将遗传变异与代谢组和转录组数据相结合。这种整合将揭示基因表达，代谢产物谱和饮食诱导的代谢紊乱的背景下复杂的生物体性状之间的关系。总的来说，我们希望揭示新的基因-饮食相互作用和以前难以捉摸的途径成分，为研究代谢疾病提供新的多组学策略。这项研究的成功执行有望推动精准医学，增强代谢健康，并加深我们对遗传学基础的核心知识。 复杂的代谢紊乱

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.