Comparative Functional Genomics of Drosophila Obesity

描述（由申请人提供）： 体脂维持的广泛变化有一个复杂的原因网络，包括细胞代谢、胰岛素信号、内分泌调节和行为。本项目旨在应用系统生物学方法，通过将这些多层次的数据串在一起，来模拟果蝇脂肪稳态的变化。我们利用基因组序列信息设计准确的检测方法来量化相关途径中90个候选基因的基因表达水平。这些测量将在一组全球分布的282个品系中进行，以通过将其与转录本丰度和相关代谢途径中酶活性的变化相关来量化脂质储存中自然变异的遗传基础。将测量的其他代谢和内分泌表型包括体重和组成、脂质分布、蜕皮激素水平、代谢率和做功能力，通过最大自由飞行功率输出进行量化。在第一个目标中，将通过贝叶斯方法拟合代谢和生理模型，以确定这些变量之间的关联，重点是将基因表达水平的变化与脂肪储存表型相关联。第二个目的是通过定量基因表达和脂肪储存表型的变化来确定脂肪稳态调节网络的进化保守性，所述脂肪储存表型在一组D. simulans杂交，以及另外10个基因组正在测序的果蝇物种。将类似的建模工作应用到第一个目标中的那些工作中，将提供对脂肪调节进化稳定性的根本原因的理解。作为第三个目标，遗传和营养扰动将被应用于进一步测试脂质储存的遗传途径的鲁棒性。我们将进行筛选以发现影响果蝇中脂质储存的P元件衍生缺陷，并且FLP-FRT方法将用于产生10-15个关键基因的靶向缺陷。将对所有这些突变体的代谢表型进行评分，以确定这些遗传病变如何与已知的候选基因相互作用。代谢表型将在四种饮食制度饲养的苍蝇中测量。通过我们的建模工作，这些数据将提供一个完整的，定量的图片如何在脂质储存的变化是由基础基因的表达变化介导的。我们预计，这些结果将通过确定哪些特定的基因和途径与脂肪储存稳态的变化最相关，并通过开发系统生物学方法来从细胞，内分泌和整个生物体属性的数据中进行推断，从而与公共卫生相关

DESCRIPTION (provided by applicant): The wide variation in the maintenance of body fat has a complex network of causes spanning cellular metabolism, insulin signaling, endocrine regulation, and behavior. This project aims to apply a systems biology approach to model variation in fat homeostasis in Drosophila melanogaster by threading together data at these multiple levels. We make use of the genome sequence information to design accurate assays to quantify gene expression levels in 90 candidate genes in relevant pathways. These measurements will be made in a set of 282 lines of globally distributed origin to quantify the genetic basis for natural variation in lipid storage by relating it to variation in transcript abundance and activities of enzymes in relevant metabolic pathways. Additional metabolic and endocrine phenotypes that will be measured include body mass and composition, lipid profiles, ecdysteroid levels, metabolic rate, and work capacity, quantified by maximal free flight power output. In the first aim, metabolic and physiological models will be fitted by Bayesian methods to identify associations among these variables, with emphasis on relating variation in gene expression levels to the fat storage phenotypes. The second aim is to determine the evolutionary conservation of the fat homeostasis regulatory network by quantifying gene expression and variation in fat storage phenotypes in lines selected for starvation resistance, in a set of D. simulans crosses, and in the 10 additional Drosophila species whose genomes are being sequenced. Applying similar modeling efforts to those performed in the first aim will provide an understanding of the underlying causes for evolutionary stability of fat regulation. As a third aim, genetic and nutritional perturbations will be applied to further test the robustness of the genetic pathways underlying lipid storage. We will perform a screen to find P-element derived deficiencies that impact lipid storage in flies and the FLP-FRT method will be used to generate targeted deficiencies of 10-15 key genes. All these mutants will be scored for metabolic phenotypes to determine how these genetic lesions interact with the known candidate genes. Metabolic phenotypes will be measured in flies reared on four dietary regimes. Through our modeling efforts, these data will provide an integrated, quantitative picture of how variation in lipid storage is mediated by variation in expression of the underlying genes. We anticipate that these results will have relevance to public health by identifying which particular genes and pathways are most relevant to variation in fat storage homeostasis, and by developing a systems biology approach to make inferences from data that span cellular, endocrine, and whole-organism attributes