The metabolic phenotype of an organism depends on a complex regulatory network, which integrates the plethora of intrinsic and external information and prioritizes the flow of nutrients accordingly. Given the rise of metabolic disorders including obesity, a detailed understanding of this regulatory network is in urgent need. Yet, our level of understanding is far from completeness and complicated by the discovery of additional layers in metabolic regulation, such as the impact of the microbial community present in the gut on the hosts’ energy storage levels. Here, we investigate the interplay between genome variation, diet and the gut microbiome in the shaping of a metabolic phenotype. For this purpose, we reared a set of fully sequenced wild type Drosophila melanogaster flies under basal and nutritionally challenged conditions and performed metabolic and microbiome profiling experiments. Our results introduce the fly as a model system to investigate the impact of genome variation on the metabolic response to diet alterations and reveal candidate single nucleotide polymorphisms associated with different metabolic traits, as well as metabolite-metabolite and metabolite-microbe correlations. Intriguingly, the dietary changes affected the microbiome composition less than anticipated. These results challenge the current view of a rapidly changing microbiome in response to environmental fluctuations.
Organisms depend on a highly connected and regulated network of biochemical reactions fueling life sustaining and growth promoting functions. While details of this metabolic network are well established, knowledge of the superordinate regulatory design principles is limited. Here, we investigated by iterative wet lab and modeling experiments the resource allocation process during the larval development of Drosophila melanogaster. We chose this system, as survival of the animals depends on the successful allocation of their available resources to the conflicting processes of growth and storage metabolite deposition. First, we generated “FlySilico”, a curated metabolic network of Drosophila, and performed time-resolved growth and metabolite measurements with larvae raised on a holidic diet. Subsequently, we performed flux balance analysis simulations and tested the predictive power of our model by simulating the impact of diet alterations on growth and metabolism. Our predictions correctly identified the essential amino acids as growth limiting factor, and metabolic flux differences in agreement with our experimental data. Thus, we present a framework to study important questions of resource allocation in a multicellular organism including process priorization and optimality principles.
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