Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life.

Protein synthesis is the most energy-consuming process in a proliferating cell, and understanding what controls protein abundances represents a key question in biology and biotechnology. We quantified absolute abundances of 5,354 mRNAs and 2,198 proteins in Saccharomyces cerevisiae under ten environmental conditions and protein turnover for 1,384 proteins under a reference condition. The overall correlation between mRNA and protein abundances across all conditions was low (0.46), but for differentially expressed proteins (n = 202), the median mRNA-protein correlation was 0.88. We used these data to model translation efficiencies and found that they vary more than 400-fold between genes. Non-linear regression analysis detected that mRNA abundance and translation elongation were the dominant factors controlling protein synthesis, explaining 61% and 15% of its variance. Metabolic flux balance analysis further showed that only mitochondrial fluxes were positively associated with changes at the transcript level. The present dataset represents a crucial expansion to the current resources for future studies on yeast physiology.

Several common oncogenic pathways have been implicated in the emergence of renowned metabolic features in cancer, which in turn are deemed essential for cancer proliferation and survival. However, the extent to which different cancers coordinate their metabolism to meet these requirements is largely unexplored. Here we show that even in the heterogeneity of metabolic regulation a distinct signature encompassed most cancers. On the other hand, clear cell renal cell carcinoma (ccRCC) strongly deviated in terms of metabolic gene expression changes, showing widespread down-regulation. We observed a metabolic shift that associates differential regulation of enzymes in one-carbon metabolism with high tumor stage and poor clinical outcome. A significant yet limited set of metabolic genes that explained the partial divergence of ccRCC metabolism correlated with loss of von Hippel-Lindau tumor suppressor (VHL) and a potential activation of signal transducer and activator of transcription 1. Further network-dependent analyses revealed unique defects in nucleotide, one-carbon, and glycerophospholipid metabolism at the transcript and protein level, which contrasts findings in other tumors. Notably, this behavior is recapitulated by recurrent loss of heterozygosity in multiple metabolic genes adjacent to VHL. This study therefore shows how loss of heterozygosity, hallmarked by VHL deletion in ccRCC, may uniquely shape tumor metabolism.cancer metabolism | systems biology | genome-scale metabolic modeling | renal cancer T here is now widespread consensus that diversion of metabolism is among the most distinguished cancer phenotypes, and it is often postulated to characterize virtually all forms of cancer (1, 2). Indeed, many common oncogenic signaling pathways have been implicated in the emergence of specific metabolic features in cancer cells that have been associated with both survival and sustained abnormal proliferation rate (2-5). However, only a fraction of the metabolic reactions potentially occurring in a generic human cell are typically involved in such processes. Only recently a systemic study using transcriptional regulation has attempted to rule out the possibility that other metabolic processes in the network may achieve equal importance in cancer cells (6), and the idea that all cancer cells display a unique metabolic phenotype has spurred disputes that mainly highlighted a lack of comprehensive evidence (7). Taken together, we contend that only a systems perspective may help to elucidate the extent to which different cancer cells coordinate their metabolic activity.In this context, systems biology approaches have been demonstrated to lead to the identification of altered metabolic processes in disease development with regard to those disorders that are driven or accompanied by metabolic reprogramming, including cancer (8-11). To this end, the reconstruction of genome-scale metabolic models (GEMs) is instrumental to knit high-throughput data into the metabolic network topology. Such integrative and network-depend...