Macropinocytosis is a highly conserved endocytic process by which extracellular fluid and its contents are internalized into cells via large, heterogeneous vesicles known as macropinosomes. Oncogenic Ras proteins have been shown to stimulate macropinocytosis but the functional contribution of this uptake mechanism to the transformed phenotype remains unknown1-3. Here we show that Ras-transformed cells utilize macropinocytosis to transport extracellular protein into the cell. The internalized protein undergoes proteolytic degradation, yielding amino acids including glutamine that can enter central carbon metabolism. Accordingly, the dependence of Ras-transformed cells on free extracellular glutamine for growth can be suppressed by the macropinocytic uptake of protein. Consistent with macropinocytosis representing an important route of tumor nutrient uptake, its pharmacological inhibition compromised the growth of Ras-transformed pancreatic tumor xenografts. These results identify macropinocytosis as a mechanism by which cancer cells support their unique metabolic needs and point to the possible exploitation of this process in the design of anti-cancer therapies.

Glucose and amino acids are key nutrients supporting cell growth. Amino acids are imported as monomers, but an alternative route induced by oncogenic KRAS involves uptake of extracellular proteins via macropinocytosis and subsequent lysosomal degradation of these proteins as a source of amino acids. In this study, we examined the metabolism of pancreatic ductal adenocarcinoma (PDAC), a poorly vascularized lethal KRAS-driven malignancy. Metabolomic comparisons of human PDAC and benign adjacent tissue revealed that tumor tissue was low in glucose, upper glycolytic intermediates, creatine phosphate and the amino acids glutamine and serine, two major metabolic substrates. Surprisingly, PDAC accumulated essential amino acids. Such accumulation could arise from extracellular proteins being degraded through macropinocytosis in quantities necessary to meet glutamine requirements, which in turn produces excess of most other amino acids. Consistent with this hypothesis, active macropinocytosis is observed in primary human PDAC specimens. Moreover, in the presence of physiological albumin, we found that cultured murine PDAC cells grow indefinitely in media lacking single essential amino acids, and replicate once in the absence of free amino acids. Growth under these conditions was characterized by simultaneous glutamine depletion and essential amino acid accumulation. Overall, our findings argue that the scavenging of extracellular proteins is an important mode of nutrient uptake in PDAC.

Cellular metabolic fluxes are determined by enzyme activities and metabolite abundances. Biochemical approaches reveal the impact of specific substrates or regulators on enzyme kinetics, but do not capture the extent to which metabolite and enzyme concentrations vary across physiological states, and therefore how cellular reactions are regulated. We measured enzyme and metabolite concentrations and metabolic fluxes across 25 steady-state yeast cultures. We then assessed the extent to which flux can be explained by a Michaelis-Menten relationship between enzyme, substrate, product, and potential regulator concentrations. This revealed three new instances of cross-pathway regulation, which we biochemically verified. These included inhibition of pyruvate kinase by citrate, which accumulated and thereby curtailed glycolytic outflow in nitrogen-limited yeast. Overall, substrate concentrations were the strongest driver of the net rates of cellular metabolic reactions, with metabolite concentrations collectively having more than double the physiological impact of enzymes.

Reference collections of multiple Drosophila lines with accumulating collections of “omics” data have proven especially valuable for the study of population genetics and complex trait genetics. Here we present a description of a resource collection of 84 strains of Drosophila melanogaster whose genome sequences were obtained after 12 generations of full-sib inbreeding. The initial rationale for this resource was to foster development of a systems biology platform for modeling metabolic regulation by the use of natural polymorphisms as perturbations. As reference lines, they are amenable to repeated phenotypic measurements, and already a large collection of metabolic traits have been assayed. Another key feature of these strains is their widespread geographic origin, coming from Beijing, Ithaca, Netherlands, Tasmania, and Zimbabwe. After obtaining 12.5× coverage of paired-end Illumina sequence reads, SNP and indel calls were made with the GATK platform. Thorough quality control was enabled by deep sequencing one line to >100×, and single-nucleotide polymorphisms and indels were validated using ddRAD-sequencing as an orthogonal platform. In addition, a series of preliminary population genetic tests were performed with these single-nucleotide polymorphism data for assessment of data quality. We found 83 segregating inversions among the lines, and as expected these were especially abundant in the African sample. We anticipate that this will make a useful addition to the set of reference D. melanogaster strains, thanks to its geographic structuring and unusually high level of genetic diversity.

Summary Ras-driven cancer cells upregulate basal autophagy that degrades and recycles intracellular proteins and organelles. Autophagy-mediated proteome degradation provides free amino acids to support metabolism and macromolecular synthesis, which confers a survival advantage in starvation and promotes tumorigenesis. While the degradation of isolated protein substrates by autophagy has been implicated in controlling cellular function, the extent and specificity by which autophagy remodels the cellular proteome and the underlying functional consequences were unknown. Here we compared the global proteome of autophagy-functional and -deficient Ras-driven cancer cells, finding that autophagy affects the majority of the proteome yet is highly selective. While levels of vesicle trafficking proteins important for autophagy are preserved during starvation-induced autophagy, deleterious inflammatory response pathway components are eliminated even under basal conditions, preventing cytokine-induced paracrine cell death. This reveals the global, functional impact of autophagy-mediated proteome remodeling on cell survival and identifies critical autophagy substrates that mediate this process.

We present IDEA (the Induction Dynamics gene Expression Atlas), a dataset constructed by independently inducing hundreds of transcription factors (TFs) and measuring timecourses of the resulting gene expression responses in budding yeast. Each experiment captures a regulatory cascade connecting a single induced regulator to the genes it causally regulates. We discuss the regulatory cascade of a single TF, Aft1, in detail; however, IDEA contains > 200 TF induction experiments with 20 million individual observations and 100,000 signal‐containing dynamic responses. As an application of IDEA, we integrate all timecourses into a whole‐cell transcriptional model, which is used to predict and validate multiple new and underappreciated transcriptional regulators. We also find that the magnitudes of coefficients in this model are predictive of genetic interaction profile similarities. In addition to being a resource for exploring regulatory connectivity between TFs and their target genes, our modeling approach shows that combining rapid perturbations of individual genes with genome‐scale time‐series measurements is an effective strategy for elucidating gene regulatory networks.

Partial diallel crossing designs are in common use among evolutionary geneticists, as well as among plant and animal breeders. When the goal is to make statements about populations represented by a given set of lines, it is desirable to maximize the number of lines sampled given a set number of crosses among them. We propose an augmented round-robin design that accomplishes this. We develop a hierarchical Bayesian model to estimate quantitative genetic parameters from our scheme. For example, we show how to partition genetic effects into specific and general combining abilities, and the method provides estimates of heritability, dominance, and genetic correlations in the face of complex and unbalanced designs. We test our approach with simulated and real data. We show that although the models slightly overestimate genetic variances, main effects are assessed accurately and precisely. We also illustrate how our approach allows the construction of posterior distributions of combinations of parameters by calculating narrow-sense heritability and a genetic correlation between activities of two enzymes.

Metabolism, the conversion of nutrients into usable energy and biochemical building blocks, is an essential feature of all cells. The genetic factors responsible for inter-individual metabolic variability remain poorly understood. To investigate genetic causes of metabolome variation, we measured the concentrations of 74 metabolites across * 100 segregants from a Saccharomyces cerevisiae cross by liquid chromatography-tandem mass spectrometry. We found 52 quantitative trait loci for 34 metabolites. These included linkages due to overt changes in metabolic genes, e.g., linking pyrimidine intermediates to the deletion of ura3. They also included linkages not directly related to metabolic enzymes, such as those for five central carbon metabolites to ira2, a Ras/PKA pathway regulator, and for the metabolites, S-adenosyl-methionine and S-adenosylhomocysteine to slt2, a MAP kinase involved in cell wall integrity. The variant of ira2 that elevates metabolite levels also increases glucose uptake and ethanol secretion. These results highlight specific examples of genetic variability, including in genes without prior known metabolic regulatory function, that impact yeast metabolism.