Automated preparation of plasma lipids, metabolites, and proteins for LC/MS-based analysis of a high-fat diet in mice.

Blood plasma is one of the most commonly analyzed and easily accessible biological samples. Here, we describe an automated liquid-liquid extraction platform that generates accurate, precise, and reproducible samples for metabolomic, lipidomic, and proteomic analyses from a single aliquot of plasma while minimizing hands-on time and avoiding contamination from plasticware. We applied mass spectrometry to examine the metabolome, lipidome, and proteome of 90 plasma samples to determine the effects of age, time of day, and a high-fat diet in mice. From 25 µl of mouse plasma, we identified 907 lipid species from 16 different lipid classes and subclasses, 233 polar metabolites, and 344 proteins. We found that the high-fat diet induced only mild changes in the polar metabolome, upregulated apolipoproteins, and induced substantial shifts in the lipidome, including a significant increase in arachidonic acid and a decrease in eicosapentaenoic acid content across all lipid classes.

Functional role of autophagy-mediated proteome remodeling in cell survival signaling and innate immunity.

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.

Learning causal networks using inducible transcription factors and transcriptome-wide time series.

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.

Genetic basis of metabolome variation in yeast.

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-adenosyl-homocysteine 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.

Microbial reaction rate estimation using proteins and proteomes.

Microbes transform their environments using diverse enzymatic reactions. However, it remains challenging to measure microbial reaction rates in natural environments. Despite advances in global quantification of enzyme abundances, the individual relationships between enzyme abundances and their reaction rates have not been systematically examined. Using matched proteomic and reaction rate data from microbial cultures, we show that enzyme abundance is often insufficient to predict its corresponding reaction rate. However, we discovered that global proteomic measurements can be used to make accurate rate predictions of individual reaction rates (median R 2 = 0.78). Accurate rate predictions required only a small number of proteins and they did not need explicit prior mechanistic knowledge or environmental context. These results indicate that proteomes are encoders of cellular reaction rates, potentially enabling proteomic measurements in situ to estimate the rates of microbially mediated reactions in natural systems.

Protein-metabolite interactomics of carbohydrate metabolism reveal regulation of lactate dehydrogenase.

Metabolic networks are interconnected and influence diverse cellular processes. The protein-metabolite interactions that mediate these networks are frequently low affinity and challenging to systematically discover. We developed mass spectrometry integrated with equilibrium dialysis for the discovery of allostery systematically (MIDAS) to identify such interactions. Analysis of 33 enzymes from human carbohydrate metabolism identified 830 protein-metabolite interactions, including known regulators, substrates, and products as well as previously unreported interactions. We functionally validated a subset of interactions, including the isoform-specific inhibition of lactate dehydrogenase by long-chain acyl-coenzyme A. Cell treatment with fatty acids caused a loss of pyruvate-lactate interconversion dependent on lactate dehydrogenase isoform expression. These protein-metabolite interactions may contribute to the dynamic, tissue-specific metabolic flexibility that enables growth and survival in an ever-changing nutrient environment.

Environmental and genetic perturbations reveal different networks of metabolic regulation.

Progress in systems biology depends on accurate descriptions of biological networks. Connections in a regulatory network are identified as correlations of gene expression across a set of environmental or genetic perturbations. To use this information to predict system behavior, we must test how the nature of perturbations affects topologies of networks they reveal. To probe this question, we focused on metabolism of Drosophila melanogaster. Our source of perturbations is a set of crosses among 92 wild-derived lines from five populations, replicated in a manner permitting separate assessment of the effects of genetic variation and environmental fluctuation. We directly assayed activities of enzymes and levels of metabolites. Using a multivariate Bayesian model, we estimated covariance among metabolic parameters and built fine-grained probabilistic models of network topology. The environmental and genetic co-regulation networks are substantially the same among five populations. However, genetic and environmental perturbations reveal qualitative differences in metabolic regulation, suggesting that environmental shifts, such as diet modifications, produce different systemic effects than genetic changes, even if the primary targets are the same.

Evaluation of Williams et al.: On multiomics and mediation.

One snapshot of the peer review process for "Multiomic profiling of the liver across diets and age in a diverse mouse population" (Williams et al., 2021).

Systems-level analysis of mechanisms regulating yeast metabolic flux.

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 previously unrecognized instances of cross-pathway regulation, which we biochemically verified. One of these involved 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.

Global diversity lines - a five-continent reference panel of sequenced Drosophila melanogaster strains.

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.

Human pancreatic cancer tumors are nutrient poor and tumor cells actively scavenge extracellular protein.

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 physiologic 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.

A hierarchical Bayesian model for a novel sparse partial diallel crossing design.

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.