We find that the metabolome of nutrient-limited yeast varies dramatically with the limiting nutrient's identity. Low glutamine is a hallmark of nitrogen limitation, ATP of phosphorus limitation, and pyruvate of carbon limitation. The availability of these metabolites can quantitatively account for the nutrient-limited yeast's growth rate.

We conducted a phenotypic, transcriptional, metabolic, and genetic analysis of quiescence in yeast induced by starvation of prototrophic cells for one of three essential nutrients (glucose, nitrogen, or phosphate) and compared those results with those obtained with cells growing slowly due to nutrient limitation. These studies address two related questions: (1) Is quiescence a state distinct from any attained during mitotic growth, and (2) does the nature of quiescence differ depending on the means by which it is induced? We found that either limitation or starvation for any of the three nutrients elicits all of the physiological properties associated with quiescence, such as enhanced cell wall integrity and resistance to heat shock and oxidative stress. Moreover, the starvations result in a common transcriptional program, which is in large part a direct extrapolation of the changes that occur during slow growth. In contrast, the metabolic changes that occur upon starvation and the genetic requirements for surviving starvation differ significantly depending on the nutrient for which the cell is starved. The genes needed by cells to survive starvation do not overlap the genes that are induced upon starvation. We conclude that cells do not access a unique and discrete G 0 state, but rather are programmed, when nutrients are scarce, to prepare for a range of possible future stressors. Moreover, these survival strategies are not unique to quiescence, but are engaged by the cell in proportion to nutrient scarcity.

BackgroundWhile human gut microbiomes vary significantly in taxonomic composition, biological pathway abundance is surprisingly invariable across hosts. We hypothesized that healthy microbiomes appear functionally redundant due to factors that obscure differences in gene abundance between individuals.ResultsTo account for these biases, we developed a powerful test of gene variability called CCoDA, which is applicable to shotgun metagenomes from any environment and can integrate data from multiple studies. Our analysis of healthy human fecal metagenomes from three separate cohorts revealed thousands of genes whose abundance differs significantly and consistently between people, including glycolytic enzymes, lipopolysaccharide biosynthetic genes, and secretion systems. Even housekeeping pathways contain a mix of variable and invariable genes, though most highly conserved genes are significantly invariable. Variable genes tend to be associated with Proteobacteria, as opposed to taxa used to define enterotypes or the dominant phyla Bacteroidetes and Firmicutes.ConclusionsThese results establish limits on functional redundancy and predict specific genes and taxa that may explain physiological differences between gut microbiomes.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-017-0244-z) contains supplementary material, which is available to authorized users.

Metabolite concentrations can regulate gene expression, which can in turn regulate metabolic activity. The extent to which functionally related transcripts and metabolites show similar patterns of concentration changes, however, remains unestablished. We measure and analyze the metabolomic and transcriptional responses of Saccharomyces cerevisiae to carbon and nitrogen starvation. Our analysis demonstrates that transcripts and metabolites show coordinated response dynamics. Furthermore, metabolites and gene products whose concentration profiles are alike tend to participate in related biological processes. To identify specific, functionally related genes and metabolites, we develop an approach based on Bayesian integration of the joint metabolomic and transcriptomic data. This algorithm finds interactions by evaluating transcript–metabolite correlations in light of the experimental context in which they occur and the class of metabolite involved. It effectively predicts known enzymatic and regulatory relationships, including a gene–metabolite interaction central to the glycolytic–gluconeogenetic switch. This work provides quantitative evidence that functionally related metabolites and transcripts show coherent patterns of behavior on the genome scale and lays the groundwork for building gene–metabolite interaction networks directly from systems-level data.

Shotgun metagenomic DNA sequencing is a widely applicable tool for characterizing the functions that are encoded by microbial communities. Several bioinformatic tools can be used to functionally annotate metagenomes, allowing researchers to draw inferences about the functional potential of the community and to identify putative functional biomarkers. However, little is known about how decisions made during annotation affect the reliability of the results. Here, we use statistical simulations to rigorously assess how to optimize annotation accuracy and speed, given parameters of the input data like read length and library size. We identify best practices in metagenome annotation and use them to guide the development of the Shotgun Metagenome Annotation Pipeline (ShotMAP). ShotMAP is an analytically flexible, end-to-end annotation pipeline that can be implemented either on a local computer or a cloud compute cluster. We use ShotMAP to assess how different annotation databases impact the interpretation of how marine metagenome and metatranscriptome functional capacity changes across seasons. We also apply ShotMAP to data obtained from a clinical microbiome investigation of inflammatory bowel disease. This analysis finds that gut microbiota collected from Crohn’s disease patients are functionally distinct from gut microbiota collected from either ulcerative colitis patients or healthy controls, with differential abundance of metabolic pathways related to host-microbiome interactions that may serve as putative biomarkers of disease.