Adaptive adjustment of significance thresholds produces large gains in microbial gene annotations and metabolic insights.

Gene function annotations enable microbial ecologists to make inferences about metabolic potential from genomes and metagenomes. However, even tools that use the same database and general approach can differ markedly in the annotations they recover. We compare three popular methods for identifying KEGG Orthologs, applying them to genomes drawn from a range of bacterial families that occupy different host-associated and free-living biomes. Our results show that by adaptively tuning sequence similarity thresholds, sensitivity can be substantially improved while maintaining accuracy. We observe the largest improvements when few reference sequences exist for a given protein family, and when annotating genomes from non-model organisms (such as gut-dwelling Lachnospiraceae). Our results suggest that straightforward heuristic adjustments can broadly improve microbial metabolic predictions.

Framework for exploring the sensory repertoire of the human gut microbiota.

Bacteria sense changes in their environment and transduce signals to adjust their cellular functions accordingly. For this purpose, bacteria employ various sensors feeding into multiple signal transduction pathways. Signal recognition by bacterial sensors is studied mainly in a few model organisms, but advances in genome sequencing and analysis offer new ways of exploring the sensory repertoire of many understudied organisms. The human gut is a natural target of this line of study: it is a nutrient-rich and dynamic environment and is home to thousands of bacterial species whose activities impact human health. Many gut commensals are also poorly studied compared to model organisms and are mainly known through their genome sequences. To begin exploring the signals human gut commensals sense and respond to, we have designed a framework that enables the identification of sensory domains, prediction of signals that they recognize, and experimental verification of these predictions. We validate this framework's functionality by systematically identifying amino acid sensors in selected bacterial genomes and metagenomes, characterizing their amino acid binding properties, and demonstrating their signal transduction potential.IMPORTANCESignal transduction is a central process governing how bacteria sense and respond to their environment. The human gut is a complex environment with many living organisms and fluctuating streams of nutrients. One gut inhabitant, Escherichia coli, is a model organism for studying signal transduction. However, E. coli is not representative of most gut microbes, and signaling pathways in the thousands of other organisms comprising the human gut microbiota remain poorly understood. This work provides a foundation for how to explore signals recognized by these organisms.

Four-part harmony between diet, drugs, microbes, and host.

In this issue of Cell Host & Microbe, Zeng et al. show that a specific gut microbe causes diet-dependent attenuation of acetaminophen toxicity in mice. This link between gut microbes and toxicity is mechanistically detailed, yet intriguingly indirect, mediated by the transformation of ingested phytochemicals as opposed to the drug itself.

Host and gut bacteria share metabolic pathways for anti-cancer drug metabolism.

Pharmaceuticals have extensive reciprocal interactions with the microbiome, but whether bacterial drug sensitivity and metabolism is driven by pathways conserved in host cells remains unclear. Here we show that anti-cancer fluoropyrimidine drugs inhibit the growth of gut bacterial strains from 6 phyla. In both Escherichia coli and mammalian cells, fluoropyrimidines disrupt pyrimidine metabolism. Proteobacteria and Firmicutes metabolized 5-fluorouracil to its inactive metabolite dihydrofluorouracil, mimicking the major host mechanism for drug clearance. The preTA operon was necessary and sufficient for 5-fluorouracil inactivation by E. coli, exhibited high catalytic efficiency for the reductive reaction, decreased the bioavailability and efficacy of oral fluoropyrimidine treatment in mice and was prevalent in the gut microbiomes of colorectal cancer patients. The conservation of both the targets and enzymes for metabolism of therapeutics across domains highlights the need to distinguish the relative contributions of human and microbial cells to drug efficacy and side-effect profiles.

phylogenize: correcting for phylogeny reveals genes associated with microbial distributions.

SUMMARY: Phylogenetic comparative methods are powerful but presently under-utilized ways to identify microbial genes underlying differences in community composition. These methods help to identify functionally important genes because they test for associations beyond those expected when related microbes occupy similar environments. We present phylogenize, a pipeline with web, QIIME 2 and R interfaces that allows researchers to perform phylogenetic regression on 16S amplicon and shotgun sequencing data and to visualize results. phylogenize applies broadly to both host-associated and environmental microbiomes. Using Human Microbiome Project and Earth Microbiome Project data, we show that phylogenize draws similar conclusions from 16S versus shotgun sequencing and reveals both known and candidate pathways associated with host colonization. AVAILABILITY AND IMPLEMENTATION: phylogenize is available at https://phylogenize.org and https://bitbucket.org/pbradz/phylogenize. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Minor Isozymes Tailor Yeast Metabolism to Carbon Availability.

Isozymes are enzymes that differ in sequence but catalyze the same chemical reactions. Despite their apparent redundancy, isozymes are often retained over evolutionary time, suggesting that they contribute to fitness. We developed an unsupervised computational method for identifying environmental conditions under which isozymes are likely to make fitness contributions. This method analyzes published gene expression data to find specific experimental perturbations that induce differential isozyme expression. In yeast, we found that isozymes are strongly enriched in the pathways of central carbon metabolism and that many isozyme pairs show anticorrelated expression during the respirofermentative shift. Building on these observations, we assigned function to two minor central carbon isozymes, aconitase 2 (ACO2) and pyruvate kinase 2 (PYK2). ACO2 is expressed during fermentation and proves advantageous when glucose is limiting. PYK2 is expressed during respiration and proves advantageous for growth on three-carbon substrates. PYK2's deletion can be rescued by expressing the major pyruvate kinase only if that enzyme carries mutations mirroring PYK2's allosteric regulation. Thus, central carbon isozymes help to optimize allosteric metabolic regulation under a broad range of potential nutrient conditions while requiring only a small number of transcriptional states. IMPORTANCE Gene duplication is one of the main evolutionary paths to new protein function. Typically, duplicated genes either accumulate mutations and degrade into pseudogenes or are retained and diverge in function. Some duplicated genes, however, show long-term persistence without apparently acquiring new function. An important class of isozymes consists of those that catalyze the same reaction in the same compartment, where knockout of one isozyme causes no known functional defect. Here we present an approach to assigning specific functional roles to seemingly redundant isozymes. First, gene expression data are analyzed computationally to identify conditions under which isozyme expression diverges. Then, knockouts are compared under those conditions. This approach revealed that the expression of many yeast isozymes diverges in response to carbon availability and that carbon source manipulations can induce fitness phenotypes for seemingly redundant isozymes. A driver of these fitness phenotypes is differential allosteric enzyme regulation, indicating isozyme divergence to achieve more-optimal control of metabolism.

Phylogeny-corrected identification of microbial gene families relevant to human gut colonization.

The mechanisms by which different microbes colonize the healthy human gut versus other body sites, the gut in disease states, or other environments remain largely unknown. Identifying microbial genes influencing fitness in the gut could lead to new ways to engineer probiotics or disrupt pathogenesis. We approach this problem by measuring the statistical association between a species having a gene and the probability that the species is present in the gut microbiome. The challenge is that closely related species tend to be jointly present or absent in the microbiome and also share many genes, only a subset of which are involved in gut adaptation. We show that this phylogenetic correlation indeed leads to many false discoveries and propose phylogenetic linear regression as a powerful solution. To apply this method across the bacterial tree of life, where most species have not been experimentally phenotyped, we use metagenomes from hundreds of people to quantify each species' prevalence in and specificity for the gut microbiome. This analysis reveals thousands of genes potentially involved in adaptation to the gut across species, including many novel candidates as well as processes known to contribute to fitness of gut bacteria, such as acid tolerance in Bacteroidetes and sporulation in Firmicutes. We also find microbial genes associated with a preference for the gut over other body sites, which are significantly enriched for genes linked to fitness in an in vivo competition experiment. Finally, we identify gene families associated with higher prevalence in patients with Crohn's disease, including Proteobacterial genes involved in conjugation and fimbria regulation, processes previously linked to inflammation. These gene targets may represent new avenues for modulating host colonization and disease. Our strategy of combining metagenomics with phylogenetic modeling is general and can be used to identify genes associated with adaptation to any environment.

Proteobacteria explain significant functional variability in the human gut microbiome.

BACKGROUND: While 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. RESULTS: To 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. CONCLUSIONS: These results establish limits on functional redundancy and predict specific genes and taxa that may explain physiological differences between gut microbiomes.

Automated and Accurate Estimation of Gene Family Abundance from Shotgun Metagenomes.

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.

A new system for comparative functional genomics of Saccharomyces yeasts.

Whole-genome sequencing, particularly in fungi, has progressed at a tremendous rate. More difficult, however, is experimental testing of the inferences about gene function that can be drawn from comparative sequence analysis alone. We present a genome-wide functional characterization of a sequenced but experimentally understudied budding yeast, Saccharomyces bayanus var. uvarum (henceforth referred to as S. bayanus), allowing us to map changes over the 20 million years that separate this organism from S. cerevisiae. We first created a suite of genetic tools to facilitate work in S. bayanus. Next, we measured the gene-expression response of S. bayanus to a diverse set of perturbations optimized using a computational approach to cover a diverse array of functionally relevant biological responses. The resulting data set reveals that gene-expression patterns are largely conserved, but significant changes may exist in regulatory networks such as carbohydrate utilization and meiosis. In addition to regulatory changes, our approach identified gene functions that have diverged. The functions of genes in core pathways are highly conserved, but we observed many changes in which genes are involved in osmotic stress, peroxisome biogenesis, and autophagy. A surprising number of genes specific to S. bayanus respond to oxidative stress, suggesting the organism may have evolved under different selection pressures than S. cerevisiae. This work expands the scope of genome-scale evolutionary studies from sequence-based analysis to rapid experimental characterization and could be adopted for functional mapping in any lineage of interest. Furthermore, our detailed characterization of S. bayanus provides a valuable resource for comparative functional genomics studies in yeast.

Phylogenetic portrait of the Saccharomyces cerevisiae functional genome.

The genome of budding yeast (Saccharomyces cerevisiae) contains approximately 5800 protein-encoding genes, the majority of which are associated with some known biological function. Yet the extent of amino acid sequence conservation of these genes over all phyla has only been partially examined. Here we provide a more comprehensive overview and visualization of the conservation of yeast genes and a means for browsing and exploring the data in detail, down to the individual yeast gene, at http://yeast-phylogroups.princeton.edu. We used data from the OrthoMCL database, which has defined orthologs from approximately 150 completely sequenced genomes, including diverse representatives of the archeal, bacterial, and eukaryotic domains. By clustering genes based on similar patterns of conservation, we organized and visualized all the protein-encoding genes in yeast as a single heat map. Most genes fall into one of eight major clusters, called "phylogroups." Gene ontology analysis of the phylogroups revealed that they were associated with specific, distinct trends in gene function, generalizations likely to be of interest to a wide range of biologists.

Yeast cells can access distinct quiescent states.

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.

Growth-limiting intracellular metabolites in yeast growing under diverse nutrient limitations.

Microbes tailor their growth rate to nutrient availability. Here, we measured, using liquid chromatography-mass spectrometry, >100 intracellular metabolites in steady-state cultures of Saccharomyces cerevisiae growing at five different rates and in each of five different limiting nutrients. In contrast to gene transcripts, where approximately 25% correlated with growth rate irrespective of the nature of the limiting nutrient, metabolite concentrations were highly sensitive to the limiting nutrient's identity. Nitrogen (ammonium) and carbon (glucose) limitation were characterized by low intracellular amino acid and high nucleotide levels, whereas phosphorus (phosphate) limitation resulted in the converse. Low adenylate energy charge was found selectively in phosphorus limitation, suggesting the energy charge may actually measure phosphorus availability. Particularly strong concentration responses occurred in metabolites closely linked to the limiting nutrient, e.g., glutamine in nitrogen limitation, ATP in phosphorus limitation, and pyruvate in carbon limitation. A simple but physically realistic model involving the availability of these metabolites was adequate to account for cellular growth rate. The complete data can be accessed at the interactive website http://growthrate.princeton.edu/metabolome.

Coordinated concentration changes of transcripts and metabolites in Saccharomyces cerevisiae.

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.