One-third of all protein-coding genes from bacterial genomes cannot be annotated with a function. Here, to investigate the functions of these genes, we present genome-wide mutant fitness data from 32 diverse bacteria across dozens of growth conditions. We identified mutant phenotypes for 11,779 protein-coding genes that had not been annotated with a specific function. Many genes could be associated with a specific condition because the gene affected fitness only in that condition, or with another gene in the same bacterium because they had similar mutant phenotypes. Of the poorly annotated genes, 2,316 had associations that have high confidence because they are conserved in other bacteria. By combining these conserved associations with comparative genomics, we identified putative DNA repair proteins; in addition, we propose specific functions for poorly annotated enzymes and transporters and for uncharacterized protein families. Our study demonstrates the scalability of microbial genetics and its utility for improving gene annotations.

XCMS Online () is a cloud-based informatic platform designed to process and visualize mass-spectrometry-based, untargeted metabolomic data. Initially, the platform was developed for two-group comparisons to match the independent, “control” versus “disease” experimental design. Here, we introduce an enhanced XCMS Online interface that enables users to perform dependent (paired) two-group comparisons, meta-analysis, and multigroup comparisons, with comprehensive statistical output and interactive visualization tools. Newly incorporated statistical tests cover a wide array of univariate analyses. Multigroup comparison allows for the identification of differentially expressed metabolite features across multiple classes of data while higher order meta-analysis facilitates the identification of shared metabolic patterns across multiple two-group comparisons. Given the complexity of these data sets, we have developed an interactive platform where users can monitor the statistical output of univariate (cloud plots) and multivariate (PCA plots) data analysis in real time by adjusting the threshold and range of various parameters. On the interactive cloud plot, metabolite features can be filtered out by their significance level (p-value), fold change, mass-to-charge ratio, retention time, and intensity. The variation pattern of each feature can be visualized on both extracted-ion chromatograms and box plots. The interactive principal component analysis includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria. The utility of XCMS functionalities is demonstrated through the metabolomic analysis of bacterial stress response and the comparison of lymphoblastic leukemia cell lines.

Highlights d Identification of specific phenotypes for 516 Bacteroides thetaiotaomicron genes d A 3-keto-glucoside hydrolase is important for disaccharide utilization d A tripartite multidrug resistance system is important for bile salt resistance d Use of alternate biosynthetic enzymes depends on ammonium availability in the gut

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The genomes of sulfate-reducing bacteria remain poorly characterized, largely due to a paucity of experimental data and genetic tools. To meet this challenge, we generated an archived library of 15,477 mapped transposon insertion mutants in the sulfate-reducing bacterium Desulfovibrio alaskensis G20. To demonstrate the utility of the individual mutants, we profiled gene expression in mutants of six regulatory genes and used these data, together with 1,313 high-confidence transcription start sites identified by tiling microarrays and transcriptome sequencing (5′ RNA-Seq), to update the regulons of Fur and Rex and to confirm the predicted regulons of LysX, PhnF, PerR, and Dde_3000, a histidine kinase. In addition to enabling single mutant investigations, the D. alaskensis G20 transposon mutants also contain DNA bar codes, which enables the pooling and analysis of mutant fitness for thousands of strains simultaneously. Using two pools of mutants that represent insertions in 2,369 unique protein-coding genes, we demonstrate that the hypothetical gene Dde_3007 is required for methionine biosynthesis. Using comparative genomics, we propose that Dde_3007 performs a missing step in methionine biosynthesis by transferring a sulfur group to O-phosphohomoserine to form homocysteine. Additionally, we show that the entire choline utilization cluster is important for fitness in choline sulfate medium, which confirms that a functional microcompartment is required for choline oxidation. Finally, we demonstrate that Dde_3291, a MerR-like transcription factor, is a choline-dependent activator of the choline utilization cluster. Taken together, our data set and genetic resources provide a foundation for systems-level investigation of a poorly studied group of bacteria of environmental and industrial importance.