Inference of Bacterial Small RNA Regulatory Networks and Integration with Transcription Factor-Driven Regulatory Networks.

Small noncoding RNAs (sRNAs) are key regulators of bacterial gene expression. Through complementary base pairing, sRNAs affect mRNA stability and translation efficiency. Here, we describe a network inference approach designed to identify sRNA-mediated regulation of transcript levels. We use existing transcriptional data sets and prior knowledge to infer sRNA regulons using our network inference tool, the Inferelator This approach produces genome-wide gene regulatory networks that include contributions by both transcription factors and sRNAs. We show the benefits of estimating and incorporating sRNA activities into network inference pipelines using available experimental data. We also demonstrate how these estimated sRNA regulatory activities can be mined to identify the experimental conditions where sRNAs are most active. We uncover 45 novel experimentally supported sRNA-mRNA interactions in Escherichia coli, outperforming previous network-based efforts. Additionally, our pipeline complements sequence-based sRNA-mRNA interaction prediction methods by adding a data-driven filtering step. Finally, we show the general applicability of our approach by identifying 24 novel, experimentally supported, sRNA-mRNA interactions in Pseudomonas aeruginosa, Staphylococcus aureus, and Bacillus subtilis Overall, our strategy generates novel insights into the functional context of sRNA regulation in multiple bacterial species.IMPORTANCE Individual bacterial genomes can have dozens of small noncoding RNAs with largely unexplored regulatory functions. Although bacterial sRNAs influence a wide range of biological processes, including antibiotic resistance and pathogenicity, our current understanding of sRNA-mediated regulation is far from complete. Most of the available information is restricted to a few well-studied bacterial species; and even in those species, only partial sets of sRNA targets have been characterized in detail. To close this information gap, we developed a computational strategy that takes advantage of available transcriptional data and knowledge about validated and putative sRNA-mRNA interactions for inferring expanded sRNA regulons. Our approach facilitates the identification of experimentally supported novel interactions while filtering out false-positive results. Due to its data-driven nature, our method prioritizes biologically relevant interactions among lists of candidate sRNA-target pairs predicted in silico from sequence analysis or derived from sRNA-mRNA binding experiments.

Expansion of the Spore Surface Polysaccharide Layer in Bacillus subtilis by Deletion of Genes Encoding Glycosyltransferases and Glucose Modification Enzymes.

Polysaccharides (PS) decorate the surface of dormant endospores (spores). In the model organism for sporulation, Bacillus subtilis, the composition of the spore PS is not known in detail. Here, we have assessed how PS synthesis enzymes produced during the late stages of sporulation affect spore surface properties. Using four methods, bacterial adhesion to hydrocarbons (BATH) assays, India ink staining, transmission electron microscopy (TEM) with ruthenium red staining, and scanning electron microscopy (SEM), we characterized the contributions of four sporulation gene clusters, spsABCDEFGHIJKL, yfnHGF-yfnED, ytdA-ytcABC, and cgeAB-cgeCDE, on the morphology and properties of the crust, the outermost spore layer. Our results show that all mutations in the sps operon result in the production of spores that are more hydrophobic and lack a visible crust, presumably because of reduced PS deposition, while mutations in cgeD and the yfnH-D cluster noticeably expand the PS layer. In addition, yfnH-D mutant spores exhibit a crust with an unusual weblike morphology. The hydrophobic phenotype from sps mutant spores was partially rescued by a second mutation inactivating any gene in the yfnHGF operon. While spsI, yfnH, and ytdA are paralogous genes, all encoding glucose-1-phosphate nucleotidyltransferases, each paralog appears to contribute in a distinct manner to the spore PS. Our data are consistent with the possibility that each gene cluster is responsible for the production of its own respective deoxyhexose. In summary, we found that disruptions to the PS layer modify spore surface hydrophobicity and that there are multiple saccharide synthesis pathways involved in spore surface properties.IMPORTANCE Many bacteria are characterized by their ability to form highly resistant spores. The dormant spore state allows these species to survive even the harshest treatments with antimicrobial agents. Spore surface properties are particularly relevant because they influence spore dispersal in various habitats from natural to human-made environments. The spore surface in Bacillus subtilis (crust) is composed of a combination of proteins and polysaccharides. By inactivating the enzymes responsible for the synthesis of spore polysaccharides, we can assess how spore surface properties such as hydrophobicity are modulated by the addition of specific carbohydrates. Our findings indicate that several sporulation gene clusters are responsible for the assembly and allocation of surface polysaccharides. Similar mechanisms could be modulating the dispersal of infectious spore-forming bacteria.

Contributions of crust proteins to spore surface properties in Bacillus subtilis.

Surface properties, such as adhesion and hydrophobicity, constrain dispersal of bacterial spores in the environment. In Bacillus subtilis, these properties are influenced by the outermost layer of the spore, the crust. Previous work has shown that two clusters, cotVWXYZ and cgeAB, encode the protein components of the crust. Here, we characterize the respective roles of these genes in surface properties using Bacterial Adherence to Hydrocarbons assays, negative staining of polysaccharides by India ink and Transmission Electron Microscopy. We showed that inactivation of crust genes caused increases in spore relative hydrophobicity, disrupted the spore polysaccharide layer, and impaired crust structure and attachment to the rest of the coat. We also found that cotO, previously identified for its role in outer coat formation, is necessary for proper encasement of the spore by the crust. In parallel, we conducted fluorescence microscopy experiments to determine the full network of genetic dependencies for subcellular localization of crust proteins. We determined that CotZ is required for the localization of most crust proteins, while CgeA is at the bottom of the genetic interaction hierarchy.

Resolving the Complex Genetic Basis of Phenotypic Variation and Variability of Cellular Growth.

In all organisms, the majority of traits vary continuously between individuals. Explaining the genetic basis of quantitative trait variation requires comprehensively accounting for genetic and nongenetic factors as well as their interactions. The growth of microbial cells can be characterized by a lag duration, an exponential growth phase, and a stationary phase. Parameters that characterize these growth phases can vary among genotypes (phenotypic variation), environmental conditions (phenotypic plasticity), and among isogenic cells in a given environment (phenotypic variability). We used a high-throughput microscopy assay to map genetic loci determining variation in lag duration and exponential growth rate in growth rate-limiting and nonlimiting glucose concentrations, using segregants from a cross of two natural isolates of the budding yeast, Saccharomyces cerevisiae We find that some quantitative trait loci (QTL) are common between traits and environments whereas some are unique, exhibiting gene-by-environment interactions. Furthermore, whereas variation in the central tendency of growth rate or lag duration is explained by many additive loci, differences in phenotypic variability are primarily the result of genetic interactions. We used bulk segregant mapping to increase QTL resolution by performing whole-genome sequencing of complex mixtures of an advanced intercross mapping population grown in selective conditions using glucose-limited chemostats. We find that sequence variation in the high-affinity glucose transporter HXT7 contributes to variation in growth rate and lag duration. Allele replacements of the entire locus, as well as of a single polymorphic amino acid, reveal that the effect of variation in HXT7 depends on genetic, and allelic, background. Amplifications of HXT7 are frequently selected in experimental evolution in glucose-limited environments, but we find that HXT7 amplifications result in antagonistic pleiotropy that is absent in naturally occurring variants of HXT7 Our study highlights the complex nature of the genotype-to-phenotype map within and between environments.

An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network.

Organisms from all domains of life use gene regulation networks to control cell growth, identity, function, and responses to environmental challenges. Although accurate global regulatory models would provide critical evolutionary and functional insights, they remain incomplete, even for the best studied organisms. Efforts to build comprehensive networks are confounded by challenges including network scale, degree of connectivity, complexity of organism-environment interactions, and difficulty of estimating the activity of regulatory factors. Taking advantage of the large number of known regulatory interactions in Bacillus subtilis and two transcriptomics datasets (including one with 38 separate experiments collected specifically for this study), we use a new combination of network component analysis and model selection to simultaneously estimate transcription factor activities and learn a substantially expanded transcriptional regulatory network for this bacterium. In total, we predict 2,258 novel regulatory interactions and recall 74% of the previously known interactions. We obtained experimental support for 391 (out of 635 evaluated) novel regulatory edges (62% accuracy), thus significantly increasing our understanding of various cell processes, such as spore formation.