No abstract

44The multi-subunit bacterial RNA polymerase (RNAP) and its associated regulators carry 45 out transcription and integrate myriad regulatory signals. Numerous studies have 46 interrogated the inner workings of RNAP, and mutations in genes encoding RNAP drive 47 adaptation of Escherichia coli to many health-and industry-relevant environments, yet a 48 paucity of systematic analyses has hampered our understanding of the fitness benefits 49 and trade-offs from altering RNAP function. Here, we conduct a chemical-genetic 50 analysis of a library of RNAP mutants. We discover phenotypes for non-essential 51 insertions, show that clustering mutant phenotypes increases their predictive power for 52 drawing functional inferences, and illuminate a connection between transcription and 53 cell division. Our findings demonstrate that RNAP chemical-genetic interactions provide 54 a general platform for interrogating structure-function relationships in vivo and for 55 identifying physiological trade-offs of mutations, including those relevant for disease and 56 biotechnology. This strategy should have broad utility for illuminating the role of other 57 important protein complexes. 58 59 60Multi-subunit RNA polymerases are responsible for transcription in all organisms. The 61 core RNA polymerase (RNAP) enzyme (β', β, α2, ω) is conserved across all domains of 62 life (Jokerst et al., 1989; Lane and Darst, 2010; Sweetser et al., 1987). Bacterial-specific 63 initiation factors, called sigmas (σs), transiently associate with the core complex to 64 provide promoter recognition and assist in melting promoter DNA during initiation 65 (Gruber and Gross, 2003). During elongation, RNAP associates with NusA, which 66 enhances pausing and intrinsic termination at specific sequences (Artsimovitch and 67 Landick, 2000), and NusG (Spt5 in archaea and eukaryotes), the only universally 68 conserved elongation factor, which modulates elongation and ρ-dependent termination 69 (Burova et al., 1995; Li et al., 1993). Termination in eubacteria is facilitated either by 70 RNA structure (intrinsic termination) or by the termination factor ρ, which uses its 71 helicase activity to release the transcript and recycle the RNAP complex ( Figure 1A). 72 Additionally, bacterial RNAPs differ from archaeal and eukaryotic RNAPs, for which the 73 core enzymes acquired peripheral subunits (e.g., Rpb4,5,[7][8][9][10]12 in RNAPII), by instead 74 having acquired lineage-specific insertions in β' and β (called sequence insertions 1-3 75 (SI1-3) in E. coli) whose functions remain largely unknown (Artsimovitch et al., 2003; 76 Lane and Darst, 2010). 77 The central role played by this enzyme complex, both in orchestrating transcription and 78 integrating diverse signals, is reflected in the pleiotropic phenotypes that arise from 79 mutations in RNAP. Efforts to evolve E. coli in maladapted environments, such as 80 growth on glycerol (Cheng et al., 2014), ethanol (Haft et al., 2014, or at elevated 81 temperatures (Tenaillon et al., 2012), have all re...

Background Microbial genetics has formed a foundation for understanding many aspects of biology. Systematic annotation that supports computational data mining should reveal further insights for microbes, microbiomes, and conserved functions beyond microbes. The Ontology of Microbial Phenotypes (OMP) was created to support such annotation. Results We define standards for an OMP-based annotation framework that supports the capture of a variety of phenotypes and provides flexibility for different levels of detail based on a combination of pre- and post-composition using OMP and other Open Biomedical Ontology (OBO) projects. A system for entering and viewing OMP annotations has been added to our online, public, web-based data portal. Conclusions The annotation framework described here is ready to support projects to capture phenotypes from the experimental literature for a variety of microbes. Defining the OMP annotation standard should support the development of new software tools for data mining and analysis in comparative phenomics.

ABSTRACTA long-standing effort in biology is to precisely define and group phenotypes that characterize a biological process, and the genes that underpin them. In Saccharomyces cerevisiae and other organisms, functional screens have generated rich lists of phenotypes associated with individual genes. However, it is often challenging to identify sets of phenotypes and genes that are most closely associated with a given biological process. Here, we focused on the 166 phenotypes arising from loss-of-function and the 86 phenotypes from gain-of-function mutations in 571 genes currently assigned to cell cycle-related ontologies in S. cerevisiae. To reduce this complexity, we applied unbiased, computational approaches of correspondence analysis to identify a minimum set of phenotypic variables that accounts for as much of the variability in the data as possible. Loss-of-function phenotypes can be reduced to 20 dimensions, while gain-of-function ones to 14 dimensions. We also pinpoint the contributions of phenotypes and genes in each set. The approach we describe not only simplifies the categorization of phenotypes associated with cell cycle progression but can also serve as a discovery tool for gene function.

A long-standing effort in biology is to precisely define and group phenotypes that characterize a biological process, and the genes that underpin them. In Saccharomyces cerevisiae and other organisms, functional screens have generated rich lists of phenotypes associated with individual genes. However, it is often challenging to identify sets of phenotypes and genes that are most closely associated with a given biological process. Here, we focused on the 166 phenotypes arising from loss-of-function and the 86 phenotypes from gain-of-function mutations in 571 genes currently assigned to cell cycle-related ontologies in S. cerevisiae. To reduce this complexity, we applied unbiased, computational approaches of correspondence analysis to identify a minimum set of phenotypic variables that accounts for as much of the variability in the data as possible. Loss-of-function phenotypes can be reduced to 20 dimensions, while gain-of-function ones to 14 dimensions. We also pinpoint the contributions of phenotypes and genes in each set. The approach we describe not only simplifies the categorization of phenotypes associated with cell cycle progression but might also potentially serve as a discovery tool for gene function.