Chemical-genetic interrogation of RNA polymerase mutants reveals structure-function relationships and physiological tradeoffs

The multi-subunit bacterial RNA polymerase (RNAP) and its associated regulators carry out transcription and integrate myriad regulatory signals. Numerous studies have interrogated the inner workings of RNAP, and mutations in genes encoding RNAP drive adaptation of Escherichia coli to many health- and industry-relevant environments, yet a paucity of systematic analyses has hampered our understanding of the fitness benefits and trade-offs from altering RNAP function. Here, we conduct a chemical-genetic analysis of a library of RNAP mutants. We discover phenotypes for non-essential insertions, show that clustering mutant phenotypes increases their predictive power for drawing functional inferences, and demonstrate that some RNA polymerase mutants both decrease average cell length and confer insensitivity to killing by cell-wall targeting antibiotics. Our findings demonstrate that RNAP chemical-genetic interactions provide a general platform for interrogating structure-function relationships in vivo and for identifying physiological trade-offs of mutations, including those relevant for disease and biotechnology. This strategy should have broad utility for illuminating the role of other important protein complexes.This dataset contains the raw images (img/*) of colony arrays and associated image analysis data (iris/*) for the estimation of colony size and colony opacity. The data files are used for downstream analysis in the chemical genetic pipeline while the raw images are made available for alternative image analysis approaches. Together with the data, metadata files (keys/*) and filtering information (filter/*) are used to generate an S-score matrix describing chemical genetic interactions for RNA polymerase mutations. A key to the fields in the data and metadata files is provided in (readme.txt). The code generating the final dataset is available has been published as a Code Ocean compute capsule. The final dataset for chemical genetic interactions and the dataset of gene expression changes in β-P153L are also available (datasets/*) Funding provided by: National Institutes of HealthCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000002Award Number: R35GM118061Funding provided by: National Science FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000001Award Number: 1659873Funding provided by: National Institutes of HealthCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000002Award Number: GM084448Funding provided by: National Institutes of HealthCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000002Award Number: AI150476The raw images of colony arrays were taken with a Powershot G10 camera (Canon) and a custom illumination configuration. Colony opacity was estimated using the software Iris v. 0.9.4 (Kritikos et al., 2017). Dataset 1 was generated using in-house MATLAB scripts. Dataset 2 was generated using in-house MATLAB scripts, in-house R scripts, and DESeq2 (Love et al., 2014). Please see the associated manuscript for the context of each dataset. References Love, M.I., Huber, W., and Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology 15, 550. Kritikos, G., Banzhaf, M., Herrera-Dominguez, L., Koumoutsi, A., Wartel, M., Zietek, M., and Typas, A. (2017). A tool named Iris for versatile high-throughput phenotyping in microorganisms. Nature microbiology 2, 17014