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.

Methods
The 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.