DOI: 10.5061/dryad.z612jm68q
title: Chemical-genetic interrogation of RNA polymerase mutants reveals structure-function relationships and physiological tradeoffs

description: 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.
description: 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
 <i>Iris</i> v. 0.9.4<i> </i>(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)<strong>.</strong> Please see the
 associated manuscript for the context of each dataset.
 <strong>References</strong> Love, M.I., Huber, W., and
 Anders, S. (2014). Moderated estimation of fold change and dispersion for
 RNA-seq data with DESeq2. Genome biology<i> 15</i>,
 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<i> 2</i>,
 17014.
description: This dataset contains the raw images (<em>img/*</em>)
 of colony arrays and associated image analysis data
 (<em>iris/*</em>) 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
 (<em>keys/</em>*) and filtering information
 (<em>filter/*</em>) 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 (<em>readme.txt</em>). 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 (<em>datasets/*</em>)