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Download fileThe Tolerome: A Database of Transcriptome-Level Contributions to Diverse Escherichia coli Resistance and Tolerance Phenotypes
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posted on 2017-10-10, 00:00 authored by Keesha
E. Erickson, James D. Winkler, Danh T. Nguyen, Ryan T. Gill, Anushree ChatterjeeTolerance and resistance
are complex biological phenotypes that
are desirable bioengineering goals for those seeking to design industrial
strains or prevent the spread of antibiotic resistance. Over decades
of research, a wealth of information has been generated to attempt
to decode a molecular basis for tolerance, but to fully achieve the
goal of engineering tolerance, researchers must be able to easily
learn from a variety of data sources. To this end, we here describe
a resource designed to enable scrutiny of diverse tolerance phenotypes.
We have curated hundreds of gene expression studies exploring the
response of Escherichia coli to chemical and environmental
perturbations, from antibiotics to biofuels and solvents and more.
Overall, our efforts give rise to a database encompassing more than
56 000 gene expression changes across 89 different stress conditions.
This resource is designed for compatibility with the Resistome database,
which includes more than 5000 strains with mutations conferring resistance
or sensitivity but no transcriptomic data. Thus, the work here results
in the first combined resource specialized to tolerance and resistance
in E. coli that supports investigations across
genomic, transcriptomic, and phenotypic levels. We leverage the database
to identify promising bioengineering targets by searching globally
across multiple stress conditions as well as by narrowing the focus
to fewer conditions of interest, such as biofuel stress and antibiotic
stress. We discuss some of the most frequently differentially expressed
or coexpressed genes, and predict which transcription factors and
sigma factors most likely contribute to gene expression profiles in
a wide array of conditions. We also compare profiles from sensitive
and resistant strains, gaining knowledge of how responses differ per
overrepresented gene ontology terms. Finally, we search for genes
that are frequently differentially expressed but not mutated, with
the expectation that these may present interesting targets for future
engineering efforts. The curated data presented here is publicly available,
and should be advantageous to those studying a variety of bacterial
tolerance phenotypes.