The Tolerome: A Database of Transcriptome-Level Contributions to Diverse Escherichia coli Resistance and Tolerance Phenotypes

Abstract

Tolerance 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.

Pseudomonas aeruginosa Presents Multiple Vital Changes in Its Proteome in the Presence of 3-Hydroxyphenylacetic Acid, a Promising Antimicrobial Agent

“…Therefore, it leads to problems in the expression of nitrogen metabolism-related proteins due to the role of σ54 in the transcription of nitrogen-related genes under stress conditions. 31 Accordingly, DNA binding transcriptional regulator NtrC ( ntrC ), which has a role in nitrogen utilization, was also undetected, which might show the reduction in nitrogen assimilation in bacteria. The phosphate regulon transcriptional regulatory protein PhoB ( phoB ), which is the major transcription factor that functions in the phosphate limitation of the bacteria, 32 was newly detected ( Table 3 ).…”

Profiling the E. coli Membrane Interactome Captured in Peptidisc Libraries

“…However, the set of gold standard interactions in E. coli is relatively small compared to the set of gold standard interactions derived from mammalian studies (Rajagopala et al 2014;Ruepp et al 2008), meaning that any estimates based entirely on this gold standard set could be susceptible to noise. Therefore, to further estimate the biological plausibility of our peptidisc interactome, we determined whether predicted interacting pairs were more likely than non-interacting pairs to be enriched for three measures of biological association: shared GO terms, positively correlated stress phenotypes, and shared binding domains (Erickson et al 2017;Mosca et al 2014). Further, these enrichment values allowed us to benchmark our interactome against the E. coli cell envelope (CE) interactome recently published by Babu and collaborators (Babu et al 2018).…”

“…We performed multiple validations of both binary interactions and protein complexes using custom Matlab code. To validate the binary interactions, we first calculated three measures of biological association: i) fraction of protein pairs sharing at least one Gene Ontology (GO) term, ii) fraction of protein pairs with a positively correlated stress phenotypes as measured in the Tolerome database (R>0, Pearson correlation; (Erickson et al 2017), and iii) fraction of protein pairs sharing at least one threedimensional interacting domains (3did) . Only GO and 3did terms that annotated >20 and <1000 proteins were used.…”

“…To computationally validate the precision of the peptidisc interactome, we benchmark the dataset against the recently published E.coli cell envelope interactome ("CE") (Babu et al 2018) and two other unpublished interactomes collected for that earlier study ("validating interactomes"). We also measure the biological plausibility of the peptidisc interactome by determining enrichment for shared gene ontology terms, binding domains, and correlation of growth phenotype (Erickson et al 2017;Mosca et al 2014).…”

Antimicrobial Resistance in the Environment

“…Tolerome (Erickson et al, 2017), was developed to meet this need through hand-curating gene expression studies in E.…”