Dataset Information

Regulatory fine-tuning of mcr-1 increases bacterial fitness and stabilises antibiotic resistance in agricultural settings.

ABSTRACT: Antibiotic resistance tends to carry fitness costs, making it difficult to understand how resistance can be maintained in the absence of continual antibiotic exposure. Here we investigate this problem in the context of mcr-1, a globally disseminated gene that confers resistance to colistin, an agricultural antibiotic that is used as a last resort for the treatment of multi-drug resistant infections. Here we show that regulatory evolution has fine-tuned the expression of mcr-1, allowing E. coli to reduce the fitness cost of mcr-1 while simultaneously increasing colistin resistance. Conjugative plasmids have transferred low-cost/high-resistance mcr-1 alleles across an incredible diversity of E. coli strains, further stabilising mcr-1 at the species level. Regulatory mutations were associated with increased mcr-1 stability in pig farms following a ban on the use of colistin as a growth promoter that decreased colistin consumption by 90%. Our study shows how regulatory evolution and plasmid transfer can combine to stabilise resistance and limit the impact of reducing antibiotic consumption.

SUBMITTER: Ogunlana L

PROVIDER: S-EPMC10579358 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Regulatory fine-tuning of mcr-1 increases bacterial fitness and stabilises antibiotic resistance in agricultural settings.

Ogunlana Lois L Kaur Divjot D Shaw Liam P LP Jangir Pramod P Walsh Timothy T Uphoff Stephan S MacLean R C RC

The ISME journal 20230918 11

Antibiotic resistance tends to carry fitness costs, making it difficult to understand how resistance can be maintained in the absence of continual antibiotic exposure. Here we investigate this problem in the context of mcr-1, a globally disseminated gene that confers resistance to colistin, an agricultural antibiotic that is used as a last resort for the treatment of multi-drug resistant infections. Here we show that regulatory evolution has fine-tuned the expression of mcr-1, allowing E. coli t ...[more]

PMID: 37723338

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Project description:To create a dataset of PSMs that does not exhibit tryptic bias, we selected PSMs with a uniform distribution of amino acids at the C-terminal peptide positions from two datasets: MassIVE-KB [Wang2018] and PROSPECT [Shouman2022]. The MassIVE-KB dataset contains 30 million PSMs and consists entirely of data generated using trypsin; hence, only a small proportion of the MassIVE-KB peptides do not end in K or R, corresponding to those that appear at the C-terminus of the corresponding protein. The PROSPECT dataset is a collection of 61 million PSMs generated from synthetic peptides. To avoid overrepresentation of some peptides in this dataset, we randomly selected at most 100 PSMs per unique peptide, similar to the processing that was done during the creation of the MassIVE-KB dataset. This pre-selection step reduced the size of the PROSPECT dataset to 12.6 million PSMs. Finally, to create a non-enzymatic dataset containing 1 million peptides, we selected 50,000 PSMs for each canonical amino acid. These PSMs were selected at random from MassIVE-KB, then supplemented as necessary from PROSPECT to obtain the desired count (see Yilmaz et al. [Yilmaz2023] Supplementary Table S1). This dataset contained PSMs from 247,859 unique peptides, which were randomly split into training, validation and test sets in the ratio 80/10/10. FTP directory contains the mgf files corresponding to train, validation and test splits. [Wang2018] M. Wang, J. Wang, J. Carver, B. Pullman, S. Won Cha, N. Bandeira, "Assembling the Community-Scale Discoverable Human Proteome", Cell Systems, Volume 7, Issue 4, 2018. [Shouman2022] O. Shouman, W. Gabriel, V. Giurcoiu, V. Sternlicht, M. Wilhelm, "PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics", NeurIPS Datasets and Benchmarks, 2022 [Yilmaz2023] M. Yilmaz*, W. Fondrie*, W. Bittremieux, R. Nelson, V. Ananth, S. Oh, and W. Noble,"Sequence-to-sequence translation from mass spectra to peptides with a transformer model", bioRxiv, 2023

Dataset Information

Regulatory fine-tuning of mcr-1 increases bacterial fitness and stabilises antibiotic resistance in agricultural settings.

Publications

Regulatory fine-tuning of mcr-1 increases bacterial fitness and stabilises antibiotic resistance in agricultural settings.

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