Dataset Information

Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning.

ABSTRACT: It is important that antibiotics prescriptions are based on antimicrobial susceptibility data to ensure effective treatment outcomes. The increasing availability of next-generation sequencing, bacterial whole genome sequencing (WGS) can facilitate a more reliable and faster alternative to traditional phenotyping for the detection and surveillance of AMR. This work proposes a machine learning approach that can predict the minimum inhibitory concentration (MIC) for a given antibiotic, here ciprofloxacin, on the basis of both genome-wide mutation profiles and profiles of acquired antimicrobial resistance genes. We analysed 704 Escherichia coli genomes combined with their respective MIC measurements for ciprofloxacin originating from different countries. The four most important predictors found by the model, mutations in gyrA residues Ser83 and Asp87, a mutation in parC residue Ser80 and presence of the qnrS1 gene, have been experimentally validated before. Using only these four predictors in a linear regression model, 65% and 93% of the test samples' MIC were correctly predicted within a two- and a four-fold dilution range, respectively. The presented work does not treat machine learning as a black box model concept, but also identifies the genomic features that determine susceptibility. The recent progress in WGS technology in combination with machine learning analysis approaches indicates that in the near future WGS of bacteria might become cheaper and faster than a MIC measurement.

SUBMITTER: Pataki BA

PROVIDER: S-EPMC7490380 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning.

Pataki Bálint Ármin BÁ Matamoros Sébastien S van der Putten Boas C L BCL Remondini Daniel D Giampieri Enrico E Aytan-Aktug Derya D Hendriksen Rene S RS Lund Ole O Csabai István I Schultsz Constance C

Scientific reports 20200914 1

It is important that antibiotics prescriptions are based on antimicrobial susceptibility data to ensure effective treatment outcomes. The increasing availability of next-generation sequencing, bacterial whole genome sequencing (WGS) can facilitate a more reliable and faster alternative to traditional phenotyping for the detection and surveillance of AMR. This work proposes a machine learning approach that can predict the minimum inhibitory concentration (MIC) for a given antibiotic, here ciprofl ...[more]

PMID: 32929164

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Project description:Gram-negative bacteria possess stress responses to maintain the integrity of the cell envelope. Stress sensors monitor outer membrane permeability, envelope protein folding, and energization of the inner membrane. The systems used by Gram-negative bacteria to sense and combat stress resulting from disruption of the peptidoglycan layer are not well characterized. The peptidoglycan layer is a single molecule that completely surrounds the cell and ensures its structural integrity. During cell growth new peptidoglcyan subunits are incorporated into the peptidoglycan layer by a series of enzymes called the penicillin-binding proteins (PBPs). To explore how Gram-negative bacteria respond to peptidoglycan stress, global gene expression analysis was used to identify Escherichia coli stress responses activated following inhibition of specific PBPs by the β-lactam antibiotics mecillinam and cefsulodin. Inhibition of PBPs with different roles in peptidoglycan synthesis has different consequences for cell morphology and viability, suggesting that not all perturbations to the peptidoglycan layer generate equivalent stresses. We demonstrate that inhibition of different PBPs resulted in both shared and unique stress responses. The regulation of capsular synthesis (Rcs) phosphorelay was activated by inhibition of all of the PBPs tested. Furthermore, we show that activation of the Rcs phosphorelay increased survival in the presence of these antibiotics, independently of capsule synthesis. Both activation of the phosphorelay and survival required signal transduction via the outer membrane lipoprotein RcsF and the response regulator RcsB. We propose that the Rcs pathway responds to peptidoglycan damage and contributes to the intrinsic resistance of E. coli to β-lactam antibiotics. We used microarrays to identify changes in gene expression resulting from treatment of Escherichia coli with the β-lactam antibiotics cefsulodin, mecillinam, or the combination. Keywords: Dose response, stress response

Project description:Gram-negative bacteria possess stress responses to maintain the integrity of the cell envelope. Stress sensors monitor outer membrane permeability, envelope protein folding, and energization of the inner membrane. The systems used by Gram-negative bacteria to sense and combat stress resulting from disruption of the peptidoglycan layer are not well characterized. The peptidoglycan layer is a single molecule that completely surrounds the cell and ensures its structural integrity. During cell growth new peptidoglcyan subunits are incorporated into the peptidoglycan layer by a series of enzymes called the penicillin-binding proteins (PBPs). To explore how Gram-negative bacteria respond to peptidoglycan stress, global gene expression analysis was used to identify Escherichia coli stress responses activated following inhibition of specific PBPs by the Î²-lactam antibiotics mecillinam and cefsulodin. Inhibition of PBPs with different roles in peptidoglycan synthesis has different consequences for cell morphology and viability, suggesting that not all perturbations to the peptidoglycan layer generate equivalent stresses. We demonstrate that inhibition of different PBPs resulted in both shared and unique stress responses. The regulation of capsular synthesis (Rcs) phosphorelay was activated by inhibition of all of the PBPs tested. Furthermore, we show that activation of the Rcs phosphorelay increased survival in the presence of these antibiotics, independently of capsule synthesis. Both activation of the phosphorelay and survival required signal transduction via the outer membrane lipoprotein RcsF and the response regulator RcsB. We propose that the Rcs pathway responds to peptidoglycan damage and contributes to the intrinsic resistance of E. coli to Î²-lactam antibiotics. We used microarrays to identify changes in gene expression resulting from treatment of Escherichia coli with the Î²-lactam antibiotics cefsulodin, mecillinam, or the combination. Experiment Overall Design: E. coli was treated with two b-lactam antibiotics, cefsulodin and mecillinam, alone at the minimum inhibitory concentration to identify the expression changes in response to antibiotic-induced peptidoglycan stress. E. coli was grown to an OD of 0.2 at which time cefsulodin or mecillinam was added. Samples of the bacteria were collected at 5 and 10 minutes after treatment with cefsulodin and 60 minutes after treatment with mecillinam. Untreated cells were also collected at the same time points as controls. Three biological replicates were collected for all time points and samples, however, only one of the 5 minute untreated samples was hybridized to the arrays. In addition, a single sample from cells treated with the combination of cefsulodin and mecillinam was hybridized to allow for comparison with samples hybridized to Affymetrix antisense genechips.

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Dataset Information

Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning.

Publications

Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning.

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