Predicting Escherichia coli loads in cascading dams with machine learning: An integration of hydrometeorology, animal density and grazing pattern

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.

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Prediction of Acquired Antimicrobial Resistance for Multiple Bacterial Species Using Neural Networks

mSystems ◽

10.1128/msystems.00774-19 ◽

2020 ◽

Vol 5 (1) ◽

Cited By ~ 6

Author(s):

D. Aytan-Aktug ◽

P. T. L. C. Clausen ◽

V. Bortolaia ◽

F. M. Aarestrup ◽

O. Lund

Keyword(s):

Machine Learning ◽

Escherichia Coli ◽

Mycobacterium Tuberculosis ◽

Antimicrobial Resistance ◽

Bacterial Species ◽

Individual Species ◽

Learning Models ◽

Content Type ◽

Multiple Species ◽

Machine Learning Models

ABSTRACT Machine learning has proven to be a powerful method to predict antimicrobial resistance (AMR) without using prior knowledge for selected bacterial species-antimicrobial combinations. To date, only species-specific machine learning models have been developed, and to the best of our knowledge, the inclusion of information from multiple species has not been attempted. The aim of this study was to determine the feasibility of including information from multiple bacterial species to predict AMR for an individual species, since this may make it easier to train and update resistance predictions for multiple species and may lead to improved predictions. Whole-genome sequence data and susceptibility profiles from 3,528 Mycobacterium tuberculosis, 1,694 Escherichia coli, 658 Salmonella enterica, and 1,236 Staphylococcus aureus isolates were included. We developed machine learning models trained by the features of the PointFinder and ResFinder programs detected to predict binary (susceptible/resistant) AMR profiles. We tested four feature representation methods to determine the most efficient way for introducing features into the models. When training the model only on the Mycobacterium tuberculosis isolates, high prediction performances were obtained for the six AMR profiles included. By adding information on ciprofloxacin from the additional 3,588 isolates, there was no reduction in performance for the other antimicrobials but an increased performance for ciprofloxacin AMR profile prediction for Mycobacterium tuberculosis and Escherichia coli. In conclusion, the species-independent models can predict multi-AMR profiles for multiple species without losing any robustness. IMPORTANCE Machine learning is a proven method to predict AMR; however, the performance of any machine learning model depends on the quality of the input data. Therefore, we evaluated different methods of representing information about mutations as well as mobilizable genes, so that the information can serve as input for a robust model. We combined data from multiple bacterial species in order to develop species-independent machine learning models that can predict resistance profiles for multiple antimicrobials and species with high performance.

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Patchy promiscuity: machine learning applied to predict the host specificity of Salmonella enterica and Escherichia coli

Microbial Genomics ◽

10.1099/mgen.0.000135 ◽

2017 ◽

Vol 3 (10) ◽

Cited By ~ 11

Author(s):

Nadejda Lupolova ◽

Tim J. Dallman ◽

Nicola J. Holden ◽

David L. Gally

Keyword(s):

Machine Learning ◽

Escherichia Coli ◽

Host Specificity ◽

Salmonella Enterica

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In Silico Design for Systems-Based Metabolic Engineering In Escherichia Coli for The Bioconversion of Aerobic Glycerol to Succinic Acid

10.21203/rs.3.rs-113854/v1 ◽

2020 ◽

Author(s):

Albert Enrique Tafur Rangel ◽

Wendy Lorena Rios Guzman ◽

Carmen Elvira Ojeda Cuella ◽

Daissy Esther Mejia Perez ◽

Ross Carlson ◽

...

Keyword(s):

Machine Learning ◽

Escherichia Coli ◽

Metabolic Engineering ◽

Succinic Acid ◽

In Silico ◽

Rational Design ◽

Industrial Biotechnology ◽

E Coli ◽

Cell Factories ◽

Transcriptomics Data

Abstract BackgroundGlycerol has become an interesting carbon source for industrial processes as consequence of the biodiesel business growth since it has shown promising results in terms of biomass/substrate yields. Selecting the appropriate metabolic targets to build efficient cell factories and maximize the desired chemical production in as little time as possible is a major challenge in industrial biotechnology. The engineering of microbial metabolism following rational design has been widely studied. However, it is a cost-, time-, and laborious-intensive process because of the cell network complexity; thus, to be proficient is needed known in advance the effects of gene deletions.ResultsAn in silico experiment was performed to model and understand the effects of metabolic engineering over the metabolism by transcriptomics data integration. In this study, systems-based metabolic engineering to predict the metabolic engineering targets was used in order to increase the bioconversion of glycerol to succinic acid by Escherichia coli. Transcriptomics analysis suggest insights of how increase the glycerol utilization of the cell for further design efficient cell factories. Three models were used; an E. coli core model, a model obtained after the integration of transcriptomics data obtained from E. coli growing in an optimized culture media, and a third one obtained after integration of transcriptomics data obtained from E. coli after adaptive laboratory evolution experiments. A total of 2402 strains were obtained from these three models. Fumarase and pyruvate dehydrogenase were frequently predicted in all the models, suggesting that these reactions are essential to increasing succinic acid production from glycerol. Finally, using flux balance analysis results for all the mutants predicted, a machine learning method was developed to predict new mutants as well as to propose optimal metabolic engineering targets and mutants based on the measurement of importance of each knockout’s (feature’s) contribution.ConclusionsThe combination of transcriptome, systems metabolic modeling, and machine learning analyses revealed versatile molecular mechanisms involved in the utilization of glycerol. These data provide a platform to improve the prediction of metabolic engineering targets to design efficient cell factories. Our results may also work a guide platform for the selection/engineering of microorganisms for production of interesting chemical compounds.

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Evaluating the Performance of Machine Learning Approaches to Predict the Microbial Quality of Surface Waters and to Optimize the Sampling Effort

Water ◽

10.3390/w13182457 ◽

2021 ◽

Vol 13 (18) ◽

pp. 2457

Author(s):

Manel Naloufi ◽

Françoise S. Lucas ◽

Sami Souihi ◽

Pierre Servais ◽

Aurélie Janne ◽

...

Keyword(s):

Machine Learning ◽

Escherichia Coli ◽

Random Forest ◽

Microbiological Quality ◽

Support Vector ◽

Sampling Effort ◽

Temperature Conductivity ◽

Adaptive Boosting ◽

Paris Area

Exposure to contaminated water during aquatic recreational activities can lead to gastrointestinal diseases. In order to decrease the exposure risk, the fecal indicator bacteria Escherichia coli is routinely monitored, which is time-consuming, labor-intensive, and costly. To assist the stakeholders in the daily management of bathing sites, models have been developed to predict the microbiological quality. However, model performances are highly dependent on the quality of the input data which are usually scarce. In our study, we proposed a conceptual framework for optimizing the selection of the most adapted model, and to enrich the training dataset. This frameword was successfully applied to the prediction of Escherichia coli concentrations in the Marne River (Paris Area, France). We compared the performance of six machine learning (ML)-based models: K-nearest neighbors, Decision Tree, Support Vector Machines, Bagging, Random Forest, and Adaptive boosting. Based on several statistical metrics, the Random Forest model presented the best accuracy compared to the other models. However, 53.2 ± 3.5% of the predicted E. coli densities were inaccurately estimated according to the mean absolute percentage error (MAPE). Four parameters (temperature, conductivity, 24 h cumulative rainfall of the previous day the sampling, and the river flow) were identified as key variables to be monitored for optimization of the ML model. The set of values to be optimized will feed an alert system for monitoring the microbiological quality of the water through combined strategy of in situ manual sampling and the deployment of a network of sensors. Based on these results, we propose a guideline for ML model selection and sampling optimization.

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Physicochemical and Structural Parameters Contributing to the Antibacterial Activity and Efflux Susceptibility of Small Molecule Inhibitors of Escherichia coli

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.01925-20 ◽

2021 ◽

Author(s):

Sara S. El Zahed ◽

Shawn French ◽

Maya A. Farha ◽

Garima Kumar ◽

Eric D. Brown

Keyword(s):

Machine Learning ◽

Escherichia Coli ◽

Antibacterial Activity ◽

Small Molecules ◽

Small Molecule ◽

In Silico ◽

Molecular Descriptors ◽

Structural Parameters ◽

Side Chain ◽

Gram Negative

Discovering new Gram-negative antibiotics has been a challenge for decades. This has been largely attributed to a limited understanding of the molecular descriptors governing Gram-negative permeation and efflux evasion. Herein, we address the contribution of efflux using a novel approach that applies multivariate analysis, machine learning, and structure-based clustering to some 4,500 actives from a small molecule screen in efflux-compromised Escherichia coli. We employed principal-component analysis and trained two decision tree-based machine learning models to investigate descriptors contributing to the antibacterial activity and efflux susceptibility of these actives. This approach revealed that the Gram-negative activity of hydrophobic and planar small molecules with low molecular stability is limited to efflux-compromised E. coli. Further, molecules with reduced branching and compactness showed increased susceptibility to efflux. Given these distinct properties that govern efflux, we developed the first machine learning model, called Susceptibility to Efflux Random Forest (SERF), as a tool to analyze the molecular descriptors of small molecules and predict those that could be susceptible to efflux pumps in silico. Here, SERF demonstrated high accuracy in identifying such molecules. Further, we clustered all 4,500 actives based on their core structures and identified distinct clusters highlighting side chain moieties that cause marked changes in efflux susceptibility. In all, our work reveals a role for physicochemical and structural parameters in governing efflux, presents a machine learning tool for rapid in silico analysis of efflux susceptibility, and provides a proof of principle for the potential of exploiting side chain modification to design novel antimicrobials evading efflux pumps.

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In silico screening of ssDNA aptamer against Escherichia coli O157:H7: a machine learning and the Pseudo K-tuple nucleotide composition based approach

Computational Biology and Chemistry ◽

10.1016/j.compbiolchem.2021.107568 ◽

2021 ◽

pp. 107568

Author(s):

Mokhtar Nosrati ◽

Jafar amani

Keyword(s):

Machine Learning ◽

Escherichia Coli ◽

In Silico ◽

Nucleotide Composition ◽

Escherichia Coli O157 ◽

In Silico Screening ◽

Ssdna Aptamer

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