Bioinformatics Approaches to the Understanding of Molecular Mechanisms in Antimicrobial Resistance

Camp, Pieter-Jan Van; Haslam, David B.; Porollo, Aleksey

doi:10.3390/ijms21041363

Cited by 42 publications

(35 citation statements)

References 20 publications

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“…(Meta-)transcriptomics data, unfortunately, and remains mostly within research realm. Complexity of reference database, inference of organisms/strains, and their relative abundance data (Cox et al, 2017), dynamic gene expression profile upon different drug treatments, and overall complexity of data preparation/generation impede the application of meta-transcriptomics data for real-time predictions (Van Camp et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Neural networks (NN), especially their currently popular application to deep learning, and require much larger training data-sets (tens of thousands to millions of input vectors/samples) than currently available for antimicrobial resistance (hundreds of samples) in order to demonstrate benefits of deep learning. Moreover, the resulting NN-based models represent a black box that would be difficult to dissect in order to see the decision making rules and factors influencing the decision (Van Camp et al, 2020). XGBoost is an extreme gradient booster for decision trees that is capable of handling correlated inputs.…”

Section: Building and Evaluating Machine Learning Modelsmentioning

confidence: 99%

“…Finally, isolates with intermediate resistance were excluded from the final predictors making the performance estimates on such samples uncertain. For more details on the bioinformatics approaches to the AMR analysis and prediction, the reader can refer to a recent review (Van Camp et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data

2020

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Background: Early detection of antimicrobial resistance in pathogens and prescription of more effective antibiotics is a fast-emerging need in clinical practice. High-throughput sequencing technology, such as whole genome sequencing (WGS), may have the capacity to rapidly guide the clinical decision-making process. The prediction of antimicrobial resistance in Gram-negative bacteria, often the cause of serious systemic infections, is more challenging as genotype-to-phenotype (drug resistance) relationship is more complex than for most Gram-positive organisms. Methods and Findings: We have used NCBI BioSample database to train and crossvalidate eight XGBoost-based machine learning models to predict drug resistance to cefepime, cefotaxime, ceftriaxone, ciprofloxacin, gentamicin, levofloxacin, meropenem, and tobramycin tested in Acinetobacter baumannii, Escherichia coli, Enterobacter cloacae, Klebsiella aerogenes, and Klebsiella pneumoniae. The input is the WGS data in terms of the coverage of known antibiotic resistance genes by shotgun sequencing reads. Models demonstrate high performance and robustness to class imbalanced datasets. Conclusion: Whole genome sequencing enables the prediction of antimicrobial resistance in Gram-negative bacteria. We present a tool that provides an in silico antibiogram for eight drugs. Predictions are accompanied with a reliability index that may further facilitate the decision making process. The demo version of the tool with pre-processed samples is available at https://vancampn.shinyapps.io/wgs2amr/.

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Section: Discussionmentioning

confidence: 99%

Section: Building and Evaluating Machine Learning Modelsmentioning

confidence: 99%

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Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data

2020

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“…This is particularly urgent for diseases like tuberculosis, mainly caused by Mtb , or nosocomial infections caused by opportunistic pathogens such as Staphylococcus aureus , Pseudomonas aeruginosa , and Acinetobacter baumannii ( Dheda et al, 2017 ; Botelho et al, 2019 ; Guo et al, 2020 ; Moubareck and Halat, 2020 ). Antibiotic resistance may be conferred through different mechanisms, such as antibiotic degradation or modification, receptor alteration, or antibiotic efflux mediated by membrane transport systems ( Machado et al, 2017 ; Singh et al, 2019 ; Van Camp et al, 2020 ). Efflux transporters are a serious problem to the efficacy of antimicrobial drugs as they confer to bacteria the capacity to rapidly export drugs and to evade current drug therapies ( Alcalde-Rico et al, 2016 ; Du et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

“…Alternative strategies to combat MDR disease are underway, like the development of sensitive tests for early diagnosis of drug-resistant TB, or identification of bacterial phenotypes through drug therapy as biomarkers of treatment success, or therapeutic targeting of host immune responses ( Honeyborne et al, 2016 ; Wilder et al, 2018 ; Thapa et al, 2019 ; Ahmed et al, 2020 ; Tsenova and Singhal, 2020 ). Studies on the molecular mechanisms involved in the acquisition of drug-resistant phenotypes in mycobacteria also represent an important strategy to combat MDR disease ( Hameed et al, 2018 ; Van Camp et al, 2020 ). In Mtb , drug resistance evolves through chromosomal mutations that impact drug degradation and modification or target alteration, which operate alongside intrinsic factors such as low cell wall permeability and an extensive efflux network ( Singh et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Characterization of the Mycobacterial MSMEG-3762/63 Efflux Pump in Mycobacterium smegmatis Drug Efflux

et al. 2020

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Multi-drug resistant tuberculosis (MDR-TB) represents a major health problem worldwide. Drug efflux and the activity of efflux transporters likely play important roles in the development of drug-tolerant and drug-resistant mycobacterial phenotypes. This study is focused on the action of a mycobacterial efflux pump as a mechanism of drug resistance. Previous studies demonstrated up-regulation of the TetR-like transcriptional regulator MSMEG_3765 in Mycobacterium smegmatis and its ortholog Rv1685c in Mycobacterium tuberculosis (Mtb) in acid-nitrosative stress conditions. MSMEG-3765 regulates the expression of the MSMEG_3762/63/65 operon, and of the orthologous region in Mtb (Rv1687c/86c/85c). MSMEG-3762 and Rv1687c are annotated as ATP-binding proteins, while MSMEG-3763 and Rv1686c are annotated as trans-membrane polypeptides, defining an ABC efflux pump in both M. smegmatis and Mtb. The two putative efflux systems share a high percentage of identity. To examine the role of the putative efflux system MSMEG-3762/63, we constructed and characterized a MSMEG-3763 deletion mutant in M. smegmatis (∆MSMEG_3763). By comparative analysis of wild type, knockout, and complemented strains, together with structural modeling and molecular docking bioinformatics analyses of the MSMEG-3763 trans-membrane protein, we define the protein complex MSMEG-3762/63 as an efflux pump. Moreover, we demonstrate involvement of this pump in biofilm development and in the extrusion of rifampicin and ciprofloxacin (CIP), antimicrobial drugs used in first- and second-line anti-TB therapies.

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ASGARD+: A New Modular Platform for Bacterial Antibiotic‐Resistant Analysis

Montero-Vargas¹,

Saenz-Rojas²,

Suárez-Esquivel

et al. 2023

Current Protocols

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ASGARD+ (Accelerated Sequential Genome-analysis and Antibiotic Resistance Detection) is a command-line platform for automatic identification of antibiotic-resistance genes in bacterial genomes, providing an easy-to-use interface to process big batches of sequence files from whole genome sequencing, with minimal configuration. It also provides a CPU-optimization algorithm that reduces the processing time. This tool consists of two main protocols. The first one, ASGARD, is based on the identification and annotation of antimicrobial resistance elements directly from the short reads using different public databases. SAGA, enables the alignment, indexing, and mapping of whole-genome samples against a reference genome for the detection and call of variants, as well as the visualization of the results through the construction of a tree of SNPs. The application of both protocols is performed using just one short command and one configuration file based on JSON syntax, which modulates each pipeline step, allowing the user to do as many interventions as needed on the different software tools that are adapted to the pipeline. The modular ASGARD+ allows researchers with little experience in bioinformatic analysis and command-line use to quickly explore bacterial genomes in depth, optimizing analysis times and obtaining accurate results.

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Bioinformatics Approaches to the Understanding of Molecular Mechanisms in Antimicrobial Resistance

Cited by 42 publications

References 20 publications

Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data

Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data

Characterization of the Mycobacterial MSMEG-3762/63 Efflux Pump in Mycobacterium smegmatis Drug Efflux

ASGARD+: A New Modular Platform for Bacterial Antibiotic‐Resistant Analysis

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