About this Research Topic
Since their discovery in 1928 and their first application in 1935, antibiotics have proven to be effective in curing many diseases of humans and animals. Soon after their application, resistant bacteria have been repeatedly reported, what is called now as antimicrobial resistance (AMR). Unfortunately, this was coupled with a decline in the number of development pipelines of novel antimicrobials. According to the most recent report of the UN environmental program, deaths due to AMR are expected to reach 10 million globally per year by 2050. The threat of AMR will influence developed as well as low and middle-income countries (mainly in Africa and Asia), yet the later will be more adversely affected. Monitoring and understanding the molecular basis of AMR evolution are thus becoming integral parts of the efforts geared towards its prevention and control.
Currently, most laboratories assess AMR via exposing the cultured bacteria to antibiotics and phenotypically estimate the growth inhibition. This approach is time consuming, not suitable for uncultured bacteria, provides low-resolution and limited scope of the phenomena. The application of high throughput next generation sequencing technologies is expected to compensate for the previously mentioned shortfalls. Techniques such as whole genome sequencing (WGS), shotgun metagenomics and matrix-assisted laser desorbition/ionization time of flight (MALDI-TOF) are being used to monitor and predict AMR via detecting mobile genetic elements (MGE) and their cargo antimicrobial resistance genes (ARG). The novel proximity ligation methods (e.g. Hi-C and 3C) is filling a gap in the conventional metagenomics by allowing the attribution of the identified ARG and MGE to their reservoir bacterial host. Bacterial transcriptomic aims to uncover molecular mechanisms (e.g. gene expression and mutations) and regulatory circuits that drive AMR emergence. Analyzing bacterial pangenomes (phylogenomics) and the associated epidemiological data (phylodynamics) can infer temporal and spatial transmission dynamics of ARG during outbreaks. Furthermore, 16s sequencing can aid to understand whether microbiota are drivers or barriers for AMR dissemination. The data generated from these approaches are usually large and complex to be analyzed by standard tools. Here comes the importance of bioinformatics to obtain meaningful insights from such high-dimensional data. Bioinformatics lie at an exciting intersection between biology, statistics and computer science. Current research indicates that machine learning (ML) and multivariate analyses hold strong potential to pave the way for automating the monitoring and prediction of AMR via complementing and supporting the applied bioinformatics approaches.
This Research Topic is meant to be a forum for research articles and reviews that exemplify and evidence the power of bioinformatics and advanced data analyses in supporting traditional microbiological techniques in tackling the problem of AMR in human, animal and environmental.
The aim of this Research Topic is to discuss articles and reviews that focus on the application and developments of bioinformatics and advanced data analyses (e.g. machine learning and modeling) in the field of AMR. The articles discussing the following topics (and related ones) are welcome:
• Using "whole genome sequencing" in monitoring and analyzing bacterial populations and MGE.
• Using "Metagenomic, metatranscriptomics and proximity ligation approaches" in analyzing AMR in complex microbial populations and to link the identified MGE or ARG with their respective bacterial reservoir.
• Using "bacterial transcriptomic" to provide mechanistic insights into the emergence and evolution of AMR for instance via analyzing gene expression and genetic variants.
• Using "phylogenomics and phylodynamics" to infer AMR transmission and epidemiology from genomic data.
• Using "microbiome analyses" to reveal the roles played by commensal microbiota as drivers or barriers for AMR burden.
• Using "structural bioinformatics" as it applied to the AMR problem, especially if combined with other genomic approaches.
• Using "metabolomics" and "proteomics, for example MALDI-TOF" for complementing the genomic analyses of AMR.
• Application of various "machine learning models and multivariate analyses" on output large data to help tackling AMR problem.
Currently, most laboratories assess AMR via exposing the cultured bacteria to antibiotics and phenotypically estimate the growth inhibition. This approach is time consuming, not suitable for uncultured bacteria, provides low-resolution and limited scope of the phenomena. The application of high throughput next generation sequencing technologies is expected to compensate for the previously mentioned shortfalls. Techniques such as whole genome sequencing (WGS), shotgun metagenomics and matrix-assisted laser desorbition/ionization time of flight (MALDI-TOF) are being used to monitor and predict AMR via detecting mobile genetic elements (MGE) and their cargo antimicrobial resistance genes (ARG). The novel proximity ligation methods (e.g. Hi-C and 3C) is filling a gap in the conventional metagenomics by allowing the attribution of the identified ARG and MGE to their reservoir bacterial host. Bacterial transcriptomic aims to uncover molecular mechanisms (e.g. gene expression and mutations) and regulatory circuits that drive AMR emergence. Analyzing bacterial pangenomes (phylogenomics) and the associated epidemiological data (phylodynamics) can infer temporal and spatial transmission dynamics of ARG during outbreaks. Furthermore, 16s sequencing can aid to understand whether microbiota are drivers or barriers for AMR dissemination. The data generated from these approaches are usually large and complex to be analyzed by standard tools. Here comes the importance of bioinformatics to obtain meaningful insights from such high-dimensional data. Bioinformatics lie at an exciting intersection between biology, statistics and computer science. Current research indicates that machine learning (ML) and multivariate analyses hold strong potential to pave the way for automating the monitoring and prediction of AMR via complementing and supporting the applied bioinformatics approaches.
This Research Topic is meant to be a forum for research articles and reviews that exemplify and evidence the power of bioinformatics and advanced data analyses in supporting traditional microbiological techniques in tackling the problem of AMR in human, animal and environmental.
The aim of this Research Topic is to discuss articles and reviews that focus on the application and developments of bioinformatics and advanced data analyses (e.g. machine learning and modeling) in the field of AMR. The articles discussing the following topics (and related ones) are welcome:
• Using "whole genome sequencing" in monitoring and analyzing bacterial populations and MGE.
• Using "Metagenomic, metatranscriptomics and proximity ligation approaches" in analyzing AMR in complex microbial populations and to link the identified MGE or ARG with their respective bacterial reservoir.
• Using "bacterial transcriptomic" to provide mechanistic insights into the emergence and evolution of AMR for instance via analyzing gene expression and genetic variants.
• Using "phylogenomics and phylodynamics" to infer AMR transmission and epidemiology from genomic data.
• Using "microbiome analyses" to reveal the roles played by commensal microbiota as drivers or barriers for AMR burden.
• Using "structural bioinformatics" as it applied to the AMR problem, especially if combined with other genomic approaches.
• Using "metabolomics" and "proteomics, for example MALDI-TOF" for complementing the genomic analyses of AMR.
• Application of various "machine learning models and multivariate analyses" on output large data to help tackling AMR problem.
Keywords: one-health, genomics, bacteria, antimicrobial resistance
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