About this Research Topic
The possibility of predicting antimicrobial resistance (AMR) profiles from genomic data of pathogenic bacteria is considered a promising strategy to help implement more efficient programs for the surveillance of antibiotic resistance. In September 2020, the World Health Organization (WHO), through its Global Antimicrobial Resistance and Use Surveillance System (GLASS) initiative, published a guiding document on the use of whole-genome sequencing (WGS) in the context of AMR surveillance. In 2022, in line with the WHO initiative, the Global Alliance for Diagnostics also published a detailed document with important guidelines for implementing genomic surveillance in this context. Among the main recommendations of these documents is the need to implement international standards for predicting AMR from genomic data since there are no published standards yet for quality control and evaluation of the effectiveness and reliability of strategies and bioinformatics analysis pipelines currently in use.
Adopting genomic strategies for AMR prediction still faces several challenges, especially in developing countries. One of the most essential difficulties involves the concrete definition of antibiotic resistance genes (ARGs), and associated mutations, which are genuinely relevant to public health in a regional context, in addition to the correct integration between the genotype and the phenotypes in tests considered 'gold standard' for determining susceptibilities to antimicrobials (AST) in clinical isolates.
In recent literature, it is also possible to identify some examples of initiatives dedicated to implementing genomics-based surveillance of AMR in clinical practice, including in regions with limited access to resources for carrying out sequencing using NGS (next-generation sequencing) platforms. From these initiatives, it was possible to detect some of the main challenges that exist in carrying out the clinical implementation of these genomic surveillance protocols, which include: (1) the limited access to infrastructure for bioinformatics analyses, as well as limited training of local human resources in the field; (2) difficult interpretation of the clinical relevance of genomic findings for AMR prediction; (3) high cost of access to reagents necessary for sequencing on NGS platforms; and (4) limited knowledge for integrating AMR genotypes and phenotypes, particularly for emerging pathogens.
This Research Topic aims to provide a platform for discussing the current strategies for genome-based AMR prediction, as well as their limitations and future use in genomic surveillance of AMR. This Research Topic invites manuscripts covering all the themes described below, as well as other related themes in the field:
- Genomics and metagenomics studies with a focus on resistance to antimicrobial agents;
- Development and/or evaluation of bioinformatics strategies for predicting AMR from genomic data;
- Rules-based approaches and Machine Learning-based approaches for predicting antimicrobial resistance;
- Databases of antimicrobial resistance genes (ARGs) and mutations associated with AMR;
- Novel metrics for evaluating the reliabilities of different approaches for genome-based AMR prediction;
- Attempts to clinical implementation of genome-based surveillance of AMR in GLASS-priority pathogens and emerging multidrug-resistant pathogens;
- Standardized and/or streamlined bioinformatics pipelines for aiding genome-based surveillance of AMR in clinical settings;
Adopting genomic strategies for AMR prediction still faces several challenges, especially in developing countries. One of the most essential difficulties involves the concrete definition of antibiotic resistance genes (ARGs), and associated mutations, which are genuinely relevant to public health in a regional context, in addition to the correct integration between the genotype and the phenotypes in tests considered 'gold standard' for determining susceptibilities to antimicrobials (AST) in clinical isolates.
In recent literature, it is also possible to identify some examples of initiatives dedicated to implementing genomics-based surveillance of AMR in clinical practice, including in regions with limited access to resources for carrying out sequencing using NGS (next-generation sequencing) platforms. From these initiatives, it was possible to detect some of the main challenges that exist in carrying out the clinical implementation of these genomic surveillance protocols, which include: (1) the limited access to infrastructure for bioinformatics analyses, as well as limited training of local human resources in the field; (2) difficult interpretation of the clinical relevance of genomic findings for AMR prediction; (3) high cost of access to reagents necessary for sequencing on NGS platforms; and (4) limited knowledge for integrating AMR genotypes and phenotypes, particularly for emerging pathogens.
This Research Topic aims to provide a platform for discussing the current strategies for genome-based AMR prediction, as well as their limitations and future use in genomic surveillance of AMR. This Research Topic invites manuscripts covering all the themes described below, as well as other related themes in the field:
- Genomics and metagenomics studies with a focus on resistance to antimicrobial agents;
- Development and/or evaluation of bioinformatics strategies for predicting AMR from genomic data;
- Rules-based approaches and Machine Learning-based approaches for predicting antimicrobial resistance;
- Databases of antimicrobial resistance genes (ARGs) and mutations associated with AMR;
- Novel metrics for evaluating the reliabilities of different approaches for genome-based AMR prediction;
- Attempts to clinical implementation of genome-based surveillance of AMR in GLASS-priority pathogens and emerging multidrug-resistant pathogens;
- Standardized and/or streamlined bioinformatics pipelines for aiding genome-based surveillance of AMR in clinical settings;
Keywords: antimicrobial resistance, genomics, next-generation sequencing, bioinformatics, surveillance
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