Francesc Coll (IBV) — Large-scale genomic analyses applied to study the genetic mechanisms of antibiotic resistance in enterococci
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Check this Success Story at our LinkedIn: Large-scale genomic analyses applied to study the genetic mechanisms of antibiotic resistance in enterococci
💡The first story of the year is about fighting antimicrobial resistance💡
📋"Large-scale genomic analyses applied to study the genetic mechanisms of antibiotic resistance in enterococci" led by Francesc Coll from Instituto de Biomedicina de Valencia
Antimicrobial resistance (AMR) is a growing global health challenge that threatens to make common infections harder to treat. For this reason, antibiotic susceptibility testing is essential to determine which bacteria will be killed by which drug. Traditional options are time consuming and alternatives are becoming increasingly necessary as AMR rises.
One promising alternative is whole-genome sequencing as, in principle, if a bacterial genome contains a known resistance marker (AMR gene or mutation) the bacterium should be resistant. However predictions are not always accurate due to the lack of accuracy of some AMR markers and bioinformatic tools, and is particularly problematic for certain antibiotics.
🖥️ Thanks to RES supercomputer #Picasso from Centro Supercomputación y Bioinformática (Universidad de Málaga) the team studied the structure and genomic context of tetracycline resistance genes present in Enterococcis faecium with unprecedented precision. They analyzed over 4.000 genomes.
They also benchmarked 4 common AMR bioinformatic tools used to detect different structural configurations of AMR genes, and compared the results with the accuracy obtained from simulations run on Picasso. They found that prediction failures stem from bioinformatic limitations when dealing with truncated or duplicated AMR genes, and from lack of representation of alleles in AMR gene databases. The team is currently working on developing a new bioinformatics tool that overcomes these limitations.
📸 The figure shows a phenotypic tree that illustrates the genetic relationships (genomic diversity and resistance profiling) of 4,000 E. faecium isolates. The metadata rings, ordered from the center outward, represent: (1) Antimicrobial susceptibility tests (dark grey = resistant isolates and light grey = susceptible isolates). (2) Genetic markers: Shows if a known tetracycline resistance gene was detected in the genome. (3) Phenotype-genotype prediction errors: Highlights only "false negatives," marking instances where bioinformatic tools incorrectly predicted the bacteria would be susceptible to the drug.
📋"Large-scale genomic analyses applied to study the genetic mechanisms of antibiotic resistance in enterococci" led by Francesc Coll from Instituto de Biomedicina de Valencia
Antimicrobial resistance (AMR) is a growing global health challenge that threatens to make common infections harder to treat. For this reason, antibiotic susceptibility testing is essential to determine which bacteria will be killed by which drug. Traditional options are time consuming and alternatives are becoming increasingly necessary as AMR rises.
One promising alternative is whole-genome sequencing as, in principle, if a bacterial genome contains a known resistance marker (AMR gene or mutation) the bacterium should be resistant. However predictions are not always accurate due to the lack of accuracy of some AMR markers and bioinformatic tools, and is particularly problematic for certain antibiotics.
🖥️ Thanks to RES supercomputer #Picasso from Centro Supercomputación y Bioinformática (Universidad de Málaga) the team studied the structure and genomic context of tetracycline resistance genes present in Enterococcis faecium with unprecedented precision. They analyzed over 4.000 genomes.
They also benchmarked 4 common AMR bioinformatic tools used to detect different structural configurations of AMR genes, and compared the results with the accuracy obtained from simulations run on Picasso. They found that prediction failures stem from bioinformatic limitations when dealing with truncated or duplicated AMR genes, and from lack of representation of alleles in AMR gene databases. The team is currently working on developing a new bioinformatics tool that overcomes these limitations.
📸 The figure shows a phenotypic tree that illustrates the genetic relationships (genomic diversity and resistance profiling) of 4,000 E. faecium isolates. The metadata rings, ordered from the center outward, represent: (1) Antimicrobial susceptibility tests (dark grey = resistant isolates and light grey = susceptible isolates). (2) Genetic markers: Shows if a known tetracycline resistance gene was detected in the genome. (3) Phenotype-genotype prediction errors: Highlights only "false negatives," marking instances where bioinformatic tools incorrectly predicted the bacteria would be susceptible to the drug.