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Bartrop, L.

Publications and source records attributed to Bartrop, L..

2 recordsLinked to original sources

ARG-PASS v2.0 expands global resistome surveillance to reveal previously unrecognized antibiotic resistance genes reaching the human gut

Antimicrobial resistance (AMR) is a significant global public health threat, and antibiotic resistance genes (ARGs) that bacteria depend on to withstand treatment are its molecular basis. How ARGs move from environmental reservoirs into the human microbiome, and eventually into human pathogens, remains one of the most important unresolved questions in AMR research. Surveillance has largely targeted ARGs resembling those already clinically characterised. The environmental reservoir remains poorly described and the routes connecting it to human microbiomes are inferred rather than observed. Describing that reservoir requires computational methods able to recognise ARGs that share little similarity with known enzymes, yet retain accuracy after experimental validation to enable scalable applications. Dietary exposure through unprocessed ferments offers a tractable setting in which such a route can be observed directly. Here we introduce ARG-PASS v2.0 (ARG-prediction by PAirwise Sequence versus Structure v2.0) and first apply it to a lake metagenome from Kolkata, India, where 10 of 11 tested candidates across diverse ARG classes were functionally confirmed after expression in Escherichia coli. Given their clinical relevance, we then predicted 918 class B1 metallo-beta-lactamases (MBLs) from MGnify and the PLSDB, with most diverging from characterised enzymes, but as widely distributed. A number of globally widespread candidates track their hosts distribution, with some recovered only from human-associated biomes, yet most span a wider ecological range. Phylogenetic inference places clinically relevant class B1 MBLs within marine diversity and suggests a marine reservoir. Cross-referencing against the Unified Human Gastrointestinal Protein catalogue identified a carbapenemase, an ANT, and class A beta-lactamases from Bacillus sonorensis and Bacillus licheniformis, detected in South Korean fermented soybean paste (doenjang) as well as gut isolates of populations consuming these foods. Together, these results point to a largely chromosomal reservoir of undescribed ARG diversity that reaches the human gut through both resident and generalist hosts. Once in the human microbiome, treatment may be compromised directly if the host is an opportunistic pathogen, or after transfer to a clinical pathogen.

microbiology↗

Conserved protein sequence-structure signatures identify emerging antibiotic resistance genes from the human microbiome

Bacteria exhibiting antimicrobial resistance (AMR) is a problem that has grown to become a significant public health challenge worldwide. Antibiotic resistance genes (ARGs), determinants of AMR, mostly emerge from non-clinical settings. Identifying previously undetected ARGs in the human microbiome which confer resistance to clinical concentrations of antibiotics is a crucial component of addressing AMR, yet can be hindered by their low homology to existing ARGs. Here, we attempt to address this by focussing on functionally important protein regions. ARG-PASS (ARG-PAirwise Sequence vs Structure) represents a novel protein function prediction method which leverages a one-class support vector machine trained on pairwise primary and tertiary distributions of structurally conserved regions of proteins encoded by ARGs. ARG-PASS was applied to six reference strains of the Human Microbiome Project. Nine candidates were selected for experimental verification and all were functionally confirmed when expressed in E.coli, belonging to ARG classes: APH, dfr, class B and C {beta}-lactamases, and penicillin binding proteins. We also used ARG-PASS directly on protein structures within the AlphaFold database and predicted a phnP gene (metallo-{beta}-lactamase fold), which is highly divergent from existing {beta}-lactamases and had activity against ampicillin. In total, 80% of the tested genes confer resistance at CLSI resistant breakpoints and the remainder represent pre-resistance genes, with activity but not at clinically relevant minimum inhibitory concentrations. We suggest pre-resistance genes may preferentially evolve into clinically relevant resistance determinants. ARG-PASS represents a novel and precise method of identifying previously uncharacterized ARGs from DNA databases, contributing to resistance surveillance and antibiotic stewardship.

microbiology↗