Biology subjects

Alcock, B. P.

Publications and source records attributed to Alcock, B. P..

6 recordsLinked to original sources

bioRxiv2026

Analysing antimicrobial resistance mobility patterns using a diverse dataset of over 8000 bacterial species

2.Advances in genomics have enhanced surveillance of antimicrobial resistance (AMR), yet the factors governing resistance gene mobility, and therefore their risk of spread, remain poorly characterized. Here, we analyzed AMR gene distributions across thousands of bacterial genomes from NCBI RefSeq, encompassing clinical, agricultural and environmental isolates, to quantify associations with plasmids and predicted mobile genomic islands. AMR genes were identified using the Resistance Gene Identifier, and in addition to plasmid identification, mobile chromosomal elements were predicted using IslandViewer 4. Analysing both the full dataset and subsets of data with corrections for sampling bias, we show that known AMR genes are significantly enriched in mobile regions overall. However, stratification by resistance mechanism supported marked heterogeneity: certain drug classes and mechanisms are strongly associated with mobile elements, whereas others are predominantly chromosomal and non-mobile. Notably, mechanisms with specialized functions showed higher mobility, consistent with their role as "ecological public goods" that need not be present in all cells to confer community-level benefit. Differences were also observed across bacteria with distinct cell envelope structures. Together, these findings lay the groundwork for predictive models of AMR gene mobility and provide a framework for incorporating gene-level mobility into AMR risk assessment and antimicrobial stewardship policy. 3. Impact statementUnderstanding which antimicrobial resistance (AMR) genes are most likely to spread is central to managing resistance in clinical, agricultural, and environmental settings. This study reports a large-scale, systematic analysis of antimicrobial resistance (AMR) gene mobility across >8000 bacterial species, spanning diverse clinical and other ecological sources. By linking resistance mechanisms to their genomic location (plasmids, genomic islands, or the rest of chromosomes), our study demonstrates that AMR genes are overall enriched in mobile elements, but that mobility varies substantially by mechanism and bacterial context. In particular, functionally specialized resistance mechanisms are more frequently associated with mobile elements, consistent with an "ecological public goods" model for AMR dissemination. These findings extend beyond gene-specific case studies to identify generalizable patterns that help explain why some resistance determinants spread more readily than others. The breadth of relevance spans clinical microbiology, evolutionary biology, environmental microbiology, agri-foods, and public health. The work provides evidence supporting an improved framework for AMR mobility risk assessment, and antimicrobial stewardship strategies, with direct implications for forecasting the durability of existing and future antibiotics. 4. Data summaryThe complete dataset and analysis code used to generate the results can be accessed via the Open Science Framework (OSF) repository under DOI:10.17605/OSF.IO/WE3TX

genomics↗

bioRxiv2025

Increasing the predictive accuracy of the Resistance Gene Identifier by evaluating antimicrobial resistance gene over- and underprediction

Computational biology is paving the way towards accessible antimicrobial resistance (AMR) gene (ARG) detection methods to complement canonical gold-standard phenotypic diagnostics for the purposes of antimicrobial surveillance. However, obtaining an accurate depiction of phenotypic resistance through genotypic methods requires addressing potential over- and underprediction of ARGs. This study assessed the manual curation accuracy of bit-score cutoff values associated with bioinformatic models within the Comprehensive Antibiotic Resistance Database (CARD) and the subsequent effects of erroneous cutoff curation, leading to potential Type I and Type II error, on its in silico resistome prediction tool, the Resistance Gene Identifier (RGI). CARD models rarely overpredicted (5 of 3,900 models with >5% false positive rates) but somewhat underpredicted (739 of 3,900 models with >5% false negative rates) resistance-associated sequences and mutations, emphasizing RGIs conservative prediction algorithms. Isolating curation inaccuracies by AMR gene family, efflux-related families were the main contributors to overprediction (likely due to human curation error), while underprediction was primarily due to beta-lactamase families, the latter finding highlighting systemic curation deficiencies.

bioinformatics↗

bioRxiv2025

CARD k-mers: Unmasking the pathogen hosts and genomic contexts of antimicrobial resistance genes in metagenomic sequences

Antimicrobial resistance (AMR) is a global health crisis requiring rapid surveillance across human, agricultural, and environmental systems. A major challenge during outbreaks is not only detecting antimicrobial resistance genes (ARGs), but also unmasking their pathogen hosts and genomic context, as ARGs alone do not fully capture AMR risk. Pathogen identification is often essential for guiding effective treatment. While culture-based methods remain the diagnostic gold standard, they are slow and sometimes impractical. Faster metagenomic (mNGS) tools typically detect either ARGs, taxonomy, or genomic context, but rarely all three, resulting in fragmented surveillance. Existing k-mer classifiers like Kraken2 and CLARK, designed for general taxonomy, often perform poorly on AMR-specific sequences. We introduce CARD k-mers, the first tool built to jointly predict species-level taxonomy and genomic context (plasmid vs. chromosome) for ARGs in short metagenomic reads. Integrated with the Comprehensive Antibiotic Resistance Database (CARD), CARD k-mers enables rapid, context-aware assignment of ARGs to their likely pathogen and genomic element origin. In benchmarking with 103,456 in-silico pathogen-specific AMR alleles, CARD k-mers outperformed Kraken2 and CLARK by 10.85% and 15.2%, respectively, and correctly classified the genomic context of 4,590 chromosome- and 176 plasmid-specific ARGs. The tool operates at speeds exceeding 675,000 metagenomic reads per minute. By delivering fast, accurate, and context-rich classification of ARGs, CARD k-mers significantly advances untargeted AMR surveillance and is accessible to users with basic command-line experience for use in both clinical and environmental pipelines. CARD k-mers is available at: https://github.com/arpcard/rgi.

bioinformatics↗

bioRxiv2024

Simultaneous detection of pathogens and antimicrobial resistance genes with the open source, cloud-based, CZ ID pipeline

Antimicrobial resistant (AMR) pathogens represent urgent threats to human health, and their surveillance is of paramount importance. Metagenomic next generation sequencing (mNGS) has revolutionized such efforts, but remains challenging due to the lack of open-access bioinformatics tools capable of simultaneously analyzing both microbial and AMR gene sequences. To address this need, we developed the Chan Zuckerberg ID (CZ ID) AMR module, an open-access, cloud-based workflow designed to integrate detection of both microbes and AMR genes in mNGS and whole-genome sequencing (WGS) data. It leverages the Comprehensive Antibiotic Resistance Database and associated Resistance Gene Identifier software, and works synergistically with the CZ ID short-read mNGS module to enable broad detection of both microbes and AMR genes. We highlight diverse applications of the AMR module through analysis of both publicly available and newly generated mNGS and WGS data from four clinical cohort studies and an environmental surveillance project. Through genomic investigations of bacterial sepsis and pneumonia cases, hospital outbreaks, and wastewater surveillance data, we gain a deeper understanding of infectious agents and their resistomes, highlighting the value of integrating microbial identification and AMR profiling for both research and public health. We leverage additional functionalities of the CZ ID mNGS platform to couple resistome profiling with the assessment of phylogenetic relationships between nosocomial pathogens, and further demonstrate the potential to capture the longitudinal dynamics of pathogen and AMR genes in hospital acquired bacterial infections. In sum, the new AMR module advances the capabilities of the open-access CZ ID microbial bioinformatics platform by integrating pathogen detection and AMR profiling from mNGS and WGS data. Its development represents a critical step toward democratizing pathogen genomic analysis and supporting collaborative efforts to combat the growing threat of AMR.

microbiology↗

bioRxiv2024

CARPDM: cost-effective antibiotic resistome profiling of metagenomic samples using targeted enrichment

Better interrogation of antimicrobial resistance requires new approaches to detect the associated genes in metagenomic samples. Targeted enrichment is an ideal method for their sequencing and characterization. However, no open-source, up-to-date hybridization probe set targeting antimicrobial resistance genes exists. Here we describe CARPDM, a probe design software package made to run alongside all future Comprehensive Antibiotic Resistance Database releases. To test its efficacy, we have created and validated two separate probe sets: AllCARD, which enriches all genes encoded in the Comprehensive Antibiotic Resistance Databases protein homolog models (n = 4,661), and clinicalCARD, which focuses on a clinically relevant subset of resistance genes (n = 323). We demonstrate that allCARD increases the number of reads mapping to resistance genes by up to 594-fold. ClinicalCARD performs similarly when clinically relevant genes are present, increasing the number of resistance-gene mapping reads by up to 598-fold. In parallel with this development, we have established a protocol to synthesize any probe set in-house, saving up to 350 dollars per reaction. Together, these probe sets, their associated design program CARPDM, and the protocol for in-house synthesis will democratize metagenomic resistome analyses, allowing researchers access to a cost-effective and efficient means to explore the antibiotic resistome.

genomics↗

bioRxiv2022

Exploring the mobilome and resistome of Enterococcus faecium in a One Health context across two continents

Enterococcus faecium is a ubiquitous opportunistic pathogen that is exhibiting increasing levels of antimicrobial resistance (AMR). Many of the genes that confer resistance and pathogenic functions are localized on mobile genetic elements (MGEs), which facilitate their transfer between lineages. Here, features including resistance determinants, virulence factors, and MGEs were profiled in a set of 1273 E. faecium genomes from two disparate geographic locations (in the UK and Canada) from a range of agricultural, clinical, and associated habitats. Neither lineages of E. faecium nor MGEs are constrained by geographic proximity, but our results show evidence of a strong association of many profiled genes and MGEs with habitat. Many features were associated with a group of clinical and municipal wastewater genomes that are likely forming a new human-associated ecotype. The evolutionary dynamics of E. faecium make it a highly versatile emerging pathogen, and its ability to acquire, transmit, and lose features presents a high risk for the emergence of new pathogenic variants and novel resistance combinations. This study provides a workflow for MGE-centric surveillance of AMR in Enterococcus that can be adapted to other pathogens.

genomics↗