Biology subjects

Raphenya, A. R.

Publications and source records attributed to Raphenya, A. R..

11 recordsLinked to original sources

bioRxiv2026

CARD:Epi - Contextualizing Antimicrobial Resistance Determinants Using Deep Learning Language Models

Bacterial outbreak publications outline the key factors involved in the uncontrolled spread of infection. Such factors include the environment, pathogens, hosts, and antimicrobial resistance genes (ARGs). Individually, each paper published in this area gives a glimpse into the devastating impact drug resistant infections have on healthcare, agriculture, and livestock. When examined together, these publications provide contextual information on ARG transmission, from the discovery of new resistance genes to their dissemination to different pathogens, hosts, and environments. We have extracted this information from publications in PubMed by using the biomedical deep-learning language model, BioBERT. We trained BioBERT on two tasks: entity recognition to identify AMR-relevant terms (i.e., ARGs, taxonomy, environments, geographical locations, etc.) and relation extraction to determine which terms identified through entity recognition contextualize ARGs. By collating results from 204,094 antimicrobial resistance publications worldwide, we have generated interpretable results about the sources where genes are commonly found. To visualize the dataset, we have created two pipelines to analyze transmission patterns of ARGs across agriculture, environments, and human populations using a Confusogram and Uniform Manifold Approximation and Projection. Overall, we have taken a large-scale approach to collect antimicrobial resistance data from a commonly overlooked resource, i.e., the systematic examination of the large body of AMR literature and have visualized how scientific literature can be used to assess transmission patterns of ARGs.

genomics↗

bioRxiv2026

Structural diversification of phage tail fibres enables recognition of diverse type IV pili

Viruses must recognize receptors on host surfaces to initiate infection, but these receptors can evolve rapidly, posing a fundamental challenge to viral persistence. The type IV pilus of Pseudomonas aeruginosa is an ideal model system to study virus-receptor coevolution because its major pilin subunit PilA exhibits extensive sequence and chemical diversity while remaining essential for phage attachment. Here, we combined large-scale comparative genomics with structural and functional analyses to determine how pilus-dependent phages maintain infectivity despite extensive receptor diversification. Pilin variation was concentrated at solvent-exposed regions, altering filament surface chemistry while preserving key subunit-subunit interfaces. Despite this variation, phages recognized divergent pilins more effectively than polyclonal antisera. However, phages differed markedly in their sensitivity to receptor perturbation: some required electrostatic and structural compatibility, whereas others tolerated substantial receptor modification, including the presence of post-translational glycosylation. Comparisons of AlphaFold3 structural models revealed two distinct tail fibre architectures associated with those phenotypes. Phages encoding tail fibres with conserved receptor-binding domains were more sensitive to receptor perturbation within a given pilin background, while those with structurally diversified binding regions infected strains expressing highly divergent pilins. Together, these findings suggest that modular diversification of receptor-binding proteins provides a structural route by which phages accommodate receptor evolution. SIGNIFICANCEUnderstanding the determinants of viral host range is essential for explaining how viruses persist in the face of receptor diversification. Using pilus-dependent bacteriophages that bind diverse type IV pilins in Pseudomonas aeruginosa, we show that differences in tail fibre architecture are associated with distinct sensitivities to receptor variation. Phages with structurally conserved receptor-binding regions are sensitive to receptor perturbation, whereas those with diversified binding architectures remain infectious across strains expressing highly divergent pilins. Our results identify a structural correlate of receptor recognition breadth and reveal how receptor-binding protein architecture contributes to viral host range and adaptation. These data can inform the selection or engineering of broad-host range phages for therapeutic applications.

microbiology↗

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↗

bioRxiv2024

hAMRonization: Enhancing antimicrobial resistance prediction using the PHA4GE AMR detection specification and tooling

The detection of antimicrobial resistance (AMR) markers directly from genomic or metagenomic data is becoming a standard clinical and public health procedure. This has resulted in the development of a number of different bioinformatic AMR prediction tools. Although many may implement similar principles, these tools differ significantly in their supported inputs, search algorithms, parameterisation, and underlying reference databases. Each of these tools generates a report of detected AMR genes or variants in a distinct, non-standard, format. This presents a huge barrier to the comparison of results and to the modularity of tools for AMR gene prediction within bioinformatic workflows. In collaboration with 17 public health laboratories across 10 countries, the Public Health Alliance for Genomic Epidemiology (PHA4GE) (https://pha4ge.org) data structures working group has developed and piloted a standardized output specification for the bioinformatic detection of AMR from microbial genomes. In this report, we discuss hAMRonization, a python package and command-line utility, which implements PHA4GEs AMR specification to combine the outputs of disparate antimicrobial resistance gene detection tools into a single unified format. hAMRonization can be easily extended and currently supports 18 different tools (both species-agnostic and species-specific) for the detection of genes and/or variants conferring AMR. The harmonized reports are available in tabular form, JSON format or through an interactive HTML file (e.g., https://maguire-lab.github.io/assets/interactive_report_demo.html) that can be opened within the browser for navigable data exploration. As of 2024-03-07 hAMRonization has been downloaded [~]12,500 times, incorporated into >9 public bioinformatic tools and workflows, and been internally adopted by several national and international public health groups. The hAMRonization tool and underlying specification are open-source and freely available through PyPI, conda and GitHub (https://github.com/pha4ge/hAMRonization).

bioinformatics↗

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↗

bioRxiv2021

Performance characteristics of next-generation sequencing for antimicrobial resistance gene detection in genomes and metagenomes

Short-read sequencing provides a culture-independent method for the detection of antimicrobial resistance (AMR) genes from single bacterial genomes and metagenomic samples. However, the performance characteristics of these approaches have not been systematically characterized. We compared assembly- and read-based approaches to determine sensitivity, positive predictive value, and sequencing limits of detection required for AMR gene detection in an Escherichia coli ST38 isolate spiked into a synthetic microbial community at varying abundances. Using an assembly-based method the limit of detection was 15X genome coverage. We are confident in AMR gene detection at target relative abundances of 100% to 1%, where a target abundance of 1% would require assembly of approximately 30 million reads to achieve 15X target coverage. Recent studies assessing AMR gene content in metagenomic samples may be inadequately sequenced to achieve high sensitivity. Our study informs future sequencing projects and analytical strategies for genomic and metagenomic AMR gene detection.

bioinformatics↗

bioRxiv2020

Sequence, infectivity and replication kinetics of SARS-CoV-2 isolated from COVID-19 patients in Canada

SARS-CoV-2 emerged in December 2019 in Wuhan, China and has since infected over 1.5 million people, of which over 107,000 have died. As SARS-CoV-2 spreads across the planet, speculations remain about the range of human cells that can be infected by SARS-CoV-2. In this study, we report the isolation of SARS-CoV-2 from two COVID-19 patients in Toronto, Canada. We determined the genomic sequences of the two isolates and identified single nucleotide changes in representative populations of our virus stocks. More importantly, we tested a wide range of human immune cells for productive infection with SARS-CoV-2. Here we confirm that human primary peripheral blood mononuclear cells (PBMCs) are not permissive to SARS-CoV-2. As SARS-CoV-2 continues to spread globally, it is essential to monitor small nucleotide polymorphisms in the virus and to continue to isolate circulating viruses to determine cell susceptibility and pathogenicity using in vitro and in vivo infection models.

microbiology↗