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Maguire, F.

Publications and source records attributed to Maguire, F..

14 recordsLinked to original sources

Influenza A virus H5Nx genotypes assigned by genetic similarity are incongruent with global phylogenies

Influenza A virus H5N1 and other H5 subtypes (H5Nx) have broad host ranges and pose a pandemic risk. Since early 2022, H5 clade 2.3.4.4b has predominated globally, causing unprecedented mortality in wild birds, poultry, and mammals, with spillovers to humans. Accurate viral classification is essential for tracking H5Nx variant emergence and spread, but reassortment among genome segments with distinct evolutionary histories complicates genotyping. H5Nx genotype assignment tools, GenoFlu in North America and Genin2 in Europe, assign segments to clusters by genetic similarity to reference sequences and define genotypes as constellations of segment clusters. However, genetic similarity does not necessarily imply recent shared ancestry because mutations may occur multiple times across the tree and branches may exhibit variable evolutionary rates. Furthermore, similarity is defined relative to references that may inadequately represent diversity. We evaluated the congruence of genetic similarity-based H5Nx clusters and genotypes with phylogenies of global sequences across eight segments. Publicly available H5Nx nucleotide sequences from 41,366 samples were assigned genotypes using GenoFlu and Genin2, and maximum likelihood phylogenies were inferred for each segment. Phylogenetic clusters were identified using TreeCluster for a range of patristic distance thresholds and CD-HIT clusters were identified for a range of genetic similarity thresholds. Clustering methods were evaluated based on phylogenetic purity, monophyly, and diversity. Reassortment within predominant GenoFlu and Genin2 genotypes was evaluated based on their phylogenetic cluster spans. Expectedly, GenoFlu was largely unable to assign genomes from outside of North America, South America, and Antarctica from 2022 to 2025, whereas Genin2 assignments struggled outside of Europe, Africa, and Western Asia. Only 14.2% of GenoFlu clusters were monophyletic, while Genin2 clusters were more variable across segments (overall 49.3% monophyletic). Despite CD-HIT yielding genetic distance clusters of similar size to TreeCluster, they were less monophyletic (44%) and encompassed greater phylogenetic diversity. We identified several genotypes, such as GenoFlu B3.13, D1.1, and D1.3, with internal segments that had diversified into contiguous clusters. Similarly, the wide phylogenetic span of GenoFlu genotypes B3.5 and A3, and Genin2 genotypes EA-2020-C, EA-2021-AB, and EA-2022-CH suggest they may erroneously combine multiple reassortants. Genetic similarity-based H5Nx genotyping tools are rapid, stable, and sufficient for broad categorization, but may group viruses with distinct evolutionary histories and fail to assign divergent viruses, risking biased surveillance, modeling, and risk assessment.

bioinformatics↗

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↗

Best of Both Worlds? Optimising Graph-Based Antimicrobial Resistance Gene Profiling in Long and Short-Read Metagenomes

Environmental surveillance using metagenomic sequencing offers a powerful way to track emerging and mobile antimicrobial resistance (AMR) genes and inform public health mitigation strategies. Read-based analysis tools can sensitively detect AMR genes in metagenomes but provide little information about the surrounding genome. This prevents easily linking detected genes with particular host species or mobile genetic elements. On the other hand, contig-based analysis tools can provide this genomic context but systematically fail to recover many AMR genes. Querying the intermediate assembly graph directly may provide a trade-off between these strengths and weaknesses. However, many existing tools capable of querying assembly graphs are designed for applications other than gene detection, such as pan-genomics, indexing, or scaffolding. Therefore, a comprehensive evaluation of six tools across four search paradigms was performed to determine the optimal graph querying tool for profiling AMR genes in both long and short-read metagenomic assembly graphs. Across mock and simulated metagenomes of varying complexity and read-type, BLAST-based graph alignment (as implemented by GraphAligner) consistently outperformed other graph alignment algorithms. Overall, graph-based methods correctly identified 21% to 46% more AMR genes in complex datasets than contig analyses; however, increases in recall were modest. Combining assembly graphs analyses with contig-based analyses identifies up to 56% additional AMR genes across both long and short-read datasets. This study highlights the challenges associated with metagenomic AMR surveillance and demonstrates that graph-based analyses offer a useful tool in maximising sensitive identification of AMR genes and their genomic context from these data. ImportanceAntimicrobial resistance (AMR) is a severe public health threat that has spurred non-governmental organisations and public health agencies to develop action plans to reduce resistance to critical antimicrobials. Surveillance of One Health environments for AMR determinants are often central parts of these action plans. Metagenomic sequencing presents a key method for clinical and public health AMR surveillance; however, algorithmic and biochemical limitations prevent linking most detected AMR genes to their associated host bacteria or mobile genetic elements. Our findings suggest that querying the assembly graph alongside assembled contigs can identify more AMR genes than contigs alone while still providing epidemiologically informative flanking sequences. Associating AMR genes with their genomic context greatly expands our ability to assess the risk they pose across different environments. These improvements in metagenomic AMR gene identification make AMR surveillance more effective for public health institutions potentially reducing the harm of resistant infections.

bioinformatics↗

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↗

Testing for a wildlife reservoir of divergent SARS-CoV-2 in white-tailed deer

The 2021 discovery of a divergent lineage (B.1.641) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in white-tailed deer (Odocoileus virginianus) from Ontario raised concerns that deer were a potential reservoir. To assess whether white-tailed deer continued to be infected with B.1.641 and to test for spillover into other species, we established a surveillance program in Ontario by sampling wildlife via existing monitoring programs and through active surveillance of captive and wild animals. Between 2022 to 2024, we tested 2,839 animals, identifying one active SARS-CoV-2 infection (a likely spillover of a recombinant XBB.2.3.11.3 lineage), but no cases of B.1.641. Overall, 93 animals (6.8%) tested positive for SARS-CoV-2 antibodies, including 89 white-tailed deer, two Virginia opossums (Didelphis virginiana), one American mink (Neogale vison), and one river otter (Lontra canadensis). In Southwestern Ontario, where B.1.641 was originally detected, 15.2% of deer samples were seropositive. Generalized Linear Models demonstrated that seropositive deer were more likely to be found in areas with a higher fall deer harvest and human population density, and closer to previous B.1.641 cases. Our data suggest that deer-associated B.1.641 may have caused a relatively localized epizootic without forming a stable reservoir. This study underscores the importance of One Health-focused surveillance.

pathology↗

De novo genome sequence assembly of the RNAi-tractable endosymbiosis model system Paramecium bursaria 186b reveals factors shaping intron repertoire

How two species engage in stable endosymbiosis is a biological quandary. The study of facultative endosymbiotic interactions has emerged as a useful approach to understand how endosymbiotic functions can arise. The ciliate protist Paramecium bursaria hosts green algae of the order Chlorellales in a facultative photo-endosymbiosis. We have recently reported RNAi as a tool for understanding gene function in Paramecium bursaria 186b, CCAP strain 1660/18 [1]. To complement this work, here we report a highly complete host genome and transcriptome sequence dataset, using both Illumina and PacBio sequencing methods to aid genome analysis and to enable the design of RNAi experiments. Our analyses demonstrate Paramecium bursaria, like other ciliates such as diverse species of Paramecia, possess numerous tiny introns. These data, combined with the alternative genetic code common to ciliates, makes gene identification and annotation challenging. To explore intron evolutionary dynamics further we show that alternative splicing leading to intron retention occurs at a higher frequency among the smaller number of longer introns, identifying a source of selection against longer introns. These data will aid the investigation of genome evolution in the Paramecia and provide additional source data for the exploration of endosymbiotic functions.

genomics↗

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↗

Sarand: Exploring Antimicrobial Resistance Gene Neighborhoods in Complex Metagenomic Assembly Graphs

Antimicrobial resistance (AMR) is a major global challenge to human and animal health. The genomic element (e.g., chromosome, plasmid, and genomic islands) and neighbouring genes associated with an AMR gene play a major role in its function, regulation, evolution, and propensity to undergo lateral gene transfer. Therefore, characterising these genomic contexts is vital to effective AMR surveillance, risk assessment, and stewardship. Metagenomic sequencing is widely used to identify AMR genes in microbial communities, but analysis of short-read data offers fragmentary information that lacks this critical contextual information. Alternatively, metagenomic assembly, in which a complex assembly graph is generated and condensed into contigs, provides some contextual information but systematically fails to recover many mobile genetic elements. Here we introduce Sarand, a method that combines the sensitivity of read-based methods with the genomic context offered by assemblies by extracting AMR genes and their associated context directly from metagenomic assembly graphs. Sarand combines BLAST-based homology searches with coverage statistics to sensitively identify and visualise AMR gene contexts while minimising inference of chimeric contexts. Using both real and simulated metagenomic data, we show that Sarand outperforms metagenomic assembly and recently developed graph-based tools in terms of precision and sensitivity for this problem. Sarand (https://github.com/beiko-lab/sarand) enables effective extraction of metagenomic AMR gene contexts to better characterize AMR evolutionary dynamics within complex microbial communities.

bioinformatics↗

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↗

Highly divergent white-tailed deer SARS-CoV-2 with potential deer-to-human transmission

Wildlife reservoirs of SARS-CoV-2 may enable viral adaptation and spillback from animals to humans. In North America, there is evidence of unsustained spillover of SARS-CoV-2 from humans to white-tailed deer (Odocoileus virginianus), but no evidence of transmission from deer to humans. Through a biosurveillance program in Ontario, Canada we identified a new and highly divergent lineage of SARS-CoV-2 in white-tailed deer. This lineage is the most divergent SARS-CoV-2 lineage identified to date, with 76 consensus mutations (including 37 previously associated with non-human animal hosts) and signatures of considerable evolution and transmission within wildlife. Phylogenetic analysis also revealed an epidemiologically linked human case. Together, our findings represent the first clear evidence of sustained evolution of SARS-CoV-2 in white-tailed deer and of deer-to-human transmission.

microbiology↗

Immunogenicity of convalescent and vaccinated sera against clinical isolates of ancestral SARS-CoV-2, beta, delta, and omicron variants

The omicron variant of concern (VOC) of SARS-CoV-2 was first reported in November 2021 in Botswana and South Africa. Omicron has evolved multiple mutations within the spike protein and the receptor binding domain (RBD), raising concerns of increased antibody evasion. Here, we isolated infectious omicron from a clinical specimen obtained in Canada. The neutralizing activity of sera from 65 coronavirus disease (COVID-19) vaccine recipients and convalescent individuals against clinical isolates of ancestral SARS-CoV-2, beta, delta, and omicron VOCs was assessed. Convalescent sera from unvaccinated individuals infected by the ancestral virus during the first wave of COVID-19 in Canada (July, 2020) demonstrated reduced neutralization against beta and omicron VOCs. Convalescent sera from unvaccinated individuals infected by the delta variant (May-June, 2021) neutralized omicron to significantly lower levels compared to the delta variant. Sera from individuals that received three doses of the Pfizer or Moderna vaccines demonstrated reduced neutralization of the omicron variant relative to ancestral SARS-CoV-2. Sera from individuals that were naturally infected with ancestral SARS-CoV-2 and subsequently received two doses of the Pfizer vaccine induced significantly higher neutralizing antibody levels against ancestral virus and all VOCs. Importantly, infection alone, either with ancestral SARS-CoV-2 or the delta variant was not sufficient to induce high neutralizing antibody titers against omicron. This data will inform current booster vaccination strategies, and we highlight the need for additional studies to identify longevity of immunity against SARS-CoV-2 and optimal neutralizing antibody levels that are necessary to prevent infection and/or severe COVID-19.

immunology↗

Emergent RNA-RNA interactions can promote stability in a nascent phototrophic endosymbiosis

Eukaryote-eukaryote endosymbiosis was responsible for the spread of chloroplast (plastid) organelles. Stability is required for the metabolic and genetic integration that drives the establishment of new organelles, yet the mechanisms which act to stabilise nascent endosymbioses - between two fundamentally selfish biological organisms - are unclear. Theory suggests that enforcement mechanisms, which punish misbehaviour, may act to stabilise such interactions by resolving conflict. However, how such mechanisms can emerge in a nascent endosymbiosis has yet to be explored. Here, we propose that endosymbiont-host RNA-RNA interactions, arising from digestion of the endosymbiont population, can result in a cost to host growth for breakdown of the endosymbiosis. Using the model nascent endosymbiosis, Paramecium bursaria - Chlorella spp., we demonstrate that this mechanism is dependent on the host RNA-interference (RNAi) system. We reveal through small RNA (sRNA) sequencing that endosymbiont-derived mRNA released upon endosymbiont digestion can be processed by the host RNAi system into 23-nt sRNA. We predict multiple regions of shared sequence identity between endosymbiont and host mRNA, and demonstrate through delivery of synthetic endosymbiont sRNA that exposure to these regions can knock-down expression of complementary host genes, resulting in a cost to host growth. This process of host gene knock-down in response to endosymbiont-derived RNA processing by host RNAi factors, which we term RNAi-collisions, represents a mechanism which can promote stability in a nascent eukaryote-eukaryote endosymbiosis. By imposing a cost for breakdown of the endosymbiosis, endosymbiont-host RNA-RNA interactions may drive maintenance of the symbiosis across fluctuating ecological conditions and symbiotic status. SIGNIFICANCE STATEMENTStable endosymbiosis between eukaryotic microbes has driven the evolution of further cellular complexity. Yet the mechanisms which can act to stabilise a nascent eukaryote-eukaryote endosymbiosis are unclear. Using the model nascent endosymbiotic system, Paramecium bursaria-Chlorella, we demonstrate that endosymbiont-host RNA-RNA interactions can drive a cost to host growth upon endosymbiont digestion, punishing the host for misbehaviour. These RNA-RNA interactions are facilitated by the host RNA-interference system. For endosymbiont mRNA sharing a high-level of sequence identity with host transcripts, this process can result in host gene knock-down. We propose that these endosymbiont-host RNA-RNA interactions- RNAi collisions-represent a viable enforcement mechanism to sanction the host for breakdown of the endosymbiosis, promoting the stability of a nascent endosymbiotic interaction.

evolutionary biology↗

Validation of a Core RNA-interference Pathway as a Tool for Reverse Genetics in the Emergent Model Phototrophic Endosymbiosis, Paramecium bursaria

Endosymbiosis was fundamental for the evolution of eukaryotic complexity. Endosymbiotic interactions can be dissected through forward and reverse-genetic experiments, such as RNA-interference (RNAi). However, distinguishing small (s)RNA pathways in a eukaryote-eukaryote endosymbiotic interaction is challenging. Here, we investigate the repertoire of RNAi pathway protein-encoding genes in the model nascent endosymbiotic system, Paramecium bursaria-Chlorella spp. Using comparative genomics and transcriptomics supported by phylogentics, we identify essential proteome components of the small interfering (si)RNA, scan (scn)RNA, and internal eliminated sequence (ies)RNA pathways. Our analyses reveal that copies of these components have been retained throughout successive whole genome duplication (WGD) events in the Paramecium clade. We then validate feeding-induced siRNA-based RNAi in P. bursaria via knock-down of the splicing factor, u2af1, which we show to be crucial to host growth. Finally, using simultaneous knock-down paradox controls to rescue the effect u2af1 knock-down, we demonstrate that feeding-induced RNAi in P. bursaria is dependent upon a core pathway of host-encoded Dcr1, Piwi and Pds1 components. Our experiments confirm the presence of a functional, host-derived RNAi pathway in P. bursaria that generates 23-nt siRNA, validating use of the P. bursaria-Chlorella spp. system to investigate the genetic basis of a nascent endosymbiosis.

molecular biology↗

Metagenome-Assembled Genome Binning Methods Disproportionately Fail for Plasmids and Genomic Islands

Metagenomic methods are an important tool in the life sciences, as they enable simultaneous characterisation of all microbes in a community without time-consuming and bias-inducing culturing. Metagenome-assembled genome (MAG) binning methods have emerged as a promising approach to recover individual genomes from metagenomic data. However, MAG binning has not been well assessed for its ability to recover mobile genetic elements (MGEs), such as plasmids and genomic islands (GIs), that have very high clinical/agricultural/environmental importance. Certain antimicrobial resistance (AMR) genes and virulence factor (VF) genes are noted to be disproportionately associated with MGEs, making studying their transmission a public health priority. However, the variable copy number and sequence composition of MGEs relative to the majority of the host genome makes them potentially problematic for MAG binning methods. To systematically investigate this, we simulated a low-complexity metagenome comprising 30 GI-rich and plasmid-containing bacterial genomes. MAGs were then recovered using 12 current prediction pipelines and evaluated for recovery of MGE-associated AMR/VF genes. Here we show that while 82-94% of chromosomes could be correctly recovered and binned, only 38-44% of GIs were recovered and, even more notably, only 1-29% of plasmid sequences were found. Most strikingly, no plasmid-borne VF or AMR genes were recovered and within GIs, only between 0-45% of AMR or VF genes were identified. We conclude that short-read MAGs are largely ineffective for the analysis of mobile genes, including those of public-health importance like AMR and VF genes. We propose that microbiome researchers should instead primarily utilise unassembled short reads and/or long-read approaches to more accurately analyse metagenomic data

bioinformatics↗