Ewan M. Harrison

Staphylococcus aureus is a major human pathogen and commensal of the nose and other body sites. S. aureus colonisation (carriage) increases the risk of infection, so identifying carriers is central to infection control; however, few studies have combined robust methods with sufficient statistical power to determine the risk factors for carriage. We recruited over 22,000 adults into the CARRIAGE cohort study, who each returned self-taken nasal swabs and a questionnaire weekly for three weeks. We show that in a multivariable analysis with correction for multiple testing that the only factors significantly associated with persistent S. aureus carriage were younger age (with carriage rates declining after 60), male sex, higher BMI, chronic skin conditions, and antibiotic use in the prior month. Many previously proposed factors, including smoking and livestock contact, were not significantly associated. Overall, we demonstrate that only a limited number of risk factors influence S. aureus colonisation

Background: Sequencing respiratory virus genomes is essential for public health surveillance and research. Although shotgun metagenomics is pathogen agnostic, its sensitivity is limited by abundant off-target host nucleic acids. Hybridization bait capture overcomes this limitation by selectively enriching viral sequences prior to sequencing. Methods: We evaluated three respiratory viral bait capture workflows (veSEQ, RVI-seq, and Illumina) to compare their performance and assess their suitability for scalable respiratory virus sequencing. Synthetic RNA controls and clinical samples containing SARS-CoV-2, influenza A, influenza B, human parainfluenza virus, and respiratory syncytial virus (RSV) were analysed. Results: All three workflows demonstrated high efficiency, reproducibility and broadly comparable performance across respiratory viruses. Complete viral genomes were consistently recovered from samples containing 10,000 viral copies, while viral reads remained detectable at substantially lower viral loads, including approximately 100 copies. Workflow optimisation reduced reagent costs and enabled laboratory automation without compromising sequencing sensitivity. Conclusion: Hybridization bait capture provides an effective and scalable approach for respiratory virus genome sequencing. Cost reductions and automation can be implemented without compromising performance, supporting its use in routine genomic surveillance and public health preparedness.

Background Genomic prediction of antimicrobial resistance (AMR) relies on the accurate detection of resistance genes or allelic variants of core genes from raw or assembled genomes sequences. For several bacterial species and antibiotics, AMR genotype-phenotype discrepancies are common, indicating that important sources of error remain unresolved. For Enterococcus faecium, we focused on identifying the sources of discrepancies for tetracycline resistance, for which genotypic detection had shown particularly low accuracy. We investigated the effect of structural variation in antibiotic resistance genes (ARGs), including gene duplications, truncations, interruptions, and mixed configurations of complete and partial gene copies, as a source of genotype-phenotype discrepancies from short-read data. We conduct further extended investigations to other antibiotic families and into another bacterial species: Escherichia coli. Methods We analyzed collections of E. faecium and E. coli genomes, integrating high-quality complete assemblies, simulated Illumina short reads, and matched AMR phenotypic data. The integrity, copy number, and allelic diversity of ARGs were examined for multiple antibiotic classes, and their impact on ARG detection and accuracy of AMR determination was assessed using several commonly used bioinformatic tools (SRST2, ARIBA and AMRFinderPlus). Results For E. faecium, after ruling out the effect of specific tet allelic variants on tetracycline susceptibility, we found that the integrity and copy number of tet(M) had a major effect on detection accuracy. Duplicated and incomplete ARGs are also common in E. faecium genomes, particularly for macrolides (erm(B)) and aminoglycosides (ant(6)-Ia and aph(3′)-IIIa). In E. coli, similar patterns were observed for tet(A), erm(B) and aminoglycoside-associated genes (aph(3′)-IIIa and ant(6)-Ia). Across ARGs in both species, short-read mapping methods wrongly reported interrupted genes as complete in some instances, while assembly-based methods often failed to resolve complete copies of duplicated genes. Detection accuracy improved when tools were adapted to account for gene integrity and when extended AMR databases incorporating species-specific alleles were included. Conclusions Our findings reveal that bioinformatic limitations in dealing with ARG copy number and completeness, and in accounting for allelic variation, underly a substantial source of genotype-phenotype errors, highlighting the need for improved AMR databases and bioinformatic tools that consider these factors to achieve reliable genomic prediction of AMR.

Staphylococcus aureus colonises the nose in humans, with individuals defined as persistent, intermittent or non-carriers. Unlike the gut microbiome, the nasal microbiome has not been studied in large numbers of people. Here, we define the nasal microbiome in ~1100 individuals from the CARRIAGE study (ISRCTN: ISRCTN10474633) and combine with S. aureus culture data. We identify seven community state types (CST), including two CSTs more common in females. Approximately 70% of those who are persistently colonised with S. aureus have a CST dominated by S. aureus , while non-carriers are distributed across the other six CSTs. Intermittent carriers are not a unique state but have microbiomes that resemble non- or persistent carriers. Persistent carriage is positively associated with S. aureus abundance, and negatively associated with three Corynebacterium species, Dolosigranulum pigrum , Staphylococcus epidermidis , and Moraxella catarrhalis ; the microbiome can be exploited with machine learning to accurately predict the persistence of S. aureus colonisation. Finally, we find that certain S. aureus lineages are better adapted to colonisation than others. Our data provides a comprehensive view of the nasal microbiome with respect to S. aureus colonisation, describing two key states: a S. aureus dominated CST in which S. aureus shapes the microbiome, and CSTs in which S. aureus is rare or absent.

The npmA gene, encoding a 16S rRNA methyltransferase, confers resistance to all clinically available aminoglycosides, posing a significant threat to effective antibiotic therapy. We analyze 1,932,812 bacterial genomes to investigate the distribution and mobilization of npmA variants. npmA is not found in Gram-negative bacteria, where it was originally described, but is identified among Gram-positive bacteria, predominantly as the npmA2 variant in the globally distributed Clostridioides difficile ST11 lineage. We also detect npmA2 in two vancomycin-resistant Enterococcus faecium isolates from a Dutch hospital. Upon sequencing and phenotypic analysis, we determine that E. faecium isolates are pan-resistant to aminoglycosides. Genomic characterization links npmA2 to a composite transposon, Tn7734, which is integrated within a previously uncharacterized Integrative and Conjugative Element (ICE) Tn7740, present in both npmA2-carrying C. difficile and E. faecium clinical isolates. Tn7740-like, but not npmA2, appears across diverse taxa, including human microbiome members. Here, we show that Tn7740 likely facilitates cross-species npmA2 mobilization between these Gram-positive bacteria and emphasize the risk of mobile genetic elements transferring pan-aminoglycoside resistance between clinically important bacterial pathogens.

Staphylococcus aureus colonises the nose in humans, with individuals defined as persistent, intermittent or non-carriers. Unlike the gut microbiome, the nasal microbiome has not been studied in large numbers of people. Here, we define the nasal microbiome in ~ 1,100 individuals and combine this with S. aureus culture data. We identify seven community state types (CST), including two CSTs found more commonly in females. Approximately 70% of those who are persistently colonised with S. aureus have a single CST microbiome dominated by S. aureus , while non-carriers are distributed across the other six CSTs. Intermittent carriers are not a unique state but have microbiomes that resemble either non- or persistent carriers. Persistent carriage is positively associated with S. aureus abundance, and negatively associated with three Corynebacterium species, Dolosigranulum pigrum , Staphylococcus epidermidis , and Moraxella catarrhalis ; the microbiome can be exploited with machine learning to accurately predict S. aureus colonisation status. Finally, we find that certain S. aureus lineages are likely better adapted to colonisation. Our data provides a comprehensive view of the nasal microbiome with respect to S. aureus colonisation, describing two key states: a S. aureus dominated CST in which S. aureus shapes the microbiome, and a group of CSTs in which S. aureus is rare or absent.

Human reads are a key contaminant in microbial metagenomics and enrichment-based studies, requiring removal for computational efficiency, biological analysis, and privacy protection. Various in silico methods exist, but their effectiveness depends on the parameters and reference genomes used. Here, we assess different methods, including the impact of the updated T2T-CHM13 human genome versus GRCh38. Using a synthetic dataset of viral and human reads, we evaluated performance metrics for multiple approaches. We found that the usage of high-sensitivity configuration of Bowtie2 with the T2T-CHM13 reference assembly significantly improves human read removal with minimal loss of specificity, albeit at higher computational cost compared to other methods investigated. Applying this approach to a publicly available microbiome dataset, we effectively removed sex-determining SNPs with little impact on microbial assembly. Our results suggest that our high-sensitivity Bowtie2 approach with the T2T-CHM13 is the best method tested to minimise identifiability risks from residual human reads.

During the winter, healthcare systems experience additional pressures due to increases in respiratory infections and staff absence. We aimed to determine the prevalence of respiratory viruses and impact on sickness absence in the SIREN healthcare worker (HCW) cohort during winter 2023/24. SIREN is a cohort study with linked testing, vaccination, demographic, symptoms and sick leave data. Participants undergo fortnightly multiplex PCR testing for SARS-CoV-2, influenza and RSV, regardless of symptoms. The proportion of participants who took sick leave, the total number and median of sick leave days taken was calculated by viral infection and vaccination status. Logistic regression was used to estimate the association between sick leave and vaccination status. 5,287 participants were included, 78·3% female, median age 54 years. There were 1,828 infections among 1,659 participants (1,161 SARS-CoV-2; 387 RSV; 280 influenza infections). Influenza and RSV peaked in December (1·2%; 1·9%), SARS-CoV-2 peaked in September and December 2023 (4·0%; 4·3%). Regardless of a known infection, 35.8% (1,892/5,287) took sick leave, resulting in 10,168 days (median 5 days per person; range 1-98 days). Vaccination was not significantly associated with reduced sick leave (adjusted odd ratios 0·98; 95%CI 0·87, 1·10). Respiratory illness caused a substantial burden on the SIREN healthcare workforce over winter, with all three viruses contributing. Reduced number of staff at work and pressures to work through illness have implications for healthcare resilience.

Staphylococcus aureus is an important human pathogen and a commensal of the human nose and skin. Survival and persistence during colonisation are likely major drivers of S. aureus evolution. Here we applied a genome-wide mutation enrichment approach to a genomic dataset of 3060 S. aureus colonization isolates from 791 individuals. Despite limited within-host genetic diversity, we observed an excess of protein-altering mutations in metabolic genes, in regulators of quorum-sensing (agrA and agrC) and in known antibiotic targets (fusA, pbp2, dfrA and ileS). We demonstrated the phenotypic effect of multiple adaptive mutations in vitro, including changes in haemolytic activity, antibiotic susceptibility, and metabolite utilisation. Nitrogen metabolism showed the strongest evidence of adaptation, with the assimilatory nitrite reductase (nasD) and urease (ureG) showing the highest mutational enrichment. We identified a nasD natural mutant with enhanced growth under urea as the sole nitrogen source. Inclusion of 4090 additional isolate genomes from 731 individuals revealed eight more genes including sasA/sraP, darA/pstA, and rsbU with signals of adaptive variation that warrant further characterisation. Our study provides a comprehensive picture of the heterogeneity of S. aureus adaptive changes during colonisation, and a robust methodological approach applicable to study in host adaptive evolution in other bacterial pathogens.