Biology subjects

Harrison, E. M.

Publications and source records attributed to Harrison, E. M..

6 recordsLinked to original sources

bioRxiv2026

Accounting for allelic diversity and multicopy gene detection improves the accuracy of antibiotic resistance genotypic determination

BackgroundGenomic 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{square}read data. We conduct further extended investigations to other antibiotic families and into another bacterial species: Escherichia coli. MethodsWe analyzed collections of E. faecium and E. coli genomes, integrating high{square}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). ResultsFor 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{square}associated genes (aph(3{square})-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{square}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{square}specific alleles were included. ConclusionsOur 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.

bioinformatics↗

bioRxiv2025

In host mutational adaptation of Mycobacterium tuberculosis complex strains

BackgroundTuberculosis (TB) is the second leading cause of death from an infectious disease worldwide. Mycobacterium tuberculosis complex (MTBC), the causative bacteria of TB, is becoming increasingly resistant to anti-TB drugs, resulting in poorer treatment outcomes. The mutations arising in MTBC strains during infection provide a record of bacterial diversification and adaptation, and indirect evidence on the selective pressures and conditions that MTBC encountered in vivo, such as those exerted by the immune system and antibiotic therapies. MethodsWe conducted a meta-analysis of MTBC sequenced strains with multiple clinical isolates sequenced from the same patient, from published studies and TB Portals. We applied stringent genomic QC criteria to keep only clonal isolates and applied robust genomic pipelines to identify mutations arising de novo during infection. A convergent evolution approach was applied to identify heavily mutated genes, operons, and candidate promoter across strains of multiple patients. We estimated the frequency of drug resistance acquisition during treatment for the subset of patients with available drug treatment data. ResultUsing 5,899 high-quality genomes from 1,056 TB patients after ensuring clonality of isolate genomes we identified limited within-host diversity was identified including 3,296 fixed mutations across 501 patients. A total of 21 genes, 25 operons and 27 promoter regions were statistically enriched by mutations compared to the rest of the genome, and additional loci with established or plausible adaptive roles approaching statistical significance. Significantly, this included multiple loci known to be involved in resistance to first-, second-, and last-line anti-TB drugs. Fluoroquinolone resistance was acquired more frequently during treatment than resistance to any other anti-TB drug. Previously reported candidate drug-resistance and -tolerance genes (prpR, Rv2571c, fadD11, helY, ndhA, Rv0139, fadE5, and mce1 operon) were also identified. Genes encoding regulators (phoR, whiB6 and mycP1) and effectors (espK and eccE1) of the virulence ESX-1 locus were frequently mutated in host. ConclusionsHere we analysed a large dataset of MTBC within-host genetic diversity. We show that frequently mutated genes in MTBC during infection reveal known and biologically plausible in host adaptations, predominantly associated drug resistance, but also in genes involved in pathogenesis. The higher resistance acquisition rate observed for fluoroquinolones may have important clinical relevance. We reveal a list of candidate loci which will require mechanistic characterisation and whose impact on disease progression will need to be investigated.

genomics↗

bioRxiv2025

BaGPipe: an automated, reproducible, and flexible pipeline for bacterial genome-wide association studies

Microbial genome-wide association study (GWAS) tools often require manual data processing steps, lack comprehensive workflows, and are limited by scalability issues, thus hindering the exploration of bacterial genetic traits. To address these challenges, we developed BaGPipe, an automated and flexible bacterial GWAS pipeline built using Nextflow and incorporating Pyseer for association analysis. BaGPipe integrates all essential components of a bacterial GWAS--spanning pre-processing, statistical analysis, and downstream visualisation--into a unified workflow that is reproducible and easy to deploy across diverse computational environments. BaGPipe was validated on a publicly available dataset of Streptococcus pneumoniae whole-genome sequences, and reproduced published findings with improved computational efficiency. BaGPipe was then applied to a dataset of Staphylococcus aureus whole-genome sequences, successfully identifying known and novel antibiotic resistance associations. By offering an accessible, efficient, and reproducible platform, BaGPipe accelerates bacterial GWAS and facilitates deeper exploration into the genetic underpinnings of phenotypic traits. Impact StatementThe increasing availability of bacterial genome sequences has created an opportunity for robust, reproducible tools to facilitate the discovery of novel genotype-phenotype associations. Despite the demonstrated utility of genome-wide association studies (GWAS) in identifying genetic determinants of disease, toxicity and antibiotic resistance, existing tools for bacterial GWAS often involve fragmented workflows requiring extensive manual intervention, limiting their adoption and reproducibility. Here, we introduce BaGPipe, a fully integrated bacterial GWAS pipeline that automates pre-processing, statistical analysis, and visualisation, thereby streamlining the entire workflow. With its flexibility, scalability, and ease of use, BaGPipe makes bacterial GWAS more accessible to researchers, enabling faster and more reliable insights into microbial genetics. This is an important step towards overcoming the computational and logistical barriers that have constrained bacterial GWAS, ultimately accelerating research into microbial evolution, resistance mechanisms, and the genetic basis of other key phenotypic traits. Data SummaryBaGPipe is freely available at https://github.com/sanger-pathogens/BaGPipe. The Streptococcus pneumoniae input dataset is available from the Pyseer tutorial (https://pyseer.readthedocs.io/en/master/tutorial.html#). The Staphylococcus aureus sequencing assemblies can be sourced from their ERS accession numbers provided in supplementary data. The reference assemblies, listed in the supplementary, can be sourced from NCBI.

bioinformatics↗

bioRxiv2023

The mutational landscape of Staphylococcus aureus during colonisation

Staphylococcus aureus is an important human pathogen but is primarily a commensal of the human nose and skin. Survival during colonisation is likely one of the major drivers of S. aureus evolution. Here we use a genome-wide mutation enrichment approach to analyse a genomic dataset of 3,060 S. aureus isolates from 791 individuals to show that despite limited within-host genetic diversity, an excess of protein-altering mutations can be found in genes encoding key metabolic pathways, in regulators of quorum-sensing and in known antibiotic targets. Nitrogen metabolism and riboflavin synthesis are the metabolic processes with strongest evidence of adaptation. Further evidence of adaptation to nitrogen availability was revealed by enrichment of mutations in the assimilatory nitrite reductase and urease, including mutations that enhance growth with urea as the sole nitrogen source. Inclusion of an additional 4,090 genomes from 802 individuals revealed eight additional genes including sasA/sraP, pstA, and rsbU with signals adaptive variation that warrant further characterisation. Our study provides the most comprehensive picture to date of the heterogeneity of adaptive changes that occur in the genomes of S. aureus during colonisation, revealing the likely importance of nitrogen metabolism, loss of quorum sensing and antibiotic resistance for successful human colonisation.

genomics↗

bioRxiv2020

MAJORA: Continuous integration supporting decentralised sequencing for SARS-CoV-2 genomic surveillance

Genomic epidemiology has become an increasingly common tool for epidemic response. Recent technological advances have made it possible to sequence genomes rapidly enough to inform outbreak response, and cheaply enough to justify dense sampling of even large epidemics. With increased availability of sequencing it is possible for agile networks of sequencing facilities to collaborate on the sequencing and analysis of epidemic genomic data. In response to the ongoing SARS-CoV-2 pandemic in the United Kingdom, the COVID-19 Genomics UK (COG-UK) consortium was formed with the aim of rapidly sequencing SARS-CoV-2 genomes as part of a national-scale genomic surveillance strategy. The network consists of universities, academic institutes, regional sequencing centres and the four UK Public Health Agencies. We describe the development and deployment of Majora, an encompassing digital infrastructure to address the challenge of collecting and integrating both genomic sequencing data and sample-associated metadata produced across the COG-UK network. The system was designed and implemented pragmatically to stand up capacity rapidly in a pandemic caused by a novel virus. This approach has underpinned the success of COG-UK, which has rapidly become the leading contributor of SARS-CoV-2 genomes to international databases and has generated over 60,000 sequences to date.

bioinformatics↗

bioRxiv2019

Resolving the fibrotic niche of human liver cirrhosis using single-cell transcriptomics

Currently there are no effective antifibrotic therapies for liver cirrhosis, a major killer worldwide. To obtain a cellular resolution of directly-relevant pathogenesis and to inform therapeutic design, we profile the transcriptomes of over 100,000 primary human single cells, yielding molecular definitions for the major non-parenchymal cell types present in healthy and cirrhotic human liver. We uncover a novel scar-associated TREM2+CD9+ macrophage subpopulation with a fibrogenic phenotype, that has a distinct differentiation trajectory from circulating monocytes. In the endothelial compartment, we show that newly-defined ACKR1+ and PLVAP+ endothelial cells expand in cirrhosis and are topographically located in the fibrotic septae. Multi-lineage ligand-receptor modelling of specific interactions between the novel scar-associated macrophages, endothelial cells and collagen-producing myofibroblasts in the fibrotic niche, reveals intra-scar activity of several major pathways which promote hepatic fibrosis. Our work dissects unanticipated aspects of the cellular and molecular basis of human organ fibrosis at a single-cell level, and provides the conceptual framework required to discover rational therapeutic targets in liver cirrhosis.

immunology↗