Biology subjects

Connor, T. R.

Publications and source records attributed to Connor, T. R..

8 recordsLinked to original sources

bioRxiv2025

Parapipe: a Pipeline for Handling Parasite NGS Datasets and its Application to Cryptosporidium

0.0Cryptosporidium, a protozoan parasite of significant public health concern, is responsible for severe diarrheal diseases, particularly in immunocompromised individuals and young children in resource-limited settings. Analysis of whole genome next generation sequencing (NGS) data is a critical next step in improving our understanding of Cryptosporidium epidemiology, transmission dynamics, and genetic diversity. However, effective analysis of NGS data in a public health context necessitates the development of robust, validated bioinformatics tools. Here, we present Parapipe, a modular ISO accreditable bioinformatics pipeline designed for high-throughput processing and analysis of Cryptosporidium NGS datasets. Built using Nextflow DSL2 and containerized with Singularity, Parapipe is portable, scalable, and capable of end-to-end analyses, including quality control, variant calling, multiplicity of infection (MOI) investigations and phylogenomic clustering analysis. Using both simulated and real-world datasets, we demonstrate Parapipes ability to resolve genetic heterogeneity, identify mixed infections, and generate high-resolution phylogenomic insights. Here, we use it to carry out a comparison between whole genome single nucleotide polymorphism (wgSNP) typing and the conventionally used gp60 molecular typing scheme. Compared to existing pipelines, Parapipe uniquely integrates MOI analysis, enabling the differentiation of mixed infections and supporting epidemiological investigations. Parapipes design facilitates integration with geographic, demographic, epidemiological and environmental data, enhancing its utility for tracking transmission pathways and outbreak sources. Parapipe represents a significant advance in utilising genomics for public health surveillance of Cryptosporidium, offering a streamlined and reproducible framework for analysis with potential application to other pathogenic protozoa. By automating complex workflows and enabling detailed genomic characterization, Parapipe provides a valuable tool for public health agencies and researchers, supporting efforts to mitigate the global burden of cryptosporidiosis.

bioinformatics↗

bioRxiv2023

Identification of two distinct phylogenomic lineages and model strains for the understudied cystic fibrosis lung pathogen Burkholderia multivorans

Burkholderia multivorans is the dominant Burkholderia pathogen recovered from lung infection in people with cystic fibrosis. However, as an understudied pathogen there are knowledge gaps in relation to its population biology, phenotypic traits and useful model strains. A phylogenomic study of B. multivorans was undertaken using a total of 283 genomes, of which 73 were sequenced and 49 phenotypically characterized as part of this study. Average nucleotide identity analysis (ANI) and phylogenetic alignment of core genes demonstrated that the B. multivorans population separated into two distinct evolutionary clades, defined as lineage 1 (n = 58 genomes) and lineage 2 (n = 221 genomes). To examine the population biology of B. multivorans, a representative subgroup of 77 B. multivorans genomes (28 from the reference databases and the 49-novel short-read genome sequences) were selected based on multilocus sequence typing (MLST), isolation source and phylogenetic placement criteria. Comparative genomics was used to identify B. multivorans lineage-specific genes: ghrB_1 in lineage 1, and glnM_2 in lineage 2, and diagnostic PCRs targeting them successfully developed. Phenotypic analysis of 49 representative B. multivorans strains showed considerable variance with the majority of isolates tested being motile and capable of biofilm formation. A striking absence of B. multivorans protease activity in vitro was observed, but no lineage-specific phenotypic differences demonstrated. Using phylogenomic and phenotypic criteria, three model B. multivorans CF strains were identified, BCC0084 (lineage 1), BCC1272 (lineage 2a) and BCC0033 lineage 2b, and their complete genome sequences determined. B. multivorans CF strains BCC0033 and BCC0084, and the environmental reference strain, ATCC 17616, were all capable of short-term survival within a murine lung infection model. By mapping the population biology, identifying lineage-specific PCRs and model strains, we provide much needed baseline resources for future studies of B. multivorans.

microbiology↗

bioRxiv2022

The origins and molecular evolution of SARS-CoV-2 lineage B.1.1.7 in the UK

The first SARS-CoV-2 variant of concern (VOC) to be designated was lineage B.1.1.7, later labelled by the World Health Organisation (WHO) as Alpha. Originating in early Autumn but discovered in December 2020, it spread rapidly and caused large waves of infections worldwide. The Alpha variant is notable for being defined by a long ancestral phylogenetic branch with an increased evolutionary rate, along which only two sequences have been sampled. Alpha genomes comprise a well-supported monophyletic clade within which the evolutionary rate is more typical of SARS-CoV-2. The Alpha epidemic continued to grow despite the continued restrictions on social mixing across the UK, and the imposition of new restrictions, in particular the English national lockdown in November 2020. While these interventions succeeded in reducing the absolute number of cases, the impact of these non-pharmaceutical interventions was predominantly to drive the decline of the SARS-CoV-2 lineages which preceded Alpha. We investigate the only two sampled sequences that fall on the branch ancestral to Alpha. We find that one is likely to be a true intermediate sequence, providing information about the order of mutational events that led to Alpha. We explore alternate hypotheses that can explain how Alpha acquired a large number of mutations yet remained largely unobserved in a region of high genomic surveillance: an under-sampled geographical location, a non-human animal population, or a chronically-infected individual. We conclude that the last hypothesis provides the best explanation of the observed behaviour and dynamics of the variant, although we find that the individual need not be immunocompromised, as persistently-infected immunocompetent hosts also display a higher within-host rate of evolution. Finally, we compare the ancestral branches and mutation profiles of other VOCs to each other, and identify that Delta appears to be an outlier both in terms of the genomic locations of its defining mutations, and its lack of rapid evolutionary rate on the ancestral branch. As new variants, such as Omicron, continue to evolve (potentially through similar mechanisms) it remains important to investigate the origins of other variants to identify ways to potentially disrupt their evolution and emergence.

evolutionary biology↗

bioRxiv2021

Exploration of polyyne biosynthetic gene cluster diversity in bacteria leads to the discovery of the Pseudomonas polyyne protegencin

Natural products that possess alkyne or polyyne moieties have been isolated from a variety of biological sources. In bacteria their biosynthesis has been defined, however, the distribution of polyyne biosynthetic gene clusters (BGCs), and their evolutionary relationship to alkyne biosynthesis, have not been addressed. We explored the distribution of alkyne biosynthesis gene cassettes throughout bacteria, revealing evidence of multiple horizontal gene transfer events. Investigating the evolutionary connection between alkyne and polyyne biosynthesis identified a monophyletic clade possessing a conserved seven-gene cassette for polyyne biosynthesis. Mapping the diversity of these conserved genes revealed a phylogenetic clade representing a polyyne BGC in Pseudomonas, pgn, and subsequent pathway mutagenesis and analytical chemistry characterised the associated metabolite, protegencin. In addition to unifying and expanding our knowledge of polyyne diversity, our results show that alkyne and polyyne biosynthetic gene clusters are promiscuous within bacteria. Systematic mapping of conserved biosynthetic genes across bacterial genomic diversity has proven to be a successful method for discovering natural products.

microbiology↗

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↗

bioRxiv2020

Kill and cure: genomic phylogeny and bioactivity of a diverse collection of Burkholderia gladioli bacteria capable of pathogenic and beneficial lifestyles

Burkholderia gladioli is one of few bacteria with a broad ecology spanning disease in humans, animals, and plants, and encompassing beneficial interactions with multiple eukaryotic hosts. It is a plant pathogen, a bongkrekic acid toxin producing food-poisoning agent, and a lung pathogen in people with cystic fibrosis (CF). Contrasting beneficial traits include antifungal production exploited by insects to protect their eggs, plant protective abilities and antibiotic biosynthesis. We explored the ecological diversity and specialized metabolite biosynthesis of 206 B. gladioli strains, phylogenomically defining 5 evolutionary clades. Historical disease pathovars (pv) B. gladioli pv. allicola and B. gladioli pv. cocovenenans were phylogenetically distinct, while B. gladioli pv. gladioli and B. gladioli pv. agaricicola were indistinguishable. Soft-rot disease and CF infection pathogenicity traits were conserved across all pathovars. Biosynthetic gene clusters for toxoflavin, caryoynencin and enacyloxin were dispersed across B. gladioli, but bongkrekic acid and gladiolin production were clade specific. Strikingly, 13% of CF-infection strains characterised (n=194) were bongkrekic acid toxin positive, uniquely linking this food-poisoning risk factor to chronic lung disease. Toxin production was suppressed by exposing strains to the antibiotic trimethoprim, providing a potential therapeutic strategy to minimise poisoning risk in CF.

microbiology↗

bioRxiv2019

Phenotypically distinct human sequence is widespread in publicly archived microbial reads: an evaluation of methods for its detection

Sequencing data from host-associated microbes can often be contaminated by the body of the investigator or research subject. Human DNA is typically removed from microbial reads either by subtractive alignment (dropping all reads that map to the human genome) or using a read classification tool to predict those of human origin, and then discarding them. To inform best practice guidelines, we benchmarked 8 alignment-based and 2 classification-based methods of human read detection using simulated data from 10 clinically prevalent bacteria and 3 viruses, into which contaminating human reads had been added. While the majority of methods successfully detected > 99% of the human reads, they were distinguishable by variance. The most precise methods, with negligible variance, were Bowtie2 and SNAP, both of which misclassified few, if any, bacterial reads (and no viral reads) as human. While correctly detecting a similar number of human reads, methods based on taxonomic classification, such as Kraken2 and Centrifuge, often misclassified bacterial reads as human, the extent of which was species-specific. Among the most sensitive methods of human read detection was BWA, although this also made the greatest number of false positive classifications. Across all methods, the set of human reads not identified as such, although often representing < 0.1% of the total reads, were non-randomly distributed along the human genome with many originating from the repeat-rich sex chromosomes. For viral reads and longer (> 300bp) bacterial reads, the highest performing approaches were classification-based, using Kraken2 or Centrifuge. For shorter (150-300bp) bacterial reads, combining multiple methods of human read detection maximised the recovery of human reads from contaminated short read datasets without being compromised by false positives. The highest-performing approach with shorter bacterial reads was a two-stage classification using Bowtie2 followed by SNAP. Using this approach, we re-examined 11,577 publicly archived bacterial readsets for hitherto undetected human contamination. We were able to extract a sufficient number of reads to call known human SNPs, including those with clinical significance, in 6% of the samples. These results show that phenotypically-distinct human sequence is widespread in publicly-archived (and nominally pure) bacterial datasets.

bioinformatics↗

bioRxiv2019

Influenza Classification from Short Reads with VAPOR Facilitates Robust Mapping Pipelines and Zoonotic Strain Detection for Routine Surveillance Applications

BackgroundInfluenza viruses are associated with a significant global public health burden. The segmented RNA genome of influenza changes continually due to mutation, and the accumulation of these changes within the antigenic recognition sites of haemagglutinin (HA) and neuraminidase (NA) in turn leads to annual epidemics. Influenza A is also zoonotic, allowing for exchange of segments between human and non-human viruses, resulting in new strains with pandemic potential. These processes necessitate a global surveillance system for influenza monitoring. To this end, whole-genome sequencing (WGS) has begun to emerge as a useful tool. However, due to the diversity and mutability of the influenza genome, and noise in short-read data, bioinformatics processing can present challenges.\n\nResultsConventional mapping approaches can be insufficient when a sub-optimal reference strain is chosen. For short-read datasets simulated from influenza H1N1 HA sequences, read recovery after single-reference mapping was routinely as low as 90% for human-origin influenza sequences, and often lower than 10% for those from avian hosts. To this end, we developed a de Bruijn Graph (DBG)-based classifier of influenza WGS datasets: VAPOR. In real data benchmarking using 257 WGS read sets with corresponding de novo assemblies, VAPOR provided classifications for all samples with a mean of >99.8% identity to assembled contigs. This resulted in an increase in the number of mapped reads by 6.8% on average, up to a maximum of 13.3%. Additionally, using simulations, we demonstrate that classification from reads may be applied to detection of reassorted strains.\n\nConclusionsVAPOR has potential to simplify bioinformatics pipelines for surveillance, providing a novel method for detection of influenza strains of human and non-human origin directly from reads, minimization of potential data loss and bias associated with conventional mapping, and allowing visualization of alignments that would otherwise require slow de novo assembly. Whilst with expertise and time these pitfalls can largely be avoided, with pre-classification they are remedied in a single step. Furthermore, our algorithm could be adapted in future to surveillance of other RNA viruses. VAPOR is available at https://github.com/connor-lab/vapor.

bioinformatics↗