Biology subjects

Hawkins, P. A.

Publications and source records attributed to Hawkins, P. A..

3 recordsLinked to original sources

bioRxiv2024

A Portable and Scalable Genomic Analysis Pipeline for Streptococcus pneumoniae Surveillance: GPS Pipeline

Ever increasing global sequencing capacity provides an unprecedented opportunity in utilising genomic information captured from whole-genome sequencing to enhance pathogen surveillance. However, there is a growing need for developing user-friendly tools to effectively analyse the increasing volume of data. To meet this need, we have developed a genomic analysis pipeline, GPS Pipeline, which is portable and scalable to analyse genomes of Streptococcus pneumoniae, a major bacterial pathogen that is estimated to cause 317,000 child deaths worldwide every year. The GPS Pipeline is based on Nextflow and containerisation technology, and designed to enable researchers generating public health relevant output, including in silico serotypes, pneumococcal lineages (i.e. GPSCs), multilocus sequence types, and antimicrobial susceptibilities against 20 commonly used antibiotics,with minimal software setup requirements and bioinformatic expertise, in order to analyse genomic data at scale with ease. The GPS Pipeline provides a streamlined workflow that improves responsiveness in genomic surveillance on pneumococci. Data SummaryThe GPS Pipeline is available on GitHub at github.com/GlobalPneumoSeq/gps-pipeline. Published data from the GPS Database is available on Monocle Data Viewer at data.monocle.sanger.ac.uk and associated sequence read files are searchable and downloadable in the European Nucleotide Archive at ebi.ac.uk/ena via their ERR accession numbers. Impact StatementThe GPS Pipeline advances global genomic surveillance of Streptococcus pneumoniae by providing a scalable, portable, and user-friendly tool for analysing whole-genome sequencing data. Leveraging Nextflow and containerisation technology, it minimises bioinformatics expertise requirements and infrastructure needs, making it particularly valuable in low- and middle-income countries where pneumococcal disease burden is high. This pipeline ensures reproducibility and stability across platforms, facilitating rapid and accurate pneumococci genomic analysis. By streamlining data processing, the GPS Pipeline enhances pathogen surveillance, generates evidence to support vaccine strategy development, and empowers researchers worldwide, ultimately contributing to improved public health outcomes.

genomics↗

bioRxiv2024

Comparison of gene-by-gene and genome-wide short nucleotide sequence based approaches to define the global population structure of Streptococcus pneumoniae

Defining the population structure of a pathogen is a key part of epidemiology, as genomically related isolates are likely to share key clinical features such as antimicrobial resistance profiles and invasiveness. Multiple different methods are currently used to cluster together closely- related genomes, potentially leading to inconsistency between studies. Here, we use a global dataset of 26,306 S. pneumoniae genomes to compare four clustering methods: gene-by- gene seven-locus multi-locus sequencing typing (MLST), core genome MLST (cgMLST)- based hierarchical clustering (HierCC) assignments, Life Identification Number (LIN) barcoding, and k-mer-based PopPUNK clustering (known as GPSCs in this species). We compare the clustering results with phylogenetic and pan-genome analyses to assess their relationship with genome diversity and evolution, as we would expect a good clustering method to form a single monophyletic cluster that has high within-cluster similarity of genomic content. We show that the four methods are generally able to accurately reflect the population structure based on these metrics, and that the methods were broadly consistent with each other. We investigated further to study the discrepancies in clusters. The greatest concordance was seen between LIN barcoding and HierCC (Adjusted Mutual Information Score = 0.950), which was expected given that both methods utilise cgMLST, but have different methods for defining an individual cluster and different core genome schema. However, the existence of differences between the two methods show that the selection of a core genome schema can introduce inconsistencies between studies. GPSC and HierCC assignments were also highly concordant (AMI = 0.946), showing that k-mer based methods which use the whole genome and do not require the careful selection of a core genome schema are just as effective at representing the population structure. Additionally, where there were differences in clustering between these methods, this could be explained by differences in the accessory genome that were not identified in cgMLST. We conclude that for S. pneumoniae, standardised and stable nomenclature is important as the number of genomes available expands. Furthermore, the research community should transition away from seven- locus MLST, and cgMLST, GPSC, and LIN assignments should be used more widely. However, to allow for easy comparison between studies and to make previous literature relevant, the reporting of multiple clustering names should be standardised within research. Data summaryGenome sequences are deposited in the European Nucleotide Archive (ENA); accession numbers. Metadata of the pneumococcal isolates in this study have been submitted as a supplementary file and are also available on the Monocle Database available at https://data.monocle.sanger.ac.uk/. The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. Impact StatementUsing a global dataset of S. pneumoniae genomes allows us to thoroughly observe and analyse discrepancies between different clustering methods. Whilst all methods in this study are used to cluster S. pneumoniae genomes, no study has yet thoroughly compared the clustering results and discrepancies. This work summarises the strengths and weaknesses of the different methods and highlights the need for consistency between studies.

genomics↗

bioRxiv2019

A novel mosaic tetracycline resistance gene tet(S/M) detected in a multidrug-resistant pneumococcal CC230 lineage that underwent capsular switching in South Africa

SynopsisO_ST_ABSObjectiveC_ST_ABSWe reported a novel tetracycline-resistant gene in Streptococcus pneumoniae and investigated its temporal spread in relation to nationwide clinical interventions.\n\nMethodsWe whole genome sequenced 12,254 pneumococcal isolates from twenty-nine countries on an Illumina HiSeq Sequencer. Serotypes, sequence types and antibiotic resistance were inferred from genomes. Phylogeny was built based on single-nucleotide variants. Temporal changes of spread were reconstructed using a birth-death model.\n\nResultsWe identified tet(S/M) in 131 pneumococcal isolates, 97 (74%) caused invasive pneumococcal diseases among young children (59% HIV-positive, where HIV status was available) in South Africa. A majority of tet(S/M)-positive isolates (129/131) belong to clonal complex (CC)230. A global phylogeny of CC230 (n=389) revealed that tet(S/M)-positive isolates formed a sub-lineage that exhibited multidrug-resistance. Using the genomic data and a birth-death model, we detected an unrecognised outbreak of this sub-lineage in South Africa between 2000 and 2004 with an expected secondary infections (R) of ~2.5. R declined to ~1.0 in 2005 and <1.0 in 2012. The declining epidemic coincided and could be related to the nationwide implementation of anti-retroviral treatment (ART) for HIV-infected individuals in 2004 and PCVs in late 2000s. Capsular switching from vaccine serotype 14 to non-vaccine serotype 23A was observed within the sub-lineage.\n\nConclusionsThe prevalence of tet(S/M) in pneumococci was low and its dissemination was due to an unrecognised outbreak of CC230 in South Africa prior to ART and PCVs. However, capsular switching in this multidrug-resistant sub-lineage highlighted its potential to continue to cause disease in the post-PCV13 era.

microbiology↗