Biomedical subjects

Lachlan J M Coin

Publications and source records attributed to Lachlan J M Coin.

3 recordsLinked to original sources

PubMed2025

Genome size estimation from long read overlaps.

MOTIVATION: Accurate genome size estimation is an important component of genomic analyses such as assembly and coverage calculation, though existing tools are primarily optimized for short-read data. RESULTS: We present LRGE, a novel tool that uses read-to-read overlap information to estimate genome size in a reference-free manner. LRGE calculates per-read genome size estimates by analysing the expected number of overlaps for each read, considering read lengths and a minimum overlap threshold. The final size is taken as the median of these estimates, ensuring robustness to outliers such as reads with no overlaps. Additionally, LRGE provides an expected confidence range for the estimate. We validate LRGE on a large, diverse bacterial dataset and confirm it generalizes to eukaryotic datasets. On bacterial genomes, LRGE outperforms k-mer-based methods in both accuracy and computational efficiency and produces genome size estimates comparable to those from assembly-based approaches, like Raven, while using significantly less computational resources. AVAILABILITY AND IMPLEMENTATION: Our method, LRGE (Long Read-based Genome size Estimation from overlaps), is implemented in Rust and is available as a precompiled binary for most architectures, a Bioconda package, a prebuilt container image, and a crates.io package as a binary (lrge) or library (liblrge). The source code is available at https://github.com/mbhall88/lrge and an archive at https://doi.org/10.5281/zenodo.17183812 under an MIT license.

Genome Size↗

PubMed2025

Invasive Streptococcus dysgalactiae subspecies equisimilis compared with Streptococcus pyogenes in Australia, 2011-23, and the emergence of a multi-continent stG62647 lineage: a retrospective clinical and genomic epidemiology study.

BACKGROUND: Streptococcus dysgalactiae subspecies equisimilis (SDSE) is closely related to Streptococcus pyogenes, with overlapping disease manifestations. We compared the clinical and genomic epidemiology of invasive SDSE with invasive S pyogenes across different settings in Australia and phylogenetically contextualised the SDSE sequences within a global cohort of genomes. METHODS: In this retrospective clinical and genomic epidemiology study, cases of invasive SDSE isolated from normally sterile sites were identified and whole-genome sequenced across five hospital networks in temperate southeast Australia (Melbourne and Sydney) and the tropical Top End of the Northern Territory. SDSE disease incidence, case demographics, clinical outcomes, and longitudinal lineage dynamics were compared between southeast Australia and the Top End and to co-collected invasive S pyogenes cases in each region. SDSE genomes and lineages were also contextualised within 1166 global SDSE sequences. Genomic transmission clusters (not necessarily direct transmission) were inferred between isolates from different individuals by single-linkage clustering at a single nucleotide polymorphism threshold of less than or equal to seven for SDSE and less than or equal to five for S pyogenes based on previous transmission analyses. FINDINGS: Between Jan 1, 2011, and Feb 28, 2023, there were 693 invasive SDSE cases and 995 invasive S pyogenes cases. Invasive SDSE occurred almost exclusively in adults. The overall invasive SDSE incidence in southeast Australia was similar to invasive S pyogenes (incidence rate ratio [IRR] 1·15, 95% CI 0·91-1·46; p=0·26) and increased over the study period (IRR 1·06 per year, 95% CI 1·05-1·08; p<0·0001) from 1·30 cases per 10 000 admissions in 2011 to 3·72 cases per 10 000 admissions in the first 2 months of 2023 (95% CI 2·13-6·07). In southeast Australia, where stringent COVID-19 non-pharmaceutical interventions (NPIs) were implemented between 2020 and 2021, the SDSE incidence plateaued during 2020-21 but did not significantly decline (IRR 1·09 compared with 2017-19, 95% CI 0·88-1·35; p=0·47). By contrast, S pyogenes incidence substantially declined in 2020-21 in southeast Australia (IRR 0·35 compared to 2017-19, 95% CI 0·22-0·52; p=0·017). In the Top End, SDSE incidence was lower than S pyogenes (IRR 0·24, 95% CI 0·19-0·31; p<0·0001). However, crude incidence remained higher than southeast Australia (crude IRR 1·24, 95% CI 1·07-1·42; p=0·0037) and disproportionately affected First Nations Australians in the Top End compared with non-First Nations individuals (IRR 3·36, 95% CI 2·33-4·85; p<0·0001). Comparing 2020-21 with 2017-19, there was no decline in SDSE (IRR 1·27, 95% CI 0·73-2·24; p=0·45) or S pyogenes (IRR 0·97, 95% CI 0·80-1·18; p=0·81) incidence in the Top End, which did not implement prolonged stringent COVID-19 NPIs. Analysing the available genomes of invasive cases and in lineages for which more than or equal to five invasive cases occurred, only 24 (6%) of 384 SDSE cases were assigned to genomic transmission clusters, compared with 271 (52%) of 524 S pyogenes cases. An stG62647 lineage encompassed 113 (26%) of 436 sequenced SDSE genomes. Analysis of available SDSE sequences from Australia, western Europe, and North America inferred concurrent international expansion of the stG62647 lineage in all three regions between 1990 and 2005. INTERPRETATION: We identified a substantial burden of invasive SDSE, dominated by the emergent stG62647 lineage. The contrasting epidemiology between species in the different Australian regions, during COVID-19 NPIs, and genomic infection patterns indicates transmission dynamic, pathogen population, and host-pathogen interaction differences between SDSE and S pyogenes and indicates implications for disease control measures. FUNDING: Australian National Health and Medical Research Council.

Humans↗

PubMed2025

Temporal and geographical lineage dynamics of invasive Streptococcus pyogenes in Australia from 2011 to 2023: a retrospective, multicentre, clinical and genomic epidemiology study.

BACKGROUND: Defining the temporal dynamics of invasive Streptococcus pyogenes (group A Streptococcus) and differences between hyperendemic and lower-incidence regions provides crucial insights into pathogen evolution and, in turn, informs preventive measures. We aimed to examine the clinical and temporal lineage dynamics of S pyogenes across different disease settings in Australia to improve understanding of drivers of pathogen diversity. METHODS: In this retrospective, multicentre, clinical and genomic epidemiology study, we identified cases of invasive S pyogenes infection from normally sterile sites between Jan 1, 2011, and Feb 28, 2023. Data were collected from five hospital networks across low-incidence regions in temperate southeast Australia and the hyperendemic, tropical, and largely remote Top End of the Northern Territory of Australia. The crude incidence rate ratio (IRR) of bloodstream S pyogenes infection comparing the Top End and southeast Australia and in First Nations people compared with non-First Nations people was estimated by quasi-Poisson regression. We estimated odds ratios (ORs) of intensive care unit (ICU) admission, in-hospital mortality, and 30-day mortality for the Top End versus southeast Australia using logistic regression. Retrieved and successfully sequenced isolates were assigned lineages at whole-genome resolution. Temporal trends in the composition of co-circulating lineages were compared between the two regions. We used an S pyogenes-specific multistrain simulated transmission model to examine the relationship between host population-specific parameters and observed pathogen lineage dynamics. The prevalence of accessory genes (those present in 5-95% of all genomes) was compared across geographies and temporal periods to investigate genomic drivers of diversity. FINDINGS: We identified 500 cases of invasive S pyogenes infection in patients in the Top End and 495 cases in patients in southeast Australia. The crude IRR of bloodstream infection for the Top End compared with southeast Australia was 5·97 (95% CI 4·61-7·73) across the entire study period; in the Top End, infection disproportionately affected First Nations people compared with non-First Nations people (5·41, 4·28-6·89). The odds of in-hospital mortality (OR 0·43, 95% CI 0·26-0·70), 30-day mortality (0·38, 0·23-0·63), and ICU admission (0·42, 0·30-0·59) were lower in the Top End than in southeast Australia. Longitudinal lineage analysis of 642 S pyogenes genomes identified waves of replacement with distinct lineages in the Top End, whereas southeast Australia had a small number of dominant lineages that persisted and cycled in frequency. The transmission model qualitatively reproduced a similar pattern of replacement with distinct lineages when using a high transmission rate, small population size, and high levels of human movement-characteristics similar to those of communities in the hyperendemic Top End. Using a lower transmission rate, larger population size, and lower levels of migration similar to those of communities in urbanised southeast Australia, the transmission model qualitatively reproduced a pattern of dominant lineages that cycled in frequency. Despite distinct circulating lineages, the prevalence of accessory genes in the bacterial population was maintained across geographies and temporal periods. INTERPRETATION: In a hyperendemic setting, the replacement of distinct S pyogenes lineages occurred in waves, which could be linked to the disproportionate burden of disease and sparse human population in this setting. The maintenance of bacterial gene frequency could be consistent with multilocus selection. These findings suggest that lineage-specific interventions-such as vaccines under development-should consider disease setting and, without broad cross-protection, might lead to lineage replacement. FUNDING: National Health and Medical Research Council, and Leducq Foundation.

Humans↗