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

Ouli Xie, Rebecca Chisholm, Leo Featherstone, An Nguyen, Andrew Hayes, Magnus Jespersen, Cameron Zachreson, Nefel Tellioglu, Gerry Tonkin-Hill, Ravindra Dotel, et al.

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## ► To cite this version:

Ouli Xie, Rebecca Chisholm, Leo Featherstone, An Nguyen, Andrew Hayes, et al.. Temporal and geographical lineage dynamics of invasive Streptococcus pyogenes in Australia from 2011 to 2023: a retrospective, multicentre, clinical and genomic epidemiology study. The Lancet Microbe, 2025, 6 (6), pp.101053. 10.1016/j.lanmic.2024.101053. hal-05162004

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Distributed under a Creative Commons Attribution 4.0 International License
# Temporal and geographical lineage dynamics of invasive *Streptococcus pyogenes* in Australia from 2011 to 2023: a retrospective, multicentre, clinical and genomic epidemiology study

Ouli Xie, Rebecca H Chisholm, Leo Featherstone, An N T Nguyen, Andrew J Hayes, Magnus G Jespersen, Cameron Zachreson, Nefel Tellioglu, Gerry Tonkin-Hill, Ravindra Dotel, Stephanie Spring, Alice Liu, Alexander Rofe, Sebastian Duchene, Norelle L Sherry, Robert W Baird, Vicki L Krause, Deborah C Holt, Lachlan J M Coin, Neela Joshi Rai, Matthew V N O'Sullivan, Katherine Bond, Jukka Corander, Benjamin P Howden, Tony M Korman, Bart J Currie, Steven Y C Tong*, Mark R Davies*

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CrossMark

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## Summary

**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.

Lancet Microbe 2025; 6: 101053

Published Online April 4, 2025

https://doi.org/10.1016/

/j.lanmic.2024.101053

*Contributed equally

Department of Infectious

Diseases (O Xie FRACP,

N Tellioglu PhD, A Liu FRACP,

K Bond FRACP,

Prof S Y C Tong PhD),

Department of Microbiology
and Immunology

(L Featherstone PhD,

A N T Nguyen PhD,

A J Hayes MPhil,

M G Jespersen PhD,

G Tonkin-Hill PhD, S Duchene PhD,

N L Sherry PhD,

Prof L J M Coin PhD, K Bond,

Prof B P Howden PhD,

M R Davies PhD), and

Microbiological Diagnostic Unit

Public Health Laboratory

(N L Sherry, Prof B P Howden),

University of Melbourne at the

Peter Doherty Institute for

Infection and Immunity,

Melbourne, VIC, Australia;

Monash Infectious Diseases,

Monash Health, Melbourne, VIC,

Australia (O Xie, S Spring FRACP,

T M Korman FRACP); Global and

Tropical Health Division,

Menzies School of Health

Research (O Xie, D C Holt PhD,

Prof B J Currie PhD) and Faculty of

Health (D C Holt), Charles Darwin

University, Darwin, NT,

Australia; Department of

Mathematical and Physical

Sciences, La Trobe University,

Melbourne, VIC, Australia

(R H Chisholm PhD); Melbourne

School of Population and Global

Health (R H Chisholm), School of

Computing and Information

Systems (C Zachreson PhD,

N Tellioglu), and Sir Peter

MacCallum Department of

Oncology (G Tonkin-Hill),

University of Melbourne,
Melbourne, VIC, Australia; School of Life and Environmental Sciences (L Featherstone) and Faculty of Medicine and Health (M V N O'Sullivan PhD), University of Sydney, Sydney, NSW, Australia; Peter MacCallum Cancer Centre, Melbourne, VIC, Australia (G Tonkin-Hill); Department of Biostatistics, Faculty of Medicine, University of Oslo, Oslo, Norway (G Tonkin-Hill, Prof J Corander PhD); Department of Infectious Diseases, Blacktown Hospital, Sydney, NSW, Australia (R Dotel PhD); Infectious Diseases Department, Royal Darwin Hospital, Darwin, NT, Australia (S Spring, Prof B J Currie); Victorian Infectious Diseases Service, The Royal Melbourne Hospital at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia (A Liu, A Rofe MBBS, K Bond, Prof S Y C Tong); Department of Microbiology, The Royal Melbourne Hospital, Melbourne, VIC, Australia (A Rofe, K Bond); Department of Computational Biology, Institut Pasteur, Paris, France (S Duchene); Department of Infectious Diseases & Immunology, Austin Health, Melbourne, VIC, Australia (N L Sherry, Prof B P Howden); Territory Pathology, Northern Territory Department of Health, Royal Darwin Hospital, Darwin, NT, Australia (R W Baird FRACP); Northern Territory Centre for Disease Control, Northern Territory Department of Health, Darwin, NT, Australia (V L Krause FAPHM); Department of Infectious Diseases (N J Rai MPH, M V N O'Sullivan) and NSW Health Pathology (M V N O'Sullivan), Westmead Hospital, Sydney, NSW, Australia; Parasites and Microbes, Wellcome Sanger Institute, Cambridge, UK (Prof J Corander); Helsinki Institute for Information Technology, Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland (Prof J Corander); Centre for Inflammatory Diseases, Monash University, Melbourne, VIC, Australia (T M Korman)

**Copyright** © 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

## Introduction

Infection with the human pathogen *Streptococcus pyogenes* (group A *Streptococcus*) can result in various manifestations, ranging from pharyngitis and impetigo to severe invasive disease and serious post-infectious sequelae, such as rheumatic heart disease.¹ More than 300 *S. pyogenes* lineages have been identified worldwide.²⁻⁴ Traditional molecular typing of *S. pyogenes* has focused on sequencing of the hypervariable virulence-encoding *emm* gene. However, *emm* type lacks resolution for detecting emergent sub-lineages and the same *emm* type can be found in distant lineages.²,⁵ Understanding temporal and geographical lineage dynamics at whole-genome resolution is crucial to elucidating drivers of pathogen diversity and, in turn, informing preventive measures such as vaccine design.

After historically low rates in 2020–21, during the COVID-19 pandemic, multiple high-income countries have since reported a resurgent incidence of invasive *S. pyogenes* infections greater than pre-pandemic levels.⁶⁻¹⁰ However, socioeconomically disadvantaged settings have long been disproportionately affected by streptococcal disease, including populations in high-income countries such as

First Nations communities in tropical northern Australia.¹¹ The inequitable burden of disease is driven in large part by social determinants, including household overcrowding, sanitation and water supply, and access to health care.¹¹,¹² Genomically, lineages in hyperendemic tropical settings might be distinct from those in highly resourced temperate regions.²,³,¹³,¹⁴ To our knowledge, there are no studies that compare these settings by examining longitudinal lineage dynamics at whole-genome resolution.

We aimed to define the clinical epidemiology and temporal lineage dynamics of invasive *S. pyogenes* disease across tropical northern and temperate southeast Australia between 2011 and 2023 at whole-genome resolution. Then, using our geographical and temporal comparisons, we investigated drivers of genomic diversity by assessing the frequency of accessory genes (those present only in some isolates) across contrasting disease settings.

## Methods

### Study design and isolates

In this retrospective, multicentre, clinical and genomic epidemiology study, we identified patients who had invasive

## Research in context

### Evidence before this study

*Streptococcus pyogenes* (group A *Streptococcus*) is a cause of serious invasive human disease, with the lineages circulating in high-resourced, lower-incidence regions distinct from those in socioeconomically disadvantaged, hyperendemic regions. Understanding longitudinal lineage dynamics at whole-genome resolution and the maintenance of diversity across different disease settings is crucial to inform disease-prevention strategies such as vaccines. We searched PubMed from database inception to Feb 19, 2024, with no language restrictions, using the search terms "*Streptococcus*" AND "*pyogenes*" AND "whole-genome" AND "sequencing", with and without the term "longitudinal". The search returned 162 publications, only six of which included the keyword "longitudinal". Existing studies indicate that particular *emm* types, such as *emm*1, dominate in high-resourced settings, whereas many distinct *emm* types circulate in disadvantaged, hyperendemic regions. However, studies mainly focused on the emergence of select *S. pyogenes* sublineages in high-resourced settings, and previous comparisons between lower-incidence and hyperendemic regions were limited by disparate sampling strategies and a cross-sectional design and often used single-gene *emm* typing. Therefore, how longitudinal lineage dynamics at whole-genome resolution compare across disease settings, and how these dynamics relate to drivers of multilineage pathogen diversity, remains unclear.

### Added value of this study

This study provides a clinical and whole-genome dataset of *S. pyogenes* infections, with data collected concurrently over more

than 10 years, contrasting hyperendemic tropical and lower-incidence temperate regions. Recurrent waves of replacement with multiple distinct lineages were observed in hyperendemic settings, contrasting with the dominance of a few lineages, which persisted and cycled in frequency, in high-resourced, lower-incidence regions. Compared with previous epidemiological reports, this study provides a possible link between population-level genomic dynamics, transmission intensity, and population demographic parameters using a multistrain model. Genomic lineage diversity was, in turn, associated with the maintenance of bacterial accessory gene prevalence, despite large differences in lineage composition across geographies and temporal periods.

### Implications of all the available evidence

These longitudinal insights at whole-genome resolution extend previous investigations of *S. pyogenes* conducted at lower *emm*-type resolution and provide a link between population parameters, such as disease burden and population size, and genomic dynamics. At a gene level, conservation of gene frequency is suggestive of multilocus negative frequency-dependent selection—eg, through host immune interactions or ecological competition—in the maintenance of multilineage diversity. Taken together, these findings indicate that preventive measures without broad cross-protection could result in lineage replacement and highlight the ongoing, pivotal role of primordial prevention in socioeconomically disadvantaged regions in which *S. pyogenes* is hyperendemic.
S *pyogenes* infections between Jan 1, 2011, and Feb 28, 2023, from five tertiary or quaternary health networks (ie, groups of hospitals operated by a single organisation) across Australia. Two networks were situated in Melbourne (VIC) and two in Sydney (NSW); these four networks were considered representative of lower-incidence, highly resourced, urbanised settings in temperate southeast Australia. The fifth network covered the tropical Top End of the Northern Territory, an area of approximately 475 000 km². Much of this area is sparsely populated, hosts many remote communities with high population mobility, and is hyperendemic for streptococcal disease.¹⁰,¹¹,¹⁵ First Nations people make up 27% of the population of the Top End and the majority of those living in remote communities.¹⁶ Three networks (one in Sydney, one in Melbourne, and the network covering the Top End) admitted adults and children; one network in each of Melbourne and Sydney admitted only adults. Detailed descriptions of the hospital networks are provided in appendix 1 (pp 1–2).

Invasive S *pyogenes* was defined when a culture of S *pyogenes* was obtained from a normally sterile site (appendix 1 p 2). Patients were identified using the local laboratory information system for sites in Melbourne and Sydney, from which we included only patients with bloodstream infections, and using the NT Notifiable Diseases Surveillance System for the Top End, from which all patients with invasive infections were included. Positive cultures obtained more than 14 days apart were considered reinfected or relapsed cases. Reinfection was defined as infection with an isolate from a different lineage or sublineage and relapse was defined as recurrence of infection with an isolate from the same sublineage. Bacterial isolates were retrieved from storage at -80°C.

A selection of invasive isolates from central Australia, a semi-arid area south of the Top End, were obtained during the same period for geographical genomic context but were not included in clinical or temporal analyses as insufficient clinical data were available.

This study received approval from the Royal Melbourne Hospital Human Research Ethics Committee (HREC/80105/MH-2021) and the Human Research Ethics Committee of the Northern Territory Department of Health and Menzies School of Health Research (2021-4181). Individual patient consent was waived as all data were available from routine clinical care.

Clinical data and outcomes

The following demographic information was extracted from patients' medical records: First Nations identification (yes or no), date of birth, and sex (all patient-reported); disease manifestation (as determined by treating clinicians); and, as outcomes, admission to the intensive care unit (ICU), length of hospital stay, in-hospital mortality, and 30-day mortality. Streptococcal toxic shock syndrome was recorded as per the 2010 US National Notifiable Diseases Surveillance System definition.¹⁷ Puerperal sepsis was defined as infection within 28 days postpartum.

Genomic sequencing and population structure analysis

Whole-genome sequencing of bacterial isolates was done on the NextSeq 550 platform (Illumina, San Diego, CA, USA) with 150-bp paired-end reads. Additional microbiological methods are described in appendix 1 (p 2). From the two networks in Sydney, isolates were available from October, 2020, to February, 2023 only.

Genomes were assembled and *emm* type, multilocus sequence type, and phylogeny of high-quality assemblies were inferred using standard pipelines (appendix 1 pp 2–3).⁴,¹⁸ Global genomic sequence clusters were assigned by PopPUNK (version 2.6.0) using a previously described model.² Genomic sequence clusters were highly concordant with the phylogeny and are henceforth referred to as lineages. Sublineages were assigned using PopPIPE (version 1.0.0) and matched against select previously identified hypervirulent lineages (appendix 1 pp 4–5).⁵,¹⁹,²⁰ Accessory genes were classified using Panaroo (version 1.3.3) and a previous mobile genetic element pipeline (appendix 1 p 5).¹⁸,²¹

Four new *S pyogenes* reference genomes were generated as a resource for future surveillance projects. Strains RDH425 (*emm*91), RDH275 (*emm*113), and RDH444 (*emm*121) were randomly selected as representatives of three well sampled lineages without publicly available reference genomes and strain RMH048 was selected as a contemporary *emm*1 isolate. High-molecular-weight DNA was sequenced using the GridION platform (Nanopore, Oxford, UK) with R10.4.1 flow cells. Complete genomes were assembled using Trycycler (version 0.5.4; appendix 1 pp 3–4).²²

Temporal and geographical lineage analysis

Geographical differences in lineage composition were quantified using the Morisita index as implemented in vegan (version 2.6),²³ a measure of community dissimilarity in which 0 indicates identical populations and 1 indicates completely different populations. Temporal trends in the composition of co-circulating lineages in the Top End and Melbourne (Sydney isolates were not included as they were not available before October, 2020) were compared between regions and in the context of non-pharmaceutical interventions implemented for COVID-19 (2020–21) using time-scaled lineage density plots. During 2020–21, Melbourne was frequently affected by stay-at-home orders in response to the COVID-19 pandemic; by contrast, the Top End implemented border closures and restriction between urban and remote communities but did not issue prolonged stay-at-home orders.²⁴,²⁵ For each lineage, a Bayesian coalescent skyline was fitted to infer an ancestral trajectory in the effective population size using BEAST (version 1.10.4).²⁶ Here, effective population size can be inferred as a proxy for overall diversity, which is expected to scale with true population size (appendix 1 pp 5–6).

To assess the broader applicability of our analysis, we used time-scaled lineage density plots to assess temporal lineage trends in previously published, publicly available datasets containing international, longitudinal, whole-genome data. We analysed a dataset of 328 genomes collected from

Correspondence to:

Assoc Prof Mark R Davies,
Department of Microbiology and
Immunology, University of
Melbourne at the Peter Doherty
Institute for Infection and
Immunity, Melbourne, VIC 3000,
Australia

mark.davies1@unimelb.edu.au

For PopPIPE see https://github.com/bacpop/PopPIPE

See Online for appendix 1
<figure><img src="image_6.png" /><figcaption>**Figure 1: Study flow chart**</figcaption></figure>

*Two cases of non-bloodstream *Streptococcus pyogenes* infection in the Top End were reinfected or relapsed cases for which the index case was culture-positive from the bloodstream.

children with invasive *S. pyogenes* infections between August, 1998, and December, 2011 in Kilifi, Kenya (representing a hyperendemic setting) and 839 genomes obtained from invasive disease surveillance between January, 2018 and February, 2023 in Denmark (a highly resourced region).⁷,¹⁴

## Lineage dynamic modelling

The effect of host population parameters on temporal lineage dynamics was modelled using an individual-based multistrain *S. pyogenes* transmission model described in a manuscript²⁷ on a preprint server. The model is agnostic to infection type (eg, invasive or non-invasive) and assumes uniform mixing and SIRS-like dynamics: hosts are either susceptible (S), infectious (I), or recovered with temporary immunity (R). Protective immunity is assumed to be 80% effective against that lineage and partially (20%) protective against other lineages. We simulated lineage dynamics in populations in which the size (N), per capita migration rate (d), and transmission intensities (R₀) were proportional to those of communities in southeast Australia and the Top End. Simulated lineage patterns were qualitatively compared with observed data considering patterns of lineage diversity, dominance, persistence, and replacement.

Further details and sensitivity analyses in which combinations of R₀ and d from one population are applied to the other, and vice versa, are described in appendix 1 (pp 6–8).

## Statistical analysis

Analyses of clinical outcomes and demographic characteristics were limited to patients with bloodstream infection to enable equal comparison across regions. Patients without complete clinical data were excluded from clinical analyses but were included in epidemiological and genomic analyses. Comparisons were made between the Top End and southeast Australia and separately over three time periods (before [2011–19], during [2020–21], and after [2022–23] COVID-19 non-pharmaceutical interventions) unless otherwise stated. All statistical analyses were conducted in R (version 4.3.1).

To address multiple infection episodes in the same individual, only the first episode was included for demographic and clinical comparisons. For comparisons of demographic characteristics, length of hospital stay, and disease manifestations, the distributions of continuous variables (age and length of hospital stay) were compared by the Wilcoxon rank sum test and the proportions of categorical variables (sex, First Nations identification, and disease manifestations)

For the multistrain transmission model see https://github.com/rhchisholm/gas-multi-strain-model
were compared with Fisher's exact test at a significance (α) of 0·05. Bonferroni correction was applied to comparisons of disease manifestations to account for multiple testing.

For analysis of clinical outcomes, odds ratios (OR, 95% CI) of ICU admission, in-hospital mortality, and 30-day mortality were estimated using logistic regression (α 0·05). As a sensitivity analysis when comparing regions, the comparisons for in-hospital mortality, 30-day mortality, and ICU admission were adjusted (adjusted OR, 95% CI, α 0·05) for age as a continuous variable, sex, First Nations identification, year of isolation, skin and soft tissue infection, and necrotising fasciitis or streptococcal toxic shock syndrome as covariates. Secondary analysis of clinical outcomes before, during, and after COVID-19 non-pharmaceutical interventions were adjusted for region only.

To examine trends in disease incidence in the Top End and southeast Australia, all episodes of bloodstream infection were included and incidence was calculated per 10 000 acute hospital admissions per year. Acute hospital admissions were defined as all emergency and maternity admissions as per the Australian Institute of Health and Welfare definitions.²⁸ As data were collected only until Feb 28, 2023, incidence in 2023 represents only the first 2 months of the year. In the Top End, which is served by a single hospital network, we also calculated population incidence using mid-year statistics from the Australian Bureau of Statistics.²⁹ Population incidence was not calculated for sites in southeast Australia because two hospitals in this region provided high-volume specialty statewide referral services and, therefore, an accurate population denominator could not be determined. Trends in disease incidence over time were estimated using quasi-Poisson regression for overdispersion with the natural log of yearly acute hospital admissions or population as offset and reported as crude incidence rate ratio (IRR, with 95% CI). The IRRs (95% CI) of the Top End and southeast Australia were compared using quasi-Poisson regression with year as a covariate. To estimate the burden of disease in First Nations Australians, the crude incidence and IRR (95% CI) affecting First Nations people compared with non-First Nations people in the Top End were estimated using population data from the nearest Australian Bureau of Statistics census year.¹⁶ The incidence of infection affecting First Nations Australians in southeast Australia was not estimated owing to few patients identifying as First Nations in this region.

To analyse genomic drivers of diversity, the frequency distribution (prevalence) of accessory genes was calculated as the proportion of genomes containing the gene among all genomes in each category. We included only intermediate-frequency genes (ie, those present in 5–95% of all genomes; core genes present in >95% of genomes and rare genes present in <5% of genomes were excluded) and compared this prevalence between different geographical regions and over two time periods before and after COVID-19 (ie, pre-2020 and post-2020) using Pearson’s correlation (coefficient of determination R², α 0·05). The correlation of lineage prevalence between regions and before and after

<figure><img src="image_7.png" /><figcaption>**Figure 2:** Geographical sampling and incidence of invasive *Streptococcus pyogenes* disease in the Top End and southeast Australia from Jan 1, 2011, to Feb 28, 2023</figcaption></figure>

(A) Geographical distribution of all *S pyogenes* cases and sequenced genomes across the tropical, hyperendemic Top End of the Northern Territory of Australia (highlighted in orange) and four hospital networks within the temperate cities of Melbourne and Sydney in southeast Australia. The pie charts show the total number of clinical cases and are centred over the cities of Darwin (in the Top End), Melbourne, and Sydney. An additional 122 genomes from central Australia (shaded in grey) were provided for genomic context but were not included in clinical or epidemiological analyses. (B) Yearly incidence of bloodstream invasive *S pyogenes* disease in the Top End (hospital and population incidence) and southeast Australia (hospital incidence only). Population incidence was not calculated for southeast Australia because an accurate catchment population could not be defined. Error bars for hospital incidence are 95% CIs. The blue shaded region from 2020 to 2021 approximates the period of the most stringent non-pharmaceutical interventions during the COVID-19 pandemic. *Up to Feb 28, 2023.*

COVID-19 was also calculated using Pearson’s correlation (R², α 0·05).

Role of the funding source

The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
<table><thead><tr><th></th><th>Top End<br>(n=383)</th><th>Southeast Australia<br>(n=417)</th><th>p value*</th><th>p value<br>(Bonferroni-corrected)</th></tr></thead><tbody><tr><td><strong>Demographics</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Age, years</td><td>53 (37-64)</td><td>53 (35-70)</td><td>0.20</td><td>..</td></tr><tr><td>Age (excluding adult-only sites, years)</td><td>53 (37-64; n=383)</td><td>52 (32-70; n=248)</td><td>0.99</td><td>..</td></tr><tr><td>Sex</td><td></td><td></td><td>0.57</td><td>..</td></tr><tr><td>Male</td><td>195 (51%)</td><td>221 (53%)</td><td></td><td></td></tr><tr><td>Female</td><td>188 (49%)</td><td>196 (47%)</td><td></td><td></td></tr><tr><td>First Nations identification</td><td></td><td></td><td>†</td><td>..</td></tr><tr><td>Yes</td><td>250 (65%)</td><td>5 (1%)</td><td></td><td></td></tr><tr><td>No</td><td>133 (35%)</td><td>412 (99%)</td><td></td><td></td></tr><tr><td><strong>Outcomes</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Length of hospital stay, days</td><td>8 (5-15)</td><td>9 (5-18)</td><td>0.15</td><td>..</td></tr><tr><td>In-hospital mortality</td><td>24 (6%)</td><td>56 (13%)</td><td>0.0010</td><td>..</td></tr><tr><td>30-day mortality</td><td>22 (6%)</td><td>57 (14%)</td><td>0.0003</td><td>..</td></tr><tr><td>Intensive care admission</td><td>61 (16%)</td><td>129 (31%)</td><td>&lt;0.0001</td><td>..</td></tr><tr><td><strong>Disease manifestation</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Primary bacteraemia</td><td>43 (11%)</td><td>58 (14%)</td><td>0.29</td><td>1.00</td></tr><tr><td>Necrotising fasciitis</td><td>12 (3%)</td><td>16 (4%)</td><td>0.70</td><td>1.00</td></tr><tr><td>Deep soft tissue infection</td><td>30 (8%)</td><td>51 (12%)</td><td>0.046</td><td>0.64</td></tr><tr><td>Superficial skin and soft tissue infection</td><td>217 (57%)</td><td>151 (36%)</td><td>&lt;0.0001</td><td>&lt;0.0001</td></tr><tr><td>Osteoarticular</td><td>41 (11%)</td><td>46 (11%)</td><td>0.91</td><td>1.00</td></tr><tr><td>Pneumonia</td><td>25 (7%)</td><td>54 (13%)</td><td>0.0028</td><td>0.040</td></tr><tr><td>Streptococcal toxic shock syndrome</td><td>24 (6%)</td><td>67 (16%)</td><td>&lt;0.0001</td><td>0.0002</td></tr><tr><td>Infective endocarditis</td><td>3 (1%)</td><td>7 (2%)</td><td>0.35</td><td>1.00</td></tr><tr><td>CNS infection</td><td>3 (1%)</td><td>10 (2%)</td><td>0.093</td><td>1.00</td></tr><tr><td>Head, ears, eyes, nose, and throat infection</td><td>17 (4%)</td><td>30 (7%)</td><td>0.13</td><td>1.00</td></tr><tr><td>Puerperal sepsis</td><td>7 (2%)</td><td>23 (6%)</td><td>0.0081</td><td>0.11</td></tr><tr><td>Genitourinary‡</td><td>6 (2%)</td><td>11 (3%)</td><td>0.34</td><td>1.00</td></tr><tr><td>Endovascular§</td><td>5 (1%)</td><td>11 (3%)</td><td>0.21</td><td>1.00</td></tr><tr><td>Other</td><td>4 (1%)</td><td>7 (2%)</td><td>0.55</td><td>1.00</td></tr></tbody></table>

Data are median (IQR) or n (%). Only individuals for whom complete clinical data were available are included. For individuals with multiple episodes of infection separated by more than 14 days, only the first episode was included. Individuals could have more than one disease manifestation. *Continuous variables (age and length of hospital stay) were compared by the Wilcoxon rank sum test and the proportions of categorical variables (sex and disease manifestations) were compared with Fisher's exact test. In-hospital mortality, 30-day mortality, and intensive care admission were compared by crude logistic regression. †p value not calculated owing to different underlying populations. ‡Not including puerperal sepsis. §Not including infective endocarditis.

**Table:** Demographics, disease manifestations, and outcomes of individuals with invasive *Streptococcus pyogenes* infection isolated from the bloodstream

## Results

Between Jan 1, 2011, and Feb 28, 2023, 495 cases of *S pyogenes* infection were recorded in patients across four health networks in southeast Australia and 500 cases were recorded in the Top End (figures 1, 2A). Of these 995 infections, 60 (6%) were reinfection or relapsed episodes in 37 individuals (four [11%] in southeast Australia and 33 [89%] in the Top End; median two episodes per individual, IQR 2–3) with each episode separated by a median of 516 days (IQR 252–1068). Of these 60 episodes, 47 (78%) samples were available for sequencing, of which only seven (15%) were genotypically from the same sub-lineage as the index case, indicating that most of these episodes were reinfections. In the Top End, where all sterile

site cultures were included, 38 (8%) of 500 infections were culture-positive from a sterile site without concurrent bloodstream infection.

Demographic characteristics, disease manifestations, and outcomes of infections are described in the table. Examining outcomes between regions, 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. Findings were similar after adjusting for demographics, year, and disease manifestations (appendix 1 p 19). Examining trends over temporal periods, no significant differences in demographics or outcomes were found before, during, and after implementation of COVID-19 non-pharmaceutical interventions (appendix 1 pp 20–21). Disease manifestations were also similar during these time periods, with the exception of fewer cases of streptococcal toxic shock syndrome during the period of implementation of COVID-19 non-pharmaceutical interventions (three [3%] of 97) than in the periods before [71 [12%] of 605] or after [17 [17%] of 98] these interventions (Bonferroni-corrected p=0.049; appendix 1 p 22).

The IRR was 5.97 (95% CI 4.61–7.73; p<0.0001) for the Top End compared with southeast Australia across the entire study period (figure 2B). The IRRs between the Top End and southeast Australia were similar for patients aged 0–18 years (6.52, 4.20–10.06; p<0.0001) and for adults older than 18 years (5.88, 4.51–7.66; p<0.0001; appendix 1 p 9). Disease in the Top End disproportionately affected First Nations compared with non-First Nations Australians (IRR 5.41, 95% CI 4.28–6.89; p<0.0001; appendix 1 p 10). Incidence in First Nations people peaked at 6.38 cases per 10 000 population per year (95% CI 4.52–8.76) in 2022 compared with 0.89 (0.50–1.47) in non-First Nations people in the same year.

A decline in the incidence of invasive *S pyogenes* infection was observed in southeast Australia during the period of maximal implementation of COVID-19 non-pharmaceutical interventions (IRR 0.32, 95% CI 0.17–0.58 for 2020–21 compared with 2011–19; p=0.0066), with a minimum of 0.53 cases per 10 000 hospital admissions in 2021. This decline was followed by an increase (2.99, 2.68–3.33 for each year from 2021 to 2023; p=0.032) to levels almost twice as high as previously recorded (4.78 cases per 10 000 hospital admissions [2.94–7.39] in the first 2 months of 2023). By contrast, no significant change in incidence was observed in the Top End during 2020–21 (0.76, 0.54–1.06 compared with 2011–19; p=0.15). However, infections increased, albeit not significantly, in the Top End after the easing of movement restrictions in 2022–23 (IRR 1.32 [0.96–1.79] for each year from 2021 to 2023; p=0.054), peaking at 16.6 cases per 10 000 hospital admissions (8.82–28.7) in the first 2 months of 2023.

Isolates for whole-genome sequencing were available from 647 (65%) of 995 cases of infection, of which 642 (99%) from 595 individuals passed quality control (figure 1, appendix 2). Patients from whom *S pyogenes* genomes were

See Online for appendix 2
<figure><img src="image_8.png" /></figure>

(Figure 3 continues on next page)

sequenced were broadly representative of the entire study cohort, although a higher proportion of cases requiring ICU admission was observed among those with sequences available (156 [27%] of 573 individuals with available clinical data) than among those without sequences available (58 [17%] of 339 individuals with available clinical data; appendix 1 pp 23–24). Combined with 122 genomes from central Australia for geographical context, we found 81 *emm* types, 112 *emm* subtypes, and 141 multilocus sequence types. Whole-genome classification found 109 distinct lineages (figure 3). Notably, 25 *emm* types encompassed more than one lineage; for example, the prevalent *emm* types *emm4* and *emm89* included three lineages each. Conversely, nine lineages included more than one *emm* type.

Lineages circulating in the Top End were similar to those
from central Australia but distinct from those in southeast

Australia (figure 3). The lineage dissimilarity, as measured
by the Morisita index, was 0·11 between Melbourne and
Sydney in southeast Australia, 0·30 between the Top
End and central Australia, and 0·80 between the Top End
and southeast Australia.

From 45 lineages with more than five isolates, we assigned
100 sublineages (appendix 1 p 11). The globally dis-
seminated sublineages M1<sub>UK</sub>, an M4 lineage carrying a
chimeric <em>emm</em> gene, and an acapsular <em>emm</em>89 lineage were
detected.<sup>5,19,20</sup>

In Melbourne, lineages representative of *emm* types typic-
ally associated with disease in high-resource settings—such
as *emm1*, *emm4*, *emm12*, and *emm89*—cycled in frequency
but were dominant (146 [50%] of 294 sequenced isolates) and
persisted over time (figures 3B, 4C). During the period of the
most stringent COVID-19 non-pharmaceutical interventions,
<figure><img src="image_9.png" /><figcaption>**Figure 3: Genomic population structure and longitudinal dynamics of invasive Streptococcus pyogenes from Jan 1, 2011, to Feb 28, 2023**</figcaption></figure>

(A) Maximum likelihood phylogenetic tree of 642 *S. pyogenes* genomes collected from Melbourne, Sydney, and the Top End, with an additional 122 genomes from central Australia provided for context (appendix p 3). Lineages are highlighted by alternating blue and grey shades from internal nodes. The scale bar denotes nucleotide substitutions per site. Shading of branches indicates ultrafast bootstrap support value. (B) Number of *S. pyogenes* isolates by lineage over the study period in Melbourne. (C) Number of *S. pyogenes* isolates by lineage over the study period in the Top End. In B and C, lineages with at least five isolates are highlighted and are labelled with the dominant *emm* type in brackets. The blue shading from 2020 to 2021 approximates the period of the most stringent non-pharmaceutical interventions during the COVID-19 pandemic. NA denotes isolates that were not available for genome sequencing. (D) Bayesian coalescent skyline plot for dominant lineages sampled in Melbourne from 2011 until the most recent sample. The mean estimated effective population size, a measure of diversity and population size, is displayed on the y-axis on a log scale and the 95% highest posterior density interval is given by the corresponding shading. The absolute value of the effective population size is not comparable across lineages. Ticks along each x-axes show sampling times. *Up to Feb 28, 2023.*

a marked decline in the traditionally dominant lineages was observed, followed by a resurgence in 2022–23. Notably, in the first 2 months of 2023, all seven *emm1* isolates were M1UK (appendix 1 p 12). The surge in cases in 2022–23 in southeast Australia was not associated with novel sublineages. The effective population size was broadly stable across dominant lineages, with fluctuations inferred well before 2011 (figure 3D, appendix 1 p 13). A declining trend in effective population size of the lineage consisting of *emm1* isolates was observed during 2020–21,

consistent with a population bottleneck in 2020–21 and lower diversity in 2022–23 associated with an increased proportion of M1UK isolates. This trend mirrors a pattern of sublineage replacement within multiple lineages (appendix 1 pp 12, 14).

In contrast to Melbourne, temporal analysis of genomic data from the Top End revealed waves of replacement with distinct lineages, with no dominant lineage over the entire study period (figures 3C, 4A). Each lineage was detected over a median of 2–0 years (95% CI 1·3–4·4). A similar pattern of
<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /><figcaption>**Figure 4: Distribution of Streptococcus pyogenes lineages over time**</figcaption></figure>
replacement without dominant lineages was found in genomes from Kilifi, Kenya, between 1998 and 2011 (appendix 1 p 15).¹⁴ Analysis of genomes obtained from invasive disease surveillance in Denmark between 2018 and 2023 showed a pattern similar to that in Melbourne (appendix 1 p 16).⁷

Using a multistrain transmission model (figure 4E), and assuming that invasive disease is proportional to the burden of all *S. pyogenes* infections, we were able to qualitatively reproduce the observed patterns of replacement with distinct lineages using population parameters similar to those of communities in the Top End: small population (n=1000), high transmission intensity (R₀=2·1), and high population migration rate (d=0·007 per week; figure 4B). Conversely, in a simulation with a larger population (n=100 000), a lower transmission intensity (R₀=1·1), and a lower migration rate (d=0·001 per week; figure 4D), we found establishment of a few dominant lineages, and a cycling of frequency between them with longer incidence peaks—similar to the pattern observed in southeast Australia. Sensitivity analyses using other combinations of parameters showed very different qualitative patterns to the observed data (appendix 1 pp 17–18), suggesting that the combined differences in transmission intensity, migration rate, and population size between southeast Australia and the Top End could contribute to the distinct epidemiologies in these areas.

To further investigate genomic drivers of the maintenance of diversity, we examined the prevalence of accessory genes across geographies and temporal periods. Despite large differences in lineage composition between the Top End and Melbourne over time, the prevalence of accessory genes was highly correlated across time and geography (Pearson's correlation p<0·0001 for all comparisons; figure 5).

Discussion

We show distinct patterns of clinical incidence and lineage dynamics across different *S. pyogenes* disease settings. Despite being a highly resourced nation, considerable inequality exists within Australia and is highlighted by the disproportionate burden of invasive *S. pyogenes* disease in First Nations people. Through modelling, we indicate a possible link between high *S. pyogenes* disease burden and population parameters such as transmission intensity, population size, and population migration, with a pattern of recurrent lineage replacement with distinct lineages in the Top End. However, despite marked differences between lineages, accessory gene content was maintained in the bacterial population, consistent with processes such as multilocus negative frequency-dependent selection as a driver of

diversity.³⁰ Together, these findings suggest that lineage-specific interventions—such as vaccines without broad cross-protective immunity—could result in lineage replacement, as has been observed with *Streptococcus pneumoniae*.³⁰

Using a co-collected, longitudinally rich dataset, we show that the crude incidence of invasive *S. pyogenes* disease is around six times higher in the Top End than in southeast Australia. Between 2020 and 2021, incidence declined in southeast Australia but not in the Top End. These differences could be due to the differing intensity of COVID-19 non-pharmaceutical interventions between regions during this time period. Despite higher incidence overall, in-hospital and 30-day mortality was lower in the Top End than in southeast Australia, even after adjustment for differences in age and disease manifestations. However, this study was not designed for detailed clinical comparisons and, as such, unmeasured confounders could be present. For example, greater exposure to *S. pyogenes* early in life in the Top End could provide greater immune protection against severe disease, such as streptococcal toxic shock syndrome. The greater proportion of skin and soft tissue manifestations associated with invasive disease in the Top End than in southeast Australia is consistent with the high burden of impetigo in the Top End.¹¹

We show that lineages circulating in the Top End were distinct from those in southeast Australia, consistent with previous reports using lower-resolution *emm* typing.³¹ Longitudinally, we observed a striking pattern of waves of replacement with distinct lineages in the Top End, extending what has been observed in individual communities with non-invasive isolates; this pattern was also mirrored, albeit less strikingly, in Kilifi, Kenya, on reanalysis of a previously published dataset.⁴,¹⁴ This finding contrasts with a pattern of a few dominant lineages that cycle in frequency in Melbourne, similar to other highly resourced settings.⁷

Using a transmission model, we were able to qualitatively reproduce observed lineage patterns by varying population size, transmission intensity (which encompasses the effects of sociodemographic factors related to disease burden), and population migration rate. Although assumptions were incorporated into the model—such as strong lineage-specific immunity with weak cross-protection—these findings suggest that transmission intensity and population size could contribute to the lineage patterns observed in this study.

When investigating drivers of diversity, we found that the accessory gene content was highly conserved despite large differences in lineage composition between geographical regions and temporal periods—consistent with negative

Observed distribution of *S. pyogenes* lineages over time in the Top End (A) and from sites in Melbourne (C). Each lineage is labelled with the most common *emm* type in brackets. The y-axes of the density plots correspond to the distribution of the isolation dates of the samples, which are not proportional to the total number of isolates (ie, the sum of the area under the curve of each plot is 1). The number of isolates is represented by points under each density curve for observed data and is labelled on the right. Only lineages with at least five isolates are shown and are ordered by date of their peak, with dominant lineages that were present throughout the study period shown at the top. These distributions are compared with corresponding simulated lineage dynamics in a Top End-like community (B), with a small population (n=1000), high transmission intensity (R₀=2·1), and high population migration rate (d=0·007 per week), and a community more similar to those in Melbourne (D), with a larger population (n=100 000), lower transmission intensity (R₀=1·1), and lower migration rate (d=0·001 per week). Rare lineages containing less than 1·5% of total infections were excluded from these simulation plots. The relative number of simulated infections is represented by the corresponding gradient in the simulated data. Lineages are arbitrary for the simulated data. Schematic of the stochastic agent-based model used to simulate lineage dynamics (E).²⁷ R₀=basic reproductive number.
<figure><img src="image_17.png" /><figcaption>**Figure 5: Distribution of accessory gene diversity between geographies and temporal periods**</figcaption></figure>

Comparison of lineage distribution between the Top End and Melbourne over the entire study period (A) and between 2020-23 (after the emergence of COVID-19) and 2011-19 (before the emergence of COVID-19) in Melbourne (C) and in the Top End (E). Comparison of accessory gene frequency between the Top End and Melbourne over the entire study period (B) and between 2020-23 (after the emergence of COVID-19) and 2011-19 (before the emergence of COVID-19) in Melbourne (D) and in the Top End (F). Only accessory genes present in 5-95% of all sequenced isolates (n=1114) were included. Lineage comparisons included only those found in at least one category for each comparison. The coefficient of determination (R²) was inferred using Pearson's correlation. Accessory genes are coloured by the category of mobile genetic element with which they were most frequently associated.

**ICE**=integrative conjugative element. **IS**=insertion sequence or transposon. **ME**=mobility element denoting an unclassified or composite mobile genetic element. **MGE**=mobile genetic element.
frequency-dependent selection, in which alleles are most favoured when they are rare.³⁰ In *S. pneumoniae*, consider-
able evidence exists for the maintenance of multi-lineage
populations driven by negative frequency-dependent selec-
tion of accessory genes.³⁰,³² This process could be mediated
by host immune interactions or ecological competition
provided by combinations of accessory genes in different
lineages.³⁰ Models incorporating negative frequency-
dependent selection were able to predict serotype replace-
ment after the introduction of *S. pneumoniae* serotype-specific
vaccines.³⁰ Although a formal negative frequency-dependent
selection model could not be fit to these data as the underlying
carriage prevalence is unknown, the strong correlation of
accessory genes suggests that similar selection pressures and
consequences of lineage-specific vaccines might also apply to
*S. pyogenes*.

This study has some limitations. Non-invasive or colo-
nising isolates were not available. As such, whether the
patterns observed in invasive disease apply across the entire
bacterial population is not known. Not all invasive *S pyogenes*
cases are bacteraemic, and the incidence of bloodstream
infection tends to underestimate the overall incidence of
invasive disease.⁶,⁹ Future population surveillance studies
could facilitate improved age-structure adjustment for
incidence and generalisability compared with hospital-
based surveillance. Cases were assigned to hospital net-
works and individual hospital-level data and information on
within-network inter-hospital patient transfers were not
collected. Therefore, potential hospital-level clustering
could not be adjusted for when comparing disease out-
comes between regions. Although cases with and without
genome sequences available mostly had similar outcomes,
there was an imbalance in cases requiring ICU admission.
However, we do not attempt to draw genotype-to-phenotype
associations, and any potential lineage differences among
cases requiring ICU admission are unlikely to have greatly
affected the overall observed lineage patterns over the
12-year study period. The parameters of the multistrain
model implemented in this study did not consider potential
phenotypic differences between lineages in the Top End and
those in Melbourne or differences between droplet trans-
mission compared with skin or contact transmission.
However, a genomic cause of tissue tropism and immune
correlates of protection remain active areas of research.
Additionally, traditional tissue tropism markers—such as
the *emm* pattern—might not be as applicable in the Top
End, where less well studied lineages can frequently be
found on both the throat and skin,¹,⁴ as in traditional lower-
incidence high-income settings. Enhancement of the
transmission model incorporating future data on the rela-
tive contribution of transmission routes to disease, lineage-
specific tissue tropism, and host structuring when simu-
lating large communities could provide even more detailed
insights into the interaction between host epidemiological
parameters and pathogen lineage dynamics.

This study highlights the value of longitudinal clinical and
whole-genome datasets from contrasting disease settings.

Our findings of lineage replacement with distinct genomic
lineages in the Top End—which could potentially be mir-
rored in other settings with a high disease burden—and
accessory genome dynamics have implications for
understanding the possible effect of disease-control meas-
ures, such as vaccines in development, on pathogen popu-
lation dynamics. Concurrent with the development of
biomedical strategies, primordial prevention of disease in
social economically disadvantaged settings remains crucial.
Going forward, the longitudinal and systematic collection of
isolates from patients with non-invasive disease or colon-
isation will be required to better understand the broader
population dynamics of *S. pyogenes*.

Contributors

OX, MRD, SYCT, RWB, VLK, DCH, MVNO, KB, JC, TMK, and BJC conceptualised the study. OX, RHC, LF, ANTN, AJH, MGJ, CZ, NT, and GT-H carried out genomic and modelling investigations. OX, RD, SS, AL, AR, NJR, NLS, and BPH curated the data. MRD and SYCT interpreted the data. OX, MRD, SYCT, RHC, LF, ANTN, AJH, MGJ, CZ, NT, GT-H, SD, LJMC, RD, SS, AL, AR, NJR, NLS, BPH, RWB, VLK, DCH, MVNO, KB, JC, TMK, and BJC analysed the data. NLS and BPH provided resources. MRD, SYCT, SD, and LJMC supervised the work. OX wrote the original draft of the manuscript, which was edited and reviewed by all authors. OX, SYCT, and MRD verified the underlying data. All authors had full access to all data in the study and accept responsibility for the decision to submit for publication.

Declaration of interests

We declare no competing interests.

Data sharing

Accession numbers for reads are listed in appendix 2 and are available on NCBI Sequence Read Archive and NCBI BioProjects (PRJNA1087113 and PRJNA857543). Sequences of the complete genomes RMH048 (CP147909), RDH425 (CP147910), RDH275 (CP149486), and RDH444 (CP147908) are available on NCBI GenBank. Full de-identified clinical data may be available from the corresponding author on reasonable request and subject to ethical approval.

Acknowledgments

We thank the clinical and research microbiology teams at Monash Health (Melbourne, VIC, Australia), Royal Melbourne Hospital (Melbourne, VIC, Australia), Menzies School of Health Research (Charles Darwin University, Darwin, NT, Australia), and Westmead Hospital (Sydney, NSW, Australia) for their assistance in retrieving bacterial isolates; the surveillance team at the Northern Territory Centre for Disease Control (Northern Territory Department of Health, Darwin, NT, Australia) for their assistance with surveillance data extraction; and Thor Bech Johannesen and the invasive group A *Streptococcus* surveillance group at Statens Serum Insitut (Copenhagen, Denmark) for providing date data for genomes from Denmark. We acknowledge the scientific contributions of Louise Judd and Taylor Harshegyi from the Innovation Hub, Centre for Pathogen Genomics, University of Melbourne (Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia) and the assistance of Doherty Applied Microbial Genomics (Peter Doherty Institute for Infection and Immunity) in conducting the short-read sequencing. OX was supported by the Australian National Health and Medical Research Council postgraduate scholarship (GNT2013831) and Avant Foundation Doctors in Training Research Scholarship (2021/000017). Components of this work were supported by a Leducq Foundation grant (22VAC01).

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