Xie Ouli, Chisholm Rebecca H, Featherstone Leo, Nguyen An N T, Hayes Andrew J, Jespersen Magnus G, Zachreson Cameron, Tellioglu Nefel, Tonkin-Hill Gerry, Dotel Ravindra, Spring Stephanie, Liu Alice, Rofe Alexander, Duchene Sebastian, Sherry Norelle L, Baird Robert W, Krause Vicki L, Holt Deborah C, Coin Lachlan J M, Rai Neela Joshi, O'Sullivan Matthew V N, Bond Katherine, Corander Jukka, Howden Benjamin P, Korman Tony M, Currie Bart J, Tong Steven Y C, Davies Mark R
Department of Infectious Diseases, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia; Monash Infectious Diseases, Monash Health, Melbourne, VIC, Australia; Global and Tropical Health Division, Menzies School of Health Research, Charles Darwin University, Darwin, NT, Australia.
Department of Mathematical and Physical Sciences, La Trobe University, Melbourne, VIC, Australia; Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia.
Lancet Microbe. 2025 Jun;6(6):101053. doi: 10.1016/j.lanmic.2024.101053. Epub 2025 Apr 4.
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.
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.
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.
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.
National Health and Medical Research Council, and Leducq Foundation.
确定侵袭性酿脓链球菌(A 组链球菌)的时间动态以及高流行地区和低发病地区之间的差异,对于了解病原体进化至关重要,进而为预防措施提供依据。我们旨在研究澳大利亚不同疾病背景下酿脓链球菌的临床和时间谱系动态,以增进对病原体多样性驱动因素的理解。
在这项回顾性、多中心、临床和基因组流行病学研究中,我们确定了 2011 年 1 月 1 日至 2023 年 2 月 28 日期间来自正常无菌部位的侵袭性酿脓链球菌感染病例。数据收集自澳大利亚东南部温带低发病地区以及澳大利亚北领地高流行、热带且大多偏远的顶端地区的五个医院网络。通过准泊松回归估计了顶端地区与澳大利亚东南部之间血流中酿脓链球菌感染的粗发病率比(IRR),以及原住民与非原住民之间的粗发病率比。我们使用逻辑回归估计了顶端地区与澳大利亚东南部相比重症监护病房(ICU)入住率、住院死亡率和 30 天死亡率的比值比(OR)。对检索到并成功测序的分离株进行全基因组分辨率的谱系分类。比较了两个地区共同流行谱系组成的时间趋势。我们使用酿脓链球菌特异性多菌株模拟传播模型来研究宿主人群特异性参数与观察到的病原体谱系动态之间的关系。比较了不同地理区域和时间段内辅助基因(存在于所有基因组的 5%至 95%中的基因)的流行情况,以研究多样性的基因组驱动因素。
我们在顶端地区的患者中确定了 500 例侵袭性酿脓链球菌感染病例,在澳大利亚东南部的患者中确定了 495 例。在整个研究期间,顶端地区与澳大利亚东南部相比,血流感染的粗 IRR 为 5.97(95%CI 4.61 - 7.73);在顶端地区,与非原住民相比,感染对原住民的影响不成比例(5.41,4.28 - 6.89)。顶端地区的住院死亡率(OR 0.43,95%CI 0.26 - 0.70)、30 天死亡率(0.38,0.23 - 0.63)和 ICU 入住率(0.42,0.30 - 0.59)的比值比低于澳大利亚东南部。对 642 个酿脓链球菌基因组的纵向谱系分析发现,顶端地区出现了不同谱系的替代浪潮,而澳大利亚东南部有少数占主导地位的谱系持续存在且频率循环变化。当使用高传播率、小种群规模和高水平的人类流动(类似于高流行顶端地区社区的特征)时,传播模型定性地再现了类似的不同谱系替代模式。使用较低的传播率、较大的种群规模和较低的迁移水平(类似于城市化的澳大利亚东南部社区)时,传播模型定性地再现了占主导地位的谱系频率循环变化的模式。尽管存在不同的流行谱系,但细菌群体中辅助基因的流行情况在不同地理区域和时间段内保持稳定。
在高流行环境中,不同酿脓链球菌谱系的替代呈波浪式发生,这可能与该环境中疾病负担不成比例和人口稀少有关。细菌基因频率的维持可能与多位点选择一致。这些发现表明,针对特定谱系的干预措施,如正在研发的疫苗,应考虑疾病背景,且若无广泛的交叉保护,可能会导致谱系替代。
澳大利亚国家卫生与医学研究委员会以及勒杜克基金会。