Miotto Olivo, Amambua-Ngwa Alfred, Amenga-Etego Lucas N, Abdel Hamid Muzamil M, Adam Ishag, Aninagyei Enoch, Apinjoh Tobias, Awandare Gordon A, Bejon Philip, Bertin Gwladys I, Bouyou-Akotet Marielle, Claessens Antoine, Conway David J, D'Alessandro Umberto, Diakite Mahamadou, Djimdé Abdoulaye, Dondorp Arjen M, Duffy Patrick, Fairhurst Rick M, Fanello Caterina I, Ghansah Anita, Ishengoma Deus S, Lawniczak Mara, Maïga-Ascofaré Oumou, Auburn Sarah, Rosanas-Urgell Anna, Wasakul Varanya, White Nina F D, Harrott Alexandria, Almagro-Garcia Jacob, Pearson Richard D, Goncalves Sonia, Ariani Cristina, Bozdech Zbynek, Hamilton William L, Simpson Victoria, Kwiatkowski Dominic P
Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand; Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK.
Medical Research Council Unit The Gambia at London School of Hygiene & Tropical Medicine, Banjul, The Gambia; London School of Hygiene and Tropical Medicine, London, UK.
Lancet Microbe. 2024 Dec;5(12):100941. doi: 10.1016/j.lanmic.2024.07.004. Epub 2024 Nov 7.
The population structure of the malaria parasite Plasmodium falciparum can reveal underlying adaptive evolutionary processes. Selective pressures to maintain complex genetic backgrounds can encourage inbreeding, producing distinct parasite clusters identifiable by population structure analyses.
We analysed population structure in 3783 P falciparum genomes from 21 countries across Africa, provided by the MalariaGEN Pf7 dataset. We used Principal Coordinate Analysis to cluster parasites, identity by descent (IBD) methods to identify genomic regions shared by cluster members, and linkage analyses to establish their co-inheritance patterns. Structural variants were reconstructed by de novo assembly and verified by long-read sequencing.
We identified a strongly differentiated cluster of parasites, named AF1, comprising 47 (1·2%) of 3783 samples analysed, distributed over 13 countries across Africa, at locations over 7000 km apart. Members of this cluster share a complex genetic background, consisting of up to 23 loci harbouring many highly differentiated variants, rarely observed outside the cluster. IBD analyses revealed common ancestry at these loci, irrespective of sampling location. Outside the shared loci, however, AF1 members appear to outbreed with sympatric parasites. The AF1 differentiated variants comprise structural variations, including a gene conversion involving the dblmsp and dblmsp2 genes, and numerous single nucleotide polymorphisms. Several of the genes harbouring these mutations are functionally related, often involved in interactions with red blood cells including invasion, egress, and erythrocyte antigen export.
We propose that AF1 parasites have adapted to some unidentified evolutionary niche, probably involving interactions with host erythrocytes. This adaptation involves a complex compendium of interacting variants that are rarely observed in Africa, which remains mostly intact despite recombination events. The term cryptotype was used to describe a common background interspersed with genomic regions of local origin.
Bill & Melinda Gates Foundation.
恶性疟原虫的种群结构能够揭示潜在的适应性进化过程。维持复杂遗传背景的选择压力会促使近亲繁殖,产生可通过种群结构分析识别的不同寄生虫簇。
我们分析了由疟疾基因组计划Pf7数据集提供的来自非洲21个国家的3783个恶性疟原虫基因组的种群结构。我们使用主坐标分析对寄生虫进行聚类,通过溯源身份(IBD)方法识别簇成员共享的基因组区域,并通过连锁分析确定它们的共同遗传模式。通过从头组装重建结构变异,并通过长读长测序进行验证。
我们识别出一个高度分化的寄生虫簇,命名为AF1,在分析的3783个样本中有47个(1.2%)属于该簇,分布在非洲的13个国家,相隔距离超过7000公里。该簇的成员共享一个复杂的遗传背景,由多达23个位点组成,这些位点含有许多高度分化的变异,在该簇之外很少观察到。IBD分析揭示了这些位点的共同祖先,与采样位置无关。然而,在共享位点之外,AF1成员似乎与同域寄生虫进行远交。AF1分化的变异包括结构变异,包括涉及dblmsp和dblmsp2基因的基因转换,以及许多单核苷酸多态性。携带这些突变的几个基因在功能上相关,通常参与与红细胞的相互作用,包括入侵、逸出和红细胞抗原输出。
我们提出AF1寄生虫已经适应了一些未明确的进化生态位,可能涉及与宿主红细胞的相互作用。这种适应涉及一组复杂的相互作用变异,在非洲很少观察到,尽管发生了重组事件,但这些变异大多保持完整。术语“隐型”用于描述散布着本地起源基因组区域的共同背景。
比尔及梅琳达·盖茨基金会。