Recovering evolutionary drivers of malarial parasites - leveraging genomes past and present

恢复疟疾寄生虫的进化驱动因素——利用过去和现在的基因组

• 批准号: MR/X034828/1
• 负责人: Lucy Van Dorp
• 金额: $ 162.4万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX034828%2F1
• 关键词: Recovering; evolutionary; drivers; malarial; parasites

Malaria has had a devastating impact on human health throughout history and currently inflicts around 600,000 deaths annually, mostly in young children and pregnant women. Malaria is caused by several species of Plasmodium which, along with humans, can infect a range of animals including bats, rodents, birds and other primates.Human-associated malaria is predominately caused by five species transmitted to humans by mosquitoes. The number of animal parasites which can infect humans however is constantly under revision. Today malaria is mostly found in tropical and sub-tropical latitudes. Yet, until quite recently, malaria was a truly global disease spanning Britain and the Mediterranean, as far North as Finland, and through to European Russia, with the last indigenous cases in Europe persisting until the late 1970s. Whilst we have increasingly good data for the present, including genetic data generated from parasites and spatial trends in disease occurrence, the type and locality of disease further back in malaria's deep history is mostly uncharacterised. This means that even for parasites with rich accompanying data today, only an incomplete picture can be gleaned on how they evolved. This limits our understanding of the long-term drivers of disease.My proposal seeks to address major outstanding questions in Plasmodium evolution using genetic data generated from infecting parasites. My work will be uniquely aided by genome sequences of parasites involved in ancient and historic infections spanning from thousands of years ago through to the 20th century. Data from past infections will be generated from a range of archived material including archaeological remains, microscope slides, vials, tissue and macaque skeletal specimens. I will focus on the human infecting species P. falciparum, P. vivax and P. malariae as well as those species found in monkeys including P. inui, P. cynomolgi and P. knowlesi, the latter implicated in extensive human infections in southeast Asia.The generation of genetic data from past infections provides new opportunities to study the evolution of human-associated parasites. Using statistical methods, I will estimate when P. falciparum, P. vivax and P. malariae first began infecting humans and map their dispersals from the deep past to now. In addition, I will interrogate specific features of the genome to identify changes which impact how we treat malaria today, such as the ability to survive treatment with antimalarial drugs.I will then consider genetic data from parasites infecting macaques in the early 20th century in Indonesia, identifying what malarial species are present and using this data to test concerns over whether macaque parasites may be able to infect humans. I will particularly focus on P. knowlesi, which is frequently transmitted from macaques to humans via mosquito vectors. I will compare the genomes of P. knowlesi both today and in the past to build a robust picture of the contact between different parasite populations including the potential transition of this parasite from macaque reservoir to specialised human parasite. Finally, since Plasmodium parasites are diverse in number and found in a very wide range of animal species, I will build a Plasmodium family tree designed to robustly recover how different species are related. I will map this information to data on the animal species each parasite can infect, sourced through an array of data mining techniques. Pairing parasite relatedness with the range of animal infections, I will model mechanisms of adaptation to different animal hosts and pinpoint those malarial parasites at highest risk of transmitting to humans.My work provides the bespoke platform and perspective required to uncover the drivers of malaria prevalence through time. I anticipate my framework will be portable to other pathogens and will ultimately enable me to substantially contribute to our understanding of infectious disease dynamics.

纵观历史，疟疾对人类健康造成了毁灭性影响，目前每年造成约 60 万人死亡，其中大部分是幼儿和孕妇。疟疾是由几种疟原虫引起的，这些疟原虫与人类一起可以感染一系列动物，包括蝙蝠、啮齿动物、鸟类和其他灵长类动物。与人类相关的疟疾主要是由通过蚊子传播给人类的五种疟疾引起的。然而，能够感染人类的​​动物寄生虫的数量却不断在修正。如今，疟疾主要见于热带和亚热带纬度地区。然而，直到最近，疟疾还是一种真正的全球性疾病，横跨英国和地中海，北至芬兰，一直到俄罗斯欧洲部分，欧洲最后的本土病例一直持续到 20 世纪 70 年代末。虽然我们目前拥有越来越多的可靠数据，包括寄生虫产生的遗传数据和疾病发生的空间趋势，但疟疾悠久历史中疾病的类型和地点大多是未知的。这意味着，即使对于当今具有丰富伴随数据的寄生虫，也只能收集到关于它们如何进化的不完整图片。这限制了我们对疾病长期驱动因素的理解。我的建议旨在利用感染寄生虫产生的遗传数据来解决疟原虫进化中的主要悬而未决的问题。我的工作将得到涉及从数千年前到 20 世纪的古代和历史性感染的寄生虫基因组序列的独特帮助。过去感染的数据将从一系列存档材料中生成，包括考古遗迹、显微镜载玻片、小瓶、组织和猕猴骨骼标本。我将重点关注感染人类的​​恶性疟原虫、间日疟原虫和三日疟原虫，以及在猴子身上发现的疟原虫、猕猴疟原虫和诺氏疟原虫，后者与东南亚广泛的人类感染有关从过去的感染中生成遗传数据为研究人类相关寄生虫的进化提供了新的机会。我将使用统计方法估计恶性疟原虫、间日疟原虫和三日疟原虫首次开始感染人类的​​时间，并绘制它们从远古至今的传播图。此外，我将探究基因组的具体特征，以确定影响我们今天治疗疟疾的变化，例如抗疟药物治疗后的生存能力。然后，我将考虑 20 世纪初期印度尼西亚猕猴感染寄生虫的遗传数据，确定存在哪些疟疾物种，并使用这些数据来测试对猕猴寄生虫是否可能感染人类的​​担忧。我将特别关注诺氏疟原虫，它经常通过蚊子媒介从猕猴传播给人类。我将比较现在和过去的诺氏疟原虫的基因组，以构建不同寄生虫种群之间接触的可靠图片，包括这种寄生虫从猕猴宿主到特殊人类寄生虫的潜在转变。最后，由于疟原虫寄生虫的数量多种多样，并且在非常广泛的动物物种中发现，我将建立一个疟原虫家谱，旨在稳健地恢复不同物种之间的相关性。我将通过一系列数据挖掘技术将这些信息映射到每种寄生虫可以感染的动物物种的数据。将寄生虫相关性与动物感染的范围相结合，我将模拟对不同动物宿主的适应机制，并查明那些传播给人类风险最高的疟疾寄生虫。我的工作提供了揭示疟疾流行驱动因素所需的定制平台和视角。时间。我预计我的框架将可移植到其他病原体，并最终使我能够为我们对传染病动态的理解做出重大贡献。