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

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 there is 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 the understanding of the long-term drivers of disease.

This 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. The focus will be on the human-infecting species P. falciparum, P. vivax, and P. malariae as well as species found in monkeys, including P. inui, P. cynomolgi, and P. knowlesi, the latter of which has been 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, estimates will be made of when P. falciparum, P. vivax, and P. malariae first began infecting humans, and their dispersals from the deep past to now will be mapped. In addition, specific features of the genome will be interrogated to identify changes that impact how malaria is treated today, such as the ability to survive treatment with antimalarial drugs.

Genetic data from parasites infecting macaques in the early 20th century in Indonesia will then be considered, with an aim to identify what malarial species are present and use this data to test concerns over whether macaque parasites may be able to infect humans. A particular focus will be placed on P. knowlesi, which is frequently transmitted from macaques to humans via mosquito vectors. The genomes of P. knowlesi will be compared 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 a macaque reservoir to a specialized human parasite. Finally, since Plasmodium parasites are diverse in number and found in a very wide range of animal species, a Plasmodium family tree will be built to robustly recover how different species are related. This information will be mapped to data on the animal species each parasite can infect, sourced through an array of data mining techniques. By pairing parasite relatedness with the range of animal infections, mechanisms of adaptation to different animal hosts will be modeled, and those malarial parasites at the highest risk of transmitting to humans will be pinpointed. A bespoke platform and perspective are provided by this work to uncover the drivers of malaria prevalence through time. It is anticipated that the framework will be portable to other pathogens and will ultimately enable a substantial contribution to the understanding of infectious disease dynamics.

Project details

Genetics and Genomics

Jan 2024 — Dec 2027

$1.57M

Denmark
United Kingdom

Plasmodium knowlesi