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 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.