A Tale of Brothers, Sisters, Aunts and Uncles: Using Genomics and Modeling to Uncover the Nature of P. Falciparum Polygenomic Infections and Cotransmission
AbstractA curious feature of malaria epidemiology is the presence of polygenomic (multiple strain) infections in natural parasite populations. Polygenomic infections are an important aspect of malaria transmission and a necessary prerequisite for outcrossing. From a public health perspective, the genomic composition of polygenomic infections can be used to better understand malaria transmission and to monitor changes in transmission intensity. From an evolutionary perspective, polygenomic infections allow genetic exchange between coinfecting strains and alter parasite population genetics.
In this thesis, I use a mix of computational biology tools, ranging from bioinformatics and sequencing analysis to mathematical modeling, to understand the genomic composition of polygenomic infections and the consequences of coinfection in the context of malaria population genetics and public health.
First, I analyzed the genetic relatedness of coinfecting strains in polygenomic infections collected from Thiès, Senegal. I show that the relatedness of coinfecting strains in polygenomic infections are incompatible with the expectations of pure superinfection, which suggests that cotransmission is common in natural populations.
Second, I used a mathematical model to quantify the expected relatedness of cotransmitted strains. I demonstrate that there are only 9 different ways that cotransmitted parasites can be related to one another. I show that the relatedness of polygenomic infections depends on the conditions of the initial infection and that different transmission lineages have different expectations of polygenomic relatedness.
Third, I analyzed the sequencing quality of lab-generated mock infections to determine whether selective whole genome amplification could be used to accurately sequence polygenomic infections. I found that selective whole genome amplification could be used to characterize the genomic composition of polygenomic infections, even when there is a significant amount of contaminating host DNA present.
Finally, I interrogate how coinfection and transmission topology affects malaria population genetics and evolution by performing evolutionary invasion analyses. This work borrows heavily from theoretical evolutionary population genetics and is designed to show how modeling can be used to highlight importance features of malaria transmission.
The use of population genomics for understanding parasite transmission and evolution hinges on our ability to integrate population genetics into existing epidemiological frameworks. The integration of these fields will require advances in both data generation and theory development. This research contributes to our understanding of malaria population genomics and the importance of coinfection and sexual recombination in the context of transmission.
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