bioRxivSystems BiologyPreprint
Evolution of hierarchical switching pattern in antigenic variation of Plasmodium falciparum under variable host immunity
Gayathri Priya IragavarapuiD, HJ Varsha, Shruthi Sridhar VembariD, Bhaswar GhoshiD
Abstract
The var genes family encoding the variants of the erythrocyte membrane protein of Plasmodium falciparum is crucial for virulence of the parasite inside host. The transcriptional output of the var genes switches from one variant to other in a mutually exclusive fashion. It is proposed that a biased hierarchical switching pattern optimizes the growth and survival of the parasite inside the host. Apart from the hierarchical switching pattern, it is also well established that the intrinsic switching rates vary widely among the var genes. The centromeric protein like Var2csa is much more stable than the genes located at the telomeric and sub-telomeric regions of the chromosomes. In this study, we explored the evolutionary advantage achieved through selecting variable switching rates. Our theoretical analysis based on a mathematical model coupled with single cell RNA-seq data suggests that the variable switching rate is beneficial when cells expressing different variants are deferentially amenable to be cleared by the immune response. In fact, the variants which are cleared by the immune systems more efficiently are more stably expressed compared to a variant attacked by the immune system much less vigorously. The cells turn off expression of the variant quickly which is not cleared very efficiently. The evolutionary simulation shows that this strategy maximizes the growth of the parasite population under the presence of immune attack by the host. In corroboration with the result, we observed that stable variant has higher binding affinity to IgM from experimental data. Our study provides an evolutionary basis of widely variable switching rates of the var genes in Plasmodium falciparum.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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