bioRxivEvolutionary Biology
Gene regulation of the avian malaria parasite Plasmodium relictum, during the different stages within the mosquito vector
V. Sekar, A. Rivero, R. Pigeault, S. Gandon, A. Drews, D. Ahren, O. Hellgren
Abstract
The malaria parasite Plasmodium relictum is one of the most widespread species of avian malaria. As is the case in its human counterparts, bird Plasmodium undergoes a complex life cycle infecting two hosts: the arthropod vector and the vertebrate host. In this study, we examine the transcriptome of P. relictum (SGS1) during crucial timepoints within its natural vector, Culex pipiens quinquefasciatus. Differential gene-expression analyses identified genes linked to the parasites life-stages at: i) a few minutes after the blood meal is ingested, ii) during peak oocyst production phase, iii) during peak sporozoite phase and iv) during the late-stages of the infection. A large amount of genes coding for functions linked to host-immune invasion and multifunctional genes was active throughout the infection cycle. One gene associated with a conserved Plasmodium membrane protein with unknown function was upregulated throughout the parasite development in the vector, suggesting an important role in the successful completion of the sporogonic cycle. Transcript annotation further revealed novel genes, which were significantly differentially expressed during the infection in the vector as well as upregulation of reticulocyte-binding proteins, which raises the possibility of the multifunctionality of these RBPs. We establish the existence of highly stage-specific pathways being overexpressed during the infection. This first study of gene-expression of a non-human Plasmodium species in its natural vector provides a comprehensive insight into the molecular mechanisms of the common avian malaria parasite P. relictum and provides essential information on the evolutionary diversity in gene regulation of the Plasmodiums vector stages.
- organism: non-human primate
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
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