Abstract:
Malaria's impact on the human population has decreased in most of the world; however, there are still areas where malaria infectivity is high (especially in Africa and Indonesia), and the parasite has become resistant to these regions. So we need to develop new methods for identifying antimalarial drug targets. To do this, we needed to compare the regulatory networks of genes from Plasmodium species responsible for human malaria transmission. So, for that, we used the approach of calculating promoter correlations (Pearson). Based on these correlations, we are selecting the promoter regions of the genome, known as switch-on regions, which initiate transcription. Transcription factors (as they are mainly responsible for gene regulatory networks). To obtain the correlations, we will use the observed-to-expected counts of small motifs in gene promoter regions. After the observed-by- expected counts, we proceed to the Pearson correlation coefficients. However, before that, we also calculated the mean and standard deviation of the data to determine the count of the top motifs. Using this, we proceed with the Pearson correlation coefficients. For regulatory network analyses and comparisons, we needed gene orthologs across Plasmodium species. We used BLAST to identify reciprocal best hits and thus obtain orthologs. Now, using these orthologs and the calculated correlations, we compared the network. We compared the networks of human-infecting Plasmodium species and observed some similarities. These networks were created as square matrices, with sizes around 5000 x 5000. Despite the similarity, we also observed differences in these networks. As a result, there is a difference in the efficacy of antimalarial drugs on one Plasmodium species compared to another Plasmodium species.
