Journal of Applied Bioinformatics & Computational BiologyISSN: 2329-9533

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Computational Analysis of RNA Sequencing Dataset Reveals Novel Associations in Response to Artemisinin-Resistant Malaria: Implications in Drug Discovery and Design

Background: Artemisinin and its derivatives, in combination with partner drugs currently represent the most effective and influential class of drugs in malaria treatment. Although Artemisinin Combination Therapy (ACT) continues to serve as a foundation for antimalarial therapy, numerous challenges have emerged over their continued usage for decades. ACT has recently been found to be losing efficacy in Southeast Asia (SEA). The current understanding of the Mechanism of Action (MOA) of artemisinin remains controversial. Several reports suggest that the artemisinin MOA involves multiple targets and it’s unlikely that the mutations in these drug targets are the cause of high level resistance. Among the reviewed drug targets, PI3 kinases and their pathway members are strongly implicated in resistance.

Material and methods: In this study, we reassessed an earlier experimental study representing an RNA sequencing dataset of a transcriptome from the trophozoite stage of the parasite in response to the artemisinin drug. We used differential gene expression analysis followed by enrichment analysis tools like GSEA and pathway analysis using Path view.

Results: Our computational analysis of the transcriptome dataset involves the usage of the most recent tools with updated annotation records. Results indicate the fatty acid biosynthesis pathway representing alpha-linolenic acid enzymes associated with artemisinin resistance. Hence, this study adds on to state that the FA biosynthesis pathway is to be targeted to overcome artemisinin resistance.

Conclusion: Further experimental studies will have implications for drug discovery and the design of second-generation antimalarials.

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