Leveraging Subtractive Genomics to Combat Visceral Leishmaniasis: New Insights into Vaccine and Drug Target Identification
Leishmaniasis is a neglected tropical disease caused by protozoan parasites of the genus Leishmania. It is endemic in more than 90 countries, with a particularly high burden in tropical and subtropical regions. The disease manifests in several forms, ranging from self-limiting cutaneous lesions to life-threatening visceral leishmaniasis (VL), depending on the parasite species and the host’s immune response. VL is predominantly caused by Leishmania donovani and is characterized by high fever, hepatosplenomegaly and anemia. Current treatment options for VL include pentavalent, antimonials, amphotericin B, miltefosine, paromomycin and pentamidine, but issues like drug toxicity, resistance and cost limit their effectiveness. So alternate strategies are necessary for the treatment of VL. This work aims to identify potential drug targets and vaccine candidates of the protozoa L. donovani using subtractive genomics. It involves subtracting protein sequences from the parasite genome to identify essential proteins that are necessary for parasite survival. The approach identifies targets that are nonhomologous to host proteins and reduces the risk of off-target effects. We identified 12 potential drug targets and 2 vaccine targets (surface antigen-like proteins) for developing effective treatment/prevention strategies for VL. We found that these proteins are involved in important metabolic pathways such as glycolysis/gluconeogenesis, purine and pyrimidine metabolism, sterol biosynthesis and DNA repair and replication. Parasite-specific targets are promising candidates for antileishmanial therapy. Targeting proteins absent in the host improves selectivity, lowers toxicity and provides a strong basis for developing safer and more effective drugs and vaccines against leishmaniasis.
Institutions
- SASTRA University (IN)
Publication Details
- Journal
- Journal of Computational Biophysics and Chemistry
- Published
- 2025-09-22
- DOI
- https://doi.org/10.1142/s2737416526500043
- Primary Topic
- Geological Modeling and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00