Unraveling Leishmania major Metacyclogenesis: A Comprehensive Analysis of Transcriptomic and Metabolomic Profiles.

BACKGROUND

Metacyclogenesis is a critical developmental process in the life cycle of Leishmania parasites, particularly in their transition from non-infective procyclic to infective metacyclic promastigotes. This transformation is closely linked to the metabolic adaptation of the parasite, optimizing its survival and study, we integrated metabolomics and transcriptomics data to gain deeper molecular mechanisms of Leishmania major metacyclogenesis.

METHODS

The metabolic profiles of procyclic and metacyclic promastigotes were first identified using ¹H-NMR spectroscopy. Multivariate statistical analysis conducted to distinguish different metabolites between the two forms. Metabolic pathway analysis was performed using the KEGG database to identify the metabolic pathways that significantly altered and overrepresented in the metabolomic profile. Finally, the differential gene expression and pathway enrichment analyses were conducted on transcriptomic data retrieved from public repositories.

RESULT

Multivariate statistical analysis revealed that 44 metabolites and ten pathways were significantly different between the two forms. Transcriptome genes during metacyclogenesis. These genes underwent GO and KEGG pathway analyses, revealing upregulated GO categories in the metacyclic phase, including protein phosphorylation, ion transport, signal transduction, and phosphorylation reactions, as well as several downregulated GO categories. Integrating metabolomic and transcriptomic data demonstrated seven significantly different KEGG pathways between procyclic and metacyclic forms, including fructose and mannose, galactose, ascorbate and aldarate, arginine and proline, histidine, inositol phosphate, and pyruvate metabolism.

CONCLUSION

Our findings suggest distinct metabolic profiles and changes in gene expression associated with the transition from procyclic to metacyclic promastigotes. By integrating diverse omics data, we could identify more reliable altered pathways and biomarkers.