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• W4310358800 abstract "Abstract Leishmania donovani , a kinetoplastid parasite causing leishmaniasis , is an opportunistic parasitic pathogen that affects immunocompromised individuals and is a common cause of Kala-azar . Specific parasite molecules can be delivered into host epithelial cells and may act as effector molecules for intracellular parasite development. So, there is a need to develop new approaches to understanding the interaction between the host and the pathogen. In our study, we built a weighted gene co-expression network using differentially expressed genes obtained through analysis of leishmaniasis-infected patients. Our goal was to identify key signature genes and pathways associated with visceral leishmaniasis infection by network biology analysis which can identify the most influential genes in the gene co-expression interaction network. We identified five prominent genes, IFNG, SC5D, LSM1, CMC2, and SAR1B, with higher interamodular connectivity, as the key signature genes. A deep neural network model- variational autoencoder was utilized to create new features, and a support vector machine validated the key signature genes. These key signature genes are involved in various biological processes like cytokine-cytokine receptor interaction, TGF-beta signaling pathway, antigen processing and presentation, IL-17 signaling pathway, Th1 and Th2 cell differentiation, and T-cell receptor signaling pathway. Besides, we also identified 04 significant miRNAs targeted with key signature genes, including hsa-miR-340-5p, hsa-miR-325-3p, hsa-miR-182-5p, hsa-miR-1271-5p/hsa-miR-96-5p . Further, analysis of the differentially expressed genes revealed that many critical cellular responses were triggered by visceral leishmaniasis infection, including immune responses and inflammatory and cell apoptosis. We get FDA-approved anti-inflammatory agents Emapalumab and Methylprednisolone as a re-proposed drug for leishmaniasis cure. Our study can enhance the understanding of the molecular pathogenesis of visceral leishmaniasis infection and have implications for the plan and execution of mRNA expression tools to support early diagnostics and treatment of visceral leishmaniasis infection." @default.
• W4310358800 created "2022-12-09" @default.
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• W4310358800 date "2022-11-29" @default.
• W4310358800 modified "2023-10-15" @default.
• W4310358800 title "A two-step approach-machine learning, variational autoencoder, and weighted gene co-expression network analysis identify key signature genes and pathways implicated in active visceral leishmaniasis" @default.
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• W4310358800 doi "https://doi.org/10.21203/rs.3.rs-2314650/v1" @default.
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