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Immuno-metabolic signaling in leishmaniasis: insights gained from mathematical modeling

MOTIVATION: Leishmaniasis is a global concern especially in underdeveloped and developing subtropical and tropical regions. The extent of infectivity in host is majorly dependent on functional polarization of macrophages. Classically activated M1 macrophage can eliminate parasite through production...

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Detalles Bibliográficos
Autores principales: Khandibharad, Shweta, Singh, Shailza
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10548086/
https://www.ncbi.nlm.nih.gov/pubmed/37799190
http://dx.doi.org/10.1093/bioadv/vbad125
Descripción
Sumario:MOTIVATION: Leishmaniasis is a global concern especially in underdeveloped and developing subtropical and tropical regions. The extent of infectivity in host is majorly dependent on functional polarization of macrophages. Classically activated M1 macrophage can eliminate parasite through production of iNOS and alternatively activated M2 macrophages can promote parasite growth through by providing shelter and nutrients to parasite. The biological processes involved in immune signaling and metabolism of host and parasite might be responsible for deciding fate of parasite. RESULTS: Using systems biology approach, we constructed two mathematical models and inter-regulatory immune-metabolic networks of M1 and M2 state, through which we identified crucial components that are associated with these phenotypes. We also demonstrated how parasite may modulate M1 phenotype for its growth and proliferation and transition to M2 state. Through our previous findings as well as from recent findings we could identify SHP-1 as a key component in regulating the immune-metabolic characterization of M2 macrophage. By targeting SHP-1 at cellular level, it might be possible to modulate immuno-metabolic mechanism and thereby control parasite survival. AVAILABILITY AND IMPLEMENTATION: Mathematical modeling is implemented as a workflow and the models are deposited in BioModel database. FactoMineR is available at: https://github.com/cran/FactoMineR/tree/master.