Biology subjects

Manjari, S.

Publications and source records attributed to Manjari, S..

3 recordsLinked to original sources

bioRxiv2026

The CYP27A1 - VDR Feedback Rheostat Controls Mitochondrial Protein Homeostasis and Preserves Hippocampal - Striatal Network Integrity to rescue Cognitive impairment in HD condition

Emerging evidence links vitamin D (VD) deficiency to cognitive and motor dysfunction in Huntingtons disease (HD), yet the underlying mechanism remains unclear. Here, we combine in vitro genetic and 3-nitropropionic acid (3-NP)-induced in vivo models of HD to define the mechanistic basis of VD-mediated neuroprotection. We demonstrate that VD supplementation restores cognitive and motor deficits while improving mitochondrial function and cellular survival. Mechanistically, VD enhances mitochondrial fusion and rescues complex II expression, a key defect in HD pathology. We identify the mitochondrial enzyme CYP27A1 as a critical mediator of this effect, as its expression is reduced in HD models but restored upon VD treatment. Overexpression of CYP27A1 recapitulates the protective effects of VD, confirming its central role in maintaining mitochondrial integrity. Furthermore, VD promotes vitamin D receptor (VDR)-dependent transcriptional activation of CYP27A1 and additional nuclear- and mitochondrial-encoded genes, establishing a regulatory feedback loop that supports mitochondrial biogenesis and function. VD supplementation also improves proteostasis via attenuating endoplasmic reticulum stress. Together, our findings uncover a previously unrecognized VD-CYP27A1 axis that links mitochondrial dysfunction to HD pathology and highlight CYP27A1 as a potential therapeutic target.

neuroscience↗

bioRxiv2024

Construction of an Immunoinformatics-Based Multi-Epitope Vaccine Candidate targeting Kyasanur Forest Disease Virus

Kyasanur Forest Disease (KFD) is one of the neglected tick-borne viral zoonoses. KFD virus was initially considered endemic to the Western Ghats region of Karnataka. Still, over the years, there have been reports of its spread to newer areas within and outside Karnataka. The absence of an effective treatment for KFD expedites the need for further research and development of novel vaccines. The present study was designed to develop a multi-epitope vaccine candidate against KFDV using immunoinformatic tools. After analyzing 74 complete KFDV genome sequences for genetic recombination and phylogeny, different prioritized B and T cell epitopes were combined using various linkers to construct the vaccine candidate. Docking analysis of the designed vaccine construct revealed a stable interaction with the TLR2-TLR6 receptor complex. After confirming the stability of the vaccine receptor complex, codon optimization was done to ensure the efficient translation of the designed multi-epitope vaccine in the prokaryotic host system, and the subsequent in-silico cloning into the pET30b(+) expression vector was carried out. Immunoinformatics analysis of the multi-epitope vaccine in the current study is satisfactory as it can significantly accelerate the initial stages of vaccine development by narrowing down potential vaccine candidates and providing insights into their design. Experimental validation of the potential multi-epitope vaccine candidate remains crucial to confirm effectiveness and safety in real-world conditions.

bioinformatics↗

bioRxiv2019

GSMN-ML- a genome scale metabolic network reconstruction of the obligate human pathogen Mycobacterium leprae

Leprosy, caused by Mycobacterium leprae, has plagued humanity for thousands of years and continues to cause morbidity, disability and stigmatization in two to three million people today. Although effective treatment is available, the disease incidence has remained approximately constant for decades so new approaches, such as vaccine or new drugs, are urgently needed for control. Research is however hampered by the pathogens obligate intracellular lifestyle and the fact that it has never been grown in vitro. Consequently, despite the availability of its complete genome sequence, fundamental questions regarding the biology of the pathogen, such as its metabolism, remain largely unexplored. In order to explore the metabolism of the leprosy bacillus with a long-term aim of developing a medium to grow the pathogen in vitro, we reconstructed an in silico genome scale metabolic model of the bacillus, GSMN-ML. The model was used to explore the growth and biomass production capabilities of the pathogen with a range of nutrient sources, such as amino acids, glucose, glycerol and metabolic intermediates. We also used the model to analyze RNA-seq data from M. leprae grown in mouse foot pads, and performed Differential Producibility Analysis (DPA) to identify metabolic pathways that appear to be active during intracellular growth of the pathogen, which included pathways for central carbon metabolism, co-factor, lipids, amino acids, nucleotides and cell wall synthesis. The GSMN-ML model is thereby a useful in silico tool that can be used to explore the metabolism of the leprosy bacillus, analyze functional genomic experimental data, generate predictions of nutrients required for growth of the bacillus in vitro and identify novel drug targets.\n\nAuthor SummaryMycobacterium leprae, the obligate human pathogen is uncultivable in axenic growth medium, and this hinders research on this pathogen, and the pathogenesis of leprosy. The development of novel therapeutics relies on the understanding of growth, survival and metabolism of this bacterium in the host, the knowledge of which is currently very limited. Here we reconstructed a metabolic network of M. leprae-GSMN-ML, a powerful in silico tool to study growth and metabolism of the leprosy bacillus. We demonstrate the application of GSMN-ML to identify the metabolic pathways, and metabolite classes that M. leprae utilizes during intracellular growth.

microbiology↗