Cannabinoid and endocannabinoid system: a promising therapeutic intervention for multiple sclerosis.

Multiple sclerosis (MS) is a chronic and complex neurodegenerative disease, distinguished by the presence of lesions in the central nervous system (CNS) due to exacerbated immunological responses that inflict oligodendrocytes and the myelin sheath of axons. In recent years, studies have focused on targeted therapeutics for MS that emphasize the role of G protein-coupled receptors (GPCRs), specifically cannabinoids receptors. Clinical studies have suggested the therapeutic potential of cannabinoids derived from Cannabis sativa in relieving pain, tremors and spasticity. Cannabinoids also appear to prevent exaggerated immune responses in CNS due to compromised blood-brain barrier. Both, endocannabinoid system (ECS) modulators and cannabinoid ligands actively promote oligodendrocyte survival by regulating signaling, migration and myelination of nerve cells. The cannabinoid receptors 1 (CB1) and 2 (CB2) of ECS are the main ones in focus for therapeutic intervention of MS. Various CB1/CB2 receptors agonists have been experimentally studied which showed anti-inflammatory properties and are considered to be effective as potential therapeutics for MS. In this review, we focused on the exacerbated immune attack on nerve cells and the role of the cannabinoids and its interaction with the ECS in CNS during MS pathology.

Insilico prediction and functional analysis of nonsynonymous SNPs in human CTLA4 gene.

The CTLA4 receptor is an immune checkpoint involved in the downregulation of T cells. Polymorphisms in this gene have been found to be associated with different diseases like rheumatoid arthritis, autosomal dominant immune dysregulation syndrome, juvenile idiopathic arthritis and autoimmune Addison's disease. Therefore, the identification of polymorphisms that have an effect on the structure and function of CTLA4 gene is important. Here we identified the most damaging missense or non-synonymous SNPs (nsSNPs) that might be crucial for the structure and function of CTLA4 using different bioinformatics tools. These in silico tools included SIFT, PROVEAN, PhD-SNP, PolyPhen-2 followed by MutPred2, I-Mutant 2.0 and ConSurf. The protein structures were predicted using Phyre2 and I-TASSER, while the gene-gene interactions were predicted by GeneMANIA and STRING. Our study identified three damaging missense SNPs rs1553657429, rs1559591863 and rs778534474 in coding region of CTLA4 gene. Among these SNPs the rs1553657429 showed a loss of potential phosphorylation site and was found to be highly conserved. The prediction of gene-gene interaction showed the interaction of CTlA4 with other genes and its importance in different pathways. This investigation of damaging nsSNPs can be considered in future while studying CTLA4 related diseases and can be of great importance in precision medicine.