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IN SILICO ANALYSIS AND MOLECULAR DOCKING STUDIES TO PREDICT THE IMPACT OF GENES ASSOCIATED WITH RHEUMATOID ARTHRITIS: A COMPUTATIONAL APPROACH

KrishiKosh

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Title IN SILICO ANALYSIS AND MOLECULAR DOCKING STUDIES TO PREDICT THE IMPACT OF GENES ASSOCIATED WITH RHEUMATOID ARTHRITIS: A COMPUTATIONAL APPROACH
 
Creator Das, Krishna Kumar
 
Contributor Rath, S N
 
Subject IN SILICO ANALYSIS, MOLECULAR DOCKING, RHEUMATOID ARTHRITIS, COMPUTATIONAL APPROACH
 
Description Autoimmune diseases (ADs) are often intractable because their cause are unknown and
is defined as "a clinical syndrome caused by the activation of T cells or B cells, or both,
in the absence of an ongoing infection or other discernible cause". As this robust
definition suggests, the causes of autoimmune diseases are not generally known.
However, the genetic background of patients is generally believed to be important. ADs
include diabetes, rheumatoid arthritis, Graves' disease, systemic lupus and
inflammatory bowel disease (IBD). The present investigation was carried out to explore
the genes and their interactions pertaining to Rheumatoid arthritis (RA) which is
through In-silico and molecular docking analysis. RA is a chronic autoimmune disease
affecting ~1% of the worldwide population, and while a number of pharmaceuticals
have been used to treat this disease. In the present study a total 267 unique genes were
mined for RA from 39 GWAS studies. The functional annotation of a total of 267 genes
was performed through Gene Ontology (GO) analysis using DAVID which reported
216 genes and 238 GO terms for biological processes (BP). The STRING database
reported the genes namely STAT4, CD40, CD28, CD247, HLA-DRB1, IRF8, IRF4,
REL, EOMES, CSF2, IL2, IL3, TYK2, CSF2 and CD5 at the core region of the RA
network of 216 BP genes. These genes may be said to play a key in RA as well as can
be differentially expressed in RA disease. The Drug association analysis of WebGestalt
has reported 28 drugs interacted with 41 genes or its corresponding proteins out of
which docking was performed for 15drugs and 18 potential targets as they are found to
be key regulators in RA disease. The molecular docking studies have reported the drugtarget
interactions of IL2-cyclosporine was -11.34, IL2RA-prednisone was -8.61,
JMJD1C-raloxifene was -9.56, PON1-simvastatin was-8.36, IL2-tacrolimus was -13.64
and IL2RA-tacrolimus was -16.27 as the highest docking scores with energy
minimization and interactions of IL2RA-cyclosporine was -2.47and CTLA4-
methimazole was -2.91 as the lowest docking scores with energy minimization out of
24 ligand-protein interactions. In this study it is clear that episode of RA differs from
person to person based on their genes, genetic interactions and expression levels that
could recommend the clinicians to go for personalized medicine rather that generalized
medicine for the patients with RA. Seeking the importance of genetic background of
RA patients further studies can be done by mining of non-synonymous SNPs associated
with genes for causing RA.
 
Date 2017-01-04T12:29:05Z
2017-01-04T12:29:05Z
2016
 
Type Thesis
 
Identifier http://krishikosh.egranth.ac.in/handle/1/94391
 
Language en
 
Relation Th;4622
 
Format application/pdf