Generic placeholder image

Recent Advances in Anti-Infective Drug Discovery

Editor-in-Chief

ISSN (Print): 2772-4344
ISSN (Online): 2772-4352

Research Article

An Integrated Approach to Identify Intrinsically Disordered Regions in Osteopontin with its Interacting Network in Rheumatoid Arthritis

Author(s): Parul Johri*, Sachidanand Singh, Prachi Sao, Sudeshna Banerjee, Mala Trivedi, Aditi Singh and Irena Kostova

Volume 18, Issue 1, 2023

Published on: 17 October, 2022

Page: [69 - 86] Pages: 18

DOI: 10.2174/2772434417666220908122654

Price: $65

Abstract

Background: Credentials of molecular diagnostic approaches are an important goal. Since protein-protein interaction (PPI) network analysis is an apposite method for molecular valuation, a PPI grid related to Intrinsically Disordered Proteins (IDPs) of RA was targeted in the present research.

Aim: The aim of the study is to analyse the role of highly disordered proteins and their functional parameters in causing Rheumatoid Arthritis (RA).

Methods: Cytoscape software helped in identifying molecular interaction networks. Intrinsically disordered proteins lack higher order structure and have functional advantages, but their dysregulation can cause several diseases. All the significant proteins responsible for RA were identified. On the basis of the data obtained, highly disordered proteins were selected. Further, MSA was done to find the similarity among the highly disordered proteins and their functional partners. To determine the most relevant functional partner( s)/interacting protein(s) out of large network, three filters were introduced in the methodology.

Results: The two filtered proteins, IBSP and FGF2, have common functions and also play a vital role in the pathways of RA. Thus, gives an in-depth knowledge of molecular mechanisms involved in Rheumatoid Arthritis and targeted therapeutics.

Conclusion: The network analysis of these proteins has been explored using Cytoscape, and the proteins with favourable values of graph centrality parameters such as IBSP and FGF2 are identified. Interesting functional cross talk such as bio mineralization, boneremodelling, angiogenesis, cell differentiation, etc., of SPP1 with IBSP and FGF2 is found, which throws light into the fact that these two proteins play a vital role in the pathways of RA.

Keywords: Rheumatoid Arthritis, Cytoscape, Protein-Protein Interaction, intrinsically disordered protein, biological annotations, KEGG

« Previous
[1]
Bell GW, Lewitter F. Visualizing networks. Methods Enzymol 2006; 411(1): 408-21.
[http://dx.doi.org/10.1016/S0076-6879(06)11022-8] [PMID: 16939803]
[2]
Dunker AK, Brown CJ, Lawson JD, Iakoucheva LM. Obradović, Z. Intrinsic disorder and protein function. Biochemistry 2002; 41(21): 6573-82.
[http://dx.doi.org/10.1021/bi012159+] [PMID: 12022860]
[3]
Franklin J, Lunt M, Bunn D, Symmons D, Silman A. Incidence of lymphoma in a large primary care derived cohort of cases of inflammatory polyarthritis. Ann Rheum Dis 2006; 65(5): 617-22.
[http://dx.doi.org/10.1136/ard.2005.044784] [PMID: 16249224]
[4]
Davey NE, Travé G, Gibson TJ. How viruses hijack cell regulation. Trends Biochem Sci 2011; 36(3): 159-69.
[http://dx.doi.org/10.1016/j.tibs.2010.10.002] [PMID: 21146412]
[5]
In Klippel JH. Dieppep. Rheumatology. St Louis Mosb 2003; 17(2): 159-68.
[6]
Iakoucheva LM, Brown CJ, Lawson JD. Obradović, Z, Dunker AK. Intrinsic disorder in cell-signaling and cancer-associated proteins. J Mol Biol 2002; 323(3): 573-84.
[http://dx.doi.org/10.1016/S0022-2836(02)00969-5] [PMID: 12381310]
[7]
Malaney P, Uversky VN, Davé V. Identification of intrinsically disordered regions in PTEN and delineation of its function via a network approach. Methods 2015; 77-78(3): 69-74.
[http://dx.doi.org/10.1016/j.ymeth.2014.10.005] [PMID: 25449897]
[8]
Petrow PK, Hummel KM, Schedel J, et al. Expression of osteopontin messenger RNA and protein in rheumatoid arthritis: Effects of osteopontin on the release of collagenase 1 from articular chondrocytes and synovial fibroblasts. Arthritis Rheum 2000; 43(7): 1597-605.
[http://dx.doi.org/10.1002/1529-0131(200007)43:7<1597:AID-ANR25>3.0.CO;2-0] [PMID: 10902765]
[9]
Snijesh VP, Matchado MS, Singh S. Classifying rheumatoid arthritis gene network signatures for identifying key regulatory molecules and their altered pathways by adopting network biology approach. Gene Rep 2018; 13(13): 199-211.
[http://dx.doi.org/10.1016/j.genrep.2018.10.013]
[10]
Chand Y, Alam MA. Network biology approach for identifying key regulatory genes by expression based study of breast cancer. Bioinformation 2012; 8(23): 1132-8.
[http://dx.doi.org/10.6026/97320630081132] [PMID: 23275709]
[11]
Safran M, Dalah I, Alexander J, et al. GeneCards Version 3: The human gene integrator. Database 2010; 2010: baq020.
[http://dx.doi.org/10.1093/database/baq020] [PMID: 20689021]
[12]
Shannon P, Markiel A, Ozier O, et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13(11): 2498-504.
[http://dx.doi.org/10.1101/gr.1239303] [PMID: 14597658]
[13]
Tompa P, Csermely P. The role of structural disorder in the function of RNA and protein chaperones. FASEB J 2004; 18(11): 1169-75.
[http://dx.doi.org/10.1096/fj.04-1584rev] [PMID: 15284216]
[14]
Warren RS, Yuan H, Matli MR, Ferrara N, Donner DB. Induction of vascular endothelial growth factor by insulin-like growth factor 1 in colorectal carcinoma. J Biol Chem 1996; 271(46): 29483-8.
[http://dx.doi.org/10.1074/jbc.271.46.29483] [PMID: 8910616]
[15]
Xie H, Vucetic S, Iakoucheva LM, et al. Functional anthology of intrinsic disorder. 1. Biological processes and functions of proteins with long disordered regions. J Proteome Res 2007; 6(5): 1882-98.
[http://dx.doi.org/10.1021/pr060392u] [PMID: 17391014]
[16]
Zhang F, Luo W, Li Y, Gao S, Lei G. Role of osteopontin in rheumatoid arthritis. Rheumatol Int 2015; 35(4): 589-95.
[http://dx.doi.org/10.1007/s00296-014-3122-z] [PMID: 25163663]
[17]
Snijesh VP, Singh S. Molecular modeling and network based approach in explaining the medicinal properties of Nyctanthes arbortristis, Lippia nodiflora for rheumatoid arthritis. J Bioinform Intell Cont 2014; 3(1): 31-8.
[http://dx.doi.org/10.1166/jbic.2014.1072]
[18]
Chand Y, Sao P, Singh S, Chandra N, Das S, Singh S. Prioritizing potential diagnostic biomarkers of Alzheimer’s disease by investigating gene expression data: A network-based approach. Alzheimers Dement 2020; 16(S4): e044322.
[http://dx.doi.org/10.1002/alz.044322]
[19]
Singh S, Vennila JJ, Snijesh VP, George G, Sunny C. Implying analytic measures for unravelling rheumatoid arthritis significant proteins through drug–target interaction. Interdiscip Sci 2016; 8(2): 122-31.
[http://dx.doi.org/10.1007/s12539-015-0108-9] [PMID: 26286007]
[20]
Blessia TF, Singh S, Vennila JJ. Unwinding the novel genes involved in the differentiation of embryonic stem cells into insulin-producing cells: A network-based approach. Interdiscip Sci 2017; 9(1): 88-95.
[http://dx.doi.org/10.1007/s12539-016-0148-9] [PMID: 26853975]
[21]
George G, Singh S, Lokappa SB, Varkey J. Gene co-expression network analysis for identifying genetic markers in Parkinson’s disease - a three-way comparative approach. Genomics 2019; 111(4): 819-30.
[http://dx.doi.org/10.1016/j.ygeno.2018.05.005] [PMID: 29852216]
[22]
Chand Y, Singh S. Prioritization of potential vaccine candidates and designing a multiepitope-based subunit vaccine against multidrug-resistant Salmonella typhi str. CT18: A subtractive proteomics and immunoinformatics approach. Microb Pathog 2021; 159: 105150.
[http://dx.doi.org/10.1016/j.micpath.2021.105150] [PMID: 34425197]
[23]
Sao P, Chand Y, Kumar A, Singh S. Potential drug target identification in Porphyromonas gingivalis using in silico subtractive metabolic pathway analysis. Bangladesh J Medical Sci 2021; 20(4): 887-96.
[http://dx.doi.org/10.3329/bjms.v20i4.54149]
[24]
Singh S, Snijesh VP, Jannet VJ. Rheumatoid Arthritis Candidate Genes Identification by Investigating Core and Periphery Interaction Structures. In: Muppalaneni, N., Gunjan, V, eds. Computational Intelligence in Medical Informatics. Springer Briefs in Applied Sciences and Technology. Springer, Singapore 2015; pp. 87-96.
[http://dx.doi.org/10.1007/978-981-287-260-9_9]
[25]
Sao P, Kishore I, Singh S, Panneerselvam K, Kumar A, Chaudhuri T. Putative target identification for gout; a network biology approach. J Bionanosci 2013; 7(6): 649-53.
[http://dx.doi.org/10.1166/jbns.2013.1166]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy