An Integrated Chikungunya Virus Database to Facilitate Therapeutic Analysis: ChkVDb

Author(s): Priya Narang, Mehak Dangi, Deepak Sharma, Alka Khichi, Anil Kumar Chhillar*.

Journal Name: Current Bioinformatics

Volume 14 , Issue 4 , 2019

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Abstract:

Background: Chikungunya infection flare-ups have manifested in nations of Africa, Asia, and Europe including Indian and Pacific seas. It causes fever and different side effects include muscle torment, migraine, sickness, exhaustion and rash. It has turned into another, startling general medical issue in numerous tropical African and Asian countries and is presently being viewed as a genuine risk. No antiviral treatment or vaccine is yet available for this ailment. The current treatment is centered just on mitigating its side effects.

Objective: The objective was to encourage the study on this viral pathogen, by the development of a database dedicated to Chikungunya Virus, that annotates and unifies the related data from various resources.

Method: It undertook a consolidated approach for Chikungunya Virus genomic, proteomic, phylogenetics and therapeutic learning, involving the entire genome sequences and their annotation utilizing different in silico tools. Annotation included the information for CpG Island, usage bias, codon context and phylogenetic analysis at both the genome and proteome levels.

Results: This database incorporates information of 41 strains of virus causing Chikungunya infection that can be accessed conveniently as well as downloaded effortlessly. Therapeutics section of this database contains data about B and T cell Epitopes, siRNAs and miRNAs that can be used as potential therapeutic targets.

Conclusion: This database can be explored by specialists and established researchers around the world to assist their research on this non-treatable virus. It is a public database available from “www.chkv.in”.

Keywords: ChkVDb, CpG Island, codon context, phylogenetics, glycosylation sites, Epitopes, siRNAs, miRNAs.

[1]
Simon F, Javelle E, Oliver M, Leparc-Goffart I, Marimoutou C. Chikungunya virus infection. Curr Infect Dis Rep 2011; 13(3): 218-28.
[2]
Cunha RVD, Trinta KS. Chikungunya virus: clinical aspects and treatment - A Review. Mem Inst Oswaldo Cruz 2017; 112(8): 523-31.
[3]
Fritel X, Rollot O, Gérardin P, et al. Chikungunya virus infection during pregnancy, Reunion, France, 2006. Emerg Infect Dis 2010; 16(3): 418-25.
[4]
Chikungunya Virus Net.com. Retrieved from: http://www.chikun-gunyavirusnet.com (Accessed on May 16, 2017).
[5]
Grivard P, Le Roux K, Laurent P, et al. Molecular and serological diagnosis of Chikungunya virus infection. Pathol Biol 2007; 55(10): 490-4.
[6]
Weaver SC, Osorio JE, Livengood JA, Chen R, Stinchcomb DT. Chikungunya virus and prospects for a vaccine. Expert Rev Vaccines 2012; 11(9): 1087-101.
[7]
Mallilankaraman K, Shedlock DJ, Bao H, et al. A DNA vaccine against chikungunya virus is protective in mice and induces neutralizing antibodies in mice and nonhuman primates. PLoS Negl Trop Dis 2011; 5(1): e928.
[8]
Agnandji ST, Lell B, Soulanoudjingar SS, et al. First results of phase 3 trial of RTS,S/AS01 malaria vaccine in African children. N Engl J Med 2011; 365(20): 1863-75.
[9]
Elliott SL, Suhrbier A, Miles JJ, et al. Phase I trial of a CD8+ T-cell peptide epitope-based vaccine for infectious mononucleosis. J Virol 2008; 82(3): 1448-57.
[10]
Kanasty R, Dorkin JR, Vegas A, Anderson D. Delivery materials for siRNA therapeutics. Nat Mater 2013; 12(11): 967-77.
[11]
Dhanda SK, Chaudhary K, Gupta S, Brahmachari SK, Raghava GP. A web-based resource for designing therapeutics against Ebola Virus. Sci Rep 2016; 6: 24782.
[12]
Wittrup A, Lieberman J. Knocking down disease: a progress report on siRNA therapeutics. Nat Rev Genet 2015; 16(9): 543-52.
[13]
Broderick JA, Zamore PD. MicroRNA therapeutics. Gene Ther 2011; 18(12): 1104-10.
[14]
Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 2016; 44(D1): D7-D19.
[15]
Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank. Nucleic Acids Res 2008; 36(Database issue): D25-30.
[16]
Rose PW, Bi C, Bluhm WF, et al. The RCSB Protein Data Bank: new resources for research and education. Nucleic Acids Res 2013; 41(Database issue): D475-82.
[17]
Grant JR, Stothard P. The CGView Server: a comparative genomics tool for circular genomes. Nucleic Acids Res 2008; 36(Web Server issue): W181-4.
[18]
Stothard P, Wishart DS. Circular genome visualization and exploration using CGView. Bioinformatics 2005; 21(4): 537-9.
[19]
Chenna R, Sugawara H, Koike T, et al. Multiple sequence alignment with the Clustal series of programs. Nucleic Acids Res 2003; 31(13): 3497-500.
[20]
Kumar S, Stecher G, Tamura K. MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets. Mol Biol Evol 2016; 33(7): 1870-4.
[21]
Behura SK, Severson DW. Comparative analysis of codon usage bias and codon context patterns between dipteran and hymenopteran sequenced genomes. PLoS One 2012; 7(8): e43111.
[22]
EMBOSS. http://www.bioinformatics.nl/cgi-bin/emboss/cusp (Accessed on: 16/May/2017)
[23]
Olson SA. EMBOSS opens up sequence analysis. European Molecular Biology Open Software Suite. Brief Bioinform 2002; 3(1): 87-91.
[24]
Pinheiro M, Afreixo V, Moura G, Freitas A, Santos MA, Oliveira JL. Statistical, computational and visualization methodologies to unveil gene primary structure features. Methods Inf Med 2006; 45(2): 163-8.
[25]
Hansen JE, Lund O, Tolstrup N, Gooley AA, Williams KL, Brunak S. NetOglyc: prediction of mucin type O-glycosylation sites based on sequence context and surface accessibility. Glycoconj J 1998; 15(2): 115-30.
[26]
Norton PA, Comunale MA, Krakover J, et al. N-linked glycosylation of the liver cancer biomarker GP73. J Cell Biochem 2008; 104(1): 136-49.
[27]
Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 2006; 22(13): 1658-9.
[28]
Huang WL, Tsai MJ, Hsu KT, Wang JR, Chen YH, Ho SY. Prediction of linear B-cell epitopes of hepatitis C virus for vaccine development. BMC Med Genomics 2015; 8(Suppl. 4): S3.
[29]
Zheng W, Zhang C, Hanlon M, Ruan J, Gao J. An ensemble method for prediction of conformational B-cell epitopes from antigen sequences. Comput Biol Chem 2014; 49: 51-8.
[30]
Zhang Q, Wang P, Kim Y, et al. Immune epitope database analysis resource (IEDB-AR). Nucleic Acids Res 2008; 36(Web Server issue): W513-8.
[31]
Singh H, Raghava GPS. ProPred1: prediction of promiscuous MHC Class-I binding sites. Bioinformatics 2003; 19(8): 1009-14.
[32]
Nielsen M, Lund O, Buus S, Lundegaard C. MHC class II epitope predictive algorithms. Immunology 2010; 130(3): 319-28.
[33]
Qureshi A, Thakur N, Kumar M. VIRsiRNApred: a web server for predicting inhibition efficacy of siRNAs targeting human viruses. J Transl Med 2013; 11: 305.
[34]
Chaudhary K, Nagpal G, Dhanda SK, Raghava GP. Prediction of Immunomodulatory potential of an RNA sequence for designing non-toxic siRNAs and RNA-based vaccine adjuvants. Sci Rep 2016; 6: 20678.
[35]
Nam JW, Shin KR, Han J, Lee Y, Kim VN, Zhang BT. Human microRNA prediction through a probabilistic co-learning model of sequence and structure. Nucleic Acids Res 2005; 33(11): 3570-81.
[36]
Liu X, He S, Skogerbø G, Gong F, Chen R. Integrated sequence-structure motifs suffice to identify microRNA precursors. PLoS One 2012; 7(3): e32797.
[37]
Hofacker IL. Vienna RNA secondary structure server. Nucleic Acids Res 2003; 31(13): 3429-31.
[38]
Larsen F, Gundersen G, Lopez R, Prydz H. CpG islands as gene markers in the human genome. Genomics 1992; 13(4): 1095-107.


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Article Details

VOLUME: 14
ISSUE: 4
Year: 2019
Page: [323 - 332]
Pages: 10
DOI: 10.2174/1574893613666181029124848
Price: $58

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