INTRODUCTION
MATERIALS AND METHODS
RESULTS
CONCLUSIONS
Collagen Triple Helix Repeat Containing 1 is a glycoprotein that expressed in vascular calcifications of carotid artery lesions. Location of this gene is 8q22.3. It expresses at higher rates in esophageal tissues. It plays a multidimensional role in malignant cancers. Overexpression of this gene affects various signalling pathways , thus mainly arises Barret’s esophagus and esophageal adenocarcinoma conditions. CTHRC1 gene regulates the activation of Wnt/PCP Pathway.
First dbSNP database is used. The nonsynonymous SNPs of CTHRC1 was reclaimed from this database. http://www.ncbi.nlm.nih.gov/snp/?term=C THRC1.
The number of non-synonymous SNPs present in the CTHRC1 gene is 3211. Out of which, 142 variants belong to the protein and 106 nsSNPs evaluated as missense type. A total of
Further sorting nsSNPs according to their energy values, it was found that only 12 SNPs were highly deleterious by analyzing with the help of bioinformatics tools such as SIFT, PROVEAN, I-MUTANT, PhD-SNPs and POLYPHEN 2. Hence, these non-synonymous SNPs were highly linked to disease progression. FUTURE SCOPE ➢These nsSNPs can be further analyzed by experimental methods to work on the structural properties of CTHRC1 gene.
But overexpression of CTHRC1 in Wnt/PCP Pathway leads to cervical cancers.
OBJECTIVES ❑To evaluate the phenotypic effect of nsSNPs within coding region and missense mutations using Insilco bioinformatic tools. ❑ To find out several highly deleterious nsSNPs within the coding region in the CTHRC1 gene.
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➢ Used in the drug designing process. ➢ Used in drug discovery.
REFERENCES
In the second step, 5 Pathogenicity Prediction Tools have been used PROVEAN, i-Mutant2.0, Polyphen-2, PhD-SNP and SIFT.
For find neighbouring proteins, String Database is very helpful. In the third method, the interacting proteins with CTHRC1 found. http://string-db.org/ RESEARCH POSTER PRESENTATION DESIGN © 2019
106 nsSNPs, submitted to SIFT server that predicted 104 nsSNPs affecting the protein structure as the SIFT score is equal to or below 0.05 and 2 nsSNPs were found to be tolerated. Further, the analysis conducted by using the I-Mutant tool, the predictions determined that nsSNPs with < 0.05 were deleterious. It was showed that DDG value of 83 SNPs was less or equal than 0, hence they decreased the stability of the protein. The 23 nsSNPs were greater than 0 thus increasing the stability.
Capriotti, E., Fariselli, P., & Casadio, R. (2005). IMutant2.0: Predicting Stability Changes Upon Mutation From the Protein Sequence or Structure. Nucleic Acids Research, 33(Web Server Issue), W306-10. DOI: 10.1093/nar/gki375
With the study evaluated from the Polyphen 2, the 35 SNPs were found to be benign (36.84%), possibly damaging SNPs were observed to be 10 (10.52%) and probably damaging nsSNPs were 50 (52.63%). The 38 nsSNPs (40.86%) were found to be deleterious and the prediction was found to be deleterious index < -2.5 on the Provean tool. The other 68 nsSNPs were concluded as neutral. The analysis depicted by the PhD-SNP concluded that 39 (37%) nsSNPs were disease variant and 67 nsSNPs (63%) were found neutral.
Choi, Y., & Chan, A. (2015). PROVEAN Web Server: A Tool to Predict the Functional Effect of Amino Acid Substitutions and Indels. Bioinformatics, 31(16), 2745-7. DOI: 10.1093/bioinformatics/btv195 Short, M., Burgers, K., & Fry, V. (2017). Esophageal Cancer. Am Fam Physician, 95(1), 2228.
CONTACTS Samonee Ralmilay samoneeralmilay321@gmail.com