RESUMEN
Colorectal cancer (CRC) is the third most prevalent cancer with the second-highest mortality rate worldwide. microRNAs (miRNAs) of cancer-derived exosomes have shown promising diagnosis potential. Recent studies have shown the metastatic potential of a specific group of microRNAs called metastasis. Therefore, down-regulation of miRNAs at the transcriptional level can reduce metastasis probability. The aim of this bioinformatics research is targeting of miRNAs precursors using CRISPR-C2c2 (Cas13a) technique. The C2c2 (Cas13a) enzyme structure was downloaded from the RCSB database, the sequence miRNAs and their precursors were collected from miRbase. The crRNAs were designed and evaluated for their specificity by using CRISPR-RT server. The modeling 3D structure of the designed crRNA was performed by RNAComposer server. Finally, HDOCK server was used to perform molecular docking to evaluate docked molecules' energy level and position. The crRNAs designed for miR-1280, miR-206, miR-195, miR- 371a, miR-34a, miR-27a, miR-224, miR-99b, miR-877, miR-495 and miR-384 that showed high structural similarity with the situation observed in normal and appropriate orientation was obtained. Despite high specificity, the correct orientation was not established in the case of crRNAs that designed to target miR-145, miR-378a, miR-199a, miR- 320a and miR-543. The predicted interactions between crRNAs and Cas13a enzyme showed that crRNAs have a strong potential to inhibit metastasis. Therefore, crRNAs may be considered as an effective anticancer agent for further research in drug development.
RESUMEN
The high mutation rates of RNA viruses, coupled with short generation times and large population sizes, allow viruses to evolve rapidly and adapt to the host environment. The rapidity of viral mutation also causes problems in developing successful vaccines and antiviral drugs. With the spread of SARS-CoV-2 worldwide, thousands of mutations have been identified, some of which have relatively high incidences, but their potential impacts on virus characteristics remain unknown. The present study analyzed mutation patterns, SARS-CoV-2 AASs retrieved from the GISAID database containing 10,500,000 samples. Python 3.8.0 programming language was utilized to pre-process FASTA data, align to the reference sequence, and analyze the sequences. Upon completion, all mutations discovered were categorized based on geographical regions and dates. The most stable mutations were found in nsp1(8% S135R), nsp12(99.3% P323L), nsp16 (1.2% R216C), envelope (30.6% T9I), spike (97.6% D614G), and Orf8 (3.5% S24L), and were identified in the United States on April 3, 2020, and England, Gibraltar, and, New Zealand, on January 1, 2020, respectively. The study of mutations is the key to improving understanding of the function of the SARS-CoV-2, and recent information on mutations helps provide strategic planning for the prevention and treatment of this disease. Viral mutation studies could improve the development of vaccines, antiviral drugs, and diagnostic assays designed with high accuracy, specifically useful during pandemics. This knowledge helps to be one step ahead of new emergence variants.