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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an unsegmented positivesense single-stranded RNA virus that belongs to the {beta}-coronavirus. This virus was the cause of a novel severe acute respiratory syndrome in 2019 (COVID-19) that emerged in Wuhan, China at the early stage of the pandemic and rapidly spread around the world. Rapid transmission and reproduction of SARS-CoV-2 threaten worldwide health with a high mortality rate from the virus. According to the significant role of non-structural protein 1 (NSP1) in inhibiting host mRNA translation, this study focuses on the link between amino acid sequences of NSP1 and alterations of them spreading around the world. The SARS-CoV-2 NSP1 protein sequences were analyzed and FASTA files were processed by Python language programming libraries. Reference sequences compared with each NSP1 sample to identify every mutation and categorize them were based on continents and frequencies. NSP1 mutations rate divided into continents were different. Based on continental studies, E87D in global vision and also in Europe notably increased. The E87D mutation has significantly risen especially in the last months of the study as the first frequent mutation observed. The remarkable mutations, H110Y and R24C, have the second and third frequencies, respectively. Based on this mutational information, despite NSP1 being a conserved sequence occurrence, these mutations change the rate of flexibility and stability of the NSP1 protein, which can eventually affect inhibiting the host translation. IMPORTANCEIn this study, we analyzed 6,510,947 sequences of non-structural protein 1 as a conserved region of SARS-CoV-2. According to the obtained results, 93.4819% of samples had no mutant regions on their amino acid sequences. Heat map data of mutational samples demonstrated high percentages of mutations that occurred in the region of 72 to 126 amino acids indicating a hot spot region of the protein. Increased rates of E87D, H110Y, and R24C mutations in the timeline of our study were reported as significant compared to available mutant samples. Analyzing the details of replacing amino acids in the most frequent E87D mutation reveals the role of this alteration in increasing molecule flexibility and destabilizing the structure of the protein.
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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. IMPORTANCEMore than two years into the global COVID-19 pandemic, the focus of attention is shifted to the emergence and spread of the SARS-CoV-2 variants that cause the evolutionary trend. Here, we analyzed and compared about 10.5 million sequences of SARS-CoV-2 to extract the stable mutations, frequencies and the substitute amino acid that changed with the wild-type one in the evolutionary trend. Also, developing and designing accurate vaccines could prepare long-term immunization against different local variants. In addition, according to the false negative results of the COVID-19 PCR test report in the diagnosis of new strains, investigating local mutation patterns could help to design local primer and vaccine.
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The coronavirus disease 19 (COVID-19) is a highly pathogenic viral infection of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), resulting in the global pandemic of 2020.A lack of therapeutic and preventive approaches including drugs and vaccines, has quickly posed significant threats to world health. A comprehensive understanding of the evolution and natural selection of SARS-CoV-2 against the host interaction and symptoms at the phenotype level could impact the candidates strategies for the fight against this virus. SARS-CoV-2 Mutation (SARS2Mutant, http://sars2mutant.com/) is a database thatprovides comprehensive analysis results based on tens of thousands of high-coverage and high-quality SARS-CoV-2 complete protein sequences. The structure of this database is designed to allow the users to search for the three different strategies among amino acid substitution mutations based on gene name, geographical zone or comparative analysis. Based on each strategy, five data types are available to the user: mutated sample frequencies, heat map of the mutated amino acid positions, timeline trend for mutation survivals and natural selections, and charts of changed amino acids and their frequencies. Due to the increase of virus protein sequence samples published daily showing the latest trends of current results, all sequences in the database are reanalyzed and updated monthly. The SARS-2Mutant database providescurrent analysis and updated data of mutation patterns and conserved regions, helpful in developing and designing targeted vaccines, primers and drug discoveries.
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BackgroundWith evidence of sustained transmission in more than 190 countries, coronavirus disease 2019 (COVID-19) has been declared a global pandemic. As such, data are urgently needed about risk factors associated with clinical outcomes. MethodsA retrospective chart review of 323 hospitalized patients with COVID-19 in Wuhan was conducted. Patients were classified into three disease severity groups (non-severe, severe, and critical), based on their initial clinical presentation. Clinical outcomes were designated as favorable and unfavorable, based on disease progression and response to treatments. Logistic regression models were performed to identify factors associated with clinical outcomes, and log-rank test was conducted for the association with clinical progression. ResultsCurrent standard treatments did not show significant improvement on patient outcomes in the study. By univariate logistic regression model, 27 risk factors were significantly associated with clinical outcomes. Further, multivariate regression indicated that age over 65 years, smoking, critical disease status, diabetes, high hypersensitive troponin I (>0.04 pg/mL), leukocytosis (>10 x 109/L) and neutrophilia (>75 x 109/L) predicted unfavorable clinical outcomes. By contrast, the use of hypnotics was significantly associated with favorable outcomes. Survival analysis also confirmed that patients receiving hypnotics had significantly better survival. ConclusionsTo our knowledge, this is the first indication that hypnotics could be an effective ancillary treatment for COVID-19. We also found that novel risk factors, such as higher hypersensitive troponin I, predicted poor clinical outcomes. Overall, our study provides useful data to guide early clinical decision making to reduce mortality and improve clinical outcomes of COVID-19. (Funded by the Natural Science Foundation of Hubei Province ZRMS2019000029 and the Top Youth Talent Program in Hubei Province.)
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One important purpose to investigate medicinal plants is to understand genes and enzymes that govern the biological metabolic process to produce bioactive compounds.Genome wide high throughput technologies such as genomics,transcriptomics,proteomics and metabolomics can help reach that goal.Such technologies can produce a vast amount of data which desperately need bioinformatics and systems biology to process,manage,distribute and understand these data.By dealing with theomicsdata,bioinformatics and systems biology can also help improve the quality of traditional medicinal materials,develop new approaches for the classification and authentication of medicinal plants,identify new active compounds,and cultivate medicinal plant species that tolerate harsh environmental conditions.In this review,the application of bioinformatics and systems biology in medicinal plants is briefly introduced.
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Germination of the seeds of Bupleurum chinense and B. falcatum activated by exogenous hormones of dif ferent concentrations was studied. The hormones used were GA3, 6-BA and IAA. The optimum concentration of the 3 hormones that activated the seed germination of B. chinense was 50?g/ml,while that for B. falcatum was 200?g/ml, 100?g/ml and 25?g/ml respectively. Among the 3 hormones, 6-BA showed the greatest effect on the 2 species of Bupleurum L. 50?g/ml 6-BA raised the germination rate of the seeds of B. chinese from 57.5 % to 84. 5 % and 100?g/ml 6-BA raised the germination rate of the seeds of B.falcatum from 28. 5 % to 76.0%
