ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new member of the Coronaviridae family, triggering more than 190 million cases and more than two million deaths in European societies. Emerging the new variants due to mutations in genomic regions is foremost responsible for influencing the infectivity and mortality potential of such a virus. In the current study, we considered mutations among spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins of SARS-CoV-2 in the Europe continent by exploring the frequencies of mutations and the timeline of emerging them. For this purpose, Amino-acid sequences (AASs) were gathered from the GISAID database, and Mutation tracking was performed by detecting any difference between samples and a reference sequence; Wuhan-2019. In the next step, we compared the achieved results with worldwide sequences. 8.6%, 63.6%, 24.7%, and 1.7% of S, E, M, and N samples did not demonstrate any mutation among European countries. Also, the regions of 508 to 635 AA, 7 to 14 AA, 66 to 88 AA, and 164 to 205 AA in S, E, M, and N samples contained the most mutations relative to the total AASs in both Europe AASs and worldwide samples. D614G, A222V, S477N, and L18F were the first to fifth frequent mutations in S AASs among European samples, and T9I, I82T, and R203M were the first frequent mutations among E, M, and S AASs of the Europe continent. Investigating the mutations among structural proteins of SARS-CoV-2 can improve the strength of therapeutic and diagnostic strategies to efficient combat the virus and even maybe efficient in predicting new emerging variants of concern.
ABSTRACT
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a role in the mortality of more than 6 million people worldwide. This virus owns the genome, which contains four structural proteins, including spike (S), envelope (E), membrane (M), and nucleocapsid (N). The occurrence of structural mutations can induce the emergence of new variants. Depending on the mutations, the variants may display different patterns of infectivity, mortality, and sensitivity toward drugs and vaccines. In this study, we analyzed samples of amino-acid sequences (AASs) for structural proteins from the coronavirus 2019 (COVID-19) declaration as a pandemic to April 2022 among American countries. The analysis process included considering mutations frequencies, locations, and evolutionary trends utilizing sequence alignment to the reference sequence. In the following, the results were compared with the same analyses among the samples of the entire world. Results displayed that despite samples of North America and international countries that own the region of 508 to 635 with the highest mutation frequency among S AASs, the region with the same characteristic was concluded as 1 to 127 in South America. Besides, the most frequent mutations in S, E, M, and N proteins from North America and worldwide samples were concluded as D614G, T9I, I82T, and R203M. In comparison, R203K was the first frequent mutation in N samples in South America. Widely comparing mutations between North America and South America and between the Americas and the world can help scientists introduce better drug and vaccine development strategies.
ABSTRACT
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.
ABSTRACT
BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new emerging coronavirus that causes coronavirus disease 2019 (COVID-19). Whole-genome tracking of the SARS-CoV-2 enhanced our understanding of the mechanism of disease, control, and prevent COVID-19 infections. Materials and MethodsIn the current study, we investigated 1221 SARS-CoV-2 protein sequences of Iranian SARS-CoV-2 in the public database of the GISAID from January 2019 to April 2022. Prepare a list of suitable samples and preprocess performed by python programming language. To compare and identify mutation patterns SARS-CoV-2 genome was aligned to the Wuhan-Hu-1 as a reference sequence. ResultsOur investigation revealed that spike-P323L, ORF9c-G50N, NSP14-I42V, spike-D614G, NSP4-T492I, nucleocapsid-R203K, nucleocapsid-G204R, membrane-A63T, membrane-Q19E, NSP5-P132H, envelope-T9I, NSP3-G489S, ORF3a-T24I, membrane-D3G, spike-S477N, Spike-D478K, nucleocapsid-S235F, spike-N501Y, nucleocapsid-D3L, and spike-P861H as the most frequent mutations among the Iranian SARS-COV-2 sequences. Furthermore, it was observed that more than 95 % of the SARS-CoV-2 genome, including NSP7, NSP8, NSP9, NSP10, NSP11, and ORF8, had no mutation when compared to the Wuhan-Hu-1. Finally, our data indicated the ORF3a-T24I, NSP3-G489S, NSP5-P132H, NSP14-I42V, envelope-T9I, nucleocapsid-D3L, membrane-Q19E, and membrane-A63T mutations might be one of the responsible factors for the surge in the SARS-CoV-2 omicron variant wave in Iran. DiscussionOur results highlight the value of real-time genomic surveillance that help to identify novel SARS-CoV-2 variants and could be applied to update SARS-CoV-2 diagnostic tools, vaccine design, and understanding of the mechanisms of adaptation to a new host environment.
ABSTRACT
BackgroundThe Coronavirus 2019 (COVID-19) was named by the World Health Organization (WHO) due to its rapid transmittable potential and high mortality rate. Based on the critical role of None Structural Proteins (NSP), NSP3, NSP4, and NSP6 in COVID-19, this study attempts to investigate the superior natural selection mutations and Epistasis among these none structural proteins. MethodsApproximately 6.5 million SARS-CoV-2 protein sequences of each NSP3, NSP4, and NSP6 nonstructural protein were analyzed from January 2020 to January 2022. Python programming language was utilized to preprocess and apply inclusion criteria on the FASTA file to prepare a list of suitable samples. NSP3, NSP4, and NSP6 were aligned to the reference sequence to compare and identify mutation patterns categorized based on frequency, geographical zone distribution, and date. To discover epistasis situations, linear regression between mutation frequency and date among candidate genes was performed to determine correlations. ResultsThe rate of NSP3, NSP4, and NSP6 mutations in divided geographical areas was different. Based on continental studies, P1228L (54.48%), P1469S (54.41%), and A488S (53.86%) mutations in NSP3, T492I (54.84%), and V167L (52.81%) in NSP4 and T77A (69.85%) mutation in NSP6 increased over time, especially in recent months. For NSP3, Europe had the highest P1228L, P1469S, and A488S mutations. For NSP4, Oceania had the highest T492I and V167L mutations, and for NSP6, Europe had the highest T77A mutation. Hot spot regions for NSP3, NSP4, and NSP6 were 1358 to 1552 AA, 150 to 200 AA, and 58 to 87 AA, respectively. Our results showed a significant correlation and co-occurrence between NSP3, NSP4, and NSP6 mutations. ConclusionWe conclude that the effect of mutations on virus stability and replication can be predicted by examining the amino acid changes of P1228L, P1469S, A488S, T492I, V167L and T77A mutations. Also, these mutations can possibly be effective on the function of proteins and their targets in the host cell.
ABSTRACT
Coronavirus Disease 2019 (COVID-19) pandemic has become the greatest threat to global health in only a matter of months. Iran struggling with COVID-19 coincidence with Nowruz vacations has led to horrendous consequences for both people and the public health workforce. Modeling approaches have been proved to be highly advantageous in taking appropriate actions in the early stages of the pandemic. To this date, no study has been conducted to model the disease to investigate the disease, especially after travel restrictions in Iran. In this study, we exploited the opportunities that Artificial neural networks offer to investigate contributing factors of early-stage coronavirus spread via generating a model to predict daily confirmed cases in Iran. We collected publicly available data of confirmed cases in 24 provinces from April 4, 2020, to May 2, 2020, with a list of explanatory factors. The factors were checked separately for any linear associations and to train and validate a multilayer perceptron network. The accuracy of the models was evaluated, the R2 scores were 0.842 for population distribution, 0.822 for health index, and 0.864 for the population in the provinces. Our results suggest the significant impact of the mentioned factors on disease spread in the time of travel restrictions when the vacation ended. Accordingly, this information can be implicated in assessing the risk of epidemics and future policy makings in this area.
