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With the global epidemic of SARS-CoV-2, it is important to monitor the variation, haplotype subgroup epidemic trends and key mutations of SARS-CoV-2 over time effectively, which is of great significance to the development of new vaccines, the update of therapeutic drugs, and the improvement of detection reagents. The AutoVEM tool developed in the present study could complete all mutations detections, haplotypes classification, haplotype subgroup epidemic trends and key mutations analysis for 131,576 SARS-CoV-2 genome sequences in 18 hours on a 1 core CPU and 2G internal storage computer. Through haplotype subgroup epidemic trends analysis of 131,576 genome sequences, the great significance of the previous 4 specific sites (C241T, C3037T, C14408T and A23403G) was further revealed, and 6 new mutation sites of highly linked (T445C, C6286T, C22227T, G25563T, C26801G and G29645T) were discovered for the first time that might be related to the infectivity, pathogenicity or host adaptability of SARS-CoV-2. In brief, we proposed an integrative method and developed an efficient automated tool to monitor haplotype subgroup epidemic trends and screen out the key mutations in the evolution of SARS-CoV-2 over time for the first time, and all data could be updated quickly to track the prevalence of previous key mutations and new key mutations because of high efficiency of the tool. In addition, the idea of combinatorial analysis in the present study can also provide a reference for the mutation monitoring of other viruses.
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ObjectivesTo reveal epidemic trend and possible origins of SARS-CoV-2 by exploring its evolution and molecular characteristics based on a large number of genomes since it has infected millions of people and spread quickly all over the world. MethodsVarious evolution analysis methods were employed. ResultsThe estimated Ka/Ks ratio of SARS-CoV-2 is 1.008 or 1.094 based on 622 or 3624 SARS-CoV-2 genomes, and the time to the most recent common ancestor (tMRCA) was inferred in late September 2019. Further 9 key specific sites of highly linkage and four major haplotypes H1, H2, H3 and H4 were found. The Ka/Ks, detected population size and development trends of each major haplotype showed H3 and H4 subgroups were going through a purify evolution and almost disappeared after detection, indicating H3 and H4 might have existed for a long time, while H1 and H2 subgroups were going through a near neutral or neutral evolution and globally increased with time. Notably the frequency of H1 was generally high in Europe and correlated to death rate (r>0.37). ConclusionsIn this study, the evolution and molecular characteristics of more than 16000 genomic sequences provided a new perspective for revealing epidemiology of SARS-CoV-2.
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Objective Type 2 diabetes mellitus ( T2DM) as a common disease around the world becomes a great threat to the health of human beings.The cynomolgus T2DM model, which preferably simulates human T2DM onset and progress, can be beneficial to the drug development and clinical treatment.In the present study, 37 of T2DM-susceptibility SNPs and the extended genome sequences were used to obtain corresponding SNPs in the T2DM cynomolgus monkeys. Methods Firstly, DNA pool screening was conducted.Then, using polymerase chain reaction to amplify and to sequence the cynomolgus homologous sequences.Using DNAStar software to analyze the differences between bases.Finally, we used analysis of variance and F test to calculate the frequency of alleles.We also used the GLM models of SAS software to analyze the association of genotype with fasting plasma glucose and glycosylated hemoglobin.Results SNP661A,SNP661B, SNP343A, SNP343B, SNP343C, SNP565A, SNP565B and SNP565C were found to have a significant difference of allele frequencies between spontaneous cases and controls.Conclusions The findings of this study suggest that SNP661A, SNP661B, SNP343A, SNP343B and SNP343C may play an important role in the establishment of cynomolgus T2DM models.
