Genome-wide identification and prediction of SARS-CoV-2 mutations show an abundance of variants: Integrated study of bioinformatics and deep neural learning

Genomic data analysis is a fundamental system for monitoring pathogen evolution and the outbreak of infectious diseases. Based on bioinformatics and deep learning, this study was designed to identify the genomic variability of SARS-CoV-2 worldwide and predict the impending mutation rate. Analysis of 259044 SARS-CoV-2 isolates identified 3334545 mutations with an average of 14.01 mutations per isolate. Globally, single nucleotide polymorphism (SNP) is the most prevalent mutational event. The prevalence of C > T (52.67%) was noticed as a major alteration across the world followed by the G > T (14.59%) and A > G (11.13%). Strains from India showed the highest number of mutations (48) followed by Scotland, USA, Netherlands, Norway, and France having up to 36 mutations. D416G, F106F, P314L, UTR:C241T, L93L, A222V, A199A, V30L, and A220V mutations were found as the most frequent mutations. D1118H, S194L, R262H, M809L, P314L, A8D, S220G, A890D, G1433C, T1456I, R233C, F263S, L111K, A54T, A74V, L183A, A316T, V212F, L46C, V48G, Q57H, W131R, G172V, Q185H, and Y206S missense mutations were found to largely decrease the structural stability of the corresponding proteins. Conversely, D3L, L5F, and S97I were found to largely increase the structural stability of the corresponding proteins. Multi-nucleotide mutations GGG > AAC, CC > TT, TG > CA, and AT > TA have come up in our analysis which are in the top 20 mutational cohort. Future mutation rate analysis predicts a 17%, 7%, and 3% increment of C > T, A > G, and A > T, respectively in the future. Conversely, 7%, 7%, and 6% decrement is estimated for T > C, G > A, and G > T mutations, respectively. T > G\A, C > G\A, and A > T\C are not anticipated in the future. Since SARS-CoV-2 is mutating continuously, our findings will facilitate the tracking of mutations and help to map the progression of the COVID-19 intensity worldwide

Global urban environmental change drives adaptation in white clover.

Urbanization transforms environments in ways that alter biological evolution. We examined whether urban environmental change drives parallel evolution by sampling 110,019 white clover plants from 6169 populations in 160 cities globally. Plants were assayed for a Mendelian antiherbivore defense that also affects tolerance to abiotic stressors. Urban-rural gradients were associated with the evolution of clines in defense in 47% of cities throughout the world. Variation in the strength of clines was explained by environmental changes in drought stress and vegetation cover that varied among cities. Sequencing 2074 genomes from 26 cities revealed that the evolution of urban-rural clines was best explained by adaptive evolution, but the degree of parallel adaptation varied among cities. Our results demonstrate that urbanization leads to adaptation at a global scale

Association between gestational weight gain and postpartum diabetes: evidence from a community based large cohort study

We have investigated the prospective association between excess gestational weight gain (GWG) and development of diabetes by 21 years post-partum using a community-based large prospective cohort study in Brisbane, Australia. There were 3386 mothers for whom complete data were available on GWG, pre-pregnancy BMI and self-reported diabetes 21 years post-partum. We used The Institute of Medicine (IOM) definition to categorize GWG as inadequate, adequate and excessive. We found 839 (25.78%) mothers gained inadequate weight, 1,353 (39.96%) had adequate weight gain and 1,194 (35.26%) had gained excessive weight during pregnancy. At 21 years post-partum, 8.40% of mothers self-reported a diagnosis of diabetes made by their doctor. In the age adjusted model, we found mothers who gained excess weight during pregnancy were 1.47(1.11,1.94) times more likely to experience diabetes at 21 years post-partum compared to the mothers who gained adequate weight. This association was not explained by the potential confounders including maternal age, parity, education, race, smoking, TV watching and exercise. However, this association was mediated by the current BMI. There was no association for the women who had normal BMI before pregnancy and gained excess weight during pregnancy. The findings of this study suggest that women who gain excess weight during pregnancy are at greater risk of being diagnosed with diabetes in later life. This relationship is likely mediated through the pathway of post-partum weight-retention and obesity. This study adds evidence to the argument that excessive GWG during pregnancy for overweight mothers has long term maternal health implications