Evaluation of daily average temperature trends in Kerala, India, using MERRA-2 reanalysis data: a climate change perspective

Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) data for a period of 40 years (1980-2019) was used to detect the trend characteristics of daily average temperature in the state of Kerala, India. Data extracted from a total of fifty locations in the state were statistically processed using Mann-Kendall (MK) and Spearman's Rho (SR) tests to detect the trend, Pettitt test to identify the single change point, and Theil-Sen's method for the calculation of the rate of change. The MERRA-2 product is validated for the study region according to statistical indicators. The daily average temperature in the state during the period of study varies between 16.56 and 32.64 °C. The spatial pattern of daily average maximum temperature shows higher temperature domains in the central and southern parts of the state. Trend characteristics of daily average temperature assessed through MK and SR tests show a significant increasing trend in all stations, with maximum values in stations located in the northern part of the state. Change point detected through the Pettitt test divided the sampling stations into three groups based on the change in daily average temperature characteristics in the years 2002 (north zone), 2009 (south zone), and 2012 (central zone), indicating nonunique spatial variability in temperature characteristics in the state. The rate of change in the daily average temperature assessed indicates an increase at the rate of an average of 0.013 °C.year^-1. During the whole study period, the daily average temperature showed an overall increase of 0.54 °C, and for the 100-year futuristic prediction, the daily average temperature in the state shows an average increase of 1.35 °C. Among the stations, a higher rate of increase in daily average temperature is shown by stations located in the eastern part of the Pathanamthitta, Idukki, and Kollam districts. Though the rise in daily average temperature is not much higher, its spatial characteristics require more attention because, in recent times, the study area has faced repeated, severe, and long drought conditions along with sunburn incidents. As an agrarian state, a change in the temperature domain will adversely affect the overall agricultural production and will evoke not only a food crisis but also economic as well as water resources issues. The result obtained can be used as holistic basic information for understanding the impending effect of climate change in the state to frame better mitigation as well as management strategies.

Emerging of composition variations of SARS-CoV-2 spike protein and human ACE2 contribute to the level of infection: in silico approaches

SARS-CoV-2 is causative of pandemic COVID-19. There is a sequence similarity between SARS-CoV-2 and SARS-CoV; however, SARS-CoV-2 RBDs (receptor-binding domain) binds 20-fold strongly with human angiotensin-converting enzyme 2 (hACE2) than SARS-CoV. The study aims to investigate protein-protein interactions (PPI) of hACE2 with SARS-CoV-2 RBD between wild and variants to detect the most influential interaction. Variants of hACE2 were retrieved from NCBI and subjected to determine the most pathogenic nsSNPs. Probability of PPIs determines the binding affinity of hACE2 genetic variants with RBD was investigated. Composition variations at the hACE2 and RBD were processed for PatchDock and refined by FireDock for the PPIs. Twelve nsSNPs were identified as the top pathogenic from SNPs (n = 7489) in hACE2 using eight bioinformatics tools. Eight RBD variants were complexed with 12 nSNPS of hACE2, and the global energy scores (Kcal/mol) were calculated and classified as very weak (-3.93 to -18.43), weak (-18.42 to -32.94), moderate (-32.94 to -47.44), strong (-47.44 to -61.95) and very strong (-61.95 to -76.46) zones. Seven composition variants in the very strong zone [G726R-G476S; R768W-V367F; Y252N-V483A; Y252N-V367F; G726R-V367F; N720D-V367F and N720D-F486L], and three in very weak [P263S-S383C; RBD-H378R; G726R-A348T] are significantly (p < 0.00001) varied for global energy score. Zonation of the five zones was established based on the scores to differentiate the effect of hACE2 and RBD variants on the binding affinity. Moreover, our findings support that the combination of hACE2 and RBD is key players for the risk of infection that should be done by further laboratory studies.Communicated by Ramaswamy H. Sarma.

Parallel profiling of antigenicity alteration and immune escape of SARS-CoV-2 Omicron and other variants

SARS-CoV-2 variants have evolved a variety of critical mutations, leading to antigenicity changes and immune escape. The recent emerging SARS-CoV-2 Omicron variant attracted global attention due to its significant resistance to current antibody therapies and vaccines. Here, we profiled the mutations of Omicron and other various circulating SARS-CoV-2 variants in parallel by computational interface analysis and in vitro experimental assays. We identified critical mutations that lead to antigenicity changes and diminished neutralization efficiency of a panel of 14 antibodies due to diverse molecular mechanisms influencing the antigen-antibody interaction. Our study identified that Omicron exhibited extraordinary potency in immune escape compared to the other variants of concern, and explores the application of computational interface analysis in SARS-CoV-2 mutation surveillance and demonstrates its potential for the early identification of concerning variants, providing preliminary guidance for neutralizing antibody therapy.