Effect of environmental factors on SARS-CoV-2 infectivity in northern hemisphere countries: a 2-year data analysis.

OBJECTIVE: The COVID-19 pandemic that emerged in December 2019 brought human life to a standstill. With over 2-year since the pandemic originated from Wuhan, SARS-CoV-2 has caused more than 6 million deaths worldwide. With the emergence of mutant strains and COVID-19 surge waves, it becomes critically important to conduct epidemiological studies that allow us to understand the role of various environmental factors on SARS-CoV-2 infectivity. Our earlier study reported a strong negative correlation between temperature and COVID-19 incidence. This research is an extension of our previous study with an attempt to understand the global analysis of COVID-19 in northern hemisphere countries. STUDY DESIGN: This research aims at achieving a better understanding of the correlation of environmental factors such as temperature, sunlight, and humidity with new cases of COVID-19 in northern hemisphere from March 2020 to February 2022. METHODS: To understand the relationship between the different environmental variants and COVID-19, a statistical approach was employed using Pearson, Spearman and Kendall analysis. RESULTS: Month-wise univariate analysis indicated a strong negative correlation of temperature and sunlight with SARS-CoV-2 infectivity, whereas inconsistencies were observed in correlation analysis in the case of humidity in winter months. Moreover, a strong negative correlation between average temperature of winter months and COVID-19 cases exists as evidenced by Pearson, Spearman, and Kendall analyses. In addition, correlation pattern between monthly temperature and COVID-19 cases of a country mimics to that of sunlight of a country. CONCLUSION: This pilot study proposes that low temperatures and low sunlight might be additional risk factors for SARS-CoV-2 infectivity, mostly in northern hemisphere countries.

Combinatorial influence of environmental temperature, obesity and cholesterol on SARS-CoV-2 infectivity.

The continuing evolution of SARS-CoV-2 variants not only causes a long-term global health concerns but also encounters the vaccine/drug effectiveness. The degree of virus infectivity and its clinical outcomes often depend on various biological parameters (e.g., age, genetic factors, diabetes, obesity and other ailments) of an individual along with multiple environmental factors (e.g., air temperature, humidity, seasons). Thus, despite the extensive search for and use of several vaccine/drug candidates, the combinative influence of these various extrinsic and intrinsic risk factors involved in the SARS-CoV-2 virus infectivity has yet to be explored. Previous studies have reported that environment temperature is negatively associated with virus infectivity for SARS-CoV-2. This study elaborates on our previous findings, investigating the link between environmental temperature and other metabolic parameters, such as average total cholesterol and obesity, with the increase in COVID-19 cases. Statistical analysis conducted on a per country basis not only supports the existence of a significant negative correlation between environmental temperature and SARS-CoV-2 infections but also found a strong positive correlation between COVID-19 cases and these metabolic parameters. In addition, a multiphase growth curve model (GCM) was built to predict the contribution of these covariates in SARS-CoV-2 infectivity. These findings, for first time, support the idea that there might be a combinatorial impact of environmental temperature, average total cholesterol, and obesity in the inflation of the SARS-CoV-2 infectivity.

Structure prediction-based insights into the patatin family of Mycobacterium tuberculosis.

Despite its genome sequencing more than two decades ago, the majority of the genes of Mycobacterium tuberculosis remain functionally uncharacterized. Patatins are one such class of proteins that, despite undergoing an expansion in this pathogenic species compared to their non-pathogenic cousins, remain largely unstudied. Recent advances in protein structure prediction using machine learning tools such as AlphaFold2 have provided high-confidence predicted structures for all M. tuberculosis proteins. Here we present detailed analyses of the patatin family of M. tuberculosis using AlphaFold-predicted structures, providing insights into likely modes of regulation, membrane interaction and substrate binding. Regulatory domains within this family of proteins include cyclic nucleotide binding, lid-like domains and other helical domains. Using structural homologues, we identified the likely membrane localization mechanisms and substrate-binding sites. These analyses reveal diversity in their regulatory capacity, mechanisms of membrane binding and likely length of fatty acid substrates. Together, this analysis suggests unique roles for the eight predicted patatins of M. tuberculosis.