Emergence of a unique SARS-CoV-2 Delta sub-cluster harboring a constellation of co-appearing non-Spike mutations.

Accumulation of diverse mutations across the structural and nonstructural genes is leading to rapid evolution of SARS-CoV-2, altering its pathogenicity. We performed whole genome sequencing of 239 SARS-CoV-2 RNA samples collected from both adult and pediatric patients across eastern India (West Bengal), during the second pandemic wave in India (April-May 2021). In addition to several common spike mutations within the Delta variant, a unique constellation of eight co-appearing non-Spike mutations was identified, which revealed a high degree of positive mutual correlation. Our results also demonstrated the dynamics of SARS-CoV-2 variants among unvaccinated pediatric patients. 41.4% of our studied Delta strains harbored this signature set of eight co-appearing non-Spike mutations and phylogenetically out-clustered other Delta sub-lineages like 21J, 21A, or 21I. This is the first report from eastern India that portrayed a landscape of co-appearing mutations in the non-Spike proteins, which might have led to the evolution of a distinct Delta subcluster. Accumulation of such mutations in SARS-CoV-2 may lead to the emergence of "vaccine-evading variants." Hence, monitoring of such non-Spike mutations will be significant in the formulation of any future vaccines against those SARS-CoV-2 variants that might evade the current vaccine-induced immunity, among both the pediatric and adult populations.

Contrasting Distribution of SARS-CoV-2 Lineages across Multiple Rounds of Pandemic Waves in West Bengal, the Gateway of East and North-East States of India.

The evolution of viral variants and their impact on viral transmission have been an area of considerable importance in this pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We analyzed the viral variants in different phases of the pandemic in West Bengal, a state in India that is important geographically, and compared the variants with other states like Delhi, Maharashtra, and Karnataka, located in other regions of the country. We have identified 57 pango-lineages in 3,198 SARS-CoV-2 genomes, alteration in their distribution, as well as contrasting profiles of amino acid mutational dynamics across different waves in different states. The evolving characteristics of Delta (B.1.617.2) sublineages and alterations in hydrophobicity profiles of the viral proteins caused by these mutations were also studied. Additionally, implications of predictive host miRNA binding/unbinding to emerging spike or nucleocapsid mutations were highlighted. Our results throw considerable light on interesting aspects of the viral genomic variation and provide valuable information for improved understanding of wave-defining mutations in unfolding the pandemic. IMPORTANCE Multiple waves of infection were observed in many states in India during the coronavirus disease 2019 (COVID19) pandemic. Fine-scale evolution of major SARS-CoV-2 lineages and sublineages during four wave-window categories: Pre-Wave 1, Wave 1, Pre-Wave 2, and Wave 2 in four major states of India: Delhi (North), Maharashtra (West), Karnataka (South), and West Bengal (East) was studied using large-scale virus genome sequencing data. Our comprehensive analysis reveals contrasting molecular profiles of the wave-defining mutations and their implications in host miRNA binding/unbinding of the lineages in the major states of India.

Computational prediction of the molecular mechanism of statin group of drugs against SARS-CoV-2 pathogenesis.

Recently published clinical data from COVID-19 patients indicated that statin therapy is associated with a better clinical outcome and a significant reduction in the risk of mortality. In this study by computational analysis, we have aimed to predict the possible mechanism of the statin group of drugs by which they can inhibit SARS-CoV-2 pathogenesis. Blind docking of the critical structural and functional proteins of SARS-CoV-2 like RNA-dependent RNA polymerase, M-protease of 3-CL-Pro, Helicase, and the Spike proteins ( wild type and mutants from different VOCs) were performed using the Schrodinger docking tool. We observed that fluvastatin and pitavastatin showed fair, binding affinities to RNA polymerase and 3-CL-Pro, whereas fluvastatin showed the strongest binding affinity to the helicase. Fluvastatin also showed the highest affinity for the SpikeDelta and a fair docking score for other spike variants. Additionally, molecular dynamics simulation confirmed the formation of a stable drug-protein complex between Fluvastatin and target proteins. Thus our study shows that of all the statins, fluvastatin can bind to multiple target proteins of SARS-CoV-2, including the spike-mutant proteins. This property might contribute to the potent antiviral efficacy of this drug.

SARS-CoV-2 mutation 614G creates an elastase cleavage site enhancing its spread in high AAT-deficient regions.

SARS-CoV-2 was first reported from China. Within three months, it evolved to 10 additional subtypes. Two evolved subtypes (A2 and A2a) carry a non-synonymous Spike protein mutation (D614G). We conducted phylodynamic analysis of over 70,000 SARS-CoV-2 coronaviruses worldwide, sequenced until July2020, and found that the mutant subtype (614G) outcompeted the pre-existing type (614D), significantly faster in Europe and North-America than in East Asia. Bioinformatically and computationally, we identified a novel neutrophil elastase (ELANE) cleavage site introduced in the G-mutant, near the S1-S2 junction of the Spike protein. We hypothesised that elevation of neutrophil elastase level at the site of infection will enhance the activation of Spike protein thus facilitating host cell entry for 614G, but not the 614D, subtype. The level of neutrophil elastase in the lung is modulated by its inhibitor α1-antitrypsin (AAT). AAT prevents lung tissue damage by elastase. However, many individuals exhibit genotype-dependent deficiency of AAT. AAT deficiency eases host-cell entry of the 614G virus, by retarding inhibition of neutrophil elastase and consequently enhancing activation of the Spike protein. AAT deficiency is highly prevalent in European and North-American populations, but much less so in East Asia. Therefore, the 614G subtype is able to infect and spread more easily in populations of the former regions than in the latter region. Our analyses provide a molecular biological and evolutionary model for the higher observed virulence of the 614G subtype, in terms of causing higher morbidity in the host (higher infectivity and higher viral load), than the non-mutant 614D subtype.

Analysis of RNA sequences of 3636 SARS-CoV-2 collected from 55 countries reveals selective sweep of one virus type.

BACKGROUND & OBJECTIVES: SARS-CoV-2 (Severe acute respiratory syndrome coronavirus-2) is evolving with the progression of the pandemic. This study was aimed to investigate the diversity and evolution of the coronavirus SARS-CoV-2 with progression of the pandemic over time and to identify similarities and differences of viral diversity and evolution across geographical regions (countries). METHODS: Publicly available data on type definitions based on whole-genome sequences of the SARS-CoV-2 sampled during December and March 2020 from 3636 infected patients spread over 55 countries were collected. Phylodynamic analyses were performed and the temporal and spatial evolution of the virus was examined. RESULTS: It was found that (i) temporal variation in frequencies of types of the coronavirus was significant; ancestral viruses of type O were replaced by evolved viruses belonging to type A2a; (ii) spatial variation was not significant; with the spread of SARS-CoV-2, the dominant virus was the A2a type virus in every geographical region; (iii) within a geographical region, there was significant micro-level variation in the frequencies of the different viral types, and (iv) the evolved coronavirus of type A2a swept rapidly across all continents. INTERPRETATION & CONCLUSIONS: SARS-CoV-2 belonging to the A2a type possesses a non-synomymous variant (D614G) that possibly eases the entry of the virus into the lung cells of the host. This may be the reason why the A2a type has an advantage to infect and survive and as a result has rapidly swept all geographical regions. Therefore, large-scale sequencing of coronavirus genomes and, as required, of host genomes should be undertaken in India to identify regional and ethnic variation in viral composition and its interaction with host genomes. Further, careful collection of clinical and immunological data of the host can provide deep learning in relation to infection and transmission of the types of coronavirus genomes.