Genomic surveillance reveals circulation of multiple variants and lineages of SARS-CoV-2 during COVID-19 pandemic in Indian city of Bengaluru (preprint)

Genomic surveillance in response to coronavirus disease (COVID-19) pandemic is crucial for tracking spread, identify variants of concern (VoCs) and understand the evolution of its etiological agent, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). India has experienced three waves of COVID-19 cases, which includes a deadly wave of COVID-19 that was driven by the Delta lineages (second/Delta wave) followed by another wave driven by the Omicron lineages (third/Omicron wave). These waves were particularly dramatic in the metropolitan cities due to high population density. We evaluated the prevalence, and mutational spectrum of SARS-CoV-2 variants/lineages in one such megapolis, Bengaluru city, across these three waves between October 2020 and June 2022. 15,134 SARS-CoV-2 samples were subjected to whole genome sequencing (WGS). Phylogenetic analysis revealed, SARS-CoV-2 variants in Bengaluru city belonged to 18 clades and 196 distinct lineages. As expected, the Delta lineages were the most dominant lineages during the second wave of COVID-19. The Omicron lineage BA.2 and its sublineages accounted for most of the COVID-19 cases in the third wave. Most number of amino acid changes were observed in spike protein. Among the 18 clades, majority of the mutations and least similarity at nucleotide sequence level with the reference genome were observed in Omicron clades.

SARS-CoV-2 infection dynamics and genomic surveillance reveals early variant transmission in urban wastewater (preprint)

Environmental surveillance (ES) of a pathogen is crucial for understanding the community load of disease. As an early warning system, ES for SARS-CoV-2 has complemented routine diagnostic surveillance by capturing near real-time virus circulation at a population level. In this longitudinal study in 28 sewershed sites in Bangalore city, we quantified SARS-CoV-2 RNA to track infection dynamics and provide evidence of change in the relative abundance of emerging variants. We describe an early warning system using the exponentially weighted moving average control chart and demonstrate how SARS-CoV-2 RNA concentrations in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 8-14 days earlier in wastewater than in clinical data. This was further corroborated by showing that the estimated number of infections is strongly correlated with SARS-CoV-2 RNA copies detected in the wastewater. Using a deconvolution matrix, we detected emerging variants of concern up to two months earlier in wastewater samples. In addition, we found a huge diversity in variants detected in wastewater compared to clinical samples. Our study highlights that quantifying viral titres, correlating it with a known number of cases in the area, and combined with genomic surveillance helps in tracking VOCs over time and space, enabling timely and making informed policy decisions.

Comparative transcriptome analyses reveal genes associated with SARS-CoV-2 infection of human lung epithelial cells.

During 2020, understanding the molecular mechanism of SARS-CoV-2 infection (the cause of COVID-19) became a scientific priority due to the devastating effects of the COVID-19. Many researchers have studied the effect of this viral infection on lung epithelial transcriptomes and deposited data in public repositories. Comprehensive analysis of such data could pave the way for development of efficient vaccines and effective drugs. In the current study, we obtained high-throughput gene expression data associated with human lung epithelial cells infected with respiratory viruses such as SARS-CoV-2, SARS, H1N1, avian influenza, rhinovirus and Dhori, then performed comparative transcriptome analysis to identify SARS-CoV-2 exclusive genes. The analysis yielded seven SARS-CoV-2 specific genes including CSF2 [GM-CSF] (colony-stimulating factor 2) and calcium-binding proteins (such as S100A8 and S100A9), which are known to be involved in respiratory diseases. The analyses showed that genes involved in inflammation are commonly altered by infection of SARS-CoV-2 and influenza viruses. Furthermore, results of protein-protein interaction analyses were consistent with a functional role of CSF2 and S100A9 in COVID-19 disease. In conclusion, our analysis revealed cellular genes associated with SARS-CoV-2 infection of the human lung epithelium; these are potential therapeutic targets.

Comparative transcriptome analyses reveal genes associated with SARS-CoV-2 infection of human lung epithelial cells (preprint)

Understanding the molecular mechanism of SARS-CoV-2 infection (the cause of COVID-19) is a scientific priority for 2020. Various research groups are working toward development of vaccines and drugs, and many have published genomic and transcriptomic data related to this viral infection. The power inherent in publicly available data can be demonstrated via comparative transcriptome analyses. In the current study, we collected high-throughput gene expression data related to human lung epithelial cells infected with SARS-CoV-2 or other respiratory viruses (SARS, H1N1, rhinovirus, avian influenza, and Dhori) and compared the effect of these viruses on the human transcriptome. The analyses identified fifteen genes specifically expressed in cells transfected with SARS-CoV-2; these included CSF2 (colony-stimulating factor 2) and S100A8 and S100A9 (calcium-binding proteins), all of which are involved in lung/respiratory disorders. The analyses showed that genes involved in the Type1 interferon signaling pathway and the apoptosis process are commonly altered by infection of SARS-CoV-2 and influenza viruses. Furthermore, results of protein-protein interaction analyses were consistent with a functional role of CSF2 in COVID-19 disease. In conclusion, our analysis has revealed cellular genes associated with SARS-CoV-2 infection of the human lung epithelium; these are potential therapeutic targets.