SARS-CoV-2 and COVID-19 Research

This paper develops a machine learning model using LightGBM to predict high-risk SARS-CoV-2 variants based on haplotype network features, achieving high accuracy in identifying variants of concern and interest.

This study expresses ACE2 from seven bat species in DF1 cells to assess their susceptibility to SARS-CoV-2 and its variants, showing varying replication levels and higher titers with human ACE2.

This study shows SARS-CoV-2 infection alters zddhc20 transcription, producing a longer ZDHHC20 protein that enhances Spike acylation and viral infectivity.

This study reports SARS-CoV-2 infection and exposure in white-tailed deer across the conterminous US, showing decreasing prevalence and increasing seroprevalence over time.

The paper analyzes SARS-CoV-2 recombinant lineages XBC and XBC.1 in the Philippines, identifying co-infection of Omicron and Delta as a potential origin and showing early cases from the country.

The paper uses machine learning to identify mutational signatures in SARS-CoV-2 genomes and links them to host antiviral molecules, showing these drive most of the virus's evolutionary changes.

This study shows human SP-A binds SARS-CoV-2 S protein and hACE2, inhibiting viral infectivity and reducing viral load in lung cells, and suggests SP-A levels in saliva correlate with COVID-19 severity.

The paper develops a computational framework to detect emerging viral variants with altered phenotypes by analyzing epistatic networks, achieving early detection of known SARS-CoV-2 strains.

The paper reports genomic surveillance of SARS-CoV-2 in Bengaluru, India, across three pandemic waves, identifying 18 clades and 196 lineages, with Delta dominant in the second wave and Omicron BA.2 dominant in the third.

The paper investigates interactions between SARS-CoV-2 variants, MERS-CoV, and DPP4 using molecular docking and simulation to identify mutations that enhance DPP4 binding and disease severity.

The paper identifies and analyzes hosts of human coronaviruses, finding all verified natural and laboratory hosts are therian mammals, and provides a standardized ontology for host knowledge.

The paper analyzes the diversity of coronaviral main protease active sites to identify potential broad-spectrum inhibitors for future pandemics.

This study develops a bivalent chimeric virus-like particle vaccine displaying CHIKV and SARS-CoV-2 RBD antigens, showing potent immunogenicity in mice.

EVPMM is a deep learning framework that predicts vital mutation sites and fitness of viral proteins by integrating co-evolution profiles, outperforming existing methods.

The paper characterizes antigen-specific CD8+ T cells post-SARS-CoV-2 vaccination and infection using multimodal sequencing, identifying unique biomarkers and clinical predictors.

The paper uses Seq2Seq and Transformer models to predict SARS-CoV-2 spike protein mutations and finds some generated sequences match known variants from 2021 and 2022.

This study performs in silico analysis of SARS-CoV-2 spike proteins from different variants to identify structural and functional variations.

This study predicts that the SARS-CoV-2 XBB.1.5 variant may spread more widely and faster than other variants based on molecular docking and evolutionary analysis.

SpikeGPT2, a deep generative model of SARS-CoV-2 spike protein sequences, predicts future mutations with high accuracy and identifies variants that may increase transmissibility and immune evasion.

This study shows that SARS-CoV-2 evolves in the absence of immune pressure, leading to antibody resistance, interferon suppression, and lineage-specific phenotypic changes.

This study shows that human iPS cell-derived sensory neurons can be infected by SARS-CoV-2 WA1/2020 and its variants delta and omicron, but cannot produce new viruses.

The paper evaluates molecular diversity of Brazilian B.1.1 SARS-CoV-2 sequences, finding low polymorphism and no genetic structuring, suggesting vaccine effectiveness.

The paper identifies genomic signatures and lineage markers of SARS-CoV-2 variants associated with mortality during the second and third waves of COVID-19 in Western Rajasthan, India.

This study analyzes SARS-CoV-2 genomes to identify two major pandemic onsets driven by different haplotypes, DL in China and GL globally.

This study adjusts for sampling bias in SARS-CoV-2 Ct values and finds that viral variant and age affect within-host viral burdens, with variant effects varying by vaccination status.

ASOs (LNA gapmers) targeting the SARS-CoV-2 RNA genome effectively reduce viral RNA load by up to 96% in vitro.

The paper identifies environmental and genetic factors influencing population differences in SARS-CoV-2 immune responses using single-cell RNA-sequencing data.

The paper identifies polymorphic regions in BA.2.12.1, BA.4, and BA.5 Spike proteins that may contribute to immunological evasion by BQ.1.1 subvariant.

This paper analyzes global SARS-CoV-2 variant transitions from 2020-2022, finding associations with vaccination rates, co-circulating variants, and natural immunity.

This study identifies DUB inhibitors that target SARS-CoV-2 PLpro, inhibiting its proteolytic activity and restoring immune responses in a mouse model.