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Tracking SARS-CoV-2 Spike Protein Mutations in the United States (2020/01 - 2021/03) Using a Statistical Learning Strategy.

ABSTRACT: The emergence and establishment of SARS-CoV-2 variants of interest (VOI) and variants of concern (VOC) highlight the importance of genomic surveillance. We propose a statistical learning strategy (SLS) for identifying and spatiotemporally tracking potentially relevant Spike protein mutations. We analyzed 167,893 Spike protein sequences from US COVID-19 cases (excluding 21,391 sequences from VOI/VOC strains) deposited at GISAID from January 19, 2020 to March 15, 2021. Alignment against the reference Spike protein sequence led to the identification of viral residue variants (VRVs), i.e., residues harboring a substitution compared to the reference strain. Next, generalized additive models were applied to model VRV temporal dynamics, to identify VRVs with significant and substantial dynamics (false discovery rate q-value <0.01; maximum VRV proportion > 10% on at least one day). Unsupervised learning was then applied to hierarchically organize VRVs by spatiotemporal patterns and identify VRV-haplotypes. Finally, homology modelling was performed to gain insight into potential impact of VRVs on Spike protein structure. We identified 90 VRVs, 71 of which have not previously been observed in a VOI/VOC, and 35 of which have emerged recently and are durably present. Our analysis identifies 17 VRVs ∼91 days earlier than their first corresponding VOI/VOC publication. Unsupervised learning revealed eight VRV-haplotypes of 4 VRVs or more, suggesting two emerging strains (B1.1.222 and B.1.234). Structural modeling supported potential functional impact of the D1118H and L452R mutations. The SLS approach equally monitors all Spike residues over time, independently of existing phylogenic classifications, and is complementary to existing genomic surveillance methods.

SUBMITTER: Zhao LP

PROVIDER: S-EPMC8219100 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundHLA-E interaction with inhibitory receptor, NKG2A attenuates NK-mediated cytotoxicity. NKG2A overexpression by SARS-CoV-2 exhausts NK cells function, whereas virus-induced down-regulation of MHC-Ia reduces its derived-leader sequence peptide levels required for proper binding of HLA-E to NKG2A. This leads HLA-E to become more complex with viral antigens and delivers them to CD8+ T cells, which facilitates cytolysis of infected cells. Now, the fact that alleles of HLA-E have different levels of expression and affinity for MHC Ia-derived peptide raises the question of whether HLA-E polymorphisms affect susceptibility to COVID-19 or its severity.Methods104 COVID-19 convalescent plasma donors with/without history of hospitalization and 18 blood donors with asymptomatic COVID-19, all were positive for anti-SARS-CoV-2 IgG antibody as well as a group of healthy control including 68 blood donors with negative antibody were subjected to HLA-E genotyping. As a privilege, individuals hadn't been vaccinated against COVID-19 and therefore naturally exposed to the SARS-CoV-2.ResultsThe absence of HLA-E*01:03 allele significantly decreases the odds of susceptibility to SARS-CoV-2 infection [p = 0.044; OR (95 %CI) = 0.530 (0.286 - 0.983)], suggesting that HLA-E*01:01 + HLA-E*01:01 genotype favors more protection against SARS-CoV-2 infection. HLA-E*01:03 + HLA-E*01:03 genotype was also significantly associated with more severe COVID-19 [p = 0.020; 2.606 (1.163 - 5.844) CONCLUSION: Here, our observation about lower susceptibility of HLA-E*01:01 + HLA-E*01:01 genotype to COVID-19 could be clinical evidence in support of some previous studies suggesting that the lower affinity of HLA-E*01:01 to peptides derived from the leader sequence of MHC class Ia may instead shift its binding to virus-derived peptides, which then facilitates target recognition by restricted conventional CD8+ T cells and leads to efficient cytolysis. On the other hand, according to other studies, less reactivity of HLA-E*01:01 with NKG2A abrogates NK cells or T cells inhibition, which may also lead to a greater cytotoxicity against SARS-CoV-2 infected cells compared to HLA-E*01:03. Taken together given HLA-E polymorphisms, the data presented here may be useful in identifying more vulnerable individuals to COVID-19 for better care and management. Especially since along with other risk factors in patients, having HLA-E*01:03 + HLA-E*01:03 genotype may also be associated with the possibility of severe cases of the disease.

Project description:Stagnating COVID-19 vaccination rates and vaccine hesitancy remain a threat to public health. Improved strategies for real-time tracking and estimation of population-level behavior regarding vaccinations are needed. The aim of this study was to evaluate whether online search trends for COIVD-19 and influenza mirror vaccination rates. State-level weekly fraction of online searches for top vaccination-related search terms and CDC vaccination data were obtained from June 1, 2020, to May 31, 2021. Next, trends in online search and vaccination data for COVID-19 and influenza were analyzed for visual and quantitative correlation patterns using Spearman's rank correlation analysis. Online searches in the US for COVID-19 vaccinations increased 2.71-fold (95% CI: 1.98-3.45) in the 4 weeks after the FDA emergency authorization compared to the precedent 4 weeks. In March-April 2021, US online searches reached a plateau that was followed by a decline of 83.3% (95% CI: 31.2%-135.3%) until May 31, 2021. The timing of peaks in online searches varied across US states. Online searches were strongly correlated with vaccination rates (r=0.71, 95% CI: 0.45 - 0.87), preceding actual reported vaccination rates in 44 of 51 states. Online search trends preceded vaccination trends by a median of 3.0 weeks (95% CI: 2.0-4.0 weeks) across all states. For influenza vaccination searches, seasonal peaks in September-October between 2016-2020 were noted. Influenza search trends highly correlated with the timing of actual vaccinations for the 2019-2020 (r=0.82, 95% CI: 0.64 - 0.93) and 2020-2021 season (r=0.91, 95% CI: 0.78 - 0.97). Search trends and real-world vaccination rates are highly correlated. Temporal alignment and correlation levels were higher for influenza vaccinations; however, only online searches for COVID-19 vaccination preceded vaccination trends. These findings indicate that US online search data can potentially guide public health efforts, including policy changes and identifying geographical areas to expand vaccination campaigns.

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Tracking SARS-CoV-2 Spike Protein Mutations in the United States (2020/01 - 2021/03) Using a Statistical Learning Strategy.

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