Background: little is known about the forecasting of new variants of SARS-COV-2 in North America and the interaction of variants with vaccine-derived neutralizing antibodies. Methods: the affinity scores of the spike receptor-binding domain (S-RBD) of B.1.1.7, B. 1.351, B.1.617, and P.1 variants in interaction with the neutralizing antibody (CV30 isolated from a patient), and human angiotensin-converting enzyme 2 (hACE2) receptor were predicted using the template-based computational modeling. From the Nextstrain global database, we identified prevalent mutations of S-RBD of SARS-CoV-2 from December 2019 to April 2021. Pre- and post-vaccination time series forecasting models were developed based on the prediction of neutralizing antibody affinity scores for S-RBD of the variants. Results: the proportion of the B.1.1.7 variant in North America is growing rapidly, but the rate will reduce due to high affinity (~90%) to the neutralizing antibody once herd immunity is reached. Currently, the rates of isolation of B. 1.351, B.1.617, and P.1 variants are slowly increasing in North America. Herd immunity is able to relatively control these variants due to their low affinity (~70%) to the neutralizing antibody. The S-RBD of B.1.617 has a 110% increased affinity score to the human angiotensin-converting enzyme 2 (hACE2) in comparison to the wild-type structure, making it highly infectious. Conclusion: The newly emerged B.1.351, B.1.617, and P.1 variants escape from vaccine-induced neutralizing immunity and continue circulating in North America in post- herd immunity era. Our study strongly suggests that a third dose of vaccine is urgently needed to cover novel variants with affinity scores (equal or less than 70%) to eliminate developing viral mutations and reduce transmission rates.
Background Little is known about the impact of the ecosystem disruption and its contribution on the non-tuberculosis mycobacteria (NTM) diseases (cases) rate in Florida (FL), a state with a high prevalence of NTM in the United States. We aimed to evaluate the epidemiological distribution of NTM in FL and identify its association with extreme weather events. Methods We used OneFlorida Clinical Research Consortium dataset and extracted data on NTM cases using ICD codes 9- CM 031.0 and ICD-10 A31 during 2012–2018. The number of hurricanes during the study period which affected FL were extracted data from the National Hurricane Center (NHC) and the National Oceanic and Atmospheric Administration (NOAA). Results Prevalence of NTM gradually increased during the study period. The rate was 2012: 14.3/100,000, 2015; 20.1/100,000 and 2018; 22.6/100,00 except in 2014 where there was an 8% decrease. The incidences were 2012; 6.5/100,00, 2015; 4.9/100,000 and in 2015; 5.4/100,000. Geographical analysis demonstrated a gradual expansion of the NTM cases in Alachua, and Marion Counties throughout the study period. Notably, the 2018 heat map showed higher prevalence of NTM in the northwestern, panhandle region of FL which had been absent in the heat maps for years 2012–2018. High number of the hurricanes was associated with the higher number of the new cases of NTM infection for years 2012, 2016–2018, while the lower number of the hurricanes was associated with the lower number of the new cases of NTM infection for years 2014–2015. Conclusion The current study found the prevalence rates of NTM disease in FL rose from 2012 to 2018. A higher prevalence was seen following the hurricanes.
We present a structure-based model of phosphorylation-dependent binding and sequestration of SARS-CoV-2 nucleocapsid protein and the impact of two consecutive amino acid changes R203K and G204R. Additionally, we studied how mutant strains affect HLA-specific antigen presentation and correlated these findings with HLA allelic population frequencies. We discovered RG>KR mutated SARS-CoV-2 expands the ability for differential expression of the N protein epitope on Major Histocompatibility Complexes (MHC) of varying Human Leukocyte Antigen (HLA) origin. The N protein LKR region K203, R204 of wild type (SARS-CoVs) and (SARS-CoV-2) observed HLA-A*30:01 and HLA-A*30:21, but mutant SARS-CoV-2 observed HLA-A*31:01 and HLA-A*68:01. Expression of HLA-A genotypes associated with the mutant strain occurred more frequently in all populations studied.ImportanceThe novel coronavirus known as SARS-CoV-2 causes a disease renowned as 2019-nCoV (or COVID-19). HLA allele frequencies worldwide could positively correlate with the severity of coronavirus cases and a high number of deaths.
Introduction: The outbreak of pneumonia known as SARS-COV-2 and newly-emerging South African (B.1.351), the United Kingdom (B.1.1.7) and Brazil (P.1) variants have led to a more infectious virus and potentially more substantial loss of neutralizing activity by natural infection or vaccine-elicited antibodies. Methods: We identified prevalent mutations using the spike receptor-binding domain (S-RBD) of SARS-CoV-2 deposited in the Nextstrain global database and comparing them to the Wuhan-Hu-1/2019 genomic sequence as a reference. Then we calculated the percentages of mutant genomes from the total regional subsample isolates from December 2019 to the end of January 2021. We developed two separate time series forecasting models for the SARS-CoV-2 B.1.1.7 variant. The computational model used the structure of the S-RBD to examine its interactions with the neutralizing antibody, named CV30 (isolated from a patient), and human angiotensin-converting enzyme 2 (hACE-2), based on a hybrid algorithm of template-based modeling to predict the affinity of S protein to the neutralizing antibodies and hACE-2 receptor. Results: The proportion of the B.1.1.7 strain in North America is growing fast. From these computations, it seems that the S-RBD and hACE-2 proteins are less favorable for the South African strain (K417N, E484K, and N501Y) as compared to the wild type structure and more favorable for B.1.1.7 and P.1 variants. In the present of crystallized CV30 neutralizing antibodies, docking scores suggest antibodies can be partially neutralize the B.1.1.7 variant, and, less efficiently, the B.1.351 and P.1 variants. Conclusion: The rapid evolution of SARS-CoV-2 has the potential to allow the newly-emerged B.1.351, and P.1 variants to escape from natural or vaccine-induced neutralizing immunity and viral spreading.
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