Molecular epidemiology analysis of early variants of SARS-CoV-2 reveals the potential impact of mutations P504L and Y541C (NSP13) in the clinical COVID-19 outcomes

SARS-CoV-2: analysis of the effects of mutations in non-structural proteins

A worldwide pandemic that started in China in late 2019 was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a single-stranded RNA virus belonging to the family Coronaviridae. Due to its structural variability and mutability, this virus continues to evolve and pose a major health threat around the world. Its characteristics, such as transmissibility, antigenicity, and resistance to drugs and vaccines, are continually altered through mutations. Examining mutational hotspots and their structural repercussions can thus aid in the development of more-effective vaccinations and treatment plans. In this study, we used full genome sequences of SARS-CoV-2 variants to predict structural changes in viral proteins. These sequences were obtained from the Global Initiative on Sharing Avian Influenza Data (GISAID), and a set of significant mutations were identified in each of the non-structural proteins (NSP1-16) and structural proteins, including the envelope, nucleocapsid, membrane, and spike proteins. The mutations were characterized as stabilizing or destabilizing based on their effect on protein dynamics and stability, and their impact on structure and function was evaluated. Among all of the proteins, NSP6 stands out as especially variable. The results of this study augment our understanding of how mutational events influence virus pathogenicity and evolution.

A novel mutation-proof, next-generation vaccine to fight against upcoming SARS-CoV-2 variants and subvariants, designed through AI enabled approaches and tools, along with the machine learning based immune simulation: A vaccine breakthrough

Emerging SARS-CoV-2 variants and subvariants are great concerns for their significant mutations, which are also responsible for vaccine escape. Therefore, the study was undertaken to develop a mutation-proof, next-generation vaccine to protect against all upcoming SARS-CoV-2 variants. We used advanced computational and bioinformatics approaches to develop a multi-epitopic vaccine, especially the AI model for mutation selection and machine learning (ML) strategies for immune simulation. AI-enabled and the top-ranked antigenic selection approaches were used to select nine mutations from 835 RBD mutations. We selected twelve common antigenic B cell and T cell epitopes (CTL and HTL) containing the nine RBD mutations and joined them with the adjuvants, PADRE sequence, and suitable linkers. The constructs' binding affinity was confirmed through docking with TLR4/MD2 complex and showed significant binding free energy (-96.67 kcal mol^-1) with positive binding affinity. Similarly, the calculated eigenvalue (2.428517e-05) from the NMA of the complex reveals proper molecular motion and superior residues' flexibility. Immune simulation shows that the candidate can induce a robust immune response. The designed mutation-proof, multi-epitopic vaccine could be a remarkable candidate for upcoming SARS-CoV-2 variants and subvariants. The study method might guide researchers in developing AI-ML and immunoinformatics-based vaccines for infectious disease.

A Comparative Study of the Plasma Chemokine Profile in COVID-19 Patients Infected with Different SARS-CoV-2 Variants

BACKGROUND

Infection caused by SARS-CoV-2 mostly affects the upper and lower respiratory tracts and causes symptoms ranging from the common cold to pneumonia with acute respiratory distress syndrome. Chemokines are deeply involved in the chemoattraction, proliferation, and activation of immune cells within inflammation. It is crucial to consider that mutations within the virion can potentially affect the clinical course of SARS-CoV-2 infection because disease severity and manifestation vary depending on the genetic variant. Our objective was to measure and assess the different concentrations of chemokines involved in COVID-19 caused by different variants of the virus.

METHODS

We used the blood plasma of patients infected with different variants of SARS-CoV-2, i.e., the ancestral Wuhan strain and the Alpha, Delta, and Omicron variants. We measured the concentrations of 11 chemokines in the samples: CCL2/MCP-1, CCL3/MIP-1α, CCL4/MIP-1β, CCL7/MCP-3, CCL11/Eotaxin, CCL22/MDC, CXCL1/GROα, CXCL8/IL-8, CXCL9/MIG, CXCL10/IP-10, and CX3CL1/Fractalkine.

RESULTS

We noted a statistically significant elevation in the concentrations of CCL2/MCP-1, CXCL8/IL-8, and CXCL1/IP-10 independently of the variant, and a drop in the CCL22/MDC concentrations.

CONCLUSIONS

The chemokine concentrations varied significantly depending on the viral variant, leading us to infer that mutations in viral proteins play a role in the cellular and molecular mechanisms of immune responses.

SARS-CoV-2 Variants and Clinical Outcomes: A Systematic Review

Background: From the start of the COVID-19 pandemic, new SARS-CoV-2 variants have emerged that potentially affect transmissibility, severity, and immune evasion in infected individuals. In the present systematic review, the impact of different SARS-CoV-2 variants on clinical outcomes is analyzed. Methods: A systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020. Two databases (PubMed and ScienceDirect) were searched for original articles published from 1 January 2020 to 23 November 2021. The articles that met the selection criteria were appraised according to the Newcastle–Ottawa Quality Assessment Scale. Results: Thirty-three articles were included, involving a total of 253,209 patients and 188,944 partial or complete SARS-CoV-2 sequences. The most reported SARS-CoV-2 variants showed changes in the spike protein, N protein, RdRp and NSP3. In 28 scenarios, SARS-CoV-2 variants were found to be associated with a mild to severe or even fatal clinical outcome, 15 articles reported such association to be statistically significant. Adjustments in eight of them were made for age, sex and other covariates. Conclusions: SARS-CoV-2 variants can potentially have an impact on clinical outcomes; future studies focused on this topic should consider several covariates that influence the clinical course of the disease.

Indonesia COVID-19 cases report using Linked Open Data

Coronavirus disease is a worldwide pandemic. The need for accurate data and information become an important thing in this pandemic situation. In Indonesia, the government provides an official website for displaying COVID-19 spread statistics. However, the data provided does not follow the 5-star open data. As a result, the data is not reusable and integrated easily into another dataset and application. In this paper, we proposed an RDF vocabulary for presenting COVID-19 data in Indonesia. In addition, two queries are presented as an example for using our vocabulary and dataset as part of Linked Open data movement.

Codon usage divergence in Delta variants (B.1.617.2) of SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spreads all over the world and brings great harm to humans in many countries. Many new SARS-CoV-2 variants appeared during its transmission. In the present study, the Delta variants (B.1.617.2) of SARS-CoV-2, which have appeared in many countries, were considered for analysis. In order to evaluate the evolutionary divergence of the Delta variants(B.1.617.2), the codon usage divergence in Delta variants (B.1.617.2) of SARS-CoV-2 was compared to that of the SARS-CoV-2 genomes emerged before June 2020. All Delta variants (B.1.617.2) and 350 early genomes of SARS-CoV-2 in the NCBI database were downloaded. Codon usage pattern including the basic composition, the GC ratio of the third position (GC3) and the first two positions (GC12) in codons, overall GC contents, the effective number of codons (ENC), the codon bias index (CBI), the relative synonymous codon usage (RSCU) values, etc., of all concerned important gene sequences were all calculated. Codon usage divergence of them was calculated via summing their standard deviations. The results suggested that base compositions in both Delta variants (B.1.617.2) of SARS-CoV-2 and the early SARS-CoV-2 genomes were similar to each other. However, the internal codon usage divergence for most genes in Delta variants (B.1.617.2) was significantly wider than that of SARS-CoV-2. The RSCU values were further used to explore the synonymous and non-synonymous mutations in the sequences of the Delta variants (B.1.617.2), and the results showed the synonymous mutations are more obvious than the non-synonymous in the concerned sequences. The related codon usage divergence analysis is helpful for further study on the adaptability and disease prognosis of the SARS-CoV-2 variants.

Structural Analysis on the Severe Acute Respiratory Syndrome Coronavirus 2 Non-structural Protein 13 Mutants Revealed Altered Bonding Network With TANK Binding Kinase 1 to Evade Host Immune System

Mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have made this virus more infectious. Previous studies have confirmed that non-structural protein 13 (NSP13) plays an important role in immune evasion by physically interacting with TANK binding kinase 1 (TBK1) to inhibit IFNβ production. Mutations have been reported in NSP13; hence, in the current study, biophysical and structural modeling methodologies were adapted to dissect the influence of major mutations in NSP13, i.e., P77L, Q88H, D260Y, E341D, and M429I, on its binding to the TBK1 and to escape the human immune system. The results revealed that these mutations significantly affected the binding of NSP13 and TBK1 by altering the hydrogen bonding network and dynamic structural features. The stability, flexibility, and compactness of these mutants displayed different dynamic features, which are the basis for immune evasion. Moreover, the binding was further validated using the MM/GBSA approach, revealing that these mutations have higher binding energies than the wild-type (WT) NSP13 protein. These findings thus justify the basis of stronger interactions and evasion for these NSP13 mutants. In conclusion, the current findings explored the key features of the NSP13 WT and its mutant complexes, which can be used to design structure-based inhibitors against the SARS-CoV-2 new variants to rescue the host immune system.

QUASISPECIES FEATURE IN SARS-CoV-2

Since the identification of the SARS-CoV-2, genus Beta- Coronavirus, in January 2020, the virus quickly spread in less than 3 months to all continents with a susceptible human population of about a 7.9billion, and still in active circulation. In the process, it has accumulated mutations leading to genetic diversity. Regular emergence of variants of concern/significance in different ecology shows genetic heterogeneity in the base population of SARS-CoV-2 that is continuously expanding with the passage of the virus in the vast susceptible human population. Natural selection of mutant occurs frequently in a positive sense (+) single-stranded (ss) RNA virus upon replication in the host. The Pressure of sub-optimal levels of virus-neutralizing antibodies and also innate immunity influence the process of genetic/ antigenic selection. The fittest of the mutants, that could be more than one, propagate and emerge as variants. The existence of different lineages, clades, and strains, as well as genetic heterogeneity of plaque purified virus population, justifies SARS-CoV-2 as ‘Quasispecies’ that refers to swarms of mutant sequences generated during replication of the viral genome, and all mutant sequences may not lead to virion. Viruses having a quasispecies nature may end up with progressive antigenic changes leading to antigenic plurality that is driven by ecology, and this phenomenon challenges vaccination-based control programs.

SARS-CoV-2 Infectivity and Severity of COVID-19 According to SARS-CoV-2 Variants: Current Evidence

BACKGROUND

We conducted this review to summarize the relation between viral mutation and infectivity of SARS-CoV-2 and also the severity of COVID-19 in vivo and in vitro.

METHOD

Articles were identified through a literature search until 31 May 2021, in PubMed, Web of Science and Google Scholar.

RESULTS

Sixty-three studies were included. To date, most studies showed that the viral mutations, especially the D614G variant, correlate with a higher infectivity than the wild-type virus. However, the evidence of the association between viral mutation and severity of the disease is scant. A SARS-CoV-2 variant with a 382-nucleotide deletion was associated with less severe infection in patients. The 11,083G > U mutation was significantly associated with asymptomatic patients. By contrast, ORF1ab 4715L and S protein 614G variants were significantly more frequent in patients from countries where high fatality rates were also reported. The current evidence showed that variants of concern have led to increased infectivity and deteriorating epidemiological situations. However, the relation between this variant and severity of COVID-19 infection was contradictory.

CONCLUSION

The COVID-19 pandemic continues to spread worldwide. It is necessary to anticipate large clinical cohorts to evaluate the virulence and transmissibility of SARS-CoV-2 mutants.