Differential mutation profile of SARS-CoV-2 proteins across deceased and asymptomatic patients

Characteristics of SARS-CoV-2 Omicron BA.5 variants in Shanghai after ending the zero-COVID policy in December 2022: a clinical and genomic analysis

Introduction

An unprecedented surge of Omicron infections appeared nationwide in China in December 2022 after the adjustment of the COVID-19 response policy. Here, we report the clinical and genomic characteristics of SARS-CoV-2 infections among children in Shanghai during this outbreak.

Methods

A total of 64 children with symptomatic COVID-19 were enrolled. SARS-CoV-2 whole genome sequences were obtained using next-generation sequencing (NGS) technology. Patient demographics and clinical characteristics were compared between variants. Phylogenetic tree, mutation spectrum, and the impact of unique mutations on SARS-CoV-2 proteins were analysed in silico.

Results

The genomic monitoring revealed that the emerging BA.5.2.48 and BF.7.14 were the dominant variants. The BA.5.2.48 infections were more frequently observed to experience vomiting/diarrhea and less frequently present cough compared to the BF.7.14 infections among patients without comorbidities in the study. The high-frequency unique non-synonymous mutations were present in BA.5.2.48 (N:Q241K) and BF.7.14 (nsp2:V94L, nsp12:L247F, S:C1243F, ORF7a:H47Y) with respect to their parental lineages. Of these mutations, S:C1243F, nsp12:L247F, and ORF7a:H47Y protein were predicted to have a deleterious effect on the protein function. Besides, nsp2:V94L and nsp12:L247F were predicted to destabilize the proteins.

Discussion

Further in vitro to in vivo studies are needed to verify the role of these specific mutations in viral fitness. In addition, continuous genomic monitoring and clinical manifestation assessments of the emerging variants will still be crucial for the effective responses to the ongoing COVID-19 pandemic.

Evaluating the transmission feasibility of SARS-CoV-2 Omicron (B.1.1.529) variant to 143 mammalian hosts: insights from S protein RBD and host ACE2 interaction studies

In comparison to previously known severe respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, the newly emerged Omicron (B.1.1.529) variant shows higher infectivity in humans. Exceptionally high infectivity of this variant raises concern of its possible transmission via other intermediate hosts. The SARS-CoV-2 infectivity is established via the association of spike (S) protein receptor binding domain (RBD) with host angiotensin I converting enzyme 2 (hACE2) receptor. In the course of this study, we investigated the interaction between Omicron S protein RBD with the ACE2 receptor of 143 mammalian hosts including human by protein-protein interaction analysis. The goal of this study was to forecast the likelihood that the virus may infect other mammalian species that coexist with or are close to humans in the household, rural, agricultural, or zoological environments. The Omicron RBD was found to interact with higher binding affinity with the ACE2 receptor of 122 mammalian hosts via different amino acid residues from the human ACE2 (hACE2). The rat (Rattus rattus) ACE2 was found to show the strongest interaction with Omicron RBD with a binding affinity of -1393.6 kcal/mol. These distinct strong binding affinity of RBD of Omicron with host ACE2 indicates a greater potential of new host transmissibility and infection via intermediate hosts. Though expected but the phylogenetic position of the mammalian species may not dictate the Omicron RBD binding to the host ACE2 receptor suggesting an involvement of multiple factors in guiding host divergence of the variant.

Interpretable and Predictive Deep Neural Network Modeling of the SARS-CoV-2 Spike Protein Sequence to Predict COVID-19 Disease Severity

Through the COVID-19 pandemic, SARS-CoV-2 has gained and lost multiple mutations in novel or unexpected combinations. Predicting how complex mutations affect COVID-19 disease severity is critical in planning public health responses as the virus continues to evolve. This paper presents a novel computational framework to complement conventional lineage classification and applies it to predict the severe disease potential of viral genetic variation. The transformer-based neural network model architecture has additional layers that provide sample embeddings and sequence-wide attention for interpretation and visualization. First, training a model to predict SARS-CoV-2 taxonomy validates the architecture's interpretability. Second, an interpretable predictive model of disease severity is trained on spike protein sequence and patient metadata from GISAID. Confounding effects of changing patient demographics, increasing vaccination rates, and improving treatment over time are addressed by including demographics and case date as independent input to the neural network model. The resulting model can be interpreted to identify potentially significant virus mutations and proves to be a robust predctive tool. Although trained on sequence data obtained entirely before the availability of empirical data for Omicron, the model can predict the Omicron's reduced risk of severe disease, in accord with epidemiological and experimental data.

Predicting COVID-19 disease severity from SARS-CoV-2 spike protein sequence by mixed effects machine learning

Epidemiological studies show that COVID-19 variants-of-concern, like Delta and Omicron, pose different risks for severe disease, but they typically lack sequence-level information for the virus. Studies which do obtain viral genome sequences are generally limited in time, location, and population scope. Retrospective meta-analyses require time-consuming data extraction from heterogeneous formats and are limited to publicly available reports. Fortuitously, a subset of GISAID, the global SARS-CoV-2 sequence repository, includes "patient status" metadata that can indicate whether a sequence record is associated with mild or severe disease. While GISAID lacks data on comorbidities relevant to severity, such as obesity and chronic disease, it does include metadata for age and sex to use as additional attributes in modeling. With these caveats, previous efforts have demonstrated that genotype-patient status models can be fit to GISAID data, particularly when country-of-origin is used as an additional feature. But are these models robust and biologically meaningful? This paper shows that, in fact, temporal and geographic biases in sequences submitted to GISAID, as well as the evolving pandemic response, particularly reduction in severe disease due to vaccination, create complex issues for model development and interpretation. This paper poses a potential solution: efficient mixed effects machine learning using GPBoost, treating country as a random effect group. Training and validation using temporally split GISAID data and emerging Omicron variants demonstrates that GPBoost models are more predictive of the impact of spike protein mutations on patient outcomes than fixed effect XGBoost, LightGBM, random forests, and elastic net logistic regression models.

Phylogenetic analysis and comparative genomics of SARS-CoV-2 from survivor and non-survivor COVID-19 patients in Cordoba, Argentina

Background

The SARS-CoV-2 virus is responsible for the COVID-19 pandemic. To better understand the evolution of SARS-CoV-2 early in the pandemic in the Province of Cordoba, Argentina, we performed a comparative genomic analysis of SARS-CoV-2 strains detected in survivors and non-survivors of COVID-19. We also carried out an epidemiological study to find a possible association between the symptoms and comorbidities of these patients with their clinical outcomes.

Results

A representative sampling was performed in different cities in the Province of Cordoba. Ten and nine complete SARS-CoV-2 genomes were obtained by next-generation sequencing of nasopharyngeal specimens from non-survivors and survivors, respectively. Phylogenetic and phylodynamic analyses revealed multiple introductions of the most common lineages in South America, including B.1, B.1.1.1, B.1.499, and N.3. Fifty-six mutations were identified, with 14% of those in common between the non-survivor and survivor groups. Specific SARS-CoV-2 mutations for survivors constituted 25% whereas for non-survivors they were 41% of the repertoire, indicating partial selectivity. The non-survivors’ variants showed higher diversity in 9 genes, with a majority in Nsp3, while the survivors’ variants were detected in 5 genes, with a higher incidence in the Spike protein. At least one comorbidity was present in 60% of non-survivor patients and 33% of survivors. Age 75–85 years (p = 0.018) and hospitalization (p = 0.019) were associated with non-survivor patients. Related to the most common symptoms, the prevalence of fever was similar in both groups, while dyspnea was more frequent among non-survivors and cough among survivors.

Conclusions

This study describes the association of clinical characteristics with the clinical outcomes of survivors and non-survivors of COVID-19 patients, and the specific mutations found in the genome sequences of SARS-CoV-2 in each patient group. Future research on the functional characterization of novel mutations should be performed to understand the role of these variations in SARS-CoV-2 pathogenesis and COVID-19 disease outcomes. These results add new genomic data to better understand the evolution of the SARS-CoV-2 variants that spread in Argentina during the first wave of the COVID-19 pandemic.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08756-6.

Safety and Immunogenicity of COVID-19 BBIBP-CorV Vaccine in Children 3–12 Years Old

Background and Objectives: In the current COVID-19 pandemic, children below the age of 12 could manifest COVID-19 symptoms and serve as a reservoir for the virus in the community. The present study was conducted to evaluate the reactogenicity, and immunogenicity of BBIBP-CorV, prior to involving this age group in the vaccination program in the kingdom of Bahrain. Subjects and Methods: The study included 582 children from 3 to 12 years old of Bahraini and non-Bahraini nationality, all of which contributed to the reactogenicity study. Of those, 401 contributed to the immunogenicity study. All children received 2 doses of BBIBP-CorV inactivated virus 3 weeks apart. To assess reactogenicity, children were followed up for 5 weeks to evaluate any vaccine-related adverse events (AE). To assess immunogenicity, blood was collected on day 0 and day 35 to assess antibody titer against S, N, and neutralizing antibody. Results: Of the 582 participants, (45.4%) were female, (54.61%) were male, with 49% in 9–12 age group. Of the 401 children contributing to the immunogenicity study, 274 (68.3%) had no prior exposure to COVID-19. The overall incidence of AE was 27.7%. No significant difference was found among different age groups. The most frequent AE was local (at the injection site) and occurred in 16% of children, followed by fever in 9.3%. No serious adverse events were reported. The Seroconversion rate was 100% among children with no prior exposure to COVID-19. Children with previous COVID-19 exposure had higher averages of anti-S (2379 U/mL compared to 409.1), anti-N (177.6 U/mL compared to 30.9) and neutralizing antibody (93.7 U/mL compared to 77.1) than children with no prior exposure at day 35. Conclusions: Two doses of COVID-19 BBIBP-CorV on the subjects aged between 3 to 12 has good safety and tolerance and can induce an effective immune response and neutralizing antibody titer.

Analyzing the interaction of human ACE2 and RBD of spike protein of SARS-CoV-2 in perspective of Omicron variant

The newly identified Omicron (B.1.1.529) variant of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has steered concerns across the world due to the possession of a large number of mutations leading to high infectivity and vaccine escape potential. The Omicron variant houses 32 mutations in spike (S) protein alone. The viral infectivity is determined mainly by the ability of S protein Receptor Binding Domain (RBD) to bind to the human Angiotensin I Converting Enzyme 2 (hACE2) receptor. In this paper, the interaction of the RBDs of SARS-CoV-2 variants with hACE2 was analyzed by using protein-protein docking and compared with the novel Omicron variant. Our findings reveal that the Omicron RBD interacts strongly with hACE2 receptor via unique amino acid residues as compared to the Wuhan and many other variants. However, the interacting residues of RBD are found to be the same in Lamda (C.37) variant. This unique binding of Omicron RBD with hACE2 suggests an increased potential of infectivity and vaccine evasion potential of the new variant. The evolutionary drive of the SARS-CoV-2 may not be exclusively driven by RBD variants but surely provides for the platform for emergence of new variants.

Effect of Aryl-Cyclohexanones and their Derivatives on Macrophage Polarization In Vitro

Macrophages are critical in both tissue homeostasis and inflammation, and shifts in their polarization have been indicated as pivotal for the resolution of inflammatory processes. Inflammation is a complex and necessary component of the immune response to stimuli that are harmful to host homeostasis and is regulated by cellular and molecular events that remain a source of ongoing investigation. Among the compounds studied that have potential against autoimmune and inflammatory diseases, cannabinoids are currently highlighted. In this work, nineteen aryl-cyclohexanones diesters and their derivatives were synthesized based on the aryl-cyclohexane skeleton of phytocannabinoids, such as cannabidiol (CBD), and were evaluated for their anti-inflammatory and macrophage polarization potential. The results showed that Compound 4 inhibited the production of nitric oxide in RAW 264.7 macrophages. Furthermore, it reduced the levels of pro-inflammatory cytokines IL-12p70, TNF-α, IFN-γ, MCP-1, and IL-6 while, at the same time, was able to increase the production of anti-inflammatory cytokines IL-4, IL-10, and IL-13. Compound 4 also reduced macrophage apoptosis, increased the expression of the CD206 (mannose receptor) and at the same time, decreased the expression of CD284 (TLR-4 receptor) on the surface of these cells. Finally, it increased the phagocytic capacity and inhibited the phosphorylation of the p65 of NF-kβ. In conclusion, Compound 4, identified as diethyl-4-hydroxy-2-(4-methoxyphenyl)-4-methyl-6-oxocyclohexane-1-3-dicarboxylate, showed significant anti-inflammatory effect, while demonstrating the ability to transform phenotypically macrophages from the M1 phenotype (pro-inflammatory) to the M2 phenotype (anti-inflammatory). This led us to hypothesize that the main mechanism of anti-inflammatory effect of this molecule is linked to its immune modulation capacity.

Updated picture of SARS-CoV-2 variants and mutations

The worldwide burden of coronavirus disease 2019 (COVID-19) is still unremittingly prosecuting, with nearly 300 million infections and over 5.3 million deaths recorded so far since the origin of the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic at the end of the year 2019. The fight against this new highly virulent beta coronavirus appears one of the most strenuous and long challenges that humanity has ever faced, since a definitive treatment has not been identified so far. The adoption of potentially useful physical preventive measures such as lockdowns, social distancing and face masking seems only partially effective for mitigating viral spread, though efficacy and continuation of such measures on the long term is questionable, due to many social and economic reasons. Many COVID-19 vaccines have been developed and are now widely used, though their effectiveness is challenged by several aspects such as low uptake and limited efficacy in some specific populations, as well as by continuous emergence of new mutations in the SARS-CoV-2 genome, accompanying the origin and spread of new variants, which in turn may contribute to further decrease the effectiveness of current vaccines and treatments. This article is hence aimed to provide an updated picture of SARS-CoV-2 variants and mutations that have emerged from November 2019 to present time (i.e., early December 2021).