Computational design of medicinal compounds to inhibit RBD-hACE2 interaction in the Omicron variant: unveiling a vulnerable target site.

The COVID-19 pandemic, caused by SARS-CoV-2, has globally affected both human health and economy. Several variants with a high potential for reinfection and the ability to evade immunity were detected shortly after the initial reported case of COVID-19. A total of 30 mutations in the spike protein (S) have been reported in the SARS-CoV-2 (BA.2) variant in India and South Africa, while half of these mutations are in the receptor-binding domain and have spread rapidly throughout the world. Drug repurposing offers potential advantages over the discovery of novel drugs, and one is that it can be delivered quickly without lengthy assessments and time-consuming clinical trials. In this study, computational drug design, such as pharmacophore-based virtual screening and MD simulation has been concentrated, in order to find a novel small molecular inhibitor that prevents hACE2 from binding to the receptor binding domain (RBD). three medicinal compound databases: North-East African, North African, and East African were screened and carried out a multi-step screening approach that identified three compounds, which are thymoquinol 2-O-beta-glucopyranoside (C1), lanneaflavonol (C2), and naringenin-4'-methoxy-7-O-Alpha-L-rhamnoside (C3), with excellent anti-viral properties against the RBD of the omicron variant. Furthermore, PAIN assay interference, computation bioactivity prediction, binding free energy, and dissociation constant were used to validate the top hits, which indicated good antiviral activity. The three compounds that were found may be useful against COVID-19, though more research is required. These findings could aid the development of novel therapeutic drugs against the emerging Omicron variant of SARS-CoV-2.

A multicentre study reveals dysbiosis in the microbial co-infection and antimicrobial resistance gene profile in the nasopharynx of COVID-19 patients.

The impact of SARS-CoV-2 infection on the nasopharyngeal microbiome has not been well characterised. We sequenced genetic material extracted from nasopharyngeal swabs of SARS-CoV-2-positive individuals who were asymptomatic (n = 14), had mild (n = 64) or severe symptoms (n = 11), as well as from SARS-CoV-2-negative individuals who had never-been infected (n = 5) or had recovered from infection (n = 7). Using robust filters, we identified 1345 taxa with approximately 0.1% or greater read abundance. Overall, the severe cohort microbiome was least diverse. Bacterial pathogens were found in all cohorts, but fungal species identifications were rare. Few taxa were common between cohorts suggesting a limited human nasopharynx core microbiome. Genes encoding resistance mechanisms to 10 antimicrobial classes (> 25% sequence coverages, 315 genes, 63 non-redundant) were identified, with ß-lactam resistance genes near ubiquitous. Patients infected with SARS-CoV-2 (asymptomatic and mild) had a greater incidence of antibiotic resistance genes and a greater microbial burden than the SARS-CoV-2-negative individuals. This should be considered when deciding how to treat COVID-19 related bacterial infections.

Immunoinformatics approach to epitope-based vaccine design against the SARS-CoV-2 in Bangladeshi patients.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the ongoing coronavirus disease 2019 (COVID-19) pandemic which has brought a great challenge to public health. After the first emergence of novel coronavirus SARS-CoV-2 in the city of Wuhan, China, in December 2019. As of March 2020, SARS-CoV-2 was first reported in Bangladesh and since then the country has experienced a steady rise in infections, resulting in 13,355,191 cases and 29,024 deaths as of 27 February 2022. Bioinformatics techniques are used to predict B cell and T cell epitopes from the new SARS-CoV-2 spike glycoprotein in order to build a unique multiple epitope vaccine. The immunogenicity, antigenicity scores, and toxicity of these epitopes were evaluated and chosen based on their capacity to elicit an immune response. RESULT: The best multi-epitope of the possible immunogenic property was created by combining epitopes. EAAAK, AAY, and GPGPG linkers were used to connect the epitopes. In several computer-based immune response analyses, this vaccine design was found to be efficient, as well as having high population coverage. CONCLUSION: This research is entirely reliant on the development of epitope-based vaccines, and these in silico findings would represent a major step forward in the development of a vaccine that might eradicate SARS-CoV-2 in Bangladeshi patients.

Immunoinformatics approach to epitope-based vaccine design against the SARS-CoV-2 in Bangladeshi patients (preprint)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the ongoing coronavirus disease 2019 (COVID-19) pandemic which has brought a great challenge to public health. After the first emergence of novel coronavirus SARS-CoV-2 in the city of Wuhan, China in December 2019. As of March 2020, SARS-CoV-2 was first reported in Bangladesh and since then the country has experienced a steady rise in infections, resulting in 13,355,191 cases and 29,024 deaths as of 27 February 2022. Bioinformatics techniques are used to predict B- and T-cell epitopes from the new SARS-CoV-2 glycoprotein in order to build a unique multiple epitope vaccine. The immunogenicity, antigenicity scores, and toxicity of these epitopes were evaluated and chosen based on their capacity to elicit an immune response. The best multi-epitope of the possible immunogenic property was created by combining epitopes. EAAAK, AAY, and GPGPG linkers were used to connect the epitopes. In several computer-based immune response analyses, this vaccine design was found to be efficient, as well as having high population coverage. This research is entirely reliant on the development of epitope-based vaccines, and these in silico findings would represent a major step forward in the development of a vaccine that might eradicate SARS-CoV-2 in Bangladeshi patients.

Genomic characterization of the dominating Beta, V2 variant carrying vaccinated (Oxford-AstraZeneca) and nonvaccinated COVID-19 patient samples in Bangladesh: A metagenomics and whole-genome approach.

Bangladesh is experiencing a second wave of COVID-19 since March 2021, despite the nationwide vaccination drive with ChAdOx1 (Oxford-AstraZeneca) vaccine from early February 2021. Here, we characterized 19 nasopharyngeal swab (NPS) samples from COVID-19 suspect patients using genomic and metagenomic approaches. Screening for SARS-CoV-2 by reverse transcriptase polymerase chain reaction and metagenomic sequencing revealed 17 samples of COVID-19 positive (vaccinated = 10, nonvaccinated = 7) and 2 samples of COVID-19 negative. We did not find any significant correlation between associated factors including vaccination status, age or sex of the patients, diversity or abundance of the coinfected organisms/pathogens, and the abundance of SARS-CoV-2. Though the first wave of the pandemic was dominated by clade 20B, Beta, V2 (South African variant) dominated the second wave (January 2021 to May 2021), while the third wave (May 2021 to September 2021) was responsible for Delta variants of the epidemic in Bangladesh including both vaccinated and unvaccinated infections. Noteworthily, the receptor binding domain (RBD) region of S protein of all the isolates harbored similar substitutions including K417N, E484K, and N501Y that signify the Beta, while D614G, D215G, D80A, A67V, L18F, and A701V substitutions were commonly found in the non-RBD region of Spike proteins. ORF7b and ORF3a genes underwent a positive selection (dN/dS ratio 1.77 and 1.24, respectively), while the overall S protein of the Bangladeshi SARS-CoV-2 isolates underwent negative selection pressure (dN/dS = 0.621). Furthermore, we found different bacterial coinfections like Streptococcus agalactiae, Neisseria meningitidis, Elizabethkingia anophelis, Stenotrophomonas maltophilia, Klebsiella pneumoniae, and Pseudomonas plecoglossicida, expressing a number of antibiotic resistance genes such as tetA and tetM. Overall, this approach provides valuable insights on the SARS-CoV-2 genomes and microbiome composition from both vaccinated and nonvaccinated patients in Bangladesh.

SARS-CoV-2 infection reduces human nasopharyngeal commensal microbiome with inclusion of pathobionts.

The microbiota of the nasopharyngeal tract (NT) play a role in host immunity against respiratory infectious diseases. However, scant information is available on interactions of SARS-CoV-2 with the nasopharyngeal microbiome. This study characterizes the effects of SARS-CoV-2 infection on human nasopharyngeal microbiomes and their relevant metabolic functions. Twenty-two (n = 22) nasopharyngeal swab samples (including COVID-19 patients = 8, recovered humans = 7, and healthy people = 7) were collected, and underwent to RNAseq-based metagenomic investigation. Our RNAseq data mapped to 2281 bacterial species (including 1477, 919 and 676 in healthy, COVID-19 and recovered metagenomes, respectively) indicating a distinct microbiome dysbiosis. The COVID-19 and recovered samples included 67% and 77% opportunistic bacterial species, respectively compared to healthy controls. Notably, 79% commensal bacterial species found in healthy controls were not detected in COVID-19 and recovered people. Similar dysbiosis was also found in viral and archaeal fraction of the nasopharyngeal microbiomes. We also detected several altered metabolic pathways and functional genes in the progression and pathophysiology of COVID-19. The nasopharyngeal microbiome dysbiosis and their genomic features determined by our RNAseq analyses shed light on early interactions of SARS-CoV-2 with the nasopharyngeal resident microbiota that might be helpful for developing microbiome-based diagnostics and therapeutics for this novel pandemic disease.