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.

The COVID-19 pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has created an urgent global situation. Therefore, it is necessary to identify the differentially expressed genes (DEGs) in COVID-19 patients to understand disease pathogenesis and the genetic factor(s) responsible for inter-individual variability and disease comorbidities. The pandemic continues to spread worldwide, despite intense efforts to develop multiple vaccines and therapeutic options against COVID-19. However, the precise role of SARS-CoV-2 in the pathophysiology of the nasopharyngeal tract (NT) is still unfathomable. This study utilized machine learning approaches to analyze 22 RNA-seq data from COVID-19 patients (n = 8), recovered individuals (n = 7), and healthy individuals (n = 7) to find disease-related differentially expressed genes (DEGs). We compared dysregulated DEGs to detect critical pathways and gene ontology (GO) connected to COVID-19 comorbidities. We found 1960 and 153 DEG signatures in COVID-19 patients and recovered individuals compared to healthy controls. In COVID-19 patients, the DEG–miRNA, and DEG–transcription factors (TFs) interactions network analysis revealed that E2F1, MAX, EGR1, YY1, and SRF were the highly expressed TFs, whereas hsa-miR-19b, hsa-miR-495, hsa-miR-340, hsa-miR-101, and hsa-miR-19a were the overexpressed miRNAs. Three chemical agents (Valproic Acid, Alfatoxin B1, and Cyclosporine) were abundant in COVID-19 patients and recovered individuals. Mental retardation, mental deficit, intellectual disability, muscle hypotonia, micrognathism, and cleft palate were the significant diseases associated with COVID-19 by sharing DEGs. Finally, the detected DEGs mediated by TFs and miRNA expression indicated that SARS-CoV-2 infection might contribute to various comorbidities. Our results provide the common DEGs between COVID-19 patients and recovered humans, which suggests some crucial insights into the complex interplay between COVID-19 progression and the recovery stage, and offer some suggestions on therapeutic target identification in COVID-19 caused by the SARS-CoV-2.

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. 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.

Background: The next-generation sequencing (NGS) technology facilitates in-depth study of host-pathogen metatranscriptome. We, therefore, implicated phylodynamic and transcriptomic approaches through NGS technology to know/understand the dengue virus (DENV) origin and host response with dengue fever.Methods: In this study, blood serum RNA was extracted from 21 dengue patients and 3 healthy individuals. Total transcriptomic data were analyzed for phylogenetic, phylodynamic, differential express gene (DEG), and gene ontology (GO) using respective bioinformatics tools.Results: The viral genome sequence revealed dengue viral genome size ranges 10647 to 10707 nucleotide. Phylogenetic and phylodynamic analysis showed that the 2021 epidemic isolates were DENV-3 genotype-I and maintained as a new clade in compared to 2019 epidemic. Transcriptome analysis showed a total of 2686 genes were DEG in dengue patients compared to control with a q-value < 0.05. DESeq2 plot counts function of the top 24 genes with the smallest q-values of differential gene expression of RNA-seq data showed that 11 genes were upregulated, whereas 13 genes were downregulated. GO analysis showed a significant upregulation (p = < 0.001) in a process of multicellular organismal, nervous system, sensory perception of chemical stimulus, and G protein-coupled receptor signaling pathways in the dengue patients. However, there were a significant downregulation (p = < 0.001) of intracellular component, cellular anatomical entity, and protein-containing complex in dengue patients. Most importantly, there was a significant increase of a class of immunoregulatory proteins in dengue patients in compared to the controls, with increased GO of immune system process. In addition, upregulation of toll receptor (TLR) signaling pathways were found in dengue patients. These TLR pathways were particularly involved for the activation of innate system coupled with adaptive immune system that probably involved the rapid elimination of dengue virus infected cells. These differentially expressed genes could be further investigated for target based prophylactic interventions for dengue.Conclusion: This is a first report describing DENV complete genomic features and differentially expressed genes in patients in Bangladesh. These genes may have diagnostic and therapeutic values for dengue infection. Continual genomic surveillance is required to further investigate the shift in dominant genotypes in relation to viral pathogenesis.

We report the sequencing of three severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from Bangladesh. We have identified a unique mutation (NSP2_V480I) in one of the sequenced genomes (isolate hCoV-19/Bangladesh/BCSIR-NILMRC-006/2020) compared to the sequences available in the Global Initiative on Sharing All Influenza Data (GISAID) database. The data from this analysis will contribute to advancing our understanding of the epidemiology of SARS-CoV-2 in Bangladesh as well as worldwide at the molecular level and will identify potential new targets for interventions.

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 (d N /d S ratio 1.77 and 1.24, respectively), while the overall S protein of the Bangladeshi SARS‐CoV‐2 isolates underwent negative selection pressure (d N /d S  = 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 tet A and tet M. Overall, this approach provides valuable insights on the SARS‐CoV‐2 genomes and microbiome composition from both vaccinated and nonvaccinated patients in Bangladesh.