ABSTRACT
The nasopharyngeal tract (NT) of human is a habitat of a diverse microbial community that work together with other gut microbes to maintain the host immunity. In our previous study, we reported that SARS-CoV-2 infection reduces human nasopharyngeal commensal microbiome (bacteria, archaea and commensal respiratory viruses) but increases the abundance of pathobionts. This study aimed to assess the possible changes in the resident fungal diversity by the inclusion of opportunistic fungi due to the infection of SARS-CoV-2 in the NT of humans. Twenty-two (n = 22) nasopharyngeal swab samples (including COVID-19 = 8, Recovered = 7, and Healthy = 7) were collected for RNAseq-based metagenomics analyses. Our results indicate that SARS-CoV-2 infection significantly increased (p < 0.05, Wilcoxon test) the population and diversity of NT fungi with a high inclusion of opportunistic pathogens. We detected 863 fungal species including 533, 445, and 188 species in COVID-19, Recovered, and Healthy individuals, respectively that indicate a distinct microbiome dysbiosis due to the SARS-CoV-2 infection. Remarkably, 37% of the fungal species were exclusively associated with SARS-CoV-2 infection, where S. cerevisiae (88.62%) and Phaffia rhodozyma (10.30%) were two top abundant species in the NT of COVID-19 patients. Importantly, 16% commensal fungal species found in the Healthy control were not detected in either COVID-19 patients or when they were recovered from the COVID-19. Pairwise Spearmans correlation test showed that several altered metabolic pathways had significant positive correlations (r > 0.5, p < 0.01) with dominant fungal species detected in three metagenomes. Taken together, our results indicate that SARS-CoV-2 infection causes significant dysbiosis of fungal microbiome and alters some metabolic pathways and expression of genes in the NT of human. Findings of our study might be helpful for developing microbiome-based diagnostics, and also devising appropriate therapeutic regimens including antifungal drugs for prevention and control of concurrent fungal coinfections in COVID-19 patients. Author summaryThe SARS-CoV-2 is a highly transmissible and pathogenic betacoronavirus that primarily enters into the human body through NT to cause fearsome COVID-19 disease. Recent high throughput sequencing and downstream bioinformatic analyses revealed that microbiome dysbiosis associated with SARS-CoV-2 infection are not limited to bacteria, and fungi are also implicated in COVID-19 development in susceptible individuals. This study demonstrates that SARS-CoV-2 infection results in remarkable depletion of NT commensal fungal microbiomes with inclusion of various opportunistic fungal pathogens. We discussed the role of these altered fungal microbiomes in the pathophysiology of the SARS-CoV-2 infection. Our results suggest that dysbiosis in fungal microbiomes and associated altered metabolic functional pathways (or genes) possibly play a determining role in the progression of SARS-CoV-2 pathogenesis. Thus, the identifiable changes in the diversity and composition of the NT fungal population and their related genomic features demonstrated in this study might lay a foundation for better understanding of the underlying mechanism of co-pathogenesis, and the ongoing development of therapeutic agents including antifungal drugs for the resolution of COVID-19 pandemic.
ABSTRACT
BackgroundThe mortality of COVID-19 disease is very high among males or elderly or individuals having comorbidities with obesity, cardiovascular diseases, lung infections, hypertension, and/or diabetes. Our study characterizes SARS-CoV-2 infected patients metagenomic features with or without type 2 diabetes to identify the microbial interactions associated with its fatal consequences. MethodThis study compared the baseline nasopharyngeal microbiome of SARS-CoV-2 infected diabetic and non-diabetic patients with controls adjusted with age and gender. The mNGS were performed using Ion GeneStudio S5 Series and the data were analyzed by the Vegan-package in R. ResultsAll three groups possessed significant bacterial diversity and dissimilarity indexes (p<0.05). Spearmans correlation coefficient network analysis illustrated 183 significant positive correlations and 13 negative correlations of pathogenic bacteria (r=0.6-1.0, p<0.05), and 109 positive correlations among normal-flora and probiotic bacteria (r>0.6, p<0.05). The SARS-CoV-2 diabetic group exhibited a significant increase of pathogens (p<0.05) and opportunistic pathogens (p<0.05) with a simultaneous decrease of normal-flora (p<0.05). The molecular docking analysis of Salivaricin, KLD4 (alpha), and enterocin produced by several enriched probiotic strains presented strong binding affinity with Shiga toxin, outer membrane proteins (ompA, omp33) or hemolysin. ConclusionThe dysbiosis of the bacterial community might be linked with severe consequences of COVID-19 infected diabetic patients, although few probiotic strains inhibited numerous pathogens in the same pathological niches. This study suggested that the promotion of normal-flora and probiotics through dietary changes and reduction of excessive pro-inflammatory states by preventing pathogenic environment might lead to a better outcome for those co-morbid patients.
ABSTRACT
The novel coronavirus disease 2019 (COVID-19) is a rapidly emerging and highly transmissible disease caused by the Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2). Understanding the microbiomes associated with the upper respiratory tract infection (URTI), chronic obstructive pulmonary disease (COPD) and COVID-19 diseases has clinical interest. We hypothesized that the diversity of microbiome compositions and their genomic features are associated with different pathological conditions of these human respiratory tract diseases (COVID-19 and non-COVID; URTI and COPD). To test this hypothesis, we analyzed 21 whole metagenome sequences (WMS) including eleven COVID-19 (BD = 6 and China = 5), six COPD (UK = 6) and four URTI (USA = 4) samples to unravel the diversity of microbiomes, their genomic features and relevant metabolic functions. The WMS data mapped to 534 bacterial, 60 archaeal and 61 viral genomes with distinct variation in the microbiome composition across the samples (COVID-19>COPD>URTI). Notably, 94.57%, 80.0% and 24.59% bacterial, archaeal and viral genera shared between the COVID-19 and non-COVID samples, respectively, however, the COVID-19 related samples had sole association with 16 viral genera other than SARS-CoV-2. Strain-level virome profiling revealed 660 and 729 strains in COVID-19 and non-COVID sequence data, respectively and of them 34.50% strains shared between the conditions. Functional annotation of metagenomics sequences of thevCOVID-19 and non-COVID groups identified the association of several biochemical pathways related to basic metabolism (amino acid and energy), ABC transporters, membrane transport, replication and repair, clustering-based subsystems, virulence, disease and defense, adhesion, regulation of virulence, programmed cell death, and primary immunodeficiency. We also detected 30 functional gene groups/classes associated with resistance to antibiotics and toxic compounds (RATC) in both COVID-19 and non-COVID microbiomes. Furthermore, a predominant higher abundance of cobalt-zinc-cadmium resistance (CZCR) and multidrug resistance to efflux pumps (MREP) genes were detected in COVID-19 metagenome. The profiles of microbiome diversity and associated microbial genomic features found in both COVID-19 and non-COVID (COPD and URTI) samples might be helpful for developing the microbiome-based diagnostics and therapeutics for COVID-19 and non-COVID respiratory diseases. However, future studies might be carried out to explore the microbiome dynamics and the cross-talk between host and microbiomes employing larger volume of samples from different ethnic groups and geoclimatic conditions.
ABSTRACT
The emerging novel coronavirus SARS-CoV-2 has created a global confusing pandemic health crisis that warrants an accurate and detailed characterization of the rapidly evolving viral genome for understanding its epidemiology, pathogenesis and containment. We explored 61,485 sequences of the Nucleocapsid (N) protein, a potent diagnostic and prophylactic target, for identifying the mutations to review their roles in RT-PCR based diagnosis and observe consequent impacts. Compared to the Wuhan reference strain, a total of 1034 unique nucleotide mutations were identified in the mutant strains (49.15%, n=30,221) globally. Of these mutations, 367 occupy primer binding sites including 3-end mismatch to primer-pair of 11 well characterized primer sets. Noteworthy, CDC (USA) recommended N2 primer set contained lower mismatch than the other primer sets. Moreover, 684 amino acid (aa) substitutions located across 317 (75.66% of total aa) unique positions including 82, 21, and 83 of those in RNA binding N-terminal domain (NTD), SR-rich region, and C-terminal dimerization domain (CTD), respectively. Moreover, 11 in-frame deletions were revealed, mostly (n =10) within the highly flexible linker region, and the rest within the NTD region. Furthermore, we predicted the possible consequences of high-frequency mutations ([≥] 20) and deletions on the tertiary structure of the N protein. Remarkably, we observed that high frequency (67.94% of mutated sequences) coevolving mutations (R203K and G204R) destabilized and decreased overall structural flexibility. Despite being proposed as the alternate target to spike protein for vaccine and therapeutics, ongoing nonsynonymous evolution of the N protein may challenge the endeavors, thus need further immunoinformatics analyses. Therefore, continuous monitoring is required for tracing the ongoing evolution of the SARS-CoV-2 N protein in prophylactic and diagnostic interventions.
ABSTRACT
In order to explore nonsynonymous mutations and deletions in the spike (S) protein of SARS-CoV-2, we comprehensively analyzed 35,750 complete S protein gene sequences from across six continents and five climate zones around the world, as documented in the GISAID database as of June 24th, 2020. Through a custom Python-based pipeline for analyzing mutations, we identified 27,801 (77.77 % of spike sequences) mutated strains compared to Wuhan-Hu-1 strain. 84.40% of these strains had only single amino-acid (aa) substitution mutations, but an outlier strain from Bosnia and Herzegovina (EPI_ISL_463893) was found to possess six aa substitutions. The D614G variant of the major G clade was found to be predominant across circulating strains in all climates. We also identified 988 unique aa substitution mutations distributed across 660 positions within the spike protein, with eleven sites showing high variability - these sites had four types of aa variations at each position. Besides, 17 in-frame deletions at four major regions (three in N-terminal domain and one just downstream of the RBD) may have possible impact on attenuation. Moreover, the mutational frequency differed significantly (p= 0.003, Kruskal-Wallis test) among the SARS-CoV-2 strains worldwide. This study presents a fast and accurate pipeline for identifying nonsynonymous mutations and deletions from large dataset for any particular protein coding sequence and presents this S protein data as representative analysis. By using separate multi-sequence alignment with MAFFT, removing ambiguous sequences and in-frame stop codons, and utilizing pairwise alignment, this method can derive nonsynonymus mutations (Reference:Position:Strain). We believe this will aid in the surveillance of any proteins encoded by SARS-CoV-2, and will prove to be crucial in tracking the ever-increasing variation of many other divergent RNA viruses in the future.
ABSTRACT
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the ongoing pandemic of coronavirus disease 2019 (COVID-19), a public health emergency of international concern declared by the World Health Organization (WHO). An immuno-informatics approach along with comparative genomic was applied to design a multi-epitope-based peptide vaccine against SARS-CoV-2 combining the antigenic epitopes of the S, M and E proteins. The tertiary structure was predicted, refined and validated using advanced bioinformatics tools. The candidate vaccine showed an average of [≥] 90.0% world population coverage for different ethnic groups. Molecular docking of the chimeric vaccine peptide with the immune receptors (TLR3 and TLR4) predicted efficient binding. Immune simulation predicted significant primary immune response with increased IgM and secondary immune response with high levels of both IgG1 and IgG2. It also increased the proliferation of T-helper cells and cytotoxic T-cells along with the increased INF-{gamma} and IL-2 cytokines. The codon optimization and mRNA secondary structure prediction revealed the chimera is suitable for high-level expression and cloning. Overall, the constructed recombinant chimeric vaccine candidate demonstrated significant potential and can be considered for clinical validation to fight against this global threat, COVID-19.
