Metagenomic analysis reveals the abundance and diversity of opportunistic fungal pathogens in the nasopharyngeal tract of COVID-19 patients

  1. M. Nazmul Hoque
  2. M. Shaminur Rahman
  3. Md. Murshed Hasan Sarkar
  4. Md Ahashan Habib
  5. M. Anwar Hossain
  6. M. Salim Khan
  7. Tofazzal Islam

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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 Spearman’s 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 summary

The 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.

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  1. ScreenIT

    SciScore for 10.1101/2022.02.17.480819: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsConsent: The study participants received a written informed consent letter consistent with the experiment.
    Field Sample Permit: The collected samples were placed in sample collection vial containing normal saline.
    Sex as a biological variableIn this study, 68.0% and 32.0% of the selected people were male and female, respectively, and their mean age was 41.86 (ranged from 22 to 72) years.
    RandomizationThe Healthy control subjects were randomly selected and these people did not show any signs and symptoms of respiratory illness.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The good quality reads from COVID-19, Recovered and Healthy samples (n = 22) were analyzed using two different bioinformatics tools: the IDSeq (an open-source cloud-based pipeline to assign taxonomy) [66] and the MG-RAST (release version 4.1) (MR) and both use mapping and assembly-based hybrid method [67].
    MG-RAST
    suggested: (MG-RAST, RRID:SCR_004814)
    For this pipeline, we employed the ‘‘Best Hit Classification’’ option to determine taxonomic abundance using the NCBI database as a reference with the following set parameters: maximum e-value of 1×10-30; minimum identity of 90% using a minimum alignment length of 20 as the set parameters.
    NCBI
    suggested: (NCBI, RRID:SCR_006472)
    For these statistical analyses, pairwise non-parametric Wilcoxon test was performed using the Phyloseq and Vegan (package 2.5.1 of R 3.4.2) programs [72].
    Phyloseq
    suggested: (phyloseq, RRID:SCR_013080)
    Vegan
    suggested: (vegan, RRID:SCR_011950)
    After filtering, 11 taxa remained for which Spearman’s correlation analysis between KEGG pathways and SEED functions pathways was done in Hmisc’s rcorr function [73] and the corrplot function [74] of the corrplot R package as mentioned in the previous section.
    KEGG
    suggested: (KEGG, RRID:SCR_012773)
    In addition, Kruskal-Wallis test was also applied at different KEGG and SEED subsystems levels through IBM SPSS (SPSS, Version 23.0, IBM Corp.,
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2022.02.17.480819v1 on bioRxiv