Metagenomic diagnosis and pathogenic network profile of SARS-CoV-2 in patients co-morbidly affected by type 2 diabetes

  1. Hassan M. Al-Emran
  2. M. Shaminur Rahman
  3. Md. Shazid Hasan
  4. A. S. M. Rubayet Ul Alam
  5. Ovinu Kibria Islam
  6. Ajwad Anwar
  7. Iqbal Kabir Jahid
  8. M. Anwar Hossain

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Abstract

Background

The 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.

Method

This 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.

Results

All three groups possessed significant bacterial diversity and dissimilarity indexes (p<0.05). Spearman’s 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.

Conclusion

The 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.

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  1. ScreenIT

    SciScore for 10.1101/2021.02.23.432535: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIACUC: Ethical Approval: Ethical approval to conduct this metagenomic study was granted by the Ethical Review Committee of the Jashore University of Science and Technology (ERC no: ERC/FBST/JUST/2020-41).
    Consent: Informed consent was taken from all the COVID-19 positive patients and healthy volunteers.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The host sequences from the trimmed files were removed by aligning to the human genome (hg38) by using Burrows-Wheeler Aligner (BWA) 18 and SAMtools 15.
    BWA
    suggested: (BWA, RRID:SCR_010910)
    SAMtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    The taxonomic assignment has been done by Kraken2 19 with NCBI RefSeq Release 201 database (Bacterial, Viral, Archaeal, and Fungal).
    RefSeq
    suggested: (RefSeq, RRID:SCR_003496)
    Phyloseq and vegan packages were employed for those statistical analyses 21.
    Phyloseq
    suggested: (phyloseq, RRID:SCR_013080)
    vegan
    suggested: (vegan, RRID:SCR_011950)
    A correlation network was constructed and visualized with Gephi (ver. 0.9.2).
    Gephi
    suggested: (Gephi, RRID:SCR_004293)
    A quantitative analysis of comparative RNA-seq data using shrinkage estimators for dispersion and fold change was employed for differential bacterial species with a statistical significance (q-value) <0.01 and absolute value of log2 (Fold Change) > 3 using DESeq2 (v4.0).
    DESeq2
    suggested: (DESeq, RRID:SCR_000154)
    Determination of protein structures and their binding affinity: The SWISS-MODEL homology modeling webtool and I-TASSER were utilized for generating the three-dimensional (3D) structures of the extracellular toxin protein of the probiotics or outer membrane protein of the pathogen found in our study.
    I-TASSER
    suggested: (I-TASSER, RRID:SCR_014627)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study had a limitation that the microbiome analyses was performed with small number of individuals studied. In developing countries, this limitation is quite common because of paradoxical situations; doubled price of metagenome reagents in developing countries, extended delivery time with short period of expiry, and unavailability of reagents during the peak times of COVID-19 infections. Therefore, there are very few data reported from those regions where the highest number of patients are having comorbidity. However, our preliminary observations and hypothesis were supported by appropriate statistical methods and the results are compared with suitable controls.

    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.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  2. Version published to 10.1101/2021.02.23.432535v1 on bioRxiv