This preprint has been published elsewhere.
DOI of the published preprint https://doi.org/10.9771/cmbio.v23i1.50821
Preprint / Version 1

SARS-CoV-2/COVID and Type 1 Diabetes Mellitus: An immunoinformatics approach

##article.authors##

  • Luis Jesuino de Oliveira Andrade Universidade Estadual de Santa Cruz - Ilhéus - Bahia https://orcid.org/0000-0002-7714-0330
    • Alcina Maria Vinhaes Bittencourt Faculdade de Medicina, Universidade Federal da Bahia, Salvador - Bahia - Brasil
      • Luís Matos de Oliveira Faculdade de Medicina - Bahia - Brasil
        • Luisa Correia Matos de Oliveira Centro Universitário Senai Cimatec - Salvador - Bahia - Brasil
          • Gabriela Correia Matos de Oliveira Faculdade de Medicina UniFTC - Salvador - Bahia - Brasil

            DOI:

            https://doi.org/10.1590/SciELOPreprints.2458

            Keywords:

            SARS-CoV-2, Type 1 diabetes, Molecular mimicry, Immunoinformatics.

            Abstract

            Contact with viruses which have an aminoacid (AA) sequence simile to that of the auto-antigens can lead to autoimmune diseases in genetically susceptible individuals. SARS-CoV-2 has been implied as a possible causer of new-onset type 1 diabetes mellitus (DM1), however, no consistent evidence yet that SARS-CoV-2 take to DM1 on your own initiative. Objective: Evaluate the possible similarity between the AA sequences of human insulin and human glutamic acid decarboxylase-65 (GAD65) with SARS-CoV-2/COVID proteins, to explain the possible trigger of DM1. Methods: AA sequences of the human insulin (4F0N), GAD65 (2OKK), and SARS-CoV-2 (SARS-Cov2 S protein at open state (7DDN), SARS-Cov2 S protein at close state (7DDD), SARS CoV-2 Spike protein (6ZB5), Crystal structure of SARS-CoV-2 nucleocapsid protein N-terminal RNA binding domain (6M3M), Crystal structure of SARS-CoV-2 nucleocapsid protein C-terminal RNA binding domain (7DE1), Crystal Structure of NSP1 from SARS-CoV-2 (7K3N), and SARS-CoV-2 S trimer (7DK3)) available in the Protein Data Bank were compared using the Pairwise Structure Alignment. Results: Sequence identity percentage (SI%) and sequence similarity percentage (SS%) were found among the 4F0N, 2OKK and SARS-CoV-2. The SI% between the 4F0N and SARS-CoV-2 ranged from 4.76% to 14.29% and SS% ranged from 5.00% to 45.45%, distributed like this: 4F0N and 7DDN = SI% 4.76 and SS% 28.57; 4F0N and 7DDD = SI% 14.39 and SS% 23.81; 4F0N and 6ZB5 = SI% 4.76 and SS% 28.57; 4F0N and 6M3M = SI% 5.00 and SS% 5;00; 4F0N and 7DE1 = SI% 4.76 and SS% 9.21; 4F0N and 7K3N = SI% 9.09 and SS% 45.45; 4F0N and 7DK3 = SI% 4.76 and SS% 28.57. The SI% between the between the 2OKK and SARS-CoV-2 ranged from 3.19% to 6,70% and SS% ranged from 10.45 % to 22.22%, distributed like this: 2OKK and 7DDN = SI% 6.70 and SS% 15.64; 2OKK and 7DDD = SI% 7.53 and SS% 18.84; 2OKK and 6ZB5 = SI% 6.68 and SS% 17.38; 2OKK and 6M3M = SI% 4.48 and SS% 10.45; 2OKK and 7DE1 = SI% 6.67 and SS% 22.22; 2OKK and 7K3N = SI% 3.19 and SS% 15.97; 2OKK and 7DK3 = SI% 3.95 and 17.98. Conclusion: Immunoinformatics data suggest a potential pathogenic link between DM1 and SARS-CoV-2/COVID. Thus, by means of molecular mimicking we check that sequences similarity among SARS-CoV-2/COVID and human insulin and human glutamic acid decarboxylase-65 may lead to production of an immune cross-response to self-antigens, with breakage of self-tolerance that can trigger DM1.

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            Posted

            06/14/2021

            How to Cite

            SARS-CoV-2/COVID and Type 1 Diabetes Mellitus: An immunoinformatics approach. (2021). In SciELO Preprints. https://doi.org/10.1590/SciELOPreprints.2458

            Section

            Health Sciences

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