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

Detalhes bibliográficos
Autor(a) principal: Andrade, Luis Jesuino de Oliveira
Data de Publicação: 2021
Outros Autores: Bittencourt , Alcina Maria Vinhaes, Oliveira , Luís Matos de, Oliveira , Luisa Correia Matos de, Oliveira , Gabriela Correia Matos de
Tipo de documento: preprint
Idioma: por
Título da fonte: SciELO Preprints
Texto Completo: https://preprints.scielo.org/index.php/scielo/preprint/view/2458
Resumo: 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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spelling SARS-CoV-2/COVID and Type 1 Diabetes Mellitus: An immunoinformatics approachSARS-CoV-2 / COVID e Diabetes Mellitus Tipo 1: Uma abordagem com imunoinformáticaSARS-CoV-2Diabetes mellitus tipo 1Mimetismo molecularImunoinformáticaSARS-CoV-2Type 1 diabetesMolecular mimicryImmunoinformatics.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.O contato com vírus que têm uma sequência de aminoácidos (AA) semelhante à dos autoantígenos podem desencadear doenças autoimunes em indivíduos geneticamente suscetíveis. SARS-CoV-2 foi sugerido como um possível causador de diabetes mellitus tipo 1 de início recente (DM1), no entanto, não há evidências consistentes de que o SARS-CoV-2 possa desencadear DM1. Objetivo: Avaliar a possível semelhança entre as sequências AA da insulina humana e da descarboxilase-65 do ácido glutâmico humano (GAD65) com as proteínas SARS-CoV-2 / COVID, para explicar o possível desencadeamento do DM1. Métodos: Sequências de AA da insulina humana (4F0N), GAD65 (2OKK) e 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))  disponíveis no Protein Data Bank foram comparadas utilizando o Pairwise Structure Alignment. Resultados: O percentual de identidade de sequências (SI%) e o percentual de similaridade de sequências (SS%) foram encontrados entre o 4F0N, 2OKK e o SARS-CoV-2. O SI% entre o 4F0N e o SARS-CoV-2 variou de 4,76% a 14,29% e o SS% variou de 5,00% a 45,45%, assim distribuídos: 4F0N e 7DDN = SI% 4,76 e SS% 28,57; 4F0N e 7DDD = SI% 14,39 e SS% 23,81; 4F0N e 6ZB5 = SI% 4,76 e SS% 28,57; 4F0N e 6M3M = SI% 5,00 e SS% 5; 00; 4F0N e 7DE1 = SI% 4,76 e SS% 9,21; 4F0N e 7K3N = SI% 9,09 e SS% 45,45; 4F0N e 7DK3 = SI% 4,76 e SS% 28,57. O SI% entre o 2OKK e o SARS-CoV-2 variou de 3,19% a 6,70% e o SS% variou de 10,45% a 22,22%, assim distribuídos: 2OKK e 7DDN = SI% 6,70 e SS% 15,64; 2OKK e 7DDD = SI% 7,53 e SS% 18,84; 2OKK e 6ZB5 = SI% 6,68 e SS% 17,38; 2OKK e 6M3M = SI% 4,48 e SS% 10,45; 2OKK e 7DE1 = SI% 6,67 e SS% 22,22; 2OKK e 7K3N = SI% 3,19 e SS% 15,97; 2OKK e 7DK3 = SI% 3,95 e 17,98. Conclusão: Os dados de imunoinformática sugerem uma potencial ligação patogênica entre SARS-CoV-2 / COVID e o DM1. Assim, por meio de mimetização molecular, verificamos que a similaridade das sequências de AA entre SARS-CoV-2 / COVID e insulina humana e a descarboxilase-65 do ácido glutâmico humano pode levar à produção de uma resposta cruzada imunológica para autoantígenos, com quebra de auto-tolerância, podendo desencadear o DM1.SciELO PreprintsSciELO PreprintsSciELO Preprints2021-06-14info:eu-repo/semantics/preprintinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://preprints.scielo.org/index.php/scielo/preprint/view/245810.1590/SciELOPreprints.2458porhttps://preprints.scielo.org/index.php/scielo/article/view/2458/4216Copyright (c) 2021 Luis Jesuino de Oliveira Andrade, Alcina Maria Vinhaes Bittencourt , Luís Matos de Oliveira , Luisa Correia Matos de Oliveira , Gabriela Correia Matos de Oliveira https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessAndrade, Luis Jesuino de OliveiraBittencourt , Alcina Maria VinhaesOliveira , Luís Matos deOliveira , Luisa Correia Matos deOliveira , Gabriela Correia Matos dereponame:SciELO Preprintsinstname:SciELOinstacron:SCI2021-06-08T04:12:26Zoai:ops.preprints.scielo.org:preprint/2458Servidor de preprintshttps://preprints.scielo.org/index.php/scieloONGhttps://preprints.scielo.org/index.php/scielo/oaiscielo.submission@scielo.orgopendoar:2021-06-08T04:12:26SciELO Preprints - SciELOfalse
dc.title.none.fl_str_mv SARS-CoV-2/COVID and Type 1 Diabetes Mellitus: An immunoinformatics approach
SARS-CoV-2 / COVID e Diabetes Mellitus Tipo 1: Uma abordagem com imunoinformática
title SARS-CoV-2/COVID and Type 1 Diabetes Mellitus: An immunoinformatics approach
spellingShingle SARS-CoV-2/COVID and Type 1 Diabetes Mellitus: An immunoinformatics approach
Andrade, Luis Jesuino de Oliveira
SARS-CoV-2
Diabetes mellitus tipo 1
Mimetismo molecular
Imunoinformática
SARS-CoV-2
Type 1 diabetes
Molecular mimicry
Immunoinformatics.
title_short SARS-CoV-2/COVID and Type 1 Diabetes Mellitus: An immunoinformatics approach
title_full SARS-CoV-2/COVID and Type 1 Diabetes Mellitus: An immunoinformatics approach
title_fullStr SARS-CoV-2/COVID and Type 1 Diabetes Mellitus: An immunoinformatics approach
title_full_unstemmed SARS-CoV-2/COVID and Type 1 Diabetes Mellitus: An immunoinformatics approach
title_sort SARS-CoV-2/COVID and Type 1 Diabetes Mellitus: An immunoinformatics approach
author Andrade, Luis Jesuino de Oliveira
author_facet Andrade, Luis Jesuino de Oliveira
Bittencourt , Alcina Maria Vinhaes
Oliveira , Luís Matos de
Oliveira , Luisa Correia Matos de
Oliveira , Gabriela Correia Matos de
author_role author
author2 Bittencourt , Alcina Maria Vinhaes
Oliveira , Luís Matos de
Oliveira , Luisa Correia Matos de
Oliveira , Gabriela Correia Matos de
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Andrade, Luis Jesuino de Oliveira
Bittencourt , Alcina Maria Vinhaes
Oliveira , Luís Matos de
Oliveira , Luisa Correia Matos de
Oliveira , Gabriela Correia Matos de
dc.subject.por.fl_str_mv SARS-CoV-2
Diabetes mellitus tipo 1
Mimetismo molecular
Imunoinformática
SARS-CoV-2
Type 1 diabetes
Molecular mimicry
Immunoinformatics.
topic SARS-CoV-2
Diabetes mellitus tipo 1
Mimetismo molecular
Imunoinformática
SARS-CoV-2
Type 1 diabetes
Molecular mimicry
Immunoinformatics.
description 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.
publishDate 2021
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