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Quantitative Analysis and Visualization of the Interaction Between Intestinal Microbiota and Type 1 Diabetes in Children Based on Multi-Databases

Background: As an important autoimmune disease, type 1 diabetes (T1D) is often diagnosed in children, but due to the complexity of the etiology of diabetes and many other factors, the disease pathogenesis of diabetes is still unclear. The intestinal microbiota has been proved to have close relations...

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Autores principales: Zhao, Mingyi, Xu, Shaokang, Cavagnaro, María José, Zhang, Wei, Shi, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8715853/
https://www.ncbi.nlm.nih.gov/pubmed/34976889
http://dx.doi.org/10.3389/fped.2021.752250
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author Zhao, Mingyi
Xu, Shaokang
Cavagnaro, María José
Zhang, Wei
Shi, Jian
author_facet Zhao, Mingyi
Xu, Shaokang
Cavagnaro, María José
Zhang, Wei
Shi, Jian
author_sort Zhao, Mingyi
collection PubMed
description Background: As an important autoimmune disease, type 1 diabetes (T1D) is often diagnosed in children, but due to the complexity of the etiology of diabetes and many other factors, the disease pathogenesis of diabetes is still unclear. The intestinal microbiota has been proved to have close relationships with T1D in recent years, which is one of the most important molecular bases of pathogenesis and prognosis factors for T1D. Using the multi-omics and multicenter sample analysis method, a number of intestinal microbiota in T1D have been discovered and explained, which has provided comprehensive and rich information. However, how to find more useful information and get an intuitive understanding that people need conveniently in the huge data sea has become the focus of attention. Therefore, quantitative analysis and visualization of the interaction between intestinal microbiota and T1D in children are urgently needed. Methods: We retrieved the detailed original data from the National Center for Biotechnology Information, GMREPO, and gutMEGA databases and other authoritative multiple projects with related research; the ranking of intestinal microbiota abundance from healthy people, overall T1D patients, and T1D in children (0–18 years old) were detailed analyzed, classified, and visualized. Results: A total of 515 bacterial species and 161 related genera were fully analyzed. Also, Prevotella copri was led by 21.25% average abundance, followed by Clostridium tertium of 10.39% in all-cross T1D patients. For children with T1D, Bacteroides vulgatus has high abundance in all age periods, whereas the abundance of each intestinal microbiota was more uniform in female samples, with the ranking from high to low as Bacteroides dorei 9.56%, P. copri 9.53%, Streptococcus pasteurianus 8.15%, and C. tertium 7.53%, whereas in male samples, P. copri was accounted for the largest by 22.72%. The interaction between intestinal microbiota and comparison between healthy people and children with T1D was also detailed analyzed. Conclusions: This study provides a new method and comprehensive perspectives for the evaluation of the interaction between intestinal microbiota and T1D in children. A set of useful information of intestinal microbiota with its internal interaction and connections has been presented, which could be a compact, immediate, and practical scientific reference for further molecular biological and clinical translational research of T1D in children.
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spelling pubmed-87158532021-12-30 Quantitative Analysis and Visualization of the Interaction Between Intestinal Microbiota and Type 1 Diabetes in Children Based on Multi-Databases Zhao, Mingyi Xu, Shaokang Cavagnaro, María José Zhang, Wei Shi, Jian Front Pediatr Pediatrics Background: As an important autoimmune disease, type 1 diabetes (T1D) is often diagnosed in children, but due to the complexity of the etiology of diabetes and many other factors, the disease pathogenesis of diabetes is still unclear. The intestinal microbiota has been proved to have close relationships with T1D in recent years, which is one of the most important molecular bases of pathogenesis and prognosis factors for T1D. Using the multi-omics and multicenter sample analysis method, a number of intestinal microbiota in T1D have been discovered and explained, which has provided comprehensive and rich information. However, how to find more useful information and get an intuitive understanding that people need conveniently in the huge data sea has become the focus of attention. Therefore, quantitative analysis and visualization of the interaction between intestinal microbiota and T1D in children are urgently needed. Methods: We retrieved the detailed original data from the National Center for Biotechnology Information, GMREPO, and gutMEGA databases and other authoritative multiple projects with related research; the ranking of intestinal microbiota abundance from healthy people, overall T1D patients, and T1D in children (0–18 years old) were detailed analyzed, classified, and visualized. Results: A total of 515 bacterial species and 161 related genera were fully analyzed. Also, Prevotella copri was led by 21.25% average abundance, followed by Clostridium tertium of 10.39% in all-cross T1D patients. For children with T1D, Bacteroides vulgatus has high abundance in all age periods, whereas the abundance of each intestinal microbiota was more uniform in female samples, with the ranking from high to low as Bacteroides dorei 9.56%, P. copri 9.53%, Streptococcus pasteurianus 8.15%, and C. tertium 7.53%, whereas in male samples, P. copri was accounted for the largest by 22.72%. The interaction between intestinal microbiota and comparison between healthy people and children with T1D was also detailed analyzed. Conclusions: This study provides a new method and comprehensive perspectives for the evaluation of the interaction between intestinal microbiota and T1D in children. A set of useful information of intestinal microbiota with its internal interaction and connections has been presented, which could be a compact, immediate, and practical scientific reference for further molecular biological and clinical translational research of T1D in children. Frontiers Media S.A. 2021-12-15 /pmc/articles/PMC8715853/ /pubmed/34976889 http://dx.doi.org/10.3389/fped.2021.752250 Text en Copyright © 2021 Zhao, Xu, Cavagnaro, Zhang and Shi. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pediatrics
Zhao, Mingyi
Xu, Shaokang
Cavagnaro, María José
Zhang, Wei
Shi, Jian
Quantitative Analysis and Visualization of the Interaction Between Intestinal Microbiota and Type 1 Diabetes in Children Based on Multi-Databases
title Quantitative Analysis and Visualization of the Interaction Between Intestinal Microbiota and Type 1 Diabetes in Children Based on Multi-Databases
title_full Quantitative Analysis and Visualization of the Interaction Between Intestinal Microbiota and Type 1 Diabetes in Children Based on Multi-Databases
title_fullStr Quantitative Analysis and Visualization of the Interaction Between Intestinal Microbiota and Type 1 Diabetes in Children Based on Multi-Databases
title_full_unstemmed Quantitative Analysis and Visualization of the Interaction Between Intestinal Microbiota and Type 1 Diabetes in Children Based on Multi-Databases
title_short Quantitative Analysis and Visualization of the Interaction Between Intestinal Microbiota and Type 1 Diabetes in Children Based on Multi-Databases
title_sort quantitative analysis and visualization of the interaction between intestinal microbiota and type 1 diabetes in children based on multi-databases
topic Pediatrics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8715853/
https://www.ncbi.nlm.nih.gov/pubmed/34976889
http://dx.doi.org/10.3389/fped.2021.752250
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