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Identification of Microbiota Biomarkers With Orthologous Gene Annotation for Type 2 Diabetes

Type 2 diabetes (T2D) is a systematic chronic metabolic condition with abnormal sugar metabolism dysfunction, and its complications are the most harmful to human beings and may be life-threatening after long-term durations. Considering the high incidence and severity at late stage, researchers have...

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Autores principales: Zhang, Yu-Hang, Guo, Wei, Zeng, Tao, Zhang, ShiQi, Chen, Lei, Gamarra, Margarita, Mansour, Romany F., Escorcia-Gutierrez, José, Huang, Tao, Cai, Yu-Dong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8299781/
https://www.ncbi.nlm.nih.gov/pubmed/34305880
http://dx.doi.org/10.3389/fmicb.2021.711244
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author Zhang, Yu-Hang
Guo, Wei
Zeng, Tao
Zhang, ShiQi
Chen, Lei
Gamarra, Margarita
Mansour, Romany F.
Escorcia-Gutierrez, José
Huang, Tao
Cai, Yu-Dong
author_facet Zhang, Yu-Hang
Guo, Wei
Zeng, Tao
Zhang, ShiQi
Chen, Lei
Gamarra, Margarita
Mansour, Romany F.
Escorcia-Gutierrez, José
Huang, Tao
Cai, Yu-Dong
author_sort Zhang, Yu-Hang
collection PubMed
description Type 2 diabetes (T2D) is a systematic chronic metabolic condition with abnormal sugar metabolism dysfunction, and its complications are the most harmful to human beings and may be life-threatening after long-term durations. Considering the high incidence and severity at late stage, researchers have been focusing on the identification of specific biomarkers and potential drug targets for T2D at the genomic, epigenomic, and transcriptomic levels. Microbes participate in the pathogenesis of multiple metabolic diseases including diabetes. However, the related studies are still non-systematic and lack the functional exploration on identified microbes. To fill this gap between gut microbiome and diabetes study, we first introduced eggNOG database and KEGG ORTHOLOGY (KO) database for orthologous (protein/gene) annotation of microbiota. Two datasets with these annotations were employed, which were analyzed by multiple machine-learning models for identifying significant microbiota biomarkers of T2D. The powerful feature selection method, Max-Relevance and Min-Redundancy (mRMR), was first applied to the datasets, resulting in a feature list for each dataset. Then, the list was fed into the incremental feature selection (IFS), incorporating support vector machine (SVM) as the classification algorithm, to extract essential annotations and build efficient classifiers. This study not only revealed potential pathological factors for diabetes at the microbiome level but also provided us new candidates for drug development against diabetes.
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spelling pubmed-82997812021-07-24 Identification of Microbiota Biomarkers With Orthologous Gene Annotation for Type 2 Diabetes Zhang, Yu-Hang Guo, Wei Zeng, Tao Zhang, ShiQi Chen, Lei Gamarra, Margarita Mansour, Romany F. Escorcia-Gutierrez, José Huang, Tao Cai, Yu-Dong Front Microbiol Microbiology Type 2 diabetes (T2D) is a systematic chronic metabolic condition with abnormal sugar metabolism dysfunction, and its complications are the most harmful to human beings and may be life-threatening after long-term durations. Considering the high incidence and severity at late stage, researchers have been focusing on the identification of specific biomarkers and potential drug targets for T2D at the genomic, epigenomic, and transcriptomic levels. Microbes participate in the pathogenesis of multiple metabolic diseases including diabetes. However, the related studies are still non-systematic and lack the functional exploration on identified microbes. To fill this gap between gut microbiome and diabetes study, we first introduced eggNOG database and KEGG ORTHOLOGY (KO) database for orthologous (protein/gene) annotation of microbiota. Two datasets with these annotations were employed, which were analyzed by multiple machine-learning models for identifying significant microbiota biomarkers of T2D. The powerful feature selection method, Max-Relevance and Min-Redundancy (mRMR), was first applied to the datasets, resulting in a feature list for each dataset. Then, the list was fed into the incremental feature selection (IFS), incorporating support vector machine (SVM) as the classification algorithm, to extract essential annotations and build efficient classifiers. This study not only revealed potential pathological factors for diabetes at the microbiome level but also provided us new candidates for drug development against diabetes. Frontiers Media S.A. 2021-07-09 /pmc/articles/PMC8299781/ /pubmed/34305880 http://dx.doi.org/10.3389/fmicb.2021.711244 Text en Copyright © 2021 Zhang, Guo, Zeng, Zhang, Chen, Gamarra, Mansour, Escorcia-Gutierrez, Huang and Cai. 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 Microbiology
Zhang, Yu-Hang
Guo, Wei
Zeng, Tao
Zhang, ShiQi
Chen, Lei
Gamarra, Margarita
Mansour, Romany F.
Escorcia-Gutierrez, José
Huang, Tao
Cai, Yu-Dong
Identification of Microbiota Biomarkers With Orthologous Gene Annotation for Type 2 Diabetes
title Identification of Microbiota Biomarkers With Orthologous Gene Annotation for Type 2 Diabetes
title_full Identification of Microbiota Biomarkers With Orthologous Gene Annotation for Type 2 Diabetes
title_fullStr Identification of Microbiota Biomarkers With Orthologous Gene Annotation for Type 2 Diabetes
title_full_unstemmed Identification of Microbiota Biomarkers With Orthologous Gene Annotation for Type 2 Diabetes
title_short Identification of Microbiota Biomarkers With Orthologous Gene Annotation for Type 2 Diabetes
title_sort identification of microbiota biomarkers with orthologous gene annotation for type 2 diabetes
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8299781/
https://www.ncbi.nlm.nih.gov/pubmed/34305880
http://dx.doi.org/10.3389/fmicb.2021.711244
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