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:: Zhang, Yu-Hang
Guo, Wei
Tao, Zeng
ShiQi, Zhang
Chen, Lei
Gamarra, Margarita
Mansour, Romany F.
Escorcia-Gutierrez, Jose
Huang, Tao
Yu Dong, Cai

Tipo de recurso:: Article of journal

Fecha de publicación:: 2021

Institución:: Corporación Universidad de la Costa

Repositorio:: REDICUC - Repositorio CUC

Idioma:: eng

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dc.title.spa.fl_str_mv	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
spellingShingle	Identification of microbiota biomarkers with orthologous gene annotation for type 2 diabetes type 2 diabetes gut microbiome machine learning feature selection support vector machine microbiota biomarkers
title_short	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_sort	Identification of microbiota biomarkers with orthologous gene annotation for type 2 diabetes
dc.creator.fl_str_mv	Zhang, Yu-Hang Guo, Wei Tao, Zeng ShiQi, Zhang Chen, Lei Gamarra, Margarita Mansour, Romany F. Escorcia-Gutierrez, Jose Huang, Tao Yu Dong, Cai
dc.contributor.author.spa.fl_str_mv	Zhang, Yu-Hang Guo, Wei Tao, Zeng ShiQi, Zhang Chen, Lei Gamarra, Margarita Mansour, Romany F. Escorcia-Gutierrez, Jose Huang, Tao Yu Dong, Cai
dc.subject.spa.fl_str_mv	type 2 diabetes gut microbiome machine learning feature selection support vector machine microbiota biomarkers
topic	type 2 diabetes gut microbiome machine learning feature selection support vector machine microbiota biomarkers
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.
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-08-19T15:34:31Z
dc.date.available.none.fl_str_mv	2021-08-19T15:34:31Z
dc.date.issued.none.fl_str_mv	2021-07-09
dc.type.spa.fl_str_mv	Artículo de revista
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dc.identifier.issn.spa.fl_str_mv	1664-302X
dc.identifier.uri.spa.fl_str_mv	https://hdl.handle.net/11323/8555
dc.identifier.doi.spa.fl_str_mv	10.3389/fmicb.2021.711244
dc.identifier.instname.spa.fl_str_mv	Corporación Universidad de la Costa
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identifier_str_mv	1664-302X 10.3389/fmicb.2021.711244 Corporación Universidad de la Costa REDICUC - Repositorio CUC
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dc.language.iso.none.fl_str_mv	eng
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dc.relation.references.spa.fl_str_mv	Arthur, R., Rohrmann, S., Møller, H., Selvin, E., Dobs, A. S., Kanarek, N., et al. (2017). Pre-diabetes and serum sex steroid hormones among US men. Andrology 5, 49–57. doi: 10.1111/andr.12287 Bakris, G. L., Agarwal, R., Anker, S. D., Pitt, B., Ruilope, L. M., Rossing, P., et al. (2020). Effect of finerenone on chronic kidney disease outcomes in type 2 diabetes. N. Engl. J. Med. 383, 2219–2229. doi: 10.1056/nejmoa2025845 Beli, E., Prabakaran, S., Krishnan, P., Evans-Molina, C., and Grant, M. B. (2019). Loss of diurnal oscillatory rhythms in gut microbiota correlates with changes in circulating metabolites in type 2 diabetic db/db mice. Nutrients 11:2310. doi: 10.3390/nu11102310 Bullard, K. M., Cowie, C. C., Lessem, S. E., Saydah, S. H., Menke, A., Geiss, L. S., et al. (2018). Prevalence of diagnosed diabetes in adults by diabetes type—United States, 2016. Morb. Mortal. Wkly. Rep. 67:359. doi: 10.15585/mmwr.mm6712a2 Carrillo-Larco, R. M., Altez-Fernandez, C., Acevedo-Rodriguez, J. G., Ortiz-Acha, K., and Ugarte-Gil, C. (2019). Leptospirosis as a risk factor for chronic kidney disease: a systematic review of observational studies. PLoS Neglect. Trop. Dis. 13:e0007458. doi: 10.1371/journal.pntd.0007458 Chatterjee, S., Khunti, K., and Davies, M. J. (2017). Type 2 diabetes. Lancet 389, 2239–2251. Chen, L., Wang, S., Zhang, Y.-H., Li, J., Xing, Z.-H., Yang, J., et al. (2017). Identify key sequence features to improve CRISPR sgRNA efficacy. IEEE Access 5, 26582–26590. doi: 10.1109/access.2017.2775703 Chen, L., Zeng, T., Pan, X., Zhang, Y. H., Huang, T., and Cai, Y. D. (2019). Identifying methylation pattern and genes associated with breast cancer subtypes. Int. J. Mol. Sci. 20:4269. doi: 10.3390/ijms20174269 Cortes, C., and Vapnik, V. (1995). Support-vector networks. Mach. Learn. 20, 273–297. Deputy, N. P., Kim, S. Y., Conrey, E. J., and Bullard, K. M. (2018). 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spelling	Zhang, Yu-HangGuo, WeiTao, ZengShiQi, ZhangChen, LeiGamarra, MargaritaMansour, Romany F.Escorcia-Gutierrez, JoseHuang, TaoYu Dong, Cai2021-08-19T15:34:31Z2021-08-19T15:34:31Z2021-07-091664-302Xhttps://hdl.handle.net/11323/855510.3389/fmicb.2021.711244Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/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.Zhang, Yu-Hang-will be generated-orcid-0000-0003-3825-0796-600Guo, Wei-will be generated-orcid-0000-0001-5445-7872-600Tao, ZengShiQi, ZhangChen, Lei-will be generated-orcid-0000-0001-6652-610X-600Gamarra, Margarita-will be generated-orcid-0000-0003-1834-2984-600Mansour, Romany F.-will be generated-orcid-0000-0002-4290-0387-600Escorcia-Gutierrez, Jose-will be generated-orcid-0000-0003-0518-3187-600Huang, Tao-will be generated-orcid-0000-0002-1888-7389-600Yu Dong, Caiapplication/pdfengCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2FRONTIERS IN MICROBIOLOGYhttps://www.frontiersin.org/articles/10.3389/fmicb.2021.711244/fulltype 2 diabetesgut microbiomemachine learningfeature selectionsupport vector machinemicrobiota biomarkersIdentification of microbiota biomarkers with orthologous gene annotation for type 2 diabetesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionArthur, R., Rohrmann, S., Møller, H., Selvin, E., Dobs, A. S., Kanarek, N., et al. (2017). Pre-diabetes and serum sex steroid hormones among US men. Andrology 5, 49–57. doi: 10.1111/andr.12287Bakris, G. L., Agarwal, R., Anker, S. D., Pitt, B., Ruilope, L. M., Rossing, P., et al. (2020). Effect of finerenone on chronic kidney disease outcomes in type 2 diabetes. N. Engl. J. Med. 383, 2219–2229. doi: 10.1056/nejmoa2025845Beli, E., Prabakaran, S., Krishnan, P., Evans-Molina, C., and Grant, M. B. (2019). Loss of diurnal oscillatory rhythms in gut microbiota correlates with changes in circulating metabolites in type 2 diabetic db/db mice. Nutrients 11:2310. doi: 10.3390/nu11102310Bullard, K. M., Cowie, C. C., Lessem, S. E., Saydah, S. H., Menke, A., Geiss, L. S., et al. (2018). Prevalence of diagnosed diabetes in adults by diabetes type—United States, 2016. Morb. Mortal. Wkly. Rep. 67:359. doi: 10.15585/mmwr.mm6712a2Carrillo-Larco, R. M., Altez-Fernandez, C., Acevedo-Rodriguez, J. G., Ortiz-Acha, K., and Ugarte-Gil, C. (2019). 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Genet. 9:515. doi: 10.3389/fgene.2018.00515PublicationORIGINALIdentification of Microbiota Biomarkers With Orthologous Gene Annotation for Type 2 Diabetes.pdfIdentification of Microbiota Biomarkers With Orthologous Gene Annotation for Type 2 Diabetes.pdfapplication/pdf6001426https://repositorio.cuc.edu.co/bitstreams/b34954bf-07ef-4125-b834-dd21caabeb67/download7ad9975e04318c91386617c52cfa3bf5MD51ORIGINALIdentification of Microbiota Biomarkers With Orthologous Gene Annotation for Type 2 Diabetes.pdfIdentification of Microbiota Biomarkers With Orthologous Gene Annotation for Type 2 Diabetes.pdfapplication/pdf6001426https://repositorio.cuc.edu.co/bitstreams/ad68b673-5619-4a46-a1c6-8a5673c3ef85/download7ad9975e04318c91386617c52cfa3bf5MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/8d50e287-43cf-4957-b70b-cdfd5a7a4f0e/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; 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Identification of microbiota biomarkers with orthologous gene annotation for type 2 diabetes

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