A network perspective on the ecology of gut microbiota and progression of type 2 diabetes: Linkages to keystone taxa in a Mexican cohort
Por:
Esquivel-Hernández D.A., Martínez-López Y.E., Sánchez-Castañeda J.P., Neri-Rosario D., Padrón-Manrique C., Giron-Villalobos D., Mendoza-Ortíz C., Resendis-Antonio O.
Publicada:
1 ene 2023
Categoría:
Endocrinology, diabetes and metabolism
Resumen:
Introduction: The human gut microbiota (GM) is a dynamic system which ecological interactions among the community members affect the host metabolism. Understanding the principles that rule the bidirectional communication between GM and its host, is one of the most valuable enterprise for uncovering how bacterial ecology influences the clinical variables in the host. Methods: Here, we used SparCC to infer association networks in 16S rRNA gene amplicon data from the GM of a cohort of Mexican patients with type 2 diabetes (T2D) in different stages: NG (normoglycemic), IFG (impaired fasting glucose), IGT (impaired glucose tolerance), IFG + IGT (impaired fasting glucose plus impaired glucose tolerance), T2D and T2D treated (T2D with a 5-year ongoing treatment). Results: By exploring the network topology from the different stages of T2D, we observed that, as the disease progress, the networks lose the association between bacteria. It suggests that the microbial community becomes highly sensitive to perturbations in individuals with T2D. With the purpose to identify those genera that guide this transition, we computationally found keystone taxa (driver nodes) and core genera for a Mexican T2D cohort. Altogether, we suggest a set of genera driving the progress of the T2D in a Mexican cohort, among them Ruminococcaceae NK4A214 group, Ruminococcaceae UCG-010, Ruminococcaceae UCG-002, Ruminococcaceae UCG-005, Alistipes, Anaerostipes, and Terrisporobacter. Discussion: Based on a network approach, this study suggests a set of genera that can serve as a potential biomarker to distinguish the distinct degree of advances in T2D for a Mexican cohort of patients. Beyond limiting our conclusion to one population, we present a computational pipeline to link ecological networks and clinical stages in T2D, and desirable aim to advance in the field of precision medicine. Copyright © 2023 Esquivel-Hernández, Martínez-López, Sánchez-Castañeda, Neri-Rosario, Padrón-Manrique, Giron-Villalobos, Mendoza-Ortíz and Resendis-Antonio.
Filiaciones:
Esquivel-Hernández D.A.:
Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Mexico City, Mexico
Martínez-López Y.E.:
Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Mexico City, Mexico
Programa de Doctorado en Ciencias Médicas, Odontológicas y de la Salud, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, Mexico
Metabolic Research Laboratory, Department of Medicine and Nutrition, University of Guanajuato, Guanajuato, León, Mexico
Sánchez-Castañeda J.P.:
Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Mexico City, Mexico
Programa de Maestría en Ciencias Bioquímicas, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, Mexico
Neri-Rosario D.:
Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Mexico City, Mexico
Programa de Maestría en Ciencias Bioquímicas, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, Mexico
Padrón-Manrique C.:
Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Mexico City, Mexico
Programa de Doctorado en Ciencias Biomédicas, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, Mexico
Giron-Villalobos D.:
Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Mexico City, Mexico
Programa de Maestría en Ciencias Bioquímicas, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, Mexico
Mendoza-Ortíz C.:
Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Mexico City, Mexico
Programa de Maestría en Ciencias Bioquímicas, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, Mexico
Resendis-Antonio O.:
Human Systems Biology Laboratory, Instituto Nacional de Medicina Genómica (INMEGEN), Mexico City, Mexico
Coordinación de la Investigación Científica – Red de Apoyo a la Investigación, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, Mexico
Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, Mexico
Green Submitted, Green Accepted, gold, All Open Access, Gold, Green
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