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.
Front Endocrinol (Lausanne). 2023 Apr 12;14:1128767. doi: 10.3389/fendo.2023.1128767. eCollection 2023.
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.
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).
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 group, , and .
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.
人类肠道微生物群（GM）是一个动态系统，其中社区成员之间的生态相互作用影响宿主代谢。了解支配 GM 与其宿主之间双向交流的原则，是揭示细菌生态学如何影响宿主临床变量的最有价值的研究之一。
在这里，我们使用 SparCC 从 2 型糖尿病（T2D）不同阶段的墨西哥患者 GM 的 16S rRNA 基因扩增子数据中推断关联网络：NG（血糖正常）、IFG（空腹血糖受损）、IGT（葡萄糖耐量受损）、IFG+IGT（空腹血糖受损加葡萄糖耐量受损）、T2D 和 T2D 治疗（正在进行 5 年治疗的 T2D）。
通过探索 T2D 不同阶段的网络拓扑结构，我们观察到随着疾病的进展，网络失去了细菌之间的关联。这表明微生物群落对 T2D 个体的扰动变得高度敏感。为了确定指导这种转变的那些属，我们通过计算找到了墨西哥 T2D 队列的关键分类群（驱动节点）和核心属。总的来说，我们建议了一组属可以作为一个墨西哥 T2D 队列中 T2D 进展的潜在生物标志物，其中包括属、属和属。
基于网络方法，本研究提出了一组属，可以作为区分墨西哥 T2D 患者不同程度进展的潜在生物标志物。除了将我们的结论限制在一个人群之外，我们还提出了一种计算方法来将生态网络与 T2D 的临床阶段联系起来，这是精准医学领域的一个理想目标。
