Department of Genetics and Molecular Biology, College of Medicine and Health Sciences, Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, UAE.
Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, UAE.
Sci Rep. 2023 Oct 20;13(1):17943. doi: 10.1038/s41598-023-45066-7.
Type 2 Diabetes Mellitus has reached epidemic levels globally, and several studies have confirmed a link between gut microbial dysbiosis and aberrant glucose homeostasis among people with diabetes. While the assumption is that abnormal metabolomic signatures would often accompany microbial dysbiosis, the connection remains largely unknown. In this study, we investigated how diet changed the gut bacteriome, mycobiome and metabolome in people with and without type 2 Diabetes.1 Differential abundance testing determined that the metabolites Propionate, U8, and 2-Hydroxybutyrate were significantly lower, and 3-Hydroxyphenyl acetate was higher in the high fiber diet compared to low fiber diet in the healthy control group. Next, using multi-omics factor analysis (MOFA2), we attempted to uncover sources of variability that drive each of the different groups (bacterial, fungal, and metabolite) on all samples combined (control and DM II). Performing variance decomposition, ten latent factors were identified, and then each latent factor was tested for significant correlations with age, BMI, diet, and gender. Latent Factor1 was the most significantly correlated. Remarkably, the model revealed that the mycobiome explained most of the variance in the DM II group (12.5%) whereas bacteria explained most of the variance in the control group (64.2% vs. 10.4% in the DM II group). The latent Factor1 was significantly correlated with dietary intake (q < 0.01). Further analyses of the impact of bacterial and fungal genera on Factor1 determined that the nine bacterial genera (Phocaeicola, Ligilactobacillus, Mesosutterella, Acidaminococcus, Dorea A, CAG-317, Caecibacter, Prevotella and Gemmiger) and one fungal genus (Malassezia furfur) were found to have high factor weights (absolute weight > 0.6). Alternatively, a linear regression model was fitted per disease group for each genus to visualize the relationship between the factor values and feature abundances, showing Xylose with positive weights and Propionate, U8, and 2-Hydroxybutyrate with negative weights. This data provides new information on the microbially derived changes that influence metabolic phenotypes in response to different diets and disease conditions in humans.
2 型糖尿病在全球范围内已达到流行水平，多项研究证实肠道微生物失调与糖尿病患者葡萄糖稳态异常之间存在关联。虽然人们认为异常代谢特征通常伴随着微生物失调，但这种联系在很大程度上仍不清楚。在这项研究中，我们调查了饮食如何改变 2 型糖尿病患者和非糖尿病患者的肠道细菌组、真菌组和代谢组。差异丰度检验确定，与低纤维饮食相比，高纤维饮食中丙酸、U8 和 2-羟丁酸的含量显著降低，而 3-羟基苯乙酸的含量较高。接下来，我们使用多组学因子分析（MOFA2），试图揭示驱动所有样本（对照和 2 型糖尿病组）中不同组（细菌、真菌和代谢物）变化的来源。进行方差分解后，确定了 10 个潜在因子，然后对每个潜在因子与年龄、BMI、饮食和性别进行显著相关性测试。潜在因子 1 相关性最强。值得注意的是，该模型显示，真菌组解释了 2 型糖尿病组大部分的方差（12.5%），而细菌组解释了对照组大部分的方差（64.2%比 2 型糖尿病组的 10.4%）。潜在因子 1 与饮食摄入显著相关（q<0.01）。进一步分析细菌和真菌属对因子 1 的影响，确定了 9 个细菌属（Phocaeicola、Ligilactobacillus、Mesosutterella、Acidaminococcus、Dorea A、CAG-317、Caecibacter、Prevotella 和 Gemmiger）和 1 个真菌属（Malassezia furfur）具有较高的因子权重（绝对权重>0.6）。或者，为每个疾病组拟合了一个线性回归模型，以可视化因子值与特征丰度之间的关系，显示木糖具有正权重，丙酸、U8 和 2-羟丁酸具有负权重。该数据提供了有关微生物衍生变化的新信息，这些变化会影响不同饮食和疾病条件下人类的代谢表型。
