生物
背景(考古学)
肠道菌群
受体
微生物群
生物信息学
孕烷X受体
计算生物学
G蛋白偶联受体
芳香烃受体
TLR4型
模式识别受体
炎症
系统生物学
信号转导
细胞生物学
免疫学
生物信息学
遗传学
先天免疫系统
基因
转录因子
核受体
古生物学
作者
Lokanand Koduru,Meiyappan Lakshmanan,Shawn Hoon,Dong-Yup Lee,Yuan Kun Lee,Dave Siak-Wei Ow
标识
DOI:10.3389/fmicb.2022.846555
摘要
The incidence and prevalence of inflammatory disorders have increased globally, and is projected to double in the next decade. Gut microbiome-based therapeutics have shown promise in ameliorating chronic inflammation. However, they are largely experimental, context- or strain-dependent and lack a clear mechanistic basis. This hinders precision probiotics and poses significant risk, especially to individuals with pre-existing conditions. Molecules secreted by gut microbiota act as ligands to several health-relevant receptors expressed in human gut, such as the G-protein coupled receptors (GPCRs), Toll-like receptor 4 (TLR4), pregnane X receptor (PXR), and aryl hydrocarbon receptor (AhR). Among these, the human AhR expressed in different tissues exhibits anti-inflammatory effects and shows activity against a wide range of ligands produced by gut bacteria. However, different AhR ligands induce varying host responses and signaling in a tissue/organ-specific manner, which remain mostly unknown. The emerging systems biology paradigm, with its powerful in silico tool repertoire, provides opportunities for comprehensive and high-throughput strain characterization. In particular, combining metabolic models with machine learning tools can be useful to delineate tissue and ligand-specific signaling and thus their causal mechanisms in disease and health. The knowledge of such a mechanistic basis is indispensable to account for strain heterogeneity and actualize precision probiotics.
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