Zhen Jianhua, Zhao Pengfei, Li Yini, Cai Yanan, Yu Wanchen, Wang Wei, Zhao Lu, Wang Hesong, Huang Guangrui, Xu Anlong
School of Life Sciences, Beijing University of Chinese Medicine, Beijing, People's Republic of China.
Oncology Department, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, People's Republic of China.
J Asthma Allergy. 2022 Jan 29;15:117-131. doi: 10.2147/JAA.S334752. eCollection 2022.
To explore changes in the gut microbiota (GM), urine metabolome and plasma proteome in individuals with allergies using multiomics analyses, and identify the key components and mechanism.
This was a cross-sectional study. All subjects were recruited to collect fecal, urine and blood samples. 16S rDNA sequencing was used to analyze the structure and function of the GM, liquid chromatography mass spectrometry was used to quantify metabolites in the urine, and data-independent acquisition quantitative proteome analysis was used to detect proteins in the plasma. Differences in GM, urine metabolites and plasma proteins between allergic and healthy individuals were displayed using principal component analysis (PCoA) and heatmap, and the co-occurrence network was visualized in Cytoscape using Spearman correlation among differential predominant genera, metabolites and proteins. The functional analysis was performed according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) dataset. The allergy-related cytokines, IL-4, IL-6 and IL-13, were measured to evaluate the effect of indole derivatives on LPS-induced macrophage activation.
GM α indexes, β distances and the relative abundance of the core differential genera in the allergic group were different from those of healthy individuals, which resulted in a separate distribution in the PCoA and enterotypes. Similarly, the concentrations of 393 metabolites and 144 proteins were different between allergic and healthy individuals. Then, 634 significant correlations were identified among 6 predominant differential genera, 24 differential metabolites and 104 differential proteins, 301 of which were negative and 333 of which were positive. Notably, a core network centered on tryptophan metabolites, indole-3-butyric acid (IBA) and indole-3-lactic acid (ILA), displayed high consistency with the results of KEGG pathway analysis. In the LPS-stimulated macrophages, IBA reduced the expression of IL-4 and IL-6, and ILA inhibited the upregulation of IL-6.
The GM, urine metabolome and plasma proteome underwent systematic change in allergic individuals compared to healthy individuals, among which indole derivatives from tryptophan metabolism might play key roles in the progression of allergies and could serve as therapeutic targets of allergy.
采用多组学分析方法探究过敏个体的肠道微生物群（GM）、尿液代谢组和血浆蛋白质组的变化，并确定关键成分和机制。
这是一项横断面研究。招募所有受试者以收集粪便、尿液和血液样本。采用16S rDNA测序分析GM的结构和功能，采用液相色谱质谱法对尿液中的代谢物进行定量，并采用数据非依赖采集定量蛋白质组分析检测血浆中的蛋白质。使用主成分分析（PCoA）和热图展示过敏个体与健康个体之间GM、尿液代谢物和血浆蛋白质的差异，并使用差异优势属、代谢物和蛋白质之间的Spearman相关性在Cytoscape中可视化共现网络。根据京都基因与基因组百科全书（KEGG）数据集进行功能分析。检测过敏相关细胞因子IL-4、IL-6和IL-13，以评估吲哚衍生物对脂多糖诱导的巨噬细胞活化的影响。
过敏组的GMα指数、β距离和核心差异属的相对丰度与健康个体不同，这导致在PCoA和肠型中呈现单独分布。同样，过敏个体与健康个体之间393种代谢物和144种蛋白质的浓度也不同。然后，在6个主要差异属、24种差异代谢物和104种差异蛋白质之间鉴定出634个显著相关性，其中301个为负相关，333个为正相关。值得注意的是，以色氨酸代谢物吲哚-3-丁酸（IBA）和吲哚-3-乳酸（ILA）为中心的核心网络与KEGG通路分析结果高度一致。在脂多糖刺激的巨噬细胞中，IBA降低了IL-4和IL-6的表达，ILA抑制了IL-6的上调。
与健康个体相比，过敏个体的GM、尿液代谢组和血浆蛋白质组发生了系统性变化，其中色氨酸代谢产生的吲哚衍生物可能在过敏进展中起关键作用，并可作为过敏的治疗靶点。
