Using high-throughput metabolomics to discover perturbed metabolic pathways and biomarkers of allergic rhinitis as potential targets to reveal the effects and mechanism of geniposide

Abstract

In this study, we probed the molecular mechanisms of metabolic biomarkers and pathways affected by the bioactive ingredient geniposide (GP), which was shown to protect against experimental allergic rhinitis in mice.

UHPLC‐Q‐Orbitrap HR‐MS‐Based Metabolomics for Profiling the Sida rhombifolia Metabolites with Different Plant Organs and Cultivation Ages

“…The values of R2 and Q2 were used to determine the model's quality. The model is acceptable if R2>0.5 and Q2>0.4 [19,20] . The OPLS‐DA model for the classification of sidaguri extract by plan ograns and age has values of R2X=0.974, R2Y=0.923, and Q2=0.855.…”

“…The model is acceptable if R2 > 0.5 and Q2 > 0.4. [19,20] The OPLS-DA model for the classification of sidaguri extract by plan ograns and age has values of R2X = 0.974, R2Y = 0.923, and Q2 = 0.855. Based on the results, the model is acceptable and fit for prediction.…”

“…[13] A total of seven metabolites from the steroid group were identified in this study. These include 20-hydroxyecdysone ( 16), 20-hydroxyecdysone-3-OÀ β-D-glucopyranoside (17), 25acetoxy-20-hydroxyecdysone-3-OÀ β-D-glucopyranoside (18), polypodine B (19), stigmasterol (20), and ecdysone (21). An example of the fragmentation pattern of steroid group metabolites is 20 5).…”

Exploring the Microbiome Analysis and Visualization Landscape

“…Visualization —The link between taxa and functions can be visualized using bipartite graphs ( Figure 7C ) or interactive stacked bar charts using highlighting, as was done in Burrito ( McNally et al, 2018 ). The result of functional profiling are typically represented in a metabolic pathway network ( Figure 7D ) ( Zhang et al, 2019 ).…”

“…Changes of microbial sample compositions are captured across time using unweighted UniFrac, whereas in TIME dynamic time warping distance is used to capture groups of taxa showing similar trends over time. TIME identifies FIGURE 7 | An overview of the visual encodings used to display feature volatility and functional profiling; (A) eveloution of relative abundance over time visualized using a linechart in q2-longitudinal (Bokulich et al, 2018), (B) associations between taxa based on Granger causality testing represented in a node-link diagram in TIME (Baksi et al, 2018), (C) taxa-function relationship displayed using a bipartite graph in BURRITO (McNally et al, 2018), (D) KEGG metabolic pathway network (Zhang et al, 2019), (E) taxa clustered based on similar trends in time in the web-app TIME (Baksi et al, 2018).…”