GC/MS-Based Metabolomics Approach to Evaluate the Effect of Jackyakgamcho-Tang on Acute Colitis

Abstract

The objective of this study was to examine the effects of Jackyakgamcho-tang (JGT) on acute colitis. GC/MS-based metabolomics and NGS-based metagenomics were applied to investigate the alteration of metabolites and microbiota in an acute colitis model. The severity of acute colitis symptoms was alleviated by JGT treatment. Induction of colitis and JGT treatment changed compositions of gut microbiota and inflammatory cytokine levels (TNF-α and IL-6). They also substantially change metabolites (i.e., lactic acid, linoleic acid, monostearin, and palmitoylglycerol). In addition, some clear correlations were observed among metabolites, cytokine, and microbiota. This study highlights the applicability of metabolomics and metagenomics study for evaluating anti-inflammatory effects of a new functional herbal medicine as a therapeutic agent for acute colitis.

Figure 1

(a) Weight, (b) colon length, and (c) colon tissue pictures of rats treated with acetic acid and Jackyakgamcho-tang (JGT). (d) Disease activity index (DAI) value was calculated by using body weight loss, stool consistency, and blood in stool scores. Significant difference at  ^#p < 0.05,  ^##p < 0.01, and  ^###p < 0.001 compared to the control group. Significant difference at  ^∗p < 0.05,  ^∗∗p < 0.01, and  ^∗∗∗p <0.001 compared to acetic acid-induced acute colitis group.

Figure 2

Effect of JGT on inflammation in acute colitis rat gut tissue. (a) Representative images by H&E staining displaying colon segments on the day of sacrifice. (b) Levels of tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6). Significant difference at  ^#p < 0.05,  ^##p < 0.01, and  ^###p < 0.001 compared to the control group. Significant difference at  ^∗p < 0.05,  ^∗∗p < 0.01, and  ^∗∗∗p <0.001 compared to acetic acid-induced acute colitis group.

Figure 3

PLS-DA scores plots for control, acetic acid-induced acute colitis, and JGT treated groups derived from GC-MS data of serum (a, b) and feces (c, d) samples. These PLS-DA models were validated by a permutation test (n = 200).

Figure 4

Box plots of identified metabolites that contributed to the discriminating PLS-DA model (VIP > 1, p < 0.05) in (a) serum and (b) feces. Peak intensities of selected mass ions from GC/MS data were used for quantification. Data were based on six replicates (n = 6). For each compound, the statistical outcome was summarized within the figure (one-way ANOVA). Alphabet represents significant difference between samples (p < 0.05).

Figure 5

Integration of variable correlations. The heat map derived from correlations among metabolites of serum and feces, inflammatory cytokines, and gut microbiota phyla.