Flaxseed Lignan Alleviates the Paracetamol-Induced Hepatotoxicity Associated with Regulation of Gut Microbiota and Serum Metabolome

Abstract

This study examined the protective effect of flaxseed lignans on liver damage caused by an overdose of paracetamol (PAM). The findings demonstrated that administering 800 mg/kg/d flaxseed lignan prior to PAM significantly decreased the serum aspartate aminotransferase (AST), alanine aminotransferase (ALT), and total bilirubin (TBi) levels, while it increased liver superoxide dismutase (SOD) and glutathione (GSH) levels in mice. Flaxseed lignan renovated the gut microbiota dysbiosis induced by PAM by promoting the proliferation of sulfonolipid (SL) producing bacteria such as Alistipes and lignan-deglycosolating bacteria such as Ruminococcus while inhibiting the growth of opportunistic pathogen bacteria such as Acinetobacter and Clostridium. Furthermore, flaxseed lignan modulated the serum metabolomic profile after PAM administration, specifically in the taurine and hypotaurine metabolism, phenylalanine metabolism, and pyrimidine metabolism. The study identified eight potential biomarkers, including enterolactone, cervonyl carnitine, acutilobin, and PC (20:3(5Z, 8Z, 11Z)/20:0). Overall, the results suggest that flaxseed lignan can alleviate PAM-induced hepatotoxicity and may be beneficial in preventing drug-induced microbiome and metabolomic disorders.

Keywords: flaxseed lignan; gut microbiota; liver injury; paracetamol; untargeted metabolome.

Figure 1

Effect of SIM and flaxseed lignan on liver function in PAM-induced hepatoxicity mice. (A) Diagram of research experimental design. (B) Photomicrograph of hepatic tissue (200×, scale bar: 100 μm). (C) Serum levels of ALT, AST, and Tbi. (D) MDA, SOD, and GSH levels in the liver tissue. CV: central vein; N: necrosis; I: inflammation. * means p < 0.05 when compared with NC group; # and ## means p < 0.05 and p < 0.01 when compared with the PAM group, respectively; ns means p > 0.05 when compared with the PAM group.

Figure 2

Effects of SIM and flaxseed lignan on microbial diversity and structure in the fecal samples. (A) α-diversity indexes are presented by box plot. (B) Non-metric multidimensional scaling (NMDS) result based on unweighted UniFrac distance matrices. (C) The composition of the top 10 gut microbiota at the phylum level. (D) The composition of the top 10 gut microbiota at the genus level. ** means p < 0.001 when compared with the PAM group.

Figure 3

Analysis of significantly regulated intestinal microbiota by Lefse (A) and Metastats (B) between the two groups. Data (n = 9) are expressed as mean ± SEM. Only taxa meeting an LDA significant threshold > 4 are shown.

Figure 4

Multivariate statistical analysis of untargeted metabolomics data in the serum samples. (A) PCA score plot based on the combined positive and negative model data. (B) Superclass distribution of detected metabolites in all samples. (C) OPLS-DA analysis of three comparison groups: PAM vs. NC (left), SIM vs. PAM (middle), and flaxseed lignan vs. PAM (right).

Figure 5

Differently expressed serum metabolites and pathways for SIM and flaxseed lignan pretreatment mice (positive ion combined with negative ion). (A) Volcano plots for the model-separated metabolites following the conditions of VIP > 1 and p (corr) < 0.01 with 95% jackknifed confidence intervals. PAM vs. NC (upper), SIM vs. PAM (middle), and flaxseed lignan vs. PAM (bottom). (B) Bubble plots for the KEGG pathway enrichment of the changed metabolites between each compared group. PAM vs. NC (upper), SIM vs. PAM (middle), and flaxseed lignan vs. PAM (bottom).

Figure 6

Correlation among regulated gut microbiota, biochemical parameters, and changed metabolites. (A) Heatmap of Spearman’s correlation between changed bacterial taxa and biochemical parameters. (B) Pie network of Spearman’s correlation between changed bacterial taxa and regulated metabolites. Significant differences are indicated as * (p < 0.05). Red lines indicate significantly positive correlation, green lines indicate significantly negative correlation.