Integrated 16S rRNA Gene Sequencing and Metabolomics Analysis to Investigate the Important Role of Osthole on Gut Microbiota and Serum Metabolites in Neuropathic Pain Mice

Abstract

Accumulating evidence suggests that neuropathic pain (NP) is closely connected to the metabolic disorder of gut microbiota, and natural products could relieve NP by regulating gut microbiota. The purpose of this study is to investigate the important regulatory effects of osthole on gut microbiota and serum metabolites in mice with chronic constriction injury (CCI). Mice's intestinal contents and serum metabolites were collected from the sham group, CCI group, and osthole treatment CCI group. The 16S rRNA gene sequencing was analyzed, based on Illumina NovaSeq platform, and ANOVA analysis were used to analyze the composition variety and screen differential expression of intestinal bacteria in the three groups. Ultra-high-performance liquid chromatography-quadrupole time of flight-tandem mass spectrometry (UHPLC-Q-TOF-MS) was used for analyzing the data obtained from serum specimens, and KEGG enrichment analysis was used to identify pathways of differential metabolites in the treatment of neuralgia mice. Furthermore, the Pearson method and Cytoscape soft were used to analyze the correlation network of differential metabolites, gut microbiota, and disease genes. The analysis results of 16S rRNA gene sequencing displayed that Bacteroidetes, Firmicutes, and Verrucomicrobia were highly correlated with NP after osthole treatment at the phylum level. Akkermansia, Lachnospiraceae_unclassified, Lachnospiraceae_NK4A136_group, Bacteroides, Lactobacillus, and Clostridiales_unclassified exhibited higher relative abundance and were considered important microbial members at genus level in neuralgia mice. Serum metabolomics results showed that 131 metabolites were considered to be significantly different in the CCI group compared to the sham group, and 44 metabolites were significantly expressed between the osthole treatment group and the CCI group. At the same time, we found that 29 differential metabolites in the two comparison groups were overlapping. Integrated analysis results showed that many intestinal microorganisms and metabolites have a strong positive correlation. The correlation network diagram displays that 10 genes were involved in the process of osthole alleviating NP through a metabolic pathway and gut microbiota, including IGF2, GDAP1, MYLK, IL18, CD55, MIR331, FHIT, F3, ERBB4, and ITGB3. Our findings have preliminarily confirmed that NP is closely related to metabolism and intestinal microbial imbalance, and osthole can improve the metabolic disorder of NP by acting on gut microbiota.

Keywords: 16S rRNA gene sequencing; gut microbiota; neuropathic pain; osthole; serum metabolomes.

FIGURE 1

The experimental flow chart. The mechanism of osthole against chronic constriction injury (CCI) neuralgia mice was analyzed by 16S rRNA and metabolomics.

FIGURE 2

Effects of osthole on the diversity of gut microbiota. (A) The rank abundance curves. (B) The Chao1 indices were used to estimate the diversity of the gut microbiota. (C) The Shannon indices were used to estimate the diversity of the gut microbiota. (D) Principal coordinates analysis (PCoA) diagram illustrating the difference in microbial composition among the three groups.

FIGURE 3

Effects of osthole on the structure of gut microbiota. Based on the quantitative data, the relative abundance of gut microbiota was displayed in a stacked bar plot, including the dissimilarity of gut microbiota in phyla level (A) and in phyla level (C). (B) The gut microbiota differed significantly among the groups in phyla level (B) and in phyla level (D). ^##p < 0.01, ^#p < 0.05 compared with sham group; *p < 0.05, compared with chronic constriction injury (CCI) group, analysis of variance (ANOVA) followed by Bonferroni post-hoc test, n = 6 mice/group.

FIGURE 4

Identification of most specific bacterial taxa by LEfSe analysis. Comparison of gut microbiota composition among experimental groups based on linear discriminant analysis (LDA) effect size (LEfSe) (A) and LDA (B). Dot sizes are proportional to the abundance of certain taxa in the taxonomic cladogram, and the greatest differences among groups after LDA using a threshold score of >4.0.

FIGURE 5

Osthole treatment changed serum metabolites in chronic constriction injury (CCI) mice. (A) Total ion flow of mice in each group under positive and negative ion mode. (B) PLS-DA 2D and 3D diagram. (C) OPLS-DA analysis. (D) The variable important for the project (VIP) value of differentially expressed MS2 metabolites.

FIGURE 6

Differential metabolite identification and pathway analysis. Expression of differential metabolites in each comparison group was represented by Volcano plot (A), Venn diagram (B), heat maps (C), and FC histogram (D). (E) Kyoto encyclopedia of genes and genomes (KEGG) analysis was used to enrich the pathway of differential metabolites. Data were calculated by the Pearson correlation method after mean centering and unit variance scaling.

FIGURE 7

Integrated analyses of metabolites, gut microbiota, and disease genes. Correlation heatmap (*p < 0.05, **p < 0.01) (A) and correlation network (B) of differential microbiota and metabolites. (C) Correlation network diagram of microbiota, metabolites, and disease gene. Data were calculated by the Pearson correlation method after mean centering and unit variance scaling.