Changes in the composition of the fecal metabolome and gut microbiota contribute to intervertebral disk degeneration in a rabbit model

Abstract

Purpose: Lower back pain (LBP), mainly caused by intervertebral disk (IVD) degeneration (IDD), is widely prevalent worldwide and is a serious socioeconomic burden. Numerous factors may trigger this degenerative process, and microbial dysbiosis has recently been implicated as one of the likely causes. However, the exact relationship between IDD and the microbiome remains obscure. In this study, we investigated the gut microbiota composition and fecal metabolic phenotype and discussed the possible influences of microbiome dysbiosis on IDD.

Methods: Fecal DNA was extracted from 16 fecal samples (eight rabbit models with IDD and eight sex- and age-matched healthy controls) and analyzed by high-throughput 16S rDNA sequencing. The fecal samples were also analyzed by liquid chromatography-mass spectrometry-based metabolomics. Multivariate analyses were conducted for the relationship between the omics data and IDD, linear discriminant analysis effect size was employed for biomarker discovery. Moreover, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to annotate the differential metabolites. The potential correlation between differential gut microbiota and metabolites was then assessed.

Results: The 16S rDNA sequencing results showed that the β-diversity of the gut microbiota was significantly different between the IDD and control groups, with distinct abundance levels of dominant genera. Moreover, 59 metabolites were significantly upregulated and 91 were downregulated in IDD rabbits versus the controls. The KEGG enrichment analysis revealed that the top pathways remarkably impacted by IDD were tyrosine metabolism, amino sugar and nucleotide sugar metabolism, benzoate degradation, ABC transporters, ascorbate and aldarate metabolism, pantothenate and CoA biosynthesis, and pyrimidine metabolism. The correlation analysis revealed that DL-tyrosine and N-acetylmuramic acid were associated with multiple differential bacterial genera, including Helicobacter and Vibrio, which may play important roles in the process of IVD degeneration.

Conclusion: Our findings revealed that IDD altered gut microbiota and fecal metabolites in a rabbit model. The correlation analysis of microbiota and metabolome provides a deeper understanding of IDD and its possible etiopathogenesis. These results also provide a direction and theoretical basis for the clinical application of fecal transplantation, probiotics, and other methods to regulate gut microbiota in the treatment of LBP caused by IDD.

Keywords: Fecal metabolomics; Gut microbiota; Intervertebral disk degeneration; Lower back pain.

Fig. 1

Establishment of the rabbit IDD model. We punctured the disk to induce IDD in rabbits. a, b MRI T2WI imaging (a) and X-ray imaging (b) of the IDD model and control groups 4 weeks after the operation. c Representative images of hematoxylin and eosin (H&E) staining of the nucleus pulposus from the IDD and control groups

Fig. 2

IDD significantly changes the species abundance of gut microbiota. Compositional alteration of gut microbiota between the rabbit IDD models and healthy controls. a, b PCoA of the weighted (a) and unweighted (b) UniFrac distances for the normal controls (red circles) and rabbit IDD models (green circles). c Relative gut microbiota abundance in the control and IDD groups at the phylum level. d LEfSe analysis revealing significant differences in the bacterial taxa of the rabbit IDD models and the normal controls. e LEfSe cladogram showing six taxonomic levels from kingdom to genus. Significantly enriched bacterial taxa obtained in healthy controls are indicated by green circles and shading. Significantly enriched bacterial taxa obtained in the rabbit IDD models are indicated by red circles and shading

Fig. 3

IDD markedly alters the fecal metabolome. LC–MS technology confirmed differences in the IDD and control group metabolic profiles. a, b Principal component analysis (PCA) score plot of the LC–MS spectra data for the IDD and control groups in positive ion (a) and negative ion (b) modes. c, d Orthogonal partial least-squares discriminant analysis (OPLS-DA) score plot of the LC–MS spectra data in positive ion (c) and negative ion (d) modes. e, f OPLS-DA permutation testing showing response of 200 permutations in the positive (e) and negative (f) ion modes. g, h The volcano charts showing the differences in positive ion metabolites (g) and negative ion metabolites (h) between the groups. Red dots (up) represent significantly upregulated metabolites (OPLS-DA VIP > 1, P < 0.05); green dots (down) represent significantly downregulated metabolites (OPLS-DA VIP > 1, P < 0.05); black dots (no significant difference) represent insignificantly changed metabolites. i Bubble diagram of the 10 most enriched KEGG pathways in the comparison of the IDD and control. The sizes of the bubbles indicate the metabolite numbers enriched in the KEGG pathways, and the color of the bubble represents the P-value

Fig. 4

Association analysis of differential gut microbiota and fecal metabolome in IDD. Correlation relationships between discriminative genus-level microorganisms and metabolites in the fecal samples of IDD. a Hierarchical clustering heat map of the Spearman correlation analysis of differential gut microbiota and fecal metabolome. b Network diagram of Spearman correlation analysis of differential gut microbiota and fecal metabolome. The discriminative genera are marked with circles, and the discriminative fecal metabolites are marked with squares