Molecular Traces of Microbial Cross-Kingdom Migration: From the Gut Ecosystem to the Intervertebral Disc Microenvironment
Hao Liu, Bin Xie, Hang Zhuo, Bowen He, Jiahui Dai, Zelin Zhou, Gengyang Shen, Binwei Chen, Jingjing Tang, Hui Ren, Xiaobing Jiang
Journal:Spine Journal
IF:5.1
DOI:10.1016/j.spinee.2026.04.027
PMID:
Published:2026-04-24
research field:分子生物学生物信息学免疫学微生物学肠-椎间盘轴系统生物学骨科学
Abstract
Background context Low back pain is a leading cause of disability worldwide, and lumbar intervertebral disc degeneration (IVDD) is strongly associated with its development. Recent studies have shown that the gut microbiota (GM) and its metabolites may be involved in the occurrence and development of IVDD through the gut-disc axis. However, the key microbes mediating this process and their specific molecular mechanisms remain unclear. Purpose This study aimed to identify the gut microbes that play a key role in the progression of IVDD using multi-omics approaches and clarify the specific mechanisms by which these microbes participate in IVDD by regulating host cell functions. Study design/Setting A single center, prospective cross-sectional study. Patient sample We prospectively included 113 patients who underwent surgical treatment for symptomatic lumbar degenerative diseases from May 2022 to May 2023, and their degenerated lumbar intervertebral disc (IVD) tissues as well as paired feces samples were collected. Outcome Measures Metagenomic next-generation sequencing (mNGS), modified Pfirrmann typing, Single-cell RNA sequencing (scRNA-seq), Bulk RNA sequencing (Bulk RNA-seq). Methods Clinical IVD samples and paired fecal samples were prospectively collected and subjected to multi-omics bioinformatics analysis. mNGS was used to analyze the microbial composition in IVD and paired fecal samples. scRNA-seq was employed to resolve the cellular heterogeneity of IVD tissues. Bulk RNA-seq was utilized to identify the characteristics of host response genes related to microbial exposure. Subsequent AUCell scoring was performed to evaluate the abundance of microbes in cell subsets. The CellChat algorithm was applied to analyze the microbe-mediated intercellular communication network of host cells. Results The raw detection rate of mNGS in IVD tissues was 100%, with a positive rate of 60.2% (68/113) after excluding background bacteria. A total of 505 genera and 1,528 microbial
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