Machine Learning Identifies Key Cells and Therapeutic Targets in Intervertebral Disc Degeneration: SASP-Driven Matrix...

Area of Science:

• Biomedical research
• Molecular biology
• Genomics

Background:

• Intervertebral disc degeneration (IVDD) is a primary cause of low back pain, but its underlying cellular and molecular drivers are not fully understood.
• Senescence-associated secretory phenotype (SASP) signaling is implicated in degenerative diseases, including IVDD.

Purpose of the Study:

• To investigate the role of SASP signaling in IVDD pathogenesis.
• To identify key molecular targets and potential therapeutic interventions for IVDD.

Main Methods:

• Integration of single-cell and bulk RNA sequencing data.
• Weighted gene co-expression network analysis (WGCNA) and machine learning algorithms (LASSO, Random Forest, Boruta).
• In vitro cell culture and in vivo murine models of IVDD, including drug screening and molecular docking.

Main Results:

• Single-cell RNA sequencing revealed heterogeneity in nucleus pulposus cells (NPCs) with varying senescence and SASP levels.
• A core regulatory network involving Bone Morphogenetic Protein 2 (BMP2) and Matrix Metalloproteinase 3 (MMP3) was identified as central to SASP in IVDD.
• A SASP scoring model based on BMP2 and MMP3 demonstrated high diagnostic accuracy.
• Simvastatin emerged as a potent dual inhibitor of BMP2/MMP3, reducing NPC senescence and apoptosis in vitro and mitigating IVDD progression in vivo.

Conclusions:

• This study elucidates a SASP-driven regulatory framework in IVDD.
• BMP2 and MMP3 are identified as promising therapeutic targets for IVDD.
• Simvastatin shows potential as a therapeutic agent for slowing disc degeneration and alleviating low back pain.