bioRxivMolecular BiologyPreprint
SIRT6 loss causes intervertebral disc degeneration in mice by promoting senescence and SASP status
Pranay L. RamtekeiD, Bahiyah WatsoniD, Mallory TociiD, Victoria A. Tran, Shira N. JohnstoniD, Maria TsingasiD, Ruteja A. BarveiD, Ramkrishna MitraiD, Richard F. LoeseriD, John A. Collins, Makarand V. RisbudiD
Abstract
Intervertebral disc degeneration is a major risk factor contributing to chronic low back and neck pain. While the etiological factors for disc degeneration vary, age is still one of the most important risk factors. Recent studies have shown the promising role of SIRT6 in mammalian aging and skeletal tissue health, however its role in the intervertebral disc health remains unexplored. We investigated the contribution of SIRT6 to disc health by studying the age-dependent spinal phenotype of mice with conditional deletion of Sirt6 in the disc (AcanCreERT2; Sirt6fl/fl). Histological studies showed a degenerative phenotype in knockout mice compared to Sirt6fl/fl control mice at 12 months which became pronounced at 24 months. RNA-Seq analysis of NP and AF tissues, quantitative histone analysis, and in vitro multiomics employing RNA-seq with ATAC-seq revealed that SIRT6-loss resulted in changes in acetylation and methylation status of specific Histone 3 lysine residues, thereby affecting DNA accessibility and transcriptomic landscape. A decrease in autophagy and an increase in DNA damage were also noted in Sirt6-deficient cells. Further mechanistic insights revealed that loss of SIRT6 increased senescence and SASP burden in the disc characterized by increased p21, {gamma}H2AX, IL-6, and TGF-{beta} abundance. Taken together our study highlights the contribution of SIRT6 in modulating DNA damage, autophagy and cell senescence, and its importance in maintaining disc health during aging thereby underscoring it as a potential therapeutic target to treat intervertebral disc degeneration.
- organism: mouse
- assay: bulk RNA-seq
- assay: ATAC-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 1
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