Molecular Therapy for Degenerative Disc Disease: Clues from Secretome Analysis of the Notochordal Cell-Rich Nucleus Pulposus
Bibliographic record
Abstract
Degenerative disc disease (DDD) is associated with spinal pain often leading to long-term disability. However, the non-chondrodystrophic canine intervertebral disc is protected from the development of DDD, ostensibly due to its retention of notochordal cells (NC) in the nucleus pulposus (NP). In this study, we hypothesized that secretome analysis of the NC-rich NP will lead to the identification of key proteins that delay the onset of DDD. Using mass-spectrometry, we identified 303 proteins including components of TGFβ- and Wnt-signaling, anti-angiogeneic factors and proteins that inhibit axonal ingrowth in the bioactive fractions of serum free, notochordal cell derived conditioned medium (NCCM). Ingenuity Pathway Analysis revealed TGFβ1 and CTGF as major hubs in protein interaction networks. In vitro treatment with TGFβ1 and CTGF promoted the synthesis of healthy extra-cellular matrix proteins, increased cell proliferation and reduced cell death in human degenerative disc NP cells. A single intra-discal injection of recombinant TGFβ1 and CTGF proteins in a pre-clinical rat-tail disc injury model restored the NC and stem cell rich NP. In conclusion, we demonstrate the potential of TGFβ1 and CTGF to mitigate the progression of disc degeneration and the potential use of these molecules in a molecular therapy to treat the degenerative disc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".