The MAPK Signaling Pathway Presents Novel Molecular Targets for Therapeutic Intervention after Traumatic Spinal Cord Injury: A Comparative Cross-Species Transcriptional Analysis
Bibliographic record
Abstract
Despite the debilitating consequences following traumatic spinal cord injury (SCI), there is a lack of safe and effective therapeutics in the clinic. The species-specific responses to SCI present major challenges and opportunities for the clinical translation of biomolecular and pharmacological interventions. Recent transcriptional analyses in preclinical SCI studies have provided a snapshot of the local SCI-induced molecular responses in different animal models. However, the variation in the pathogenesis of traumatic SCI across species is yet to be explored. This study aims to identify and characterize the common and inconsistent SCI-induced differentially expressed genes across species to identify potential therapeutic targets of translational relevance. A comprehensive search of open-source transcriptome datasets identified four cross-compatible microarray experiments in rats, mice, and salamanders. We observed consistent expressional changes in extracellular matrix components across the species. Conversely, salamanders showed downregulation of intracellular MAPK signaling compared to rodents. Additionally, sequence conservation and interactome analyses highlighted the well-preserved sequences of Fn1 and Jun with extensive protein-protein interaction networks. Lastly, in vivo immunohistochemical staining for fibronectin was used to validate the observed expressional pattern. These transcriptional changes in extracellular and MAPK pathways present potential therapeutic targets for traumatic SCI with promising translational relevance.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".