Mitogen-Activated Protein Kinase-Activated Protein Kinase 2 (MK2) Contributes to Secondary Damage after Spinal Cord Injury
Bibliographic record
Abstract
The inflammatory response contributes importantly to secondary tissue damage and functional deficits after spinal cord injury (SCI). In this work, we identified mitogen-activated protein kinase (MAPK)-activated protein kinase 2 (MAPKAPK2 or MK2), a downstream substrate of p38 MAPK, as a potential target using microarray analysis of contused spinal cord tissue taken at the peak of the inflammatory response. There was increased expression and phosphorylation of MK2 after SCI, with phospho-MK2 expressed in microglia/macrophages, neurons and astrocytes. We examined the role of MK2 in spinal cord contusion injury using MK2(-/-) mice. These results show that locomotor recovery was significantly improved in MK2(-/-) mice, compared with wild-type controls. MK2(-/-) mice showed reduced neuron and myelin loss, and increased sparing of serotonergic fibers in the ventral horn caudal to the injury site. We also found differential expression of matrix metalloproteinase-2 and 9 in MK2(-/-) and wild-type mice after SCI. Significant reduction was also seen in the expression of proinflammatory cytokines and protein nitrosylation in the injured spinal cord of MK2(-/-) mice. Our previous work has shown that macrophages lacking MK2 have an anti-inflammatory phenotype. We now show that there is no difference in the number of macrophages in the injured spinal cord between the two mouse strains and little if any difference in their phagocytic capacity, suggesting that macrophages lacking MK2 have a beneficial phenotype. These findings suggest that a lack of MK2 can reduce tissue damage after SCI and improve locomotor recovery. MK2 may therefore be a useful target to treat acute SCI.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".