Evidence of microglial involvement in the childhood abuse-associated increase in perineuronal nets in the ventromedial prefrontal cortex
Bibliographic record
Abstract
• Microglia regulate perineuronal nets, a specialized form of extracellular matrix. • Child abuse associates with lowered expression of microglial MMP-9 and TIMP-2. • IL33R and CX3CR1 levels are decreased in lysate and isolated CD11b + cells. Microglia, known for their diverse roles in the central nervous system, have recently been recognized for their involvement in degrading the extracellular matrix. Perineuronal nets (PNNs), a specialized form of this matrix, are crucial for stabilizing neuronal connections and constraining plasticity. Our group recently reported increased PNN densities in the ventromedial prefrontal cortex (vmPFC) of depressed individuals that died by suicide in adulthood after experiencing childhood abuse (DS-CA) compared to matched controls. To explore potential underlying mechanisms, we employed a comprehensive approach in similar postmortem vmPFC samples, combining a human matrix metalloproteinase and chemokine array, isolation of CD11b-positive microglia and enzyme-linked immunosorbent assays (ELISA). Our findings indicate a significant downregulation of matrix metalloproteinase (MMP)-9 and tissue inhibitors of metalloproteinases (TIMP)-2 in both whole vmPFC grey matter and isolated microglial cells from DS-CA samples. Furthermore, our experiments reveal that a history of child abuse is associated with diminished levels of microglial CX3CR1 and IL33R in both vmPFC whole lysate and CD11b + isolated cells. However, levels of the CX3CR1 ligand, CX3CL1 (Fractalkine), did not differ between groups. While these data suggest potential long-lasting alterations in microglial markers in the vmPFC of individuals exposed to severe childhood adversity, direct functional assessments were not conducted. Nonetheless, these findings offer insight into how childhood abuse may contribute to PNN alterations via microglial-related mechanisms.
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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 | 0.000 | 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".