Microglia promote maladaptive plasticity in autonomic circuitry after spinal cord injury in mice
Bibliographic record
Abstract
Robust structural remodeling and synaptic plasticity occurs within spinal autonomic circuitry after severe high-level spinal cord injury (SCI). As a result, normally innocuous visceral or somatic stimuli elicit uncontrolled activation of spinal sympathetic reflexes that contribute to systemic disease and organ-specific pathology. How hyperexcitable sympathetic circuitry forms is unknown, but local cues from neighboring glia likely help mold these maladaptive neuronal networks. Here, we used a mouse model of SCI to show that microglia surrounded active glutamatergic interneurons and subsequently coordinated multi-segmental excitatory synaptogenesis and expansion of sympathetic networks that control immune, neuroendocrine, and cardiovascular functions. Depleting microglia during critical periods of circuit remodeling after SCI prevented maladaptive synaptic and structural plasticity in autonomic networks, decreased the frequency and severity of autonomic dysreflexia, and prevented SCI-induced immunosuppression. Forced turnover of microglia in microglia-depleted mice restored structural and functional indices of pathological dysautonomia, providing further evidence that microglia are key effectors of autonomic plasticity. Additional data show that microglia-dependent autonomic plasticity required expression of triggering receptor expressed on myeloid cells 2 (Trem2) and α2δ-1-dependent synaptogenesis. These data suggest that microglia are primary effectors of autonomic neuroplasticity and dysautonomia after SCI in mice. Manipulating microglia may be a strategy to limit autonomic complications after SCI or other forms of neurologic disease.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".