Microglial NMDA receptors drive pro‐inflammatory responses via PARP‐1/TRMP2 signaling
Bibliographic record
Abstract
Abstract Chronic neuroinflammation driven by microglia is a characteristic feature associated with numerous neurodegenerative diseases. While acute inflammation can assist with recovery and repair, prolonged microglial pro‐inflammatory responses are known to exacerbate neurodegenerative processes. Yet, detrimental outcomes of extended microglial activation are counterbalanced by beneficial outcomes including phagocytosis and release of trophic factors promoting neuronal viability. Our past work has shown that the nuclear enzyme poly(ADP‐ribose) polymerase‐1 (PARP‐1) is a key signaling hub driving pro‐inflammatory microglia responses, but the signaling pathway maintaining PARP‐1 activation remains elusive. While best understood for its role in promoting DNA repair, our group has shown that PARP‐1 activity can be stimulated via Ca2+ influx‐dependent ERK1/2‐mediated phosphorylation. However, to date, the route of Ca2+ entry responsible for stimulating PARP‐1 has not been identified. A likely candidate is via Ca2+‐permeable transient receptor potential melastatin 2 (TRPM2) channels activated downstream of PARP‐1 in a cascade that involves ADP‐ribose (ADPR) production by poly(ADP‐ribose) glycohydrolase (PARG). Here we demonstrate that NMDA receptor (NMDAR) stimulation in primary cultured microglia induces their proliferation, morphological activation and release of pro‐inflammatory mediators. These responses were contingent on the recruitment of PARP‐1, PARG and Ca2+ permeable TRPM2 channels. Furthermore, we show that Ca2+ influx is necessary to activate PARP‐1/TRPM2 signaling, in an ERK1/2‐dependent, but DNA damage independent, manner. Our findings, showing that PARP‐1/TRPM2 mediate the pro‐inflammatory effects of NMDAR stimulation, provides a unifying mechanism linking elevated glutamate levels to chronic neuroinflammation.
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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 | 0.001 | 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".