Role of microglial NMDA receptor-initiated PARP-1/TRPM2 signaling in driving chronic neuroinflammation
Bibliographic record
Abstract
An important component of neurodegenerative disorders, like Alzheimer’s disease (AD), is the prolonged inflammatory response driven by continuous microglial poly (ADP-ribose) polymerase-1 (PARP-1) activation. However, mechanisms that promote sustained microglial PARP-1 activation and maintaining microglial activity remains elusive. But we know that PARP-1 enzymatic activity requires Ca2+ influx, a process that is independent of DNA damage, and this could be through microglial N-methyl D-aspartate receptors (NMDARs). Notably, PARP-1 mediated ADP-ribose production causes activation of Ca2+ permeable non-selective cation channel, transient receptor potential melastatin-2 (TRPM2). Hence, we hypothesize that NMDAR activation by amyloid beta oligomers (AβO) or NMDA initiates PARP-1 mediated ADPR production and TRPM2 activation. This in-turn leads to TRPM2-dependent, self-sustaining PARP-1 activation, which promotes pro-inflammatory responses. Primary microglia treated with AβO and NMDA were assessed for functional TRPM2 currents using whole cell voltage-clamp electrophysiology. Using nitric oxide assay and qRT-PCR, AβO/NMDA treated microglia were also evaluated to identify the contribution of NMDAR/PARP-1/TRPM2 in promoting pro-inflammatory responses. Our findings, demonstrates that glutamate signaling via NMDARs, promoting detrimental microglial responses, suggests a unifying mechanism linking elevated glutamate levels, associated for example with AD, hypoxic-ischemic injury, or traumatic brain injury, 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".