LRRK2 and WAVE2 regulate microglial‐transition through distinct morphological phenotypes to induce neurotoxicity in a novel two‐hit in vitro model of neurodegeneration
Bibliographic record
Abstract
We report a novel in vitro classification system that tracks microglial activation state and their potential neurotoxicity. Mixed live-cell imaging was used to characterize transition through distinct morphological phenotypes, production of reactive oxygen species (ROS), formation of reactive microglial aggregates, and subsequent cytokine production. Transwell cultures were used to determine microglial migration (control and lipopolysaccharide (LPS) treated) to glutamate pre-stressed or healthy neurons. This two-hit paradigm was developed to model the vast evidence that neurodegenerative conditions, like Parkinson's disease (PD), may stem from the collective impact of multiple environmental stressors. We found that healthy neurons were resistant to microglial-mediated inflammation, whereas glutamate pre-stressed neurons were highly susceptible and in fact, appeared to recruit microglia. The LPS treated microglia progressed through distinct morphological states and expressed high levels of ROS and formed large cellular aggregates. Recent evidence implicates leucine-rich repeat kinase 2 (LRRK2) as an important player in the microglial inflammatory state, as well as in the genesis of PD. We found that inhibition of the LRRK2 signaling pathway using the kinase inhibitor cis-2,6-dimethyl-4-(6-(5-(1-methylcyclopropoxy)-1H-indazol-3-yl)pyrimidin-4-yl)morpholine (MLi2) or inhibition of the actin regulatory protein, Wiskott-Aldrich syndrome family Verprolin-homologous Protein-2 (WAVE2), stunted microglial activation and prevented neurotoxicity. Furthermore, inhibition of LRRK2 kinase activity reduced pro-inflammatory chemokines including MIP-2, CRG-2, and RANTES. These data together support the notion that LRRK2 and WAVE2 are important mediators of cytokine production and cytoskeletal rearrangement necessary for microglial-induced neurotoxicity. Furthermore, our model demonstrated unique microglial phenotypic changes that might be mechanistically important for better understanding neuron-microglial crosstalk.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".