Changes in Phosphorylation of the TREM2‐PLCG2 Signaling Pathway Components Influences Microglia Function
Bibliographic record
Abstract
BACKGROUND: Missense mutations in the TREM2 gene are associated with increased risk of Alzheimer's disease (AD). Aβ is a known ligand of TREM2 binding directly and activating a signalling pathway involving PLCG2. TREM2 itself signals through its association with DAP12 and recruits SYK through its cytosolic immune-receptor tyrosine-based activation motifs. This study will provide insights into relevant TREM2-PLCG2-associated cellular processes to help to identify novel interacting proteins and dissect cellular mechanisms involved in AD. METHOD: Aβ and anti-TREM2 activating antibody were used to stimulate TREM2 signalling in mouse microglia followed by western blotting to interrogate the phosphorylation state of various interactors under basal and stimulated conditions. Co-IP and Immunofluorescence studies were performed to investigate protein interactors in this signalling cascade. Migration and Aβ engulfment assays were used as downstream functional readouts of TREM2 stimulation. Results are representative of at least three independent biological replicate experiments. RESULT: We discovered that the endogenous TREM2-DAP12-PLCG2 signaling complex interacts with numerous novel high affinity components in microglia. We found that the levels of phospho-SYK (Tyr525/526) and non-canonical phospho-tyrosines in PLCG2 were significantly increased by TREM-2 activation. Increased Phospho-PLCG2 levels triggered changes in microglia function reflected in functional assays such as cell migration and cargo engulfment assays. CONCLUSION: We discovered that stimulation of TREM2 in microglia led to a novel signalling cascade involving PLCG2 and other proteins which mediated important microglia functions. Some of these proteins are components of the B cell receptor signaling machinery acting via PLCG2 while others are known/new AD-related risk genes. The elucidation of the TREM2-PLCG2 pathway and its downstream signalling partners provides important insights into the mechanisms of normal microglia function and in the pathogenesis of AD.
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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) | 0.002 | 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".