TREM2-activating antibodies abrogate the negative pleiotropic effects of the Alzheimer's disease variant Trem2R47H on murine myeloid cell function
Bibliographic record
Abstract
Triggering receptor expressed on myeloid cells 2 (TREM2) is an orphan immune receptor expressed on cells of myeloid lineage such as macrophages and microglia. The rare variant R47H TREM2 is associated with an increased risk for Alzheimer's disease, supporting the hypothesis that TREM2 loss of function may exacerbate disease progression. However, a complete knockout of the TREM2 gene in different genetic models of neurodegenerative diseases has been reported to result in both protective and deleterious effects on disease-related end points and myeloid cell function. Here, we describe a Trem2 R47H transgenic mouse model and report that even in the absence of additional genetic perturbations, this variant clearly confers a loss of function on myeloid cells. The Trem2 R47H variant–containing myeloid cells exhibited subtle defects in survival and migration and displayed an unexpected dysregulation of cytokine responses in a lipopolysaccharide challenge environment. These subtle phenotypic defects with a gradation in severity across genotypes were confirmed in whole-genome RNA-Seq analyses of WT, Trem2 −/− , and Trem2 R47H myeloid cells under challenge conditions. Of note, TREM2-activating antibodies that boost proximal signaling abrogated survival defects conferred by the variant and also modulated migration and cytokine responses in an antibody-, ligand-, and challenge-dependent manner. In some instances, these antibodies also boosted WT myeloid cell function. Our studies provide a first glimpse into the boost in myeloid cell function that can be achieved by pharmacological modulation of TREM2 activity that can potentially be ameliorative in neurodegenerative diseases such as Alzheimer's 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 | 0.000 |
| 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 | 0.001 |
| 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".