Divergent Neuroinflammatory Regulation of Microglial TREM Expression and Involvement of NF-κB
Bibliographic record
Abstract
The triggering receptor expressed on myeloid cells (TREM) family of proteins are cell surface receptors with important roles in regulation of myeloid cell inflammatory activity. In the central nervous system, TREM2 is implicated in further roles in microglial homeostasis, neuroinflammation and neurodegeneration. Different TREM receptors appear to have contrasting roles in controlling myeloid immune activity therefore the relative and co-ordinated regulation of their expression is important to understand but is currently poorly understood. We sought to determine how microglial TREM expression is affected under neuroinflammatory conditions in vitro and in vivo. Our data show that microglial Trem1 and Trem2 gene expression are regulated in an opposing manner by lipopolysaccharide (LPS) in vitro in both adult murine and human microglia. LPS caused a significant induction of Trem1 and a contrasting suppression of Trem2 expression. We also observed similar divergent Trem1 and Trem2 responses in vivo in response to acute brain inflammation and acute cerebral ischaemia. Our data show that inhibition of NF-B activation prevents the LPS-induced alterations in both Trem1 and Trem2 expression in vitro indicating NF-kB as a common signalling intermediate controlling these divergent responses. Distinct patterns of microglial Trem1 induction and Trem2 suppression to different Toll-like receptor (TLR) ligands were also evident, notably with Trem1 induction restricted to those ligands activating TLRs signalling via TRIF. Our data show co-ordinated but divergent regulation of microglial TREM receptor expression with a central role for NF-B. Neuroinflammatory conditions that alter the balance in TREM expression could therefore be an important influence on microglial inflammatory and homeostatic activity with implications for neuroinflammatory and neurodegenerative 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) | 0.000 | 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) | 0.001 | 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".