MerTK Is a Functional Regulator of Myelin Phagocytosis by Human Myeloid Cells
Bibliographic record
Abstract
Multifocal inflammatory lesions featuring destruction of lipid-rich myelin are pathologic hallmarks of multiple sclerosis. Lesion activity is assessed by the extent and composition of myelin uptake by myeloid cells present in such lesions. In the inflamed CNS, myeloid cells are comprised of brain-resident microglia, an endogenous cell population, and monocyte-derived macrophages, which infiltrate from the systemic compartment. Using microglia isolated from the adult human brain, we demonstrate that myelin phagocytosis is dependent on the polarization state of the cells. Myelin ingestion is significantly enhanced in cells exposed to TGF-β compared with resting basal conditions and markedly reduced in classically activated polarized cells. Transcriptional analysis indicated that TGF-β-treated microglia closely resembled M0 cells. The tyrosine kinase phagocytic receptor MerTK was one of the most upregulated among a select number of differentially expressed genes in TGF-β-treated microglia. In contrast, MerTK and its known ligands, growth arrest-specific 6 and Protein S, were downregulated in classically activated cells. MerTK expression and myelin phagocytosis were higher in CNS-derived microglia than observed in monocyte-derived macrophages, both basally and under all tested polarization conditions. Specific MerTK inhibitors reduced myelin phagocytosis and the resultant anti-inflammatory biased cytokine responses for both cell types. Defining and modulating the mechanisms that regulate myelin phagocytosis has the potential to impact lesion and disease evolution in multiple sclerosis. Relevant effects would include enhancing myelin clearance, increasing anti-inflammatory molecule production by myeloid cells, and thereby permitting subsequent tissue repair.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".