MER Tyrosine Kinase Mediates Efferocytosis Through a Novel β2 Integrin-Activating Signalling Pathway
Bibliographic record
Abstract
Abstract Efferocytosis – the phagocytic removal of apoptotic cells – is a central component of tissue homeostasis and contributes to inflammatory and autoimmune conditions including atherosclerosis and multiple sclerosis. MER tyrosine kinase (MERTK) is the predominant or sole efferocytic receptor in multiple tissues, and is the predominant efferocytic receptor on macrophages. MERTK is an opsonin-dependent receptor tyrosine kinase that triggers a poorly understood signaling pathway to mediate apoptotic cell engulfment. Using immunoprecipitation, mass spectrometry, and super-resolution microscopy, we have identified a pre-formed receptor complex on the macrophage plasma membrane comprised of ~100 nm clusters of MERTK, β2 integrins, and multiple signaling molecules including Src-family kinases, PI3-kinases, and the integrin regulatory proteins ILK and FAK. MERTK is unable to mediate efferocytosis in the absence of β2 integrins or their opsonins, while β2 integrins require activation via MERTK signaling to induce the engulfment of apoptotic cells. Using FRET microscopy, we determined that MERTK directly induces the conformational change of β2 integrins from the low to high-affinity form via a PI3-kinase, FAK and ILK-dependent signaling pathway. Activation of this pathway then drives the coalescence of the MERTK/β2 integrin clusters into a larger synapse through which efferocytosis occurs. The identification of the MERTK signaling pathway and the role of β2 integrins in this pathway provides new insights into the function of this critical homeostatic receptor and provides new insights into how MERTK mutations and signaling defects may contribute to inflammatory and autoimmune diseases.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".