Elucidation of the Signaling Pathway of MERTK
Bibliographic record
Abstract
Mer tyrosine kinase (MERTK) is a receptor tyrosine kinase which mediates efferocytosis-the recognition and uptake of apoptotic cells. MERTK serves as the predominant efferocytic receptor in several tissues, including in the heart, with single nucleotide polymorphisms associated with up to a 75% increased risk of atherosclerosis in humans. MERTK facilitates the internalization of apoptotic cells via a MERTK/integrin pathway that involves, in part, PI-3-kinase signaling and signaling via Src-family kinases. While these elements of the MERTK signaling pathway have been identified, much of MERTK’s signaling mechanisms remain to be elucidated. Pharmacological inhibitors were used to block signaling through canonical phagocytic signaling pathways, and through signalling molecules identified as part of the MERTK signalosome by mass spectrometry, and the efferocytosis of MERTK-specific targets assessed using a human macrophage cell line. These experiments identified PI3K, Src, Syk, ERK, and ILK as regulators of MERTK efferocytic function. To identify non-canonical pathways, we began the development of a CRISPR-tagged endogenous MERTK allele which can be selectively activated in a human macrophage cell line without inadvertent activation of other phagocytic receptors; using this cell line and phosphoprotein mass spectrometry, future studies will be able to identify non-canonical tyrosine-kinase dependent pathways mediating MERTK efferocytic function. Identification of these pathways will elucidate the signaling pathway utilized by MERTK, and potentially may identify signaling pathways which drive aberrant MERTK function during diseases such as atherosclerosis.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".