Fibronectin leucine-rich transmembrane protein 2 drives monocyte differentiation into macrophages via the UNC5B-Akt/mTOR axis
Bibliographic record
Abstract
Upon migrating into the tissues, hematopoietic stem cell (HSC)-derived monocytes differentiate into macrophages, playing a crucial role in determining innate immune responses towards external pathogens and internal stimuli. However, the regulatory mechanisms underlying monocyte-to-macrophage differentiation remain largely unexplored. Here we divulge a previously uncharacterized but essential role for an axon guidance molecule, fibronectin leucine-rich transmembrane protein 2 (FLRT2), in monocyte-to-macrophage maturation. FLRT2 is almost undetectable in human monocytic cell lines, human peripheral blood mononuclear cells (PBMCs), and mouse primary monocytes but significantly increases in fully differentiated macrophages. Myeloid-specific deletion of FLRT2 (Flrt2ΔMyel) contributes to decreased peritoneal monocyte-to-macrophage generation in mice in vivo, accompanied by impaired macrophage functions. Gain- and loss-of-function studies support the promoting effect of FLRT2 on THP-1 cell and human PBMC differentiation into macrophages. Mechanistically, FLRT2 directly interacts with Unc-5 netrin receptor B (UNC5B) via its extracellular domain (ECD) and activates Akt/mTOR signaling. In vivo administration of mTOR agonist MYH1485 reverses the impaired phenotypes observed in Flrt2ΔMyel mice. Together, these results identify FLRT2 as a novel pivotal endogenous regulator of monocyte differentiation into macrophages. Targeting the FLRT2/UNC5B-Akt/mTOR axis may provide potential therapeutic strategies directly relevant to human diseases associated with aberrant monocyte/macrophage differentiation.
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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".