EGFR Phosphorylates and Associates with EFNB1 to Regulate Cell Adhesion to Fibronectin
Bibliographic record
Abstract
Ephrin-Bs (EFNB1-3) are ligands for members of the largest subfamily of receptor tyrosine kinases (RTKs) in humans, the EPH receptors. Interestingly, ephrin-Bs are transmembrane proteins that may also act as receptors themselves upon EPH binding, activating so-called reverse signaling pathways that are critical for multiple cellular processes. Although a number of ephrin-B signaling effectors have been identified, the molecular mechanisms underlying ephrin-B-driven cellular processes remain unresolved, suggesting that multiple signaling effectors are yet to be discovered. Here, we employed proximity labeling proteomics to delineate the proximity network of EFNB1 in steady state and under active reverse signaling conditions. This allowed us to identify 90 uncharacterized EFNB1 proximity partners, from which we could distinguish three main groups: EPH receptor stimulation-dependent, stimulation-independent, and negatively modulated by EPH receptor stimulation. We further investigated the functional relationship between EFNB1 and one of the candidates identified, the epidermal growth factor receptor (EGFR). We found that EFNB1 and EGFR associate in cells and showed that the formation of this complex relies on EFNB1's PDZ-binding motif (PBM). Strikingly, we demonstrate that EGFR directly phosphorylates tyrosine residues within EFNB1's PBM, which results in the disruption of the EFNB1-EGFR complex. Furthermore, we show that the EFNB1-EGFR association is required for EFNB1-dependent cell adhesion to fibronectin. Taken together, our results shed light on a functional relationship between EFNB1 and EGFR.
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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 | 0.001 |
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".