Dynamic extracellular proximal interaction profiling reveals Low-Density Lipoprotein Receptor as a new Epidermal Growth Factor signaling pathway component
Bibliographic record
Abstract
ABSTRACT Plasma membrane proteins are critical mediators of cell-cell and cell-environment interactions, pivotal in intracellular signal transmission vital for cellular functionality. Proximity-dependent biotinylation approaches such as BioID combined with mass spectrometry have begun illuminating the landscape of proximal protein interactions within intracellular compartments. However, their deployment in studies of the extracellular environment remains scarce. Here, we present extracellular TurboID (ecTurboID), a method designed to profile cell surface interactions in living cells on short timescales. We first report on the careful optimization of experimental and data analysis strategies that enable the capture of extracellular protein interaction information. Leveraging the ecTurboID technique, we unveiled the proximal interactome of multiple plasma membrane proteins, notably the epidermal growth factor receptor (EGFR). This led to identifying the low-density lipoprotein receptor (LDLR) as a newfound extracellular protein associating with EGFR, contingent upon the presence of the EGF ligand. We showed that 15 minutes of EGF stimulation induced LDLR localization to the plasma membrane to associate with proteins involved in EGFR regulation. This modified proximity labelling methodology allows us to dynamically study the associations between plasma membrane proteins in the extracellular environment. One Sentence Summary We developed extracellular TurboID (ecTurboID) as a new proximity dependent biotinylation approach that can capture dynamic interactions at the cell surface, identifying Low-Density Lipoprotein Receptor as a new ligand-dependent extracellular partner of Epidermal Growth Factor Receptor.
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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) | 0.002 | 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".