Mapping the signaling network of BIN2 kinase using TurboID-mediated biotin labeling and phosphoproteomics
Bibliographic record
Abstract
Elucidating enzyme-substrate relationships in posttranslational modification (PTM) networks is crucial for understanding signal transduction pathways but is technically difficult because enzyme-substrate interactions tend to be transient. Here, we demonstrate that TurboID-based proximity labeling (TbPL) effectively and specifically captures the substrates of kinases and phosphatases. TbPL-mass spectrometry (TbPL-MS) identified over 400 proximal proteins of Arabidopsis thaliana BRASSINOSTEROID-INSENSITIVE2 (BIN2), a member of the GLYCOGEN SYNTHASE KINASE 3 (GSK3) family that integrates signaling pathways controlling diverse developmental and acclimation processes. A large portion of the BIN2-proximal proteins showed BIN2-dependent phosphorylation in vivo or in vitro, suggesting that these are BIN2 substrates. Protein-protein interaction network analysis showed that the BIN2-proximal proteins include interactors of BIN2 substrates, revealing a high level of interactions among the BIN2-proximal proteins. Our proteomic analysis establishes the BIN2 signaling network and uncovers BIN2 functions in regulating key cellular processes such as transcription, RNA processing, translation initiation, vesicle trafficking, and cytoskeleton organization. We further discovered significant overlap between the GSK3 phosphorylome and the O-GlcNAcylome, suggesting an evolutionarily ancient relationship between GSK3 and the nutrient-sensing O-glycosylation pathway. Our work presents a powerful method for mapping PTM networks, a large dataset of GSK3 kinase substrates, and important insights into the signaling network that controls key cellular functions underlying plant growth and acclimation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".