Comparing the BAD Protein Interactomes in 2D and 3D Cell Culture Using Proximity Labeling
Bibliographic record
Abstract
Protein-protein interaction studies using proximity labeling techniques, such as biotin ligase-based BioID, have become integral in understanding cellular processes. Most studies utilize conventional 2D cell culture systems, potentially missing important differences in protein behavior found in 3D tissues. In this study, we investigated the protein-protein interactions of a protein, Bcl-2 Agonist of cell death (BAD), and compared conventional 2D culture conditions to a 3D system, wherein cells were embedded within a 3D extracellular matrix (ECM) mimic. Using BAD fused to the engineered biotin ligase miniTurbo (BirA*), we identified both overlapping and distinct BAD interactomes under 2D and 3D conditions. The known BAD binding proteins 14-3-3 isoforms and Bcl-XL interacted with BAD in both 2D and 3D. Of the 131 BAD-interactors identified, 56% were specific to 2D, 14% were specific to 3D, and 30% were common to both conditions. Interaction network analysis demonstrated differential associations between 2D and 3D interactomes, emphasizing the impact of the culture conditions on protein interactions. The 2D-3D overlap interactome encapsulated the apoptotic program, which is a well-known role of BAD. The 3D unique pathways were enriched in ECM signaling, suggestive of hitherto unknown functions for BAD. Thus, exploring protein-protein interactions in 3D provides novel clues into cell behavior. This exciting approach has the potential to bridge the knowledge gap between tractable 2D cell culture and organoid-like 3D systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".