BioID2-Based Tau Interactome Reveals Novel and Known Protein Interactions Associated with Multiple Cellular Pathways
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Pathological inclusions composed of tau are hallmarks of neurodegenerative diseases termed tauopathies, the most common of which is Alzheimer’s disease. Accumulating evidence suggests that tau is involved in a multitude of physiological functions that are regulated, in part, by direct and/or transient protein interactions. Deciphering the tau interactome is critical for understanding the physiological and pathological roles of tau. This work aimed to identify potential tau interactors using the in situ protein labeling biotin identification (BioID2) method. Advantages of this approach include in-cell interactor labeling and an enhanced likelihood of detecting transient and/or weak interactions. We identified 324 potential tau interactors spanning multiple cellular compartments and pathways. We validated tau interactions with selected candidates using two independent approaches: proximity ligation assay and co-immunoprecipitation (co-IP) which included cytoskeletal proteins (MAP2 and MAP6), nucleus-associated proteins (FUS and prune1), and synaptic proteins (synapsin-1 and neurabin-2). Importantly, this approach revealed potential novel interactors that were not clearly identified by other interaction approaches such as co-IP. Thus, this approach is a powerful tool to identify potential members of the tau interactome via in situ labeling. This work helps expand our understanding of tau’s physiological roles, which may also advance our understanding of its role in neurodegenerative diseases.
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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 | 0.000 |
| 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".