TAZ interactome analysis using nanotrap-based affinity purification–mass spectrometry
Bibliographic record
Abstract
Characterization of protein-protein interactions (PPIs) is a fundamental goal in the post-genomic era. Here, we document a generally applicable approach to identify cellular protein interactomes using a combination of nanobody-based affinity purification (AP) coupled with liquid chromatography and tandem mass spectrometry (LC-MS/MS). The Hippo signaling regulator TAZ (also known as WWTR1) functions as a transcriptional co-repressor or activator depending on its PPI network; we therefore undertook an unbiased proteomic screen to identify TAZ PPIs in striated muscle cells. A GFP nanotrap-based AP approach coupled with protein identification through LC-MS/MS was used to document a comprehensive list of known and novel TAZ interactome components. Informatic analysis of the interactome documented known components of the Hippo signaling pathway and multiple epigenetic regulators such as the NuRD, FACT and SWI/SNF complexes and the pro-myogenic CARM1 methyltransferase. Hippo pathway reporter gene (HOP/HIP) analysis indicated that CARM1 represses TAZ transcriptional co-activator function, promoting TAZ Ser89 phosphorylation and TAZ cytoplasmic sequestration. MS analysis revealed that CARM1 dimethylates TAZ at Arg77 in a PGPR*LAGG consensus peptide, resulting in enhanced TAZ Ser89 phosphorylation. These studies underline the utility of a nanobody-based AP approach for interactome analysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".