Elucidation of the 14-3-3ζ interactome reveals critical roles of RNA splicing factors during adipogenesis
Bibliographic record
Abstract
Abstract Adipogenesis is facilitated by a complex signaling network requiring strict temporal and spatial organization of effector molecules. Molecular scaffolds, such as 14-3-3 proteins, coordinate such events, and we have previously identified 14-3-3ζ as an essential scaffold in adipocyte differentiation. The interactome of 14-3-3ζ is large and diverse, and it is possible that novel adipogenic factors may be present within it. Mouse embryonic fibroblasts from mice over-expressing a TAP-epitope-tagged 14-3-3ζ molecule were generated, and following the induction of adipogenesis, TAP-14-3-3ζ complexes were purified, followed by mass spectrometry analysis to determine the 14-3-3ζ interactome. Over 100 proteins were identified as being unique to adipocyte differentiation, of which 56 were novel interacting partners. Previously established regulators of adipogenesis (ie, Ptrf/Cavin1 and Phb2) were found within the 14-3-3ζ interactome, confirming the ability of this approach to identify regulators of adipocyte differentiation. An enrichment of proteins in the interactome related to RNA metabolism, processing, and splicing was identified, and analysis of transcriptomic data revealed that 14-3-3ζ depletion in 3T3-L1 cells affected the alternative splicing of mRNA during adipocyte differentiation. Of the RNA splicing factors within the 14-3-3ζ interactome, depletion of Hnrnpf, Hnrnpk, Ddx6, and Sfpq by siRNA revealed essential roles of these proteins in adipogenesis and their roles in the alternative splicing of Lpin1 . In summary, novel adipogenic factors can be detected within the 14-3-3ζ interactome, and further characterization of additional proteins within the 14-3-3ζ interactome has the potential of identifying novel targets to block the expansion of adipose tissue mass that occurs in obesity.
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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) | 0.001 | 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".