Dissection of a Novel Autocrine Signaling Pathway via Quantitative Secretome and Interactome Mapping
Bibliographic record
Abstract
Epidermal homeostasis is a balancing act governed by a multitude of underlying regulatory events, and several growth factors and signaling pathways have been implicated in regulation of the balance between proliferation and differentiation in keratinocytes. We show here that the signal transducer/transcription factor FIZ1 (Flt3 interacting zinc finger protein-1) is a previously unknown player in this regulatory axis, promoting an increase in proliferation of HaCaT human immortalized keratinocytes that is driven by more rapid G1/S progression and mediated by activation of the MAP/ERK kinase pathway. Utilizing quantitative SILAC-based secretome analysis, we identified the insulin growth factor binding protein IGFBP3 as the key mediating factor, demonstrating that elevated FIZ1 levels promote increased IGFBP3 expression and secretion and a concurrent increased sensitivity to IGF1 signaling, while antibody-based neutralization of IGFBP3 abrogates the FIZ1-induced growth advantage. To identify underlying protein-protein interactions likely to govern these events, we mapped the interactome of FIZ1 and found eight novel binding partners that form complexes with the protein in the cytoplasm and nucleus. These include signal transduction and transcription factors and the cell cycle regulatory NDR (Nuclear Dbf2-related) kinases. Our results provide further insight into the complex balance of epidermal homeostasis and identify FIZ1 as a novel therapeutic target.
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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) | 0.001 | 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".