SPRING is a Dedicated Licensing Factor for SREBP-Specific Activation by S1P
Bibliographic record
Abstract
SREBP transcription factors are central regulators of lipid metabolism. Their proteolytic activation requires ER to the Golgi translocation and subsequent cleavage by site-1-protease (S1P). Produced as a proprotein, S1P undergoes autocatalytic cleavage from its precursor S1PA to mature S1PC form. Here, we report that SPRING (previously C12ORF29) and S1P interact through their ectodomains, and that this facilitates the autocatalytic cleavage of S1PA into its mature S1PC form. Reciprocally, we identified a S1P recognition-motif in SPRING and demonstrate that S1P-mediated cleavage leads to secretion of the SPRING ectodomain in cells, and in liver-specific Spring knockout (LKO) mice transduced with AAV-mSpring. By reconstituting SPRING variants into SPRINGKO cells we show that the SPRING ectodomain supports proteolytic maturation of S1P and SREBP signaling, but that S1P-mediated SPRING cleavage is not essential for these processes. Absence of SPRING modestly diminishes proteolytic maturation of S1PA→C and trafficking of S1PC to the Golgi. However, despite reaching the Golgi in SPRINGKO cells, S1PC fails to rescue SREBP signaling. Remarkably, whereas SREBP signaling was severely attenuated in SPRINGKO cells and LKO mice, that of ATF6, another S1P substrate, was unaffected in these models. Collectively, our study positions SPRING as a dedicated licensing factor for SREBP-specific activation by S1P.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".