Identification of Sec23ip, Part of 14-3-3γ Protein Network, as a Regulator of Acute Steroidogenesis in MA-10 Leydig Cells
Bibliographic record
Abstract
Testosterone production occurs in the Leydig cells of the testes and is essential for virilization, development, reproduction, and quality of life. Although the steroidogenic proteins involved in cholesterol conversion to testosterone (T) are well characterized, the causes of reduced T during fetal, neonatal, and adult life remain uncertain. It is well established that normal cellular function is achieved through fine-tuning of multiple rather than single protein networks. Our objective was to use mass spectrometry (MS)-based proteomics to identify which cellular pathways, other than the steroidogenic machinery, influence testosterone production in MA-10 mouse tumor Leydig cells. The 14-3-3 family of scaffolds mediate protein-protein interactions facilitating the crosstalk between protein networks. We previously showed that in MA-10 cells, 14-3-3γ is a critical regulator of steroidogenesis. Therefore, identifying proteins that interact with 14-3-3γ during steroidogenesis could provide clues into the other networks involved. Using liquid chromatography (LC)-MS, we identified 688 proteins that interact with 14-3-3γ and thus potentially impact MA-10 cell steroidogenesis. The identified proteins belong to multiple protein networks, including endoplasmic reticulum-Golgi cargo sorting and vesicle biogenesis, micro ribonucleic acid-induced gene silencing, inflammation, and vesicle trafficking, to name a few. We found that silencing one of the candidates, Sec23ip, a protein known to be involved in vesicle trafficking, resulted in decreased steroidogenesis. We further showed that in Sec23ip-silenced MA-10 cells, cholesterol mobilization from the cytoplasmic membrane to mitochondria is impaired. Taken together these data suggest that Sec23ip is involved in cholesterol trafficking to supply cholesterol for acute steroidogenesis through its interactions with 14-3-3γ.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".