Induction of Androgen Formation in the Male by a TAT-VDAC1 Fusion Peptide Blocking 14-3-3ɛ Protein Adaptor and Mitochondrial VDAC1 Interactions
Bibliographic record
Abstract
Low testosterone (T), a major cause of male hypogonadism and infertility, is linked to mood changes, fatigue, osteoporosis, reduced bone-mass index, and aging. The treatment of choice, T replacement therapy, has been linked with increased risk for prostate cancer and luteinizing hormone (LH) suppression, and shown to lead to infertility, cardiovascular diseases, and obesity. Alternate methods to induce T with lower side effects are desirable. In search of the mechanisms regulating T synthesis in the testes, we identified the 14-3-3ɛ protein adaptor as a negative regulator of steroidogenesis. Steroidogenesis begins in mitochondria. 14-3-3ɛ interacts with the outer mitochondrial membrane voltage-dependent anion channel (VDAC1) protein, forming a scaffold that limits the availability of cholesterol for steroidogenesis. We report the development of a tool able to induce endogenous T formation. Peptides able to penetrate testes conjugated to 14-3-3ɛ site of interaction with VDAC1 blocked 14-3-3ɛ-VDAC1 interactions while at the same time increased VDAC1-translocator protein (18 kDa) interactions that induced steroid formation in rat testes, leading to increased serum T levels. These peptides rescued intratesticular and serum T formation in adult male rats treated with gonadotropin-releasing hormone antagonist, which dampened LH and T production.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".