Slit/Robo signaling regulates Leydig cell steroidogenesis
Bibliographic record
Abstract
BACKGROUND: First identified as a regulator of neuronal axon guidance, Slit/Robo signaling has since been implicated in additional physiologic and pathologic processes, such as angiogenesis, organogenesis and cancer progression. However, its roles in the regulation of testis function have been little explored. METHODS: Immunohistochemistry and RT-qPCR analyses were performed to detect the expression of Slit/Robo signaling effectors in the adult mouse testis. To identify the roles and mechanisms of Slit/Robo signaling in the regulation of steroidogenesis, RT-qPCR, immunoblotting and hormone measurements were carried out using Leydig cells (primary cultures and the MA10 cell line) treated with exogenous SLIT ligands, and testes from Robo1-null mice. RESULTS: Slit1, -2 and -3 and Robo1 and -2 expression was detected in the adult mouse testis, particularly in Leydig cells. In vitro treatment of Leydig cells with exogenous SLIT ligands led to a decrease in the expression of the steroidogenic genes Star, Cyp11a1, and Cyp17a1. SLIT2 treatment decreased the phosphorylation of the key steroidogenic gene regulator CREB, possibly in part by suppressing AKT activity. Furthermore, SLIT2 treatment reduced the responsiveness of MA10 cells to luteinizing hormone by decreasing the expression of Lhcgr. Consistent with these in vitro results, an increase in testicular Star mRNA levels and intra-testicular testosterone concentrations were found in Robo1-null mice. Finally, we showed that the expression of the Slit and Robo genes in Leydig cells is enhanced by testosterone treatment in vitro, by an AR-independent mechanism. CONCLUSION: Taken together, these results suggest that Slit/Robo signaling represents a novel mechanism that regulates Leydig cell steroidogenesis. It may act in an autocrine/paracrine manner to mediate negative feedback by testosterone on its own synthesis. Video Abstract.
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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) | 0.002 | 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".