Prolactin increases SMN expression and survival in a mouse model of severe spinal muscular atrophy via the STAT5 pathway
Bibliographic record
Abstract
Spinal muscular atrophy (SMA) is an autosomal recessive neurodegenerative disease that is characterized by the loss of motor neurons, resulting in progressive muscle atrophy. It is caused by the loss of functional survival motor neuron (SMN) protein due to mutations or deletion in the SMN1 gene. A potential treatment strategy for SMA is to upregulate levels of SMN protein. Several agents that activate STAT5 in human and mouse cell lines enhance SMN expression from the SMN2 gene and can compensate, at least in part, for the loss of production of a functional protein from SMN1. Here, we have shown that prolactin (PRL) increases SMN levels via activation of the STAT5 pathway. PRL increased SMN mRNA and protein levels in cultured human and mouse neuronal cells. Administration of STAT5-specific siRNA blocked the effects of PRL, indicating that the PRL-induced transcriptional upregulation of the SMN-encoding gene was mediated by activation of STAT5. Furthermore, systemic administration of PRL to WT mice induced SMN expression in the brain and spinal cord. Critically, PRL treatment increased SMN levels, improved motor function, and enhanced survival in a mouse model of severe SMA. Our results confirm earlier work suggesting STAT5 pathway activators as potential therapeutic compounds for the treatment of SMA and identify PRL as one such promising agent.
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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 | 0.001 | 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 | 0.001 |
| 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".