Human Growth Hormone Increases SMN Expression and Survival in Severe Spinal Muscular Atrophy Mouse Model
Bibliographic record
Abstract
BACKGROUND: Autosomal recessive spinal muscle atrophy (SMA) is characterized by the loss of α motor neurons resulting in progressive muscle loss and respiratory failure. SMA is one of the most common inherited causes of infant death with a carrier frequency of 1 in 50 and a calculated prevalence of about 1 in 11,000 live births in the US. The low amount of functional survival motor neuron (SMN) protein due to mutations or deletion in the SMN1 gene causes SMA. OBJECTIVE: A potential treatment strategy for SMA is to upregulate levels of SMN protein originating from the paralog SMN2 gene compensating in part for the absence of the SMN1 gene. Our group has previously shown that activation of the STAT5 pathway by lactation hormone prolactin (PRL) increased SMN levels, improved motor function and enhanced survival in a severe SMA mouse model. Given that human growth hormone (HGH) is also known to activate the STAT5 signalling pathway and is already used extensively in clinical settings, we thus elected to assess its impact on SMN levels. METHODS AND RESULTS: Administration of HGH in NT2 cells activated STAT5 pathway which resulted into significant induction in SMN protein levels. Furthermore, systemic administration of HGH to transgenic SMA mice induced SMN protein levels in the brain and spinal cord samples. Critically, HGH treatment improved disease phenotype and increased survival in two severe SMA mouse models. CONCLUSIONS: Our results confirm earlier work suggesting STAT5 pathway activators as potential therapeutic compounds for the treatment of SMA and identify HGH 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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".