Age-dependent SMN expression in disease-relevant tissue and implications for SMA treatment
Bibliographic record
Abstract
BACKGROUNDSpinal muscular atrophy (SMA) is caused by deficient expression of survival motor neuron (SMN) protein. New SMN-enhancing therapeutics are associated with variable clinical benefits. Limited knowledge of baseline and drug-induced SMN levels in disease-relevant tissues hinders efforts to optimize these treatments.METHODSSMN mRNA and protein levels were quantified in human tissues isolated during expedited autopsies.RESULTSSMN protein expression varied broadly among prenatal control spinal cord samples, but was restricted at relatively low levels in controls and SMA patients after 3 months of life. A 2.3-fold perinatal decrease in median SMN protein levels was not paralleled by comparable changes in SMN mRNA. In tissues isolated from nusinersen-treated SMA patients, antisense oligonucleotide (ASO) concentration and full-length (exon 7 including) SMN2 (SMN2-FL) mRNA level increases were highest in lumbar and thoracic spinal cord. An increased number of cells showed SMN immunolabeling in spinal cord of treated patients, but was not associated with an increase in whole-tissue SMN protein levels.CONCLUSIONSA normally occurring perinatal decrease in whole-tissue SMN protein levels supports efforts to initiate SMN-inducing therapies as soon after birth as possible. Limited ASO distribution to rostral spinal and brain regions in some patients likely limits clinical response of motor units in these regions for those patients. These results have important implications for optimizing treatment of SMA patients and warrant further investigations to enhance bioavailability of intrathecally administered ASOs.FUNDINGSMA Foundation, SMART, NIH (R01-NS096770, R01-NS062869), Ionis Pharmaceuticals, and PTC Therapeutics. Biogen provided support for absolute real-time RT-PCR.
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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 | 0.001 | 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) | 0.001 | 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".