Sacsin levels in PBMCs: A diagnostic assay for SACS variants in peripheral blood cells – A PROSPAX study
Bibliographic record
Abstract
BACKGROUND: Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) is a common recessive ataxia that is still underdiagnosed worldwide. An easily accessible diagnostic biomarker might help to diagnostically confirm patients presenting SACS variants of unknown significance (VUS) or atypical phenotypes. OBJECTIVES: To detect sacsin in peripheral blood mononuclear cells (PBMCs) and to validate its diagnostic biomarker quality to discriminate biallelic SACS patients (including patients with VUS and/or atypical phenotypes) against healthy controls, non-ARSACS spastic ataxia patients, and heterozygous SACS carriers. METHODS: Sacsin protein levels in PBMCs were assessed in patients versus controls and validated in skin-derived fibroblasts. RESULTS: Patients with biallelic SACS variants - including patients with VUS and/or atypical phenotypes - showed loss of sacsin in PBMCs, with discriminative performance against healthy, heterozygous, and non-ARSACS controls. This included all investigated SACS missense variants. Also, C-terminal variants escaping nonsense-mediated decay, while not differing from controls in expression level, showed lower molecular weight in this assay. CONCLUSIONS: Assessing sacsin levels using PBMCs offers an easy, peripherally accessible diagnostic biomarker for ARSACS, with PBMCs being much less invasive and easier to handle than fibroblasts. Additionally, this might be a potential target-engagement blood biomarker for sacsin-increasing therapies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".