NDST1 Preferred Promoter Confirmation and Identification of Corresponding Transcriptional Inhibitors as Substrate Reduction Agents for Multiple Mucopolysaccharidosis Disorders
Bibliographic record
Abstract
The stepwise degradation of glycosaminoglycans (GAGs) is accomplished by twelve lysosomal enzymes. Deficiency in any of these enzymes will result in the accumulation of the intermediate substrates on the pathway to the complete turnover of GAGs. The accumulation of these undegraded substrates in almost any tissue is a hallmark of all Mucopolysaccharidoses (MPS). Present therapeutics based on enzyme replacement therapy and bone marrow transplantation have low effectiveness for the treatment of MPS with neurological complications since enzymes used in these therapies are unable to cross the blood brain barrier. Small molecule-based approaches are more promising in addressing neurological manifestations. In this report we identify a target for developing a substrate reduction therapy (SRT) for six MPS resulting from the abnormal degradation of heparan sulfate (HS). Using the minimal promoter of NDST1, one of the first modifying enzymes of HS precursors, we established a luciferase based reporter gene assay capable of identifying small molecules that could potentially reduce HS maturation and therefore lessen HS accumulation in certain MPS. From the screen of 1,200 compounds comprising the Prestwick Chemical library we identified SAHA, a histone deacetylase inhibitor, as the drug that produced the highest inhibitory effects in the reporter assay. More importantly SAHA treated fibroblasts expressed lower levels of endogenous NDST1 and accumulated less 35S GAGs in patient cells. Thus, by using our simple reporter gene assay we have demonstrated that by inhibiting the transcription of NDST1 with small molecules, identified by high throughput screening, we can also reduce the level of sulfated HS substrate in MPS patient cells, potentially leading to SRT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".