Knockdown of chimeric glucocerebrosidase by green fluorescent protein-directed small interfering RNA.
Bibliographic record
Abstract
Gaucher disease, the most common type of lysosomal storage disorder, is characterized by an inherited deficiency of the membrane-associated hydrolase, glucocerebrosidase. Glucocerebrosidase catalyzes the hydrolysis of glucocerebroside to ceramide and glucose, a crucial step in the recycling of membrane sphingolipids. The exorbitant cost of the current treatment standard for Gaucher disease, enzyme replacement therapy, prevents many from receiving treatment. This limitation has led to a wide-spread search for more efficient and cost-effective methods of protein production and alternate therapies, resulting in a closer examination of glucocerebrosidase biosynthesis and current treatment techniques. The use of specific small interfering RNAs (siRNAs) to knock down target genes is an attractive option for studying such processes, though a glucocerebrosidase-specific siRNA has yet to be reported. We note, however, that green fluorescent protein (GFP)-directed siRNAs can not only provide a positive control to test siRNA delivery and system integrity, but also serve as a means to knock down a fusion partner without having to design siRNAs specific to the partner. After effectively co-transfecting COS-1 cells with enhanced GFP (EGFP)-tagged glucocerebrosidase constructs and GFP-directed siRNAs, we report successful knockdown of all EGFP-containing constructs at both the RNA and protein levels. This provides a method of examining enzyme biosynthesis and treatment options. Furthermore, this technique is applicable to other systems, since we have demonstrated the usefulness of GFP as a siRNA target in mammalian cells when fused to another gene of interest.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".