Selective Fluorogenic β-Glucocerebrosidase Substrates for Convenient Analysis of Enzyme Activity in Cell and Tissue Homogenates
Bibliographic record
Abstract
Within mammals, there are often several functionally related glycoside hydrolases, which makes monitoring their activities problematic. This problem is particularly acute for the enzyme β-glucocerebrosidase (GCase), the malfunction of which is a key driver of Gaucher's disease (GD) and a major risk factor for Parkinson's disease (PD). Humans harbor two other functionally related β-glucosidases known as GBA2 and GBA3, and the currently used fluorogenic substrates are not selective, which has driven the use of complicated subtractive assays involving the use of detergents and inhibitors. Here we describe the preparation of fluorogenic substrates based on the widely used nonselective substrate resorufin β-d-glucopyranoside. Using recombinant enzymes, we show that these substrates are highly selective for GCase. We also demonstrate their value through the analysis of GCase activity in brain tissue homogenates from transgenic mice expressing mutant human GCase and patient fibroblasts expressing mutant GCase. This approach simplifies the analysis of cell and tissue homogenates and should facilitate the analysis of clinical and laboratory tissues and samples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".