A dual-functional substrate for quantitation of substrate levels and GCase activity in living cells
Bibliographic record
Abstract
is the most common genetic risk factor for the development of Parkinson's disease (PD). To gain a better understanding how these mutations drive development of PD and how GCase is regulated within cells, the field needs chemical reporters of GCase activity that can be used within living cells. Fluorogenic substrates are one method that can be used to quantify enzyme activities within cells yet existing substrates for GCase have limitations. In particular, the inability to monitor cellular uptake of substrate limits the ability to disentangle impairments in uptake of substrate from impairments in lysosomal GCase activity. Here we report on the preparation and biological characterisation of LysoRF-GBA - a new chemical tool which can be used to quantitatively measure both the cellular levels of intact substrate and lysosomal GCase activity within lysosomes. We demonstrate that, by using LysoRF-GBA, endogenous GCase activity can be measured within live neuroblastoma cells. The selectivity of this substrate for GCase, relative to other cellular enzymes, was validated by genetic and pharmacological perturbation of GCase. By using LysoRF-GBA and concomitantly monitoring levels of both cleaved product and intact substrate, we were able to measure GCase engagement with a known pharmacological chaperone and discriminate between pharmacological agents that affect GCase activity from those that impair endocytosis. Further, the ability to monitor intracellular levels of intact LysoRF-GBA also enabled us to measure its time dependent accumulation within cells, providing insight into when steady state levels of this substrate are reached. LysoRF-GBA therefore shows high potential to be exploited as a tool for the discovery of compounds that could beneficially modulate its activity for benefit in diseases including PD.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".