An Inducible Luminescent System to Explore Parkinson’s Disease-Associated Genes
Bibliographic record
Abstract
With emerging genetic association studies, new genes and pathways are revealed as causative factors in the development of Parkinson’s disease (PD). However, many of these PD genes are poorly characterized in terms of their function, subcellular localization, and interaction with other components in cellular pathways. This represents a major obstacle towards a better understanding of the molecular causes of PD, with deeper molecular studies often hindered by a lack of high-quality, validated antibodies for detecting the corresponding proteins of interest. In this study, we leveraged the nanoluciferase-derived LgBiT-HiBiT system by generating a cohort of tagged PD genes in both induced pluripotent stem cells (iPSCs) and iPSC-derived neuronal cells. To promote luminescence signals within cells, a master iPSC line was generated, in which LgBiT expression is under the control of a doxycycline-inducible promoter. LgBiT could bind to HiBiT when present either alone or when tagged onto different PD-associated proteins encoded by the genes GBA1, GPNMB, LRRK2, PINK1, PRKN, SNCA, VPS13C, and VPS35. Several HiBiT-tagged proteins could already generate luminescence in iPSCs in response to the doxycycline induction of LgBiT, with the enzyme glucosylceramidase beta 1 (GCase), encoded by GBA1, being one such example. Moreover, the GCase chaperone ambroxol elicited an increase in the luminescence signal in HiBiT-tagged GBA1 cells, correlating with an increase in the levels of GCase in dopaminergic cells. Taken together, we have developed and validated a Doxycycline-inducible luminescence system to serve as a sensitive assay for the quantification, localization, and activity of HiBiT-tagged PD-associated proteins with reliable sensitivity and efficiency.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".