Cardiac ryanodine receptor N-terminal region biosensors identify novel inhibitors via FRET-based high-throughput screening
Bibliographic record
Abstract
The N-terminal region (NTR) of ryanodine receptor (RyR) channels is critical for the regulation of Ca 2+ release during excitation–contraction (EC) coupling in muscle. The NTR hosts numerous mutations linked to skeletal (RyR1) and cardiac (RyR2) myopathies, highlighting its potential as a therapeutic target. Here, we constructed two biosensors by labeling the mouse RyR2 NTR at domains A, B, and C with FRET pairs. Using fluorescence lifetime (FLT) detection of intramolecular FRET signal, we developed high-throughput screening (HTS) assays with these biosensors to identify small-molecule RyR modulators. We then screened a small validation library and identified several hits. Hits with saturable FRET dose–response profiles and previously unreported effects on RyR were further tested using [ 3 H]ryanodine binding to isolated sarcoplasmic reticulum vesicles to determine effects on intact RyR opening in its natural membrane. We identified three novel inhibitors of both RyR1 and RyR2 and two RyR1-selective inhibitors effective at nanomolar Ca 2+ . Two of these hits activated RyR1 only at micromolar Ca 2+ , highlighting them as potential enhancers of excitation–contraction coupling. To determine whether such hits can inhibit RyR leak in muscle, we further focused on one, an FDA-approved natural antibiotic, fusidic acid (FA). In skinned skeletal myofibers and permeabilized cardiomyocytes, FA inhibited RyR leak with no detrimental effect on skeletal myofiber excitation–contraction coupling. However, in intact cardiomyocytes, FA induced arrhythmogenic Ca 2+ transients, a cautionary observation for a compound with an otherwise solid safety record. These results indicate that HTS campaigns using the NTR biosensor can identify compounds with therapeutic potential.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".