Neuronal cell-based high-throughput screen for enhancers of mitochondrial function reveals luteolin as a modulator of mitochondria-endoplasmic reticulum coupling
Bibliographic record
Abstract
Abstract Background Mitochondrial dysfunction is a common feature of aging, neurodegeneration, and metabolic diseases. Hence, mitotherapeutics may be valuable disease modifiers for a large number of conditions. In this study, we have set up a large-scale screening platform for mitochondrial-based modulators with promising therapeutic potential. Results Using differentiated human neuroblastoma cells, we screened 1200 FDA-approved compounds and identified 61 molecules that significantly increased cellular ATP without any cytotoxic effect. Following dose response curve-dependent selection, we identified the flavonoid luteolin as a primary hit. Further validation in neuronal models indicated that luteolin increased mitochondrial respiration in primary neurons, despite not affecting mitochondrial mass, structure, or mitochondria-derived reactive oxygen species. However, we found that luteolin increased contacts between mitochondria and endoplasmic reticulum (ER), contributing to increased mitochondrial calcium (Ca 2+ ) and Ca 2+ -dependent pyruvate dehydrogenase activity. This signaling pathway likely contributed to the observed effect of luteolin on enhanced mitochondrial complexes I and II activities. Importantly, we observed that increased mitochondrial functions were dependent on the activity of ER Ca 2+ -releasing channels inositol 1,4,5-trisphosphate receptors (IP 3 Rs) both in neurons and in isolated synaptosomes. Additionally, luteolin treatment improved mitochondrial and locomotory activities in primary neurons and Caenorhabditis elegans expressing an expanded polyglutamine tract of the huntingtin protein. Conclusion We provide a new screening platform for drug discovery validated in vitro and ex vivo. In addition, we describe a novel mechanism through which luteolin modulates mitochondrial activity in neuronal models with potential therapeutic validity for treatment of a variety of human diseases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".