mtDNA extramitochondrial replication mediates mitochondrial defect effects
Bibliographic record
Abstract
A high ratio of severe mitochondrial defects causes multiple human mitochondrial diseases. However, until now, the in vivo rescue signal of such mitochondrial defect effects has not been clear. Here, we built fly mitochondrial defect models by knocking down the essential mitochondrial genes dMterf4 and dMrps23 . Following genome-wide RNAi screens, we found that knockdown of Med8/Tfb4/mtSSB/PolG2/mtDNA-helicase rescued dMterf4/dMrps23 RNAi-mediated mitochondrial defect effects. Extremely surprisingly, they drove mtDNA replication outside mitochondria through the Med8/Tfb4-mtSSB/PolG2/mtDNA-helicase axis to amplify cytosolic mtDNA, leading to activation of the cGAS-Sting-like IMD pathway to partially mediate dMterf4 / dMrps23 RNAi-triggered effects. Moreover, we found that the Med8/Tfb4-mtSSB/PolG2/mtDNA-helicase axis also mediated other fly mitochondrial gene defect-triggered dysfunctions and Drosophila aging. Overall, our study demarcates the Med8/Tfb4-mtSSB/PolG2/mtDNA-helicase axis as a candidate mechanism to mediate mitochondrial defect effects through driving mtDNA extramitochondrial replication; dysfunction of this axis might be used for potential treatments for many mitochondrial and age-related 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) | 0.002 | 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".