Mitochondrial transplantation: Triumphs, challenges, and impacts on nuclear genome remodelling
Bibliographic record
Abstract
Mitochondria are membrane-bound organelles of eukaryotic cells that play crucial roles in cell functioning and homeostasis, including ATP generation for cellular energy. Mitochondrial function is associated with several complex diseases and disorders, including cardiovascular, cardiometabolic, neurodegenerative diseases and some cancers. The risk for these diseases and disorders is often associated with mitochondrial dysfunction, particularly the quantitative and qualitative features of the mitochondrial genome. Emerging results implicate mito-nuclear crosstalk as the mechanism by which mtDNA variation affects complex disease outcomes. Experimental approaches are emerging for the targeting of mitochondria as a potential therapeutic for several of these diseases, particularly in the form of mitochondrial transplantation. Current approaches to mitochondrial transplantation generally involve isolating healthy mitochondria from donor cells and introducing them to diseased recipients towards amelioration of mitochondrial dysfunction. Using such a protocol, several reports have shown recovery of mitochondrial function and improved disease outcomes post-mitochondrial transplantation, highlighting its potential as a therapeutic method for several complex, severe and debilitating diseases. Additionally, the mitochondrial genome can be modified prior to transplantation to target disease-associated site-specific mutations and to reduce the ratio of mutant-to-WT alleles. These promising results may underlie the potential impact of mitochondrial transplantation on mito-nuclear genome interactions in the setting of the disease. Further, we recommend that mitochondrial transplantation experimentation include an assessment of potential impacts on remodelling of the nuclear genome, particularly the nuclear epigenome and transcriptome. Herein, we review these and other triumphs and challenges of mitochondrial transplantation as a potential novel therapeutic for mitochondria-associated 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".