Preclinical models of mitochondrial dysfunction: mtDNA and nuclear-encoded regulators in diverse pathologies
Bibliographic record
Abstract
Mitochondrial-driven diseases encompass a diverse group of single-gene and complex disorders, all linked to mitochondrial dysfunction, with significant impacts on human health. While there are rare mitochondrial diseases in which the primary defect resides in mutations in mitochondrial DNA, it is increasingly clear that acquired mitochondrial dysfunction, both genetically- and epigenetically-mediated, complicates common complex diseases, including diabetes, cardiovascular disease and ischemia reperfusion injury, cancer, pulmonary hypertension, and neurodegenerative diseases. It is also recognized that mitochondrial abnormalities not only act by altering metabolism but, through effects on mitochondrial dynamics, can regulate numerous cellular processes including intracellular calcium handling, cell proliferation, apoptosis and quality control. This review examines the crucial role of preclinical models in advancing our understanding of mitochondrial genetic contributions to these conditions. It follows the evolution of models of mitochondrial-driven diseases, from earlier in vitro and in vivo systems to the use of more innovative approaches, such as CRISPR-based gene editing and mitochondrial replacement therapies. By assessing both the strengths and limitations of these models, we highlight their contributions to uncovering disease mechanisms, identifying therapeutic targets, and facilitating novel discoveries. Challenges in translating preclinical findings into clinical applications are also addressed, along with strategies to enhance the accuracy and relevance of these models. This review outlines the current state of the field, the future trajectory of mitochondrial disease modeling, and its potential impact on patient care.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".