The Role of Impaired Mitochondrial Dynamics in MFN2-Mediated Pathology
Bibliographic record
Abstract
The Mitofusin 2 protein (MFN2), encoded by the MFN2 gene, was first described for its role in mediating mitochondrial fusion. However, MFN2 is now recognized to play additional roles in mitochondrial autophagy (mitophagy), mitochondrial motility, lipid transfer, and as a tether to other organelles including the endoplasmic reticulum (ER) and lipid droplets. The tethering role of MFN2 is an important mediator of mitochondrial-ER contact sites (MERCs), which themselves have many important functions that regulate mitochondria, including calcium homeostasis and lipid metabolism. Exemplifying the importance of MFN2, pathogenic variants in MFN2 are established to cause the peripheral neuropathy Charcot-Marie-Tooth Disease Subtype 2A (CMT2A). However, the mechanistic basis for disease is not clear. Moreover, additional pathogenic phenotypes such as lipomatosis, distal myopathy, optic atrophy, and hearing loss, can also sometimes be present in patients with CMT2A. Given these variable patient phenotypes, and the many cellular roles played by MFN2, the mechanistic underpinnings of the cellular impairments by which MFN2 dysfunction leads to disease are likely to be complex. Here, we will review what is known about the various functions of MFN2 that are impaired by pathogenic variants causing CMT2A, with a specific emphasis on the ties between MFN2 variants and MERCs.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".