Not So Rare: Diseases Based on Mutant Proteins Controlling Endoplasmic Reticulum-Mitochondria Contact (MERC) Tethering
Bibliographic record
Abstract
ions, and reactive oxygen species. This crosstalk is a major determinant of cell metabolism, since it allows the ER to control mitochondrial oxidative phosphorylation and the Krebs cycle, while conversely, it allows the mitochondria to provide sufficient ATP to control ER proteostasis. MERC metabolic signaling is under the control of tethers and a multitude of regulatory proteins. Many of these proteins have recently been discovered to give rise to rare diseases if their genes are mutated. Surprisingly, these diseases share important hallmarks and cause neurological defects, sometimes paired with, or replaced by skeletal muscle deficiency. Typical symptoms include developmental delay, intellectual disability, facial dysmorphism and ophthalmologic defects. Seizures, epilepsy, deafness, ataxia, or peripheral neuropathy can also occur upon mutation of a MERC protein. Given that most MERC tethers and regulatory proteins have secondary functions, some MERC protein-based diseases do not fit into this categorization. Typically, however, the proteins affected in those diseases have dominant functions unrelated to their roles in MERCs tethering or their regulation. We are discussing avenues to pharmacologically target genetic diseases leading to MERC defects, based on our novel insight that MERC defects lead to common characteristics in rare diseases. These shared characteristics of MERCs disorders raise the hope that they may allow for similar treatment options.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.003 | 0.003 |
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".