Poly (A) tail length of human mitochondrial mRNAs is tissue-specific and a mutation in LRPPRC results in transcript-specific patterns of deadenylation
Bibliographic record
Abstract
Mutations in LRPPRC cause Leigh Syndrome French Canadian (LSFC), an early onset neurodegenerative disease, with differential tissue involvement. The molecular basis for tissue specificity in this disease remains unknown. LRPPRC, an RNA binding protein, forms a stable complex with SLIRP, which binds to, and stabilizes mitochondrial mRNAs. In cell culture and animal models, loss of LRPPRC function results in transcript-specific alterations in the steady-state levels of mitochondrial mRNAs and poly (A) tail length, the mechanisms for which are not understood. The poly (A) tail length of mitochondrial mRNAs has not been investigated in human tissues from heathy subjects or LSFC patients. Here we have mapped the 3'-termini of mature mitochondrial mRNAs in three tissues (skeletal muscle, heart, and liver) from a healthy individual and an LSFC patient. We show that the poly (A) tail length of mitochondrial mRNAs varies amongst tissues, and that the missense mutation in LRPPRC that causes LSFC results in tissue- and transcript-specific deadenylation of a subset of mitochondrial mRNAs, likely contributing the nature and severity of the biochemical phenotype in different tissues. We also found a relatively large fraction of short transcripts lacking a stop codon, some with short poly (A) tails, in patient tissue, suggesting that mutations in LRPPRC may also impair proper 3' end processing of some mRNAs.
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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) | 0.001 | 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".