Mitochondrial and Nuclear Genomic Responses to Loss of LRPPRC Expression
Bibliographic record
Abstract
Rapid advances in genotyping and sequencing technology have dramatically accelerated the discovery of genes underlying human disease. Elucidating the function of such genes and understanding their role in pathogenesis, however, remain challenging. Here, we introduce a genomic strategy to characterize such genes functionally, and we apply it to LRPPRC, a poorly studied gene that is mutated in Leigh syndrome, French-Canadian type (LSFC). We utilize RNA interference to engineer an allelic series of cellular models in which LRPPRC has been stably silenced to different levels of knockdown efficiency. We then combine genome-wide expression profiling with gene set enrichment analysis to identify cellular responses that correlate with the loss of LRPPRC. Using this strategy, we discovered a specific role for LRPPRC in the expression of all mitochondrial DNA-encoded mRNAs, but not the rRNAs, providing mechanistic insights into the enzymatic defects observed in the disease. Our analysis shows that nuclear genes encoding mitochondrial proteins are not collectively affected by the loss of LRPPRC. We do observe altered expression of genes related to hexose metabolism, prostaglandin synthesis, and glycosphingolipid biology that may either play an adaptive role in cell survival or contribute to pathogenesis. The combination of genetic perturbation, genomic profiling, and pathway analysis represents a generic strategy for understanding disease pathogenesis.
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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 | 0.000 |
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".