Identification of a gene causing human cytochrome <i>c</i> oxidase deficiency by integrative genomics
Why this work is in the frame
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Bibliographic record
Abstract
Identifying the genes responsible for human diseases requires combining information about gene position with clues about biological function. The recent availability of whole-genome data sets of RNA and protein expression provides powerful new sources of functional insight. Here we illustrate how such data sets can expedite disease-gene discovery, by using them to identify the gene causing Leigh syndrome, French-Canadian type (LSFC, Online Mendelian Inheritance in Man no. 220111), a human cytochrome c oxidase deficiency that maps to chromosome 2p16-21. Using four public RNA expression data sets, we assigned to all human genes a "score" reflecting their similarity in RNA-expression profiles to known mitochondrial genes. Using a large survey of organellar proteomics, we similarly classified human genes according to the likelihood of their protein product being associated with the mitochondrion. By intersecting this information with the relevant genomic region, we identified a single clear candidate gene, LRPPRC. Resequencing identified two mutations on two independent haplotypes, providing definitive genetic proof that LRPPRC indeed causes LSFC. LRPPRC encodes an mRNA-binding protein likely involved with mtDNA transcript processing, suggesting an additional mechanism of mitochondrial pathophysiology. Similar strategies to integrate diverse genomic information can be applied likewise to other disease pathways and will become increasingly powerful with the growing wealth of diverse, functional genomics data.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it