Lipidomics unveils lipid dyshomeostasis and low circulating plasmalogens as biomarkers in a monogenic mitochondrial disorder
Bibliographic record
Abstract
Mitochondrial dysfunction characterizes many rare and common age-associated diseases. The biochemical consequences, underlying clinical manifestations, and potential therapeutic targets, remain to be better understood. We tested the hypothesis that lipid dyshomeostasis in mitochondrial disorders goes beyond mitochondrial fatty acid β-oxidation, particularly in liver. This was achieved using comprehensive untargeted and targeted lipidomics in a case-control cohort of patients with Leigh syndrome French-Canadian variant (LSFC), a mitochondrial disease caused by mutations in LRPPRC, and in mice harboring liver-specific inactivation of Lrpprc (H-Lrpprc-/-). We discovered a plasma lipid signature discriminating LSFC patients from controls encompassing lower levels of plasmalogens and conjugated bile acids, which suggest perturbations in peroxisomal lipid metabolism. This premise was reinforced in H-Lrpprc-/- mice, which compared with littermates recapitulated a similar, albeit stronger peroxisomal metabolic signature in plasma and liver including elevated levels of very-long-chain acylcarnitines. These mice also presented higher transcript levels for hepatic markers of peroxisome proliferation in addition to lipid remodeling reminiscent of nonalcoholic fatty liver diseases. Our study underscores the value of lipidomics to unveil unexpected mechanisms underlying lipid dyshomeostasis ensuing from mitochondrial dysfunction herein implying peroxisomes and liver, which likely contribute to the pathophysiology of LSFC, but also other rare and common mitochondrial diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".