Unravelling a role of LRPPRC in peroxisomal lipid metabolism through lipidomic investigations in human and mouse
Bibliographic record
Abstract
Rationale LRPPRC (leucine‐rich pentatricopeptide repeat‐containing protein) is commonly described as a regulator of RNA metabolism in both mitochondria and nuclei, but the overall biological impact of this role remains unclear. Integrative genomics analyses have revealed that mutations in the LRPPRC gene were causing the cytochrome c oxidase defect in the French‐Canadian variant of Leigh Syndrome (LSFC) (Mootha et al. 2003). More recently, a targeted metabolomics study in LSFC patients highlighted specific markers reflecting mitochondrial metabolic perturbations for various nutrients including fatty acids (Thompson‐Legault et al. 2015). The role of LRPPRC in lipid metabolism remains, however, to be better deciphered. Goal & Methods To dissect the role of LRPPRC in lipid metabolism, we have analyzed plasma from LSFC patients as well as plasma and livers from hepato‐specific KO‐LRPPRC mice using a combination of mass spectrometry (MS)‐based approaches. These include: (1) an untargeted and comprehensive lipidomic workflow, which enables the coverage of > 1300 unique lipid entities, and (2) targeted lipidomic analyses to probe fatty acid (FA) metabolism through acylcarnitines (ACs) profiling (covering >100 species) or cholesterol catabolism into bile acids (BAs; 9 (un)conjugated species). Results The lipidomic profile observed in LSFC patients is typically characteristic of peroxisomal dysfunction as revealed by a: (i) 2‐fold decrease in plasmalogens (Pls; p<0.01), (ii) 2‐fold decrease in conjugate BAs such as glyco‐(G), glycodeoxy‐(GD) (p<0.001) and tauro‐(T) (p<0.05) cholic acids (CA), and (iii) 1.5‐fold increase in specific AC species, namely those with odd chains or > 20 carbons (p<0.05). These results in humans were corroborated in transgenic mice. Indeed, in plasma, there is a 1.5‐fold decrease in Pls (p<0.05). In addition, in liver, several results point out to a remodeling of peroxisomal metabolism. These include: (i) an imbalance in BA conjugation (10‐fold increase in CA, p<0.01; 1.4‐fold decrease in GDCA, p<0.05) and a 4‐ to 10‐fold increase in odd chain and >20 carbons ACs (p<0.01). This notion is further substantiated by changes in gene and/or protein expression levels for: (i) catalase, a classical marker of peroxisome content (+16%; protein, p<0.001), (ii) ACOX1, marker of peroxisomal β‐oxidation (−40%; protein, p<0.05) and (iii) several peroxins (Pex) involved in peroxisome biogenesis and transport, namely Pex11β, Pex14, Pex19, Pex1 and Pex 10 (up to +60%; mRNA; p<0.05). Conclusion Collectively there results highlight a novel role for LRPPRC in the regulation of lipid metabolism beyond mitochondria, namely in peroxisomes. Whether peroxisomal lipid perturbations are specific to LRPPRC gene defect or represent a common feature of mitochondrial dysfunction remain, however, to be further ascertained. Support or Funding Information This work was supported by the “Fondation Grand défi Pierre Lavoie”.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".