MétaCan
Menu
Back to cohort

Unravelling a role of LRPPRC in peroxisomal lipid metabolism through lipidomic investigations in human and mouse

2017· article· en· W4389019719 on OpenAlexaffabout
Matthieu Ruiz, Alexanne Cuillerier, Frédérique Dupuis, Pascaline Morue, Bertrand Bouchard, Isabelle Robillard‐Frayne, Anik Forest, Caroline Daneault, Julie Legault, Lise Coderre, Yan Burelle, John D. Rioux, Christine Des Rosiers

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsMontreal Heart Institute
Fundersnot available
KeywordsPeroxisomeLipid metabolismBiochemistryLipidomicsMetabolismBiologyChemistryGene

Abstract

fetched live from OpenAlex

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”.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.021
GPT teacher head0.265
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicMitochondrial Function and PathologyFrench-language works237,207