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Record W4408148164 · doi:10.1016/j.jare.2025.03.004

Nutrient supplementation mitigates retinal dysfunction in Acox1 knockout mice with impaired peroxisomal fatty acid oxidation

2025· article· en· W4408148164 on OpenAlexaff
Myriam Boeck, Hitomi Yagi, Chuck T. Chen, Yan Zeng, Deokho Lee, Shen Nian, Taku Kasai, Jeff Lee, Victoria Hirst, Chaomei Wang, Katherine Neilsen, Tori Rodrick, Andrew McCutcheon, Miao Yu, Irfan J. Lodhi, Sasha A. Singh, Masanori Aikawa, Richard P. Bazinet, Zhongjie Fu

Bibliographic record

VenueJournal of Advanced Research · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPeroxisome Proliferator-Activated Receptors
Canadian institutionsUniversity of Toronto
FundersNational Eye InstituteNational Institutes of HealthKowa CompanyMassachusetts Lions Eye Research FundNational Institute of Diabetes and Digestive and Kidney DiseasesBoston Children's Hospital
KeywordsPeroxisomeBeta oxidationKnockout mouseRetinalFatty acidNutrientChemistryInternal medicineEndocrinologyBiochemistryMedicineGene

Abstract

fetched live from OpenAlex

INTRODUCTION: Dyslipidemia contributes to many retinal diseases, but underlying lipid processing pathways are not fully understood. Peroxisomes oxidize very long-chain fatty acids and generate docosahexaenoic acid (DHA). Mutations in peroxisomal genes can result in severe neural retinal dysfunction. However, therapeutic approaches for peroxisomal diseases remain scarce, and dietary strategies yield inconsistent results. OBJECTIVES: This study sought to elucidate retinal metabolic adaptations resulting from impaired peroxisomal fatty acid oxidation and to evaluate the therapeutic potential of nutrient supplementation in peroxisomal retinal disease. METHODS: In mice with global knockout (KO) of acyl-coenzyme A oxidase 1 (Acox1), encoding the first and rate-limiting enzyme in peroxisomal fatty acid oxidation, the retina was characterized at postnatal day (P) 30 during development. Retinal thickness, photoreceptor structure, and function were examined. Proteome analysis was utilized for molecular mechanistic investigation. Metabolomics and fatty acid profiling were conducted to study metabolic alterations in the retina. Nutrient intervention was performed to test if providing deficient nutrients could attenuate the observed retinal dysfunction. RESULTS: In P30 Acox1 KO mice, we observed impaired neural retinal signaling, accompanied by reduced expression of genes involved in phototransduction. Proteomics suggested diminished glucose and mitochondrial metabolism, supported by decreased mitochondrial number and mitochondrial DNA copy number. Metabolomics showed reduced abundance of retinal pyruvate, and pyruvate supplementation from P30-P60 attenuated neural retinal dysfunction in Acox1 KO mice at P60. Furthermore, Acox1 KO mice at P30 exhibited a significant decrease in omega-3 (n-3) fatty acids and a compensatory increase in n-6 fatty acids. Dietary supplementation with DHA (n-3) or DHA plus arachidonic acid (n-6) from P30-P60 mitigated the progression of retinal dysfunction in Acox1 KO mice. CONCLUSION: Retinal dysfunction, decreased mitochondrial number, and metabolic imbalance were observed in mice with impaired peroxisomal fatty acid oxidation. Nutrient intervention may offer a promising therapeutic approach for peroxisomal diseases.

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)0.000
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
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.329
Teacher spread0.315 · 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".

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Citations3
Published2025
Admission routes1
Has abstractyes

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