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Record W6926108073 · doi:10.20381/ruor-27398

Molecular Pathophysiology and Stem Cell Treatment for Mitochondrial Diseases: Insights from the French-Canadian Variant of Leigh Syndrome

2022· other· en· W6926108073 on OpenAlexaboutno aff

Bibliographic record

VenueuO Research (University of Ottawa) · 2022
Typeother
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsPathophysiologyPhenotypeMitochondrionMitochondrial diseaseDiseaseGeneRespiratory chainMitochondrial DNAOxidative stress

Abstract

fetched live from OpenAlex

The French-Canadian variant of Leigh syndrome (LSFC) is a distinct and particularly severe presentation of Leigh syndrome characterized by the onset of unpredictable acidotic crises leading to death of 80% of them before the age of five. This autosomal recessive disorder is caused by mutations in LRPPRC, encoding an mRNA binding protein of the same name with a high affinity for mitochondrial transcripts. As a result of the mutations, levels of LRPPRC are decreased in all tissues and cause a severe deficiency of complex IV of the respiratory chain, with a deeper involvement of brain and liver. To gain better knowledge on the pathophysiology of this disease, and of the impact of the OXPHOS defect on the liver, our research consortium developed a mouse model of the disease harboring a liver specific inactivation of Lrpprc (H-Lrpprc). The goal of this thesis is to investigate the in vivo consequences of hepatic Lrpprc inactivation and to test potential therapy for mitochondrial diseases. The characterization of this model and the analysis of the mitochondrial phenotype are presented in Chapter 2 (Cuillerier et al, Human Molecular Genetics, 2017). Despite this severe phenotype, H-Lrpprc mice show no signs of overt liver failure and maintain energy levels, suggesting mechanisms are in place to sustain residual complex IV function. The underlying compensatory mechanisms granting these mice a remarkable resilience were explored and are presented in Chapter 4 (Cuillerier et al, Communications Biology, 2021). Along this project, we developed a protocol, and the optimized conditions of this method are described in Chapter 3 (Cuillerier and Burelle, JoVE, 2019). Although great progress has been made, there are currently no effective or curative treatments for LSFC and mitochondrial diseases. Recently, extensive pre-clinical and clinical studies supported the emergence and safety of mesenchymal stem cells therapy in the treatment of various diseases. Following transplantation, MSCs promote repair through various mechanisms including secretion of cytokines/exosomes, and transfer of mitochondria directly to target cells with impaired mitochondria offering a possibility to replace mutant dysfunctional organelles, which is relevant in the context of genetic mitochondrial diseases. Based on this, the objective of the last chapter of this thesis is to test the therapeutic potential of MSCs for genetic mitochondrial disorders using MSC-based approaches and LSFC as a disease model. Unfortunately, we encountered several obstacles along the way, including the departure of our main collaborator and stem cell expert, and delays in experimental procedures due to the COVID-19 pandemic. Consequently, this study was not completed at the moment of submission of this thesis, and is therefore presented as a pilot study in the form of a manuscript in Chapter 5. Overall, these projects unveiled alterations of mitochondrial functions that go beyond OXPHOS, a complex network of compensatory mechanisms in place to palliate these defects, and finally, encouraging preliminary results suggest MSC therapy could be beneficial for the treatment of mitochondrial 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.998
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.051
GPT teacher head0.350
Teacher spread0.299 · 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 designObservational
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

Citations1
Published2022
Admission routes1
Has abstractyes

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