Untargeted lipidomics to identify characteristic signatures and mechanisms of mitochondrial diseases according to genetic origin
Bibliographic record
Abstract
Background: The major challenge in mitochondrial diseases relies in their great heterogeneity, in terms of genetic origin (i.e. nuclear or mitochondrial), mutation diversity and clinical manifestations. The underlying mechanisms are still unclear and often considered common to most mitochondrial diseases. Hypothesis: According to the genetic origin, the different lipidomic profiles will reveal common as well as mutation-dependent signatures. Aims: i) Establish the characteristic signatures of human fibroblasts from mitochondrial diseases of distinct genetic origins: either nuclear: LRPPRC and MTFMT, or mitochondrial: NDUFS4, UQCRC2 and ECHS1 compared with healthy fibroblasts and, based on the established signatures, ii) identify the underlying mechanisms. Methods: We used a mass spectrometry-based untargeted lipidomics approach to scan for thousands lipid signals. Based on the identified signatures, selected players were evaluated by quantitative PCR. Results: Principal component analysis shows that each cell line differs from the others. Our lipidomics analyses show that few lipids were modulated in a similar way irrespective of the genetic origin, and especially the increase in cholesterol esters. In contrast, ganglioside metabolism was differentially changed according to the genetic origin. More specifically, while GM3 forms were mostly and significantly increased for MTFMT, UQCRC2 and ECHS1 and moderately increased for NDUFS4, GM3 were decreased for LRPPRC. In addition, the GA1 and GM2 forms produced from GM3 were reduced in all cell lines except for LRPPRC and NDUFS4 where their signals remained unchanged. Since the formation of GA1 and GM2 involves the B4GALNT1 enzyme, we measured its mRNA expression and observed a drastic decrease up to 90% for MTFMT, while it remained unchanged for LRPPRC. Conclusion: The use of untargeted lipidomics is a relevant and powerful approach for the identification of specific signatures and underlying mechanisms of mitochondrial disorders, illustrated here by differentially affected ganglioside metabolism according to genetic origin, providing a starting point that will ultimately guide towards nutritional and therapeutic alternatives. Montreal Heart Institute Fundation, Lactic Acidosis Association and FRQS. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".