Mutations in the <i>Saccharomyces cerevisiae</i> Succinate Dehydrogenase Result in Distinct Metabolic Phenotypes Revealed Through <sup>1</sup>H NMR-Based Metabolic Footprinting
Bibliographic record
Abstract
Metabolomics is a powerful method of examining the intricate connections between mutations, metabolism, and disease. Metabolic footprinting examines the extracellular metabolome or exometabolome. We employed NMR-based metabolic footprinting and multivariate statistical analysis to examine a yeast model of mitochondrial dysfunction. Succinate dehydrogenase (SDH) is a component of both the tricarboxylic acid cycle and the mitochondrial respiratory chain. Mutations in the human SDH are linked to a variety of cancers or neurodegenerative disorders, highlighting the genotype/phenotype complexity associated with SDH dysfunction. To gain insight into the underlying global metabolic consequences of SDH dysfunction, we examined the metabolic footprints of SDH3 and SDH4 mutants. We identified and quantified 36 metabolites in the exometabolome. Our results indicate that SDH mutations cause significant alterations to several areas of yeast metabolism. Multivariate statistical analysis allowed us to discriminate between the different metabotypes of individual mutants, including mutants that were phenotypically indistinguishable. Metabotypes were highly correlated to mutant growth yields, suggesting that the characterization of metabotypes offers a rapid means of investigating the phenotype of a new mutation. Our study provides novel insight into the metabolic effects of SDH dysfunction and highlights the effectiveness of metabolic footprinting for examining complex disorders, such as mitochondrial diseases.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".