Multi-omics Reveal that c-Src Modulates the Mitochondrial Phosphotyrosine Proteome and Metabolism According to Nutrient Availability
Bibliographic record
Abstract
BACKGROUND/AIMS: Src kinase family members, including c-Src, are involved in numerous signaling pathways and have been observed inside different cellular compartments. Notably, c-Src modulates carbohydrate and fatty acid metabolism and is involved in the metabolic rewiring of cancer cells. This kinase is found within mitochondria where it targets different proteins to impact on the organelle functions and overall metabolism. Surprisingly, no global metabolic characterization of Src has been performed although c-Src knock-out mice have been available for 30 years. Considering that c-Src is sensitive to various metabolites, c-Src might represent a crucial player in metabolic adjustments induced by nutrient stress. The aim of this work was to characterize the impact of c-Src on mitochondrial activity and overall metabolism using multi-omic characterization. METHODS: mice were fed ad libitum or fasted during 24h and were then analyzed using multi-omics. RESULTS: We observed that deletion of c-Src is linked to lower phosphorylation of Y412-NDUFA8, inhibition of oxygen consumption and accumulation of metabolites involved in glycolysis, TCA cycle and amino acid metabolism in mice fed ad libitum. Finally, metabolomics and (phosphotyrosine) proteomics are differently impacted by Src according to nutrient availability. CONCLUSION: The findings presented here highlight that c-Src reduces mitochondrial metabolism and impacts the metabolic adjustment induced by nutrient stress.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".