Modeling compound lipid homeostasis using stable isotope tracing
Bibliographic record
Abstract
Lipids represent the most diverse pool of metabolites found in cells, facilitating compartmentation, signaling, and other functions. Dysregulation of lipid metabolism is linked to disease states such as cancer and neurodegeneration. However, limited tools are available for quantifying metabolic fluxes across the lipidome. To directly measure reaction fluxes encompassing compound lipid homeostasis, we applied stable isotope tracing, liquid chromatography-high-resolution mass spectrometry, and network-based isotopologue modeling to non-small cell lung cancer (NSCLC) models. Compound lipid metabolic flux analysis (CL-MFA) enables the concurrent quantitation of fatty acid synthesis, elongation, headgroup assembly, and salvage reactions within virtually any biological system. Here, we resolve liver kinase B1 (LKB1)-mediated regulation of sphingolipid recycling in NSCLC cells and precision-cut lung slice cultures. We also demonstrate that widely used tissue culture conditions drive cells to upregulate fatty acid synthase flux to supraphysiological levels. Finally, we identify previously uncharacterized isozyme specificity of ceramide synthase inhibitors. These results highlight the ability of CL-MFA to quantify lipid cycling in biological systems to discover biological function and elucidate molecular mechanisms in membrane lipid metabolism.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".