Trans-omic analysis reveals fed and fasting insulin signal across phosphoproteome, transcriptome, and metabolome
Bibliographic record
Abstract
SUMMARY The concentration and temporal pattern of insulin selectively regulate multiple cellular functions. To understand how insulin dynamics are interpreted by cells, we constructed a trans-omic network of insulin action in FAO hepatoma cells from three networks—a phosphorylation-dependent cellular functions regulatory network using phosphoproteomic data, a transcriptional regulatory network using phosphoproteomic and transcriptomic data, and a metabolism regulatory network using phosphoproteomic and metabolomic data. With the trans-omic regulatory network, we identified selective regulatory networks that mediate differential responses to insulin. Akt and Erk, hub molecules of insulin signaling, encode information of a wide dynamic range of dose and time of insulin. Down-regulated genes and metabolites in glycolysis had high sensitivity to insulin (fasting insulin signal); up-regulated genes and dicarboxylic acids in the TCA cycle had low sensitivity (fed insulin signal). This integrated analysis enables molecular insight into how cells interpret physiologically fed and fasting insulin signals. Highlights We constructed a trans-omic network of insulin action using multi-omic data. The trans-omic network integrates phosphorylation, transcription, and metabolism. We classified signaling, transcriptome, and metabolome by sensitivity to insulin. We identified fed and fasting insulin signal flow across the trans-omic network.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".