Hormone-controlled cooperative binding of transcription factors drives synergistic induction of fasting-regulated genes
Bibliographic record
Abstract
During fasting, hepatocytes produce glucose in response to hormonal signals. Glucagon and glucocorticoids are principal fasting hormones that cooperate in regulating glucose production via gluconeogenesis. However, how these hormone signals are integrated and interpreted to a biological output is unknown. Here, we use genome-wide profiling of gene expression, enhancer dynamics and transcription factor (TF) binding in primary mouse hepatocytes to uncover the mode of cooperation between glucagon and glucocorticoids. We found that compared to a single treatment with each hormone, a dual treatment directs hepatocytes to a pro-gluconeogenic gene program by synergistically inducing gluconeogenic genes. The cooperative mechanism driving synergistic gene expression is based on 'assisted loading' whereby a glucagon-activated TF (cAMP responsive element binding protein; CREB) leads to enhancer activation which facilitates binding of the glucocorticoid receptor (GR) upon glucocorticoid stimulation. Glucagon does not only activate single enhancers but also activates enhancer clusters, thereby assisting the loading of GR also across enhancer units within the cluster. In summary, we show that cells integrate extracellular signals by an enhancer-specific mechanism: one hormone-activated TF activates enhancers, thereby assisting the loading of a TF stimulated by a second hormone, leading to synergistic gene induction and a tailored transcriptional response to fasting.
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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) | 0.000 | 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) | 0.001 | 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".