Fasting Hormones Synergistically Induce Amino Acid Catabolism Genes to Promote Gluconeogenesis
Bibliographic record
Abstract
BACKGROUND & AIMS: Gluconeogenesis from amino acids (AAs) maintains glucose homeostasis during fasting. Although glucagon is known to regulate AA catabolism, the contribution of other hormones to it and the scope of transcriptional regulation dictating AA catabolism are unknown. We explored the role of the fasting hormones glucagon and glucocorticoids in transcriptional regulation of AA catabolism genes and AA-dependent gluconeogenesis. METHODS: We tested the RNA expression of AA catabolism genes and glucose production in primary mouse hepatocytes treated with fasting hormones (glucagon, corticosterone) and feeding hormones (insulin, fibroblast growth factor 19). We analyzed genomic data of chromatin accessibility and chromatin immunoprecipitation in mice and primary mouse hepatocytes. We performed chromatin immunoprecipitation in livers of fasted mice to show binding of cAMP responsive element binding protein (CREB) and the glucocorticoid receptor (GR). RESULTS: Fasting induced the expression of 31 genes with various roles in AA catabolism. Of them, 15 were synergistically induced by co-treatment of glucagon and corticosterone. Synergistic gene expression relied on the activity of both CREB and GR and was abolished by treatment with either insulin or fibroblast growth factor 19. Enhancers adjacent to synergistically induced genes became more accessible and were bound by CREB and GR on fasting. Akin to the gene expression pattern, gluconeogenesis from AAs was synergistically induced by glucagon and corticosterone in a CREB- and GR-dependent manner. CONCLUSIONS: Transcriptional regulation of AA catabolism genes during fasting is widespread and is driven by glucagon (via CREB) and corticosterone (via GR). Glucose production in hepatocytes is also synergistically augmented, showing that glucagon alone is insufficient in fully activating gluconeogenesis.
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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) | 0.003 | 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".