PGC1A regulates the IRS1:IRS2 ratio during fasting to influence hepatic metabolism downstream of insulin
Bibliographic record
Abstract
Precise modulation of hepatic glucose metabolism is crucial during the fasting and feeding cycle and is controlled by the actions of circulating insulin and glucagon. The insulin-signaling pathway requires insulin receptor substrate 1 (IRS1) and IRS2, which are found to be dysregulated in diabetes and obesity. The peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC1A) is a fasting-induced transcriptional coactivator. In nonalcoholic fatty liver disease and in patients with type 2 diabetes, low hepatic PGC1A levels are associated with insulin resistance. However, how PGC1A activity impacts the hepatic insulin-signaling pathway is still unclear. We used gain- and loss-of-function models in mouse primary hepatocytes and measured hepatocyte insulin response by gene and protein expression and ex vivo glucose production. We found that the PGC1A level determines the relative ratio of IRS1 and IRS2 in hepatocytes, impacting insulin receptor signaling via protein kinase B/AKT (AKT). PGC1A drove the expression of IRS2 downstream of glucagon signaling while simultaneously reducing IRS1 expression. We illustrate that glucagon- or PGC1A-induced IRS2 expression was dependent on cAMP Response Element Binding Protein activity and that this was essential for suppression of hepatocyte gluconeogenesis in response to insulin in vitro. We also show that increased hepatic PGC1A improves glucose homeostasis in vivo, revealing a counterregulatory role for PGC1A in repressing uncontrolled glucose production in response to insulin signaling. These data highlight a mechanism by which PGC1A plays dual roles in the control of gluconeogenesis during the fasting-to-fed transition through regulated balance between IRS1 and IRS2 expression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".