PGC-1α isoforms coordinate to balance hepatic metabolism and apoptosis in inflammatory environments
Bibliographic record
Abstract
OBJECTIVE: The liver is regularly exposed to changing metabolic and inflammatory environments. It must sense and adapt to metabolic need while balancing resources required to protect itself from insult. Peroxisome proliferator activated receptor gamma coactivator-1 alpha (PGC-1α) is a transcriptional coactivator expressed as multiple, alternatively spliced variants transcribed from different promoters that coordinate metabolic adaptation and protect against inflammation. It is not known how PGC-1α integrates extracellular signals to balance metabolic and anti-inflammatory outcomes. METHODS: Primary mouse hepatocytes were used to evaluate the role(s) of different PGC-1α proteins in regulating hepatic metabolism and inflammatory signaling downstream of tumor necrosis factor alpha (TNFα). Gene expression and signaling analysis were combined with biochemical measurement of apoptosis using gain- and loss-of-function in vitro and in vivo. RESULTS: Hepatocytes expressed multiple isoforms of PGC-1α, including PGC-1α4, which microarray analysis showed had common and isoform-specific functions linked to metabolism and inflammation compared with canonical PGC-1α1. Whereas PGC-1α1 primarily impacted gene programs of nutrient metabolism and mitochondrial biology, TNFα signaling showed several pathways related to innate immunity and cell death downstream of PGC-1α4. Gain- and loss-of-function models illustrated that PGC-1α4 uniquely enhanced expression of anti-apoptotic gene programs and attenuated hepatocyte apoptosis in response to TNFα or lipopolysaccharide (LPS). This was in contrast to PGC-1α1, which decreased the expression of a wide inflammatory gene network but did not prevent hepatocyte death in response to cytokines. CONCLUSIONS: PGC-1α variants have distinct, yet complementary roles in hepatic responses to metabolism and inflammation, and we identify PGC-1α4 as an important mitigator of apoptosis.
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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".