PGC-1α isoforms coordinate to balance hepatic metabolism and apoptosis in inflammatory environments
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it