Activation of dsRNA-Dependent Protein Kinase R by miR-378 Sustains Metabolic Inflammation in Hepatic Insulin Resistance
Bibliographic record
Abstract
MicroRNAs (miRNAs) are noncoding small RNAs that regulate various pathophysiological cellular processes. Here, we report that expression of the miR-378 family was significantly induced by metabolic inflammatory inducers, a high-fructose diet, and inflammatory cytokine tumor necrosis factor-α. Hepatic miRNA profiling revealed that expression of miR-378a was highly upregulated, which, in turn, targeted the 3′-untranslated region of PPARα mRNA, impaired mitochondrial fatty acid β-oxidation, and induced mitochondrial and endoplasmic reticulum stress. More importantly, the upregulated miR-378a can directly bind to and activate the double-strand RNA (dsRNA)–dependent protein kinase R (PKR) to sustain the metabolic stress. In vivo, genetic depletion of miR-378a prevented PKR activation and ameliorated inflammatory stress and insulin resistance. Counterbalancing the upregulated miR-378a using nanoparticles encapsulated with an anti-miR-378a oligonucleotide restored PPARα activity, inhibited PKR activation and ER stress, and improved insulin sensitivity in fructose-fed mice. Our study delineated a novel mechanism of miR-378a in the pathogenesis of metabolic inflammation and insulin resistance through targeting metabolic signaling at both mRNA (e.g., PPARα) and protein (e.g., PKR) molecules. This novel finding of functional interaction between miRNAs (e.g., miR-378a) and cellular RNA binding proteins (e.g., PKR) is biologically significant because it greatly broadens the potential targets of miRNAs in cellular pathophysiological processes.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".