HIF-1α Regulation of Cytokine Production following TLR3 Engagement in Murine Bone Marrow–Derived Macrophages Is Dependent on Viral Nucleic Acid Length and Glucose Availability
Bibliographic record
Abstract
Hypoxia-inducible factor-1α (HIF-1α) is an important regulator of glucose metabolism and inflammatory cytokine production in innate immune responses. Viruses modulate HIF-1α to support viral replication and the survival of infected cells, but it is unclear if this transcription factor also plays an important role in regulating antiviral immune responses. In this study, we found that short and long dsRNA differentially engage TLR3, inducing distinct levels of proinflammatory cytokine production (TNF-α and IL-6) in bone marrow-derived macrophages from C57BL/6 mice. These responses are associated with differential accumulation of HIF-1α, which augments NF-κB activation. Unlike TLR4 responses, increased HIF-1α following TLR3 engagement is not associated with significant alterations in glycolytic activity and was more pronounced in low glucose conditions. We also show that the mechanisms supporting HIF-1α stabilization may differ following stimulation with short versus long dsRNA and that pyruvate kinase M2 and mitochondrial reactive oxygen species play a central role in these processes. Collectively, this work suggests that HIF-1α may fine-tune proinflammatory cytokine production during early antiviral immune responses, particularly when there is limited glucose availability or under other conditions of stress. Our findings also suggest we may be able to regulate the magnitude of proinflammatory cytokine production during antiviral responses by targeting proteins or molecules that contribute to HIF-1α stabilization.
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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 | 0.001 |
| 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".