Dysregulated glucose metabolism drives hyperinflammation and immune-mediated pathology during viral infection 4291
Bibliographic record
Abstract
Abstract Description Successful host defense during viral infection requires tight control of inflammation to mount an effective immune response while limiting damage to the host. Loss of immune regulation can result in a cytokine storm leading to major immunopathology and serious disease consequences. Evidence has shown that the pathology of acute viral infections is not mediated by viral load, but rather this hyperinflammatory response. Cellular metabolism plays a key role in immune activation as immune cells have specific metabolic requirements that dictate their functional fate. Thus, metabolic processes can shape the intensity of the inflammatory response. Nevertheless, the mechanisms driving cytokine storm remain poorly understood. Here we investigated how metabolism in the local tissue environment regulates the inflammatory response to viral infection. We and others found that mice deficient in the type I IFN receptor (Ifnar-/-) are more susceptible to cytokine storm from influenza A virus (IAV) infection than wild-type (WT) mice, independent of viral load. We show that Ifnar-/- mice have a significantly altered lung metabolite profile during IAV infection, compared to WT mice. These early metabolic changes are associated with heightened glucose metabolism in immune cells, contributing to the development of immune-mediated pathology. Importantly, these results highlight the therapeutic potential of modulating immune metabolism to effectively treat cytokine storm. Funding Sources Supported by Canadian Institute of Health Research (CIHR) project grant and a CIHR Canada Graduate Scholarship - Master’s (CGS-M) Topic Categories Immune Response Regulation: Cellular Mechanisms (IRC)
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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) | 0.002 | 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".