Cutting Edge: <i>Mycobacterium tuberculosis</i> Induces Aerobic Glycolysis in Human Alveolar Macrophages That Is Required for Control of Intracellular Bacillary Replication
Bibliographic record
Abstract
Recent advances in immunometabolism link metabolic changes in stimulated macrophages to production of IL-1β, a crucial cytokine in the innate immune response to Mycobacterium tuberculosis. To investigate this pathway in the host response to M. tuberculosis, we performed metabolic and functional studies on human alveolar macrophages, human monocyte-derived macrophages, and murine bone marrow-derived macrophages following infection with the bacillus in vitro. M. tuberculosis infection induced a shift from oxidative phosphorylation to aerobic glycolysis in macrophages. Inhibition of this shift resulted in decreased levels of proinflammatory IL-1β and decreased transcription of PTGS2, increased levels of anti-inflammatory IL-10, and increased intracellular bacillary survival. Blockade or absence of IL-1R negated the impact of aerobic glycolysis on intracellular bacillary survival, demonstrating that infection-induced glycolysis limits M. tuberculosis survival in macrophages through induction of IL-1β. Drugs that manipulate host metabolism may be exploited as adjuvants for future therapeutic and vaccination strategies.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".