Fever‐Induced Heat Shock Protein‐70 Regulates Macrophage IL‐1β and IL‐10 Secretion During <i>Mycobacterium tuberculosis</i> Infection
Bibliographic record
Abstract
Fever is a common clinical symptom in patients with tuberculosis (TB). During fever, heat-shock proteins (HSPs), such as HSP70, are expressed, which are molecular chaperones regulating protein folding and may also have immunomodulatory properties. How fever modulates immune responses during TB and by which mechanisms is unknown. In this study, we investigated the effects of fever, and specifically the role of HSP70, on Mycobacterium tuberculosis (Mtb)-induced macrophage inflammatory responses. Human monocyte-derived macrophages (MDM) were infected with Mtb at 37°C or 40°C to mimic febrile conditions. Fever suppresses Mtb-induced IL-1β and IL-10 gene expression and secretion from MDM, but enhances Mtb-induced HSP70 secretion and intracellular accumulation in MDM. Extracellular HSP70 and HSP70-expressing macrophages are abundant in granulomas in TB patient biopsies. HSP70 antagonism decreases Mtb-induced IL-1β secretion during febrile conditions but has no significant effect on IL-10 secretion. Pretreatment of MDM with recombinant HSP70 significantly increases Mtb-induced IL-1β at 37°C. Finally, extracellular HSP70 negatively regulates further HSP70 secretion from MDM during Mtb infection. Overall, fever and subsequent HSP70 expression modulates proinflammatory innate immune response in TB, which may have implications for the development of host-directed therapies.
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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 | 0.000 |
| 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".