Regulators of mycobacterial granuloma formation – CCL2 and VEGF-A
Bibliographic record
Abstract
Abstract Granulomas are macrophage dominated lesions and are a central feature of mycobacterial infections. These cell aggregates are dynamically changing structures as the life span of the granuloma recruited effector cells is relatively short and the cells have to be replaced. CCL2 and VEGF-A are required for granuloma maintenance as blocking the action of these chemokines results in reduction of granuloma size and number after BCG or Mtb infections. We seek to elucidate the relevance of these two cytokines in the timeline of mycobacterial infections and their relative contribution to granuloma formation. CD11c+ cell immigration is impaired, costimulatory molecule expression is lower and granulomas are smaller while the bacterial burden is higher in the liver when CCR2KO mice are infected i.p. with M. bovis BCG. Similarly, in animals that are selectively deficient in macrophage VEGF-A production the granulomas are smaller. Granuloma cells produce VEGF-A. Caseating granulomas, like the ones induced in C3HeB/FeJ (Kramnik) mice are hypoxic and hypoxia is a strong inducer of VEGF-A production. Sarcoid lesions induced by BCG or Mtb in B6 mice are not hypoxic but we show that ATP released from dead cells induces VEGF-A in a subpopulation of macrophages in the granulomas. Additionally, VEGF production proved to be dependent on granuloma size: bigger granulomas produce larger quantities of VEGF-A while smaller lesions produce less or none. This finding suggests that unlike other chemokines such as CCL2, which is described to be involved in both granuloma initiation and maintenance, VEGF-A may support an increased lesion size in the late acute phase of infection. Regulating cellular recruitment may provide new therapies for granulomatous diseases.
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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".