Expression of CCR2 and VEGFR1 is required for monocyte migration into <i>Mycobacterium tuberculosis</i>-induced lung granulomas.
Bibliographic record
Abstract
Abstract Granulomas provide protection against Mycobacterium tuberculosis (Mtb) infection and dissemination but also contribute to long term pathogen survival in the host. Macrophages are the dominant cell type in mycobacterial granulomas, playing a central role in bacterial control. Most macrophages recruited to the lung during infection are derived from monocytes in the blood. Our lab has previously shown that continuous cell replacement is important in mycobacterial lesions and CCL2 and granuloma macrophage-produced VEGF-A are required for granuloma maintenance as blocking the action of these chemokines results in the reduction of granuloma size and number in BCG and Mtb infection. Here we show that expression of CCR2 and VEGFR1 by monocytes is required for the migration of these cells into Mtb-induced lung granulomas. Blockade of CCR2 and VEGFR1 with Propagermanium or SU5416, respectively, reduced monocyte migration toward granuloma cells in vitro or in vivo. A 1:1:1 mixture of color-coded WT, Flt1 fl/fl x LyzMCre and CCR2−/− BM-derived monocytes was adoptively transferred into C57BL/6J recipient mice. The ratio of transferred cells in the lungs, spleen, blood, and BM was measured 1 day and 10 days after transfer. By day 10, most of the transferred cells were present in the lung parenchyma. Loss of VEGFR1 on the transferred monocytes decreased monocyte migration to the lung parenchyma dramatically while homing of CCR2−/− cells was inhibited to every tested site. In the granulomatous lung the main source of VEGF-A is the macrophage. We are developing macrophage-targeted blockade of VEGF-A to reduce granulomatous pathology with the reduction of off-target effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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, 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".