Recruited macrophages fail to restrict intracellular growth of <i>Mycobacterium tuberculosis</i>
Bibliographic record
Abstract
Abstract Mycobacterium tuberculosis (Mtb), a bacterium that predominantly afflicts the lungs, infects over 10 million people per year and causes 1.7 million deaths per year. Immediately following infection, subsets of pulmonary myeloid cells will become infected with Mtb, starting with alveolar macrophages, and eventually spreading to neutrophils, recruited macrophages, monocytes and dendritic cells. These cells differ in their ability to restrict intracellular growth of Mtb. Using reporter Mtb that constitutively express mCherry and express GFP under the tetracycline promoter, we can differentiate between live Mtb (mCherry+ GFP+) and dead Mtb (mCherry+) by flow cytometry. We show that a larger percentage of CD11b+ recruited macrophages and Ly6C+ monocytes in the lungs contain live Mtb and fewer dead Mtb compared to other myeloid populations. This has been validated by sorting infected myeloid cells by flow cytometry and plating colony forming units (CFU). Recruited macrophages averaged 5,000–8,000 live bacilli per 1,000 sorted cells, while alveolar macrophages, neutrophils and dendritic cells harbored 1,000–2,000 live bacteria/1,000 sorted cells. Furthermore, genome-wide transcriptional profiling of these sorted myeloid populations reveal substantial differences in anti-bacterial mechanisms, metabolism and chemokine production between recruited macrophages and other myeloid populations. These include changes in production of interferons, lysosomal genes and tumor necrosis factor superfamily. Altogether, these data reveal that recruited macrophages are unable to restrict the intracellular growth and create a favorable environment for Mtb growth through their deficiency in anti-bacterial cytokines.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".