Type 3 interferons expressed in tuberculous granulomas may influence signaling in epithelioid macrophages
Bibliographic record
Abstract
Abstract Type 3 interferons, also known as interferon lambda (IFNλ), play important roles in mucosal immunity and protection against viral infection, but their roles in tuberculosis (TB) remain to be investigated. IFNλ have properties that partially overlap with type 1 interferons (IFNα/β), a group of cytokines whose expression positively correlates with TB severity. Conversely, IFNλ can downregulate neutrophilic inflammation, and because neutrophils can be pathologic in TB, IFNλ may have currently-unappreciated protective effects. To better understand IFNλ expression in tuberculous lung granulomas, we performed RT-PCR analysis and immunohistochemistry on cynomolgus macaques with TB, a nonhuman primate with human-like pathology, to determine whether IFNλ is expressed in granulomas and to identify IFNλ-expressing cells at the site of disease. We found that mRNAs for the IFNλ isoforms IFNλ1 (IL-29), IFNλ3 (IL-28B), and IFNλ4 were expressed in normal lung tissue and granulomas, and there was a significant amount of heterogeneity in granuloma IFNλ expression. In normal lung tissue, IFNλ protein expression was most pronounced in ciliated epithelial cells, but there were differences in subcellular localization between isoforms. We identified differences in the subcellular localization of IFNλ4 in different granuloma macrophage subsets, with IFNλ4 in lymphocyte cuff-region macrophages present in the macrophage cytoplasm while in epithelioid macrophages, IFNλ4 was most abundant inside the macrophage nuclei. These data suggest that IFNλ4 may be differentially-expressed by granulomas macrophage subsets and have a previously-unappreciated signaling role in epithelioid macrophages.
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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".