Type III interferons are expressed in tuberculosis granulomas and promote an inflammatory phenotype in macrophages that differs from type I interferon
Bibliographic record
Abstract
Abstract Humans and non-human primates express four subtypes of type III interferons (IFNλs; IFNλ1-IFNλ4). Unlike type I interferons, which have been thoroughly investigated in tuberculosis (TB), the role of IFNλs and their effects on immunity in TB remain unknown, especially at the disease site. To better understand the role of IFNλs in TB, we examined IFNλ1 and IFNλ4 expression in cynomolgus macaques with TB and investigated the effects of IFNα/β, IFNλ1 and IFNλ4 signaling on macaque macrophages. Using IHC, we identified differential IFNλ1 and IFNλ4 expression in macrophages and neutrophils, including IFNλ4 localization in the nuclei of epithelioid macrophages. CD206+ macrophages and CD3+ cells in the airway expressed IFNλR while CD11c+ macrophages in the lymphocyte cuff of the granuloma expressed IFNλR. To measure IFNλ1 and IFNλ4’s effect on macrophage gene expression and compare these cytokines against IFNα/β, we used NanoString transcriptional profiling and Ingenuity Pathway Analysis (IPA) on cytokine-stimulated macrophages to identify differentially regulated pathways. We found that IFNα/β upregulated the greatest number of ISGs, followed by IFNλ1, whereas IFNλ4 stimulation had minimal effect on gene expression. Interestingly, pro-inflammatory genes including IL-1β, IL-8, TLR1, BATF were upregulated by IFNλ1 while IL-1β and TLR1 were downregulated by IFNα/β. To determine the effects of IFNλs on anti-mycobacterial macrophage responses, we used a reporter Mtb strain to determine how IFNλ1 and IFNλ4 effect the viability of Mycobacterium tuberculosis. Our data suggest that IFNλs have non-redundant properties with type 1 interferons that may promote macrophage activation, inflammation, and antibacterial activity in TB.
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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".