Interleukin-26 Differentially Modulates Human Macrophage Inflammatory Response to Distinct <i>Mycobacterium tuberculosis</i> Whole Cell Lysates
Bibliographic record
Abstract
Background: Interleukin-26 (IL-26) is an antimicrobial peptide that may contribute to the elimination of intracellular Mycobacterium tuberculosis (Mtb). The aim of the study was to investigate how IL-26 affected the response of human macrophages to Mtb whole cell lysates from different lineages. Methods: Human macrophages were treated with IL-26 (monomer or dimer) before stimulation with Mtb lysates from Indo-Oceanic, HN878, East-African-Indian, and CDC1551 strains. Results: Nuclear factor kappa B (NF-κB) activation was similar in untreated cells after stimulation with all lysates, but was diminished in IL-26 monomer-treated macrophages in response to Indo-Oceanic lysates. Macrophage exposure to dimeric IL-26 led to a higher NF-κB activation in response to CDC1551 lysates, but lower to HN878 lysates. No changes were observed in interferon regulatory factor (IRF) activation. Overall, IL-26 modulates NF-κB activation in a strain and confirmation-dependent manner. This modulation influences the macrophage inflammatory response to Mtb. Our results suggest that IL-26 may promote M1 polarization and enhance anti-Mtb immunity through the NF-κB pathway. This is particularly true in response to high-cytokine-inducing strains like CDC1551. Conclusion: More studies are needed to clarify IL-26’s dual role in inflammation and immune regulation, and to explore its therapeutic potential in tuberculosis (TB) treatment and vaccine strategies.
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 | 0.000 |
| 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".