Cytokines IL-17, TNF and IFN-γ Alter the Expression of Antimicrobial Peptides and Proteins Disparately: A Targeted Proteomics Analysis using SOMAscan Technology
Bibliographic record
Abstract
Antimicrobial peptides, also known as host defence peptides, are immunomodulatory molecules required to resolve infections. Antimicrobial peptides and proteins (APPs) are important in the control of infections in the lungs. Despite evidence that APPs exhibit a wide range of immune functions and modulate inflammation, the effect of inflammatory cytokines on the expression of APPs is not completely defined. In this study, we profiled the expression of 39 different APPs in human bronchial epithelial cells (HBEC) using Slow Off-rate Modified Aptamer (SOMAmer)-based protein array, in the presence and absence of three different inflammatory cytokines (IL-17, TNF and IFN-γ). Expression of 13 different APPs was altered in response to IL-17, TNF or IFN-γ. Independent validations of selected proteins from the proteomics screen i.e., those that were significantly enhanced by >2-fold change (p < 0.01) using western blots conclusively demonstrated that inflammatory cytokines alter the expression of APPs differentially. For example, the abundance of cathepsin S was enhanced by only IFN-γ, whereas lipocalin-2 was increased by IL-17 alone. Abundance of elafin increased in presence of IL-17 or TNF, but decreased in response to IFN-γ. Whereas the abundance of cathepsin V decreased following stimulation with IL-17, TNF and IFN-γ. The results of this study demonstrate that inflammatory cytokines alter the expression of APPs disparately. This suggests that the composition of the inflammatory cytokine milieu may influence APPs abundance and thus alter the processes required for infection control and regulation of inflammation in the lungs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".