The innate defense regulator peptides IDR-HH2, IDR-1002, and IDR-1018 modulate human neutrophil functions
Bibliographic record
Abstract
Although HDPs were originally hypothesized to act as antimicrobial agents, they also have been shown to broadly modulate the immune response through the activation of different cell types. We recently developed a series of novel, synthetic peptides, termed IDRs, which are conceptually based on a natural HDP, bovine bactenecin. We showed that IDR-1 and IDR-1002 protect the host against bacterial infections through the induction of chemokines. The objective of this study was to investigate the effects of the IDRs on various functions of human neutrophils. Here, we demonstrated that IDR-HH2, IDR-1002, and IDR-1018 modulated the expression of neutrophil adhesion and activation markers. Moreover, these IDRs enhanced neutrophil adhesion to endothelial cells in a β₂ integrin-dependent manner and induced neutrophil migration and chemokine production. The IDR peptides also increased the release of the neutrophil-generated HDPs (antimicrobial), human α-defensins, and LL-37 and augmented neutrophil-mediated killing of Escherichia coli. Notably, the IDRs significantly suppressed LPS-mediated neutrophil degranulation, the release of ROS, and the production of the inflammatory cytokines TNF-α and IL-10, consistent with their ability to dampen inflammation. As evidenced by the inhibitory effects of MAPK-specific inhibitors, IDRs activated the MAPK pathway that was required for chemokine production. In conclusion, our study provides novel evidence regarding the contribution of the IDR peptides to the innate immune response through the modulation of neutrophil functions. The results described here may aid in the development of IDRs as novel, anti-infective and immunomodulatory agents.
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 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".