Synthetic Cationic Peptide IDR-1002 Provides Protection against Bacterial Infections through Chemokine Induction and Enhanced Leukocyte Recruitment
Bibliographic record
Abstract
With the rapid rise in the incidence of multidrug resistant infections, there is substantial interest in host defense peptides as templates for production of new antimicrobial therapeutics. Natural peptides are multifunctional mediators of the innate immune response, with some direct antimicrobial activity and diverse immunomodulatory properties. We have previously developed an innate defense regulator (IDR) 1, with protective activity against bacterial infection mediated entirely through its effects on the immunity of the host, as a novel approach to anti-infective therapy. In this study, an immunomodulatory peptide IDR-1002 was selected from a library of bactenecin derivatives based on its substantially more potent ability to induce chemokines in human PBMCs. The enhanced chemokine induction activity of the peptide in vitro correlated with stronger protective activity in vivo in the Staphylococcus aureus-invasive infection model, with a >5-fold reduction in the protective dose in direct comparison with IDR-1. IDR-1002 also afforded protection against the Gram-negative bacterial pathogen Escherichia coli. Chemokine induction by IDR-1002 was found to be mediated through a Gi-coupled receptor and the PI3K, NF-kappaB, and MAPK signaling pathways. The protective activity of the peptide was associated with in vivo augmentation of chemokine production and recruitment of neutrophils and monocytes to the site of infection. These results highlight the importance of the chemokine induction activity of host defense peptides and demonstrate that the optimization of the ex vivo chemokine-induction properties of peptides is a promising method for the rational development of immunomodulatory IDR peptides with enhanced anti-infective activity.
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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) | 0.000 | 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 | 0.001 |
| 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".