Synthetic Immunomodulatory Peptide IDR-1002 Enhances Monocyte Migration and Adhesion on Fibronectin
Bibliographic record
Abstract
Regulation of the immune system by immunomodulatory agents, such as the synthetic innate defense regulator (IDR) peptides, has been proposed as a potential strategy to strengthen host immune responses against infection. IDR peptides confer protection in vivo against a range of bacterial infections and have been developed as components of single-dose vaccine adjuvants due to their ability to modulate innate immunity, correlating with an increased recruitment of monocytes to sites of infection or immunization. However, the mechanisms by which IDR peptides augment monocyte recruitment remain poorly defined. Anti-infective peptide IDR-1002 was demonstrated here to lack direct monocyte chemoattractive activity yet enhance, by up to 5-fold, the ability of human monocytes to migrate on fibronectin towards chemokines. This effect correlated with an increased adhesion of monocytes and THP-1 cells to fibronectin by IDR-1002 and other IDR peptides and the adhesion of THP-1 cells to fibronectin occurred in a β(1)-integrin-dependent manner, corresponding with an increased activation of β(1)-integrins and the phosphoinositide 3-kinase (PI3K)-Akt pathway. PI3K- and Akt-specific inhibitors abrogated IDR-1002-induced adhesion and activation of β(1)-integrins, whereas p38 and MEK1 inhibitors did not affect, or moderately inhibited, adhesion, respectively. Furthermore, IDR-1002 enhancement of monocyte migration towards chemokines and activation of β(1)-integrins was abrogated in the presence of PI3K- and Akt-specific inhibitors. In summary, IDR-1002 enhanced monocyte migration on fibronectin through promotion of β(1)-integrin-mediated interactions regulated by the PI3K-Akt pathway, revealing a mechanism by which IDR-1002 promotes monocyte recruitment.
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".