Self-assembly of Tyrosine-containing Peptides into Injectable Hydrogels with Distinct Nanostructures is Key in Determining Inflammatory Response of Macrophages
Bibliographic record
Abstract
Self-assembling peptides (SAPs) are fully defined nanobiomaterials offering unprecedented opportunities to control nanostructure and chemical attributes to investigate and manipulate cellular signals. To investigate the influence of chemical and morphological characteristics on inflammatory signalling in native immunity, we designed five beta-sheet SAPs: EFEFKFEFK (EF8), YEFEFKFEFK (YEF8), YEFEFKFEFK (EF8Y), YEFEFKFEFK (YEF8Y) and EYEFKFEFK (EYF8) (F: phenylalanine; E: glutamic acid; K: lysine, Y: tyrosine). The position of tyrosine in the peptide sequence dictated the distinct self-assembly into nanostructures, with sequences EF8Y, YEF8Y, EYF8 self-assembling into thin nanofibers d≈3.8 ± 0.2 nm, YEF8 self-assembling into rod-like flat ribbons d>20 nm and EF8 (control) consisting of both types of self-assembled nanostructures. These distinct nanostructures induced contrasting inflammatory response of monocytic model THP-1 cells-derived macrophages (MΦs). Presence of soluble EF8 nanofibers (at 2 mM) induced anti-inflammatory response and polarization towards an M2 state, whereas YEF8 displayed tendency for inducing pro-inflammatory response and polarization towards M1 state. The EF8Y, YEF8Y, EYF8 SAPs did not induce an inflammatory response in our models. These results were validated using peripheral blood mononuclear cells (PBMCs)-derived MΦs from human donors, confirming the critical role of the EF8 and YEF8 SAPs as possible orchestrators of the repair of tissues or inducers of pro-inflammatory state, respectively. These findings will facilitate the utilization of this family of SAPs as immunomodulatory nanobiomaterials potentially changing the course of inflammation during the progression of various diseases.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".