Screening Bioactive Nanoparticles in Phagocytic Immune Cells for Inhibitors of Toll-like Receptor Signaling
Bibliographic record
Abstract
Pharmacological regulation of Toll-like receptor (TLR) responses holds great promise in the treatment of many inflammatory diseases. However, there have been limited compounds available so far to attenuate TLR signaling and there have been no clinically approved TLR inhibitors (except the anti-malarial drug hydroxychloroquine) in clinical use. In light of rapid advances in nanotechnology, manipulation of immune responsiveness using nano-devices may provide a new strategy to treat these diseases. Herein, we present a high throughput screening method for quickly identifying novel bioactive nanoparticles that inhibit TLR signaling in phagocytic immune cells. This screening platform is built on THP-1 cell-based reporter cells with colorimetric and luciferase assays. The reporter cells are engineered from the human THP-1 monocytic cell line by stable integration of two inducible reporter constructs. One expresses a secreted embryonic alkaline phosphatase (SEAP) gene under the control of a promoter inducible by the transcription factors NF-κB and AP-1, and the other expresses a secreted luciferase reporter gene under the control of promoters inducible by interferon regulatory factors (IRFs).Upon TLR stimulation, the reporter cells activate transcription factors and subsequently produce SEAP and/or luciferase, which can be detected using their corresponding substrate reagents. Using a library of peptide-gold nanoparticle (GNP) hybrids established in our previous studies as an example, we identified one peptide-GNP hybrid that could effectively inhibit the two arms of TLR4 signaling cascade triggered by its prototypical ligand, lipopolysaccharide (LPS). The findings were validated by standard biochemical techniques including immunoblotting. Further analysis established that this lead hybrid had a broad inhibitory spectrum, acting on multiple TLR pathways, including TLR2, 3, 4, and 5. This experimental approach allows a rapid assessment of whether a nanoparticle (or other therapeutic compounds) can modulate specific TLR signaling in phagocytic immune cells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".