Ultrasmall silica nanoparticles directly ligate the T cell receptor complex
Bibliographic record
Abstract
The impact of ultrasmall nanoparticles (<10-nm diameter) on the immune system is poorly understood. Recently, ultrasmall silica nanoparticles (USSN), which have gained increasing attention for therapeutic applications, were shown to stimulate T lymphocytes directly and at relatively low-exposure doses. Delineating underlying mechanisms and associated cell signaling will hasten therapeutic translation and is reported herein. Using competitive binding assays and molecular modeling, we established that the T cell receptor (TCR):CD3 complex is required for USSN-induced T cell activation, and that direct receptor complex-particle interactions are permitted both sterically and electrostatically. Activation is not limited to αβ TCR-bearing T cells since those with γδ TCR showed similar responses, implying that USSN mediate their effect by binding to extracellular domains of the flanking CD3 regions of the TCR complex. We confirmed that USSN initiated the signaling pathway immediately downstream of the TCR with rapid phosphorylation of both ζ-chain-associated protein 70 and linker for activation of T cells protein. However, T cell proliferation or IL-2 secretion were only triggered by USSN when costimulatory anti-CD28 or phorbate esters were present, demonstrating that the specific impact of USSN is in initiation of the primary, nuclear factor of activated T cells-pathway signaling from the TCR complex. Hence, we have established that USSN are partial agonists for the TCR complex because of induction of the primary T cell activation signal. Their ability to bind the TCR complex rapidly, and then to dissolve into benign orthosilicic acid, makes them an appealing option for therapies targeted at transient TCR:CD3 receptor binding.
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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) | 0.000 | 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".