Engineering of novel Class II Transactivator gene delivery systems as molecular adjuvants to improve genetic immunotherapies by inducing <i>de novo</i> MHC II expression in human cells
Bibliographic record
Abstract
Abstract Class II Transactivator (CIITA) induces transcription of MHC class II genes. This protein can potentially be used to improve genetic immunotherapies by converting non-immune cells into cells capable of presenting antigens to CD4+ T helper cells. However, CIITA expression is complex, tightly controlled and remains unclear whether distinct non-immune cells differ in the regulation of this transcription factor. In the present study, we describe a strategy to develop gene delivery systems capable of promoting the efficient expression of CIITA in non-immune cell lines and in primary human cells in an ex vivo skin explant model. A DNA plasmid and a lentiviral vector were produced, both carrying the human CIITA DNA sequence in silico designed to avoid cis-regulatory elements, and genetically optimized for expression efficacy in human cells. Different human cell types undergoing CIITA overexpression presented high-level de novo expression of MHC II molecules, validating the delivery systems as suitable tools for the evaluation of CIITA potential as a molecular adjuvant for genetic immunizations. Further, we directly compared different non-immune cells according to exogenous CIITA transcriptional activity, protein expression levels and proteasome degradation. Here we show for the first time that distinct types of non-immune cells differentially regulate the transcription factor, and ultimately the cell surface expression of MHC II, through a cell type-specific control of CIITA proteasomal degradation. Our findings contribute to the understanding of the CIITA post-translational regulation by non-immune cells, which can greatly influence the use of this regulator of MHC II genes as a vaccine adjuvant.
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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".