Host immune responses in <i>ex vivo</i> approaches to cutaneous gene therapy targeted to keratinocytes
Bibliographic record
Abstract
Epidermal gene therapy may benefit a variety of inherited skin disorders and certain systemic diseases. Both in vivo and ex vivo approaches of gene transfer have been used to target human epidermal stem cells and achieve long-term transgene expression in immunodeficient mouse/human chimera models. Immunological responses however, especially in situations where a neoantigen is expressed, are likely to curtail expression and thereby limit the therapy. In vivo gene transfer to skin has been shown to induce transgene-specific immune responses. Ex vivo gene transfer approaches, where keratinocytes are transduced in culture and transplanted back to patient, however, may avoid signals provided to the immune system by in vivo administration of vectors. In the current study, we have developed a stable epidermal graft platform in immunocompetent mice to analyze host responses in ex vivo epidermal gene therapy. Using green fluorescent protein (GFP) as a neoantigen and an ex vivo retrovirus-mediated gene transfer to mouse primary epidermal cultures depleted of antigen-presenting cells (APCs), we show induction of GFP-specific immune responses leading to the clearance of transduced cells. Similar approach in immunocompetent mice tolerant to GFP resulted in permanent engraftment of transduced cells and continued GFP expression. Activation of transgene-specific immune responses in ex vivo gene transfer targeted to keratinocytes require cross-presentation of transgene product to APCs, a process that is most amenable to immune modulation. This model may be used to explore strategies to divert transgene-specific immune responses to less destructive or tolerogenic ones.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".