Highly Efficient Stable Expression of Indoleamine 2,3 Dioxygenase Gene in Primary Fibroblasts
Bibliographic record
Abstract
Indoleamine 2,3 dioxygenase (IDO) is a potent immunomodulatory enzyme that has recently attracted significant attention for its potential application as an inducer of immunotolerance in transplantation. We have previously demonstrated that a collagen matrix populated with IDO-expressing fibroblasts can be applied successfully in suppressing islet allogeneic immune response. Meanwhile, a critical aspect of such immunological intervention relies largely on effective long-term expression of the IDO gene. Moreover, gene manipulation of primary cells is known to be challenging due to unsatisfactory expression of the exogenous gene. In this study, a lentiviral gene delivery system has been employed to transduce primary fibroblasts. We used polybrene to efficiently deliver the IDO gene into primary fibroblasts and showed a significant increase (about tenfold) in the rate of gene transfection. In addition, by the use of fluorescence-activated cell sorting, a 95% pure population of IDO-expressing fibroblasts was successfully obtained. The efficiency of the IDO expression and the activity of the enzyme have been confirmed by Western blotting, fluorescence-activated cell sorting analysis, and Kynurenine assay, respectively. The findings of this study revealed simple and effective strategies through which an efficient and stable expression of IDO can be achieved for primary cells which, in turn, significantly improves its potential as a tool for achieving immunotolerance in different types of transplantation.
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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) | 0.001 | 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".