Retroviral expression of MIR2 decreases both surface MHC class I and the alloimmune CTL response
Bibliographic record
Abstract
The immune response to allogeneic cells in tissue-engineered constructs is a major barrier to their successful application in the treatment of many human diseases. Specifically, the T cell-mediated immune response, initiated through the recognition of cell surface MHCI molecules, is the primary cause of acute cellular allograft rejection. In this study, we altered expression of MHCI through viral immunomodulatory mechanisms to examine whether allogeneic cells could be made to 'mimic' viral evasion of a host CTL response. We demonstrate the successful application of a retroviral vector in vitro to overexpress the Kaposi's sarcoma-associated herpesvirus immunomodulatory protein, MIR2, in human monocyte-like myeloid progenitor cells. This approach led to differential downregulation of cell surface MHCI, ICAM-1 and B7-2 molecules. We also demonstrate that downregulation of immunoactive molecules has the functional effect of significantly reducing T cell-mediated cytotoxicity without altering NK-mediated cytotoxicity in vitro. These results provide proof-of-concept that viral immune evasion strategies allow cell-based tissue-engineered constructs to delay or even prevent acute cellular immune rejection in vivo. Importantly, this methodology could facilitate the development of universal donor cells for tissue engineering applications.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".