Circular <scp>RNA MAP2K2</scp>‐modified immunosuppressive dendritic cells for preventing alloimmune rejection in organ transplantation
Bibliographic record
Abstract
Abstract Long‐term patient and graft survival has been achieved in organ transplantation but at the expense of toxic side effects that are associated with long‐term use of nonspecific immunosuppressive drugs. Discovering new regulators of dendritic cells is the key for development of an ideal treatment to prevent immune rejection. We hypothesized that knockdown of circMAP2K2 induces immunosuppressive DCs and that treatment with circMAP2K2 silenced‐DCs can prevent alloimmune rejection. DCs were cultured and transfected with siRNA for circMAP2K2. circMAP2K2 levels were measured by qRT‐PCR. DC's maturation and immune function were assessed by flow cytometry and mixed lymphocyte reactions. The function of circMAP2K2 was illustrated by a series of RIP and IP. The therapeutics of engineered DCs was tested in a mouse heart transplantation model. We found that circMAP2K2 was highly expressed in mature DCs. Knockdown of circMAP2K2 reduced expression of MHCII, CD40 and CD80, attenuated the ability of DCs to activate allogeneic naïve T cells, and enhanced CD4 + CD25 + FOXP3 + regulatory T cells (Treg). circMAP2K2‐induced immunosuppressive DCs by interacting with SENP3. Treatment with circMAP2K2‐knockdown DCs attenuated alloimmune rejection and prolonged allograft survival in a murine heart transplantation model. The immune suppression induced in vivo was donor‐antigen specific. In conclusion, knockdown of circMAP2K2 can induce immunosuppressive DCs which are able to inhibit overactive immune response, highlighting a new promising therapeutic approach for immune disorder diseases.
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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".