Mesenchymal Stem Cells Engineered to Inhibit Complement-Mediated Damage
Bibliographic record
Abstract
Mesenchymal stem cells (MSC) preferentially migrate to damaged tissues and, due to their immunomodulatory and trophic properties, contribute to tissue repair. Although MSC express molecules, such as membrane cofactor protein (CD46), complement decay-accelerating factor (CD55), and protectin (CD59), which confer protection from complement-mediated lysis, MSC are recruited and activated by anaphylatoxins after transplantation, potentially causing MSC death and limiting therapeutic benefit. We have previously demonstrated that transduction of MSC with a retrovirus encoding HCMV-US proteins resulted in higher levels of MSC engraftment due to decreased HLA-I expression. Here, we investigate whether engineering MSC to express US2 (MSC-US2), US3 (MSC-US3), US6 (MSC-US6), or US11 (MSC-US11) HCMV proteins can alter complement recognition, thereby better protecting MSC from complement attack and lysis. HCMV-US proteins increased MSC CD59 expression at different levels as determined by flow cytometric evaluation of the median fluorescence intensity ratio (MFI). A significant increase in CD59 expression was seen in MSC-US2, MSC-US3, and MSC-US6, but not in MSC-US11. Only MSC-US2 displayed increased expression of CD46, while US2 and US3 proteins were both able to augment the percentage of MSC expressing this molecule. Regardless of the HCMV protein expressed, none changed CD55 MFI; however, expression of US6, US11, and US2 each increased the percentage of MSC that were positive for this molecule. Because US2 protein was the most efficient in up-regulating all three complement regulatory proteins, we used a functional complement-mediated cytotoxicity assay to investigate whether MSC-US2 were protected from complement-mediated lysis. We demonstrated that over-expression of the US2 protein reduced complement lysis by 59.10±12.89% when compared to untransduced MSC. This is the first report, to our knowledge, describing a role of HCMV-US proteins in complement evasion, and our data shows that over-expression of US2 protein on MSC could serve as a strategy to protect these cells from complement lysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".