Inhibition of c-Rel expression in myeloid and lymphoid cells with distearoyl -phosphatidylserine (DSPS) liposomal nanoparticles encapsulating therapeutic siRNA
Bibliographic record
Abstract
c-Rel, a member of the nuclear factor kappa B (NF-κB) family, is preferentially expressed by immune cells and is known to regulate inflammation, autoimmune diseases and cancer. However, there is a lack of therapeutic intervention to specifically inhibit c-Rel in immune cells. Recent success with Pfizer and Moderna mRNA lipid-encapsulated vaccines as well as FDA approved medicines based on siRNA prompted us to test a lipid nanoparticle-based strategy to silence c-Rel in immune cells. Specifically, we encapsulated c-Rel-targeting siRNA into distearoyl-phosphatidylserine (DSPS)-containing nanoparticles. DSPS is a saturated phospholipid that serves as the "eat-me" signal for professional phagocytes such as macrophages and neutrophils of the immune system. We demonstrated here that incorporation of DSPS in liposome nanoparticles (LNP) improved their uptake by immune cells. LNP containing high concentrations of DSPS were highly effective to transfect not only macrophages and neutrophils, but also lymphocytes, with limited toxicity to cells. However, LNP containing low concentrations of DSPS were more effective to transfect myeloid cells than lymphoid cells. Importantly, DSPS-LNP loaded with a c-Rel siRNA were highly effective to inhibit c-Rel expression in several professional phagocytes tested, which lasted for several days. Taken together, our results suggest that DSPS-LNP armed with c-Rel siRNA could be exploited to target immune cells to limit the development of inflammatory diseases or cancer caused by c-Rel upregulation. In addition, this newly developed DSPS-LNP system may be further tested to encapsulate and deliver other small molecule drugs to immune cells, especially macrophages, neutrophils, and lymphocytes for the treatment of 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 | 0.000 |
| 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".