Abstract 214: Balancing Innate Immunity Activation and Death Signals for Vascular Regeneration
Bibliographic record
Abstract
Background: Our recent discoveries have shown that innate signaling supports effective nuclear reprogramming (Lee & Sayed et al. Cell) and transdifferentiation to ECs (Sayed et al. Circ). By activating innate immunity (via TLR3), viral vectors used for reprogramming cause global epigenetic changes that favor an environment conducive for reprogramming. Indeed, activation of innate transcription factors (TFs) enhances EC regeneration, however, activation of the same pathway could induce: 1) apoptosis via activation of Casp8; and 2) programmed necrosis by receptor-interacting protein kinase 3 (RIP3), when Casp8 is absent. Hypothesis: We speculate that death pathways that are activated by these TFs compromise reprogramming. We previously showed that disruption of Casp8 led to mid-gestational death of mice due to unleashing of RIP3-dependent death pathways, preventing the formation of vascular endothelium and hematopoietic cells. By contrast, when Casp8 -/- mice were crossed with Rip3 -/- , the DKO mice developed normally with no stem cell defect. We hypothesize that these pathways exist as biological constraints on epigenetic plasticity and that pharmacological or genetic ablation could enhance EC reprogramming. Results: To test our hypothesis, we transdifferentiated WT and Casp8 -/- Rip3 -/- DKO MEFs using our protocol, employing an innate immunity modifier and EC growth factors. Intriguingly, our preliminary data showed that DKO MEFs transdifferentiated to induced-EC (iECs) with >10-fold higher yield when compared to WT (yields ~30%). Genetic and functional assays showed that iECs generated from DKO MEFs were comparable to WT, indicating that removal of apoptotic pathways did not lead to aberrant reprogramming. Moreover, pharmacological inhibitors of death receptors when combined with our small molecule cocktail showed a similar increase in iEC generation in human fibroblasts. Conclusion: This study is a first step toward development of a regenerative strategy for PAD on the use of ECs derived from small molecules and growth factors without use of viral vectors encoding TFs. We intend to derive an effective and feasible technology for therapeutic transdifferentiation for ischemic syndromes to promote healing with tissue rather than a scar.
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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) | 0.004 | 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".