Abstract 30: Leveraging the Innate Immunity Pathway for Transdifferentiation of Fibroblasts to Endothelial Cells
Bibliographic record
Abstract
Background Cell-based approaches to regenerate the endothelium holds promise, with one such candidate source being induced pluripotent stem cells (iPSCs). We have investigated the potential of endothelial cells (ECs) derived from iPSCs to promote the perfusion of ischemic tissue in a murine model of peripheral artery disease (PAD). However, it may be more efficient to transdifferentiate fibroblasts to ECs directly. Recent reports have suggested that direct reprogramming to ECs is feasible, however still requires the use of viral vectors encoding transcription factors, thus making them clinically unsafe. Hypothesis We recently discovered that retroviral vectors encoding the reprogramming factors, by activating the Toll-like receptor 3 (TLR3) pathway, make nuclear reprogramming possible by increasing epigenetic plasticity and favoring an open chromatin state (Lee & Sayed et al. Cell). Based on this recognition that innate immunity favors an open chromatin state, we hypothesized that activation of TLR3, together with external microenvironmental cues that drive EC specification, might induce transdifferentiation of fibroblasts into ECs (“iECs”). Results Intriguingly, our preliminary data showed that TLR3 agonist Poly I:C, combined with exogenous endothelial growth factors, was sufficient to transdifferentiate fibroblasts into iECs (in the absence of viral vectors or transcription factors). These iECs exhibited all the characteristics of EC phenotype comparable to HMVECs including ability to form capillary-like structures and incorporating acetylated-LDL. Moreover, loss-of function studies showed that activation of TLR3 plays a role in the efficient transdifferentiation of human cells when using Poly I:C. Furthermore, iECs significantly improved limb perfusion and neovascularization in the ischemic hindlimb compared to control groups. 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 transcription factors. Moreover, it has allowed us to generate a small molecule strategy for therapeutic transdifferentiation that might be applied for direct reprogramming in vivo.
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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 | 0.002 |
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".