Hypoxia-Inducible Factor Drives Vascularization of Modularly Assembled Engineered Tissue
Bibliographic record
Abstract
Robust vascularization is critical for engineering tissues of clinically relevant size and cell loads. Delineating the rate-limiting steps underlying vascularization is necessary to creating strategies for faster, better vascularization of tissue constructs. We used two inhibitory methods to dissect the role of hypoxia-inducible factor (HIF) in vascularization-inducing engineered tissues, in this study constructed from self-assembly of submillimeter-sized tissues injected subcutaneously. Both systemic pharmacological inhibition using digoxin, and genetic inhibition (short hairpin RNA-transduced endothelial cells [ECs]) reduced vascularization and oxygenation within constructs, but elicited different mechanisms of action. Systemic inhibition negatively impacted early (day 3) recruitment of host-derived endothelial progenitor cells and macrophages/monocytes to the implant. Genetic inhibition in graft-derived ECs impaired their survival in low serum conditions as well as their baseline angiogenic function. Altogether, our study demonstrates that HIF is an important driver of vascularization in tissue constructs. While hypoxia is assumed to be an important feature of tissue engineering, this study directly connects inhibition of vascularization to HIF inhibition. Using two inhibitory methods, we demonstrated that hypoxia-inducible factor (HIF) plays an important role in vascularizing and oxygenating modularly-assembled engineered tissues. Each inhibitory technique elucidated a different mechanism by which this occurred. Whereas systemic inhibition negatively impacted early recruitment of host-derived cells, genetic inhibition in grafted endothelial cells was detrimental to their survival. Taken together, our study suggests that methods of HIF-mediated mechanisms could be harnessed to tune the extent and rate of vascularization in engineered tissue constructs.
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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) | 0.000 | 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".