Leading the charge in sprouting angiogenesis: The roles of PKA and PDE4D in regulating tip cell invasion
Bibliographic record
Abstract
During angiogenic sprouting, vascular endothelial cells (VECs) either act as “tip cells” that guide blood vessel formation, or as the follower “stalk cells”, which proliferate to lengthen the newly developing vessel. While cAMP‐signaling regulates numerous VEC functions required for angiogenic sprouting, few studies have attempted to deconstruct the influences of this ubiquitous second messenger on individual functions of tip and/or stalk cells. Thus, this study was undertaken to address whether cAMP‐signaling selectively acted on VEC expressing tip or stalk phenotypes, and to begin to explore how individual components of the cAMP‐signaling system coordinated these events. Specifically, a spheroid model of angiogenic sprouting was used to assess whether knocking down the cAMP effectors expressed in human arterial VECs (HAVECs) (i.e. PKA, Epac1), or the dominant cAMP PDEs expressed by these cells (i.e. PDE4D, PDE3B), impacted tip cell matrix invasion. Using this approach, we show that while knocking down PKA‐Cα expression in HAVECs markedly enhanced tip cell invasion, knocking down EPAC1 had no effect. With respect to the dominance of individual PDE‐family variants in these processes, knocking down PDE4D expression promoted angiogenic sprouting as measured by tip cell invasion, while knocking down PDE3B markedly inhibited these processes. Indeed, tip cell matrix invasion was nearly abolished in PDE3B knockdown spheroids. Our data are consistent with the notion that individual PDE isoenzymes are involved in distinct regulatory pathways, and suggest that PDE3B and PDE4D differentially regulate angiogenic sprouting in response to pro‐angiogenic stimuli, potentially through PKA. Support or Funding Information Canadian Institutes of Health Research (CIHR)
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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) | 0.001 | 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".