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Analysis of VEGF-A Regulated Gene Expression in Endothelial Cells to Identify Genes Linked to Angiogenesis

Overview of attention for article published in PLOS ONE, September 2011
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Title
Analysis of VEGF-A Regulated Gene Expression in Endothelial Cells to Identify Genes Linked to Angiogenesis
Published in
PLOS ONE, September 2011
DOI 10.1371/journal.pone.0024887
Pubmed ID
Authors

Corban G. Rivera, Sofie Mellberg, Lena Claesson-Welsh, Joel S. Bader, Aleksander S. Popel

Abstract

Angiogenesis is important for many physiological processes, diseases, and also regenerative medicine. Therapies that inhibit the vascular endothelial growth factor (VEGF) pathway have been used in the clinic for cancer and macular degeneration. In cancer applications, these treatments suffer from a "tumor escape phenomenon" where alternative pathways are upregulated and angiogenesis continues. The redundancy of angiogenesis regulation indicates the need for additional studies and new drug targets. We aimed to (i) identify novel and missing angiogenesis annotations and (ii) verify their significance to angiogenesis. To achieve these goals, we integrated the human interactome with known angiogenesis-annotated proteins to identify a set of 202 angiogenesis-associated proteins. Across endothelial cell lines, we found that a significant fraction of these proteins had highly perturbed gene expression during angiogenesis. After treatment with VEGF-A, we found increasing expression of HIF-1α, APP, HIV-1 tat interactive protein 2, and MEF2C, while endoglin, liprin β1 and HIF-2α had decreasing expression across three endothelial cell lines. The analysis showed differential regulation of HIF-1α and HIF-2α. The data also provided additional evidence for the role of endothelial cells in Alzheimer's disease.

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Geographical breakdown

Country Count As %
Malaysia 1 1%
Netherlands 1 1%
Chile 1 1%
Canada 1 1%
Spain 1 1%
United States 1 1%
Unknown 77 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 29%
Researcher 15 18%
Professor > Associate Professor 8 10%
Student > Master 6 7%
Student > Doctoral Student 4 5%
Other 14 17%
Unknown 12 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 33%
Biochemistry, Genetics and Molecular Biology 18 22%
Medicine and Dentistry 16 19%
Engineering 4 5%
Computer Science 2 2%
Other 7 8%
Unknown 9 11%