A compartment model of VEGF distribution in blood, healthy and diseased tissues.
BACKGROUND
Angiogenesis is a process by which new capillaries are formed from pre-existing blood vessels in physiological (e.g., exercise, wound healing) or pathological (e.g., ischemic limb as in peripheral arterial disease, cancer) contexts. This neovascular mechanism is mediated by the vascular endothelial growth factor (VEGF) family of cytokines. Although VEGF is often targeted in anti-angiogenic therapies, there is little knowledge about how its concentration may vary between tissues and the vascular system. A compartment model is constructed to study the VEGF distribution in the tissue (including matrix-bound, cell surface receptor-bound and free VEGF isoforms) and in the blood. We analyze the sensitivity of this distribution to the secretion rate, clearance rate and vascular permeability of VEGF.


RESULTS
We find that, in a physiological context, VEGF concentration varies approximately linearly with the VEGF secretion rate. VEGF concentration in blood but not in tissue is dependent on the vascular permeability of healthy tissue. Model simulations suggest that relative VEGF increases are similar in blood and tissue during exercise and return to baseline within several hours. In a pathological context (tumor), we find that blood VEGF concentration is relatively insensitive to increased vascular permeability in tumors, to the secretion rate of VEGF by tumors and to the clearance. However, it is sensitive to the vascular permeability in the healthy tissue. Finally, the VEGF distribution profile in healthy tissue reveals that about half of the VEGF is complexed with the receptor tyrosine kinase VEGFR2 and the co-receptor Neuropilin-1. In diseased tissues, this binding can be reduced to 15% while VEGF bound to the extracellular matrix and basement membranes increases.


CONCLUSIONS
The results are of importance for physiological conditions (e.g., exercise) and pathological conditions (e.g., peripheral arterial disease, coronary artery disease, cancer). This mathematical model can serve as a tool for understanding the VEGF distribution in physiological and pathological contexts as well as a foundation to investigate pro- or anti-angiogenic strategies.
Stefanini
Marianne O
MO
Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21205, USA. stefanini@jhmi.edu
Wu
Florence T H
FT
Mac Gabhann
Feilim
F
Popel
Aleksander S
AS
D000818:Animals; D001485:Basement Membrane / Q000378:metabolism; D001692:Biological Transport; D001798:Blood Proteins / Q000032:analysis / Q000378:metabolism; D002199:Capillary Permeability; D003599:Cytoskeleton / Q000378:metabolism; D015444:Exercise / Q000502:physiology; D006801:Humans; D008954:Models, Biological; D009369:Neoplasms / Q000378:metabolism*; D039942:Neuropilin-1 / Q000378:metabolism; D010805:Physical Conditioning, Animal / Q000502:physiology; D020033:Protein Isoforms / Q000378:metabolism; D042461:Vascular Endothelial Growth Factor A / Q000032:analysis* / Q000235:genetics / Q000378:metabolism*; D040301:Vascular Endothelial Growth Factor Receptor-2 / Q000378:metabolism
D001798:Blood Proteins; D020033:Protein Isoforms; D042461:Vascular Endothelial Growth Factor A; D039942:Neuropilin-1; D040301:Vascular Endothelial Growth Factor Receptor-2