基质金属蛋白酶
生物
细胞生物学
内皮干细胞
明胶酶
血管生成
细胞外基质
分子生物学
化学
癌症研究
体外
生物化学
作者
Nirmala Chandrasekar,Sushma L. Jasti,Raymond Sawaya,Anthanassios P. Kyritsis,Santhi D. Konduri,Francis Ali‐Osman,Jasti S. Rao,Sanjeeva Mohanam
标识
DOI:10.1002/1097-0215(20001201)88:5<766::aid-ijc13>3.0.co;2-y
摘要
Radiation-induced damage to the central nervous system (CNS) is believed to target glial or endothelial cells or both, although the pathophysiology of the process is poorly understood. We therefore used a coculture system, in which glioblastoma SNB19 cells induced bovine retinal endothelial (BRE) cells to form capillary-like structures, to examine the role of ionizing radiation in modulating the production of matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinase-1 (TIMP-1). In particular, we irradiated both BRE cells and cocultures of BRE and SNB19 cells with a single dose of X-rays and then estimated the levels of MMP-2, MMP-9 and TIMP-1. Gelatin zymography revealed a continuous increase in the levels of MMP-2 and MMP-9 during capillary-like structure formation. Of note, the levels of both MMP-2 and MMP-9 were markedly higher in irradiated cocultures at 72 hr after irradiation than in untreated cocultures. Northern blot analysis also demonstrated an increased expression of MMP-9 mRNA in the irradiated cocultures. In addition, TIMP-1 mRNA and protein levels increased up to 48 hr in both irradiated and nonirradiated BRE cells and in nonirradiated cocultures, but there was a significant decrease in the TIMP-1 mRNA and protein levels in irradiated cocultures. It takes about 72 hr for capillaries to form in nonirradiated cocultures, but these capillary networks fail to form in endothelial cells in irradiated cocultures. These findings establish that radiation differentially affects the production of MMP-2, MMP-9 and TIMP-1 during glial-endothelial morphogenesis and suggest mechanisms by which microvessels in the CNS respond to radiation. Int. J. Cancer 88:766–771, 2000. © 2000 Wiley-Liss, Inc.
科研通智能强力驱动
Strongly Powered by AbleSci AI