Literature DB >> 17442415

Hypoxia alters the adhesive properties of lymphatic endothelial cells. A transcriptional and functional study.

M Irigoyen1, E Ansó, E Martínez, M Garayoa, J J Martínez-Irujo, A Rouzaut.   

Abstract

Recent advances in our understanding of the molecular biology of lymphatic endothelial cells have revealed that these vessels, besides their known function in tissue homeostasis and immunity, constitute conduits for the tumor cells to metastasize. One of the factors that contribute to tumor spread is the acquisition of an angiogenic phenotype as a response to the onset of tumor hypoxia. To our knowledge, little is known about the effects of low oxygen levels on the lymphatic vasculature. Therefore, we used cDNA microarrays to study the transcriptional changes occurring in hypoxia exposed lymphatic endothelial cells. Our analysis was then complemented by functional assays showing that these cells responded with increased attachment to the extracellular matrix, delayed proliferation and production of reactive oxygen species. Differential expression of genes involved in these processes such as NADPH oxidase 4, the tissue inhibitor of metalloproteinase 3, and TGFbeta induced protein I, was found. Hypoxia was also found to increase mRNA levels of the cytokine CXCL-12 and its receptor CXCR4. Moreover, adhesion experiments revealed that hypoxia increased the binding of non-small cell lung carcinoma cells to this endothelium in a CXCR4 dependent way. We thus illustrate the response of lymphatic endothelial cells to hypoxia and suggest targets to study tumor metastasis through these vessels.

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Year:  2007        PMID: 17442415     DOI: 10.1016/j.bbamcr.2007.03.001

Source DB:  PubMed          Journal:  Biochim Biophys Acta        ISSN: 0006-3002


  17 in total

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9.  Transcriptome Analysis of Hypoxic Lymphatic Endothelial Cells Indicates Their Potential to Contribute to Extracellular Matrix Rearrangement.

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10.  Genome-wide identification of hypoxia-inducible factor binding sites and target genes by a probabilistic model integrating transcription-profiling data and in silico binding site prediction.

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