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Open Access Research article

A SAGE based approach to human glomerular endothelium: defining the transcriptome, finding a novel molecule and highlighting endothelial diversity

Guerkan Sengoelge1*, Wolfgang Winnicki1, Anne Kupczok2, Arndt von Haeseler2, Michael Schuster3, Walter Pfaller4, Paul Jennings4, Ansgar Weltermann5, Sophia Blake6 and Gere Sunder-Plassmann1

Author Affiliations

1 Department of Medicine III, Division of Nephrology and Dialysis, Medical University of Vienna, Waehringer Guertel 18 – 20, A-1090 Vienna, Austria

2 Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, Medical University of Vienna/University of Vienna, Dr. Bohr-Gasse 9, A-1030 Vienna, Austria

3 EMBL, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK

4 Department of Physiology, Medical University of Innsbruck, Christoph-Probst-Platz 1, Innrain 52, A-6020 Innsbruck, Austria

5 Department of Haematology and Oncology, Elisabethinen Hospital, Fadingerstraße 1, A-4020 Linz, Austria

6 London Research Institute, Lincoln's Inn Fields Laboratories, Cancer Research UK, 44 Lincoln's Inn Fields, London WC2A 3LY, UK

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BMC Genomics 2014, 15:725  doi:10.1186/1471-2164-15-725

Published: 27 August 2014

Abstract

Background

Large scale transcript analysis of human glomerular microvascular endothelial cells (HGMEC) has never been accomplished. We designed this study to define the transcriptome of HGMEC and facilitate a better characterization of these endothelial cells with unique features. Serial analysis of gene expression (SAGE) was used for its unbiased approach to quantitative acquisition of transcripts.

Results

We generated a HGMEC SAGE library consisting of 68,987 transcript tags. Then taking advantage of large public databases and advanced bioinformatics we compared the HGMEC SAGE library with a SAGE library of non-cultured ex vivo human glomeruli (44,334 tags) which contained endothelial cells. The 823 tags common to both which would have the potential to be expressed in vivo were subsequently checked against 822,008 tags from 16 non-glomerular endothelial SAGE libraries. This resulted in 268 transcript tags differentially overexpressed in HGMEC compared to non-glomerular endothelia. These tags were filtered using a set of criteria: never before shown in kidney or any type of endothelial cell, absent in all nephron regions except the glomerulus, more highly expressed than statistically expected in HGMEC. Neurogranin, a direct target of thyroid hormone action which had been thought to be brain specific and never shown in endothelial cells before, fulfilled these criteria. Its expression in glomerular endothelium in vitro and in vivo was then verified by real-time-PCR, sequencing and immunohistochemistry.

Conclusions

Our results represent an extensive molecular characterization of HGMEC beyond a mere database, underline the endothelial heterogeneity, and propose neurogranin as a potential link in the kidney-thyroid axis.

Keywords:
Bioinformatics; Endothelial diversity; Glomerular endothelial cell; Neurogranin; Serial analysis of gene expression