Integration of flow studies for robust selection of mechanoresponsive genes

Nataly Maimari, Ryan M. Pedrigi, Alessandra Russo, Krysia Broda, Rob Krams

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

Blood flow is an essential contributor to plaque growth, composition and initiation. It is sensed by endothelial cells, which react to blood flow by expressing > 1000 genes. The sheer number of genes implies that one needs genomic techniques to unravel their response in dis­ease. Individual genomic studies have been performed but lack sufficient power to identify subtle changes in gene expression. In this study, we investigated whether a systematic meta-analysis of available microarray studies can improve their consistency. We identified 17 studies using microarrays, of which six were performed in vivo and 11 in vitro. The in vivo studies were disregarded due to the lack of the shear profile. Of the in vitro studies, a cross-platform integration of human studies (HUVECs in flow cells) showed high concordance (> 90 %). The human data set identified > 1600 genes to be shear responsive, more than any other study and in this gene set all known mechanosensitive genes and pathways were present. A detailed network analysis indicated a power distribution (e. g. the presence of hubs), without a hierarchical organisation. The average cluster coeffi­cient was high and further analysis indicated an aggregation of 3 and 4 element motifs, indicating a high prevalence of feedback and feed forward loops, similar to prokaryotic cells. In conclusion, this initial study presented a novel method to integrate human-based mechan-osensitive studies to increase its power. The robust network was large, contained all known mechanosensitive pathways and its structure re­vealed hubs, and a large aggregate of feedback and feed forward loops.

Original languageEnglish (US)
Pages (from-to)474-483
Number of pages10
JournalThrombosis and Haemostasis
Volume115
Issue number3
DOIs
Publication statusPublished - Jan 1 2016

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Keywords

  • Bioinformatics
  • Human
  • Mechanobiology
  • Microarrays
  • Network biology
  • Network structure

ASJC Scopus subject areas

  • Hematology

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