This review provides an overview of the effect of blood flow on endothelial cell (EC) signalling pathways, applying microarray technologies to cultured cells, and in vivo studies of normal and atherosclerotic animals. It is found that in cultured ECs, 5-10% of genes are up- or down-regulated in response to fluid flow, whereas only 3-6% of genes are regulated by varying levels of fluid flow. Of all genes, 90% are regulated by the steady part of fluid flow and 10% by pulsatile components. The associated gene profiles show high variability from experiment to experiment depending on experimental conditions, and importantly, the bioinformatical methods used to analyse the data. Despite this high variability, the current data sets can be summarized with the concept of endothelial priming. In this concept, fluid flows confer protection by an up-regulation of anti-atherogenic, anti-thrombotic, and anti-inflammatory gene signatures. Consequently, predilection sites of atherosclerosis, which are associated with low-shear stress, confer low protection for atherosclerosis and are, therefore, more sensitive to high cholesterol levels. Recent studies in intact non-atherosclerotic animals confirmed these in vitro studies, and suggest that a spatial component might be present. Despite the large variability, a few signalling pathways were consistently present in the majority of studies. These were the MAPK, the nuclear factor-κB, and the endothelial nitric oxide synthase-NO pathways.

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 mechanosensitive 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.

Mechanotransduction, the ability of cells to detect and react to mechanical forces, is increasingly playing a critical role in a variety of physiological and pathophysiological processes. While the focus has previously been on the MAPK, NF-ϰB and ROS generating pathways, ancient embryological pathways have reached little attention. Recently, a surge of new studies have been published on these pathways and their role in mechanotransduction and this review paper aims to provide a concise overview on the latest studies and brings them in to a larger perspective. Special emphasis is on the non-canonical aspects of the Wnt, TGF-beta and Notch pathways and their role in flow.

In spite of extensive recent progress, a comprehensive understanding of how actin cytoskeleton remodelling supports stable junctions remains to be established. Here we design a platform that integrates actin functions with optimized phenotypic clustering and identify new cytoskeletal proteins, their functional hierarchy and pathways that modulate E-cadherin adhesion. Depletion of EEF1A, an actin bundling protein, increases E-cadherin levels at junctions without a corresponding reinforcement of cell–cell contacts. This unexpected result reflects a more dynamic and mobile junctional actin in EEF1A-depleted cells. A partner for EEF1A in cadherin contact maintenance is the formin DIAPH2, which interacts with EEF1A. In contrast, depletion of either the endocytic regulator TRIP10 or the Rho GTPase activator VAV2 reduces E-cadherin levels at junctions. TRIP10 binds to and requires VAV2 function for its junctional localization. Overall, we present new conceptual insights on junction stabilization, which integrate known and novel pathways with impact for epithelial morphogenesis, homeostasis and diseases.

The discovery of the human genome has unveiled new fields of genomics, transcriptomics, and proteomics, which has produced paradigm shifts on how to study disease mechanisms, wherein a current central focus is the understanding of how gene signatures and gene networks interact within cells. These gene function studies require manipulating genes either through activation or inhibition, which can be achieved by temporarily permeabilizing the cell membrane through transfection to deliver cDNA or RNAi. An efficient transfection technique is electroporation, which applies an optimized electric pulse to permeabilize the cells of interest. When the molecules are applied on top of seeded cells, it is called “direct” transfection and when the nucleic acids are printed on the substrate and the cells are seeded on top of them, it is termed “reverse” transfection. Direct transfection has been successfully applied in previous studies, whereas reverse transfection has recently gained more attention in the context of high‐throughput experiments. Despite the emerging importance, studies comparing the efficiency of the two methods are lacking. In this study, a model for electroporation of cells in situ is developed to address this deficiency. The results indicate that reverse transfection is less efficient than direct transfection. However, the model also predicts that by increasing the concentration of deliverable molecules by a factor of 2 or increasing the applied voltage by 20%, reverse transfection can be approximately as efficient as direct transfection.