Differential responses of normal human coronary artery endothelial cells against multiple cytokines comparatively assessed by gene expression profiles

Abstract

Endothelial cells play an important role in terms of biological functions by responding to a variety of stimuli in the blood. However, little is known about the molecular mechanism involved in rendering the variety in the cellular response. To investigate the variety of the cellular responses against exogenous stimuli at the gene expression level, we attempted to describe the cellular responses with comprehensive gene expression profiles, dissect them into multiple response patterns, and characterize the response patterns according to the information accumulated so far on the genes included in the patterns. We comparatively analyzed in parallel the gene expression profiles obtained with DNA microarrays from normal human coronary artery endothelial cells (HCAECs) stimulated with multiple cytokines, interleukin-1b, tumor necrosis factor-a, interferon-b, interferon-c, and oncostatin M, which are profoundly involved in various functional responses of endothelial cells. These analyses revealed that the cellular responses of HCAECs against these cytokines included at least 15 response patterns specific to a single cytokine or common to multiple cytokines. Moreover, we statistically extracted genes contained within the individual response patterns and characterized the response patterns with the genes referring to the previously accumulated findings including the biological process defined by the Gene Ontology Consortium (GO). Out of the 15 response patterns in which at least one gene was successfully extracted through the statistical approach, 11 response patterns were differentially characterized by representing the number of genes contained in individual criteria of the biological process in the GO only. The approach to dissect cellular responses into response patterns and to characterize the pattern at the gene expression level may contribute to the gaining of insight for untangling the diversity of cellular functions.

IFN‐γ‐receptor signaling ameliorates transplant vasculopathy through attenuation of CD8+ T‐cell‐mediated injury of vascular endothelial cells

“…A particularly attractive scenario would be the potential exploitation of the cell type-specific signal transduction pathways downstream of the IFNGR in EC [52], which may harbor specific targets that could permit stimulation of the peripheral inhibitory signals while sparing the stimulatory pathway in immune cells. Taken together, pro-inflammatory IFNGR signaling in EC appears to be a central regulatory step in the control of EC-specific CD8 + T-cell responses and hence, in the promotion of shielding the graft from CD8 + T-cell-mediated damage.…”

Tight control – decision-making during T cell–vascular endothelial cell interaction

“…Hence, the ligation of PD-1 or the HVEM-receptor BTLA on EC-specific effector T cells represents an attractive therapeutic target to avoid excessive EC damage during inflammation. Furthermore, cell type-specific signal transduction pathways downstream of the IFN-γ receptor in ECs (Miura et al, 2006) may harbor specific targets that could permit stimulation of peripheral inhibitory signals. Clearly, further research is warranted to better understand how proinflammatory stimuli can be translated locally into anti-inflammatory signals for the benefit of vascular and tissue integrity.…”

Novel clusters of highly expressed genes accompany genomic amplification in breast cancers

“…Since elevated expression of ERBB2 and its neighboring genes in the chromosomal region of 17q12-21 has been extensively analyzed in breast cancer, we first focused on gene expression in chromosome 17 among the 35 breast cancer cell lines. We obtained the gene expression profiles of these cell lines as ratios against the human common reference RNA [13] (designated log ratios), extracted the expression data for genes localized on chromosome 17, and assembled the data according to the position on the chromosome to clarify the relationship between expression ratios and chromosomal positions. Thereafter, to emphasize the presence of gene clusters with elevated expression levels, we processed each log ratio of gene expression into a log value relative to the mean average log ratio of each gene among 35 profiles (designated subtracted log ratios).…”

“…Poly(A) + RNA was extracted from cells harvested at approximately 70% confluency after incubation in fresh media for 16 h. Two micrograms of poly(A) + RNA was labeled with Cyanine 5-dUTP or Cyanine 3-dUTP (PerkinElmer, MA, USA). Human common reference RNA was prepared by mixing equal amounts of poly(A) + RNA extracted from 22 human cancer cell lines to reduce Abbreviations: CGH, comparative genomic hybridization cell type-specific bias of expression [13]. Hybridization was performed with a labeling and hybridization kit (MicroDiagnostic).…”