Towards interactive exploration of gene expression patterns

Porcine aortic endothelial cell genes responsive to selected inflammatory stimulators

Use of porcine tissues has been suggested as a promising solution for severe shortage of transplantable human organs. The immediate hurdle for xenotransplantation is acute immune/inflammatory vascular rejection of the transplant. Because endothelial cells play a key role in the initiation and the amplification of inflammation, alteration of gene expression in human endothelial cells, by various inflammatory stimulators has been studied extensively. However, transcriptional changes induced by human and other inflammatory stimulators in porcine endothelial cells have thus far not been studied. In this study, we treated porcine endothelial cells with human tumor necrosis factor (TNF)-alpha, porcine interferon (IFN)-gamma, H(2)O(2) and lypopolysaccharide (LPS) and profiled transcriptional change at 1 hr, 6 hr and 24 hr, using pig oligonucleotide 13K microarray. We found that mRNA species such as chemokine (C-X-C motif) ligand 6 (CXCL6) and Cathepsin S were significantly induced in porcine endothelial cells, as was previously reported with human endothelial cell. We also found that mRNA species including secreted frizzled-related protein 2 (SFRP2), radical S-adenosyl methionine domain containing 2 (RSAD2), structure specific recognition protein 1 (SSRP1) also were highly overexpressed in porcine endothelial cells. This result shows clues to understand underlying mechanisms of xenotransplantation rejection and the highly responsive porcine genes may serve as novel targets to be regulated for improving the function of grafted porcine donor organs.

Time course analysis of gene expression during light-induced photoreceptor cell death and regeneration in albino zebrafish

Constant intense light causes apoptosis of rod and cone photoreceptors in adult albino zebrafish. The photoreceptors subsequently regenerate from proliferating inner nuclear layer (INL) progenitor cells that migrate to the outer nuclear layer (ONL) and differentiate into rods and cones. To identify gene expression changes during this photoreceptor regeneration response, a microarray analysis was performed at five time points during the light treatment. The time course included an early time point during photoreceptor death (16 h), later time points during progenitor cell proliferation and migration (31, 51, and 68 h) and a 96 h time point, which likely corresponds to the initial photoreceptor differentiation. Mean expression values for each gene were calculated at each time point relative to the control (0 h light exposure) and statistical analysis by one-way ANOVA identified 4567 genes exhibiting significant changes in gene expression along the time course. The genes within this data set were clustered based on their temporal expression patterns and proposed functions. Quantitative real-time PCR validated the microarray expression profiles for selected genes, including stat3 whose expression increased markedly during the light exposure. Based on immunoblots, both total and activated Stat3 protein expression also increased during the light treatment. Immunolocalization of Stat3 on retinal tissue sections demonstrated increased expression in photoreceptors and Müller glia by 16 h of light exposure. Some of the Stat3-positive Müller cells expressed PCNA at 31 h, suggesting that Stat3 may play a role in signaling a subset of Müller cells to proliferate during the regeneration response.

Clustering methods for microarray gene expression data

Within the field of genomics, microarray technologies have become a powerful technique for simultaneously monitoring the expression patterns of thousands of genes under different sets of conditions. A main task now is to propose analytical methods to identify groups of genes that manifest similar expression patterns and are activated by similar conditions. The corresponding analysis problem is to cluster multi-condition gene expression data. The purpose of this paper is to present a general view of clustering techniques used in microarray gene expression data analysis.