15-Lipoxygenase-1 re-expression in colorectal cancer alters endothelial cell features through enhanced expression of TSP-1 and ICAM-1

15-lipoxygenase-1 (15-LOX-1) oxygenates linoleic acid to 13(S)-hydroxyoctadecadienoic acid (RODE). The enzyme is widely suppressed in different cancers and its re-expression has tumor suppressive effects. 15-LOX-1 has been shown to inhibit neoangiogenesis in colorectal cancer (CRC); in the present study we confirm this phenomenon and describe the mechanistic basis. We show that re-expression of 15-LOX-1 in CRC cell lines resulted in decreased transcriptional activity of HIF1 alpha and reduced the expression and secretion of VEGF in both normoxic and hypoxic conditions. Conditioned medium (CM) was obtained from CRC or prostate cancer cell lines re-expressing 15-LOX-1 (15-LOX-1CM). 15-LOX-1CM treated aortic rings from 6-week old C57BL/6 mice showed significantly less vessel sprouting and more organized structure of vascular network. Human umbilical vein endothelial cells (HUVECs) incubated with 15-LOX-1CM showed reduced motility, enhanced expression of intercellular cell adhesion molecule (ICAM-1) and reduced tube formation but no change in proliferation or cell cycle distribution. HUVECs incubated with 13(S)-HODE partially phenocopied the effects of 15-LOX-1CM, showed reduced motility and enhanced expression of ICAM-1, but did not reduce tube formation, implying the importance of additional factors. Therefore, a Proteome Profiler Angiogenesis Array was carried out, which showed that Thrombospondin-1 (TSP-1), a matrix glycoprotein known to strongly inhibit neovascularization, was expressed significantly more in HUVECs incubated with 15-LOX-1CM. TSP-1 blockage in HUVECs reduced the expression of ICAM-1 and enhanced cell motility, thereby providing a mechanism for reduced angiogenesis. The anti-angiogenic effects of 15-LOX-1 through enhanced expressions of ICAM-1 and TSP-1 are novel findings and should be explored further to develop therapeutic options

CAP-RNAseq: An online tool for Clustering, Annotation and Prioritization of RNAseq data

Transcriptome analysis has been an effective high throughput method for examining the regulation and function of genomes. However, analysis of RNAseq experiments with more than two groups/factors is complex and can benefit from clustering followed by annotation and prioritization of genes. While a few advanced analysis pipelines are available for such data sets, none of them make the gene selection process automated and hence easier for validation experiments. To fill this gap, we have developed a web tool named CAP-RNAseq, which creates co-expressed gene clusters using logCPM (voom transformed) values, and then annotates and prioritizes genes in the selected cluster. Users can upload their raw count RNAseq data, followed by application of ANOVA to filter/reduce the data to obtain genes showing significant changes between any two groups. k-means clustering is then applied with the user-specified cluster numbers; and the emerging clusters can be visualized and enriched functionally with mSigDB, GO and KEGG databases. CAP-RNAseq also suggests genes for future validation by qRT-PCR and Western Blotting. The algorithm used for this prioritization implements distance correlation (multivariate independence), which measures the correlation between the consensus profile of a cluster and profile of each gene in that cluster. Then, top correlated genes are shown in a table from which the user can select a gene for primers to be designed. Moreover, the Human Protein Atlas data has been integrated into CAP-RNAseq to visualize the protein levels of suggested or user-selected genes in selected cancer types. CAP-RNAseq has been designed and implemented for the R-Shiny platform and is user-friendly. We present several case studies for its use in breast cancer and hormone replacement therapy based on our own RNAseq datasets.This study has been funded by TUSEB (Grant No. 4405

Quantification of SLIT-ROBO transcripts in hepatocellular carcinoma reveals two groups of genes with coordinate expression

Background: SLIT-ROBO families of proteins mediate axon pathfinding and their expression is not solely confined to nervous system. Aberrant expression of SLIT-ROBO genes was repeatedly shown in a wide variety of cancers, yet data about their collective behavior in hepatocellular carcinoma (HCC) is missing. Hence, we quantified SLIT-ROBO transcripts in HCC cell lines, and in normal and tumor tissues from liver

Dose- and time-dependent expression patterns of zebrafish orthologs of selected E2F target genes in response to serum starvation/replenishment

Targets of E2F transcription factors effectively regulate the cell cycle from worms to humans. Furthermore, the dysregulation of E2F transcription modules plays a highly conserved role in cancers of human and zebrafish. Studying E2F target expression under a given cellular state, such as quiescence, might lead to a better understanding of the conserved patterns of expression in different taxa. In the present study, we used literature searches and phylogeny to identify several targets of E2F transcription factors that are known to be serum-responsive; namely, PCNA, MYBL2, MCM7, TYMS, and CTGF. The transcriptional serum response of zebrafish orthologs of these genes were quantified under different doses (i.e., 0, 0.1, 1, 3, and 10% FBS) and time points (i.e., 6, 24 and 48 hours, h) using quantitative RT-PCR (qRT-PCR) in the zebrafish fibroblast cells (ZF4). Our results indicated that mRNA expression of zebrafish pcna, mybl2, mcm7 and tyms drastically decreased while that of ctgf increased with decreasing serum levels as observed in mammals. These genes responded to serum starvation at 24 and 48 h and to the mitogenic stimuli as early as 6 h except for ctgf whose expression was significantly altered at 24 h. The zebrafish Mcm7 protein levels also were modulated by serum starvation/replenishment. The present study provides a foundation for the comparative analysis of quantitative expression patterns for genes involved in regulation of cell cycle using a zebrafish serum response model