Deaf-1 regulates epithelial cell proliferation and side-branching in the mammary gland

BACKGROUND: The transcription factor DEAF-1 has been identified as a high affinity binding partner of the LIM-only protein LMO4 that plays important roles in mammary gland development and breast cancer. Here we investigated the influence of DEAF-1 on human and mouse mammary epithelial cells both in vitro and in vivo and identified a potential target gene. RESULTS: Overexpression of DEAF-1 in human breast epithelial MCF10A cells enhanced cell proliferation in the mammary acini that develop in 3D cultures. To investigate the effects of Deaf-1 on mammary gland development and oncogenesis, we generated MMTV-Deaf-1 transgenic mice. Increased ductal side-branching was observed in young virgin mammary glands, accompanied by augmented cell proliferation. In addition, the ratio of the progesterone receptor isoforms PRA and PRB, previously implicated in regulating ductal side-branching, was altered. Affymetrix gene profiling studies revealed Rac3 as a potential target gene and quantitative RT-PCR analysis confirmed that Rac3 was upregulated by Deaf-1 in immortalized mouse mammary epithelial cells. Furthermore, MMTV-Deaf-1 transgenic mammary glands were found to have elevated levels of Rac3 mRNA, suggesting that it is a bona fide target. CONCLUSION: We have demonstrated that overexpression of Deaf-1 enhances the proliferation of human breast epithelial cells in vitro and mouse epithelial cells in vivo. Transgenic mammary glands overexpressing Deaf-1 exhibited a modest side-branching phenotype, accompanied by an increase in the number of BrdU-positive cells and a decrease in the proportion of PRA-expressing cells. Although proliferation was enhanced in Deaf-1 transgenic mice, overexpression of this gene was not sufficient to induce the formation of mammary tumors. In addition, our studies identified Rac3, encoding a small Rho-like GTPase, as a potential target of Deaf-1 in mouse mammary epithelial cells

GENE-Counter: A Computational Pipeline for the Analysis of RNA-Seq Data for Gene Expression Differences

GENE-counter is a complete Perl-based computational pipeline for analyzing RNA-Sequencing (RNA-Seq) data for differential gene expression. In addition to its use in studying transcriptomes of eukaryotic model organisms, GENE-counter is applicable for prokaryotes and non-model organisms without an available genome reference sequence. For alignments, GENE-counter is configured for CASHX, Bowtie, and BWA, but an end user can use any Sequence Alignment/Map (SAM)-compliant program of preference. To analyze data for differential gene expression, GENE-counter can be run with any one of three statistics packages that are based on variations of the negative binomial distribution. The default method is a new and simple statistical test we developed based on an over-parameterized version of the negative binomial distribution. GENE-counter also includes three different methods for assessing differentially expressed features for enriched gene ontology (GO) terms. Results are transparent and data are systematically stored in a MySQL relational database to facilitate additional analyses as well as quality assessment. We used next generation sequencing to generate a small-scale RNA-Seq dataset derived from the heavily studied defense response of Arabidopsis thaliana and used GENE-counter to process the data. Collectively, the support from analysis of microarrays as well as the observed and substantial overlap in results from each of the three statistics packages demonstrates that GENE-counter is well suited for handling the unique characteristics of small sample sizes and high variability in gene counts

A Molecular Signature of Proteinuria in Glomerulonephritis

Proteinuria is the most important predictor of outcome in glomerulonephritis and experimental data suggest that the tubular cell response to proteinuria is an important determinant of progressive fibrosis in the kidney. However, it is unclear whether proteinuria is a marker of disease severity or has a direct effect on tubular cells in the kidneys of patients with glomerulonephritis. Accordingly we studied an in vitro model of proteinuria, and identified 231 “albumin-regulated genes” differentially expressed by primary human kidney tubular epithelial cells exposed to albumin. We translated these findings to human disease by studying mRNA levels of these genes in the tubulo-interstitial compartment of kidney biopsies from patients with IgA nephropathy using microarrays. Biopsies from patients with IgAN (n = 25) could be distinguished from those of control subjects (n = 6) based solely upon the expression of these 231 “albumin-regulated genes.” The expression of an 11-transcript subset related to the degree of proteinuria, and this 11-mRNA subset was also sufficient to distinguish biopsies of subjects with IgAN from control biopsies. We tested if these findings could be extrapolated to other proteinuric diseases beyond IgAN and found that all forms of primary glomerulonephritis (n = 33) can be distinguished from controls (n = 21) based solely on the expression levels of these 11 genes derived from our in vitro proteinuria model. Pathway analysis suggests common regulatory elements shared by these 11 transcripts. In conclusion, we have identified an albumin-regulated 11-gene signature shared between all forms of primary glomerulonephritis. Our findings support the hypothesis that albuminuria may directly promote injury in the tubulo-interstitial compartment of the kidney in patients with glomerulonephritis

The privatisation of public enterprises: Australian research findings

In 1995, with the financial support from the Australian Research Council, a study on Australian Privatisation was conducted which included the investigation of circumstances under which privatisation policy has been adopted by successive Australian governments, assess and claims of its progenitors, and document its social, political, and financial impact. Over three years about fifty key informant interviews were conducted, and these were supplemented with the analysis of both published and unpublished documents

Limma: linear models for microarray data

This free open-source software implements academic research by the authors and co-workers. If you use it, please support the project by citing the appropriate journal articles listed in Section 2.1.Content

Vol. 22 no. 7 2006, pages 897–899 BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/btl025

single channel microarray dat