Expression of Groucho/TLE proteins during pancreas development

<p>Abstract</p> <p>Background</p> <p>The full-length mammalian homologs of <it>groucho</it>, Tle1, 2, 3, and 4, act as transcriptional corepressors and are recruited by transcription factors containing an eh1 or WRPW/Y domain. Many transcription factors critical to pancreas development contain a Gro/TLE interaction domain and several have been shown to require Gro/TLE interactions for proper function during neuronal development. However, a detailed analysis of the expression patterns of the Gro/TLE proteins in pancreas development has not been performed. Moreover, little is known about the ability of Gro/TLE proteins to interact with transcription factors in the pancreas.</p> <p>Results</p> <p>We describe the expression of Gro/TLE family members, and of 34 different transcription factors that contain a Gro/TLE interaction motif, in the pancreas utilizing nine SAGE libraries created from the developing and adult pancreas, as well as the <it>GenePaint </it>database. Next, we show the dynamic expression of <it>Tle1</it>, <it>2</it>, <it>3</it>, <it>4, 5 </it>and <it>6 </it>during pancreas development by qRT-PCR. To further define the cell-type specificity of the expression of these proteins we use immunofluorescence to co-localize them with Pdx1 at embryonic day 12.5 (E12.5), Ngn3 at E14.5, Pdx1, Nkx2-2, Insulin, Glucagon, Pancreatic polypeptide and Somatostatin at E18.5, as well as Insulin and Glucagon in the adult. We then show that Tle2 can interact with Nkx2-2, Hes1, Arx, and Nkx6-1 which are all critical factors in pancreas development. Finally, we demonstrate that Tle2 modulates the repressive abilities of Arx in a β-cell line.</p> <p>Conclusion</p> <p>Although Tle1, 2, 3, and 4 show overlapping expression in pancreatic progenitors and in the adult islet, the expression of these factors is restricted to different cell types during endocrine cell maturation. Of note, Tle2 and Tle3 are co-expressed with Gro/TLE interaction domain containing transcription factors that are essential for endocrine pancreas development. We further demonstrate that Tle2 can interact with several of these factors and that Tle2 modulate Arx's repressive activity. Taken together our studies suggest that Gro/TLE proteins play a role in the repression of target genes during endocrine cell specification.</p

Identification of transcripts with enriched expression in the developing and adult pancreas

The expression profile of different developmental stages of the murine pancreas and predictions of transcription factor interactions, provides a framework for pancreas regulatory networks and development

Differentiation of Mouse Embryonic Stem Cells into Endoderm without Embryoid Body Formation

Pluripotent embryonic stem cells hold a great promise as an unlimited source of tissue for treatment of chronic diseases such as Type 1 diabetes. Herein, we describe a protocol using all-trans-retinoic acid, basic fibroblast growth factor and dibutyryl cAMP (DBcAMP) in the absence of embryoid body formation, for differentiation of murine embryonic stem cells into definitive endoderm that may serve as pancreatic precursors. The produced cells were analyzed by quantitative PCR, immunohistochemistry and static insulin release assay for markers of trilaminar embryo, and pancreas. Differentiated cells displayed increased Sox17 and Foxa2 expression consistent with definitive endoderm production. There was minimal production of Sox7, an extraembryonic endoderm marker, and Oct4, a marker of pluripotency. There was minimal mesoderm or neuroectoderm formation based on expression levels of the markers brachyury and Sox1, respectively. Various assays revealed that the cell clusters generated by this protocol express markers of the pancreatic lineage including insulin I, insulin II, C-peptide, PDX-1, carboxypeptidase E, pan-cytokeratin, amylase, glucagon, PAX6, Ngn3 and Nkx6.1. This protocol using all-trans-retinoic acid, DBcAMP, in the absence of embryoid bodies, generated cells that have features of definitive endoderm that may serve as pancreatic endocrine precursors

Integrated analysis of the prostate cancer small-nucleolar transcriptome reveals <i>SNORA55</i> as a driver of prostate cancer progression

Metastasis is the primary cause of death in prostate cancer (PCa) patients. Small nucleolar RNAs (snoRNAs) have long been considered "housekeeping" genes with no relevance for cancer biology. Emerging evidence has challenged this assumption, suggesting that snoRNA expression is frequently modulated during cancer progression. Despite this, no study has systematically addressed the prognostic and functional significance of snoRNAs in PCa. We performed RNA Sequencing on paired metastatic/non-metastatic PCa xenografts derived from clinical specimens. The clinical significance of differentially expressed snoRNAs was further investigated in two independent primary PCa cohorts (131 and 43 patients, respectively). The snoRNA demonstrating the strongest association with clinical outcome was quantified in PCa patient-derived serum samples and its functional relevance was investigated in PCa cells via gene expression profiling, pathway analysis and gene silencing. Our comparison revealed 21 differentially expressed snoRNAs in the metastatic vs. non-metastatic xenografts. Of those, 12 were represented in clinical databases and were further analyzed. SNORA55 emerged as a predictor of shorter relapse-free survival (results confirmed in two independent databases). SNORA55 was reproducibly detectable in serum samples from PCa patients. SNORA55 silencing in PCa cell lines significantly inhibited cell proliferation and migration. Pathway analysis revealed that SNORA55 expression is significantly associated with growth factor signaling and pro-inflammatory cytokine expression in PCa. Our results demonstrate that SNORA55 up-regulation predicts PCa progression and that silencing this non-coding gene affects PCa cell proliferation and metastatic potential, thus positioning it as both a novel biomarker and therapeutic target

Meta-Analysis of Differentiating Mouse Embryonic Stem Cell Gene Expression Kinetics Reveals Early Change of a Small Gene Set

Stem cell differentiation involves critical changes in gene expression. Identification of these should provide endpoints useful for optimizing stem cell propagation as well as potential clues about mechanisms governing stem cell maintenance. Here we describe the results of a new meta-analysis methodology applied to multiple gene expression datasets from three mouse embryonic stem cell (ESC) lines obtained at specific time points during the course of their differentiation into various lineages. We developed methods to identify genes with expression changes that correlated with the altered frequency of functionally defined, undifferentiated ESC in culture. In each dataset, we computed a novel statistical confidence measure for every gene which captured the certainty that a particular gene exhibited an expression pattern of interest within that dataset. This permitted a joint analysis of the datasets, despite the different experimental designs. Using a ranking scheme that favored genes exhibiting patterns of interest, we focused on the top 88 genes whose expression was consistently changed when ESC were induced to differentiate. Seven of these (103728_at, 8430410A17Rik, Klf2, Nr0b1, Sox2, Tcl1, and Zfp42) showed a rapid decrease in expression concurrent with a decrease in frequency of undifferentiated cells and remained predictive when evaluated in additional maintenance and differentiating protocols. Through a novel meta-analysis, this study identifies a small set of genes whose expression is useful for identifying changes in stem cell frequencies in cultures of mouse ESC. The methods and findings have broader applicability to understanding the regulation of self-renewal of other stem cell types

A Seriation Approach for Visualization-Driven Discovery of Co-Expression Patterns in Serial Analysis of Gene Expression (SAGE) Data

Background: Serial Analysis of Gene Expression (SAGE) is a DNA sequencing-based method for large-scale gene expression profiling that provides an alternative to microarray analysis. Most analyses of SAGE data aimed at identifying co-expressed genes have been accomplished using various versions of clustering approaches that often result in a number of false positives. Principal Findings: Here we explore the use of seriation, a statistical approach for ordering sets of objects based on their similarity, for large-scale expression pattern discovery in SAGE data. For this specific task we implement a seriation heuristic we term ‘progressive construction of contigs ’ that constructs local chains of related elements by sequentially rearranging margins of the correlation matrix. We apply the heuristic to the analysis of simulated and experimental SAGE data and compare our results to those obtained with a clustering algorithm developed specifically for SAGE data. We show using simulations that the performance of seriation compares favorably to that of the clustering algorithm on noisy SAGE data. Conclusions: We explore the use of a seriation approach for visualization-based pattern discovery in SAGE data. Using both simulations and experimental data, we demonstrate that seriation is able to identify groups of co-expressed genes more accurately than a clustering algorithm developed specifically for SAGE data. Our results suggest that seriation is a usefu