Scube activity is necessary for Hedgehog signal transduction in vivo

The Hedgehog (HH) signaling pathway is a central regulator of embryonic development, controlling the pattern and proliferation of a wide variety of organs. Previous studies have implicated the secreted protein, Scube2, in HH signal transduction in the zebrafish embryo (Hollway et al., 2006; Kawakami et al., 2005; Woods and Talbot, 2005) although the nature of the molecular function of Scube2 in this process has remained undefined. This analysis has been compounded by the fact that removal of Scube2 activity in the zebrafish embryo leads to only subtle defects in HH signal transduction in vivo (Barresi et al., 2000; Hollway et al., 2006; Ochi and Westerfield, 2007; van Eeden et al., 1996; Wolff et al., 2003). Here we present the discovery of two additional scube genes in zebrafish, scube1 and scube3, and demonstrate their roles in facilitating HH signal transduction. Knocking down the function of all three scube genes simultaneously phenocopies a complete loss of HH signal transduction in the embryo, revealing that Scube signaling is essential for HH signal transduction in vivo. We further define the molecular role of scube2 in HH signaling.     

READ: RIKEN Expression Array Database

READ, the RIKEN Expression Array Database, is a database of expression profile data from the RIKEN mouse cDNA microarray. It stores the microarray experimental data and information, and provides Web interfaces for researchers to use to retrieve, analyze and display their data. The goals for READ are to serve as a storage site for microarray data from ongoing research in the RIKEN mouse encyclopedia project and to provide useful links and tools to decipher biologically important information. The gene information is based mainly on the fully annotated FANTOM database. READ can be accessed at http://read.gsc.riken.go.jp/. READ also provides a search tool [READ integrates gene expression neighbor (RINGENE)] for genes with similarities in expression profiling.     

Mouse strain specific gene expression differences for illumina microarray expression profiling in embryos

ABSTRACT: BACKGROUND: In the field of mouse genetics the advent of technologies like microarray based expression profiling dramatically increased data availability and sensitivity, yet these advanced methods are often vulnerable to the unavoidable heterogeneity of in vivo material and might therefore reflect differentially expressed genes between mouse strains of no relevance to a targeted experiment. The aim of this study was not to elaborate on the usefulness of microarray analysis in general, but to expand our knowledge regarding this potential "background noise" for the widely used Illumina microarray platform surpassing existing data which focused primarily on the adult sensory and nervous system, by analyzing patterns of gene expression at different embryonic stages using wild type strains and modern transgenic models of often non-isogenic backgrounds. RESULTS: Wild type embryos of 11 mouse strains commonly used in transgenic and molecular genetic studies at three developmental time points were subjected to Illumina microarray expression profiling in a strain-by-strain comparison. Our data robustly reflects known gene expression patterns during mid-gestation development. Decreasing diversity of the input tissue and/or increasing strain diversity raised the sensitivity of the array towards the genetic background. Consistent strain sensitivity of some probes was attributed to genetic polymorphisms or probe design related artifacts. CONCLUSION: Our study provides an extensive reference list of gene expression profiling background noise of value to anyone in the field of developmental biology and transgenic research performing microarray expression profiling with the widely used Illumina microarray platform. Probes identified as strain specific background noise further allow for microarray expression profiling on its own to be a valuable tool for establishing genealogies of mouse inbred strains.     

Prediction of gene expression in embryonic structures of Drosophila melanogaster

Understanding how sets of genes are coordinately regulated in space and time to generate the diversity of cell types that characterise complex metazoans is a major challenge in modern biology. The use of high-throughput approaches, such as large-scale in situ hybridisation and genome-wide expression profiling via DNA microarrays, is beginning to provide insights into the complexities of development. However, in many organisms the collection and annotation of comprehensive in situ localisation data is a difficult and time-consuming task. Here, we present a widely applicable computational approach, integrating developmental time-course microarray data with annotated in situ hybridisation studies, that facilitates the de novo prediction of tissue-specific expression for genes that have no in vivo gene expression localisation data available. Using a classification approach, trained with data from microarray and in situ hybridisation studies of gene expression during Drosophila embryonic development, we made a set of predictions on the tissue-specific expression of Drosophila genes that have not been systematically characterised by in situ hybridisation experiments. The reliability of our predictions is confirmed by literature-derived annotations in FlyBase, by overrepresentation of Gene Ontology biological process annotations, and, in a selected set, by detailed gene-specific studies from the literature. Our novel organism-independent method will be of considerable utility in enriching the annotation of gene function and expression in complex multicellular organisms.     

Inference of a genetic network by a combined approach of cluster analysis and graphical Gaussian modeling

MOTIVATION: Recent advances in DNA microarray technologies have made it possible to measure the expression levels of thousands of genes simultaneously under different conditions. The data obtained by microarray analyses are called expression profile data. One type of important information underlying the expression profile data is the 'genetic network,' that is, the regulatory network among genes. Graphical Gaussian Modeling (GGM) is a widely utilized method to infer or test relationships among a plural of variables. RESULTS: In this study, we developed a method combining the cluster analysis with GGM for the inference of the genetic network from the expression profile data. The expression profile data of 2467 Saccharomyces cerevisiae genes measured under 79 different conditions (Eisen et al., PROC: Natl Acad. Sci. USA, 95, 14683-14868, 1998) were used for this study. At first, the 2467 genes were classified into 34 clusters by a cluster analysis, as a preprocessing for GGM. Then, the expression levels of the genes in each cluster were averaged for each condition. The averaged expression profile data of 34 clusters were subjected to GGM, and a partial correlation coefficient matrix was obtained as a model of the genetic network of S. cerevisiae. The accuracy of the inferred network was examined by the agreement of our results with the cumulative results of experimental studies.     

Pancreas development in zebrafish: early dispersed appearance of endocrine hormone expressing cells and their convergence to form the definitive islet

To begin to understand pancreas development and the control of endocrine lineage formation in zebrafish, we have examined the expression pattern of several genes shown to act in vertebrate pancreatic development: pdx-1, insulin (W. M. Milewski et al., 1998, Endocrinology 139, 1440-1449), glucagon, somatostatin (F. Argenton et al., 1999, Mech. Dev. 87, 217-221), islet-1 (Korzh et al., 1993, Development 118, 417-425), nkx2.2 (Barth and Wilson, 1995, Development 121, 1755-1768), and pax6.2 (Nornes et al., 1998, Mech. Dev. 77, 185-196). To determine the spatial relationship between the exocrine and the endocrine compartments, we have cloned the zebrafish trypsin gene, a digestive enzyme expressed in differentiated pancreatic exocrine cells. We found expression of all these genes in the developing pancreas throughout organogenesis. Endocrine cells first appear in a scattered fashion in two bilateral rows close to the midline during mid-somitogenesis and converge during late-somitogenesis to form a single islet dorsal to the nascent duodenum. We have examined development of the endocrine lineage in a number of previously described zebrafish mutations. Deletion of chordamesoderm in floating head (Xnot homolog) mutants reduces islet formation to small remnants, but does not delete the pancreas, indicating that notochord is involved in proper pancreas development, but not required for differentiation of pancreatic cell fates. In the absence of knypek gene function, which is involved in convergence movements, the bilateral endocrine primordia do not merge. Presence of trunk paraxial mesoderm also appears to be instrumental for convergence since the bilateral endocrine primordia do not merge in spadetail mutants. We discuss our findings on zebrafish pancreatogenesis in the light of evolution of the pancreas in chordates.     

Web Tools for Rice Transcriptome Analyses

Gene expression databases provide profiling data for the expression of thousands of genes to researchers worldwide. Oligonucleotide microarray technology is a useful tool that has been employed to produce gene expression profiles in most species. In rice, there are five genome-wide DNA microarray platforms: NSF 45K, BGI/Yale 60K, Affymetrix, Agilent Rice 44K, and NimbleGen 390K. Presently, more than 1,700 hybridizations of microarray gene expression data are available from public microarray depositing databases such as NCBI gene expression omnibus and Arrayexpress at EBI. More processing or reformatting of public gene expression data is required for further applications or analyses. Web-based databases for expression meta-analyses are useful for guiding researchers in designing relevant research schemes. In this review, we summarize various databases for expression meta-analyses of rice genes and web tools for further applications, such as the development of co-expression network or functional gene network.     

Plant–Pathogen Interactions: What Microarray Tells About It?

Plant defense responses are mediated by elementary regulatory proteins that affect expression of thousands of genes. Over the last decade, microarray technology has played a key role in deciphering the underlying networks of gene regulation in plants that lead to a wide variety of defence responses. Microarray is an important tool to quantify and profile the expression of thousands of genes simultaneously, with two main aims: (1) gene discovery and (2) global expression profiling. Several microarray technologies are currently in use; most include a glass slide platform with spotted cDNA or oligonucleotides. Till date, microarray technology has been used in the identification of regulatory genes, end-point defence genes, to understand the signal transduction processes underlying disease resistance and its intimate links to other physiological pathways. Microarray technology can be used for in-depth, simultaneous profiling of host/pathogen genes as the disease progresses from infection to resistance/susceptibility at different developmental stages of the host, which can be done in different environments, for clearer understanding of the processes involved. A thorough knowledge of plant disease resistance using successful combination of microarray and other high throughput techniques, as well as biochemical, genetic, and cell biological experiments is needed for practical application to secure and stabilize yield of many crop plants. This review starts with a brief introduction to microarray technology, followed by the basics of plant–pathogen interaction, the use of DNA microarrays over the last decade to unravel the mysteries of plant–pathogen interaction, and ends with the future prospects of this technology.     

Mutant-specific gene expression profiling identifies SRY-related HMG box 11b (SOX11b) as a novel regulator of vascular development in zebrafish

Previous studies have identified two zebrafish mutants, cloche and groom of cloche, which lack the majority of the endothelial lineage at early developmental stages. However, at later stages, these avascular mutant embryos generate rudimentary vessels, indicating that they retain the ability to generate endothelial cells despite this initial lack of endothelial progenitors. To further investigate molecular mechanisms that allow the emergence of the endothelial lineage in these avascular mutant embryos, we analyzed the gene expression profile using microarray analysis on isolated endothelial cells. We find that the expression of the genes characteristic of the mesodermal lineages are substantially elevated in the kdrl ⁺ cells isolated from avascular mutant embryos. Subsequent validation and analyses of the microarray data identifies Sox11b, a zebrafish ortholog of SRY-related HMG box 11 (SOX11), which have not previously implicated in vascular development. We further define the function sox11b during vascular development, and find that Sox11b function is essential for developmental angiogenesis in zebrafish embryos, specifically regulating sprouting angiogenesis. Taken together, our analyses illustrate a complex regulation of endothelial specification and differentiation during vertebrate development.     

Mixture modelling of gene expression data from microarray experiments

MOTIVATION: Hierarchical clustering is one of the major analytical tools for gene expression data from microarray experiments. A major problem in the interpretation of the output from these procedures is assessing the reliability of the clustering results. We address this issue by developing a mixture model-based approach for the analysis of microarray data. Within this framework, we present novel algorithms for clustering genes and samples. One of the byproducts of our method is a probabilistic measure for the number of true clusters in the data. RESULTS: The proposed methods are illustrated by application to microarray datasets from two cancer studies; one in which malignant melanoma is profiled (Bittner et al., Nature, 406, 536-540, 2000), and the other in which prostate cancer is profiled (Dhanasekaran et al., 2001, submitted).     

Gene profiling for determining pluripotent genes in a time course microarray experiment

In microarray experiments, it is often of interest to identifygenes which have a prespecified gene expression profile withrespect to time. Methods available in the literature are, however,typically not stringent enough in identifying such genes, particularlywhen the profile requires equivalence of gene expression levelsat certain time points. In this paper, the authors introducea new methodology, called gene profiling, that uses simultaneousdifferential and equivalent gene expression level testing torank genes according to a prespecified gene expression profile.Gene profiling treats the vector of true gene expression levelsas a linear combination of appropriate vectors, for example,vectors that give the required criteria for the profile. Thisgene profile model is fitted to the data, and the resultingparameter estimates are summarized in a single test statisticthat is then used to rank the genes. The theoretical underpinningsof gene profiling (equivalence testing, intersection–uniontests) are discussed in this paper, and the gene profiling methodologyis applied to our motivating stem-cell experiment.