On the simultaneous use of clinical and microarray expression data in the cluster analysis of tissue samples

This paper considers a model-based approach to the clustering of tissue samples of a very large number of genes from microarray experiments. It is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. Frequently in practice, there are also clinical data available on those cases on which the tissue samples have been obtained. Here we investigate how to use the clinical data in conjunction with the microarray gene expression data to cluster the tissue samples. We propose two mixture model-based approaches in which the number of components in the mixture model corresponds to the number of clusters to be imposed on the tissue samples. One approach specifies the components of the mixture model to be the conditional distributions of the microarray data given the clinical data with the mixing proportions also conditioned on the latter data. Another takes the components of the mixture model to represent the joint distributions of the clinical and microarray data. The approaches are demonstrated on some breast cancer data, as studied recently in van't Veer et al. (2002).

Tissue-specific gene expression in soybean (Glycine max) detected by cDNA microarray analysis

We have constructed cDNA microarrays for soybean (Glycine max L. Merrill), containing approximately 4,100 Unigene ESTs derived from axenic roots, to evaluate their application and utility for functional genomics of organ differentiation in legumes. We assessed microarray technology by conducting studies to evaluate the accuracy of microarray data and have found them to be both reliable and reproducible in repeat hybridisations. Several ESTs showed high levels (>50 fold) of differential expression in either root or shoot tissue of soybean. A small number of physiologically interesting, and differentially expressed sequences found by microarray analysis were verified by both quantitative real-time RT-PCR and Northern blot analysis. There was a linear correlation (r(2) = 0.99, over 5 orders of magnitude) between microarray and quantitative real-time RT-PCR data. Microarray analysis of soybean has enormous potential not only for the discovery of new genes involved in tissue differentiation and function, but also to study the expression of previously characterised genes, gene networks and gene interactions in wild-type, mutant or transgenic; plants.

Genetic study of alcoholism and novel gene expression in the alcoholic brain

Alcohol dependence may result from neuroadaptation involving alteration of gene expression after long-term alcohol exposure. The systematic study of gene expression profiles of the human alcoholic brain was initiated using the method of polymerase chain reaction (PCR)-differential display and was followed by DNA microarray. To date, more than 100 alcohol-responsive genes have been identified from the frontal cortex, motor cortex and nucleus accumbens of the human brain. These genes have a wide range of functions in the brain and indicate diverse actions of alcohol on neuronal function. This review discusses the current information on the genetic basis of alcoholism and the induction and characterization of these alcohol-responsive genes.

Identifying the molecular phenotype of renal progenitor cells

Although many of the molecular interactions in kidney development are now well understood, the molecules involved in the specification of the metanephric mesenchyme from surrounding intermediate mesoderm and, hence, the formation of the renal progenitor population are poorly characterized. In this study, cDNA microarrays were used to identify genes enriched in the murine embryonic day 10.5 (E10.5) uninduced metanephric mesenchyme, the renal progenitor population, in comparison with more rostral derivatives of the intermediate mesoderm. Microarray data were analyzed using R statistical software to determine accurately genes differentially expressed between these populations. Microarray outliers were biologically verified, and the spatial expression pattern of these genes at E10.5 and subsequent stages of early kidney development was determined by RNA in situ hybridization. This approach identified 21 genes preferentially expressed by the E10.5 metanephric mesenchyme, including Ewing sarcoma homolog, 14-3-3 theta, retinoic acid receptor-alpha, stearoyl-CoA desaturase 2, CD24, and cadherin-11, that may be important in formation of renal progenitor cells. Cell surface proteins such as CD24 and cadherin-11 that were strongly and specifically expressed in the uninduced metanephric mesenchyme and mark the renal progenitor population may prove useful in the purification of renal progenitor cells by FACS. These findings may assist in the isolation and characterization of potential renal stem cells for use in cellular therapies for kidney disease.

Cell cycle alterations in biopsied olfactory neuroepithelium in schizophrenia and bipolar I disorder using cell culture and gene expression analyses

We previously demonstrated that olfactory cultures front individuals with schizophrenia had increased cell proliferation compared to Cultures from healthy controls. The aims of this study were to (a) replicate this observation in a new group Of individuals with schizophrenia, (b) examine the specificity of these findings by including individuals with bipolar I disorder and (c) explore gene expression differences that may underlie cell cycle differences in these diseases. Compared to controls (n = 10), there was significantly more mitosis in schizophrenia patient cultures (it = 8) and significantly more cell death in the bipolar I disorder patient cultures (n=8). Microarray data showed alterations to the cell cycle and phosphatidylinositol signalling pathways in schizophrenia and bipolar I disorder, respectively. Whilst caution is required in the interpretation of the array results, the study provides evidence indicating that cell proliferation and cell death in olfactory neuroepithelial cultures is differentially altered in schizophrenia and bipolar disorder. (c) 2005 Elsevier B.V. All rights reserved.

Development of the Minimum Information Specification for in situ Hybridization and Immunohistochemistry Experiments (MISFISHIE)

We describe the creation process of the Minimum Information Specification for In Situ Hybridization and Immunohistochemistry Experiments (MISFISHIE). Modeled after the existing minimum information specification for microarray data, we created a new specification for gene expression localization experiments, initially to facilitate data sharing within a consortium. After successful use within the consortium, the specification was circulated to members of the wider biomedical research community for comment and refinement. After a period of acquiring many new suggested requirements, it was necessary to enter a final phase of excluding those requirements that were deemed inappropriate as a minimum requirement for all experiments. The full specification will soon be published as a version 1.0 proposal to the community, upon which a more full discussion must take place so that the final specification may be achieved with the involvement of the whole community. This paper is part of the special issue of OMICS on data standards.

Real-Time Gene-Expression Extraction with Fast Classification (FC) Networks

Fast Classification (FC) networks were inspired by a biologically plausible mechanism for short term memory where learning occurs instantaneously. Both weights and the topology for an FC network are mapped directly from the training samples by using a prescriptive training scheme. Only two presentations of the training data are required to train an FC network. Compared with iterative learning algorithms such as Back-propagation (which may require many hundreds of presentations of the training data), the training of FC networks is extremely fast and learning convergence is always guaranteed. Thus FC networks may be suitable for applications where real-time classification is needed. In this paper, the FC networks are applied for the real-time extraction of gene expressions for Chlamydia microarray data. Both the classification performance and learning time of the FC networks are compared with the Multi-Layer Proceptron (MLP) networks and support-vector-machines (SVM) in the same classification task. The FC networks are shown to have extremely fast learning time and comparable classification accuracy.