Gene expression analysis by transcript profiling coupled to a gene database query.

We describe an mRNA profiling technique for determining differential gene expression that utilizes, but does not require, prior knowledge of gene sequences. This method permits high-throughput reproducible detection of most expressed sequences with a sensitivity of greater than 1 part in 100,000. Gene identification by database query of a restriction endonuclease fingerprint, confirmed by competitive PCR using gene-specific oligonucleotides, facilitates gene discovery by minimizing isolation procedures. This process, called GeneCalling, was validated by analysis of the gene expression profiles of normal and hypertrophic rat hearts following in vivo pressure overload.     

Gene expression profiling of human diseases by serial analysis of gene expression

Until recently, the approach to understanding the molecular basis of complex syndromes such as cancer, coronary artery disease, and diabetes was to study the behavior of individual genes. However, it is generally recognized that expression of a number of genes is coordinated both spatially and temporally and that this coordination changes during the development and progression of diseases. Newly developed functional genomic approaches, such as serial analysis of gene expression (SAGE) and DNA microarrays have enabled researchers to determine the expression pattern of thousands of genes simultaneously. One attractive feature of SAGE compared to microarrays is its ability to quantify gene expression without prior sequence information or information about genes that are thought to be expressed. SAGE has been successfully applied to the gene expression profiling of a number of human diseases. In this review, we will first discuss SAGE technique and contrast it to microarray. We will then highlight new biological insights that have emerged from its application to the study of human diseases.     

On predicting medulloblastoma metastasis by gene expression profiling

Accurately predicting clinical outcome or metastatic status from gene expression profiles remains one of the biggest hurdles facing the adoption of predictive medicine. Recently, MacDonald et al. (Nat. Genet. 2001, 29, 143-152) used gene expression profiles, from samples taken at diagnosis, to distinguish between clinically designated metastatic and nonmetastatic primary medulloblastomas, helping to elucidate the genetic mechanisms underlying metastasis and suggesting novel therapeutic targets. The obtained accuracy of predicting metastatic status does not, however, reach statistical significance on Fisher's exact test, although 22 training samples were used to make each prediction via leave-one-out testing. This paper introduces readily implemented nonlinear filters to transform sequences of gene expression levels into output signals that are significantly easier to classify and predict metastasis. It is shown that when only 3 exemplars each from the metastatic and nonmetastatic classes were assumed known, a predictor was constructed whose accuracy is statistically significant over the remaining profiles set aside as a test set. The predictor was as effective in recognizing metastatic as nonmetastatic medulloblastomas, and may be helpful in deciding which patients require more aggressive therapy. The same predictor was similarly effective on an independent set of 5 nonmetastatic tumors and 3 metastatic cell lines also used by MacDonald et al.     

Comparative gene expression profiling by oligonucleotide fingerprinting.

The use of hybridisation of synthetic oligonucleotides to cDNAs under high stringency to characterise gene sequences has been demonstrated by a number of groups. We have used two cDNA libraries of 9 and 12 day mouse embryos (24 133 and 34 783 clones respectively) in a pilot study to characterise expressed genes by hybridisation with 110 hybridisation probes. We have identified 33 369 clusters of cDNA clones, that ranged in representation from 1 to 487 copies (0.7%). 737 were assigned to known rodent genes, and a further 13 845 showed significant homologies. A total of 404 clusters were identified as significantly differentially represented (P < 0.01) between the two cDNA libraries. This study demonstrates the utility of the fingerprinting approach for the generation of comparative gene expression profiles through the analysis of cDNAs derived from different biological materials.     

Partition decoupling for multi-gene analysis of gene expression profiling data

Background

Molecular dynamics (MD) simulations are powerful tools to investigate the conformational dynamics of proteins that is often a critical element of their function. Identification of functionally relevant conformations is generally done clustering the large ensemble of structures that are generated. Recently, Self-Organising Maps (SOMs) were reported performing more accurately and providing more consistent results than traditional clustering algorithms in various data mining problems. We present a novel strategy to analyse and compare conformational ensembles of protein domains using a two-level approach that combines SOMs and hierarchical clustering.

Results

The conformational dynamics of the α-spectrin SH3 protein domain and six single mutants were analysed by MD simulations. The Cα's Cartesian coordinates of conformations sampled in the essential space were used as input data vectors for SOM training, then complete linkage clustering was performed on the SOM prototype vectors. A specific protocol to optimize a SOM for structural ensembles was proposed: the optimal SOM was selected by means of a Taguchi experimental design plan applied to different data sets, and the optimal sampling rate of the MD trajectory was selected. The proposed two-level approach was applied to single trajectories of the SH3 domain independently as well as to groups of them at the same time. The results demonstrated the potential of this approach in the analysis of large ensembles of molecular structures: the possibility of producing a topological mapping of the conformational space in a simple 2D visualisation, as well as of effectively highlighting differences in the conformational dynamics directly related to biological functions.

Conclusions

The use of a two-level approach combining SOMs and hierarchical clustering for conformational analysis of structural ensembles of proteins was proposed. It can easily be extended to other study cases and to conformational ensembles from other sources.     

Global gene expression profiling and cluster analysis in Xenopus laevis

We have undertaken a large-scale microarray gene expression analysis using cDNAs corresponding to 21,000 Xenopus laevis ESTs. mRNAs from 37 samples, including embryos and adult organs, were profiled. Cluster analysis of embryos of different stages was carried out and revealed expected affinities between gastrulae and neurulae, as well as between advanced neurulae and tadpoles, while egg and feeding larvae were clearly separated. Cluster analysis of adult organs showed some unexpected tissue-relatedness, e.g. kidney is more related to endodermal than to mesodermal tissues and the brain is separated from other neuroectodermal derivatives. Cluster analysis of genes revealed major phases of co-ordinate gene expression between egg and adult stages. During the maternal-early embryonic phase, genes maintaining a rapidly dividing cell state are predominantly expressed (cell cycle regulators, chromatin proteins). Genes involved in protein biosynthesis are progressively induced from mid-embryogenesis onwards. The larval-adult phase is characterised by expression of genes involved in metabolism and terminal differentiation. Thirteen potential synexpression groups were identified, which encompass components of diverse molecular processes or supra-molecular structures, including chromatin, RNA processing and nucleolar function, cell cycle, respiratory chain/Krebs cycle, protein biosynthesis, endoplasmic reticulum, vesicle transport, synaptic vesicle, microtubule, intermediate filament, epithelial proteins and collagen. Data filtering identified genes with potential stage-, region- and organ-specific expression. The dataset was assembled in the iChip microarray database, , which allows user-defined queries. The study provides insights into the higher order of vertebrate gene expression, identifies synexpression groups and marker genes, and makes predictions for the biological role of numerous uncharacterized genes.     

Integrated gene expression profiling and linkage analysis in the rat

The combined application of genome-wide expression profiling from microarray experiments with genetic linkage analysis enables the mapping of expression quantitative trait loci (eQTLs) which are primary control points for gene expression across the genome. This approach allows for the dissection of primary and secondary genetic determinants of gene expression. The cis-acting eQTLs in practice are easier to investigate than the trans-regulated eQTLs because they are under simpler genetic control and are likely to be due to sequence variants within the gene itself or its neighboring regulatory elements. These genes are therefore candidates both for variation in gene expression and for contributions to whole-body phenotypes, particularly when these are located within known and relevant physiologic QTLs. Multiple trans-acting eQTLs tend to cluster to the same genetic location, implying shared regulatory control mechanisms that may be amenable to network analysis to identify gene clusters within the same metabolic pathway. Such clusters may ultimately underlie development of individual complex, whole-body phenotypes. The combined expression and linkage approach has been applied successfully in several mammalian species, including the rat which has specific features that demonstrate its value as a model for studying complex traits.     

Rapid analysis of gene expression (RAGE) facilitates universal expression profiling.

Current techniques for analysis of gene expression either monitor one gene at a time, for example northern hybridization or RT-PCR methods, or are designed for the simultaneous analysis of thousands of genes, for example microarray hybridization or serial analysis of gene expression. To provide a flexible, intermediate scale alternative, a PCR-based method for the rapid analysis of gene expression has been developed which allows expression changes to be determined in either a directed search of known genes, or an undirected survey of unknown genes. A single set of reagents and reaction conditions allows analyses of most genes in any eukaryote. The method is useful for assaying on the order of tens to hundreds of genes in multiple samples. Control experiments indicate reliable detection of changes in gene expression 2-fold and greater, and sensitivity of detection better than 1 in 10 000. Analyses of over 400 genes in a mouse system transgenic for the E2F1 gene have identified several new downstream targets of E2F1, including Brca1 and Cdk7, in addition to several unidentified genes that are upregulated in the transgenic mice. Changes in expression of several genes related to apoptosis suggest a possible potentiation of apoptotic pathways in the transgenic keratinocytes.