PathAct: a novel method for pathway analysis using gene expression profiles

We developed PathAct, a novel method for pathway analysis to investigate the biological and clinical implications of the gene expression profiles. The advantage of PathAct in comparison with the conventional pathway analysis methods is that it can estimate pathway activity levels for individual patient quantitatively in the form of a pathway-by-sample matrix. This matrix can be used for further analysis such as hierarchical clustering and other analysis methods. To evaluate the feasibility of PathAct, comparison with frequently used gene-enrichment analysis methods was conducted using two public microarray datasets. The dataset #1 was that of breast cancer patients, and we investigated pathways associated with triple-negative breast cancer by PathAct, compared with those obtained by gene set enrichment analysis (GSEA). The dataset #2 was another breast cancer dataset with disease-free survival (DFS) of each patient. Contribution by each pathway to prognosis was investigated by our method as well as the Database for Annotation, Visualization and Integrated Discovery (DAVID) analysis. In the dataset #1, four out of the six pathways that satisfied p < 0.05 and FDR < 0.30 by GSEA were also included in those obtained by the PathAct method. For the dataset #2, two pathways (“Cell Cycle” and “DNA replication”) out of four pathways by PathAct were commonly identified by DAVID analysis. Thus, we confirmed a good degree of agreement among PathAct and conventional methods. Moreover, several applications of further statistical analyses such as hierarchical cluster analysis by pathway activity, correlation analysis and survival analysis between pathways were conducted.     

Combining gene annotations and gene expression data in model-based clustering: weighted method

It has been increasingly recognized that incorporating prior knowledge into cluster analysis can result in more reliable and meaningful clusters. In contrast to the standard modelbased clustering with a global mixture model, which does not use any prior information, a stratified mixture model was recently proposed to incorporate gene functions or biological pathways as priors in model-based clustering of gene expression profiles: various gene functional groups form the strata in a stratified mixture model. Albeit useful, the stratified method may be less efficient than the global analysis if the strata are non-informative to clustering. We propose a weighted method that aims to strike a balance between a stratified analysis and a global analysis: it weights between the clustering results of the stratified analysis and that of the global analysis; the weight is determined by data. More generally, the weighted method can take advantage of the hierarchical structure of most existing gene functional annotation systems, such as MIPS and Gene Ontology (GO), and facilitate choosing appropriate gene functional groups as priors. We use simulated data and real data to demonstrate the feasibility and advantages of the proposed method.     

Sequential DEXAS: a method for obtaining DNA sequences from genomic DNA and blood in one reaction

Sequential DEXAS (direct exponential amplification and sequencing), a one step amplification and sequencing procedure that allows accurate, inexpensive and rapid DNA sequence determination directly from genomic DNA, is described. This method relies on the simultaneous use of two DNA polymerases that differ both in their ability to incorporate dideoxynucleotides and in the time at which they are activated during the reaction. One enzyme, which incorporates deoxynucleotides and performs amplification of the target DNA sequence, is supplied in an active state whereas the other enzyme, which incorporates dideoxynucleotides and performs the sequencing reaction, is supplied in an inactive state but becomes activated by a temperature step during the thermocycling. Thus, in the initial stage of the reaction, target amplification occurs, while in the second stage the sequencing reaction takes place. We show that Sequential DEXAS yields high quality sequencing results directly from genomic DNA as well as directly from human blood without any prior isolation or purification of DNA.     

Analysis of a Gibbs sampler method for model-based clustering of gene expression data

MOTIVATION: Over the last decade, a large variety of clustering algorithms have been developed to detect coregulatory relationships among genes from microarray gene expression data. Model-based clustering approaches have emerged as statistically well-grounded methods, but the properties of these algorithms when applied to large-scale data sets are not always well understood. An in-depth analysis can reveal important insights about the performance of the algorithm, the expected quality of the output clusters, and the possibilities for extracting more relevant information out of a particular data set. RESULTS: We have extended an existing algorithm for model-based clustering of genes to simultaneously cluster genes and conditions, and used three large compendia of gene expression data for Saccharomyces cerevisiae to analyze its properties. The algorithm uses a Bayesian approach and a Gibbs sampling procedure to iteratively update the cluster assignment of each gene and condition. For large-scale data sets, the posterior distribution is strongly peaked on a limited number of equiprobable clusterings. A GO annotation analysis shows that these local maxima are all biologically equally significant, and that simultaneously clustering genes and conditions performs better than only clustering genes and assuming independent conditions. A collection of distinct equivalent clusterings can be summarized as a weighted graph on the set of genes, from which we extract fuzzy, overlapping clusters using a graph spectral method. The cores of these fuzzy clusters contain tight sets of strongly coexpressed genes, while the overlaps exhibit relations between genes showing only partial coexpression. AVAILABILITY: GaneSh, a Java package for coclustering, is available under the terms of the GNU General Public License from our website at http://bioinformatics.psb.ugent.be/software     

Five small heat shock protein genes from Chilo suppressalis: characteristics of gene, genomic organization, structural analysis, and transcription profiles

Small heat shock proteins (sHSPs) are the most diverse but also the most poorly known family of molecular chaperones, and they play essential roles in various biological processes. The striped stem borer, Chilo suppressalis (Insecta: Lepidoptera: Pyralidae), is one of the most serious pests of rice, causing extensive damage and yield loss. In this study, we isolated and characterized five members of the sHSPs family—Cshsp19.8, Cshsp21.4, Cshsp21.5, Cshsp21.7a, and Cshsp21.7b—from C. suppressalis. The cDNAs of these genes encoded proteins of 177, 187, 191, 191, and 191 amino acids with isoelectric points of 7.0, 5.6, 6.1, 6.3, and 6.3, respectively. While Cshsp19.8, Cshsp21.5, and Cshsp21.7b had no introns, Cshsp21.4 and Cshsp21.7a contained one and two introns, respectively. Structural analysis indicated that all five Cshsps possessed conserved arginine and a V/IXI/V motif, which is related to hydrophobic characteristics of sHSPs. The five heat shock proteins can be classified into two main groups: an orthologous type (Cshsp21.4 and Cshsp21.7a) and a species-specific type (Cshsp19.8, Cshsp21.5, and Cshsp21.7b). Real-time quantitative PCR analyses revealed that Cshsp19.8, Cshsp21.5, Cshsp21.7a, and Cshsp21.7b all exhibited their highest expression levels within Malpighian tubules or the hindgut, while such levels were found in the head for Cshsp21.4. The expression of Csshsps at different developmental stages revealed that the mRNA levels of Cshsp19.8, Cshsp21.4, Cshsp21.5, and Cshsp21.7b peaked in adults, whereas the highest level of Cshsp21.7a was observed in first instar larvae. Cshsp19.8 and Cshsp21.7b were both upregulated dramatically by heat and cold, and Cshsp21.5 could be induced by cold stress. Neither Cshsp21.4 nor Cshsp21.7a responded to heat or cold. These results demonstrated that different Csshsps play distinctive roles in the regulation of the physiological activities in C. suppressalis.     

Global gene expression during the human organogenesis: from transcription profiles to function predictions

Human embryogenesis includes an integrated set of complex yet coordinated development of different organs and tissues, which is regulated by the spatiotemporal expression of many genes. Deciphering the gene regulation profile is essential for understanding the molecular basis of human embryo development. While molecular and genetic studies in mouse have served as a valuable tool to understand mammalian development, significant differences exists in human and mouse development at morphological and genomic levels. Thus it is important to carry out research directly on human embryonic development. Here we will review some recent studies on gene regulation during human embryogenesis with particular focus on the period of organogenesis, which had not been well studied previously. We will highlight a gene expression database of human embryos from the 4(th) to the 9(th) week. The analysis of gene regulation during this period reveals that genes functioning in a given developmental process tend to be coordinately regulated during human embryogenesis. This feature allows us to use this database to identify new genes important for a particular developmental process/pathway and deduce the potential function of a novel gene during organogenesis. Such a gene expression atlas should serve as an important resource for molecular study of human development and pathogenesis.     

Genomic and proteomic profiling II: Comparative assessment of gene expression profiles in leiomyomas, keloids, and surgically-induced scars

Background  

Leiomyoma have often been compared to keloids because of their fibrotic characteristic and higher rate of occurrence among African Americans as compared to other ethnic groups. To evaluate such a correlation at molecular level this study comparatively analyzed leiomyomas with keloids, surgical scars and peritoneal adhesions to identify genes that are either commonly and/or individually distinguish these fibrotic disorders despite differences in the nature of their development and growth.