Prediction of temporal gene expression. Metabolic opimization by re-distribution of enzyme activities.

A computational approach is used to analyse temporal gene expression in the context of metabolic regulation. It is based on the assumption that cells developed optimal adaptation strategies to changing environmental conditions. Time-dependent enzyme profiles are calculated which optimize the function of a metabolic pathway under the constraint of limited total enzyme amount. For linear model pathways it is shown that wave-like enzyme profiles are optimal for a rapid substrate turnover. For the central metabolism of yeast cells enzyme profiles are calculated which ensure long-term homeostasis of key metabolites under conditions of a diauxic shift. These enzyme profiles are in close correlation with observed gene expression data. Our results demonstrate that optimality principles help to rationalize observed gene expression profiles.     

Linear modes of gene expression determined by independent component analysis

MOTIVATION: The expression of genes is controlled by specific combinations of cellular variables. We applied Independent Component Analysis (ICA) to gene expression data, deriving a linear model based on hidden variables, which we term 'expression modes'. The expression of each gene is a linear function of the expression modes, where, according to the ICA model, the linear influences of different modes show a minimal statistical dependence, and their distributions deviate sharply from the normal distribution. RESULTS: Studying cell cycle-related gene expression in yeast, we found that the dominant expression modes could be related to distinct biological functions, such as phases of the cell cycle or the mating response. Analysis of human lymphocytes revealed modes that were related to characteristic differences between cell types. With both data sets, the linear influences of the dominant modes showed distributions with large tails, indicating the existence of specifically up- and downregulated target genes. The expression modes and their influences can be used to visualize the samples and genes in low-dimensional spaces. A projection to expression modes helps to highlight particular biological functions, to reduce noise, and to compress the data in a biologically sensible way.     

AtGGM2014, an Arabidopsis gene co-expression network for functional studies

Gene co-expression networks provide an important tool for systems biology studies. Using microarray data from the Array Express database, we constructed an Arabidopsis gene co-expression network, termed At GGM2014, based on the graphical Gaussian model, which contains 102,644 co-expression gene pairs among 18,068 genes. The network was grouped into 622 gene co-expression modules. These modules function in diverse house-keeping, cell cycle, development, hormone response, metabolism, and stress response pathways. We developed a tool to facilitate easy visualization of the expression patterns of these modules either in a tissue context or their regulation under different treatment conditions. The results indicate that at least six modules with tissue-specific expression pattern failed to record modular regulation under various stress conditions. This discrepancy could be best explained by the fact that experiments to study plant stress responses focused mainly on leaves and less on roots, and thus failed to recover specific regulation pattern in other tissues. Overall, the modular structures revealed by our network provide extensive information to generate testable hypotheses about diverse plant signaling pathways. At GGM2014 offers a constructive tool for plant systems biology studies.     

Comparative analysis of gene expression profiles of acute hepatic failure and that of liver regeneration in rat

To explore the relevance of rat liver regeneration (LR) to acute hepatic failure (AHF), Rat Genome 230 2.0 Array was used to detect their gene expression profiles in this study, and the reliability of the detection results was confirmed by real-time-PCR. 1012 genes were found to be significantly changed in AHF occurrence and 948 genes in LR. Hierarchical clustering analysis showed that physiological activities of AHF and those of LR had no time correlation. Hierarchical clustering analysis (which is performed to group genes based on the similarity of expression patterns) showed that physiological activities of AHF and those of LR had no time correlation. K-means clustering analysis (which is used to check the difference in the relevant predictor variables between different groups is significant or not) demonstrated that gene expression trend of C1 group (genes relate to categories of stimulus–response and cell apoptosis, etc.) in AHF and in LR was extremely similar, that those of their C2 group (categories of regulation of homeostasis and hormone stimulation, etc.) were contrary, and that those of their C3 (material and energy metabolism and oxidation reduction, etc.), C4 (Cell cycle-related genes) and C5 (cell proliferation-related genes) groups were also similar with the gene expression changes of LR more abundant. GO classifications and functional clustering analysis (which was used to statistics the numbers or composition of proteins or genes at a function level) revealed that cellular processes including immune response, inflammatory reaction, cell migration and adhesion, etc. were increased both in AHF and in LR, whereas material and energy metabolism were decreased. Of them, stimulus response, inflammatory reaction and regulation of apoptosis, etc. were stronger in AHF occurrence than in LR, but ion homeostasis, hormonal response, regulation of cell division and proliferation, etc. were weaker in AHF occurrence. Gene expression changes and physiological activities of AHF and those of LR not only have similarities but also differences.     

RNA-dependent regulation of the cell wall stress response

Stress response requires the precise modulation of gene expression in response to changes in growth conditions. This report demonstrates that selective nuclear mRNA degradation is required for both the cell wall stress response and the regulation of the cell wall integrity checkpoint. More specifically, the deletion of the yeast nuclear dsRNA-specific ribonuclease III (Rnt1p) increased the expression of the mRNAs associated with both the morphogenesis checkpoint and the cell wall integrity pathway, leading to an attenuation of the stress response. The over-expression of selected Rnt1p substrates, including the stress associated morphogenesis protein kinase Hsl1p, in wild-type cells mimicked the effect of RNT1 deletion on cell wall integrity, and their mRNAs were directly cleaved by the recombinant enzyme in vitro. The data supports a model for gene regulation in which nuclear mRNA degradation optimizes the cell response to stress and links it to the cell cycle.     

A patient-gene model for temporal expression profiles in clinical studies.

Pharmacogenomics and clinical studies that measure the temporal expression levels of patients can identify important pathways and biomarkers that are activated during disease progression or in response to treatment. However, researchers face a number of challenges when trying to combine expression profiles from these patients. Unlike studies that rely on lab animals or cell lines, individuals vary in their baseline expression and in their response rate. In this paper we present a generative model for such data. Our model represents patient expression data using two levels, a gene level, which corresponds to a common response pattern, and a patient level, which accounts for the patient specific expression patterns and response rate. Using an EM algorithm, we infer the parameters of the model. We used our algorithm to analyze multiple sclerosis patient response to interferon-beta. As we show, our algorithm was able to improve upon prior methods for combining patients data. In addition, our algorithm was able to correctly identify patient specific response patterns.     

Introns within ribosomal protein genes regulate the production and function of yeast ribosomes

In budding yeast, the most abundantly spliced pre-mRNAs encode ribosomal proteins (RPs). To investigate the contribution of splicing to ribosome production and function, we systematically eliminated introns from all RP genes to evaluate their impact on RNA expression, pre-rRNA processing, cell growth, and response to stress. The majority of introns were required for optimal cell fitness or growth under stress. Most introns are found in duplicated RP genes, and surprisingly, in the majority of cases, deleting the intron from one gene copy affected the expression of the other in a nonreciprocal manner. Consistently, 70% of all duplicated genes were asymmetrically expressed, and both introns and gene deletions displayed copy-specific phenotypic effects. Together, our results indicate that splicing in yeast RP genes mediates intergene regulation and implicate the expression ratio of duplicated RP genes in modulating ribosome function.     

Circadian variation of the response of T cells to antigen

Circadian clocks regulate many important aspects of physiology, and their disturbance leads to various medical conditions. Circadian variations have been found in immune system variables, including daily rhythms in circulating WBC numbers and serum concentration of cytokines. However, control of immune functional responses by the circadian clock has remained relatively unexplored. In this study, we show that mouse lymph nodes exhibit rhythmic clock gene expression. T cells from lymph nodes collected over 24 h show a circadian variation in proliferation after stimulation via the TCR, which is blunted in Clock gene mutant mice. The tyrosine kinase ZAP70, which is just downstream of the TCR in the T cell activation pathway and crucial for T cell function, exhibits rhythmic protein expression. Lastly, mice immunized with OVA peptide-loaded dendritic cells in the day show a stronger specific T cell response than mice immunized at night. These data reveal circadian control of the Ag-specific immune response and a novel regulatory mode of T cell proliferation, and may provide clues for more efficient vaccination strategies.     

Modelling regulatory pathways in E. coli from time series expression profiles

MOTIVATION: Cells continuously reprogram their gene expression network as they move through the cell cycle or sense changes in their environment. In order to understand the regulation of cells, time series expression profiles provide a more complete picture than single time point expression profiles. Few analysis techniques, however, are well suited to modelling such time series data. RESULTS: We describe an approach that naturally handles time series data with the capabilities of modelling causality, feedback loops, and environmental or hidden variables using a Dynamic Bayesian network. We also present a novel way of combining prior biological knowledge and current observations to improve the quality of analysis and to model interactions between sets of genes rather than individual genes. Our approach is evaluated on time series expression data measured in response to physiological changes that affect tryptophan metabolism in E. coli. Results indicate that this approach is capable of finding correlations between sets of related genes.     

Automatic discovery of regulatory patterns in promoter regions based on whole cell expression data and functional annotation

MOTIVATION: The whole genomes submitted to GenBank contain valuable information about the function of genes as well as the upstream sequences and whole cell expression provides valuable information on gene regulation. To utilize these large amounts of data for a biological understanding of the regulation of gene expression, new automatic methods for pattern finding are needed. RESULTS: Two word-analysis algorithms for automatic discovery of regulatory sequence elements have been developed. We show that sequence patterns correlated to whole cell expression data can be found using Kolmogorov-Smirnov tests on the raw data, thereby eliminating the need for clustering co-regulated genes. Regulatory elements have also been identified by systematic calculations of the significance of correlations between words found in the functional annotation of genes and DNA words occurring in their promoter regions. Application of these algorithms to the Saccharomyces cerevisiae genome and publicly available DNA array data sets revealed a highly conserved 9-mer occurring in the upstream regions of genes coding for proteasomal subunits. Several other putative and known regulatory elements were also found. AVAILABILITY: Upon request.