Characterization and Comparison of the Tissue-Related Modules in Human and Mouse

BACKGROUND: Due to the advances of high throughput technology and data-collection approaches, we are now in an unprecedented position to understand the evolution of organisms. Great efforts have characterized many individual genes responsible for the interspecies divergence, yet little is known about the genome-wide divergence at a higher level. Modules, serving as the building blocks and operational units of biological systems, provide more information than individual genes. Hence, the comparative analysis between species at the module level would shed more light on the mechanisms underlying the evolution of organisms than the traditional comparative genomics approaches. RESULTS: We systematically identified the tissue-related modules using the iterative signature algorithm (ISA), and we detected 52 and 65 modules in the human and mouse genomes, respectively. The gene expression patterns indicate that all of these predicted modules have a high possibility of serving as real biological modules. In addition, we defined a novel quantity, "total constraint intensity," a proxy of multiple constraints (of co-regulated genes and tissues where the co-regulation occurs) on the evolution of genes in module context. We demonstrate that the evolutionary rate of a gene is negatively correlated with its total constraint intensity. Furthermore, there are modules coding the same essential biological processes, while their gene contents have diverged extensively between human and mouse. CONCLUSIONS: Our results suggest that unlike the composition of module, which exhibits a great difference between human and mouse, the functional organization of the corresponding modules may evolve in a more conservative manner. Most importantly, our findings imply that similar biological processes can be carried out by different sets of genes from human and mouse, therefore, the functional data of individual genes from mouse may not apply to human in certain occasions

Evolution of a Membrane Protein Regulon in Saccharomyces

Expression variation is widespread between species. The ability to distinguish regulatory change driven by natural selection from the consequences of neutral drift remains a major challenge in comparative genomics. In this work, we used observations of mRNA expression and promoter sequence to analyze signatures of selection on groups of functionally related genes in Saccharomycete yeasts. In a survey of gene regulons with expression divergence between Saccharomyces cerevisiae and S. paradoxus, we found that most were subject to variation in trans-regulatory factors that provided no evidence against a neutral model. However, we identified one regulon of membrane protein genes controlled by unlinked cis- and trans-acting determinants with coherent effects on gene expression, consistent with a history of directional, nonneutral evolution. For this membrane protein group, S. paradoxus alleles at regulatory loci were associated with elevated expression and altered stress responsiveness relative to other yeasts. In a phylogenetic comparison of promoter sequences of the membrane protein genes between species, the S. paradoxus lineage was distinguished by a short branch length, indicative of strong selective constraint. Likewise, sequence variants within the S. paradoxus population, but not across strains of other yeasts, were skewed toward low frequencies in promoters of genes in the membrane protein regulon, again reflecting strong purifying selection. Our results support a model in which a distinct expression program for the membrane protein genes in S. paradoxus has been preferentially maintained by negative selection as the result of an increased importance to organismal fitness. These findings illustrate the power of integrating expression- and sequence-based tests of natural selection in the study of evolutionary forces that underlie regulatory change

Virtual Mutagenesis of the Yeast Cyclins Genetic Network Reveals Complex Dynamics of Transcriptional Control Networks

Study of genetic networks has moved from qualitative description of interactions between regulators and regulated genes to the analysis of the interaction dynamics. This paper focuses on the analysis of dynamics of one particular network – the yeast cyclins network. Using a dedicated mathematical model of gene expression and a procedure for computation of the parameters of the model from experimental data, a complete numerical model of the dynamics of the cyclins genetic network was attained. The model allowed for performing virtual experiments on the network and observing their influence on the expression dynamics of the genes downstream in the regulatory cascade. Results show that when the network structure is more complicated, and the regulatory interactions are indirect, results of gene deletion are highly unpredictable. As a consequence of quantitative behavior of the genes and their connections within the network, causal relationship between a regulator and target gene may not be discovered by gene deletion. Without including the dynamics of the system into the network, its functional properties cannot be studied and interpreted correctly

svdPPCS: an effective singular value decomposition-based method for conserved and divergent co-expression gene module identification

<p>Abstract</p> <p>Background</p> <p>Comparative analysis of gene expression profiling of multiple biological categories, such as different species of organisms or different kinds of tissue, promises to enhance the fundamental understanding of the universality as well as the specialization of mechanisms and related biological themes. Grouping genes with a similar expression pattern or exhibiting co-expression together is a starting point in understanding and analyzing gene expression data. In recent literature, gene module level analysis is advocated in order to understand biological network design and system behaviors in disease and life processes; however, practical difficulties often lie in the implementation of existing methods.</p> <p>Results</p> <p>Using the singular value decomposition (SVD) technique, we developed a new computational tool, named svdPPCS (<b>SVD</b>-based <b>P</b>attern <b>P</b>airing and <b>C</b>hart <b>S</b>plitting), to identify conserved and divergent co-expression modules of two sets of microarray experiments. In the proposed methods, gene modules are identified by splitting the two-way chart coordinated with a pair of left singular vectors factorized from the gene expression matrices of the two biological categories. Importantly, the cutoffs are determined by a data-driven algorithm using the well-defined statistic, SVD-p. The implementation was illustrated on two time series microarray data sets generated from the samples of accessory gland (ACG) and malpighian tubule (MT) tissues of the line W¹¹⁸of <it>M. drosophila</it>. Two conserved modules and six divergent modules, each of which has a unique characteristic profile across tissue kinds and aging processes, were identified. The number of genes contained in these models ranged from five to a few hundred. Three to over a hundred GO terms were over-represented in individual modules with FDR < 0.1. One divergent module suggested the tissue-specific relationship between the expressions of mitochondrion-related genes and the aging process. This finding, together with others, may be of biological significance. The validity of the proposed SVD-based method was further verified by a simulation study, as well as the comparisons with regression analysis and cubic spline regression analysis plus PAM based clustering.</p> <p>Conclusions</p> <p>svdPPCS is a novel computational tool for the comparative analysis of transcriptional profiling. It especially fits the comparison of time series data of related organisms or different tissues of the same organism under equivalent or similar experimental conditions. The general scheme can be directly extended to the comparisons of multiple data sets. It also can be applied to the integration of data sets from different platforms and of different sources.</p

Computational Analysis of Constraints on Noncoding Regions, Coding Regions and Gene Expression in Relation to Plasmodium Phenotypic Diversity

Malaria-causing Plasmodium species exhibit marked differences including host choice and preference for invading particular cell types. The genetic bases of phenotypic differences between parasites can be understood, in part, by investigating constraints on gene expression and genic sequences, both coding and regulatory.We investigated the evolutionary constraints on sequence and expression of parasitic genes by applying comparative genomics approaches to 6 Plasmodium genomes and 2 genome-wide expression studies. We found that the coding regions of Plasmodium transcription factor and sexual development genes are relatively less constrained, as are those of genes encoding CCCH zinc fingers and invasion proteins, which all play important roles in these parasites. Transcription factors and genes with stage-restricted expression have conserved upstream regions and so do several gene classes critical to the parasite's lifestyle, namely, ion transport, invasion, chromatin assembly and CCCH zinc fingers. Additionally, a cross-species comparison of expression patterns revealed that Plasmodium-specific genes exhibit significant expression divergence.Overall, constraints on Plasmodium's protein coding regions confirm observations from other eukaryotes in that transcription factors are under relatively lower constraint. Proteins relevant to the parasite's unique lifestyle also have lower constraint on their coding regions. Greater conservation between Plasmodium species in terms of promoter motifs suggests tight regulatory control of lifestyle genes. However, an interspecies divergence in expression patterns of these genes suggests that either expression is controlled via genomic or epigenomic features not encoded in the proximal promoter sequence, or alternatively, the combinatorial interactions between motifs confer species-specific expression patterns

Growth landscape formed by perception and import of glucose in yeast

An important challenge in systems biology is to quantitatively describe microbial growth using a few measurable parameters that capture the essence of this complex phenomenon. Two key events at the cell membrane—extracellular glucose sensing and uptake—initiate the budding yeast’s growth on glucose. However, conventional growth models focus almost exclusively on glucose uptake. Here we present results from growth-rate experiments that cannot be explained by focusing on glucose uptake alone. By imposing a glucose uptake rate independent of the sensed extracellular glucose level, we show that despite increasing both the sensed glucose concentration and uptake rate, the cell’s growth rate can decrease or even approach zero. We resolve this puzzle by showing that the interaction between glucose perception and import, not their individual actions, determines the central features of growth, and characterize this interaction using a quantitative model. Disrupting this interaction by knocking out two key glucose sensors significantly changes the cell’s growth rate, yet uptake rates are unchanged. This is due to a decrease in burden that glucose perception places on the cells. Our work shows that glucose perception and import are separate and pivotal modules of yeast growth, the interaction of which can be precisely tuned and measured.National Institutes of Health (U.S.). Pioneer AwardNatural Sciences and Engineering Research Council of Canada (NSERC). Graduate Fellowshi

QUBIC: a qualitative biclustering algorithm for analyses of gene expression data

Biclustering extends the traditional clustering techniques by attempting to find (all) subgroups of genes with similar expression patterns under to-be-identified subsets of experimental conditions when applied to gene expression data. Still the real power of this clustering strategy is yet to be fully realized due to the lack of effective and efficient algorithms for reliably solving the general biclustering problem. We report a QUalitative BIClustering algorithm (QUBIC) that can solve the biclustering problem in a more general form, compared to existing algorithms, through employing a combination of qualitative (or semi-quantitative) measures of gene expression data and a combinatorial optimization technique. One key unique feature of the QUBIC algorithm is that it can identify all statistically significant biclusters including biclusters with the so-called ‘scaling patterns’, a problem considered to be rather challenging; another key unique feature is that the algorithm solves such general biclustering problems very efficiently, capable of solving biclustering problems with tens of thousands of genes under up to thousands of conditions in a few minutes of the CPU time on a desktop computer. We have demonstrated a considerably improved biclustering performance by our algorithm compared to the existing algorithms on various benchmark sets and data sets of our own. QUBIC was written in ANSI C and tested using GCC (version 4.1.2) on Linux. Its source code is available at: http://csbl.bmb.uga.edu/∼maqin/bicluster. A server version of QUBIC is also available upon request

Parallel evolution of the make–accumulate–consume strategy in Saccharomyces and Dekkera yeasts

Saccharomyces yeasts degrade sugars to two-carbon components, in particular ethanol, even in the presence of excess oxygen. This characteristic is called the Crabtree effect and is the background for the 'make–accumulate–consume' life strategy, which in natural habitats helps Saccharomyces yeasts to out-compete other microorganisms. A global promoter rewiring in the Saccharomyces cerevisiae lineage, which occurred around 100 mya, was one of the main molecular events providing the background for evolution of this strategy. Here we show that the Dekkera bruxellensis lineage, which separated from the Saccharomyces yeasts more than 200 mya, also efficiently makes, accumulates and consumes ethanol and acetic acid. Analysis of promoter sequences indicates that both lineages independently underwent a massive loss of a specific cis-regulatory element from dozens of genes associated with respiration, and we show that also in D. bruxellensis this promoter rewiring contributes to the observed Crabtree effect

Using Pre-existing Microarray Datasets to Increase Experimental Power: Application to Insulin Resistance

Although they have become a widely used experimental technique for identifying differentially expressed (DE) genes, DNA microarrays are notorious for generating noisy data. A common strategy for mitigating the effects of noise is to perform many experimental replicates. This approach is often costly and sometimes impossible given limited resources; thus, analytical methods are needed which increase accuracy at no additional cost. One inexpensive source of microarray replicates comes from prior work: to date, data from hundreds of thousands of microarray experiments are in the public domain. Although these data assay a wide range of conditions, they cannot be used directly to inform any particular experiment and are thus ignored by most DE gene methods. We present the SVD Augmented Gene expression Analysis Tool (SAGAT), a mathematically principled, data-driven approach for identifying DE genes. SAGAT increases the power of a microarray experiment by using observed coexpression relationships from publicly available microarray datasets to reduce uncertainty in individual genes' expression measurements. We tested the method on three well-replicated human microarray datasets and demonstrate that use of SAGAT increased effective sample sizes by as many as 2.72 arrays. We applied SAGAT to unpublished data from a microarray study investigating transcriptional responses to insulin resistance, resulting in a 50% increase in the number of significant genes detected. We evaluated 11 (58%) of these genes experimentally using qPCR, confirming the directions of expression change for all 11 and statistical significance for three. Use of SAGAT revealed coherent biological changes in three pathways: inflammation, differentiation, and fatty acid synthesis, furthering our molecular understanding of a type 2 diabetes risk factor. We envision SAGAT as a means to maximize the potential for biological discovery from subtle transcriptional responses, and we provide it as a freely available software package that is immediately applicable to any human microarray study