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Pairing of competitive and topologically distinct regulatory modules enhances patterned gene expression.

ABSTRACT: Biological networks are inherently modular, yet little is known about how modules are assembled to enable coordinated and complex functions. We used RNAi and time series, whole-genome microarray analyses to systematically perturb and characterize components of a Caenorhabditis elegans lineage-specific transcriptional regulatory network. These data are supported by selected reporter gene analyses and comprehensive yeast one-hybrid and promoter sequence analyses. Based on these results, we define and characterize two modules composed of muscle- and epidermal-specifying transcription factors that function together within a single cell lineage to robustly specify multiple cell types. The expression of these two modules, although positively regulated by a common factor, is reliably segregated among daughter cells. Our analyses indicate that these modules repress each other, and we propose that this cross-inhibition coupled with their relative time of induction function to enhance the initial asymmetry in their expression patterns, thus leading to the observed invariant gene expression patterns and cell lineage. The coupling of asynchronous and topologically distinct modules may be a general principle of module assembly that functions to potentiate genetic switches.

SUBMITTER: Yanai I

PROVIDER: S-EPMC2267734 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

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Pairing of competitive and topologically distinct regulatory modules enhances patterned gene expression.

Yanai Itai I Baugh L Ryan LR Smith Jessica J JJ Roehrig Casey C Shen-Orr Shai S SS Claggett Julia M JM Hill Andrew A AA Slonim Donna K DK Hunter Craig P CP

Molecular systems biology 20080212

Biological networks are inherently modular, yet little is known about how modules are assembled to enable coordinated and complex functions. We used RNAi and time series, whole-genome microarray analyses to systematically perturb and characterize components of a Caenorhabditis elegans lineage-specific transcriptional regulatory network. These data are supported by selected reporter gene analyses and comprehensive yeast one-hybrid and promoter sequence analyses. Based on these results, we define ...[more]

PMID: 18277379

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Project description:Biological networks are inherently modular, yet little is known about how modules are assembled to enable coordinated and complex functions. We used RNAi and time-series, whole-genome microarray analyses to systematically perturb and characterize components of a C. elegans lineage-specific transcriptional regulatory network. These data are supported by select reporter gene analyses and comprehensive yeast-one-hybrid and promoter sequence analyses. Based on these results we define and characterize two modules composed of muscle- and epidermal-specifying transcription factors that function together within a single cell lineage to robustly specify multiple cell types. The expression of these two modules, although positively regulated by a common factor, is reliably segregated among daughter cells. Our analyses indicate that these modules repress each other, and we propose that this cross-inhibition coupled with their relative time of induction function to enhance the initial asymmetry in their expression patterns, thus leading to the observed invariant gene expression patterns and cell lineage. The coupling of asynchronous and topologically distinct modules may be a general principle of module assembly that functions to potentiate genetic switches. Experiment Overall Design: To study the function of the C-lineage gene regulatory network we used RNAi to knockdown expression of the 13 TFs expressed early in the C lineage, and then used whole-genome microarrays to assess each perturbation for changes in mRNA abundance. RNAi has several advantages as a method to perturb gene function: it reduces both maternal and zygotic transcripts, thus avoiding maternal rescue effects; for essential genes it allows collection of fully affected progeny; and it enables comparison between perturbations. Measuring transcript levels on microarrays is also advantageous, because it is highly parallel, allowing direct comparisons between genes within each perturbation, and it is comprehensive, thus enabling discovery of regulated genes. In addition, it allows direct comparison to previous efforts to characterize the pal-1 regulatory network (Baugh et al, 2005a; Baugh et al, 2005b). To increase sensitivity and specificity, we used mex-3 mutant mothers whose progeny are primarily composed of C-like lineages (Draper et al, 1996). In each case, RNA was collected from embryos two and three cell cycles after the initial zygotic expression of the targeted TF mRNA (Figure 1). We note that our sampling of only two time points might underestimate the magnitude of the observed effect of RNAi on mRNA levels, particularly if the expression level of the assayed mRNA peaks before or after the selected time points. The paralogous tbx-8 and tbx-9 genes, which were previously demonstrated to be functionally redundant (Pocock et al, 2004b; Baugh et al, 2005a), were simultaneously targeted by RNAi in order to assay a more penetrant effect on gene expression. Experiment Overall Design: mex-3 (zu155) mutant worms (JJ518) were used in this experiment. RNAi was administered by soaking and RNA was collected from embryos, both as previously described (Baugh 2005; Baugh et al, 2005b). Cohorts of ten embryos were used for each RNA preparation, and half of the RNA was used for linear amplification. The Artus â??ExpressArt mRNA Amplification Kitâ?? was used for amplification. The manufacturerâ??s protocol was modified in round one with a ten-fold increase in concentration of primers A, B, and C and a four-fold decrease in cDNA synthesis reaction volumes. Microarray hybridization onto Affymetrix C. elegans GeneChip, scanning, data reduction and analysis were done as previously described (Baugh et al, 2003). Expression values were normalized using RMA (Bolstad et al, 2003).

Project description:BACKGROUND: Compendia of gene expression profiles under chemical and genetic perturbations constitute an invaluable resource from a systems biology perspective. However, the perturbational nature of such data imposes specific challenges on the computational methods used to analyze them. In particular, traditional clustering algorithms have difficulties in handling one of the prominent features of perturbational compendia, namely partial coexpression relationships between genes. Biclustering methods on the other hand are specifically designed to capture such partial coexpression patterns, but they show a variety of other drawbacks. For instance, some biclustering methods are less suited to identify overlapping biclusters, while others generate highly redundant biclusters. Also, none of the existing biclustering tools takes advantage of the staple of perturbational expression data analysis: the identification of differentially expressed genes. RESULTS: We introduce a novel method, called ENIGMA, that addresses some of these issues. ENIGMA leverages differential expression analysis results to extract expression modules from perturbational gene expression data. The core parameters of the ENIGMA clustering procedure are automatically optimized to reduce the redundancy between modules. In contrast to the biclusters produced by most other methods, ENIGMA modules may show internal substructure, i.e. subsets of genes with distinct but significantly related expression patterns. The grouping of these (often functionally) related patterns in one module greatly aids in the biological interpretation of the data. We show that ENIGMA outperforms other methods on artificial datasets, using a quality criterion that, unlike other criteria, can be used for algorithms that generate overlapping clusters and that can be modified to take redundancy between clusters into account. Finally, we apply ENIGMA to the Rosetta compendium of expression profiles for Saccharomyces cerevisiae and we analyze one pheromone response-related module in more detail, demonstrating the potential of ENIGMA to generate detailed predictions. CONCLUSION: It is increasingly recognized that perturbational expression compendia are essential to identify the gene networks underlying cellular function, and efforts to build these for different organisms are currently underway. We show that ENIGMA constitutes a valuable addition to the repertoire of methods to analyze such data.

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Pairing of competitive and topologically distinct regulatory modules enhances patterned gene expression.

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Pairing of competitive and topologically distinct regulatory modules enhances patterned gene expression.

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