A systems approach to mapping DNA damage response pathways

A systems approach to delineate functions of paralogous transcription factors: role of the Yap family in the DNA damage response

Duplication of genes encoding transcription factors plays an essential role in driving phenotypic variation. Because regulation can occur at multiple levels, it is often difficult to discern how each duplicated factor achieves its regulatory specificity. In these cases, a "systems approach" may distinguish the role of each factor by integrating complementary large-scale measurements of the regulatory network. To explore such an approach, we integrate growth phenotypes, promoter binding profiles, and gene expression patterns to model the DNA damage response network controlled by the Yeast-specific AP-1 (YAP) family of transcription factors. This analysis reveals that YAP regulatory specificity is achieved by at least three mechanisms: (i) divergence of DNA-binding sequences into two subfamilies; (ii) condition-specific combinatorial regulation by multiple Yap factors; and (iii) interactions of Yap 1, 4, and 6 with chromatin remodeling proteins. Additional microarray experiments establish that Yap 4 and 6 regulate gene expression through interactions with the histone deacetylase, Hda1. The data further highlight differences among Yap paralogs in terms of their regulatory mode of action (activation vs. repression). This study suggests how other large TF families might be disentangled in the future.

Towards a molecular characterisation of pathological pathways

The dominant conceptual reductionism in drug discovery has resulted in many promising drug candidates to fail during the last clinical phases, mainly due to a lack of knowledge about the patho-physiological pathways they are acting on. Consequently, to increase the revenues of the drug discovery process, we need to improve our understanding of the molecular mechanisms underlying complex cellular processes and consider each potential drug target in its full biological context. Here, we review several strategies that combine computational and experimental techniques, and suggest a systems pathology approach that will ultimately lead to a better comprehension of the molecular bases of disease.

Functional genomics in radiation biology: a gateway to cellular systems-level studies

Cells respond to ionizing radiation through an intricate network of interacting signaling cascades that are engaged in the regulation of diverse cellular functions, such as cell cycle arrest, DNA repair, and apoptosis. While changes in protein modification, activity, and sub-cellular localization may directly mediate these responses, alterations in gene expression also represent a central component of the pathways involved. Studies of altered gene expression have historically played an important role in elucidating the molecular mechanisms underlying cellular radiation response. In recent years, functional genomics approaches, such as microarray profiling, have been developed that can simultaneously monitor changes in gene expression across essentially the entire genome. However, analogous methods for global measurements of protein expression or modification have lagged behind. As global transcription profiling has become increasingly accessible, the quantity of information on gene expression responses to irradiation has increased dramatically. While many such experiments have provided improved insight into various aspects of radiation response, the diversity of experimental models and details of radiation dose, timing, and data analysis that have been employed means that no single consistent picture has emerged yet. More sophisticated methods for data analysis, data mining, and reverse engineering to reconstruct the underlying response pathways are continually being developed, and can extract additional value from profiling studies. As methods for the global study of other biomolecules become more routine, it will be important to integrate the results of radiation response profiling across multiple biological levels, and to build from simpler experimental systems toward more complex multi-cellular and in vivo systems. The future development of "integromic" models of radiation response should add substantially to the understanding gained from gene expression studies alone.

CASCADE: a novel quasi all paths-based network analysis algorithm for clustering biological interactions

BACKGROUND

Quantitative characterization of the topological characteristics of protein-protein interaction (PPI) networks can enable the elucidation of biological functional modules. Here, we present a novel clustering methodology for PPI networks wherein the biological and topological influence of each protein on other proteins is modeled using the probability distribution that the series of interactions necessary to link a pair of distant proteins in the network occur within a time constant (the occurrence probability).

RESULTS

CASCADE selects representative nodes for each cluster and iteratively refines clusters based on a combination of the occurrence probability and graph topology between every protein pair. The CASCADE approach is compared to nine competing approaches. The clusters obtained by each technique are compared for enrichment of biological function. CASCADE generates larger clusters and the clusters identified have p-values for biological function that are approximately 1000-fold better than the other methods on the yeast PPI network dataset. An important strength of CASCADE is that the percentage of proteins that are discarded to create clusters is much lower than the other approaches which have an average discard rate of 45% on the yeast protein-protein interaction network.

CONCLUSION

CASCADE is effective at detecting biologically relevant clusters of interactions.

Interpreting physiological responses to environmental change through gene expression profiling

Identification of differentially expressed genes in response to environmental change offers insights into the roles of the transcriptome in the regulation of physiological responses. A variety of methods are now available to implement large-scale gene expression screens, and each method has specific advantages and disadvantages. Construction of custom cDNA microarrays remains the most popular route to implement expression screens in the non-model organisms favored by comparative physiologists, and we highlight some factors that should be considered when embarking along this path. Using a carp cDNA microarray, we have undertaken a broad, system-wide gene expression screen to investigate the physiological mechanisms underlying cold and hypoxia acclimation. This dataset provides a starting point from which to explore a range of specific mechanistic hypotheses at all levels of organization, from individual biochemical pathways to the level of the whole organism. We demonstrate the utility of two data analysis methods, Gene Ontology profiling and rank-based statistical methods, to summarize the probable physiological function of acclimation-induced gene expression changes, and to prioritize specific genes as candidates for further study.

Protein damage by reactive electrophiles: targets and consequences

It has been 60 years since the Millers first described the covalent binding of carcinogens to tissue proteins. Protein covalent binding was gradually overshadowed by the emergence of DNA adduct formation as the dominant paradigm in chemical carcinogenesis but re-emerged in the early 1970s as a critical mechanism of drug and chemical toxicity. Technology limitations hampered the characterization of protein adducts until the emergence of mass spectrometry-based proteomics in the late 1990s. The time since then has seen rapid progress in the characterization of the protein targets of electrophiles and the consequences of protein damage. Recent integration of novel affinity chemistries for electrophile probes, shotgun proteomics methods, and systems modeling tools has led to the identification of hundreds of protein targets of electrophiles in mammalian systems. The technology now exists to map the targets of damage to critical components of signaling pathways and metabolic networks and to understand mechanisms of damage at a systems level. The implementation of sensitive, specific analyses for protein adducts from both xenobiotic-derived and endogenous electrophiles offers a means to link protein damage to clinically relevant health effects of both chemical exposures and disease processes.

Genomewide identification of protein binding locations using chromatin immunoprecipitation coupled with microarray

Interactions between cis-acting elements and proteins play a key role in transcriptional regulation of all known organisms. To better understand these interactions, researchers developed a method that couples chromatin immunoprecipitation with microarrays (also known as ChIP-chip), which is capable of providing a whole-genome map of protein-DNA interactions. This versatile and high-throughput strategy is initiated by formaldehyde-mediated cross-linking of DNA and proteins, followed by cell lysis, DNA fragmentation, and immunopurification. The immunoprecipitated DNA fragments are then purified from the proteins by reverse-cross-linking followed by amplification, labeling, and hybridization to a whole-genome tiling microarray against a reference sample. The enriched signals obtained from the microarray then are normalized by the reference sample and used to generate the whole-genome map of protein-DNA interactions. The protocol described here has been used for discovering the genomewide distribution of RNA polymerase and several transcription factors of Escherichia coli.

Transient transcriptional responses to stress are generated by opposing effects of mRNA production and degradation

The state of the transcriptome reflects a balance between mRNA production and degradation. Yet how these two regulatory arms interact in shaping the kinetics of the transcriptome in response to environmental changes is not known. We subjected yeast to two stresses, one that induces a fast and transient response, and another that triggers a slow enduring response. We then used microarrays following transcriptional arrest to measure genome-wide decay profiles under each condition. We found condition-specific changes in mRNA decay rates and coordination between mRNA production and degradation. In the transient response, most induced genes were surprisingly destabilized, whereas repressed genes were somewhat stabilized, exhibiting counteraction between production and degradation. This strategy can reconcile high steady-state level with short response time among induced genes. In contrast, the stress that induces the slow response displays the more expected behavior, whereby most induced genes are stabilized, and repressed genes are destabilized. Our results show genome-wide interplay between mRNA production and degradation, and that alternative modes of such interplay determine the kinetics of the transcriptome in response to stress.

Trm9-catalyzed tRNA modifications link translation to the DNA damage response

Transcriptional and posttranslational signals are known mechanisms that promote efficient responses to DNA damage. We have identified Saccharomyces cerevisiae tRNA methyltransferase 9 (Trm9) as an enzyme that prevents cell death via translational enhancement of DNA damage response proteins. Trm9 methylates the uridine wobble base of tRNAARG(UCU) and tRNAGLU(UUC). We used computational and molecular approaches to predict that Trm9 enhances the translation of some transcripts overrepresented with specific arginine and glutamic acid codons. We found that translation elongation factor 3 (YEF3) and the ribonucleotide reductase (RNR1 and RNR3) large subunits are overrepresented with specific arginine and glutamic acid codons, and we demonstrated that Trm9 significantly enhances Yef3, Rnr1, and Rnr3 protein levels. Additionally, we identified 425 genes, which included YEF3, RNR1, and RNR3, with a unique codon usage pattern linked to Trm9. We propose that Trm9-specific tRNA modifications enhance codon-specific translation elongation and promote increased levels of key damage response proteins.

YEASTRACT-DISCOVERER: new tools to improve the analysis of transcriptional regulatory associations in Saccharomyces cerevisiae

The Yeast search for transcriptional regulators and consensus tracking (YEASTRACT) information system (www.yeastract.com) was developed to support the analysis of transcription regulatory associations in Saccharomyces cerevisiae. Last updated in September 2007, this database contains over 30 990 regulatory associations between Transcription Factors (TFs) and target genes and includes 284 specific DNA binding sites for 108 characterized TFs. Computational tools are also provided to facilitate the exploitation of the gathered data when solving a number of biological questions, in particular the ones that involve the analysis of global gene expression results. In this new release, YEASTRACT includes DISCOVERER, a set of computational tools that can be used to identify complex motifs over-represented in the promoter regions of co-regulated genes. The motifs identified are then clustered in families, represented by a position weight matrix and are automatically compared with the known transcription factor binding sites described in YEASTRACT. Additionally, in this new release, it is possible to generate graphic depictions of transcriptional regulatory networks for documented or potential regulatory associations between TFs and target genes. The visual display of these networks of interactions is instrumental in functional studies. Tutorials are available on the system to exemplify the use of all the available tools.

The protein degradation response of Saccharomyces cerevisiae to classical DNA-damaging agents

Genome wide experiments indicate both proteasome- and vacuole-mediated protein degradation modulate sensitivity to classical DNA-damaging agents. Here, we show that global protein degradation is significantly increased upon methyl methanesulfonate (MMS) exposure. In addition, global protein degradation is similarly increased upon exposure to 4-nitroquinoline-N-oxide (4NQO) and UV and, to a lesser extent, tert-butyl hydroperoxide. The proteasomal inhibitor MG132 decreases both MMS-induced and 4NQO-induced protein degradation, while addition of the vacuolar inhibitor phenylmethanesulfonyl fluoride does not. The addition of both inhibitors grossly inhibits cell growth upon MMS exposure over and above the growth inhibition induced by MMS alone. The MMS-induced protein degradation response remains unchanged in several ubiquitin-proteasome and vacuolar mutants, presumably because these mutants are not totally deficient in either essential pathway. Furthermore, MMS-induced protein degradation is independent of Mec1, Mag1, Rad23, and Rad6, suggesting that the protein degradation response is not transduced through the classical Mec1 DNA damage response pathway or through repair intermediates generated by the base excision, nucleotide excision, or postreplication-DNA repair pathways. These results identify the regulation of protein degradation as an important factor in the recovery of cells from toxicity induced by classical DNA-damaging agents.

Learning regulatory programs that accurately predict differential expression with MEDUSA

Inferring gene regulatory networks from high-throughput genomic data is one of the central problems in computational biology. In this paper, we describe a predictive modeling approach for studying regulatory networks, based on a machine learning algorithm called MEDUSA. MEDUSA integrates promoter sequence, mRNA expression, and transcription factor occupancy data to learn gene regulatory programs that predict the differential expression of target genes. Instead of using clustering or correlation of expression profiles to infer regulatory relationships, MEDUSA determines condition-specific regulators and discovers regulatory motifs that mediate the regulation of target genes. In this way, MEDUSA meaningfully models biological mechanisms of transcriptional regulation. MEDUSA solves the problem of predicting the differential (up/down) expression of target genes by using boosting, a technique from statistical learning, which helps to avoid overfitting as the algorithm searches through the high-dimensional space of potential regulators and sequence motifs. Experimental results demonstrate that MEDUSA achieves high prediction accuracy on held-out experiments (test data), that is, data not seen in training. We also present context-specific analysis of MEDUSA regulatory programs for DNA damage and hypoxia, demonstrating that MEDUSA identifies key regulators and motifs in these processes. A central challenge in the field is the difficulty of validating reverse-engineered networks in the absence of a gold standard. Our approach of learning regulatory programs provides at least a partial solution for the problem: MEDUSA's prediction accuracy on held-out data gives a concrete and statistically sound way to validate how well the algorithm performs. With MEDUSA, statistical validation becomes a prerequisite for hypothesis generation and network building rather than a secondary consideration.

Integrating physical and genetic maps: from genomes to interaction networks

Physical and genetic mapping data have become as important to network biology as they once were to the Human Genome Project. Integrating physical and genetic networks currently faces several challenges: increasing the coverage of each type of network; establishing methods to assemble individual interaction measurements into contiguous pathway models; and annotating these pathways with detailed functional information. A particular challenge involves reconciling the wide variety of interaction types that are currently available. For this purpose, recent studies have sought to classify genetic and physical interactions along several complementary dimensions, such as ordered versus unordered, alleviating versus aggravating, and first versus second degree.

SPINE: a framework for signaling-regulatory pathway inference from cause-effect experiments

MOTIVATION

The complex program of gene expression allows the cell to cope with changing genetic, developmental and environmental conditions. The accumulating large-scale measurements of gene knockout effects and molecular interactions allow us to begin to uncover regulatory and signaling pathways within the cell that connect causal to affected genes on a network of physical interactions.

RESULTS

We present a novel framework, SPINE, for Signaling-regulatory Pathway INferencE. The framework aims at explaining gene expression experiments in which a gene is knocked out and as a result multiple genes change their expression levels. To this end, an integrated network of protein-protein and protein-DNA interactions is constructed, and signaling pathways connecting the causal gene to the affected genes are searched for in this network. The reconstruction problem is translated into that of assigning an activation/repression attribute with each protein so as to explain (in expectation) a maximum number of the knockout effects observed. We provide an integer programming formulation for the latter problem and solve it using a commercial solver. We validate the method by applying it to a yeast subnetwork that is involved in mating. In cross-validation tests, SPINE obtains very high accuracy in predicting knockout effects (99%). Next, we apply SPINE to the entire yeast network to predict protein effects and reconstruct signaling and regulatory pathways. Overall, we are able to infer 861 paths with confidence and assign effects to 183 genes. The predicted effects are found to be in high agreement with current biological knowledge.

AVAILABILITY

The algorithm and data are available at http://cs.tau.ac.il/~roded/SPINE.html.

Microbial regulatory and metabolic networks

Reconstruction of transcriptional regulatory and metabolic networks is the foundation of large-scale microbial systems and synthetic biology. An enormous amount of information including the annotated genomic sequences and the genomic locations of DNA-binding regulatory proteins can be used to define metabolic and regulatory networks in cells. In particular, advances in experimental methods to map regulatory networks in microbial cells have allowed reliable data-driven reconstruction of these networks. Recent work on metabolic engineering and experimental evolution of microbes highlights the key role of global regulatory networks in controlling specific metabolic processes and the need to consider the integrated function of multiple types of networks for both scientific and engineering purposes.

Specific transcriptional responses induced by 8-methoxypsoralen and UVA in yeast

Treatment of eukaryotic cells with 8-methoxypsoralen plus UVA irradiation (8-MOP/UVA) induces pyrimidine monoadducts and interstrand crosslinks and initiates a cascade of events leading to cytotoxic, mutagenic and carcinogenic responses. Transcriptional activation plays an important part in these responses. Our previous study in Saccharomyces cerevisiae showed that the repair of these lesions involves the transient formation of DNA double-strand breaks and the enhanced expression of landmark DNA damage response genes such as RAD51, RNR2 and DUN1, as well as the Mec1/Rad53 kinase signaling cascade. We have now used DNA microarrays to examine genome-wide transcriptional changes produced after induction of 8-MOP/UVA photolesions. We found that 128 genes were strongly induced and 29 genes strongly repressed. Modifications in gene expression concern numerous biological processes. Compared to other genotoxic treatments, c. 42% of the response genes were specific to 8-MOP/UVA treatment. In addition to common DNA damage response genes and genes induced by environmental stresses, a large fraction of 8-MOP/UVA response genes correspond to membrane-related functions.

Prediction of phenotype and gene expression for combinations of mutations

Molecular interactions provide paths for information flows. Genetic interactions reveal active information flows and reflect their functional consequences. We integrated these complementary data types to model the transcription network controlling cell differentiation in yeast. Genetic interactions were inferred from linear decomposition of gene expression data and were used to direct the construction of a molecular interaction network mediating these genetic effects. This network included both known and novel regulatory influences, and predicted genetic interactions. For corresponding combinations of mutations, the network model predicted quantitative gene expression profiles and precise phenotypic effects. Multiple predictions were tested and verified.

Antimutagenicity of cinnamaldehyde and vanillin in human cells: Global gene expression and possible role of DNA damage and repair

Vanillin (VAN) and cinnamaldehyde (CIN) are dietary flavorings that exhibit antimutagenic activity against mutagen-induced and spontaneous mutations in bacteria. Although these compounds were antimutagenic against chromosomal mutations in mammalian cells, they have not been studied for antimutagenesis against spontaneous gene mutations in mammalian cells. Thus, we initiated studies with VAN and CIN in human mismatch repair-deficient (hMLH1(-)) HCT116 colon cancer cells, which exhibit high spontaneous mutation rates (mutations/cell/generation) at the HPRT locus, permitting analysis of antimutagenic effects of agents against spontaneous mutation. Long-term (1-3 weeks) treatment of HCT116 cells with VAN at minimally toxic concentrations (0.5-2.5mM) reduced the spontaneous HPRT mutant fraction (MF, mutants/10(6) survivors) in a concentration-related manner by 19-73%. A similar treatment with CIN at 2.5-7.5microM yielded a 13-56% reduction of the spontaneous MF. Short-term (4-h) treatments also reduced the spontaneous MF by 64% (VAN) and 31% (CIN). To investigate the mechanisms of antimutagenesis, we evaluated the ability of VAN and CIN to induce DNA damage (comet assay) and to alter global gene expression (Affymetrix GeneChip) after 4-h treatments. Both VAN and CIN induced DNA damage in both mismatch repair-proficient (HCT116+chr3) and deficient (HCT116) cells at concentrations that were antimutagenic in HCT116 cells. There were 64 genes whose expression was changed similarly by both VAN and CIN; these included genes related to DNA damage, stress responses, oxidative damage, apoptosis, and cell growth. RT-PCR results paralleled the Affymetrix results for four selected genes (HMOX1, DDIT4, GCLM, and CLK4). Our results show for the first time that VAN and CIN are antimutagenic against spontaneous mutations in mammalian (human) cells. These and other data lead us to propose that VAN and CIN may induce DNA damage that elicits recombinational DNA repair, which reduces spontaneous mutations.

A genome-wide analysis in Saccharomyces cerevisiae demonstrates the influence of chromatin modifiers on transcription

Chromatin structure is important in transcription regulation. Many factors influencing chromatin structure have been identified, but the transcriptional programs in which they participate are still poorly understood. Chromatin modifiers participate in transcriptional control together with DNA-bound transcription factors. High-throughput experimental methods allow the genome-wide identification of binding sites for transcription factors as well as quantification of gene expression under various environmental and genetic conditions. We have developed a new methodology that uses the vast amount of available data to dissect the contribution of chromatin structure to transcription. We measure and characterize the dependence of transcription factor function on specific chromatin modifiers. We apply our methodology to S. cerevisiae, using a compendium of 170 gene expression profiles of strains defective for chromatin modifiers, taken from 26 different studies. Our method succeeds in identifying known intricate genetic interactions between chromatin modifiers and transcription factors and uncovers many previously unknown genetic interactions, giving the first genome-wide picture of the contribution of chromatin structure to transcription in a eukaryote.

How to infer gene networks from expression profiles

Inferring, or 'reverse-engineering', gene networks can be defined as the process of identifying gene interactions from experimental data through computational analysis. Gene expression data from microarrays are typically used for this purpose. Here we compared different reverse-engineering algorithms for which ready-to-use software was available and that had been tested on experimental data sets. We show that reverse-engineering algorithms are indeed able to correctly infer regulatory interactions among genes, at least when one performs perturbation experiments complying with the algorithm requirements. These algorithms are superior to classic clustering algorithms for the purpose of finding regulatory interactions among genes, and, although further improvements are needed, have reached a discreet performance for being practically useful.

The ins and outs of ATP-dependent chromatin remodeling in budding yeast: biophysical and proteomic perspectives

ATP-dependent chromatin remodeling is performed by multi-subunit protein complexes. Over the last years, the identity of these factors has been unveiled in yeast and many parallels have been drawn with animal and plant systems, indicating that sophisticated chromatin transactions evolved prior to their divergence. Here we review current knowledge pertaining to the molecular mode of action of ATP-dependent chromatin remodeling, from single molecule studies to genome-wide genetic and proteomic studies. We focus on the budding yeast versions of SWI/SNF, RSC, DDM1, ISWI, CHD1, INO80 and SWR1.

A systems approach to measuring the binding energy landscapes of transcription factors

A major goal of systems biology is to predict the function of biological networks. Although network topologies have been successfully determined in many cases, the quantitative parameters governing these networks generally have not. Measuring affinities of molecular interactions in high-throughput format remains problematic, especially for transient and low-affinity interactions. We describe a high-throughput microfluidic platform that measures such properties on the basis of mechanical trapping of molecular interactions. With this platform we characterized DNA binding energy landscapes for four eukaryotic transcription factors; these landscapes were used to test basic assumptions about transcription factor binding and to predict their in vivo function.

Functional genomics of the yeast DNA-damage response

Two high-throughput studies of the DNA-damage response in yeast reveal new regulatory pathways and genes involved.

High-throughput approaches are beginning to have an impact on many areas of yeast biology. Two recent studies, using different experimental platforms, provide insight into new pathways involved in the response of yeast to DNA damage.

Reconstructing dynamic regulatory maps

Even simple organisms have the ability to respond to internal and external stimuli. This response is carried out by a dynamic network of protein-DNA interactions that allows the specific regulation of genes needed for the response. We have developed a novel computational method that uses an input-output hidden Markov model to model these regulatory networks while taking into account their dynamic nature. Our method works by identifying bifurcation points, places in the time series where the expression of a subset of genes diverges from the rest of the genes. These points are annotated with the transcription factors regulating these transitions resulting in a unified temporal map. Applying our method to study yeast response to stress, we derive dynamic models that are able to recover many of the known aspects of these responses. Predictions made by our method have been experimentally validated leading to new roles for Ino4 and Gcn4 in controlling yeast response to stress. The temporal cascade of factors reveals common pathways and highlights differences between master and secondary factors in the utilization of network motifs and in condition-specific regulation.

T. Systems biology of innate immunity

Systems Biology has emerged as an exciting research approach in molecular biology and functional genomics that involves a systematic use of genomic, proteomic, and metabolomic technologies for the construction of network-based models of biological processes. These endeavors, collectively referred to as systems biology establish a paradigm by which to systematically interrogate, model, and iteratively refine our knowledge of the regulatory events within a cell. Here, we present a new systems approach, integrating DNA and transcript expression information, specifically designed to identify transcriptional networks governing the macrophage immune response to lipopolysaccharide (LPS). Using this approach, we are not only able to infer a global macrophage transcriptional network, but also time-specific sub-networks that are dynamically active across the LPS response. We believe that our system biological approach could be useful for identifying other complex networks mediating immunological responses.

Analyzing the dose-dependence of the Saccharomyces cerevisiae global transcriptional response to methyl methanesulfonate and ionizing radiation

BACKGROUND

One of the most crucial tasks for a cell to ensure its long term survival is preserving the integrity of its genetic heritage via maintenance of DNA structure and sequence. While the DNA damage response in the yeast Saccharomyces cerevisiae, a model eukaryotic organism, has been extensively studied, much remains to be elucidated about how the organism senses and responds to different types and doses of DNA damage. We have measured the global transcriptional response of S. cerevisiae to multiple doses of two representative DNA damaging agents, methyl methanesulfonate (MMS) and gamma radiation.

RESULTS

Hierarchical clustering of genes with a statistically significant change in transcription illustrated the differences in the cellular responses to MMS and gamma radiation. Overall, MMS produced a larger transcriptional response than gamma radiation, and many of the genes modulated in response to MMS are involved in protein and translational regulation. Several clusters of coregulated genes whose responses varied with DNA damaging agent dose were identified. Perhaps the most interesting cluster contained four genes exhibiting biphasic induction in response to MMS dose. All of the genes (DUN1, RNR2, RNR4, and HUG1) are involved in the Mec1p kinase pathway known to respond to MMS, presumably due to stalled DNA replication forks. The biphasic responses of these genes suggest that the pathway is induced at lower levels as MMS dose increases. The genes in this cluster with a threefold or greater transcriptional response to gamma radiation all showed an increased induction with increasing gamma radiation dosage.

CONCLUSION

Analyzing genome-wide transcriptional changes to multiple doses of external stresses enabled the identification of cellular responses that are modulated by magnitude of the stress, providing insights into how a cell deals with genotoxicity.

Metabolic networks in motion: 13C-based flux analysis

Many properties of complex networks cannot be understood from monitoring the components—not even when comprehensively monitoring all protein or metabolite concentrations—unless such information is connected and integrated through mathematical models. The reason is that static component concentrations, albeit extremely informative, do not contain functional information per se. The functional behavior of a network emerges only through the nonlinear gene, protein, and metabolite interactions across multiple metabolic and regulatory layers. I argue here that intracellular reaction rates are the functional end points of these interactions in metabolic networks, hence are highly relevant for systems biology. Methods for experimental determination of metabolic fluxes differ fundamentally from component concentration measurements; that is, intracellular reaction rates cannot be detected directly, but must be estimated through computer model-based interpretation of stable isotope patterns in products of metabolism.

Connectivity in the yeast cell cycle transcription network: inferences from neural networks

A current challenge is to develop computational approaches to infer gene network regulatory relationships based on multiple types of large-scale functional genomic data. We find that single-layer feed-forward artificial neural network (ANN) models can effectively discover gene network structure by integrating global in vivo protein:DNA interaction data (ChIP/Array) with genome-wide microarray RNA data. We test this on the yeast cell cycle transcription network, which is composed of several hundred genes with phase-specific RNA outputs. These ANNs were robust to noise in data and to a variety of perturbations. They reliably identified and ranked 10 of 12 known major cell cycle factors at the top of a set of 204, based on a sum-of-squared weights metric. Comparative analysis of motif occurrences among multiple yeast species independently confirmed relationships inferred from ANN weights analysis. ANN models can capitalize on properties of biological gene networks that other kinds of models do not. ANNs naturally take advantage of patterns of absence, as well as presence, of factor binding associated with specific expression output; they are easily subjected to in silico "mutation" to uncover biological redundancies; and they can use the full range of factor binding values. A prominent feature of cell cycle ANNs suggested an analogous property might exist in the biological network. This postulated that "network-local discrimination" occurs when regulatory connections (here between MBF and target genes) are explicitly disfavored in one network module (G2), relative to others and to the class of genes outside the mitotic network. If correct, this predicts that MBF motifs will be significantly depleted from the discriminated class and that the discrimination will persist through evolution. Analysis of distantly related Schizosaccharomyces pombe confirmed this, suggesting that network-local discrimination is real and complements well-known enrichment of MBF sites in G1 class genes.

Unraveling transcription regulatory networks by protein-DNA and protein-protein interaction mapping

Metazoan genomes contain thousands of protein-coding and noncoding RNA genes, most of which are differentially expressed, i.e., at different locations or at different times during development, function, or pathology of the organism. Differential gene expression is achieved in part by the action of regulatory transcription factors (TFs) that bind to cis-regulatory elements that are often located in or near their target genes. Each TF likely regulates many targets in the context of intricate transcription regulatory networks. Up to 10% of a genome may encode TFs, but only a handful of these have been studied in detail. Here, I will discuss the different steps involved in the mapping and analysis of transcription regulatory networks, including the identification of network nodes (TFs and their target sequences) and edges (TF-TF dimers and TF-DNA target interactions), integration with other data types, and network properties and emerging principles that provide insights into differential gene expression.

Exploring transcriptional regulatory networks in the worm

Using a yeast one-hybrid assay, Deplancke et al. (2006) report in this issue a protein-DNA interaction network for 72 gene promoters and 117 regulatory proteins expressed in cells of the nematode digestive tract. This study is a first step toward mapping transcriptional regulatory networks in distinct metazoan cell lineages and organs using a "gene-centered" approach.