A systems approach to mapping DNA damage response pathways

Cell-cycle and DNA damage regulation of the DNA mismatch repair protein Msh2 occurs at the transcriptional and post-transcriptional level

DNA mismatch repair during replication is a conserved process essential for maintaining genomic stability. Mismatch repair is also implicated in cell-cycle arrest and apoptosis after DNA damage. Because yeast and human mismatch repair systems are well conserved, we have employed the budding yeast Saccharomyces cerevisiae to understand the regulation and function of the mismatch repair gene MSH2. Using a luciferase-based transcriptional reporter, we defined a 218-bp region upstream of MSH2 that contains cell-cycle and DNA damage responsive elements. The 5' end of the MSH2 transcript was mapped by primer extension and was found to encode a small upstream open reading frame (uORF). Mutagenesis of the uORF start codon or of the uORF stop codon, which creates a continuous reading frame with MSH2, increased Msh2 steady-state protein levels ∼2-fold. Furthermore, we found that the cell-cycle transcription factors Swi6, Swi4, and Mbp1-along with SCB/MCB cell-cycle binding sites upstream of MSH2-are all required for full basal expression of MSH2. Mutagenesis of the cell-cycle boxes resulted in a minor reduction in basal Msh2 levels and a 3-fold defect in mismatch repair. Disruption of the cell-cycle boxes also affected growth in a DNA polymerase-defective strain background where mismatch repair is essential, particularly in the presence of the DNA damaging agent methyl methane sulfonate (MMS). Promoter replacements conferring constitutive expression of MSH2 revealed that the transcriptional induction in response to MMS is required to maintain induced levels of Msh2. Turnover experiments confirmed an elevated rate of degradation in the presence of MMS. Taken together, the data show that the DNA damage regulation of Msh2 occurs at the transcriptional and post-transcriptional levels. The transcriptional and translational control elements identified are conserved in mammalian cells, underscoring the use of yeast as a model system to examine the regulation of MSH2.

Differential analysis of high-throughput quantitative genetic interaction data

Synthetic genetic arrays have been very effective at measuring genetic interactions in yeast in a high-throughput manner and recently have been expanded to measure quantitative changes in interaction, termed 'differential interactions', across multiple conditions. Here, we present a strategy that leverages statistical information from the experimental design to produce a novel, quantitative differential interaction score, which performs favorably compared to previous differential scores. We also discuss the added utility of differential genetic-similarity in differential network analysis. Our approach is preferred for differential network analysis, and our implementation, written in MATLAB, can be found at http://chianti.ucsd.edu/~gbean/compute_differential_scores.m.

Alignment and integration of complex networks by hypergraph-based spectral clustering

Complex networks possess a rich, multiscale structure reflecting the dynamical and functional organization of the systems they model. Often there is a need to analyze multiple networks simultaneously, to model a system by more than one type of interaction, or to go beyond simple pairwise interactions, but currently there is a lack of theoretical and computational methods to address these problems. Here we introduce a framework for clustering and community detection in such systems using hypergraph representations. Our main result is a generalization of the Perron-Frobenius theorem from which we derive spectral clustering algorithms for directed and undirected hypergraphs. We illustrate our approach with applications for local and global alignment of protein-protein interaction networks between multiple species, for tripartite community detection in folksonomies, and for detecting clusters of overlapping regulatory pathways in directed networks.

Regulatory network structure as a dominant determinant of transcription factor evolutionary rate

The evolution of transcriptional regulatory networks has thus far mostly been studied at the level of cis-regulatory elements. To gain a complete understanding of regulatory network evolution we must also study the evolutionary role of trans-factors, such as transcription factors (TFs). Here, we systematically assess genomic and network-level determinants of TF evolutionary rate in yeast, and how they compare to those of generic proteins, while carefully controlling for differences of the TF protein set, such as expression level. We found significantly distinct trends relating TF evolutionary rate to mRNA expression level, codon adaptation index, the evolutionary rate of physical interaction partners, and, confirming previous reports, to protein-protein interaction degree and regulatory in-degree. We discovered that for TFs, the dominant determinants of evolutionary rate lie in the structure of the regulatory network, such as the median evolutionary rate of target genes and the fraction of species-specific target genes. Decomposing the regulatory network by edge sign, we found that this modular evolution of TFs and their targets is limited to activating regulatory relationships. We show that fast evolving TFs tend to regulate other TFs and niche-specific processes and that their targets show larger evolutionary expression changes than targets of other TFs. We also show that the positive trend relating TF regulatory in-degree and evolutionary rate is likely related to the species-specificity of the transcriptional regulation modules. Finally, we discuss likely causes for TFs' different evolutionary relationship to the physical interaction network, such as the prevalence of transient interactions in the TF subnetwork. This work suggests that positive and negative regulatory networks follow very different evolutionary rules, and that transcription factor evolution is best understood at a network- or systems-level.

Identifying biologically interpretable transcription factor knockout targets by jointly analyzing the transcription factor knockout microarray and the ChIP-chip data

Background

Transcription factor knockout microarrays (TFKMs) provide useful information about gene regulation. By using statistical methods for detecting differentially expressed genes between the gene expression microarray data of the mutant and wild type strains, the TF knockout targets of the knocked-out TF can be identified. However, the identified TF knockout targets may contain a certain amount of false positives due to the experimental noises inherent in the high-throughput microarray technology. Even if the identified TF knockout targets are true, the molecular mechanisms of how a TF regulates its TF knockout targets remain unknown by this kind of statistical approaches.

Results

To solve these two problems, we developed a method to filter out the false positives in the original TF knockout targets (identified by statistical approaches) so that the biologically interpretable TF knockout targets can be extracted. Our method can further generate experimentally testable hypotheses of the molecular mechanisms of how a TF regulates its biologically interpretable TF knockout targets. The details of our method are as follows. First, a TF binding network was constructed using the ChIP-chip data deposited in the YEASTRACT database. Then for each original TF knockout target, it is said to be biologically interpretable if a path (in the TF binding network) from the knocked-out TF to this target could be identified by our path search algorithm. The identified path explains how the TF may regulate this target either directly by binding to its promoter or indirectly through intermediate TFs. After checking all the original TF knockout targets, the biologically interpretable ones could be extracted and the false positives could be filtered out. We validated the biological significance of our refined (i.e., biologically interpretable) TF knockout targets by assessing their functional enrichment, expression coherence, and the prevalence of protein-protein interactions. Our refined TF knockout targets outperform the original TF knockout targets across all measures.

Conclusions

By jointly analyzing the TFKM and ChIP-chip data, our method can extract the biologically interpretable TF knockout targets by identifying paths (in the TF binding network) from the knocked-out TF to these targets. The identified paths form experimentally testable hypotheses regarding the molecular mechanisms of how a TF may regulate its knockout targets. About seven hundred hypotheses generated by our methods have been experimentally validated in the literature. Our work demonstrates that integrating different data sources is a powerful approach to study complex biological systems.

Topology of transcriptional regulatory networks: testing and improving

With the increasing amount and complexity of data generated in biological experiments it is becoming necessary to enhance the performance and applicability of existing statistical data analysis methods. This enhancement is needed for the hidden biological information to be better resolved and better interpreted. Towards that aim, systematic incorporation of prior information in biological data analysis has been a challenging problem for systems biology. Several methods have been proposed to integrate data from different levels of information most notably from metabolomics, transcriptomics and proteomics and thus enhance biological interpretation. However, in order not to be misled by the dominance of incorrect prior information in the analysis, being able to discriminate between competing prior information is required. In this study, we show that discrimination between topological information in competing transcriptional regulatory network models is possible solely based on experimental data. We use network topology dependent decomposition of synthetic gene expression data to introduce both local and global discriminating measures. The measures indicate how well the gene expression data can be explained under the constraints of the model network topology and how much each regulatory connection in the model refuses to be constrained. Application of the method to the cell cycle regulatory network of Saccharomyces cerevisiae leads to the prediction of novel regulatory interactions, improving the information content of the hypothesized network model.

Chromatin proteins: key responders to stress

Environments can be ever-changing and stresses are commonplace. In order for organisms to survive, they need to be able to respond to change and adapt to new conditions. Fortunately, many organisms have systems in place that enable dynamic adaptation to immediate stresses and changes within the environment. Much of this cellular response is coordinated by modulating the structure and accessibility of the genome. In eukaryotic cells, the genome is packaged and rolled up by histone proteins to create a series of DNA/histone core structures known as nucleosomes; these are further condensed into chromatin. The degree and nature of the condensation can in turn determine which genes are transcribed. Histones can be modified chemically by a large number of proteins that are thereby responsible for dynamic changes in gene expression. In this Primer we discuss findings from a study published in this issue of PLoS Biology by Weiner et al. that highlight how chromatin structure and chromatin binding proteins alter transcription in response to environmental changes and stresses. Their study reveals the importance of chromatin in mediating the speed and amplitude of stress responses in cells and suggests that chromatin is a critically important component of the cellular response to stress.

Identifying responsive modules by mathematical programming: an application to budding yeast cell cycle

High-throughput biological data offer an unprecedented opportunity to fully characterize biological processes. However, how to extract meaningful biological information from these datasets is a significant challenge. Recently, pathway-based analysis has gained much progress in identifying biomarkers for some phenotypes. Nevertheless, these so-called pathway-based methods are mainly individual-gene-based or molecule-complex-based analyses. In this paper, we developed a novel module-based method to reveal causal or dependent relations between network modules and biological phenotypes by integrating both gene expression data and protein-protein interaction network. Specifically, we first formulated the identification problem of the responsive modules underlying biological phenotypes as a mathematical programming model by exploiting phenotype difference, which can also be viewed as a multi-classification problem. Then, we applied it to study cell-cycle process of budding yeast from microarray data based on our biological experiments, and identified important phenotype- and transition-based responsive modules for different stages of cell-cycle process. The resulting responsive modules provide new insight into the regulation mechanisms of cell-cycle process from a network viewpoint. Moreover, the identification of transition modules provides a new way to study dynamical processes at a functional module level. In particular, we found that the dysfunction of a well-known module and two new modules may directly result in cell cycle arresting at S phase. In addition to our biological experiments, the identified responsive modules were also validated by two independent datasets on budding yeast cell cycle.

A systems biology approach to identify molecular pathways altered by HDAC inhibition in osteosarcoma

Osteosarcoma (OS) is the most common primary tumor in humans and dogs affecting the skeleton, and spontaneously occurring OS in dogs serves as an extremely useful model. Unacceptable toxicities using current treatment protocols prevent further dose-intensification from being a viable option to improve patient survival and thus, novel treatment strategies must be developed. Histone deacetylase inhibitors (HDACi) have recently emerged as a promising class of therapeutics demonstrating an ability to enhance the anti-tumor activity of traditional chemotherapeutics. To date, gene expression analysis of OS cell lines treated with HDACi has not been reported, and evaluation of the resultant gene expression changes may provide insight into the mechanisms that lead to success of HDACi. Canine OS cells, treated with a clinically relevant concentration of the HDACi valproic acid (VPA), were used for expression analysis on the Affymetrix canine v2.0 genechip. Differentially expressed genes were grouped into pathways based upon functional annotation; pathway analysis was performed with MetaCore and Ingenuity Pathways Analysis software. Validation of microarray results was performed by a combination of qRT-PCR and functional/biochemical assays revealing oxidative phosphorylation, cytoskeleton remodeling, cell cycle, and ubiquitin-proteasome among those pathways most affected by HDACi. The mitomycin C-bioactivating enzyme NQ01 also demonstrated upregulation following VPA treatment, leading to synergistic reductions in cell viability. These results provide a better understanding of the mechanisms by which HDACi exert their effect in OS, and have the potential to identify biomarkers that may serve as novel targets and/or predictors of response to HDACi-containing combination therapies in OS.

Genome-wide assessment of oxidatively generated DNA damage

In the tide of science nouveau after the completion of genome projects of various species, there appeared a movement to understand an organism as a system rather than the sum of cells directed for certain functions. With the advent and spread of microarray techniques, systematic and comprehensive genome-wide approaches have become reasonably possible and more required on the investigation of DNA damage and the subsequent repair. The immunoprecipitation-based technique combined with high-density microarrays or next-generation sequencing is one of the promising methods to provide access to such novel research strategies. Oxygen is necessary for most of the life on earth for electron transport. However, reactive oxygen species are inevitably generated, giving rise to steady-state levels of DNA damage in the genome, that may cause mutations leading to cancer, ageing and degenerative diseases. Previously, we showed that there are many factors involved in the genomic distribution of oxidatively generated DNA damage including chromosome territory, and proposed this sort of research area as oxygenomics. Recently, RNA is also recognized as a target of this kind of modification.

Differential network biology

Protein and genetic interaction maps can reveal the overall physical and functional landscape of a biological system. To date, these interaction maps have typically been generated under a single condition, even though biological systems undergo differential change that is dependent on environment, tissue type, disease state, development or speciation. Several recent interaction mapping studies have demonstrated the power of differential analysis for elucidating fundamental biological responses, revealing that the architecture of an interactome can be massively re-wired during a cellular or adaptive response. Here, we review the technological developments and experimental designs that have enabled differential network mapping at very large scales and highlight biological insight that has been derived from this type of analysis. We argue that differential network mapping, which allows for the interrogation of previously unexplored interaction spaces, will become a standard mode of network analysis in the future, just as differential gene expression and protein phosphorylation studies are already pervasive in genomic and proteomic analysis.

Curated collection of yeast transcription factor DNA binding specificity data reveals novel structural and gene regulatory insights

Background

Transcription factors (TFs) play a central role in regulating gene expression by interacting with cis-regulatory DNA elements associated with their target genes. Recent surveys have examined the DNA binding specificities of most Saccharomyces cerevisiae TFs, but a comprehensive evaluation of their data has been lacking.

Results

We analyzed in vitro and in vivo TF-DNA binding data reported in previous large-scale studies to generate a comprehensive, curated resource of DNA binding specificity data for all characterized S. cerevisiae TFs. Our collection comprises DNA binding site motifs and comprehensive in vitro DNA binding specificity data for all possible 8-bp sequences. Investigation of the DNA binding specificities within the basic leucine zipper (bZIP) and VHT1 regulator (VHR) TF families revealed unexpected plasticity in TF-DNA recognition: intriguingly, the VHR TFs, newly characterized by protein binding microarrays in this study, recognize bZIP-like DNA motifs, while the bZIP TF Hac1 recognizes a motif highly similar to the canonical E-box motif of basic helix-loop-helix (bHLH) TFs. We identified several TFs with distinct primary and secondary motifs, which might be associated with different regulatory functions. Finally, integrated analysis of in vivo TF binding data with protein binding microarray data lends further support for indirect DNA binding in vivo by sequence-specific TFs.

Conclusions

The comprehensive data in this curated collection allow for more accurate analyses of regulatory TF-DNA interactions, in-depth structural studies of TF-DNA specificity determinants, and future experimental investigations of the TFs' predicted target genes and regulatory roles.

YeTFaSCo: a database of evaluated yeast transcription factor sequence specificities

The yeast Saccharomyces cerevisiae is a prevalent system for the analysis of transcriptional networks. As a result, multiple DNA-binding sequence specificities (motifs) have been derived for most yeast transcription factors (TFs). However, motifs from different studies are often inconsistent with each other, making subsequent analyses complicated and confusing. Here, we have created YeTFaSCo (The Yeast Transcription Factor Specificity Compendium, http://yetfasco.ccbr.utoronto.ca/), an extensive collection of S. cerevisiae TF specificities. YeTFaSCo differs from related databases by being more comprehensive (including 1709 motifs for 256 proteins or protein complexes), and by evaluating the motifs using multiple objective quality metrics. The metrics include correlation between motif matches and ChIP-chip data, gene expression patterns, and GO terms, as well as motif agreement between different studies. YeTFaSCo also features an index of ‘expert-curated’ motifs, each associated with a confidence assessment. In addition, the database website features tools for motif analysis, including a sequence scanning function and precomputed genome-browser tracks of motif occurrences across the entire yeast genome. Users can also search the database for motifs that are similar to a query motif.

Strategies to discover regulatory circuits of the mammalian immune system

Recent advances in technologies for genome- and proteome-scale measurements and perturbations promise to accelerate discovery in every aspect of biology and medicine. Although such rapid technological progress provides a tremendous opportunity, it also demands that we learn how to use these tools effectively. One application with great potential to enhance our understanding of biological systems is the unbiased reconstruction of genetic and molecular networks. Cells of the immune system provide a particularly useful model for developing and applying such approaches. Here, we review approaches for the reconstruction of signalling and transcriptional networks, with a focus on applications in the mammalian innate immune system.

Construction of regulatory networks using expression time-series data of a genotyped population

The inference of regulatory and biochemical networks from large-scale genomics data is a basic problem in molecular biology. The goal is to generate testable hypotheses of gene-to-gene influences and subsequently to design bench experiments to confirm these network predictions. Coexpression of genes in large-scale gene-expression data implies coregulation and potential gene-gene interactions, but provide little information about the direction of influences. Here, we use both time-series data and genetics data to infer directionality of edges in regulatory networks: time-series data contain information about the chronological order of regulatory events and genetics data allow us to map DNA variations to variations at the RNA level. We generate microarray data measuring time-dependent gene-expression levels in 95 genotyped yeast segregants subjected to a drug perturbation. We develop a Bayesian model averaging regression algorithm that incorporates external information from diverse data types to infer regulatory networks from the time-series and genetics data. Our algorithm is capable of generating feedback loops. We show that our inferred network recovers existing and novel regulatory relationships. Following network construction, we generate independent microarray data on selected deletion mutants to prospectively test network predictions. We demonstrate the potential of our network to discover de novo transcription-factor binding sites. Applying our construction method to previously published data demonstrates that our method is competitive with leading network construction algorithms in the literature.

Transcriptional regulation and the diversification of metabolism in wine yeast strains

Transcription factors and their binding sites have been proposed as primary targets of evolutionary adaptation because changes to single transcription factors can lead to far-reaching changes in gene expression patterns. Nevertheless, there is very little concrete evidence for such evolutionary changes. Industrial wine yeast strains, of the species Saccharomyces cerevisiae, are a geno- and phenotypically diverse group of organisms that have adapted to the ecological niches of industrial winemaking environments and have been selected to produce specific styles of wine. Variation in transcriptional regulation among wine yeast strains may be responsible for many of the observed differences and specific adaptations to different fermentative conditions in the context of commercial winemaking. We analyzed gene expression profiles of wine yeast strains to assess the impact of transcription factor expression on metabolic networks. The data provide new insights into the molecular basis of variations in gene expression in industrial strains and their consequent effects on metabolic networks important to wine fermentation. We show that the metabolic phenotype of a strain can be shifted in a relatively predictable manner by changing expression levels of individual transcription factors, opening opportunities to modify transcription networks to achieve desirable outcomes.

Systems analysis of ATF3 in stress response and cancer reveals opposing effects on pro-apoptotic genes in p53 pathway

Stress-inducible transcription factors play a pivotal role in cellular adaptation to environment to maintain homeostasis and integrity of the genome. Activating transcription factor 3 (ATF3) is induced by a variety of stress and inflammatory conditions and is over-expressed in many kinds of cancer cells. However, molecular mechanisms underlying pleiotropic functions of ATF3 have remained elusive. Here we employed systems analysis to identify genome-wide targets of ATF3 that is either induced by an alkylating agent methyl methanesulfonate (MMS) or over-expressed in a prostate tumour cell line LNCaP. We show that stress-induced and cancer-associated ATF3 is recruited to 5,984 and 1,423 targets, respectively, in the human genome, 89% of which are common. Notably, ATF3 targets are highly enriched for not only ATF/CRE motifs but also binding sites of several other stress-inducible transcription factors indicating an extensive network of stress response factors in transcriptional regulation of target genes. Further analysis of effects of ATF3 knockdown on these targets revealed that stress-induced ATF3 regulates genes in metabolic pathways, cell cycle, apoptosis, cell adhesion, and signalling including insulin, p53, Wnt, and VEGF pathways. Cancer-associated ATF3 is involved in regulation of distinct sets of genes in processes such as calcium signalling, Wnt, p53 and diabetes pathways. Notably, stress-induced ATF3 binds to 40% of p53 targets and activates pro-apoptotic genes such as TNFRSF10B/DR5 and BBC3/PUMA. Cancer-associated ATF3, by contrast, represses these pro-apoptotic genes in addition to CDKN1A/p21. Taken together, our data reveal an extensive network of stress-inducible transcription factors and demonstrate that ATF3 has opposing, cell context-dependent effects on p53 target genes in DNA damage response and cancer development.

Genomic phenotyping of the essential and non-essential yeast genome detects novel pathways for alkylation resistance

BACKGROUND

A myriad of new chemicals has been introduced into our environment and exposure to these agents can damage cells and induce cytotoxicity through different mechanisms, including damaging DNA directly. Analysis of global transcriptional and phenotypic responses in the yeast S. cerevisiae provides means to identify pathways of damage recovery upon toxic exposure.

RESULTS

Here we present a phenotypic screen of S. cerevisiae in liquid culture in a microtiter format. Detailed growth measurements were analyzed to reveal effects on ~5,500 different haploid strains that have either non-essential genes deleted or essential genes modified to generate unstable transcripts. The pattern of yeast mutants that are growth-inhibited (compared to WT cells) reveals the mechanisms ordinarily used to recover after damage. In addition to identifying previously-described DNA repair and cell cycle checkpoint deficient strains, we also identified new functional groups that profoundly affect MMS sensitivity, including RNA processing and telomere maintenance.

CONCLUSIONS

We present here a data-driven method to reveal modes of toxicity of different agents that impair cellular growth. The results from this study complement previous genomic phenotyping studies as we have expanded the data to include essential genes and to provide detailed mutant growth analysis for each individual strain. This eukaryotic testing system could potentially be used to screen compounds for toxicity, to identify mechanisms of toxicity, and to reduce the need for animal testing.

Deciphering the regulatory codes in bacterial genomes

Interactions between cis-regulatory elements and trans-acting factors are fundamental for cellular functions such as transcription. With the revolution in microarrays and sequencing technologies, genome-wide binding locations of trans-acting factors are being determined in large numbers. The richness of the genome-scale information has revealed that the nature of the bacterial transcriptome and regulome are considerably more complex than previously expected. In addition, the emerging view of the bacterial transcriptome is revising the concept of the operon organization of the genome. This review describes current advances in the genome-scale analysis of the interaction between cis-regulatory elements and trans-acting factors in microorganisms.

Relating protein adduction to gene expression changes: a systems approach

Modification of proteins by reactive electrophiles such as the 4-hydroxy-2-nonenal (HNE) plays a critical role in oxidant-associated human diseases. However, little is known about protein adduction and the mechanism by which protein damage elicits adaptive effects and toxicity. We developed a systems approach for relating protein adduction to gene expression changes through the integration of protein adduction, gene expression, protein-DNA interaction, and protein-protein interaction data. Using a random walk strategy, we expanded a list of responsive transcription factors inferred from gene expression studies to upstream signaling networks, which in turn allowed overlaying protein adduction data on the network for the prediction of stress sensors and their associated regulatory mechanisms. We demonstrated the general applicability of transcription factor-based signaling network inference using 103 known pathways. Applying our workflow on gene expression and protein adduction data from HNE-treatment not only rediscovered known mechanisms of electrophile stress but also generated novel hypotheses regarding protein damage sensors. Although developed for analyzing protein adduction data, the framework can be easily adapted for phosphoproteomics and other types of protein modification data.

Deregulation upon DNA damage revealed by joint analysis of context-specific perturbation data

Background

Deregulation between two different cell populations manifests itself in changing gene expression patterns and changing regulatory interactions. Accumulating knowledge about biological networks creates an opportunity to study these changes in their cellular context.

Results

We analyze re-wiring of regulatory networks based on cell population-specific perturbation data and knowledge about signaling pathways and their target genes. We quantify deregulation by merging regulatory signal from the two cell populations into one score. This joint approach, called JODA, proves advantageous over separate analysis of the cell populations and analysis without incorporation of knowledge. JODA is implemented and freely available in a Bioconductor package 'joda'.

Conclusions

Using JODA, we show wide-spread re-wiring of gene regulatory networks upon neocarzinostatin-induced DNA damage in Human cells. We recover 645 deregulated genes in thirteen functional clusters performing the rich program of response to damage. We find that the clusters contain many previously characterized neocarzinostatin target genes. We investigate connectivity between those genes, explaining their cooperation in performing the common functions. We review genes with the most extreme deregulation scores, reporting their involvement in response to DNA damage. Finally, we investigate the indirect impact of the ATM pathway on the deregulated genes, and build a hypothetical hierarchy of direct regulation. These results prove that JODA is a step forward to a systems level, mechanistic understanding of changes in gene regulation between different cell populations.

Multiple sequence-specific factors generate the nucleosome-depleted region on CLN2 promoter

Nucleosome-depleted regions (NDRs) are ubiquitous on eukaryotic promoters. The formation of many NDRs cannot be readily explained by previously proposed mechanisms. Here, we carry out a focused study on a physiologically important NDR in the yeast CLN2 promoter (CLN2pr). We show that this NDR does not result from intrinsically unfavorable histone-DNA interaction. Instead, we identified eight conserved factor binding sites, including that of Reb1, Mcm1, and Rsc3, that cause the local nucleosome depletion. These nucleosome-depleting factors (NDFs) work redundantly, and simultaneously mutating all their binding sites eliminates CLN2pr NDR. The loss of the NDR induces unreliable "on/off" expression in individual cell cycles, but in the presence of the NDR, NDFs have little direct effect on transcription. We present bioinformatic evidence that the formation of many NDRs across the genome involves multiple NDFs. Our findings also provide significant insight into the composition and spatial organization of functional promoters.

Ixr1 is required for the expression of the ribonucleotide reductase Rnr1 and maintenance of dNTP pools

The Saccharomyces cerevisiae Dun1 protein kinase is a downstream target of the conserved Mec1-Rad53 checkpoint pathway. Dun1 regulates dNTP pools during an unperturbed cell cycle and after DNA damage by modulating the activity of ribonucleotide reductase (RNR) by multiple mechanisms, including phosphorylation of RNR inhibitors Sml1 and Dif1. Dun1 also activates DNA-damage-inducible genes by inhibiting the Crt1 transcriptional repressor. Among the genes repressed by Crt1 are three out of four RNR genes: RNR2, RNR3, and RNR4. The fourth RNR gene, RNR1, is also DNA damage-inducible, but is not controlled by Crt1. It has been shown that the deletion of DUN1 is synthetic lethal with the deletion of IXR1, encoding an HMG-box-containing DNA binding protein, but the reason for this lethality is not known. Here we demonstrate that the dun1 ixr1 synthetic lethality is caused by an inadequate RNR activity. The deletion of IXR1 results in decreased dNTP levels due to a reduced RNR1 expression. The ixr1 single mutants compensate for the reduced Rnr1 levels by the Mec1-Rad53-Dun1-Crt1–dependent elevation of Rnr3 and Rnr4 levels and downregulation of Sml1 levels, explaining why DUN1 is indispensible in ixr1 mutants. The dun1 ixr1 synthetic lethality is rescued by an artificial elevation of the dNTP pools. We show that Ixr1 is phosphorylated at several residues and that Ser366, a residue important for the interaction of HMG boxes with DNA, is required for Ixr1 phosphorylation. Ixr1 interacts with DNA at multiple loci, including the RNR1 promoter. Ixr1 levels are decreased in Rad53-deficient cells, which are known to have excessive histone levels. A reduction of the histone gene dosage in the rad53 mutant restores Ixr1 levels. Our results demonstrate that Ixr1, but not Dun1, is required for the proper RNR1 expression both during an unperturbed cell cycle and after DNA damage.

Dun1 is a non-essential protein kinase important for the maintenance of genome stability in budding yeast. Earlier studies found that simultaneous deletion of DUN1 and IXR1 results in lethality, but the reason for this so-called synthetic lethality is not clear. Ixr1 is implicated in DNA repair based on its ability to bind to DNA modified by the anticancer drug cisplatin. Here, we investigated the mechanism behind the ixr1 dun1 synthetic lethality. We demonstrate that yeast strains lacking Ixr1 have decreased amounts of dNTPs, the building blocks of DNA. This is because Ixr1 is required for the normal expression of Rnr1, one of the essential subunits of the enzyme ribonucleotide reductase (RNR), which catalyzes the rate-limiting step in the production of all four dNTPs. Cells lacking Ixr1 compensate the decreased expression of Rnr1 by the increased expression of other RNR genes and degradation of RNR inhibitors. These compensatory processes require Dun1. Hence, cells lacking both Dun1 and Ixr1 have dNTP pools that are too low for survival. Our work identifies a new important player in the synthesis of the building blocks of DNA.

Microbial whole-cell arrays

The coming of age of whole‐cell biosensors, combined with the continuing advances in array technologies, has prepared the ground for the next step in the evolution of both disciplines – the whole‐cell array. In the present review, we highlight the state‐of‐the‐art in the different disciplines essential for a functional bacterial array. These include the genetic engineering of the biological components, their immobilization in different polymers, technologies for live cell deposition and patterning on different types of solid surfaces, and cellular viability maintenance. Also reviewed are the types of signals emitted by the reporter cell arrays, some of the transduction methodologies for reading these signals and the mathematical approaches proposed for their analysis. Finally, we review some of the potential applications for bacterial cell arrays, and list the future needs for their maturation: a richer arsenal of high‐performance reporter strains, better methodologies for their incorporation into hardware platforms, design of appropriate detection circuits, the continuing development of dedicated algorithms for multiplex signal analysis and – most importantly – enhanced long‐term maintenance of viability and activity on the fabricated biochips.

Unraveling condition-dependent networks of transcription factors that control metabolic pathway activity in yeast

While typically many expression levels change in transcription factor mutants, only few of these changes lead to functional changes. The predictive capability of expression and DNA binding data for such functional changes in metabolism is very limited.

Large-scale ¹³C-flux data reveal the condition specificity of transcriptional control of metabolic function.

Transcription control in yeast focuses on the switch between respiration and fermentation.

Follow-up modeling on the basis of transcriptomics and proteomics data suggest the newly discovered Gcn4 control of respiration to be mediated via PKA and Snf1.

Effective control and modulation of cellular behavior is of paramount importance in medicine (Kreeger and Lauffenburger, 2010) and biotechnology (Haynes and Silver, 2009), and requires profound understanding of control mechanisms. In this study, we aim to elucidate the extent to which transcription factors control the operation of yeast metabolism. As a quantitative readout of metabolic function, we monitored the traffic of small molecules through various pathways of central metabolism by ¹³C-flux analysis (Sauer, 2006). The choosen growth conditions represent two different regulatory states of reduced (galactose) and maximal carbon source repression (glucose), as well as a different nitrogen metabolism and two common, permanent stress conditions.

Depending on the growth condition, between 7 and 13% of the deleted transcription factors altered the determined flux ratios (Figure 3). Of the six quantified flux ratios, only the glycolysis/pentose phosphate pathway split, and the convergent ratio of anaplerosis and TCA cycle were controlled by the deleted transcription factors. Thus, we concluded that 23 transcription factors control flux distributions under at least one of the tested growth conditions, leading to 42 condition-dependent interactions of transcription factors with metabolic pathway activity (Figure 4). With two exceptions, all other identified transcription factors interactions controlled the TCA cycle flux. This condition-specific active control of metabolic function could not have been predicted from DNA binding and expression data; that is, 26.1% false negatives, 48.6% true positives.

Of the 23 transcription factors that controlled TCA cycle flux distributions under the tested conditions, only Bas1, Gcn4, Gcr2 and Pho2 exerted control under more than one condition. We identified Cit1, Mdh1 and Idh1/2 with a proteomics approach as the relevant target enzyme that increase the TCA cycle flux. Next, we asked whether Bas1, Gcr2, Gcn4 and Pho2 act directly on the TCA cycle or mediate their effect indirectly. Based on the transcriptomics data, the pattern of differentially activated transcription factors inferred by the differential expression of their target genes suggested reduced glucose repression in all four mutants as the common mechanism.

Starting from the currently largest set of ¹³C-based flux distributions, we identified networks of individual transcription factors that control metabolic pathway activity. These networks of active metabolic control have the following properties. First, they are highly condition dependent, as at most four transcription factors control the same metabolic flux distribution under more than one growth conditions. Second, they focus almost exclusively on the TCA cycle, thereby controlling the switch between respiratory and fermentative metabolism. Third, with four to 14 active transcription factors, they are small compared with gene regulation networks that were obtained from expression and DNA binding data. For the metabolic network studied here, robustness is also apparent from the fact that upregulated TCA cycle fluxes were not sufficient to achieve full respiratory metabolism; that is, absent or low ethanol formation. Several explanations could potentially explain the observed robustness. The most likely explanation is that environmental signals might be transmitted by different signaling pathways to several transcription factors, whose orchestrated action on multiple target genes is necessary to achieve a functional flux response. This hypothesis would explain why several transcription factors exert flux effects on the same pathway, but each flux effect is relatively small, as further, coordinated manipulations would be necessary to further improve the respiratory flux. Our findings demonstrate the importance of identifying and quantifying the extent to which regulatory effectors alter cellular function.

Which transcription factors control the distribution of metabolic fluxes under a given condition? We address this question by systematically quantifying metabolic fluxes in 119 transcription factor deletion mutants of Saccharomyces cerevisiae under five growth conditions. While most knockouts did not affect fluxes, we identified 42 condition-dependent interactions that were mediated by a total of 23 transcription factors that control almost exclusively the cellular decision between respiration and fermentation. This relatively sparse, condition-specific network of active metabolic control contrasts with the much larger gene regulation network inferred from expression and DNA binding data. Based on protein and transcript analyses in key mutants, we identified three enzymes in the tricarboxylic acid cycle as the key targets of this transcriptional control. For the transcription factor Gcn4, we demonstrate that this control is mediated through the PKA and Snf1 signaling cascade. The discrepancy between flux response predictions, based on the known regulatory network architecture and our functional ¹³C-data, demonstrates the importance of identifying and quantifying the extent to which regulatory effectors alter cellular functions.

Exploring the bZIP transcription factor regulatory network in Neurospora crassa

Transcription factors (TFs) are key nodes of regulatory networks in eukaryotic organisms, including filamentous fungi such as Neurospora crassa. The 178 predicted DNA-binding TFs in N. crassa are distributed primarily among six gene families, which represent an ancient expansion in filamentous ascomycete genomes; 98 TF genes show detectable expression levels during vegetative growth of N. crassa, including 35 that show a significant difference in expression level between hyphae at the periphery versus hyphae in the interior of a colony. Regulatory networks within a species genome include paralogous TFs and their respective target genes (TF regulon). To investigate TF network evolution in N. crassa, we focused on the basic leucine zipper (bZIP) TF family, which contains nine members. We performed baseline transcriptional profiling during vegetative growth of the wild-type and seven isogenic, viable bZIP deletion mutants. We further characterized the regulatory network of one member of the bZIP family, NCU03905. NCU03905 encodes an Ap1-like protein (NcAp-1), which is involved in resistance to multiple stress responses, including oxidative and heavy metal stress. Relocalization of NcAp-1 from the cytoplasm to the nucleus was associated with exposure to stress. A comparison of the NcAp-1 regulon with Ap1-like regulons in Saccharomyces cerevisiae, Schizosaccharomyces pombe, Candida albicans and Aspergillus fumigatus showed both conservation and divergence. These data indicate how N. crassa responds to stress and provide information on pathway evolution.

Autophagy-dependent regulation of the DNA damage response protein ribonucleotide reductase 1

Protein synthesis and degradation are posttranscriptional pathways used by cells to regulate protein levels. We have developed a systems biology approach to identify targets of posttranscriptional regulation and we have employed this system in Saccharomyces cerevisiae to study the DNA damage response. We present evidence that 50% to 75% of the transcripts induced by alkylation damage are regulated posttranscriptionally. Significantly, we demonstrate that two transcriptionally-induced DNA damage response genes, RNR1 and RNR4, fail to show soluble protein level increases after DNA damage. To determine one of the associated mechanisms of posttranscriptional regulation, we tracked ribonucleotide reductase 1 (Rnr1) protein levels during the DNA damage response. We show that RNR1 is actively translated after damage and that a large fraction of the corresponding Rnr1 protein is packaged into a membrane-bound structure and transported to the vacuole for degradation, with these last two steps dependent on autophagy proteins. We found that inhibition of target of rapamycin (TOR) signaling and subsequent induction of autophagy promoted an increase in targeting of Rnr1 to the vacuole and a decrease in soluble Rnr1 protein levels. In addition, we demonstrate that defects in autophagy result in an increase in soluble Rnr1 protein levels and a DNA damage phenotype. Our results highlight roles for autophagy and TOR signaling in regulating a specific protein and demonstrate the importance of these pathways in optimizing the DNA damage response.

The living microarray: a high-throughput platform for measuring transcription dynamics in single cells

Background

Current methods of measuring transcription in high-throughput have led to significant improvements in our knowledge of transcriptional regulation and Systems Biology. However, endpoint measurements obtained from methods that pool populations of cells are not amenable to studying time-dependent processes that show cell heterogeneity.

Results

Here we describe a high-throughput platform for measuring transcriptional changes in real time in single mammalian cells. By using reverse transfection microarrays we are able to transfect fluorescent reporter plasmids into 600 independent clusters of cells plated on a single microscope slide and image these clusters every 20 minutes. We use a fast-maturing, destabilized and nuclear-localized reporter that is suitable for automated segmentation to accurately measure promoter activity in single cells. We tested this platform with synthetic drug-inducible promoters that showed robust induction over 24 hours. Automated segmentation and tracking of over 11 million cell images during this period revealed that cells display substantial heterogeneity in their responses to the applied treatment, including a large proportion of transfected cells that do not respond at all.

Conclusions

The results from our single-cell analysis suggest that methods that measure average cellular responses, such as DNA microarrays, RT-PCR and chromatin immunoprecipitation, characterize a response skewed by a subset of cells in the population. Our method is scalable and readily adaptable to studying complex systems, including cell proliferation, differentiation and apoptosis.

Nutritional regulation of division of labor in honey bees: toward a systems biology perspective

Organisms adapt their behavior and physiology to environmental conditions through processes of phenotypic plasticity. In one well-studied example, the division of labor among worker honey bees involves a stereotyped yet plastic pattern of behavioral and physiological maturation. Early in life, workers perform brood care and other in-hive tasks and have large internal nutrient stores; later in life, they forage for nectar and pollen outside the hive and have small nutrient stores. The pace of maturation depends on colony conditions, and the environmental, physiological, and genomic mechanisms by which this occurs are being actively investigated. Here we review current knowledge of the mechanisms by which a key environmental variable, nutritional status, influences worker honey bee division of labor. These studies demonstrate that changes in individual nutritional status and conserved food-related molecular and hormonal pathways regulate the age at which individual bees begin to forage. We then outline ways in which systems biology approaches, enabled by the sequencing of the honey bee genome, will allow researchers to gain deeper insight into nutritional regulation of honey bee behavior, and phenotypic plasticity in general.

TF-centered downstream gene set enrichment analysis: Inference of causal regulators by integrating TF-DNA interactions and protein post-translational modifications information

Background

Inference of causal regulators responsible for gene expression changes under different conditions is of great importance but remains rather challenging. To date, most approaches use direct binding targets of transcription factors (TFs) to associate TFs with expression profiles. However, the low overlap between binding targets of a TF and the affected genes of the TF knockout limits the power of those methods.

Results

We developed a TF-centered downstream gene set enrichment analysis approach to identify potential causal regulators responsible for expression changes. We constructed hierarchical and multi-layer regulation models to derive possible downstream gene sets of a TF using not only TF-DNA interactions, but also, for the first time, post-translational modifications (PTM) information. We verified our method in one expression dataset of large-scale TF knockout and another dataset involving both TF knockout and TF overexpression. Compared with the flat model using TF-DNA interactions alone, our method correctly identified five more actual perturbed TFs in large-scale TF knockout data and six more perturbed TFs in overexpression data. Potential regulatory pathways downstream of three perturbed regulators— SNF1, AFT1 and SUT1 —were given to demonstrate the power of multilayer regulation models integrating TF-DNA interactions and PTM information. Additionally, our method successfully identified known important TFs and inferred some novel potential TFs involved in the transition from fermentative to glycerol-based respiratory growth and in the pheromone response. Downstream regulation pathways of SUT1 and AFT1 were also supported by the mRNA and/or phosphorylation changes of their mediating TFs and/or “modulator” proteins.

Conclusions

The results suggest that in addition to direct transcription, indirect transcription and post-translational regulation are also responsible for the effects of TFs perturbation, especially for TFs overexpression. Many TFs inferred by our method are supported by literature. Multiple TF regulation models could lead to new hypotheses for future experiments. Our method provides a valuable framework for analyzing gene expression data to identify causal regulators in the context of TF-DNA interactions and PTM information.

Comparative transcriptome profiling analyses during the lag phase uncover YAP1, PDR1, PDR3, RPN4, and HSF1 as key regulatory genes in genomic adaptation to the lignocellulose derived inhibitor HMF for Saccharomyces cerevisiae

BACKGROUND

The yeast Saccharomyces cerevisiae is able to adapt and in situ detoxify lignocellulose derived inhibitors such as furfural and HMF. The length of lag phase for cell growth in response to the inhibitor challenge has been used to measure tolerance of strain performance. Mechanisms of yeast tolerance at the genome level remain unknown. Using systems biology approach, this study investigated comparative transcriptome profiling, metabolic profiling, cell growth response, and gene regulatory interactions of yeast strains and selective gene deletion mutations in response to HMF challenges during the lag phase of growth.

RESULTS

We identified 365 candidate genes and found at least 3 significant components involving some of these genes that enable yeast adaptation and tolerance to HMF in yeast. First, functional enzyme coding genes such as ARI1, ADH6, ADH7, and OYE3, as well as gene interactions involved in the biotransformation and inhibitor detoxification were the direct driving force to reduce HMF damages in cells. Expressions of these genes were regulated by YAP1 and its closely related regulons. Second, a large number of PDR genes, mainly regulated by PDR1 and PDR3, were induced during the lag phase and the PDR gene family-centered functions, including specific and multiple functions involving cellular transport such as TPO1, TPO4, RSB1, PDR5, PDR15, YOR1, and SNQ2, promoted cellular adaptation and survival in order to cope with the inhibitor stress. Third, expressed genes involving degradation of damaged proteins and protein modifications such as SHP1 and SSA4, regulated by RPN4, HSF1, and other co-regulators, were necessary for yeast cells to survive and adapt the HMF stress. A deletion mutation strain Δrpn4 was unable to recover the growth in the presence of HMF.

CONCLUSIONS

Complex gene interactions and regulatory networks as well as co-regulations exist in yeast adaptation and tolerance to the lignocellulose derived inhibitor HMF. Both induced and repressed genes involving diversified functional categories are accountable for adaptation and energy rebalancing in yeast to survive and adapt the HMF stress during the lag phase of growth. Transcription factor genes YAP1, PDR1, PDR3, RPN4, and HSF1 appeared to play key regulatory rules for global adaptation in the yeast S. cerevisiae.

Functional genomics complements quantitative genetics in identifying disease-gene associations

An ultimate goal of genetic research is to understand the connection between genotype and phenotype in order to improve the diagnosis and treatment of diseases. The quantitative genetics field has developed a suite of statistical methods to associate genetic loci with diseases and phenotypes, including quantitative trait loci (QTL) linkage mapping and genome-wide association studies (GWAS). However, each of these approaches have technical and biological shortcomings. For example, the amount of heritable variation explained by GWAS is often surprisingly small and the resolution of many QTL linkage mapping studies is poor. The predictive power and interpretation of QTL and GWAS results are consequently limited. In this study, we propose a complementary approach to quantitative genetics by interrogating the vast amount of high-throughput genomic data in model organisms to functionally associate genes with phenotypes and diseases. Our algorithm combines the genome-wide functional relationship network for the laboratory mouse and a state-of-the-art machine learning method. We demonstrate the superior accuracy of this algorithm through predicting genes associated with each of 1157 diverse phenotype ontology terms. Comparison between our prediction results and a meta-analysis of quantitative genetic studies reveals both overlapping candidates and distinct, accurate predictions uniquely identified by our approach. Focusing on bone mineral density (BMD), a phenotype related to osteoporotic fracture, we experimentally validated two of our novel predictions (not observed in any previous GWAS/QTL studies) and found significant bone density defects for both Timp2 and Abcg8 deficient mice. Our results suggest that the integration of functional genomics data into networks, which itself is informative of protein function and interactions, can successfully be utilized as a complementary approach to quantitative genetics to predict disease risks. All supplementary material is available at http://cbfg.jax.org/phenotype.

Many recent efforts to understand the genetic origins of complex diseases utilize statistical approaches to analyze phenotypic traits measured in genetically well-characterized populations. While these quantitative genetics methods are powerful, their success is limited by sampling biases and other confounding factors, and the biological interpretation of results can be challenging since these methods are not based on any functional information for candidate loci. On the other hand, the functional genomics field has greatly expanded in past years, both in terms of experimental approaches and analytical algorithms. However, functional approaches have been applied to understanding phenotypes in only the most basic ways. In this study, we demonstrate that functional genomics can complement traditional quantitative genetics by analytically extracting protein function information from large collections of high throughput data, which can then be used to predict genotype-phenotype associations. We applied our prediction methodology to the laboratory mouse, and we experimentally confirmed a role in osteoporosis for two of our predictions that were not candidates from any previous quantitative genetics study. The ability of our approach to produce accurate and unique predictions implies that functional genomics can complement quantitative genetics and can help address previous limitations in identifying disease genes.

Sequential recruitment of SAGA and TFIID in a genomic response to DNA damage in Saccharomyces cerevisiae

Eukaryotic genes respond to their environment by changing the expression of selected genes. The question we address here is whether distinct transcriptional responses to different environmental signals elicit distinct modes of assembly of the transcription machinery. In particular, we examine transcription complex assembly by the stress-directed SAGA complex versus the housekeeping assembly factor TFIID. We focus on genomic responses to the DNA damaging agent methyl methanesulfonate (MMS) in comparison to responses to acute heat shock, looking at changes in genome-wide factor occupancy measured by chromatin immunoprecipitation-microchip (ChIP-chip) and ChIP-sequencing analyses. Our data suggest that MMS-induced genes undergo transcription complex assembly sequentially, first involving SAGA and then involving a slower TFIID recruitment, whereas heat shock genes utilize the SAGA and TFIID pathways rapidly and in parallel. Also Crt1, the repressor of model MMS-inducible ribonucleotide reductase genes, was found not to play a wider role in repression of DNA damage-inducible genes. Taken together, our findings reveal a distinct involvement of gene and chromatin regulatory factors in response to DNA damage versus heat shock and suggest different implementations of the SAGA and TFIID assembly pathways that may depend upon whether a sustained or transient change in gene expression ensues.

Protein evolution in yeast transcription factor subnetworks

When averaged over the full yeast protein-protein interaction and transcriptional regulatory networks, protein hubs with many interaction partners or regulators tend to evolve significantly more slowly due to increased negative selection. However, genome-wide analysis of protein evolution in the subnetworks of associations involving yeast transcription factors (TFs) reveals that TF hubs do not tend to evolve significantly more slowly than TF non-hubs. This result holds for all four major types of TF hubs: interaction hubs, regulatory in-degree and out-degree hubs, as well as co-regulatory hubs that jointly regulate target genes with many TFs. Furthermore, TF regulatory in-degree hubs tend to evolve significantly more quickly than TF non-hubs. Most importantly, the correlations between evolutionary rate (K(A)/K(S)) and degrees for TFs are significantly more positive than those for generic proteins within the same global protein-protein interaction and transcriptional regulatory networks. Compared to generic protein hubs, TF hubs operate at a higher level in the hierarchical structure of cellular networks, and hence experience additional evolutionary forces (relaxed negative selection or positive selection through network rewiring). The striking difference between the evolution of TF hubs and generic protein hubs demonstrates that components within the same global network can be governed by distinct organizational and evolutionary principles.

Evolution of transcriptional regulatory networks in yeast populations

Saccharomyces cerevisiae is the most thoroughly studied eukaryote at the cellular, molecular, and genetic level. Recent boost in whole-genome sequencing, array-based allelic variation mapping, and genome-wide transcriptional profiling have unprecedentedly advanced knowledge on cell biology and evolution of this organism. It is now possible to investigate how evolution shapes the functional architecture of yeast genomes and how this architecture relates to the evolution of the regulatory networks controlling the expression of genes that make up an organism. A survey of the information on genetic and whole-genome expression variations in yeast populations shows that a significant score of gene expression variation is dependent on genotype-by-environment interaction. In some cases, large trans effects are the result of mutations in the promoters of key master regulator genes. Yet trans-variation in environmental sensor proteins appears to explain the majority of the expression patterns differentiating strains in natural populations. The challenge is now to use this information to model how individual genetic polymorphisms interact in a condition-dependent fashion to produce phenotypic change. In this study, we show how fruitful application of systems biology to the progress of science and medicine requires the use of evolution as a lens to reconstruct the hierarchical structure of regulation of biological systems. The lessons learned in yeast can be of paramount importance in advancing the application of genomics and systems biology to emerging fields including personalized medicine.

Discovering conditional co-regulated protein complexes by integrating diverse data sources

Abstract

Identification of response-modulated genetic interactions by sensitivity-based epistatic analysis

Background

High-throughput genomics has enabled the global mapping of genetic interactions based on the phenotypic impact of combinatorial genetic perturbations. An important next step is to understand how these networks are dynamically remodelled in response to environmental stimuli. Here, we report on the development and testing of a method to identify such interactions. The method was developed from first principles by treating the impact on cellular growth of environmental perturbations equivalently to that of gene deletions. This allowed us to establish a novel neutrality function marking the absence of epistasis in terms of sensitivity phenotypes rather than fitness. We tested the method by identifying fitness- and sensitivity-based interactions involved in the response to drug-induced DNA-damage of budding yeast Saccharomyces cerevisiae using two mutant libraries - one containing transcription factor deletions, and the other containing deletions of DNA repair genes.

Results

Within the library of transcription factor deletion mutants, we observe significant differences in the sets of genetic interactions identified by the fitness- and sensitivity-based approaches. Notably, among the most likely interactions, only ~50% were identified by both methods. While interactions identified solely by the sensitivity-based approach are modulated in response to drug-induced DNA damage, those identified solely by the fitness-based method remained invariant to the treatment. Comparison of the identified interactions to transcriptional profiles and protein-DNA interaction data indicate that the sensitivity-based method improves the identification of interactions involved in the DNA damage response. Additionally, for the library containing DNA repair mutants, we observe that the sensitivity-based method improves the grouping of functionally related genes, as well as the identification of protein complexes, involved in DNA repair.

Conclusion

Our results show that the identification of response-modulated genetic interactions can be improved by incorporating the effect of a changing environment directly into the neutrality function marking the absence of epistasis. We expect that this extension of conventional epistatic analysis will facilitate the development of dynamic models of gene networks from quantitative measurements of genetic interactions. While the method was developed for growth phenotype, it should apply equally well for other phenotypes, including the expression of fluorescent reporters.

Genome-scale approaches to investigate oxidative DNA damage

In the trend of biological science after the completion of the human genome project, appreciation of an organism as a system rather than the sum of many molecular functions is necessary. On the investigation of DNA damage and repair, therefore, the orientation toward systematic and comprehensive genome-scale approaches is rapidly growing. The immunoprecipitation-based technique combined with high-density microarrays is one of the promising methods to provide access to such novel research strategies. We propose this sort of research area as oxygenomics.

A survey of yeast genomic assays for drug and target discovery

Over the past decade, the development and application of chemical genomic assays using the model organism Saccharomyces cerevisiae has provided powerful methods to identify the mechanism of action of known drugs and novel small molecules in vivo. These assays identify drug target candidates, genes involved in buffering drug target pathways and also help to define the general cellular response to small molecules. In this review, we examine current yeast chemical genomic assays and summarize the potential applications of each approach.

A systems-biology approach to modular genetic complexity

Multiple high-throughput genetic interaction studies have provided substantial evidence of modularity in genetic interaction networks. However, the correspondence between these network modules and specific pathways of information flow is often ambiguous. Genetic interaction and molecular interaction analyses have not generated large-scale maps comprising multiple clearly delineated linear pathways. We seek to clarify the situation by discerning the difference between genetic modules and classical pathways. We review a method to optimize the discovery of biologically meaningful genetic modules based on a previously described context-dependent information measure to obtain maximally informative networks. We compare the results of this method with the established measures of network clustering and find that it balances global and local clustering information in networks. We further discuss the consequences for genetic interaction networks and propose a framework for the analysis of genetic modularity.

An information-flow-based model with dissipation, saturation and direction for active pathway inference

Background

Biological systems process the genetic information and environmental signals through pathways. How to map the pathways systematically and efficiently from high-throughput genomic and proteomic data is a challenging open problem. Previous methods design different heuristics but do not describe explicitly the behaviours of the information flow.

Results

In this study, we propose new concepts of dissipation, saturation and direction to decipher the information flow behaviours in the pathways and thereby infer the biological pathways from a given source to its target. This model takes into account explicitly the common features of the information transmission and provides a general framework to model the biological pathways. It can incorporate different types of bio-molecular interactions to infer the signal transduction pathways and interpret the expression quantitative trait loci (eQTL) associations. The model is formulated as a linear programming problem and thus is solved efficiently. Experiments on the real data of yeast indicate that the reproduced pathways are highly consistent with the current knowledge.

Conclusions

Our model explicitly treats the biological pathways as information flows with dissipation, saturation and direction. The effective applications suggest that the three new concepts may be valid to describe the organization rules of biological pathways. The deduced linear programming should be a promising tool to infer the various biological pathways from the high-throughput data.

Characterizing regulatory path motifs in integrated networks using perturbational data

Pathicular – a Cytoscape plugin for analysing cellular responses to transcription factor perturbations is presented

We introduce Pathicular http://bioinformatics.psb.ugent.be/software/details/Pathicular, a Cytoscape plugin for studying the cellular response to perturbations of transcription factors by integrating perturbational expression data with transcriptional, protein-protein and phosphorylation networks. Pathicular searches for 'regulatory path motifs', short paths in the integrated physical networks which occur significantly more often than expected between transcription factors and their targets in the perturbational data. A case study in Saccharomyces cerevisiae identifies eight regulatory path motifs and demonstrates their biological significance.