A systems approach to mapping DNA damage response pathways

Integrated cellular network of transcription regulations and protein-protein interactions

Background

With the accumulation of increasing omics data, a key goal of systems biology is to construct networks at different cellular levels to investigate cellular machinery of the cell. However, there is currently no satisfactory method to construct an integrated cellular network that combines the gene regulatory network and the signaling regulatory pathway.

Results

In this study, we integrated different kinds of omics data and developed a systematic method to construct the integrated cellular network based on coupling dynamic models and statistical assessments. The proposed method was applied to S. cerevisiae stress responses, elucidating the stress response mechanism of the yeast. From the resulting integrated cellular network under hyperosmotic stress, the highly connected hubs which are functionally relevant to the stress response were identified. Beyond hyperosmotic stress, the integrated network under heat shock and oxidative stress were also constructed and the crosstalks of these networks were analyzed, specifying the significance of some transcription factors to serve as the decision-making devices at the center of the bow-tie structure and the crucial role for rapid adaptation scheme to respond to stress. In addition, the predictive power of the proposed method was also demonstrated.

Conclusions

We successfully construct the integrated cellular network which is validated by literature evidences. The integration of transcription regulations and protein-protein interactions gives more insight into the actual biological network and is more predictive than those without integration. The method is shown to be powerful and flexible and can be used under different conditions and for different species. The coupling dynamic models of the whole integrated cellular network are very useful for theoretical analyses and for further experiments in the fields of network biology and synthetic biology.

High-resolution transcription atlas of the mitotic cell cycle in budding yeast

BACKGROUND

Extensive transcription of non-coding RNAs has been detected in eukaryotic genomes and is thought to constitute an additional layer in the regulation of gene expression. Despite this role, their transcription through the cell cycle has not been studied; genome-wide approaches have only focused on protein-coding genes. To explore the complex transcriptome architecture underlying the budding yeast cell cycle, we used 8 bp tiling arrays to generate a 5 minute-resolution, strand-specific expression atlas of the whole genome.

RESULTS

We discovered 523 antisense transcripts, of which 80 cycle or are located opposite periodically expressed mRNAs, 135 unannotated intergenic non-coding RNAs, of which 11 cycle, and 109 cell-cycle-regulated protein-coding genes that had not previously been shown to cycle. We detected periodic expression coupling of sense and antisense transcript pairs, including antisense transcripts opposite of key cell-cycle regulators, like FAR1 and TAF2.

CONCLUSIONS

Our dataset presents the most comprehensive resource to date on gene expression during the budding yeast cell cycle. It reveals periodic expression of both protein-coding and non-coding RNA and profiles the expression of non-annotated RNAs throughout the cell cycle for the first time. This data enables hypothesis-driven mechanistic studies concerning the functions of non-coding RNAs.

Different sets of QTLs influence fitness variation in yeast

We have carried out a combination of in-lab-evolution (ILE) and congenic crosses to identify the gene sets that contribute to the ability of yeast cells to survive under alkali stress.

Each selected line acquired a different set of mutations, all resulting in the same phenotype. We identified a total of 15 genes in ILE and 17 candidates in the congenic approach, and studied their individual contribution to the phenotype.

The total additive effect of the QTLs was much larger than the difference between the ancestor and the evolved strains, suggesting epistatic interactions between the QTLs.

None of the genes identified encode structural components of the pH machinery. Instead, most encode regulatory functions, such as ubiquitin ligases, chromatin remodelers, GPI anchoring and copper/iron sensing transcription factors.

The majority of phenotypes in nature are complex traits affected by multiple genes [usually called quantitative trait loci (QTLs)], as well as by environmental factors. Many traits with practical importance such as crop yield in plants and susceptibility to various diseases in humans fall under this category. Understanding the architecture of complex traits has become the new frontier of genetic research, and many studies have greatly contributed to this field. However, to date, the genetic basis of only a few of these traits has been identified, and many questions regarding the architecture of complex traits and the accumulation of QTLs during evolution still remain unanswered. Among them are: How many QTLs affect complex phenotypes? What is the effect of each QTL? How do complex traits change during evolution? Is the adaptation process repeatable?, etc. In order to identify the QTLs that affect one of the important components of fitness variability in yeast, and to answer some of the questions above, we combined in-lab evolution (ILE) with the construction of congenic lines to isolate and map several gene sets that contribute to the ability of yeast cells to survive under alkali stress.

We carried out an ILE experiment, in which we grew yeast populations under increasing alkali stress to enrich for beneficial mutations. This process was followed by hybridizations to tiling arrays to identify the mutations acquired during the laboratory selective process. The ILE procedure revealed mutations in 15 genes, thus defining the QTLs and mechanisms that affect, in a quantitative fashion, the ability to cope with alkali stress. Our results indicate that during ILE several populations acquired different sets of QTLs that conferred the same phenotype. We identified each individual mutation in these strains, and validated and estimated their contribution to the phenotype. The total additive effect of the QTLs was much larger than the difference between the ancestor and the evolved strains, suggesting epistatic interactions between the QTLs.

In addition to the ILE, we have studied the mechanisms regulating fitness under alkali stress at natural habitats. We used a clinically isolated strain able to grow at high pH and a standard laboratory strain with a limited ability to sustain high pH as the parents of series of backcrosses to construct congenic lines up to the 8th generation. Seventeen genomic intervals that are candidates to contain QTLs were thus identified. In order to detect the contributing QTL in each interval, a predictive algorithm was applied, which scored the candidate genes in each genomic interval based on their interactions and similarity to the ILE genes. The algorithm was validated by testing the effect of the predicted candidate gene's deletions on the phenotype. Twelve out of 29 deletions were found to affect the trait (P-value 0.023).

Interestingly, our results show that almost all beneficial mutations affected regulatory genes, and not structural components of the pH homeostasis machinery (such as proton pumps, which control the cell's pH). The genes identified affect global regulators, such as ubiquitin ligases, proteins involved in GPI anchoring, copper sensing and chromatin remodelers. Thus, we show that adaptive changes tend to occur in genes with wide influence, rather than in genes narrowly affecting the phenotype selected for.

One example of genes identified both in the ILE and in the congenic lines is the copper-sensing transcription factor MAC1, and its downstream targets CTR1 and CTR3, which encode copper transporters. Different mutations at the same residue (Cys 271) were found in four out of five independent ILE lines. These mutations inactivate a copper-sensing region of Mac1 and cause up-regulation of its target genes. The CTR1 and CTR3 genes were identified in the congenic lines. Moreover, we found that a Ty transposable element is responsible for the decreased expression of CTR3 in some strains, and its excision caused transcriptional activation, affecting the ability to thrive at high pH.

This work provides insights on both evolutionary and genetic issues (such as the appearance of adaptive mutations and the architecture of complex traits), while at the same time providing information about the mechanisms that contribute to growth at high pH, a subject with ramifications for cell physiology, pathogenicity, and stress response.

Most of the phenotypes in nature are complex and are determined by many quantitative trait loci (QTLs). In this study we identify gene sets that contribute to one important complex trait: the ability of yeast cells to survive under alkali stress. We carried out an in-lab evolution (ILE) experiment, in which we grew yeast populations under increasing alkali stress to enrich for beneficial mutations. The populations acquired different sets of affecting alleles, showing that evolution can provide alternative solutions to the same challenge. We measured the contribution of each allele to the phenotype. The sum of the effects of the QTLs was larger than the difference between the ancestor phenotype and the evolved strains, suggesting epistatic interactions between the QTLs. In parallel, a clinical isolated strain was used to map natural QTLs affecting growth at high pH. In all, 17 candidate regions were found. Using a predictive algorithm based on the distances in protein-interaction networks, candidate genes were defined and validated by gene disruption. Many of the QTLs found by both methods are not directly implied in pH homeostasis but have more general, and often regulatory, roles.

Antibiotic sensitivity profiles determined with an Escherichia coli gene knockout collection: generating an antibiotic bar code

We have defined a sensitivity profile for 22 antibiotics by extending previous work testing the entire KEIO collection of close to 4,000 single-gene knockouts in Escherichia coli for increased susceptibility to 1 of 14 different antibiotics (ciprofloxacin, rifampin [rifampicin], vancomycin, ampicillin, sulfamethoxazole, gentamicin, metronidazole, streptomycin, fusidic acid, tetracycline, chloramphenicol, nitrofurantoin, erythromycin, and triclosan). We screened one or more subinhibitory concentrations of each antibiotic, generating more than 80,000 data points and allowing a reduction of the entire collection to a set of 283 strains that display significantly increased sensitivity to at least one of the antibiotics. We used this reduced set of strains to determine a profile for eight additional antibiotics (spectinomycin, cephradine, aztreonem, colistin, neomycin, enoxacin, tobramycin, and cefoxitin). The profiles for the 22 antibiotics represent a growing catalog of sensitivity fingerprints that can be separated into two components, multidrug-resistant mutants and those mutants that confer relatively specific sensitivity to the antibiotic or type of antibiotic tested. The latter group can be represented by a set of 20 to 60 strains that can be used for the rapid typing of antibiotics by generating a virtual bar code readout of the specific sensitivities. Taken together, these data reveal the complexity of intrinsic resistance and provide additional targets for the design of codrugs (or combinations of drugs) that potentiate existing antibiotics.

An overview of the computational analyses and discovery of transcription factor binding sites

Here we provide a pragmatic, high-level overview of the computational approaches and tools for the discovery of transcription factor binding sites. Unraveling transcription regulatory networks and their malfunctions such as cancer became feasible due to recent stellar progress in experimental techniques and computational analyses. While predictions of isolated sites still pose notorious challenges, cis-regulatory modules (clusters) of binding sites can now be identified with high accuracy. Further support comes from conserved DNA segments, co-regulation, transposable elements, nucleosomes, and three-dimensional chromosomal structures. We introduce computational tools for the analysis and interpretation of chromatin immunoprecipitation, next-generation sequencing, SELEX, and protein-binding microarray results. Because immunoprecipitation produces overly large DNA segments and well over half of the sequencing reads from constitute background noise, methods are presented for background correction, sequence read mapping, peak calling, false discovery rate estimation, and co-localization analyses. To discover short binding site motifs from extensive immunoprecipitation segments, we recommend algorithms and software based on expectation maximization and Gibbs sampling. Data integration using several databases further improves performance. Binding sites can be visualized in genomic and chromatin context using genome browsers. Binding site information, integrated with co-expression in large compendia of gene expression experiments, allows us to reveal complex transcriptional regulatory networks.

Mutagen structure and transcriptional response: induction of distinct transcriptional profiles in Salmonella TA100 by the drinking-water mutagen MX and its homologues

The relationship between chemical structure and biological activity has been examined for various compounds and endpoints for decades. To explore this question relative to global gene expression, we performed microarray analysis of Salmonella TA100 after treatment under conditions of mutagenesis by the drinking-water mutagen MX and two of its structural homologues, BA-1, and BA-4. Approximately 50% of the genes expressed differentially following MX treatment were unique to MX; the corresponding percentages for BA-1 and BA-4 were 91 and 80, respectively. Among these mutagens, there was no overlap of altered Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways or RegulonDB regulons. Among the 25 Comprehensive Microbial Resource functions altered by these mutagens, only four were altered by more than one mutagen. Thus, the three structural homologues produced distinctly different transcriptional profiles, with none having a single altered KEGG pathway in common. We tested whether structural similarity between a xenobiotic and endogenous metabolites could explain transcriptional changes. For the 830 intracellular metabolites in Salmonella that we examined, BA-1 had a high degree of structural similarity to 2-isopropylmaleate, which is the substrate for isopropylmalate isomerase. The transcription of the gene for this enzyme was suppressed twofold in BA-1-treated cells. Finally, the distinct transcriptional responses of the three structural homologues were not predicted by a set of phenotypic anchors, including mutagenic potency, cytotoxicity, mutation spectra, and physicochemical properties. Ultimately, explanations for varying transcriptional responses induced by compounds with similar structures await an improved understanding of the interactions between small molecules and the cellular machinery.

Computational methods for analyzing dynamic regulatory networks

Regulatory and other networks in the cell change in a highly dynamic way over time and in response to internal and external stimuli. While several different types of high-throughput experimental procedures are available to study systems in the cell, most only measure static properties of such networks. Information derived from sequence data is inherently static, and most interaction data sets are measured in a static way as well. In this chapter we discuss one of the few abundant sources for temporal information, time series expression data. We provide an overview of the methods suggested for clustering this type of data to identify functionally related genes. We also discuss methods for inferring causality and interactions using lagged correlations and regression analysis. Finally, we present methods for combining time series expression data with static data to reconstruct dynamic regulatory networks. We point to software tools implementing the methods discussed in this chapter. As more temporal measurements become available, the importance of analyzing such data and of combining it with other types of data will greatly increase.

Microbial 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 chapter, 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.

Uncovering transcriptional interactions via an adaptive fuzzy logic approach

BACKGROUND

To date, only a limited number of transcriptional regulatory interactions have been uncovered. In a pilot study integrating sequence data with microarray data, a position weight matrix (PWM) performed poorly in inferring transcriptional interactions (TIs), which represent physical interactions between transcription factors (TF) and upstream sequences of target genes. Inferring a TI means that the promoter sequence of a target is inferred to match the consensus sequence motifs of a potential TF, and their interaction type such as AT or RT is also predicted. Thus, a robust PWM (rPWM) was developed to search for consensus sequence motifs. In addition to rPWM, one feature extracted from ChIP-chip data was incorporated to identify potential TIs under specific conditions. An interaction type classifier was assembled to predict activation/repression of potential TIs using microarray data. This approach, combining an adaptive (learning) fuzzy inference system and an interaction type classifier to predict transcriptional regulatory networks, was named AdaFuzzy.

RESULTS

AdaFuzzy was applied to predict TIs using real genomics data from Saccharomyces cerevisiae. Following one of the latest advances in predicting TIs, constrained probabilistic sparse matrix factorization (cPSMF), and using 19 transcription factors (TFs), we compared AdaFuzzy to four well-known approaches using over-representation analysis and gene set enrichment analysis. AdaFuzzy outperformed these four algorithms. Furthermore, AdaFuzzy was shown to perform comparably to 'ChIP-experimental method' in inferring TIs identified by two sets of large scale ChIP-chip data, respectively. AdaFuzzy was also able to classify all predicted TIs into one or more of the four promoter architectures. The results coincided with known promoter architectures in yeast and provided insights into transcriptional regulatory mechanisms.

CONCLUSION

AdaFuzzy successfully integrates multiple types of data (sequence, ChIP, and microarray) to predict transcriptional regulatory networks. The validated success in the prediction results implies that AdaFuzzy can be applied to uncover TIs in yeast.

Dissection of a complex transcriptional response using genome-wide transcriptional modelling

Modern genomics technologies generate huge data sets creating a demand for systems level, experimentally verified, analysis techniques. We examined the transcriptional response to DNA damage in a human T cell line (MOLT4) using microarrays. By measuring both mRNA accumulation and degradation over a short time course, we were able to construct a mechanistic model of the transcriptional response. The model predicted three dominant transcriptional activity profiles—an early response controlled by NFκB and c-Jun, a delayed response controlled by p53, and a late response related to cell cycle re-entry. The method also identified, with defined confidence limits, the transcriptional targets associated with each activity. Experimental inhibition of NFκB, c-Jun and p53 confirmed that target predictions were accurate. Model predictions directly explained 70% of the 200 most significantly upregulated genes in the DNA-damage response. Genome-wide transcriptional modelling (GWTM) requires no prior knowledge of either transcription factors or their targets. GWTM is an economical and effective method for identifying the main transcriptional activators in a complex response and confidently predicting their targets.

A combined expression-interaction model for inferring the temporal activity of transcription factors

Methods suggested for reconstructing regulatory networks can be divided into two sets based on how the activity level of transcription factors (TFs) is inferred. The first group of methods relies on the expression levels of TFs, assuming that the activity of a TF is highly correlated with its mRNA abundance. The second treats the activity level as unobserved and infers it from the expression of the genes that the TF regulates. While both types of methods were successfully applied, each suffers from drawbacks that limit their accuracy. For the first set, the assumption that mRNA levels are correlated with activity is violated for many TFs due to post-transcriptional modifications. For the second, the expression level of a TF which might be informative is completely ignored. Here we present the post-transcriptional modification model (PTMM) that, unlike previous methods, utilizes both sources of data concurrently. Our method uses a switching model to determine whether a TF is transcriptionally or post-transcriptionally regulated. This model is combined with a factorial HMM to reconstruct the interactions in a dynamic regulatory network. Using simulated and real data, we show that PTMM outperforms the other two approaches discussed above. Using real data, we also show that PTMM can recover meaningful TF activity levels and identify post-transcriptionally modified TFs, many of which are supported by other sources. Supporting website: www.sb.cs.cmu.edu/PTMM/PTMM.html.

Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses

Models of mammalian regulatory networks controlling gene expression have been inferred from genomic data but have largely not been validated. We present an unbiased strategy to systematically perturb candidate regulators and monitor cellular transcriptional responses. We applied this approach to derive regulatory networks that control the transcriptional response of mouse primary dendritic cells to pathogens. Our approach revealed the regulatory functions of 125 transcription factors, chromatin modifiers, and RNA binding proteins, which enabled the construction of a network model consisting of 24 core regulators and 76 fine-tuners that help to explain how pathogen-sensing pathways achieve specificity. This study establishes a broadly applicable, comprehensive, and unbiased approach to reveal the wiring and functions of a regulatory network controlling a major transcriptional response in primary mammalian cells.

Perspectives on the DNA damage and replication checkpoint responses in Saccharomyces cerevisiae

The DNA damage and replication checkpoints are believed to primarily slow the progression of the cell cycle to allow DNA repair to occur. Here we summarize known aspects of the Saccharomyces cerevisiae checkpoints including how these responses are integrated into downstream effects on the cell cycle, chromatin, DNA repair, and cytoplasmic targets. Analysis of the transcriptional response demonstrates that it is far more complex and less relevant to the repair of DNA damage than the bacterial SOS response. We also address more speculative questions regarding potential roles of the checkpoint during the normal S-phase and how current evidence hints at a checkpoint activation mechanism mediated by positive feedback that amplifies initial damage signals above a minimum threshold.

Sugar metabolism, redox balance and oxidative stress response in the respiratory yeast Kluyveromyces lactis

A lot of studies have been carried out on Saccharomyces cerevisiae, an yeast with a predominant fermentative metabolism under aerobic conditions, which allows exploring the complex response induced by oxidative stress. S. cerevisiae is considered a eukaryote model for these studies. We propose Kluyveromyces lactis as a good alternative model to analyse variants in the oxidative stress response, since the respiratory metabolism in this yeast is predominant under aerobic conditions and it shows other important differences with S. cerevisiae in catabolic repression and carbohydrate utilization. The knowledge of oxidative stress response in K. lactis is still a developing field. In this article, we summarize the state of the art derived from experimental approaches and we provide a global vision on the characteristics of the putative K. lactis components of the oxidative stress response pathway, inferred from their sequence homology with the S. cerevisiae counterparts. Since K. lactis is also a well-established alternative host for industrial production of native enzymes and heterologous proteins, relevant differences in the oxidative stress response pathway and their potential in biotechnological uses of this yeast are also reviewed.

Predicting eukaryotic transcriptional cooperativity by Bayesian network integration of genome-wide data

Transcriptional cooperativity among several transcription factors (TFs) is believed to be the main mechanism of complexity and precision in transcriptional regulatory programs. Here, we present a Bayesian network framework to reconstruct a high-confidence whole-genome map of transcriptional cooperativity in Saccharomyces cerevisiae by integrating a comprehensive list of 15 genomic features. We design a Bayesian network structure to capture the dominant correlations among features and TF cooperativity, and introduce a supervised learning framework with a well-constructed gold-standard dataset. This framework allows us to assess the predictive power of each genomic feature, validate the superior performance of our Bayesian network compared to alternative methods, and integrate genomic features for optimal TF cooperativity prediction. Data integration reveals 159 high-confidence predicted cooperative relationships among 105 TFs, most of which are subsequently validated by literature search. The existing and predicted transcriptional cooperativities can be grouped into three categories based on the combination patterns of the genomic features, providing further biological insights into the different types of TF cooperativity. Our methodology is the first supervised learning approach for predicting transcriptional cooperativity, compares favorably to alternative unsupervised methodologies, and can be applied to other genomic data integration tasks where high-quality gold-standard positive data are scarce.

Transcriptional regulatory circuits: predicting numbers from alphabets

Transcriptional regulatory circuits govern how cis and trans factors transform signals into messenger RNA (mRNA) expression levels. With advances in quantitative and high-throughput technologies that allow measurement of gene expression state in different conditions, data that can be used to build and test models of transcriptional regulation is being generated at a rapid pace. Here, we review experimental and computational methods used to derive detailed quantitative circuit models on a small scale and cruder, genome-wide models on a large scale. We discuss the potential of combining small- and large-scale approaches to understand the working and wiring of transcriptional regulatory circuits.

Predicting functionality of protein-DNA interactions by integrating diverse evidence

Chromatin immunoprecipitation (ChIP-chip) experiments enable capturing physical interactions between regulatory proteins and DNA in vivo. However, measurement of chromatin binding alone is not sufficient to detect regulatory interactions. A detected binding event may not be biologically relevant, or a known regulatory interaction might not be observed under the growth conditions tested so far. To correctly identify physical interactions between transcription factors (TFs) and genes and to determine their regulatory implications under various experimental conditions, we integrated ChIP-chip data with motif binding sites, nucleosome occupancy and mRNA expression datasets within a probabilistic framework. This framework was specifically tailored for the identification of functional and non-functional DNA binding events. Using this, we estimate that only 50% of condition-specific protein–DNA binding in budding yeast is functional. We further investigated the molecular factors determining the functionality of protein–DNA interactions under diverse growth conditions. Our analysis suggests that the functionality of binding is highly condition-specific and highly dependent on the presence of specific cofactors. Hence, the joint analysis of both, functional and non-functional DNA binding, may lend important new insights into transcriptional regulation.

Contact:workman@cbs.dtu.dk

Understanding DNA damage response and DNA repair pathways: applications to more targeted cancer therapeutics

Radiation therapy and many of the commonly used cancer chemotherapeutic drugs target DNA for cytotoxicity. Indeed, the subsequent DNA damage response (DDR) to these cancer treatments in both malignant and normal cells/tissues determines the therapeutic index (TI) of the treatment. The DDR is a complex set of cell processes involving multiple DNA repair, cell cycle regulation, and cell death/survival pathways (or networks) with both damage specificity and coordination of the DDR to different types of DNA damage. Over the last decade, significant progress has been made in elucidating these complex cellular and molecular networks involved in the DDR in human tumor and normal tissues. Based on what has been learned about these processes using experimental in vitro and in vivo models, DDR and DNA pathways are now potential targets for cancer therapy. This article presents an overview of our current understanding of the DDR, including the key DNA repair pathways involved in determining the cytotoxicity to several classes of chemotherapy drugs (CT) as well as ionizing radiation (IR). Since many different types of human cancers can arise from genetic or epigenetic changes in the DDR and DNA repair pathways, this article also covers recent developments in cancer therapeutics that attempt to target these specific tumor-related DDR/DNA repair defects as monotherapy or, more commonly, when combined with conventional cancer treatments.

Backup in gene regulatory networks explains differences between binding and knockout results

The complementarity of gene expression and protein–DNA interaction data led to several successful models of biological systems. However, recent studies in multiple species raise doubts about the relationship between these two datasets. These studies show that the overwhelming majority of genes bound by a particular transcription factor (TF) are not affected when that factor is knocked out. Here, we show that this surprising result can be partially explained by considering the broader cellular context in which TFs operate. Factors whose functions are not backed up by redundant paralogs show a fourfold increase in the agreement between their bound targets and the expression levels of those targets. In addition, we show that incorporating protein interaction networks provides physical explanations for knockout effects. New double knockout experiments support our conclusions. Our results highlight the robustness provided by redundant TFs and indicate that in the context of diverse cellular systems, binding is still largely functional.

A network of conserved damage survival pathways revealed by a genomic RNAi screen

Damage initiates a pleiotropic cellular response aimed at cellular survival when appropriate. To identify genes required for damage survival, we used a cell-based RNAi screen against the Drosophila genome and the alkylating agent methyl methanesulphonate (MMS). Similar studies performed in other model organisms report that damage response may involve pleiotropic cellular processes other than the central DNA repair components, yet an intuitive systems level view of the cellular components required for damage survival, their interrelationship, and contextual importance has been lacking. Further, by comparing data from different model organisms, identification of conserved and presumably core survival components should be forthcoming. We identified 307 genes, representing 13 signaling, metabolic, or enzymatic pathways, affecting cellular survival of MMS-induced damage. As expected, the majority of these pathways are involved in DNA repair; however, several pathways with more diverse biological functions were also identified, including the TOR pathway, transcription, translation, proteasome, glutathione synthesis, ATP synthesis, and Notch signaling, and these were equally important in damage survival. Comparison with genomic screen data from Saccharomyces cerevisiae revealed no overlap enrichment of individual genes between the species, but a conservation of the pathways. To demonstrate the functional conservation of pathways, five were tested in Drosophila and mouse cells, with each pathway responding to alkylation damage in both species. Using the protein interactome, a significant level of connectivity was observed between Drosophila MMS survival proteins, suggesting a higher order relationship. This connectivity was dramatically improved by incorporating the components of the 13 identified pathways within the network. Grouping proteins into "pathway nodes" qualitatively improved the interactome organization, revealing a highly organized "MMS survival network." We conclude that identification of pathways can facilitate comparative biology analysis when direct gene/orthologue comparisons fail. A biologically intuitive, highly interconnected MMS survival network was revealed after we incorporated pathway data in our interactome analysis.

A complex-centric view of protein network evolution

The recent availability of protein-protein interaction networks for several species makes it possible to study protein complexes in an evolutionary context. In this article, we present a novel network-based framework for reconstructing the evolutionary history of protein complexes. Our analysis is based on generalizing evolutionary measures for single proteins to the level of whole subnetworks, comprehensively considering a broad set of computationally derived complexes and accounting for both sequence and interaction changes. Specifically, we compute sets of orthologous complexes across species, and use these to derive evolutionary rate and age measures for protein complexes. We observe significant correlations between the evolutionary properties of a complex and those of its member proteins, suggesting that protein complexes form early in evolution and evolve as coherent units. Additionally, our approach enables us to directly quantify the extent to which gene duplication has played a role in the evolution of complexes. We find that about one quarter of the sets of orthologous complexes have originated from evolutionary cores of homodimers that underwent duplication and divergence, testifying to the important role of gene duplication in protein complex evolution.

Compensatory evolution for a gene deletion is not limited to its immediate functional network

Background

Genetic disruption of an important phenotype should favor compensatory mutations that restore the phenotype. If the genetic basis of the phenotype is modular, with a network of interacting genes whose functions are specific to that phenotype, compensatory mutations are expected among the genes of the affected network. This perspective was tested in the bacteriophage T3 using a genome deleted of its DNA ligase gene, disrupting DNA metabolism.

Results

In two replicate, long-term adaptations, phage compensatory evolution accommodated the low ligase level provided by the host without reinventing its own ligase. In both lines, fitness increased substantially but remained well below that of the intact genome. Each line accumulated over a dozen compensating mutations during long-term adaptation, and as expected, many of the compensatory changes were within the DNA metabolism network. However, several compensatory changes were outside the network and defy any role in DNA metabolism or biochemical connection to the disruption. In one line, these extra-network changes were essential to the recovery. The genes experiencing compensatory changes were moderately conserved between T3 and its relative T7 (25% diverged), but the involvement of extra-network changes was greater in T3.

Conclusion

Compensatory evolution was only partly limited to the known functionally interacting partners of the deleted gene. Thus gene interactions contributing to fitness were more extensive than suggested by the functional properties currently ascribed to the genes. Compensatory evolution offers an easy method of discovering genome interactions among specific elements that does not rest on an a priori knowledge of those elements or their interactions.

High-resolution DNA-binding specificity analysis of yeast transcription factors

Transcription factors (TFs) regulate the expression of genes through sequence-specific interactions with DNA-binding sites. However, despite recent progress in identifying in vivo TF binding sites by microarray readout of chromatin immunoprecipitation (ChIP-chip), nearly half of all known yeast TFs are of unknown DNA-binding specificities, and many additional predicted TFs remain uncharacterized. To address these gaps in our knowledge of yeast TFs and their cis regulatory sequences, we have determined high-resolution binding profiles for 89 known and predicted yeast TFs, over more than 2.3 million gapped and ungapped 8-bp sequences ("k-mers"). We report 50 new or significantly different direct DNA-binding site motifs for yeast DNA-binding proteins and motifs for eight proteins for which only a consensus sequence was previously known; in total, this corresponds to over a 50% increase in the number of yeast DNA-binding proteins with experimentally determined DNA-binding specificities. Among other novel regulators, we discovered proteins that bind the PAC (Polymerase A and C) motif (GATGAG) and regulate ribosomal RNA (rRNA) transcription and processing, core cellular processes that are constituent to ribosome biogenesis. In contrast to earlier data types, these comprehensive k-mer binding data permit us to consider the regulatory potential of genomic sequence at the individual word level. These k-mer data allowed us to reannotate in vivo TF binding targets as direct or indirect and to examine TFs' potential effects on gene expression in approximately 1,700 environmental and cellular conditions. These approaches could be adapted to identify TFs and cis regulatory elements in higher eukaryotes.

Toward accurate reconstruction of functional protein networks

Genome-scale screening studies are gradually accumulating a wealth of data on the putative involvement of hundreds of genes/proteins in various cellular responses or functions. A fundamental challenge is to chart out the protein pathways that underlie these systems. Previous approaches to the problem have either employed a local optimization criterion, aiming to infer each pathway independently, or a global criterion, searching for the overall most parsimonious subnetwork. Here, we study the trade-off between the two approaches and present a new intermediary scheme that provides explicit control over it. We demonstrate its utility in the analysis of the apoptosis network in humans, and the telomere length maintenance (TLM) system in yeast. Our results show that in the majority of real-life cases, the intermediary approach provides the most plausible solutions. We use a new set of perturbation experiments measuring the role of essential genes in telomere length regulation to further study the TLM network. Surprisingly, we find that the proteasome plays an important role in telomere length regulation through its associations with transcription and DNA repair circuits.

Bridging high-throughput genetic and transcriptional data reveals cellular responses to alpha-synuclein toxicity

Cells respond to stimuli by changes in various processes, including signaling pathways and gene expression. Efforts to identify components of these responses increasingly depend on mRNA profiling and genetic library screens. By comparing the results of these two assays across various stimuli, we found that genetic screens tend to identify response regulators, whereas mRNA profiling frequently detects metabolic responses. We developed an integrative approach that bridges the gap between these data using known molecular interactions, thus highlighting major response pathways. We used this approach to reveal cellular pathways responding to the toxicity of alpha-synuclein, a protein implicated in several neurodegenerative disorders including Parkinson's disease. For this we screened an established yeast model to identify genes that when overexpressed alter alpha-synuclein toxicity. Bridging these data and data from mRNA profiling provided functional explanations for many of these genes and identified previously unknown relations between alpha-synuclein toxicity and basic cellular pathways.

Coordination of DNA mismatch repair and base excision repair processing of chemotherapy and radiation damage for targeting resistant cancers

DNA damage processing by mismatch repair (MMR) and/or base excision repair (BER) can determine the therapeutic index following treatment of human cancers using radiation therapy and several classes of chemotherapy drugs. Over the last decade, basic and translational cancer research in DNA repair has led to an increased understanding of how these two DNA repair pathways can modify cytotoxicity to chemotherapy and/or ionizing radiation treatments in both normal and malignant tissues. This Molecular Pathways article provides an overview of the current understanding of mechanisms involved in MMR and BER damage processing, including insights into possible coordination of these two DNA repair pathways after chemotherapy and/or ionizing radiation damage. It also introduces principles of systems biology that have been applied to better understand the complexities and coordination of MMR and BER in processing these DNA damages. Finally, it highlights novel therapeutic approaches to target resistant (or DNA damage tolerant) human cancers using chemical and molecular modifiers of chemotherapy and/or ionizing radiation including poly (ADP-ribose) polymerase inhibitors, methoxyamine and iododeoxyuridine (and the prodrug, 5-iodo-2-pyrimidinone-2'-deoxyribose).

Reconstruction of biochemical networks in microorganisms

Systems analysis of metabolic and growth functions in microbial organisms is rapidly developing and maturing. Such studies are enabled by reconstruction, at the genomic scale, of the biochemical reaction networks that underlie cellular processes. The network reconstruction process is organism specific and is based on an annotated genome sequence, high-throughput network-wide data sets and bibliomic data on the detailed properties of individual network components. Here we describe the process that is currently used to achieve comprehensive network reconstructions and discuss how these reconstructions are curated and validated. This review should aid the growing number of researchers who are carrying out reconstructions for particular target organisms.

A network biology approach to aging in yeast

In this study, a reverse-engineering strategy was used to infer and analyze the structure and function of an aging and glucose repressed gene regulatory network in the budding yeast Saccharomyces cerevisiae. The method uses transcriptional perturbations to model the functional interactions between genes as a system of first-order ordinary differential equations. The resulting network model correctly identified the known interactions of key regulators in a 10-gene network from the Snf1 signaling pathway, which is required for expression of glucose-repressed genes upon calorie restriction. The majority of interactions predicted by the network model were confirmed using promoter-reporter gene fusions in gene-deletion mutants and chromatin immunoprecipitation experiments, revealing a more complex network architecture than previously appreciated. The reverse-engineered network model also predicted an unexpected role for transcriptional regulation of the SNF1 gene by hexose kinase enzyme/transcriptional repressor Hxk2, Mediator subunit Med8, and transcriptional repressor Mig1. These interactions were validated experimentally and used to design new experiments demonstrating Snf1 and its transcriptional regulators Hxk2 and Mig1 as modulators of chronological lifespan. This work demonstrates the value of using network inference methods to identify and characterize the regulators of complex phenotypes, such as aging.

A fitness-based interferential genetics approach using hypertoxic/inactive gene alleles as references

Genetics, genomics, and biochemistry have all been of immense help in characterizing macromolecular cell entities and their interactions. Still, obtaining an overall picture of the functioning of even a simple unicellular species has remained a challenging task. One possible way to obtain a comprehensive picture has been described: by capitalizing on the observation that the overexpression on a multicopy plasmid of apparently any wild-type gene in yeast can lead to some negative effect on cell fitness (referring to the concept of "gene toxicity"), the FIG (fitness-based interferential genetics) approach was devised for selecting normal genes that are in antagonistic (and potentially also agonistic) relationship with a particular gene used as a reference. Herein, we take a complementary approach to FIG, by first selecting a "hypertoxic" allele of the reference gene--which easily provides the general possibility of obtaining gene products with the remarkable property of being inactive without altering their macromolecular interactivity--and then looking for the genes that interact functionally with this reference. Thus, FIG and the present approach (Trap-FIG), both taking advantage of the negative effects on cell fitness induced by various quantitative modulations in cellular networks, could potentially pave the way for the emergence of efficient in situ biochemistry.

Predicting genetic interactions with random walks on biological networks

Background

Several studies have demonstrated that synthetic lethal genetic interactions between gene mutations provide an indication of functional redundancy between molecular complexes and pathways. These observations help explain the finding that organisms are able to tolerate single gene deletions for a large majority of genes. For example, system-wide gene knockout/knockdown studies in S. cerevisiae and C. elegans revealed non-viable phenotypes for a mere 18% and 10% of the genome, respectively. It has been postulated that the low percentage of essential genes reflects the extensive amount of genetic buffering that occurs within genomes. Consistent with this hypothesis, systematic double-knockout screens in S. cerevisiae and C. elegans show that, on average, 0.5% of tested gene pairs are synthetic sick or synthetic lethal. While knowledge of synthetic lethal interactions provides valuable insight into molecular functionality, testing all combinations of gene pairs represents a daunting task for molecular biologists, as the combinatorial nature of these relationships imposes a large experimental burden. Still, the task of mapping pairwise interactions between genes is essential to discovering functional relationships between molecular complexes and pathways, as they form the basis of genetic robustness. Towards the goal of alleviating the experimental workload, computational techniques that accurately predict genetic interactions can potentially aid in targeting the most likely candidate interactions. Building on previous studies that analyzed properties of network topology to predict genetic interactions, we apply random walks on biological networks to accurately predict pairwise genetic interactions. Furthermore, we incorporate all published non-interactions into our algorithm for measuring the topological relatedness between two genes. We apply our method to S. cerevisiae and C. elegans datasets and, using a decision tree classifier, integrate diverse biological networks and show that our method outperforms established methods.

Results

By applying random walks on biological networks, we were able to predict synthetic lethal interactions at a true positive rate of 95 percent against a false positive rate of 10 percent in S. cerevisiae. Similarly, in C. elegans, we achieved a true positive rate of 95 against a false positive rate of 7 percent. Furthermore, we demonstrate that the inclusion of non-interacting gene pairs results in a considerable performance improvement.

Conclusion

We presented a method based on random walks that accurately captures aspects of network topology towards the goal of classifying potential genetic interactions as either synthetic lethal or non-interacting. Our method, which is generalizable to all types of biological networks, is likely to perform well with limited information, as estimated by holding out large portions of the synthetic lethal interactions and non-interactions.

Prioritizing genes for pathway impact using network analysis

Prioritization, or ranking, of gene lists is becoming increasingly important for analyzing data generated from high-throughput assays like expression profiling and RNAi-based screening. This is especially the case when specific genes in a list need to be further validated using low-throughput experiments. In addition to gene set overlap enrichment methods, a complementary approach is to examine molecular interaction networks. These can provide putative functional insights based on gene connectivity, especially when many genes contain little or no annotation. For bench and computational biologists alike, using networks requires an informed selection of interaction data for network construction and strategies for managing network complexity. Moreover, graph theory and social network analysis methods can be used to isolate critical subnetworks and quantify network properties. Here, I discuss the basic components of networks, implications of their structure for functional interpretation, and common metrics used for prioritization. Although this is still an ongoing area of research, networks are providing new ways for gauging pathway impact in large-scale data sets.

Chemogenomic approaches to elucidation of gene function and genetic pathways

The approximately 6,000 strains in the yeast deletion collection can be studied in a single culture by using a microarray to detect the 20 bp DNA "barcodes" or "tags" contained in each strain. Barcode intensities measured by microarray are compared across time-points or across conditions to analyze the relative fitness of each strain. The development of this pooled fitness assay has greatly facilitated the functional annotation of the yeast genome by making genome-wide gene-deletion studies faster and easier, and has led to the development of high throughput methods for studying drug action in yeast. Pooled screens can be used for identifying gene functions, measuring the functional relatedness of gene pairs to group genes into pathways, identifying drug targets, and determining a drug's mechanism of action. This process involves five main steps: preparing aliquots of pooled cells, pooled growth, isolation of genomic DNA and PCR amplification of the barcodes, array hybridization, and data analysis. In addition to yeast fitness applications, the general method of studying pooled samples with barcode arrays can also be adapted for use with other types of samples, such as mutant collections in other organisms, siRNA vectors, and molecular inversion probes.

Systems based mapping demonstrates that recovery from alkylation damage requires DNA repair, RNA processing, and translation associated networks

The identification of cellular responses to damage can promote mechanistic insight into stress signalling. We have screened a library of 3968 Escherichia coli gene-deletion mutants to identify 99 gene products that modulate the toxicity of the alkylating agent methyl methanesulfonate (MMS). We have developed an ontology mapping approach to identify functional categories over-represented with MMS-toxicity modulating proteins and demonstrate that, in addition to DNA re-synthesis (replication, recombination, and repair), proteins involved in mRNA processing and translation influence viability after MMS damage. We have also mapped our MMS-toxicity modulating proteins onto an E. coli protein interactome and identified a sub-network consisting of 32 proteins functioning in DNA repair, mRNA processing, and translation. Clustering coefficient analysis identified seven highly connected MMS-toxicity modulating proteins associated with translation and mRNA processing, with the high connectivity suggestive of a coordinated response. Corresponding results from reporter assays support the idea that the SOS response is influenced by activities associated with the mRNA-translation interface.

A specific transcriptional response of yeast cells to camptothecin dependent on the Swi4 and Mbp1 factors

Topoisomerase I (Top1) is the specific target of the anticancer drug camptothecin (CPT) that interferes with enzyme activity promoting Top1-mediated DNA breaks and inhibition of DNA and RNA synthesis. To define the specific transcriptional response to CPT, we have determined the CPT-altered transcription profiles in yeast by using a relatively low concentration of the drug. CPT could alter global expression profiles only if a catalytically active Top1p was expressed in the cell, demonstrating that drug interference with Top1 was the sole trigger of the response. A total of 95 genes showed a statistically-significant alterations. Gene Ontology term analyses suggested that the cell response was mainly to the inhibition of nucleic acid synthesis and cell cycle progression. Promoter sequence analyses of the 22 up-regulated genes and expression studies in gene-deleted strains showed that the transcription factors, Swi4p and Mbp1p, mediate at least partially the transcriptional response to CPT. The MBP1 gene deletion abrogates a transient cell growth delay caused by CPT whereas the SWI4 gene deletion increases yeast resistance to CPT. Thus, the findings show that yeast cells have a highly selective and sensitive transcriptional response to CPT depending on SWI4 and MBP1 genes suggesting a complex regulation of cell cycle progression by the two factors in the presence of CPT.

Comparison of responses to double-strand breaks between Escherichia coli and Bacillus subtilis reveals different requirements for SOS induction

DNA double-strand breaks are particularly deleterious lesions that can lead to genomic instability and cell death. We investigated the SOS response to double-strand breaks in both Escherichia coli and Bacillus subtilis. In E. coli, double-strand breaks induced by ionizing radiation resulted in SOS induction in virtually every cell. E. coli strains incapable of SOS induction were sensitive to ionizing radiation. In striking contrast, we found that in B. subtilis both ionizing radiation and a site-specific double-strand break causes induction of prophage PBSX and SOS gene expression in only a small subpopulation of cells. These results show that double-strand breaks provoke global SOS induction in E. coli but not in B. subtilis. Remarkably, RecA-GFP focus formation was nearly identical following ionizing radiation challenge in both E. coli and B. subtilis, demonstrating that formation of RecA-GFP foci occurs in response to double-strand breaks but does not require or result in SOS induction in B. subtilis. Furthermore, we found that B. subtilis cells incapable of inducing SOS had near wild-type levels of survival in response to ionizing radiation. Moreover, B. subtilis RecN contributes to maintaining low levels of SOS induction during double-strand break repair. Thus, we found that the contribution of SOS induction to double-strand break repair differs substantially between E. coli and B. subtilis.

Global analysis of protein damage by the lipid electrophile 4-hydroxy-2-nonenal

Lipid peroxidation yields a variety of electrophiles, which are thought to contribute to the molecular pathogenesis of diseases involving oxidative stress, yet little is known of the scope of protein damage caused by lipid electrophiles. We identified protein targets of the prototypical lipid electrophile 4-hydroxy-2-nonenal (HNE) in RKO cells treated with 50 or 100 μm HNE. HNE Michael adducts were biotinylated by reaction with biotinamidohexanoic acid hydrazide, captured with streptavidin, and the captured proteins were resolved by one dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis, digested with trypsin, and identified by liquid chromatography-tandem mass spectrometry. Of the 1500+ proteins identified, 417 displayed a statistically significant increase in adduction with increasing HNE exposure concentration. We further identified 18 biotin hydrazide-modified, HNE-adducted peptides by specific capture using anti-biotin antibody and analysis by high resolution liquid chromatography-tandem mass spectrometry. A subset of the identified HNE targets were validated with a streptavidin capture and immunoblotting approach, which enabled detection of adducts at HNE exposures as low as 1 μm. Protein interaction network analysis indicated several subsystems impacted by endogenous electrophiles in oxidative stress, including the 26 S proteasomal and chaperonin containing TCP-1 (CCT) systems involved in protein-folding and degradation, as well as the COP9 signalosome, translation initiation complex, and a large network of ribonucleoproteins. Global analyses of protein lipid electrophile adducts provide a systems-level perspective on the mechanisms of diseases involving oxidative stress.

Activity motifs reveal principles of timing in transcriptional control of the yeast metabolic network

Significant insight about biological networks arises from the study of network motifs--overly abundant network subgraphs--but such wiring patterns do not specify when and how potential routes within a cellular network are used. To address this limitation, we introduce activity motifs, which capture patterns in the dynamic use of a network. Using this framework to analyze transcription in Saccharomyces cerevisiae metabolism, we find that cells use different timing activity motifs to optimize transcription timing in response to changing conditions: forward activation to produce metabolic compounds efficiently, backward shutoff to rapidly stop production of a detrimental product and synchronized activation for co-production of metabolites required for the same reaction. Measuring protein abundance over a time course reveals that mRNA timing motifs also occur at the protein level. Timing motifs significantly overlap with binding activity motifs, where genes in a linear chain have ordered binding affinity to a transcription factor, suggesting a mechanism for ordered transcription. Finely timed transcriptional regulation is therefore abundant in yeast metabolism, optimizing the organism's adaptation to new environmental conditions.

A molecular bar-coded DNA repair resource for pooled toxicogenomic screens

DNA damage from exogenous and endogenous sources can promote mutations and cell death. Fortunately, cells contain DNA repair and damage signaling pathways to reduce the mutagenic and cytotoxic effects of DNA damage. The identification of specific DNA repair proteins and the coordination of DNA repair pathways after damage has been a central theme to the field of genetic toxicology and we have developed a tool for use in this area. We have produced 99 molecular bar-coded Escherichia coli gene-deletion mutants specific to DNA repair and damage signaling pathways, and each bar-coded mutant can be tracked in pooled format using bar-code specific microarrays. Our design adapted bar-codes developed for the Saccharomyces cerevisiae gene-deletion project, which allowed us to utilize an available microarray product for pooled gene-exposure studies. Microarray-based screens were used for en masse identification of individual mutants sensitive to methyl methanesulfonate (MMS). As expected, gene-deletion mutants specific to direct, base excision, and recombinational DNA repair pathways were identified as MMS-sensitive in our pooled assay, thus validating our resource. We have demonstrated that molecular bar-codes designed for S. cerevisiae are transferable to E. coli, and that they can be used with pre-existing microarrays to perform competitive growth experiments. Further, when comparing microarray to traditional plate-based screens both overlapping and distinct results were obtained, which is a novel technical finding, with discrepancies between the two approaches explained by differences in output measurements (DNA content versus cell mass). The microarray-based classification of Deltatag and DeltadinG cells as depleted after MMS exposure, contrary to plate-based methods, led to the discovery that Deltatag and DeltadinG cells show a filamentation phenotype after MMS exposure, thus accounting for the discrepancy. A novel biological finding is the observation that while DeltadinG cells filament in response to MMS they exhibit wild-type sulA expression after exposure. This decoupling of filamentation from SulA levels suggests that DinG is associated with the SulA-independent filamentation pathway.

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.

Inferring condition-specific modulation of transcription factor activity in yeast through regulon-based analysis of genomewide expression

BACKGROUND

A key goal of systems biology is to understand how genomewide mRNA expression levels are controlled by transcription factors (TFs) in a condition-specific fashion. TF activity is frequently modulated at the post-translational level through ligand binding, covalent modification, or changes in sub-cellular localization. In this paper, we demonstrate how prior information about regulatory network connectivity can be exploited to infer condition-specific TF activity as a hidden variable from the genomewide mRNA expression pattern in the yeast Saccharomyces cerevisiae.

METHODOLOGY/PRINCIPAL FINDINGS

We first validate experimentally that by scoring differential expression at the level of gene sets or "regulons" comprised of the putative targets of a TF, we can accurately predict modulation of TF activity at the post-translational level. Next, we create an interactive database of inferred activities for a large number of TFs across a large number of experimental conditions in S. cerevisiae. This allows us to perform TF-centric analysis of the yeast regulatory network.

CONCLUSIONS/SIGNIFICANCE

We analyze the degree to which the mRNA expression level of each TF is predictive of its regulatory activity. We also organize TFs into "co-modulation networks" based on their inferred activity profile across conditions, and find that this reveals functional and mechanistic relationships. Finally, we present evidence that the PAC and rRPE motifs antagonize TBP-dependent regulation, and function as core promoter elements governed by the transcription regulator NC2. Regulon-based monitoring of TF activity modulation is a powerful tool for analyzing regulatory network function that should be applicable in other organisms. Tools and results are available online at http://bussemakerlab.org/RegulonProfiler/.

Transcription factor control of growth rate dependent genes in Saccharomyces cerevisiae: a three factor design

Background

Characterization of cellular growth is central to understanding living systems. Here, we applied a three-factor design to study the relationship between specific growth rate and genome-wide gene expression in 36 steady-state chemostat cultures of Saccharomyces cerevisiae. The three factors we considered were specific growth rate, nutrient limitation, and oxygen availability.

Results

We identified 268 growth rate dependent genes, independent of nutrient limitation and oxygen availability. The transcriptional response was used to identify key areas in metabolism around which mRNA expression changes are significantly associated. Among key metabolic pathways, this analysis revealed de novo synthesis of pyrimidine ribonucleotides and ATP producing and consuming reactions at fast cellular growth. By scoring the significance of overlap between growth rate dependent genes and known transcription factor target sets, transcription factors that coordinate balanced growth were also identified. Our analysis shows that Fhl1, Rap1, and Sfp1, regulating protein biosynthesis, have significantly enriched target sets for genes up-regulated with increasing growth rate. Cell cycle regulators, such as Ace2 and Swi6, and stress response regulators, such as Yap1, were also shown to have significantly enriched target sets.

Conclusion

Our work, which is the first genome-wide gene expression study to investigate specific growth rate and consider the impact of oxygen availability, provides a more conservative estimate of growth rate dependent genes than previously reported. We also provide a global view of how a small set of transcription factors, 13 in total, contribute to control of cellular growth rate. We anticipate that multi-factorial designs will play an increasing role in elucidating cellular regulation.

Determination of antibiotic hypersensitivity among 4,000 single-gene-knockout mutants of Escherichia coli

We have tested the entire Keio collection of close to 4,000 single-gene knockouts in Escherichia coli for increased susceptibility to one of seven different antibiotics (ciprofloxacin, rifampin, vancomycin, ampicillin, sulfamethoxazole, gentamicin, or metronidazole). We used high-throughput screening of several subinhibitory concentrations of each antibiotic and reduced more than 65,000 data points to a set of 140 strains that display significantly increased sensitivities to at least one of the antibiotics, determining the MIC in each case. These data provide targets for the design of "codrugs" that can potentiate existing antibiotics. We have made a number of double mutants with greatly increased sensitivity to ciprofloxacin, and these overcome the resistance generated by certain gyrA mutations. Many of the gene knockouts in E. coli are hypersensitive to more than one antibiotic. Together, all of these data allow us to outline the cell's "intrinsic resistome," which provides innate resistance to antibiotics.

Identification and dissection of a complex DNA repair sensitivity phenotype in Baker's yeast

Complex traits typically involve the contribution of multiple gene variants. In this study, we took advantage of a high-density genotyping analysis of the BY (S288c) and RM strains of Saccharomyces cerevisiae and of 123 derived spore progeny to identify the genetic loci that underlie a complex DNA repair sensitivity phenotype. This was accomplished by screening hybrid yeast progeny for sensitivity to a variety of DNA damaging agents. Both the BY and RM strains are resistant to the ultraviolet light-mimetic agent 4-nitroquinoline 1-oxide (4-NQO); however, hybrid progeny from a BYxRM cross displayed varying sensitivities to the drug. We mapped a major quantitative trait locus (QTL), RAD5, and identified the exact polymorphism within this locus responsible for 4-NQO sensitivity. By using a backcrossing strategy along with array-assisted bulk segregant analysis, we identified one other locus, MKT1, and a QTL on Chromosome VII that also link to the hybrid 4-NQO-sensitive phenotype but confer more minor effects. This work suggests an additive model for sensitivity to 4-NQO and provides a strategy for mapping both major and minor QTL that confer background-specific phenotypes. It also provides tools for understanding the effect of genetic background on sensitivity to genotoxic agents.

Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks

A key goal of biology is to construct networks that predict complex system behavior. We combine multiple types of molecular data, including genotypic, expression, transcription factor binding site (TFBS), and protein-protein interaction (PPI) data previously generated from a number of yeast experiments, in order to reconstruct causal gene networks. Networks based on different types of data are compared using metrics devised to assess the predictive power of a network. We show that a network reconstructed by integrating genotypic, TFBS and PPI data is the most predictive. This network is used to predict causal regulators responsible for hot spots of gene expression activity in a segregating yeast population. We also show that the network can elucidate the mechanisms by which causal regulators give rise to larger-scale changes in gene expression activity. We then prospectively validate predictions, providing direct experimental evidence that predictive networks can be constructed by integrating multiple, appropriate data types.

Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering

Genome-wide identification of bona-fide targets of transcription factors in mammalian cells is still a challenge. We present a novel integrated computational and experimental approach to identify direct targets of a transcription factor. This consists of measuring time-course (dynamic) gene expression profiles upon perturbation of the transcription factor under study, and in applying a novel "reverse-engineering" algorithm (TSNI) to rank genes according to their probability of being direct targets. Using primary keratinocytes as a model system, we identified novel transcriptional target genes of TRP63, a crucial regulator of skin development. TSNI-predicted TRP63 target genes were validated by Trp63 knockdown and by ChIP-chip to identify TRP63-bound regions in vivo. Our study revealed that short sampling times, in the order of minutes, are needed to capture the dynamics of gene expression in mammalian cells. We show that TRP63 transiently regulates a subset of its direct targets, thus highlighting the importance of considering temporal dynamics when identifying transcriptional targets. Using this approach, we uncovered a previously unsuspected transient regulation of the AP-1 complex by TRP63 through direct regulation of a subset of AP-1 components. The integrated experimental and computational approach described here is readily applicable to other transcription factors in mammalian systems and is complementary to genome-wide identification of transcription-factor binding sites.

Protein networks in disease

During a decade of proof-of-principle analysis in model organisms, protein networks have been used to further the study of molecular evolution, to gain insight into the robustness of cells to perturbation, and for assignment of new protein functions. Following these analyses, and with the recent rise of protein interaction measurements in mammals, protein networks are increasingly serving as tools to unravel the molecular basis of disease. We review promising applications of protein networks to disease in four major areas: identifying new disease genes; the study of their network properties; identifying disease-related subnetworks; and network-based disease classification. Applications in infectious disease, personalized medicine, and pharmacology are also forthcoming as the available protein network information improves in quality and coverage.