Rewiring of genetic networks in response to DNA damage

Comprehensive mapping of DNA damage: from static genetic maps to condition-specific maps

DNA damage can dramatically affect the cell; thus, cells developed multiple pathways to detect and repair such damage. In this issue, Guénolé et al. (2013) systematically map genetic interactions in different DNA damage conditions, uncovering specific repair pathways.

From systems to structure: bridging networks and mechanism

There is a wide gap between the generation of large-scale biological data sets and more-detailed, structural and mechanistic studies. However, recent studies that explicitly combine data from systems and structural biological approaches are having a profound effect on our ability to predict how mutations and small molecules affect atomic-level mechanisms, disrupt systems-level networks, and ultimately lead to changes in organismal fitness. In fact, we argue that a shared framework for analysis of nonadditive genetic and thermodynamic responses to perturbations will accelerate the integration of reductionist and global approaches. A stronger bridge between these two areas will allow for a deeper and more-complete understanding of complex biological phenomenon and ultimately provide needed breakthroughs in biomedical research.

Networks in proteomics analysis of cancer

Proteomics provides direct biological information on proteins but is still a limited platform. Borrowing from genomics, its cancer-specific applications can be broadly categorized as (1) pure diagnostics, (2) biomarkers, (3) identification of root causes and (4) identification of cancer-specific network rewirings. Biological networks capture complex relationships between proteins and provide an appropriate means of contextualization. While playing significantly larger roles, especially in 1 and 3, progress in proteomics-specific network-based methods is lagging as compared to genomics. Rapid hardware advances and improvements in proteomic identification and quantification have given rise to much better quality data alongside advent of new network-based analysis methods. However, a tighter integration between analytics and hardware is still essential for network analysis to play more significant roles in proteomics analysis.

Quantitative genetic-interaction mapping in mammalian cells

Mapping genetic interactions (GIs) by simultaneously perturbing pairs of genes is a powerful tool for understanding complex biological phenomena. Here we describe an experimental platform for generating quantitative GI maps in mammalian cells using a combinatorial RNA interference strategy. We performed ∼11,000 pairwise knockdowns in mouse fibroblasts, focusing on 130 factors involved in chromatin regulation to create a GI map. Comparison of the GI and protein-protein interaction (PPI) data revealed that pairs of genes exhibiting positive GIs and/or similar genetic profiles were predictive of the corresponding proteins being physically associated. The mammalian GI map identified pathways and complexes but also resolved functionally distinct submodules within larger protein complexes. By integrating GI and PPI data, we created a functional map of chromatin complexes in mouse fibroblasts, revealing that the PAF complex is a central player in the mammalian chromatin landscape.

A systematic mammalian genetic interaction map reveals pathways underlying ricin susceptibility

Genetic interaction (GI) maps, comprising pairwise measures of how strongly the function of one gene depends on the presence of a second, have enabled the systematic exploration of gene function in microorganisms. Here, we present a two-stage strategy to construct high-density GI maps in mammalian cells. First, we use ultracomplex pooled shRNA libraries (25 shRNAs/gene) to identify high-confidence hit genes for a given phenotype and effective shRNAs. We then construct double-shRNA libraries from these to systematically measure GIs between hits. A GI map focused on ricin susceptibility broadly recapitulates known pathways and provides many unexpected insights. These include a noncanonical role for COPI, a previously uncharacterized protein complex affecting toxin clearance, a specialized role for the ribosomal protein RPS25, and functionally distinct mammalian TRAPP complexes. The ability to rapidly generate mammalian GI maps provides a potentially transformative tool for defining gene function and designing combination therapies based on synergistic pairs.

Analysis of protein-protein interactions in cross-talk pathways reveals CRKL protein as a novel prognostic marker in hepatocellular carcinoma

Deciphering the network of signaling pathways in cancer via protein-protein interactions (PPIs) at the cellular level is a promising approach but remains incomplete. We used an in situ proximity ligation assay to identify and quantify 67 endogenous PPIs among 21 interlinked pathways in two hepatocellular carcinoma (HCC) cells, Huh7 (minimally migratory cells) and Mahlavu (highly migratory cells). We then applied a differential network biology analysis and determined that the novel interaction, CRKL-FLT1, has a high centrality ranking, and the expression of this interaction is strongly correlated with the migratory ability of HCC and other cancer cell lines. Knockdown of CRKL and FLT1 in HCC cells leads to a decrease in cell migration via ERK signaling and the epithelial-mesenchymal transition process. Our immunohistochemical analysis shows high expression levels of the CRKL and CRKL-FLT1 pair that strongly correlate with reduced disease-free and overall survival in HCC patient samples, and a multivariate analysis further established CRKL and the CRKL-FLT1 as novel prognosis markers. This study demonstrated that functional exploration of a disease network with interlinked pathways via PPIs can be used to discover novel biomarkers.

How networks change with time

Motivation: Biological networks change in response to genetic and environmental cues. Changes are reflected in the abundances of biomolecules, the composition of protein complexes and other descriptors of the biological state. Methods to infer the dynamic state of a cell would have great value for understanding how cells change over time to accomplish biological goals.

Results: A new method predicts the dynamic state of protein complexes in a cell, with protein expression inferred from transcription profile time courses and protein complexes inferred by joint analysis of protein co-expression and protein–protein interaction maps. Two algorithmic advances are presented: a new method, DHAC (Dynamical Hierarchical Agglomerative Clustering), for clustering time-evolving networks; and a companion method, MATCH-EM, for matching corresponding clusters across time points. With link prediction as an objective assessment metric, DHAC provides a substantial advance over existing clustering methods. An application to the yeast metabolic cycle demonstrates how waves of gene expression correspond to individual protein complexes. Our results suggest regulatory mechanisms for assembling the mitochondrial ribosome and illustrate dynamic changes in the components of the nuclear pore.

Availability: All source code and data are available under the Boost Software License as supplementary material, at www.baderzone.org, and at sourceforge.net/projects/dhacdist

Contact: joel.bader@jhu.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Differential RNAi screening provides insights into the rewiring of signalling networks during oxidative stress

Reactive Oxygen Species (ROS) are a natural by-product of cellular growth and proliferation, and are required for fundamental processes such as protein-folding and signal transduction. However, ROS accumulation, and the onset of oxidative stress, can negatively impact cellular and genomic integrity. Signalling networks have evolved to respond to oxidative stress by engaging diverse enzymatic and non-enzymatic antioxidant mechanisms to restore redox homeostasis. The architecture of oxidative stress response networks during periods of normal growth, and how increased ROS levels dynamically reconfigure these networks are largely unknown. In order to gain insight into the structure of signalling networks that promote redox homeostasis we first performed genome-scale RNAi screens to identify novel suppressors of superoxide accumulation. We then infer relationships between redox regulators by hierarchical clustering of phenotypic signatures describing how gene inhibition affects superoxide levels, cellular viability, and morphology across different genetic backgrounds. Genes that cluster together are likely to act in the same signalling pathway/complex and thus make "functional interactions". Moreover we also calculate differential phenotypic signatures describing the difference in cellular phenotypes following RNAi between untreated cells and cells submitted to oxidative stress. Using both phenotypic signatures and differential signatures we construct a network model of functional interactions that occur between components of the redox homeostasis network, and how such interactions become rewired in the presence of oxidative stress. This network model predicts a functional interaction between the transcription factor Jun and the IRE1 kinase, which we validate in an orthogonal assay. We thus demonstrate the ability of systems-biology approaches to identify novel signalling events.

Estimation of Gene Regulatory Networks

Complex gene regulatory networks, not individual genes, control cellular function. Genes and gene products act together to determine cellular phenotypes. Estimation of these networks is necessary to understand cellular mechanisms, detect differences in gene regulation between cell types, and predict cellular response to interventions. A plethora of algorithms have been developed to infer network structure from experimental data. Here we provide a general introduction to the estimation of gene regulatory networks and the classes of proposed algorithms.

Dissection of DNA damage responses using multiconditional genetic interaction maps

To protect the genome, cells have evolved a diverse set of pathways designed to sense, signal, and repair multiple types of DNA damage. To assess the degree of coordination and crosstalk among these pathways, we systematically mapped changes in the cell's genetic network across a panel of different DNA-damaging agents, resulting in ~1,800,000 differential measurements. Each agent was associated with a distinct interaction pattern, which, unlike single-mutant phenotypes or gene expression data, has high statistical power to pinpoint the specific repair mechanisms at work. The agent-specific networks revealed roles for the histone acetyltranferase Rtt109 in the mutagenic bypass of DNA lesions and the neddylation machinery in cell-cycle regulation and genome stability, while the network induced by multiple agents implicates Irc21, an uncharacterized protein, in checkpoint control and DNA repair. Our multiconditional genetic interaction map provides a unique resource that identifies agent-specific and general DNA damage response pathways.

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.

Phosphoproteomic analysis of protein kinase C signaling in Saccharomyces cerevisiae reveals Slt2 mitogen-activated protein kinase (MAPK)-dependent phosphorylation of eisosome core components

The cell wall integrity (CWI) pathway of the model organism Saccharomyces cerevisiae has been thoroughly studied as a paradigm of the mitogen-activated protein kinase (MAPK) pathway. It consists of a classic MAPK module comprising the Bck1 MAPK kinase kinase, two redundant MAPK kinases (Mkk1 and Mkk2), and the Slt2 MAPK. This module is activated under a variety of stimuli related to cell wall homeostasis by Pkc1, the only member of the protein kinase C family in budding yeast. Quantitative phosphoproteomics based on stable isotope labeling of amino acids in cell culture is a powerful tool for globally studying protein phosphorylation. Here we report an analysis of the yeast phosphoproteome upon overexpression of a PKC1 hyperactive allele that specifically activates CWI MAPK signaling in the absence of external stimuli. We found 82 phosphopeptides originating from 43 proteins that showed enhanced phosphorylation in these conditions. The MAPK S/T-P target motif was significantly overrepresented in these phosphopeptides. Hyperphosphorylated proteins provide putative novel targets of the Pkc1-cell wall integrity pathway involved in diverse functions such as the control of gene expression, protein synthesis, cytoskeleton maintenance, DNA repair, and metabolism. Remarkably, five components of the plasma-membrane-associated protein complex known as eisosomes were found among the up-regulated proteins. We show here that Pkc1-induced phosphorylation of the eisosome core components Pil1 and Lsp1 was not exerted directly by Pkc1, but involved signaling through the Slt2 MAPK module.

The zinc cluster protein Sut1 contributes to filamentation in Saccharomyces cerevisiae

Sut1 is a transcriptional regulator of the Zn(II)(2)Cys(6) family in the budding yeast Saccharomyces cerevisiae. The only function that has been attributed to Sut1 is sterol uptake under anaerobic conditions. Here, we show that Sut1 is also expressed in the presence of oxygen, and we identify a novel function for Sut1. SUT1 overexpression blocks filamentous growth, a response to nutrient limitation, in both haploid and diploid cells. This inhibition by Sut1 is independent of its function in sterol uptake. Sut1 downregulates the expression of GAT2, HAP4, MGA1, MSN4, NCE102, PRR2, RHO3, and RHO5. Several of these Sut1 targets (GAT2, HAP4, MGA1, RHO3, and RHO5) are essential for filamentation in haploids and/or diploids. Furthermore, the expression of the Sut1 target genes, with the exception of MGA1, is induced during filamentous growth. We also show that SUT1 expression is autoregulated and inhibited by Ste12, a key transcriptional regulator of filamentation. We propose that Sut1 partially represses the expression of GAT2, HAP4, MGA1, MSN4, NCE102, PRR2, RHO3, and RHO5 when nutrients are plentiful. Filamentation-inducing conditions relieve this repression by Sut1, and the increased expression of Sut1 targets triggers filamentous growth.

Integrative analysis of a cross-loci regulation network identifies App as a gene regulating insulin secretion from pancreatic islets

Complex diseases result from molecular changes induced by multiple genetic factors and the environment. To derive a systems view of how genetic loci interact in the context of tissue-specific molecular networks, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant (B6) and diabetes-susceptible (BTBR) mouse strains made genetically obese by the Leptin^ob/ob mutation (Lep^ob). High-density genotypes, diabetes-related clinical traits, and whole-transcriptome expression profiling in five tissues (white adipose, liver, pancreatic islets, hypothalamus, and gastrocnemius muscle) were determined for all mice. We performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait. Among five tissues under study, there are extensive protein–protein interactions between genes responding to different loci in adipose and pancreatic islets that potentially jointly participated in the regulation of plasma insulin. We developed a novel ranking scheme based on cross-loci protein-protein network topology and gene expression to assess each gene's potential to regulate plasma insulin. Unique candidate genes were identified in adipose tissue and islets. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose or a membrane-permeant cAMP analog, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.

Alzheimer's disease and type 2 diabetes are two common aging-related diseases. Numerous studies have shown that the two diseases are associated. However, the mechanisms of such connection are not clear. Both diseases are complex diseases that are induced by multiple genetic factors and the environment. To understand the molecular network regulated by complex genetic factors causing type 2 diabetes, we constructed an F2 intercross comprised of >500 mice from diabetes-resistant and diabetic mouse strains. We measured genotypes, clinical traits, and expression profiling in five tissues for each mouse. We then performed an integrative analysis to investigate the inter-relationship among genetic factors, expression traits, and plasma insulin, a hallmark diabetes trait, and developed a novel method for inferring key regulators for regulating plasma insulin. In islets, the Alzheimer's gene App was identified as a top candidate regulator. Islets from 17-week-old, but not 10-week-old, App knockout mice showed increased insulin secretion in response to glucose, in agreement with the predictions of the network model. Our result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes.

Interactions of NADP-reducing enzymes across varying environmental conditions: a model of biological complexity

Interactions across biological networks are often quantified under a single set of conditions; however, cellular behaviors are dynamic and interactions can be expected to change in response to molecular context and environment. To determine the consistency of network interactions, we examined the enzyme network responsible for the reduction of nicotinamide adenine dinucleotide phosphate (NADP) to NADPH across three different conditions: oxidative stress, starvation, and desiccation. Synthetic, activity-variant alleles were used in Drosophila melanogaster for glucose-6-phosphate dehydrogenase (G6pd), cytosolic isocitrate dehydrogenase (Idh), and cytosolic malic enzyme (Men) along with seven different genetic backgrounds to lend biological relevance to the data. The responses of the NADP-reducing enzymes and two downstream phenotypes (lipid and glycogen concentration) were compared between the control and stress conditions. In general, responses in NADP-reducing enzymes were greater under conditions of oxidative stress, likely due to an increased demand for NADPH. Interactions between the enzymes were altered by environmental stress in directions and magnitudes that are consistent with differential contributions of the different enzymes to the NADPH pool: the contributions of G6PD and IDH seem to be accentuated by oxidative stress, and MEN by starvation. Overall, we find that biological network interactions are strongly influenced by environmental conditions, underscoring the importance of examining networks as dynamic entities.

Comparative chemogenomics to examine the mechanism of action of dna-targeted platinum-acridine anticancer agents

Platinum-based drugs have been used to successfully treat diverse cancers for several decades. Cisplatin, the original compound of this class, cross-links DNA, resulting in cell cycle arrest and cell death via apoptosis. Cisplatin is effective against several tumor types, yet it exhibits toxic side effects and tumors often develop resistance. To mitigate these liabilities while maintaining potency, we generated a library of non-classical platinum-acridine hybrid agents and assessed their mechanisms of action using a validated genome-wide screening approach in Saccharomyces cerevisiae and in the distantly related yeast Schizosaccharomyces pombe. Chemogenomic profiles from both S. cerevisiae and S. pombe demonstrate that several of the platinum-acridines damage DNA differently than cisplatin based on their requirement for distinct modules of DNA repair.

Differential network entropy reveals cancer system hallmarks

The cellular phenotype is described by a complex network of molecular interactions. Elucidating network properties that distinguish disease from the healthy cellular state is therefore of critical importance for gaining systems-level insights into disease mechanisms and ultimately for developing improved therapies. By integrating gene expression data with a protein interaction network we here demonstrate that cancer cells are characterised by an increase in network entropy. In addition, we formally demonstrate that gene expression differences between normal and cancer tissue are anticorrelated with local network entropy changes, thus providing a systemic link between gene expression changes at the nodes and their local correlation patterns. In particular, we find that genes which drive cell-proliferation in cancer cells and which often encode oncogenes are associated with reductions in network entropy. These findings may have potential implications for identifying novel drug targets.

Bioinformatic identification of genes suppressing genome instability

Unbiased forward genetic screens for mutations causing increased gross chromosomal rearrangement (GCR) rates in Saccharomyces cerevisiae are hampered by the difficulty in reliably using qualitative GCR assays to detect mutants with small but significantly increased GCR rates. We therefore developed a bioinformatic procedure using genome-wide functional genomics screens to identify and prioritize candidate GCR-suppressing genes on the basis of the shared drug sensitivity suppression and similar genetic interactions as known GCR suppressors. The number of known suppressors was increased from 75 to 110 by testing 87 predicted genes, which identified unanticipated pathways in this process. This analysis explicitly dealt with the lack of concordance among high-throughput datasets to increase the reliability of phenotypic predictions. Additionally, shared phenotypes in one assay were imperfect predictors for shared phenotypes in other assays, indicating that although genome-wide datasets can be useful in aggregate, caution and validation methods are required when deciphering biological functions via surrogate measures, including growth-based genetic interactions.

Prime, repair, restore: the active role of chromatin in the DNA damage response

The view of DNA packaging into chromatin as a mere obstacle to DNA repair is evolving. In this review, we focus on histone variants and heterochromatin proteins as chromatin components involved in distinct levels of chromatin organization to integrate them as real players in the DNA damage response (DDR). Based on recent data, we highlight how some of these chromatin components play active roles in the DDR and contribute to the fine-tuning of damage signaling, DNA and chromatin repair. To take into account this integrated view, we revisit the existing access-repair-restore model and propose a new working model involving priming chromatin for repair and restoration as a concerted process. We discuss how this impacts on both genomic and epigenomic stability and plasticity.

qPCA: a scalable assay to measure the perturbation of protein-protein interactions in living cells

One of the most important challenges in systems biology is to understand how cells respond to genetic and environmental perturbations. Here we show that the yeast DHFR-PCA, coupled with high-resolution growth profiling (DHFR-qPCA), is a straightforward assay to study the modulation of protein-protein interactions (PPIs) in vivo as a response to genetic, metabolic and drug perturbations. Using the canonical Protein Kinase A (PKA) pathway as a test system, we show that changes in PKA activity can be measured in living cells as a modulation of the interaction between its regulatory (Bcy1) and catalytic (Tpk1 and Tpk2) subunits in response to changes in carbon metabolism, caffeine and methyl methanesulfonate (MMS) treatments and to modifications in the dosage of its enzymatic regulators, the phosphodiesterases. Our results show that the DHFR-qPCA is easily implementable and amenable to high-throughput. The DHFR-qPCA will pave the way to the study of the effects of drug, genetic and environmental perturbations on in vivo PPI networks, thus allowing the exploration of new spaces of the eukaryotic interactome.

A multidimensional matrix for systems biology research and its application to interaction networks

A multidimensional matrix containing 76 parameters from 21 transcriptomics, proteomics, interactomics, phenotypic and sequence-based data sets, in which each data set covered most of the Saccharomyces cerevisiae proteome, was compiled for systems biology research. The maximal information coefficient (MIC) was used to measure correlations between every pair of parameters. Out of 2850 possible comparisons, 340 pairs of variables (12%) showed statistically significant MIC scores. There were 321 relationships that were expected; these included relationships within physicochemical parameters of proteins, between abundance levels of genes/proteins and expression noise, and between different types of intracellular networks. We found 19 potentially novel relationships between different types of "-omics" data. The strongest of these involved genetic interaction networks, which were correlated with pleiotropy and cell-to-cell variability in protein expression. Protein disorder also showed a number of significant relationships with protein abundance, signaling and regulatory networks. Significant cross-talk was seen between the signaling and kinase interaction networks. Investigation of this revealed densely connected kinase clusters and significant signaling between them, along with signaling centers that act as integrators or broadcasters of intracellular information. These centers may allow for redundancy and a means of dampening noise in networks under a variety of genetic or environmental perturbations.

Hierarchical modularity and the evolution of genetic interactomes across species

To date, cross-species comparisons of genetic interactomes have been restricted to small or functionally related gene sets, limiting our ability to infer evolutionary trends. To facilitate a more comprehensive analysis, we constructed a genome-scale epistasis map (E-MAP) for the fission yeast Schizosaccharomyces pombe, providing phenotypic signatures for ~60% of the nonessential genome. Using these signatures, we generated a catalog of 297 functional modules, and we assigned function to 144 previously uncharacterized genes, including mRNA splicing and DNA damage checkpoint factors. Comparison with an integrated genetic interactome from the budding yeast Saccharomyces cerevisiae revealed a hierarchical model for the evolution of genetic interactions, with conservation highest within protein complexes, lower within biological processes, and lowest between distinct biological processes. Despite the large evolutionary distance and extensive rewiring of individual interactions, both networks retain conserved features and display similar levels of functional crosstalk between biological processes, suggesting general design principles of genetic interactomes.

Programmable single-cell mammalian biocomputers

Synthetic biology has advanced the design of standardized control devices that program cellular functions and metabolic activities in living organisms. Rational interconnection of these synthetic switches resulted in increasingly complex designer networks that execute input-triggered genetic instructions with precision, robustness and computational logic reminiscent of electronic circuits. Using trigger-controlled transcription factors, which independently control gene expression, and RNA-binding proteins that inhibit the translation of transcripts harbouring specific RNA target motifs, we have designed a set of synthetic transcription–translation control devices that could be rewired in a plug-and-play manner. Here we show that these combinatorial circuits integrated a two-molecule input and performed digital computations with NOT, AND, NAND and N-IMPLY expression logic in single mammalian cells. Functional interconnection of two N-IMPLY variants resulted in bitwise intracellular XOR operations, and a combinatorial arrangement of three logic gates enabled independent cells to perform programmable half-subtractor and half-adder calculations. Individual mammalian cells capable of executing basic molecular arithmetic functions isolated or coordinated to metabolic activities in a predictable, precise and robust manner may provide new treatment strategies and bio-electronic interfaces in future gene-based and cell-based therapies.

Mass spectrometry-based proteomics and network biology

In the life sciences, a new paradigm is emerging that places networks of interacting molecules between genotype and phenotype. These networks are dynamically modulated by a multitude of factors, and the properties emerging from the network as a whole determine observable phenotypes. This paradigm is usually referred to as systems biology, network biology, or integrative biology. Mass spectrometry (MS)-based proteomics is a central life science technology that has realized great progress toward the identification, quantification, and characterization of the proteins that constitute a proteome. Here, we review how MS-based proteomics has been applied to network biology to identify the nodes and edges of biological networks, to detect and quantify perturbation-induced network changes, and to correlate dynamic network rewiring with the cellular phenotype. We discuss future directions for MS-based proteomics within the network biology paradigm.

Functional repurposing revealed by comparing S. pombe and S. cerevisiae genetic interactions

We present a genetic interaction map of pairwise measures including ∼40% of nonessential S. pombe genes. By comparing interaction maps for fission and budding yeast, we confirmed widespread conservation of genetic relationships within and between complexes and pathways. However, we identified an important subset of orthologous complexes that have undergone functional "repurposing": the evolution of divergent functions and partnerships. We validated three functional repurposing events in S. pombe and mammalian cells and discovered that (1) two lumenal sensors of misfolded ER proteins, the kinase/nuclease Ire1 and the glucosyltransferase Gpt1, act together to mount an ER stress response; (2) ESCRT factors regulate spindle-pole-body duplication; and (3) a membrane-protein phosphatase and kinase complex, the STRIPAK complex, bridges the cis-Golgi, the centrosome, and the outer nuclear membrane to direct mitotic progression. Each discovery opens new areas of inquiry and-together-have implications for model organism-based research and the evolution of genetic systems.

Dissecting DNA damage response pathways by analysing protein localization and abundance changes during DNA replication stress

Relocalization of proteins is a hallmark of the DNA damage response. We use high-throughput microscopic screening of the yeast GFP fusion collection to develop a systems-level view of protein reorganization following drug-induced DNA replication stress. Changes in protein localization and abundance reveal drug-specific patterns of functional enrichments. Classification of proteins by subcellular destination enables the identification of pathways that respond to replication stress. We analysed pairwise combinations of GFP fusions and gene deletion mutants to define and order two previously unknown DNA damage responses. In the first, Cmr1 forms subnuclear foci that are regulated by the histone deacetylase Hos2 and are distinct from the typical Rad52 repair foci. In a second example, we find that the checkpoint kinases Mec1/Tel1 and the translation regulator Asc1 regulate P-body formation. This method identifies response pathways that were not detected in genetic and protein interaction screens, and can be readily applied to any form of chemical or genetic stress to reveal cellular response pathways.

One Hand Clapping: detection of condition-specific transcription factor interactions from genome-wide gene activity data

We present One Hand Clapping (OHC), a method for the detection of condition-specific interactions between transcription factors (TFs) from genome-wide gene activity measurements. OHC is based on a mapping between transcription factors and their target genes. Given a single case–control experiment, it uses a linear regression model to assess whether the common targets of two arbitrary TFs behave differently than expected from the genes targeted by only one of the TFs. When applied to osmotic stress data in S. cerevisiae, OHC produces consistent results across three types of expression measurements: gene expression microarray data, RNA Polymerase II ChIP-chip binding data and messenger RNA synthesis rates. Among the eight novel, condition-specific TF pairs, we validate the interaction between Gcn4p and Arr1p experimentally. We apply OHC to a large gene activity dataset in S. cerevisiae and provide a compendium of condition-specific TF interactions.

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.

Multiple genetic interaction experiments provide complementary information useful for gene function prediction

Genetic interactions help map biological processes and their functional relationships. A genetic interaction is defined as a deviation from the expected phenotype when combining multiple genetic mutations. In Saccharomyces cerevisiae, most genetic interactions are measured under a single phenotype - growth rate in standard laboratory conditions. Recently genetic interactions have been collected under different phenotypic readouts and experimental conditions. How different are these networks and what can we learn from their differences? We conducted a systematic analysis of quantitative genetic interaction networks in yeast performed under different experimental conditions. We find that networks obtained using different phenotypic readouts, in different conditions and from different laboratories overlap less than expected and provide significant unique information. To exploit this information, we develop a novel method to combine individual genetic interaction data sets and show that the resulting network improves gene function prediction performance, demonstrating that individual networks provide complementary information. Our results support the notion that using diverse phenotypic readouts and experimental conditions will substantially increase the amount of gene function information produced by genetic interaction screens.

MoNetFamily: a web server to infer homologous modules and module-module interaction networks in vertebrates

A module is a fundamental unit forming with highly connected proteins and performs a certain kind of biological functions. Modules and module–module interaction (MMI) network are essential for understanding cellular processes and functions. The MoNetFamily web server can identify the modules, homologous modules (called module family) and MMI networks across multiple species for the query protein(s). This server first finds module candidates of the query by using BLASTP to search the module template database (1785 experimental and 1252 structural templates). MoNetFamily then infers the homologous modules of the selected module candidate using protein–protein interaction (PPI) families. According to homologous modules and PPIs, we statistically calculated MMIs and MMI networks across multiple species. For each module candidate, MoNetFamily identifies its neighboring modules and their MMIs in module networks of Homo sapiens, Mus musculus and Danio rerio. Finally, MoNetFamily shows the conserved proteins, PPI profiles and functional annotations of the module family. Our results indicate that the server can be useful for MMI network (e.g. 1818 modules and 9678 MMIs in H. sapiens) visualizations and query annotations using module families and neighboring modules. We believe that the server is able to provide valuable insights to determine homologous modules and MMI networks across multiple species for studying module evolution and cellular processes. The MoNetFamily sever is available at http://monetfamily.life.nctu.edu.tw.

Altered expression and activities of enzymes involved in thiamine diphosphate biosynthesis in Saccharomyces cerevisiae under oxidative and osmotic stress

Thiamine diphosphate (TDP) serves as a cofactor for enzymes engaged in pivotal carbohydrate metabolic pathways, which are known to be modulated under stress conditions to ensure the cell survival. Recent reports have proven a protective role of thiamine (vitamin B(1)) in the response of plants to abiotic stress. This work aimed at verifying a hypothesis that also baker's yeast, which can synthesize thiamine de novo similarly to plants and bacteria, adjust thiamine metabolism to adverse environmental conditions. Our analyses on the gene expression and enzymatic activity levels generally showed an increased production of thiamine biosynthesis enzymes (THI4 and THI6/THI6), a TDP synthesizing enzyme (THI80/THI80) and a TDP-requiring enzyme, transketolase (TKL1/TKL) by yeast subjected to oxidative (1 mM hydrogen peroxide) and osmotic (1 M sorbitol) stress. However, these effects differed in magnitude, depending on yeast growth phase and presence of thiamine in growth medium. A mutant thi4Δ with increased sensitivity to oxidative stress exhibited enhanced TDP biosynthesis as compared with the wild-type strain. Similar tendencies were observed in mutants yap1Δ and hog1Δ defective in the signaling pathways of the defense against oxidative and osmotic stress, respectively, suggesting that thiamine metabolism can partly compensate damages of yeast general defense systems.

Regulation of cell wall biogenesis in Saccharomyces cerevisiae: the cell wall integrity signaling pathway

The yeast cell wall is a strong, but elastic, structure that is essential not only for the maintenance of cell shape and integrity, but also for progression through the cell cycle. During growth and morphogenesis, and in response to environmental challenges, the cell wall is remodeled in a highly regulated and polarized manner, a process that is principally under the control of the cell wall integrity (CWI) signaling pathway. This pathway transmits wall stress signals from the cell surface to the Rho1 GTPase, which mobilizes a physiologic response through a variety of effectors. Activation of CWI signaling regulates the production of various carbohydrate polymers of the cell wall, as well as their polarized delivery to the site of cell wall remodeling. This review article centers on CWI signaling in Saccharomyces cerevisiae through the cell cycle and in response to cell wall stress. The interface of this signaling pathway with other pathways that contribute to the maintenance of cell wall integrity is also discussed.

Pathway analysis of genomic data: concepts, methods, and prospects for future development

Genome-wide data sets are increasingly being used to identify biological pathways and networks underlying complex diseases. In particular, analyzing genomic data through sets defined by functional pathways offers the potential of greater power for discovery and natural connections to biological mechanisms. With the burgeoning availability of next-generation sequencing, this is an opportune moment to revisit strategies for pathway-based analysis of genomic data. Here, we synthesize relevant concepts and extant methodologies to guide investigators in study design and execution. We also highlight ongoing challenges and proposed solutions. As relevant analytical strategies mature, pathways and networks will be ideally placed to integrate data from diverse -omics sources to harness the extensive, rich information related to disease and treatment mechanisms.

A novel framework for the comparative analysis of biological networks

Genome sequencing projects provide nearly complete lists of the individual components present in an organism, but reveal little about how they work together. Follow-up initiatives have deciphered thousands of dynamic and context-dependent interrelationships between gene products that need to be analyzed with novel bioinformatics approaches able to capture their complex emerging properties. Here, we present a novel framework for the alignment and comparative analysis of biological networks of arbitrary topology. Our strategy includes the prediction of likely conserved interactions, based on evolutionary distances, to counter the high number of missing interactions in the current interactome networks, and a fast assessment of the statistical significance of individual alignment solutions, which vastly increases its performance with respect to existing tools. Finally, we illustrate the biological significance of the results through the identification of novel complex components and potential cases of cross-talk between pathways and alternative signaling routes.

The yeast mitogen-activated protein kinase Slt2 is involved in the cellular response to genotoxic stress

BACKGROUND

The maintenance of genomic integrity is essential for cell viability. Complex signalling pathways (DNA integrity checkpoints) mediate the response to genotoxic stresses. Identifying new functions involved in the cellular response to DNA-damage is crucial. The Saccharomyces cerevisiae SLT2 gene encodes a member of the mitogen-activated protein kinase (MAPK) cascade whose main function is the maintenance of the cell wall integrity. However, different observations suggest that SLT2 may also have a role related to DNA metabolism.

RESULTS

This work consisted in a comprehensive study to connect the Slt2 protein to genome integrity maintenance in response to genotoxic stresses. The slt2 mutant strain was hypersensitive to a variety of genotoxic treatments, including incubation with hydroxyurea (HU), methylmetanosulfonate (MMS), phleomycin or UV irradiation. Furthermore, Slt2 was activated by all these treatments, which suggests that Slt2 plays a central role in the cellular response to genotoxic stresses. Activation of Slt2 was not dependent on the DNA integrity checkpoint. For MMS and UV, Slt2 activation required progression through the cell cycle. In contrast, HU also activated Slt2 in nocodazol-arrested cells, which suggests that Slt2 may respond to dNTP pools alterations. However, neither the protein level of the distinct ribonucleotide reductase subunits nor the dNTP pools were affected in a slt2 mutant strain. An analysis of the checkpoint function revealed that Slt2 was not required for either cell cycle arrest or the activation of the Rad53 checkpoint kinase in response to DNA damage. However, slt2 mutant cells showed an elongated bud and partially impaired Swe1 degradation after replicative stress, indicating that Slt2 could contribute, in parallel with Rad53, to bud morphogenesis control after genotoxic stresses.

CONCLUSIONS

Slt2 is activated by several genotoxic treatments and is required to properly cope with DNA damage. Slt2 function is important for bud morphogenesis and optimal Swe1 degradation under replicative stress. The MAPK Slt2 appears as a new player in the cellular response to genotoxic stresses.

Physical links between the nuclear envelope protein Mps3, three alternate replication factor C complexes, and a variant histone in Saccharomyces cerevisiae

Viability of cell progeny upon cell division require that genomes are replicated, repaired, and maintained with high fidelity. Central to both DNA replication and repair are Replication Factor C (RFC) complexes which catalyze the unloading/loading of sliding clamps such as PCNA or 9-1-1 complexes on DNA. Budding yeast contain four alternate RFC complexes which play partially redundant roles. Rfc1, Ctf18, Rad24, and Elg1 are all large subunits that bind, in a mutually exclusive fashion to RFC 2-5 small subunits. Ctf18, Rad24, and Elg1 are of particular interest because, in addition to their roles in maintaining genome integrity, all three play critical roles in sister chromatid tethering reactions that appear coupled to their roles in DNA replication/repair. Intriguingly, the nuclear envelope protein Mps3 similarly exhibits roles in repair and cohesion, leading us to hypothesize that Mps3 and RFCs function through a singular mechanism. Here we report that the nuclear envelope protein Mps3 physically associates with all three of these large RFC complex subunits (Ctf18, Elg1, and Rad24). In addition we report a physical interaction between Mps3 and the histone variant Htz1, a factor previously shown to promote DNA repair. In combination, these findings reveal a direct link between the nuclear envelope and chromatin and provide support for a model that telomeres and chromatin interact with the nuclear envelope during both DNA repair and sister chromatid pairing reactions.

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.

Reverse-engineering human regulatory networks

The explosion of genomic, transcriptomic, proteomic, metabolomic, and other omics data is challenging the research community to develop rational models for their organization and interpretation to generate novel biological knowledge. The development and use of gene regulatory networks to mechanistically interpret this data is an important development in molecular biology, usually captured under the banner of systems biology. As a result, the repertoire of methods for the reconstruction of comprehensive and cell-context-specific maps of regulatory interactions, or interactomes, has also exploded in the past few years. In this review, we focus on Network Biology and more specifically on methods for reverse engineering transcriptional, post-transcriptional, and post-translational human interaction networks and show how their interrogation is starting to impact our understanding of cellular pathophysiology and one's ability to predict cellular phenotypes from genome-wide molecular observations.

An information theoretic approach to constructing robust Boolean gene regulatory networks

We introduce a class of finite systems models of gene regulatory networks exhibiting behavior of the cell cycle. The model is an extension of a Boolean network model. The system spontaneously cycles through a finite set of internal states, tracking the increase of an external factor such as cell mass, and also exhibits checkpoints in which errors in gene expression levels due to cellular noise are automatically corrected. We present a 7-gene network based on Projective Geometry codes, which can correct, at every given time, one gene expression error. The topology of a network is highly symmetric and requires using only simple Boolean functions that can be synthesized using genes of various organisms. The attractor structure of the Boolean network contains a single cycle attractor. It is the smallest nontrivial network with such high robustness. The methodology allows construction of artificial cell cycle gene regulatory networks with the number of phases larger than in natural cell cycle.

Identifying disease genes and module biomarkers by differential interactions

OBJECTIVE

A complex disease is generally caused by the mutation of multiple genes or by the dysfunction of multiple biological processes. Systematic identification of causal disease genes and module biomarkers can provide insights into the mechanisms underlying complex diseases, and help develop efficient therapies or effective drugs.

MATERIALS AND METHODS

In this paper, we present a novel approach to predict disease genes and identify dysfunctional networks or modules, based on the analysis of differential interactions between disease and control samples, in contrast to the analysis of differential gene or protein expressions widely adopted in existing methods.

RESULTS AND DISCUSSION

As an example, we applied our method to the study of three-stage microarray data for gastric cancer. We identified network modules or module biomarkers that include a set of genes related to gastric cancer, implying the predictive power of our method. The results on holdout validation data sets show that our identified module can serve as an effective module biomarker for accurately detecting or diagnosing gastric cancer, thereby validating the efficiency of our method.

CONCLUSION

We proposed a new approach to detect module biomarkers for diseases, and the results on gastric cancer demonstrated that the differential interactions are useful to detect dysfunctional modules in the molecular interaction network, which in turn can be used as robust module biomarkers.

A quantitative analysis of monochromaticity in genetic interaction networks

BACKGROUND

A genetic interaction refers to the deviation of phenotypes from the expected when perturbing two genes simultaneously. Studying genetic interactions help clarify relationships between genes, such as compensation and masking, and identify gene groups of functional modules. Recently, several genome-scale experiments for measuring quantitative (positive and negative) genetic interactions have been conducted. The results revealed that genes in the same module usually interact with each other in a consistent way (pure positive or negative); this phenomenon was designated as monochromaticity. Monochromaticity might be the underlying principle that can be utilized to unveil the modularity of cellular networks. However, no appropriate quantitative measurement for this phenomenon has been proposed.

RESULTS

In this study, we propose the monochromatic index (MCI), which is able to quantitatively evaluate the monochromaticity of potential functional modules of genes, and the MCI was used to study genetic landscapes in different cellular subsystems. We demonstrated that MCI not only amend the deficiencies of MP-score but also properly incorporate the background effect. The results showed that not only within-complex but also between-complex connections present significant monochromatic tendency. Furthermore, we also found that significantly higher proportion of protein complexes are connected by negative genetic interactions in metabolic network, while transcription and translation system adopts relatively even number of positive and negative genetic interactions to link protein complexes.

CONCLUSION

In summary, we demonstrate that MCI improves deficiencies suffered by MP-score, and can be used to evaluate monochromaticity in a quantitative manner. In addition, it also helps to unveil features of genetic landscapes in different cellular subsystems. Moreover, MCI can be easily applied to data produced by different types of genetic interaction methodologies such as Synthetic Genetic Array (SGA), and epistatic miniarray profile (E-MAP).

Niche adaptation by expansion and reprogramming of general transcription factors

Numerous lineage-specific expansions of the transcription factor B (TFB) family in archaea suggests an important role for expanded TFBs in encoding environment-specific gene regulatory programs. Given the characteristics of hypersaline lakes, the unusually large numbers of TFBs in halophilic archaea further suggests that they might be especially important in rapid adaptation to the challenges of a dynamically changing environment. Motivated by these observations, we have investigated the implications of TFB expansions by correlating sequence variations, regulation, and physical interactions of all seven TFBs in Halobacterium salinarum NRC-1 to their fitness landscapes, functional hierarchies, and genetic interactions across 2488 experiments covering combinatorial variations in salt, pH, temperature, and Cu stress. This systems analysis has revealed an elegant scheme in which completely novel fitness landscapes are generated by gene conversion events that introduce subtle changes to the regulation or physical interactions of duplicated TFBs. Based on these insights, we have introduced a synthetically redesigned TFB and altered the regulation of existing TFBs to illustrate how archaea can rapidly generate novel phenotypes by simply reprogramming their TFB regulatory network.

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.

Genetic interaction maps in Escherichia coli reveal functional crosstalk among cell envelope biogenesis pathways

As the interface between a microbe and its environment, the bacterial cell envelope has broad biological and clinical significance. While numerous biosynthesis genes and pathways have been identified and studied in isolation, how these intersect functionally to ensure envelope integrity during adaptive responses to environmental challenge remains unclear. To this end, we performed high-density synthetic genetic screens to generate quantitative functional association maps encompassing virtually the entire cell envelope biosynthetic machinery of Escherichia coli under both auxotrophic (rich medium) and prototrophic (minimal medium) culture conditions. The differential patterns of genetic interactions detected among >235,000 digenic mutant combinations tested reveal unexpected condition-specific functional crosstalk and genetic backup mechanisms that ensure stress-resistant envelope assembly and maintenance. These networks also provide insights into the global systems connectivity and dynamic functional reorganization of a universal bacterial structure that is both broadly conserved among eubacteria (including pathogens) and an important target.

Proper assembly of the cell envelope is essential for bacterial growth, environmental adaptation, and drug resistance. Yet, while the biological roles of the many genes and pathways involved in biosynthesis of the cell envelope have been studied extensively in isolation, how the myriad components intersect functionally to maintain envelope integrity under different growth conditions has not been explored systematically. Genome-scale genetic interaction screens have increasingly been performed to great impact in yeast; no analogous comprehensive studies have yet been reported for bacteria despite their prominence in human health and disease. We addressed this by using a synthetic genetic array technology to generate quantitative maps of genetic interactions encompassing virtually all the components of the cell envelope biosynthetic machinery of the classic model bacterium E. coli in two common laboratory growth conditions (rich and minimal medium). From the resulting networks of high-confidence genetic interactions, we identify condition-specific functional dependencies underlying envelope assembly and global remodeling of genetic backup mechanisms that ensure envelope integrity under environmental challenge.

Heat shock partially dissociates the overlapping modules of the yeast protein-protein interaction network: a systems level model of adaptation

Network analysis became a powerful tool giving new insights to the understanding of cellular behavior. Heat shock, the archetype of stress responses, is a well-characterized and simple model of cellular dynamics. S. cerevisiae is an appropriate model organism, since both its protein-protein interaction network (interactome) and stress response at the gene expression level have been well characterized. However, the analysis of the reorganization of the yeast interactome during stress has not been investigated yet. We calculated the changes of the interaction-weights of the yeast interactome from the changes of mRNA expression levels upon heat shock. The major finding of our study is that heat shock induced a significant decrease in both the overlaps and connections of yeast interactome modules. In agreement with this the weighted diameter of the yeast interactome had a 4.9-fold increase in heat shock. Several key proteins of the heat shock response became centers of heat shock-induced local communities, as well as bridges providing a residual connection of modules after heat shock. The observed changes resemble to a 'stratus-cumulus' type transition of the interactome structure, since the unstressed yeast interactome had a globally connected organization, similar to that of stratus clouds, whereas the heat shocked interactome had a multifocal organization, similar to that of cumulus clouds. Our results showed that heat shock induces a partial disintegration of the global organization of the yeast interactome. This change may be rather general occurring in many types of stresses. Moreover, other complex systems, such as single proteins, social networks and ecosystems may also decrease their inter-modular links, thus develop more compact modules, and display a partial disintegration of their global structure in the initial phase of crisis. Thus, our work may provide a model of a general, system-level adaptation mechanism to environmental changes.