Conservation and rewiring of functional modules revealed by an epistasis map in fission yeast

Association genetics in Populus reveals the interactions between Pt-miR397a and its target genes

Recent studies have revealed associations between single nucleotide polymorphisms (SNPs) in microRNA (miRNA) genes and diseases. However, association studies to decipher the interactions between miRNAs and their target genes remain to be conducted. Here, we investigated the association of growth and wood traits with SNPs in Pt-miR397a and its targets, in 261 individuals from a natural population of Populus tomentosa. Of the 57 SNPs identified in Pt-miR397a, three strongly affect its secondary stability, and SNPs in target sites in Pt-LAC20 and Pt-HSP40 changed the binding affinity of Pt-miR397a. Single-SNP association analysis revealed that SNPs in Pt-miR397a significantly associated with α-cellulose content and stem volume, and SNPs in target genes also associated with growth and wood-property traits. Multi-SNP association analysis with additive and dominant models found that SNPs in six potential target genes associated with at least one trait in common with Pt-miR397a, revealing a possible genetic interaction between Pt-miR397a and its targets. Furthermore, epistasis analysis revealed epistatic interactions between SNPs in Pt-miR397a and its target genes. Thus, our study indicated that SNPs in Pt-miR397a and six target genes affect wood formation and that association studies can reveal the interactions between miRNAs and their target genes.

Lineage-Specific Viral Hijacking of Non-canonical E3 Ubiquitin Ligase Cofactors in the Evolution of Vif Anti-APOBEC3 Activity

HIV-1 encodes the accessory protein Vif, which hijacks a host Cullin-RING ubiquitin ligase (CRL) complex as well as the non-canonical cofactor CBFβ, to antagonize APOBEC3 antiviral proteins. Non-canonical cofactor recruitment to CRL complexes by viral factors, to date, has only been attributed to HIV-1 Vif. To further study this phenomenon, we employed a comparative approach combining proteomic, biochemical, structural, and virological techniques to investigate Vif complexes across the lentivirus genus, including primate (HIV-1 and simian immunodeficiency virus macaque [SIVmac]) and non-primate (FIV, BIV, and MVV) viruses. We find that CBFβ is completely dispensable for the activity of non-primate lentiviral Vif proteins. Furthermore, we find that BIV Vif requires no cofactor and that MVV Vif requires a novel cofactor, cyclophilin A (CYPA), for stable CRL complex formation and anti-APOBEC3 activity. We propose modular conservation of Vif complexes allows for potential exaptation of functions through the acquisition of non-CRL-associated host cofactors while preserving anti-APOBEC3 activity.

Ku stabilizes replication forks in the absence of Brc1

DNA replication errors are a major source of genome instability in all organisms. In the fission yeast Schizosaccharomyces pombe, the DNA damage response protein Brc1 binds phospho-histone H2A (γH2A)-marked chromatin during S-phase, but how Brc1 protects genome integrity remains unclear. Here we report that the non-homologous end-joining (NHEJ) protein Ku becomes critical for survival of replication stress in brc1∆ cells. Ku’s protective activity in brc1∆ cells does not involve its canonical NHEJ function or its roles in protecting telomeres or shielding DNA ends from Exo1 exonuclease. In brc1∆ pku80∆ cells, nuclear foci of Rad52 homologous recombination (HR) protein increase and Mus81-Eme1 Holliday junction resolvase becomes critical, indicating increased replication fork instability. Ku’s localization at a ribosomal DNA replication fork barrier associated with frequent replisome-transcriptosome collisions increases in brc1∆ cells and increased collisions correlate with an enhanced requirement for Brc1. These data indicate that Ku stabilizes replication forks in the absence of Brc1.

Differential genetic interactions of yeast stress response MAPK pathways

Genetic interaction screens have been applied with great success in several organisms to study gene function and the genetic architecture of the cell. However, most studies have been performed under optimal growth conditions even though many functional interactions are known to occur under specific cellular conditions. In this study, we have performed a large-scale genetic interaction analysis in Saccharomyces cerevisiae involving approximately 49 × 1,200 double mutants in the presence of five different stress conditions, including osmotic, oxidative and cell wall-altering stresses. This resulted in the generation of a differential E-MAP (or dE-MAP) comprising over 250,000 measurements of conditional interactions. We found an extensive number of conditional genetic interactions that recapitulate known stress-specific functional associations. Furthermore, we have also uncovered previously unrecognized roles involving the phosphatase regulator Bud14, the histone methylation complex COMPASS and membrane trafficking complexes in modulating the cell wall integrity pathway. Finally, the osmotic stress differential genetic interactions showed enrichment for genes coding for proteins with conditional changes in phosphorylation but not for genes with conditional changes in gene expression. This suggests that conditional genetic interactions are a powerful tool to dissect the functional importance of the different response mechanisms of the cell.

Genetic interaction mapping reveals a role for the SWI/SNF nucleosome remodeler in spliceosome activation in fission yeast

Although numerous regulatory connections between pre-mRNA splicing and chromatin have been demonstrated, the precise mechanisms by which chromatin factors influence spliceosome assembly and/or catalysis remain unclear. To probe the genetic network of pre-mRNA splicing in the fission yeast Schizosaccharomyces pombe, we constructed an epistatic mini-array profile (E-MAP) and discovered many new connections between chromatin and splicing. Notably, the nucleosome remodeler SWI/SNF had strong genetic interactions with components of the U2 snRNP SF3 complex. Overexpression of SF3 components in ΔSWI/SNF cells led to inefficient splicing of many fission yeast introns, predominantly those with non-consensus splice sites. Deletion of SWI/SNF decreased recruitment of the splicing ATPase Prp2, suggesting that SWI/SNF promotes co-transcriptional spliceosome assembly prior to first step catalysis. Importantly, defects in SWI/SNF as well as SF3 overexpression each altered nucleosome occupancy along intron-containing genes, illustrating that the chromatin landscape both affects—and is affected by—co-transcriptional splicing.

It has recently become apparent that most introns are removed from pre-mRNA while the transcript is still engaged with RNA polymerase II (RNAPII). To gain insight into possible roles for chromatin in co-transcriptional splicing, we generated a genome-wide genetic interaction map in fission yeast and uncovered numerous connections between splicing and chromatin. The SWI/SNF remodeling complex is typically thought to activate gene expression by relieving barriers to polymerase elongation imposed by nucleosomes. Here we show that this remodeler is important for an early step in splicing in which Prp2, an RNA-dependent ATPase, is recruited to the assembling spliceosome to promote catalytic activation. Interestingly, introns with sub-optimal splice sites are particularly dependent on SWI/SNF, suggesting the impact of nucleosome dynamics on the kinetics of spliceosome assembly and catalysis. By monitoring nucleosome occupancy, we show significant alterations in nucleosome density in particular splicing and chromatin mutants, which generally paralleled the levels of RNAPII. Taken together, our findings challenge the notion that nucleosomes simply act as barriers to elongation; rather, we suggest that polymerase pausing at nucleosomes can activate gene expression by allowing more time for co-transcriptional splicing.

Genetic Interaction Landscape Reveals Critical Requirements for Schizosaccharomyces pombe Brc1 in DNA Damage Response Mutants

Brc1, which was first identified as a high-copy, allele-specific suppressor of a mutation impairing the Smc5-Smc6 holocomplex in Schizosaccharomyces pombe, protects genome integrity during normal DNA replication and when cells are exposed to toxic compounds that stall or collapse replication forks. The C-terminal tandem BRCT (BRCA1 C-terminus) domain of fission yeast Brc1 docks with phosphorylated histone H2A (γH2A)-marked chromatin formed by ATR/Rad3 checkpoint kinase at arrested and damaged replication forks; however, how Brc1 functions in relation to other genome protection modules remains unclear. Here, an epistatic mini-array profile reveals critical requirements for Brc1 in mutants that are defective in multiple DNA damage response pathways, including checkpoint signaling by Rad3-Rad26/ATR-ATRIP kinase, DNA repair by Smc5-Smc6 holocomplex, replication fork stabilization by Mrc1/claspin and Swi1-Swi3/Timeless-Tipin, and control of ubiquitin-regulated proteolysis by the COP9 signalosome (CSN). Exogenous genotoxins enhance these negative genetic interactions. Rad52 and RPA foci are increased in CSN-defective cells, and loss of γH2A increases genotoxin sensitivity, indicating a critical role for the γH2A-Brc1 module in stabilizing replication forks in CSN-defective cells. A negative genetic interaction with the Nse6 subunit of Smc5-Smc6 holocomplex indicates that the DNA repair functions of Brc1 and Smc5-Smc6 holocomplex are at least partially independent. Rtt107, the Brc1 homolog in Saccharomyces cerevisiae, has a very different pattern of genetic interactions, indicating evolutionary divergence of functions and DNA damage responses.

Drug synergy drives conserved pathways to increase fission yeast lifespan

Aging occurs over time with gradual and progressive loss of physiological function. Strategies to reduce the rate of functional loss and mitigate the subsequent onset of deadly age-related diseases are being sought. We demonstrated previously that a combination of rapamycin and myriocin reduces age-related functional loss in the Baker’s yeast Saccharomyces cerevisiae and produces a synergistic increase in lifespan. Here we show that the same drug combination also produces a synergistic increase in the lifespan of the fission yeast Schizosaccharomyces pombe and does so by controlling signal transduction pathways conserved across a wide evolutionary time span ranging from yeasts to mammals. Pathways include the target of rapamycin complex 1 (TORC1) protein kinase, the protein kinase A (PKA) and a stress response pathway, which in fission yeasts contains the Sty1 protein kinase, an ortholog of the mammalian p38 MAP kinase, a type of Stress Activated Protein Kinase (SAPK). These results along with previous studies in S. cerevisiae support the premise that the combination of rapamycin and myriocin enhances lifespan by regulating signaling pathways that couple nutrient and environmental conditions to cellular processes that fine-tune growth and stress protection in ways that foster long term survival. The molecular mechanisms for fine-tuning are probably species-specific, but since they are driven by conserved nutrient and stress sensing pathways, the drug combination may enhance survival in other organisms.

Rapid epigenetic adaptation to uncontrolled heterochromatin spreading

Heterochromatin, a highly compact chromatin state characterized by histone H3K9 methylation and HP1 protein binding, silences the underlying DNA and influences the expression of neighboring genes. However, the mechanisms that regulate heterochromatin spreading are not well understood. In this study, we show that the conserved Mst2 histone acetyltransferase complex in fission yeast regulates histone turnover at heterochromatin regions to control heterochromatin spreading and prevents ectopic heterochromatin assembly. The combined loss of Mst2 and the JmjC domain protein Epe1 results in uncontrolled heterochromatin spreading and massive ectopic heterochromatin, leading to severe growth defects due to the inactivation of essential genes. Interestingly, these cells quickly recover by accumulating heterochromatin at genes essential for heterochromatin assembly, leading to their reduced expression to restrain heterochromatin spreading. Our studies discover redundant pathways that control heterochromatin spreading and prevent ectopic heterochromatin assembly and reveal a fast epigenetic adaptation response to changes in heterochromatin landscape.

DOI:http://dx.doi.org/10.7554/eLife.06179.001

The DNA in the nucleus of a cell is wrapped around histone proteins to form a compact structure known as chromatin. Chromatin's structure can control how the genes in DNA are expressed. Loosely packed chromatin contains active genes, whereas densely packed chromatin (also called ‘heterochromatin’) contains silenced genes that are not expressed. The assembly of DNA into heterochromatin needs to be carefully controlled. Otherwise, the DNA next to heterochromatin regions can become densely packed as well (via a process called ‘heterochromatin spreading’), and the genes within this DNA are incorrectly silenced. Incorrect gene silencing is often associated with diseases such as cancer.

Cells add chemical groups onto the histone proteins to influence how chromatin is compacted. Densely packed chromatin contains histones with many methyl groups but few acetyl groups. A protein called Epe1, which potentially removes methyl groups, helps to prevent heterochromatin spreading in yeast cells. Wang et al. found that an enzyme called Mst2, which adds acetyl groups onto histones, also limits heterochromatin spreading and prevents extra heterochromatin from assembling at undesirable locations.

Wang et al. then generated yeast cells that lacked both Epe1 and Mst2. At first, these cells were sickly and unable to grow, because several essential genes were incorrectly silenced due to rampant heterochromatin spreading. However, the cells quickly overcame this growth defect by gaining an additional mutation. Normally mutations occur through changes in DNA sequences. However, Wang et al. found that the cells acquired this mutation by packing a gene required for heterochromatin assembly into heterochromatin. This in turn stopped more chromatin from becoming packed too densely. Changes to chromatin can also be passed on to the yeast's offspring, and such a change could help the offspring to better cope with changes in heterochromatin levels. Future work could test how often inheritable changes to chromatin modification help organisms adapt to environmental stresses, or if similar changes allow cancer cells to become tolerant to anticancer drugs.

DOI:http://dx.doi.org/10.7554/eLife.06179.002

Data Imputation in Epistatic MAPs by Network-Guided Matrix Completion

Epistatic miniarray profile (E-MAP) is a popular large-scale genetic interaction discovery platform. E-MAPs benefit from quantitative output, which makes it possible to detect subtle interactions with greater precision. However, due to the limits of biotechnology, E-MAP studies fail to measure genetic interactions for up to 40% of gene pairs in an assay. Missing measurements can be recovered by computational techniques for data imputation, in this way completing the interaction profiles and enabling downstream analysis algorithms that could otherwise be sensitive to missing data values. We introduce a new interaction data imputation method called network-guided matrix completion (NG-MC). The core part of NG-MC is low-rank probabilistic matrix completion that incorporates prior knowledge presented as a collection of gene networks. NG-MC assumes that interactions are transitive, such that latent gene interaction profiles inferred by NG-MC depend on the profiles of their direct neighbors in gene networks. As the NG-MC inference algorithm progresses, it propagates latent interaction profiles through each of the networks and updates gene network weights toward improved prediction. In a study with four different E-MAP data assays and considered protein-protein interaction and gene ontology similarity networks, NG-MC significantly surpassed existing alternative techniques. Inclusion of information from gene networks also allowed NG-MC to predict interactions for genes that were not included in original E-MAP assays, a task that could not be considered by current imputation approaches.

Yeast Phenomics: An Experimental Approach for Modeling Gene Interaction Networks that Buffer Disease

The genome project increased appreciation of genetic complexity underlying disease phenotypes: many genes contribute each phenotype and each gene contributes multiple phenotypes. The aspiration of predicting common disease in individuals has evolved from seeking primary loci to marginal risk assignments based on many genes. Genetic interaction, defined as contributions to a phenotype that are dependent upon particular digenic allele combinations, could improve prediction of phenotype from complex genotype, but it is difficult to study in human populations. High throughput, systematic analysis of S. cerevisiae gene knockouts or knockdowns in the context of disease-relevant phenotypic perturbations provides a tractable experimental approach to derive gene interaction networks, in order to deduce by cross-species gene homology how phenotype is buffered against disease-risk genotypes. Yeast gene interaction network analysis to date has revealed biology more complex than previously imagined. This has motivated the development of more powerful yeast cell array phenotyping methods to globally model the role of gene interaction networks in modulating phenotypes (which we call yeast phenomic analysis). The article illustrates yeast phenomic technology, which is applied here to quantify gene X media interaction at higher resolution and supports use of a human-like media for future applications of yeast phenomics for modeling human disease.

Genome-Wide Fitness and Genetic Interactions Determined by Tn-seq, a High-Throughput Massively Parallel Sequencing Method for Microorganisms

The lagging annotation of bacterial genomes and the inherent genetic complexity of many phenotypes is hindering the discovery of new drug targets and the development of new antimicrobial agents and vaccines. This unit presents Tn-seq, a method that has made it possible to quantitatively determine fitness for most genes in a microorganism and to screen for quantitative genetic interactions on a genome-wide scale and in a high-throughput fashion. Tn-seq can thus direct studies on the annotation of genes and untangle complex phenotypes. The method is based on the construction of a saturated transposon insertion library. After library selection, changes in the frequency of each insertion mutant are determined by sequencing flanking regions en masse. These changes are used to calculate each mutant's fitness. The method was originally developed for the Gram-positive bacterium Streptococcus pneumoniae, a causative agent of pneumonia and meningitis, but has now been applied to several different microbial species.

Rewiring of genetic networks in response to modification of genetic background

Genome-scale genetic interaction networks are progressively contributing to map the molecular circuitry that determines cellular behavior. To what extent this mapping changes in response to different environmental or genetic conditions is, however, largely unknown. Here, we assembled a genetic network using an in silico model of metabolism in yeast to explicitly ask how separate genetic backgrounds alter network structure. Backgrounds defined by single deletions of metabolically active enzymes induce strong rewiring when the deletion corresponds to a catabolic gene, evidencing a broad redistribution of fluxes to alternative pathways. We also show how change is more pronounced in interactions linking genes in distinct functional modules and in those connections that present weak epistasis. These patterns reflect overall the distributed robustness of catabolism. In a second class of genetic backgrounds, in which a number of neutral mutations accumulate, we dominantly observe modifications in the negative interactions that together with an increase in the number of essential genes indicate a global reduction in buffering. Notably, neutral trajectories that originate considerable changes in the wild-type network comprise mutations that diminished the environmental plasticity of the corresponding metabolism, what emphasizes a mechanistic integration of genetic and environmental buffering. More generally, our work demonstrates how the specific mechanistic causes of robustness influence the architecture of multiconditional genetic interaction maps.

Systems cell biology

Systems cell biology melds high-throughput experimentation with quantitative analysis and modeling to understand many critical processes that contribute to cellular organization and dynamics. Recently, there have been several advances in technology and in the application of modeling approaches that enable the exploration of the dynamic properties of cells. Merging technology and computation offers an opportunity to objectively address unsolved cellular mechanisms, and has revealed emergent properties and helped to gain a more comprehensive and fundamental understanding of cell biology.

Synthetic lethal vulnerabilities of cancer

The great majority of targeted anticancer drugs inhibit mutated oncogenes that display increased activity. Yet many tumors do not contain such actionable aberrations, such as those harboring loss-of-function mutations. The notion of targeting synthetic lethal vulnerabilities in cancer cells has provided an alternative approach to exploiting more of the genetic and epigenetic changes acquired during tumorigenesis. Here, we review synthetic lethality as a therapeutic concept that exploits the inherent differences between normal cells and cancer cells. Furthermore, we provide an overview of the screening approaches that can be used to identify synthetic lethal interactions in human cells and present several recently identified interactions that may be pharmacologically exploited. Finally, we indicate some of the challenges of translating synthetic lethal interactions into the clinic and how these may be overcome.

Linear motif-mediated interactions have contributed to the evolution of modularity in complex protein interaction networks

The modular architecture of protein-protein interaction (PPI) networks is evident in diverse species with a wide range of complexity. However, the molecular components that lead to the evolution of modularity in PPI networks have not been clearly identified. Here, we show that weak domain-linear motif interactions (DLIs) are more likely to connect different biological modules than strong domain-domain interactions (DDIs). This molecular division of labor is essential for the evolution of modularity in the complex PPI networks of diverse eukaryotic species. In particular, DLIs may compensate for the reduction in module boundaries that originate from increased connections between different modules in complex PPI networks. In addition, we show that the identification of biological modules can be greatly improved by including molecular characteristics of protein interactions. Our findings suggest that transient interactions have played a unique role in shaping the architecture and modularity of biological networks over the course of evolution.

Modular architecture is important for the evolution of cellular systems. Modular rearrangements facilitate functional innovations and modular insulations provide robustness to perturbations. However, molecular-level understanding of the mechanisms underlying modular network evolution is currently not well understood. Here we show that strong domain-domain interactions (DDIs) and weak domain-linear motif interactions (DLIs) made different contributions to the evolution of the modular architecture of PPI networks. Especially, DLIs mediate between-module interactions, and that their relative abundance has dramatically increased in metazoan species. Linear motifs have been identified as evolutionary interaction switches since subtle amino acid changes can cause the short sequences in linear motifs to appear and disappear. Our results suggest that subtle changes in linear motifs have contributed to the rewiring of functional modules and, consequently, to functional innovations in metazoan species.

LocalAli: an evolutionary-based local alignment approach to identify functionally conserved modules in multiple networks

MOTIVATION

Sequences and protein interaction data are of significance to understand the underlying molecular mechanism of organisms. Local network alignment is one of key systematic ways for predicting protein functions, identifying functional modules and understanding the phylogeny from these data. Most of currently existing tools, however, encounter their limitations, which are mainly concerned with scoring scheme, speed and scalability. Therefore, there are growing demands for sophisticated network evolution models and efficient local alignment algorithms.

RESULTS

We developed a fast and scalable local network alignment tool called LocalAli for the identification of functionally conserved modules in multiple networks. In this algorithm, we firstly proposed a new framework to reconstruct the evolution history of conserved modules based on a maximum-parsimony evolutionary model. By relying on this model, LocalAli facilitates interpretation of resulting local alignments in terms of conserved modules, which have been evolved from a common ancestral module through a series of evolutionary events. A meta-heuristic method simulated annealing was used to search for the optimal or near-optimal inner nodes (i.e. ancestral modules) of the evolutionary tree. To evaluate the performance and the statistical significance, LocalAli were tested on 26 real datasets and 1040 randomly generated datasets. The results suggest that LocalAli outperforms all existing algorithms in terms of coverage, consistency and scalability, meanwhile retains a high precision in the identification of functionally coherent subnetworks.

AVAILABILITY

The source code and test datasets are freely available for download under the GNU GPL v3 license at https://code.google.com/p/localali/.

CONTACT

jialu.hu@fu-berlin.de or knut.reinert@fu-berlin.de.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Evolutionarily conserved genetic interactions with budding and fission yeast MutS identify orthologous relationships in mismatch repair-deficient cancer cells

Background

The evolutionarily conserved DNA mismatch repair (MMR) system corrects base-substitution and insertion-deletion mutations generated during erroneous replication. The mutation or inactivation of many MMR factors strongly predisposes to cancer, where the resulting tumors often display resistance to standard chemotherapeutics. A new direction to develop targeted therapies is the harnessing of synthetic genetic interactions, where the simultaneous loss of two otherwise non-essential factors leads to reduced cell fitness or death. High-throughput screening in human cells to directly identify such interactors for disease-relevant genes is now widespread, but often requires extensive case-by-case optimization. Here we asked if conserved genetic interactors (CGIs) with MMR genes from two evolutionary distant yeast species (Saccharomyces cerevisiae and Schizosaccharomyzes pombe) can predict orthologous genetic relationships in higher eukaryotes.

Methods

High-throughput screening was used to identify genetic interaction profiles for the MutSα and MutSβ heterodimer subunits (msh2Δ, msh3Δ, msh6Δ) of fission yeast. Selected negative interactors with MutSβ (msh2Δ/msh3Δ) were directly analyzed in budding yeast, and the CGI with SUMO-protease Ulp2 further examined after RNA interference/drug treatment in MSH2-deficient and -proficient human cells.

Results

This study identified distinct genetic profiles for MutSα and MutSβ, and supports a role for the latter in recombinatorial DNA repair. Approximately 28% of orthologous genetic interactions with msh2Δ/msh3Δ are conserved in both yeasts, a degree consistent with global trends across these species. Further, the CGI between budding/fission yeast msh2 and SUMO-protease Ulp2 is maintained in human cells (MSH2/SENP6), and enhanced by Olaparib, a PARP inhibitor that induces the accumulation of single-strand DNA breaks. This identifies SENP6 as a promising new target for the treatment of MMR-deficient cancers.

Conclusion

Our findings demonstrate the utility of employing evolutionary distance in tractable lower eukaryotes to predict orthologous genetic relationships in higher eukaryotes. Moreover, we provide novel insights into the genome maintenance functions of a critical DNA repair complex and propose a promising targeted treatment for MMR deficient tumors.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-014-0068-4) contains supplementary material, which is available to authorized users.

The CINs of the centromere

Replication stress is a significant contributor to genome instability. Recent studies suggest that the centromere is particularly susceptible to replication stress and prone to rearrangements and genome damage, as well as chromosome loss. This effect is enhanced by loss of heterochromatin. The resulting changes in genetic organization, including chromosome loss, increased mutation and loss of heterozygosity, are important contributors to malignant growth.

Prediction of dynamical drug sensitivity and resistance by module network rewiring-analysis based on transcriptional profiling

Revealing functional reorganization or module rewiring between modules at network levels during drug treatment is important to systematically understand therapies and drug responses. The present article proposed a novel model of module network rewiring to characterize functional reorganization of a complex biological system, and described a new framework named as module network rewiring-analysis (MNR) for systematically studying dynamical drug sensitivity and resistance during drug treatment. MNR was used to investigate functional reorganization or rewiring on the module network, rather than molecular network or individual molecules. Our experiments on expression data of patients with Hepatitis C virus infection receiving Interferon therapy demonstrated that consistent module genes derived by MNR could be directly used to reveal new genotypes relevant to drug sensitivity, unlike the other differential analyses of gene expressions. Our results showed that functional connections and reconnections among consistent modules bridged by biological paths were necessary for achieving effective responses of a drug. The hierarchical structures of the temporal module network can be considered as spatio-temporal biomarkers to monitor the efficacy, efficiency, toxicity, and resistance of the therapy. Our study indicates that MNR is a useful tool to identify module biomarkers and further predict dynamical drug sensitivity and resistance, characterize complex dynamic processes for therapy response, and provide biologically systematic clues for pharmacogenomic applications.

Functional genomics platform for pooled screening and generation of mammalian genetic interaction maps

Systematic genetic interaction maps in microorganisms are powerful tools for identifying functional relationships between genes and for defining the function of uncharacterized genes. We have recently implemented this strategy in mammalian cells as a two-stage approach. First, genes of interest are robustly identified in a pooled genome-wide screen using complex shRNA libraries. Second, phenotypes for all pairwise combinations of 'hit' genes are measured in a double-shRNA screen and used to construct a genetic interaction map. Our protocol allows for rapid pooled screening under various conditions without a requirement for robotics, in contrast to arrayed approaches. Each round of screening can be implemented in ∼2 weeks, with additional time for analysis and generation of reagents. We discuss considerations for screen design, and we present complete experimental procedures, as well as a full computational analysis suite for the identification of hits in pooled screens and generation of genetic interaction maps. Although the protocol outlined here was developed for our original shRNA-based approach, it can be applied more generally, including to CRISPR-based approaches.

Genetic interaction analysis of point mutations enables interrogation of gene function at a residue-level resolution: exploring the applications of high-resolution genetic interaction mapping of point mutations

We have achieved a residue-level resolution of genetic interaction mapping - a technique that measures how the function of one gene is affected by the alteration of a second gene - by analyzing point mutations. Here, we describe how to interpret point mutant genetic interactions, and outline key applications for the approach, including interrogation of protein interaction interfaces and active sites, and examination of post-translational modifications. Genetic interaction analysis has proven effective for characterizing cellular processes; however, to date, systematic high-throughput genetic interaction screens have relied on gene deletions or knockdowns, which limits the resolution of gene function analysis and poses problems for multifunctional genes. Our point mutant approach addresses these issues, and further provides a tool for in vivo structure-function analysis that complements traditional biophysical methods. We also discuss the potential for genetic interaction mapping of point mutations in human cells and its application to personalized medicine.

Yeast X-chromosome-associated protein 5 (Xap5) functions with H2A.Z to suppress aberrant transcripts

Chromatin regulatory proteins affect diverse developmental and environmental response pathways via their influence on nuclear processes such as the regulation of gene expression. Through a genome-wide genetic screen, we implicate a novel protein called X-chromosome-associated protein 5 (Xap5) in chromatin regulation. We show that Xap5 is a chromatin-associated protein acting in a similar manner as the histone variant H2A.Z to suppress expression of antisense and repeat element transcripts throughout the fission yeast genome. Xap5 is highly conserved across eukaryotes, and a plant homolog rescues xap5 mutant yeast. We propose that Xap5 likely functions as a chromatin regulator in diverse organisms.

The proper splicing of RNAi factors is critical for pericentric heterochromatin assembly in fission yeast

Heterochromatin preferentially assembles at repetitive DNA elements, playing roles in transcriptional silencing, recombination suppression, and chromosome segregation. The RNAi machinery is required for heterochromatin assembly in a diverse range of organisms. In fission yeast, RNA splicing factors are also required for pericentric heterochromatin assembly, and a prevailing model is that splicing factors provide a platform for siRNA generation independently of their splicing activity. Here, by screening the fission yeast deletion library, we discovered four novel splicing factors that are required for pericentric heterochromatin assembly. Sequencing total cellular RNAs from the strongest of these mutants, cwf14Δ, showed intron retention in mRNAs of several RNAi factors. Moreover, introducing cDNA versions of RNAi factors significantly restored pericentric heterochromatin in splicing mutants. We also found that mutations of splicing factors resulted in defective telomeric heterochromatin assembly and mis-splicing the mRNA of shelterin component Tpz1, and that replacement of tpz1+ with its cDNA partially rescued heterochromatin defects at telomeres in splicing mutants. Thus, proper splicing of RNAi and shelterin factors contributes to heterochromatin assembly at pericentric regions and telomeres.

Increasing the precision of orthology-based complex prediction through network alignment

Macromolecular assemblies play an important role in almost all cellular processes. However, despite several large-scale studies, our current knowledge about protein complexes is still quite limited, thus advocating the use of in silico predictions to gather information on complex composition in model organisms. Since protein–protein interactions present certain constraints on the functional divergence of macromolecular assemblies during evolution, it is possible to predict complexes based on orthology data. Here, we show that incorporating interaction information through network alignment significantly increases the precision of orthology-based complex prediction. Moreover, we performed a large-scale in silico screen for protein complexes in human, yeast and fly, through the alignment of hundreds of known complexes to whole organism interactomes. Systematic comparison of the resulting network alignments to all complexes currently known in those species revealed many conserved complexes, as well as several novel complex components. In addition to validating our predictions using orthogonal data, we were able to assign specific functional roles to the predicted complexes. In several cases, the incorporation of interaction data through network alignment allowed to distinguish real complex components from other orthologous proteins. Our analyses indicate that current knowledge of yeast protein complexes exceeds that in other organisms and that predicting complexes in fly based on human and yeast data is complementary rather than redundant. Lastly, assessing the conservation of protein complexes of the human pathogen Mycoplasma pneumoniae, we discovered that its complexes repertoire is different from that of eukaryotes, suggesting new points of therapeutic intervention, whereas targeting the pathogen’s Restriction enzyme complex might lead to adverse effects due to its similarity to ATP-dependent metalloproteases in the human host.

Role of DNA replication in establishment and propagation of epigenetic states of chromatin

DNA replication is the fundamental process of duplication of the genetic information that is vital for survival of all living cells. The basic mechanistic steps of replication initiation, elongation and termination are conserved among bacteria, lower eukaryotes, like yeast and metazoans. However, the details of the mechanisms are different. Furthermore, there is a close coordination between chromatin assembly pathways and various components of replication machinery whereby DNA replication is coupled to "chromatin replication" during cell cycle. Thereby, various epigenetic modifications associated with different states of gene expression in differentiated cells and the related chromatin structures are faithfully propagated during the cell division through tight coupling with the DNA replication machinery. Several examples are found in lower eukaryotes like budding yeast and fission yeast with close parallels in metazoans.

Genome-Wide Fitness and Genetic Interactions Determined by Tn-seq, a High-Throughput Massively Parallel Sequencing Method for Microorganisms

The lagging annotation of bacterial genomes and the inherent genetic complexity of many phenotypes is hindering the discovery of new drug targets and the development of new antimicrobial agents and vaccines. This unit presents Tn-seq, a method that has made it possible to quantitatively determine fitness for most genes in a microorganism and to screen for quantitative genetic interactions on a genome-wide scale and in a high-throughput fashion. Tn-seq can thus direct studies on the annotation of genes and untangle complex phenotypes. The method is based on the construction of a saturated transposon insertion library. After library selection, changes in the frequency of each insertion mutant are determined by sequencing flanking regions en masse. These changes are used to calculate each mutant's fitness. The method was originally developed for the Gram-positive bacterium Streptococcus pneumoniae, a causative agent of pneumonia and meningitis, but has now been applied to several different microbial species.

gitter: a robust and accurate method for quantification of colony sizes from plate images

Colony-based screens that quantify the fitness of clonal populations on solid agar plates are perhaps the most important source of genome-scale functional information in microorganisms. The images of ordered arrays of mutants produced by such experiments can be difficult to process because of laboratory-specific plate features, morphed colonies, plate edges, noise, and other artifacts. Most of the tools developed to address this problem are optimized to handle a single setup and do not work out of the box in other settings. We present gitter, an image analysis tool for robust and accurate processing of images from colony-based screens. gitter works by first finding the grid of colonies from a preprocessed image and then locating the bounds of each colony separately. We show that gitter produces comparable colony sizes to other tools in simple cases but outperforms them by being able to handle a wider variety of screens and more accurately quantify colony sizes from difficult images. gitter is freely available as an R package from http://cran.r-project.org/web/packages/gitter under the LGPL. Tutorials and demos can be found at http://omarwagih.github.io/gitter.

Identification of a BET family bromodomain/casein kinase II/TAF-containing complex as a regulator of mitotic condensin function

Condensin is a central regulator of mitotic genome structure with mutants showing poorly condensed chromosomes and profound segregation defects. Here, we identify NCT, a complex comprising the Nrc1 BET-family tandem bromodomain protein (SPAC631.02), casein kinase II (CKII), and several TAFs, as a regulator of condensin function. We show that NCT and condensin bind similar genomic regions but only briefly colocalize during the periods of chromosome condensation and decondensation. This pattern of NCT binding at the core centromere, the region of maximal condensin enrichment, tracks the abundance of acetylated histone H4, as regulated by the Hat1-Mis16 acetyltransferase complex and recognized by the first Nrc1 bromodomain. Strikingly, mutants in NCT or Hat1-Mis16 restore the formation of segregation-competent chromosomes in cells containing defective condensin. These results are consistent with a model where NCT targets CKII to chromatin in a cell-cycle-directed manner in order to modulate the activity of condensin during chromosome condensation and decondensation.

Quantitative genome-wide genetic interaction screens reveal global epistatic relationships of protein complexes in Escherichia coli

Large-scale proteomic analyses in Escherichia coli have documented the composition and physical relationships of multiprotein complexes, but not their functional organization into biological pathways and processes. Conversely, genetic interaction (GI) screens can provide insights into the biological role(s) of individual gene and higher order associations. Combining the information from both approaches should elucidate how complexes and pathways intersect functionally at a systems level. However, such integrative analysis has been hindered due to the lack of relevant GI data. Here we present a systematic, unbiased, and quantitative synthetic genetic array screen in E. coli describing the genetic dependencies and functional cross-talk among over 600,000 digenic mutant combinations. Combining this epistasis information with putative functional modules derived from previous proteomic data and genomic context-based methods revealed unexpected associations, including new components required for the biogenesis of iron-sulphur and ribosome integrity, and the interplay between molecular chaperones and proteases. We find that functionally-linked genes co-conserved among γ-proteobacteria are far more likely to have correlated GI profiles than genes with divergent patterns of evolution. Overall, examining bacterial GIs in the context of protein complexes provides avenues for a deeper mechanistic understanding of core microbial systems.

Genome-wide genetic interaction (GI) screens have been performed in yeast, but no analogous large-scale studies have yet been reported for bacteria. Here, we have used E. coli synthetic genetic array (eSGA) technology developed by our group to quantitatively map GIs to reveal epistatic dependencies and functional cross-talk among ∼600,000 digenic mutant combinations. By combining this epistasis information with functional modules derived by our group's earlier efforts from proteomic and genomic context (GC)-based methods, we identify several unexpected pathway-level dependencies, functional links between protein complexes, and biological roles of uncharacterized bacterial gene products. As part of the study, two of our pathway predictions from GI screens were validated experimentally, where we confirmed the role of these new components in iron-sulphur biogenesis and ribosome integrity. We also extrapolated the epistatic connectivity diagram of E. coli to 233 distantly related γ-proteobacterial species lacking GI information, and identified co-conserved genes and functional modules important for bacterial pathogenesis. Overall, this study describes the first genome-scale map of GIs in gram-negative bacterium, and through integrative analysis with previously derived protein-protein and GC-based interaction networks presents a number of novel insights into the architecture of bacterial pathways that could not have been discerned through either network alone.

Development of ultra-high-density screening tools for microbial "omics"

High-throughput genetic screens in model microbial organisms are a primary means of interrogating biological systems. In numerous cases, such screens have identified the genes that underlie a particular phenotype or a set of gene-gene, gene-environment or protein-protein interactions, which are then used to construct highly informative network maps for biological research. However, the potential test space of genes, proteins, or interactions is typically much larger than current screening systems can address. To push the limits of screening technology, we developed an ultra-high-density, 6144-colony arraying system and analysis toolbox. Using budding yeast as a benchmark, we find that these tools boost genetic screening throughput 4-fold and yield significant cost and time reductions at quality levels equal to or better than current methods. Thus, the new ultra-high-density screening tools enable researchers to significantly increase the size and scope of their genetic screens.

A Pil1-Sle1-Syj1-Tax4 functional pathway links eisosomes with PI(4,5)P2 regulation

Stable compartments of the plasma membrane promote a wide range of cellular functions. In yeast cells, cytosolic structures called eisosomes generate prominent cortical invaginations of unknown function. Through a series of genetic screens in fission yeast, we found that the eisosome proteins Pil1 and Sle1 function with the synaptojanin-like lipid phosphatase Syj1 and its ligand Tax4. This genetic pathway connects eisosome function with the hydrolysis of phosphatidylinositol (4,5)-bisphosphate [PI(4,5)P2] in cells. Defects in PI(4,5)P2 regulation led to eisosome defects, and we found that the core eisosome protein Pil1 can bind to and tubulate liposomes containing PI(4,5)P2. Mutations in components of the Pil1-Sle1-Syj1-Tax4 pathway suppress the growth and morphology defects of TORC2 mutants, indicating that eisosome-dependent regulation of PI(4,5)P2 feeds into signal transduction pathways. We propose that the geometry of membrane invaginations generates spatial and temporal signals for lipid-mediated signaling events in cells.

All or nothing: protein complexes flip essentiality between distantly related eukaryotes

In the budding yeast Saccharomyces cerevisiae, the subunits of any given protein complex are either mostly essential or mostly nonessential, suggesting that essentiality is a property of molecular machines rather than individual components. There are exceptions to this rule, however, that is, nonessential genes in largely essential complexes and essential genes in largely nonessential complexes. Here, we provide explanations for these exceptions, showing that redundancy within complexes, as revealed by genetic interactions, can explain many of the former cases, whereas “moonlighting,” as revealed by membership of multiple complexes, can explain the latter. Surprisingly, we find that redundancy within complexes cannot usually be explained by gene duplication, suggesting alternate buffering mechanisms. In the distantly related Schizosaccharomyces pombe, we observe the same phenomenon of modular essentiality, suggesting that it may be a general feature of eukaryotes. Furthermore, we show that complexes flip essentiality in a cohesive fashion between the two species, that is, they tend to change from mostly essential to mostly nonessential, or vice versa, but not to mixed patterns. We show that these flips in essentiality can be explained by differing lifestyles of the two yeasts. Collectively, our results support a previously proposed model where proteins are essential because of their involvement in essential functional modules rather than because of specific topological features such as degree or centrality.

Chemical genetics of rapamycin-insensitive TORC2 in S. cerevisiae

Current approaches for identifying synergistic targets use cell culture models to see if the combined effect of clinically available drugs is better than predicted by their individual efficacy. New techniques are needed to systematically and rationally identify targets and pathways that may be synergistic targets. Here, we created a tool to screen and identify molecular targets that may synergize with new inhibitors of target of rapamycin (TOR), a conserved protein that is a major integrator of cell proliferation signals in the nutrient-signaling pathway. Although clinical results from TOR complex 1 (TORC1)-specific inhibition using rapamycin analogs have been disappointing, trials using inhibitors that also target TORC2 have been promising. To understand this increased therapeutic efficacy and to discover secondary targets for combination therapy, we engineered Tor2 in S. cerevisiae to accept an orthogonal inhibitor. We used this tool to create a chemical epistasis miniarray profile (ChE-MAP) by measuring interactions between the chemically inhibited Tor2 kinase and a diverse library of deletion mutants. The ChE-MAP identified known TOR components and distinguished between TORC1- and TORC2-dependent functions. The results showed a TORC2-specific interaction with the pentose phosphate pathway, a previously unappreciated TORC2 function that suggests a role for the complex in balancing the high energy demand required for ribosome biogenesis.

Genetic interaction networks: better understand to better predict

A genetic interaction (GI) between two genes generally indicates that the phenotype of a double mutant differs from what is expected from each individual mutant. In the last decade, genome scale studies of quantitative GIs were completed using mainly synthetic genetic array technology and RNA interference in yeast and Caenorhabditis elegans. These studies raised questions regarding the functional interpretation of GIs, the relationship of genetic and molecular interaction networks, the usefulness of GI networks to infer gene function and co-functionality, the evolutionary conservation of GI, etc. While GIs have been used for decades to dissect signaling pathways in genetic models, their functional interpretations are still not trivial. The existence of a GI between two genes does not necessarily imply that these two genes code for interacting proteins or that the two genes are even expressed in the same cell. In fact, a GI only implies that the two genes share a functional relationship. These two genes may be involved in the same biological process or pathway; or they may also be involved in compensatory pathways with unrelated apparent function. Considering the powerful opportunity to better understand gene function, genetic relationship, robustness and evolution, provided by a genome-wide mapping of GIs, several in silico approaches have been employed to predict GIs in unicellular and multicellular organisms. Most of these methods used weighted data integration. In this article, we will review the later knowledge acquired on GI networks in metazoans by looking more closely into their relationship with pathways, biological processes and molecular complexes but also into their modularity and organization. We will also review the different in silico methods developed to predict GIs and will discuss how the knowledge acquired on GI networks can be used to design predictive tools with higher performances.

High-resolution network biology: connecting sequence with function

Proteins are not monolithic entities; rather, they can contain multiple domains that mediate distinct interactions, and their functionality can be regulated through post-translational modifications at multiple distinct sites. Traditionally, network biology has ignored such properties of proteins and has instead examined either the physical interactions of whole proteins or the consequences of removing entire genes. In this Review, we discuss experimental and computational methods to increase the resolution of protein-protein, genetic and drug-gene interaction studies to the domain and residue levels. Such work will be crucial for using interaction networks to connect sequence and structural information, and to understand the biological consequences of disease-associated mutations, which will hopefully lead to more effective therapeutic strategies.

Predicting complex phenotype-genotype interactions to enable yeast engineering: Saccharomyces cerevisiae as a model organism and a cell factory

There is an increasing use of systems biology approaches in both "red" and "white" biotechnology in order to enable medical, medicinal, and industrial applications. The intricate links between genotype and phenotype may be explained through the use of the tools developed in systems biology, synthetic biology, and evolutionary engineering. Biomedical and biotechnological research are among the fields that could benefit most from the elucidation of this complex relationship. Researchers have studied fitness extensively to explain the phenotypic impacts of genetic variations. This elaborate network of dependencies and relationships so revealed are further complicated by the influence of environmental effects that present major challenges to our achieving an understanding of the cellular mechanisms leading to healthy or diseased phenotypes or optimized production yields. An improved comprehension of complex genotype-phenotype interactions and their accurate prediction should enable us to more effectively engineer yeast as a cell factory and to use it as a living model of human or pathogen cells in intelligent screens for new drugs. This review presents different methods and approaches undertaken toward improving our understanding and prediction of the growth phenotype of the yeast Saccharomyces cerevisiae as both a model and a production organism.

From structure to systems: high-resolution, quantitative genetic analysis of RNA polymerase II

RNA polymerase II (RNAPII) lies at the core of dynamic control of gene expression. Using 53 RNAPII point mutants, we generated a point mutant epistatic miniarray profile (pE-MAP) comprising ∼60,000 quantitative genetic interactions in Saccharomyces cerevisiae. This analysis enabled functional assignment of RNAPII subdomains and uncovered connections between individual regions and other protein complexes. Using splicing microarrays and mutants that alter elongation rates in vitro, we found an inverse relationship between RNAPII speed and in vivo splicing efficiency. Furthermore, the pE-MAP classified fast and slow mutants that favor upstream and downstream start site selection, respectively. The striking coordination of polymerization rate with transcription initiation and splicing suggests that transcription rate is tuned to regulate multiple gene expression steps. The pE-MAP approach provides a powerful strategy to understand other multifunctional machines at amino acid resolution.

The strength of genetic interactions scales weakly with mutational effects

Background

Genetic interactions pervade every aspect of biology, from evolutionary theory, where they determine the accessibility of evolutionary paths, to medicine, where they can contribute to complex genetic diseases. Until very recently, studies on epistatic interactions have been based on a handful of mutations, providing at best anecdotal evidence about the frequency and the typical strength of genetic interactions. In this study, we analyze a publicly available dataset that contains the growth rates of over five million double knockout mutants of the yeast Saccharomyces cerevisiae.

Results

We discuss a geometric definition of epistasis that reveals a simple and surprisingly weak scaling law for the characteristic strength of genetic interactions as a function of the effects of the mutations being combined. We then utilized this scaling to quantify the roughness of naturally occurring fitness landscapes. Finally, we show how the observed roughness differs from what is predicted by Fisher's geometric model of epistasis, and discuss the consequences for evolutionary dynamics.

Conclusions

Although epistatic interactions between specific genes remain largely unpredictable, the statistical properties of an ensemble of interactions can display conspicuous regularities and be described by simple mathematical laws. By exploiting the amount of data produced by modern high-throughput techniques, it is now possible to thoroughly test the predictions of theoretical models of genetic interactions and to build informed computational models of evolution on realistic fitness landscapes.

A lipid E-MAP identifies Ubx2 as a critical regulator of lipid saturation and lipid bilayer stress

Biological membranes are complex, and the mechanisms underlying their homeostasis are incompletely understood. Here, we present a quantitative genetic interaction map (E-MAP) focused on various aspects of lipid biology, including lipid metabolism, sorting, and trafficking. This E-MAP contains ∼250,000 negative and positive genetic interaction scores and identifies a molecular crosstalk of protein quality control pathways with lipid bilayer homeostasis. Ubx2p, a component of the endoplasmic-reticulum-associated degradation pathway, surfaces as a key upstream regulator of the essential fatty acid (FA) desaturase Ole1p. Loss of Ubx2p affects the transcriptional control of OLE1, resulting in impaired FA desaturation and a severe shift toward more saturated membrane lipids. Both the induction of the unfolded protein response and aberrant nuclear membrane morphologies observed in cells lacking UBX2 are suppressed by the supplementation of unsaturated FAs. Our results point toward the existence of dedicated bilayer stress responses for membrane homeostasis.

Comparison of profile similarity measures for genetic interaction networks

Analysis of genetic interaction networks often involves identifying genes with similar profiles, which is typically indicative of a common function. While several profile similarity measures have been applied in this context, they have never been systematically benchmarked. We compared a diverse set of correlation measures, including measures commonly used by the genetic interaction community as well as several other candidate measures, by assessing their utility in extracting functional information from genetic interaction data. We find that the dot product, one of the simplest vector operations, outperforms most other measures over a large range of gene pairs. More generally, linear similarity measures such as the dot product, Pearson correlation or cosine similarity perform better than set overlap measures such as Jaccard coefficient. Similarity measures that involve L2-normalization of the profiles tend to perform better for the top-most similar pairs but perform less favorably when a larger set of gene pairs is considered or when the genetic interaction data is thresholded. Such measures are also less robust to the presence of noise and batch effects in the genetic interaction data. Overall, the dot product measure performs consistently among the best measures under a variety of different conditions and genetic interaction datasets.

Systematic triple-mutant analysis uncovers functional connectivity between pathways involved in chromosome regulation

Genetic interactions reveal the functional relationships between pairs of genes. In this study, we describe a method for the systematic generation and quantitation of triple mutants, termed triple-mutant analysis (TMA). We have used this approach to interrogate partially redundant pairs of genes in S. cerevisiae, including ASF1 and CAC1, two histone chaperones. After subjecting asf1Δ cac1Δ to TMA, we found that the Swi/Snf Rdh54 protein compensates for the absence of Asf1 and Cac1. Rdh54 more strongly associates with the chromatin apparatus and the pericentromeric region in the double mutant. Moreover, Asf1 is responsible for the synthetic lethality observed in cac1Δ strains lacking the HIRA-like proteins. A similar TMA was carried out after deleting both CLB5 and CLB6, cyclins that regulate DNA replication, revealing a strong functional connection to chromosome segregation. This approach can reveal functional redundancies that cannot be uncovered through traditional double-mutant analyses.

Integrated platform for genome-wide screening and construction of high-density genetic interaction maps in mammalian cells

A major challenge of the postgenomic era is to understand how human genes function together in normal and disease states. In microorganisms, high-density genetic interaction (GI) maps are a powerful tool to elucidate gene functions and pathways. We have developed an integrated methodology based on pooled shRNA screening in mammalian cells for genome-wide identification of genes with relevant phenotypes and systematic mapping of all GIs among them. We recently demonstrated the potential of this approach in an application to pathways controlling the susceptibility of human cells to the toxin ricin. Here we present the complete quantitative framework underlying our strategy, including experimental design, derivation of quantitative phenotypes from pooled screens, robust identification of hit genes using ultra-complex shRNA libraries, parallel measurement of tens of thousands of GIs from a single double-shRNA experiment, and construction of GI maps. We describe the general applicability of our strategy. Our pooled approach enables rapid screening of the same shRNA library in different cell lines and under different conditions to determine a range of different phenotypes. We illustrate this strategy here for single- and double-shRNA libraries. We compare the roles of genes for susceptibility to ricin and Shiga toxin in different human cell lines and reveal both toxin-specific and cell line-specific pathways. We also present GI maps based on growth and ricin-resistance phenotypes, and we demonstrate how such a comparative GI mapping strategy enables functional dissection of physical complexes and context-dependent pathways.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Navigating personalized medicine dependent on modular flexibility

Deconstructing networks and rewiring alterable modules in a rational way is critical to optimize drug discovery and develop personalized medicine.

Cross-species protein interactome mapping reveals species-specific wiring of stress response pathways

The fission yeast Schizosaccharomyces pombe has more metazoan-like features than the budding yeast Saccharomyces cerevisiae, yet it has similarly facile genetics. We present a large-scale verified binary protein-protein interactome network, "StressNet," based on high-throughput yeast two-hybrid screens of interacting proteins classified as part of stress response and signal transduction pathways in S. pombe. We performed systematic, cross-species interactome mapping using StressNet and a protein interactome network of orthologous proteins in S. cerevisiae. With cross-species comparative network studies, we detected a previously unidentified component (Snr1) of the S. pombe mitogen-activated protein kinase Sty1 pathway. Coimmunoprecipitation experiments showed that Snr1 interacted with Sty1 and that deletion of snr1 increased the sensitivity of S. pombe cells to stress. Comparison of StressNet with the interactome network of orthologous proteins in S. cerevisiae showed that most of the interactions among these stress response and signaling proteins are not conserved between species but are "rewired"; orthologous proteins have different binding partners in both species. In particular, transient interactions connecting proteins in different functional modules were more likely to be rewired than conserved. By directly testing interactions between proteins in one yeast species and their corresponding binding partners in the other yeast species with yeast two-hybrid assays, we found that about half of the interactions that are traditionally considered "conserved" form modified interaction interfaces that may potentially accommodate novel functions.

A proteome-wide visual screen identifies fission yeast proteins localizing to DNA double-strand breaks

DNA double-strand breaks (DSBs) are a major threat to genome integrity. Proteins involved in DNA damage checkpoint signaling and DSB repair often relocalize and concentrate at DSBs. Here, we used an ORFeome library of the fission yeast Schizosaccharomyces pombe to systematically identify proteins targeted to DSBs. We found 51 proteins that, when expressed from a strong exogenous promoter on the ORFeome plasmids, were able to form a distinct nuclear focus at an HO endonuclease-induced DSB. The majority of these proteins have known connections to DNA damage response, but few have been visualized at a specific DSB before. Among the screen hits, 37 can be detected at DSBs when expressed from native promoters. We classified them according to the focus emergence timing of the endogenously tagged proteins. Eight of these 37 proteins are yet unnamed. We named these eight proteins DNA-break-localizing proteins (Dbls) and performed preliminary functional analysis on two of them, Dbl1 (SPCC2H8.05c) and Dbl2 (SPCC553.01c). We found that Dbl1 and Dbl2 contribute to the normal DSB targeting of checkpoint protein Rad26 (homolog of human ATRIP) and DNA repair helicase Fml1 (homolog of human FANCM), respectively. As the first proteome-wide inventory of DSB-localizing proteins, our screen result will be a useful resource for understanding the mechanisms of eukaryotic DSB response.

Srb5/Med18-mediated termination of transcription is dependent on gene looping

We have earlier demonstrated the involvement of Mediator subunit Srb5/Med18 in the termination of transcription for a subset of genes in yeast. Srb5/Med18 could affect termination either indirectly by modulating CTD-Ser(2) phosphorylation near the 3' end of a gene or directly by physically interacting with the cleavage and polyadenylation factor or cleavage factor 1 (CF1) complex and facilitating their recruitment to the terminator region. Here, we show that the CTD-Ser(2) phosphorylation pattern on Srb5/Med18-dependent genes remains unchanged in the absence of Srb5 in cells. Coimmunoprecipitation analysis revealed the physical interaction of Srb5/Med18 with the CF1 complex. No such interaction of Srb5/Med18 with the cleavage and polyadenylation factor complex, however, could be detected. The Srb5/Med18-CF1 interaction was not observed in the looping defective sua7-1 strain. Srb5/Med18 cross-linking to the 3' end of genes was also abolished in the sua7-1 strain. Chromosome conformation capture analysis revealed that the looped architecture of Srb5/Med18-dependent genes was abrogated in srb5(-) cells. Furthermore, Srb5-dependent termination of transcription was compromised in the looping defective sua7-1 cells. The overall conclusion of these results is that gene looping plays a crucial role in Srb5/Med18 facilitated termination of transcription, and the looped gene architecture may have a general role in termination of transcription in budding yeast.