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

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.

Replication fork stability is essential for the maintenance of centromere integrity in the absence of heterochromatin

The centromere of many eukaryotes contains highly repetitive sequences marked by methylation of histone H3K9 by Clr4(KMT1). This recruits multiple heterochromatin proteins, including Swi6 and Chp1, to form a rigid centromere and ensure accurate chromosome segregation. In the absence of heterochromatin, cells show an increased rate of recombination in the centromere, as well as chromosome loss. These defects are severely aggravated by loss of replication fork stability. Thus, heterochromatin proteins and replication fork protection mechanisms work in concert to prevent abnormal recombination, preserve centromere integrity, and ensure faithful chromosome segregation.

Genotype to phenotype: lessons from model organisms for human genetics

To what extent can variation in phenotypic traits such as disease risk be accurately predicted in individuals? In this Review, I highlight recent studies in model organisms that are relevant both to the challenge of accurately predicting phenotypic variation from individual genome sequences ('whole-genome reverse genetics') and for understanding why, in many cases, this may be impossible. These studies argue that only by combining genetic knowledge with in vivo measurements of biological states will it be possible to make accurate genetic predictions for individual humans.

Cellular robustness conferred by genetic crosstalk underlies resistance against chemotherapeutic drug doxorubicin in fission yeast

Doxorubicin is an anthracycline antibiotic that is among one of the most commonly used chemotherapeutic agents in the clinical setting. The usage of doxorubicin is faced with many problems including severe side effects and chemoresistance. To overcome these challenges, it is important to gain an understanding of the underlying molecular mechanisms with regards to the mode of action of doxorubicin. To facilitate this aim, we identified the genes that are required for doxorubicin resistance in the fission yeast Schizosaccharomyces pombe. We further demonstrated interplay between factors controlling various aspects of chromosome metabolism, mitochondrial respiration and membrane transport. In the nucleus we observed that the subunits of the Ino80, RSC, and SAGA complexes function in the similar epistatic group that shares significant overlap with the homologous recombination genes. However, these factors generally act in synergistic manner with the chromosome segregation regulator DASH complex proteins, possibly forming two major arms for regulating doxorubicin resistance in the nucleus. Simultaneous disruption of genes function in membrane efflux transport or the mitochondrial respiratory chain integrity in the mutants defective in either Ino80 or HR function resulted in cumulative upregulation of drug-specific growth defects, suggesting a rewiring of pathways that synergize only when the cells is exposed to the cytotoxic stress. Taken together, our work not only identified factors that are required for survival of the cells in the presence of doxorubicin but has further demonstrated that an extensive molecular crosstalk exists between these factors to robustly confer doxorubicin resistance.

A yeast phenomic model for the gene interaction network modulating CFTR-ΔF508 protein biogenesis

Background

The overall influence of gene interaction in human disease is unknown. In cystic fibrosis (CF) a single allele of the cystic fibrosis transmembrane conductance regulator (CFTR-ΔF508) accounts for most of the disease. In cell models, CFTR-ΔF508 exhibits defective protein biogenesis and degradation rather than proper trafficking to the plasma membrane where CFTR normally functions. Numerous genes function in the biogenesis of CFTR and influence the fate of CFTR-ΔF508. However it is not known whether genetic variation in such genes contributes to disease severity in patients. Nor is there an easy way to study how numerous gene interactions involving CFTR-ΔF would manifest phenotypically.

Methods

To gain insight into the function and evolutionary conservation of a gene interaction network that regulates biogenesis of a misfolded ABC transporter, we employed yeast genetics to develop a 'phenomic' model, in which the CFTR-ΔF508-equivalent residue of a yeast homolog is mutated (Yor1-ΔF670), and where the genome is scanned quantitatively for interaction. We first confirmed that Yor1-ΔF undergoes protein misfolding and has reduced half-life, analogous to CFTR-ΔF. Gene interaction was then assessed quantitatively by growth curves for approximately 5,000 double mutants, based on alteration in the dose response to growth inhibition by oligomycin, a toxin extruded from the cell at the plasma membrane by Yor1.

Results

From a comparative genomic perspective, yeast gene interactions influencing Yor1-ΔF biogenesis were representative of human homologs previously found to modulate processing of CFTR-ΔF in mammalian cells. Additional evolutionarily conserved pathways were implicated by the study, and a ΔF-specific pro-biogenesis function of the recently discovered ER membrane complex (EMC) was evident from the yeast screen. This novel function was validated biochemically by siRNA of an EMC ortholog in a human cell line expressing CFTR-ΔF508. The precision and accuracy of quantitative high throughput cell array phenotyping (Q-HTCP), which captures tens of thousands of growth curves simultaneously, provided powerful resolution to measure gene interaction on a phenomic scale, based on discrete cell proliferation parameters.

Conclusion

We propose phenomic analysis of Yor1-ΔF as a model for investigating gene interaction networks that can modulate cystic fibrosis disease severity. Although the clinical relevance of the Yor1-ΔF gene interaction network for cystic fibrosis remains to be defined, the model appears to be informative with respect to human cell models of CFTR-ΔF. Moreover, the general strategy of yeast phenomics can be employed in a systematic manner to model gene interaction for other diseases relating to pathologies that result from protein misfolding or potentially any disease involving evolutionarily conserved genetic pathways.

Centromeric heterochromatin assembly in fission yeast--balancing transcription, RNA interference and chromatin modification

Distinct regions of the eukaryotic genome are packaged into different types of chromatin, with euchromatin representing gene rich, transcriptionally active regions and heterochromatin more condensed and gene poor. The assembly and maintenance of heterochromatin is important for many aspects of genome control, including silencing of gene transcription, suppression of recombination, and to ensure proper chromosome segregation. The precise mechanisms underlying heterochromatin establishment and maintenance are still unclear, but much progress has been made towards understanding this process during the last few years, particularly from studies performed in fission yeast. In this review, we hope to provide a conceptual model of centromeric heterochromatin in fission yeast that integrates our current understanding of the competing forces of transcription, replication, and RNA decay that influence its assembly and propagation.

Mediator regulates non-coding RNA transcription at fission yeast centromeres

Background

In fission yeast, centromeric heterochromatin is necessary for the fidelity of chromosome segregation. Propagation of heterochromatin in dividing cells requires RNA interference (RNAi) and transcription of centromeric repeats by RNA polymerase II during the S phase of the cell cycle.

Results

We found that the Med8-Med18-Med20 submodule of the Mediator complex is required for the transcriptional regulation of native centromeric dh and dg repeats and for the silencing of reporter genes inserted in centromeric heterochromatin. Mutations in the Med8-Med18-Med20 submodule did not alter Mediator occupancy at centromeres; however, they led to an increased recruitment of RNA polymerase II to centromeres and reduced levels of centromeric H3K9 methylation accounting for the centromeric desilencing. Further, we observed that Med18 and Med20 were required for efficient processing of dh transcripts into siRNA. Consistent with defects in centromeric heterochromatin, cells lacking Med18 or Med20 displayed elevated rates of mitotic chromosome loss.

Conclusions

Our data demonstrate a role for the Med8-Med18-Med20 Mediator submodule in the regulation of non-coding RNA transcription at Schizosaccharomyces pombe centromeres. In wild-type cells this submodule limits RNA polymerase II access to the heterochromatic DNA of the centromeres. Additionally, the submodule may act as an assembly platform for the RNAi machinery or regulate the activity of the RNAi pathway. Consequently, Med8-Med18-Med20 is required for silencing of centromeres and proper mitotic chromosome segregation.

Csi1 links centromeres to the nuclear envelope for centromere clustering

Csi1 promotes centromere clustering by linking centromeres to the SUN domain protein Sad1 in the nuclear envelope.

In the fission yeast Schizosaccharomyces pombe, the centromeres of each chromosome are clustered together and attached to the nuclear envelope near the site of the spindle pole body during interphase. The mechanism and functional importance of this arrangement of chromosomes are poorly understood. In this paper, we identified a novel nuclear protein, Csi1, that localized to the site of centromere attachment and interacted with both the inner nuclear envelope SUN domain protein Sad1 and centromeres. Both Csi1 and Sad1 mutants exhibited centromere clustering defects in a high percentage of cells. Csi1 mutants also displayed a high rate of chromosome loss during mitosis, significant mitotic delays, and sensitivity to perturbations in microtubule–kinetochore interactions and chromosome numbers. These studies thus define a molecular link between the centromere and nuclear envelope that is responsible for centromere clustering.

Mapping bacterial functional networks and pathways in Escherichia Coli using synthetic genetic arrays

Phenotypes are determined by a complex series of physical (e.g. protein-protein) and functional (e.g. gene-gene or genetic) interactions (GI)(1). While physical interactions can indicate which bacterial proteins are associated as complexes, they do not necessarily reveal pathway-level functional relationships1. GI screens, in which the growth of double mutants bearing two deleted or inactivated genes is measured and compared to the corresponding single mutants, can illuminate epistatic dependencies between loci and hence provide a means to query and discover novel functional relationships(2). Large-scale GI maps have been reported for eukaryotic organisms like yeast(3-7), but GI information remains sparse for prokaryotes(8), which hinders the functional annotation of bacterial genomes. To this end, we and others have developed high-throughput quantitative bacterial GI screening methods(9, 10). Here, we present the key steps required to perform quantitative E. coli Synthetic Genetic Array (eSGA) screening procedure on a genome-scale(9), using natural bacterial conjugation and homologous recombination to systemically generate and measure the fitness of large numbers of double mutants in a colony array format. Briefly, a robot is used to transfer, through conjugation, chloramphenicol (Cm) - marked mutant alleles from engineered Hfr (High frequency of recombination) 'donor strains' into an ordered array of kanamycin (Kan) - marked F- recipient strains. Typically, we use loss-of-function single mutants bearing non-essential gene deletions (e.g. the 'Keio' collection(11)) and essential gene hypomorphic mutations (i.e. alleles conferring reduced protein expression, stability, or activity(9, 12, 13)) to query the functional associations of non-essential and essential genes, respectively. After conjugation and ensuing genetic exchange mediated by homologous recombination, the resulting double mutants are selected on solid medium containing both antibiotics. After outgrowth, the plates are digitally imaged and colony sizes are quantitatively scored using an in-house automated image processing system(14). GIs are revealed when the growth rate of a double mutant is either significantly better or worse than expected(9). Aggravating (or negative) GIs often result between loss-of-function mutations in pairs of genes from compensatory pathways that impinge on the same essential process(2). Here, the loss of a single gene is buffered, such that either single mutant is viable. However, the loss of both pathways is deleterious and results in synthetic lethality or sickness (i.e. slow growth). Conversely, alleviating (or positive) interactions can occur between genes in the same pathway or protein complex(2) as the deletion of either gene alone is often sufficient to perturb the normal function of the pathway or complex such that additional perturbations do not reduce activity, and hence growth, further. Overall, systematically identifying and analyzing GI networks can provide unbiased, global maps of the functional relationships between large numbers of genes, from which pathway-level information missed by other approaches can be inferred(9).

The expanding scope of DNA sequencing

In just seven years, next-generation technologies have reduced the cost and increased the speed of DNA sequencing by four orders of magnitude, and experiments requiring many millions of sequencing reads are now routine. In research, sequencing is being applied not only to assemble genomes and to investigate the genetic basis of human disease, but also to explore myriad phenomena in organismic and cellular biology. In the clinic, the utility of sequence data is being intensively evaluated in diverse contexts, including reproductive medicine, oncology and infectious disease. A recurrent theme in the development of new sequencing applications is the creative 'recombination' of existing experimental building blocks. However, there remain many potentially high-impact applications of next-generation DNA sequencing that are not yet fully realized.

Targeting C4-demethylating genes in the cholesterol pathway sensitizes cancer cells to EGF receptor inhibitors via increased EGF receptor degradation

Persistent signaling by the oncogenic EGF receptor (EGFR) is a major source of cancer resistance to EGFR targeting. We established that inactivation of 2 sterol biosynthesis pathway genes, SC4MOL (sterol C4-methyl oxidase-like) and its partner, NSDHL (NADP-dependent steroid dehydrogenase-like), sensitized tumor cells to EGFR inhibitors. Bioinformatics modeling of interactions for the sterol pathway genes in eukaryotes allowed us to hypothesize and then extensively validate an unexpected role for SC4MOL and NSDHL in controlling the signaling, vesicular trafficking, and degradation of EGFR and its dimerization partners, ERBB2 and ERBB3. Metabolic block upstream of SC4MOL with ketoconazole or CYP51A1 siRNA rescued cancer cell viability and EGFR degradation. Inactivation of SC4MOL markedly sensitized A431 xenografts to cetuximab, a therapeutic anti-EGFR antibody. Analysis of Nsdhl-deficient Bpa(1H/+) mice confirmed dramatic and selective loss of internalized platelet-derived growth factor receptor in fibroblasts, and reduced activation of EGFR and its effectors in regions of skin lacking NSDHL.

SIGNIFICANCE

This work identifies a critical role for SC4MOL and NSDHL in the regulation of EGFR signaling and endocytic trafficking and suggests novel strategies to increase the potency of EGFR antagonists in tumors.

Targeting C4-demethylating genes in the cholesterol pathway sensitizes cancer cells to EGF receptor inhibitors via increased EGF receptor degradation

Persistent signaling by the oncogenic EGF receptor (EGFR) is a major source of cancer resistance to EGFR targeting. We established that inactivation of 2 sterol biosynthesis pathway genes, SC4MOL (sterol C4-methyl oxidase-like) and its partner, NSDHL (NADP-dependent steroid dehydrogenase-like), sensitized tumor cells to EGFR inhibitors. Bioinformatics modeling of interactions for the sterol pathway genes in eukaryotes allowed us to hypothesize and then extensively validate an unexpected role for SC4MOL and NSDHL in controlling the signaling, vesicular trafficking, and degradation of EGFR and its dimerization partners, ERBB2 and ERBB3. Metabolic block upstream of SC4MOL with ketoconazole or CYP51A1 siRNA rescued cancer cell viability and EGFR degradation. Inactivation of SC4MOL markedly sensitized A431 xenografts to cetuximab, a therapeutic anti-EGFR antibody. Analysis of Nsdhl-deficient Bpa(1H/+) mice confirmed dramatic and selective loss of internalized platelet-derived growth factor receptor in fibroblasts, and reduced activation of EGFR and its effectors in regions of skin lacking NSDHL.

SIGNIFICANCE

This work identifies a critical role for SC4MOL and NSDHL in the regulation of EGFR signaling and endocytic trafficking and suggests novel strategies to increase the potency of EGFR antagonists in tumors.

A genetic screen to discover pathways affecting cohesin function in Schizosaccharomyces pombe identifies chromatin effectors

Cohesion, the force that holds sister chromatids together from the time of DNA replication until separation at the metaphase to anaphase transition, is mediated by the cohesin complex. This complex is also involved in DNA damage repair, chromosomes condensation, and gene regulation. To learn more about the cellular functions of cohesin, we conducted a genetic screen in Schizosaccharomyces pombe with two different cohesin mutants (eso1-G799D and mis4-242). We found synthetic negative interactions with deletions of genes involved in DNA replication and heterochromatin formation. We also found a few gene deletions that rescued the growth of eso1-G799D at the nonpermissive temperature, and these genes partially rescue the lagging chromosome phenotype. These genes are all chromatin effectors. Overall, our screen revealed an intimate association between cohesin and chromatin.

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.

Swi1 associates with chromatin through the DDT domain and recruits Swi3 to preserve genomic integrity

Swi1 and Swi3 form the replication fork protection complex and play critical roles in proper activation of the replication checkpoint and stabilization of replication forks in the fission yeast Schizosaccharomyces pombe. However, the mechanisms by which the Swi1-Swi3 complex regulates these processes are not well understood. Here, we report functional analyses of the Swi1-Swi3 complex in fission yeast. Swi1 possesses the DDT domain, a putative DNA binding domain found in a variety of chromatin remodeling factors. Consistently, the DDT domain-containing region of Swi1 interacts with DNA in vitro, and mutations in the DDT domain eliminate the association of Swi1 with chromatin in S. pombe cells. DDT domain mutations also render cells highly sensitive to S-phase stressing agents and induce strong accumulation of Rad22-DNA repair foci, indicating that the DDT domain is involved in the activity of the Swi1-Swi3 complex. Interestingly, DDT domain mutations also abolish Swi1's ability to interact with Swi3 in cells. Furthermore, we show that Swi1 is required for efficient chromatin association of Swi3 and that the Swi1 C-terminal domain directly interacts with Swi3. These results indicate that Swi1 associates with chromatin through its DDT domain and recruits Swi3 to function together as the replication fork protection complex.

Static network structure can be used to model the phenotypic effects of perturbations in regulatory networks

MOTIVATION

Biological processes are dynamic, whereas the networks that depict them are typically static. Quantitative modeling using differential equations or logic-based functions can offer quantitative predictions of the behavior of biological systems, but they require detailed experimental characterization of interaction kinetics, which is typically unavailable. To determine to what extent complex biological processes can be modeled and analyzed using only the static structure of the network (i.e. the direction and sign of the edges), we attempt to predict the phenotypic effect of perturbations in biological networks from the static network structure.

RESULTS

We analyzed three networks from different sources: The EGFR/MAPK and PI3K/AKT network from a detailed experimental study, the TNF regulatory network from the STRING database and a large network of all NCI-curated pathways from the Protein Interaction Database. Altogether, we predicted the effect of 39 perturbations (e.g. by one or two drugs) on 433 target proteins/genes. In up to 82% of the cases, an algorithm that used only the static structure of the network correctly predicted whether any given protein/gene is upregulated or downregulated as a result of perturbations of other proteins/genes.

CONCLUSION

While quantitative modeling requires detailed experimental data and heavy computations, which limit its scalability for large networks, a wiring-based approach can use available data from pathway and interaction databases and may be scalable. These results lay the foundations for a large-scale approach of predicting phenotypes based on the schematic structure of networks.

Chromatin-Associated Proteins Revealed by SILAC-Proteomic Analysis Exhibit a High Likelihood of Requirement for Growth Fitness under DNA Damage Stress

Chromatin-associated nonhistone proteins (CHRAPs) are readily collected from the DNaseI digested crude chromatin preparation. In this study, we show that the absolute abundance-based label-free quantitative proteomic analysis fail to identify potential CHRAPs from the CHRAP-prep. This is because that the most-highly abundant cytoplasmic proteins such as ribosomal proteins are not effectively depleted in the CHRAP-prep. Ribosomal proteins remain the top-ranked abundant proteins in the CHRAP-prep. On the other hand, we show that relative abundance-based SILAC-mediated quantitative proteomic analysis is capable of discovering the potential CHRAPs in the CHRAP-prep when compared to the whole-cell-extract. Ribosomal proteins are depleted from the top SILAC ratio-ranked proteins. In contrast, nucleus-localized proteins or potential CHRAPs are enriched in the top SILAC-ranked proteins. Consistent with this, gene-ontology analysis indicates that CHRAP-associated functions such as transcription, regulation of chromatin structures, and DNA replication and repair are significantly overrepresented in the top SILAC-ranked proteins. Some of the novel CHRAPs are confirmed using the traditional method. Notably, phenotypic assessment reveals that the top SILAC-ranked proteins exhibit the high likelihood of requirement for growth fitness under DNA damage stress. Taken together, our results indicate that the SILAC-mediated proteomic approach is capable of determining CHRAPs without prior knowledge.

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.

Discovering the hidden sub-network component in a ranked list of genes or proteins derived from genomic experiments

Genomic experiments (e.g. differential gene expression, single-nucleotide polymorphism association) typically produce ranked list of genes. We present a simple but powerful approach which uses protein–protein interaction data to detect sub-networks within such ranked lists of genes or proteins. We performed an exhaustive study of network parameters that allowed us concluding that the average number of components and the average number of nodes per component are the parameters that best discriminate between real and random networks. A novel aspect that increases the efficiency of this strategy in finding sub-networks is that, in addition to direct connections, also connections mediated by intermediate nodes are considered to build up the sub-networks. The possibility of using of such intermediate nodes makes this approach more robust to noise. It also overcomes some limitations intrinsic to experimental designs based on differential expression, in which some nodes are invariant across conditions. The proposed approach can also be used for candidate disease-gene prioritization. Here, we demonstrate the usefulness of the approach by means of several case examples that include a differential expression analysis in Fanconi Anemia, a genome-wide association study of bipolar disorder and a genome-scale study of essentiality in cancer genes. An efficient and easy-to-use web interface (available at http://www.babelomics.org) based on HTML5 technologies is also provided to run the algorithm and represent the network.

Analyzing fission yeast multidrug resistance mechanisms to develop a genetically tractable model system for chemical biology

Chemical inhibitors can help analyze dynamic cellular processes, particularly when probes are active in genetically tractable model systems. Although fission yeast has served as an important model system, which shares more cellular processes (e.g., RNAi) with humans than budding yeast, its use for chemical biology has been limited by its multidrug resistance (MDR) response. Using genomics and genetics approaches, we identified the key transcription factors and drug-efflux transporters responsible for fission yeast MDR and designed strains sensitive to a wide-range of chemical inhibitors, including commonly used probes. We used this strain, along with acute chemical inhibition and high-resolution imaging, to examine metaphase spindle organization in a "closed" mitosis. Together, our findings suggest that our fission yeast strains will allow the use of several inhibitors as probes, discovery of new inhibitors, and analysis of drug action.

Conserved rules govern genetic interaction degree across species

Background

Synthetic genetic interactions have recently been mapped on a genome scale in the budding yeast Saccharomyces cerevisiae, providing a functional view of the central processes of eukaryotic life. Currently, comprehensive genetic interaction networks have not been determined for other species, and we therefore sought to model conserved aspects of genetic interaction networks in order to enable the transfer of knowledge between species.

Results

Using a combination of physiological and evolutionary properties of genes, we built models that successfully predicted the genetic interaction degree of S. cerevisiae genes. Importantly, a model trained on S. cerevisiae gene features and degree also accurately predicted interaction degree in the fission yeast Schizosaccharomyces pombe, suggesting that many of the predictive relationships discovered in S. cerevisiae also hold in this evolutionarily distant yeast. In both species, high single mutant fitness defect, protein disorder, pleiotropy, protein-protein interaction network degree, and low expression variation were significantly predictive of genetic interaction degree. A comparison of the predicted genetic interaction degrees of S. pombe genes to the degrees of S. cerevisiae orthologs revealed functional rewiring of specific biological processes that distinguish these two species. Finally, predicted differences in genetic interaction degree were independently supported by differences in co-expression relationships of the two species.

Conclusions

Our findings show that there are common relationships between gene properties and genetic interaction network topology in two evolutionarily distant species. This conservation allows use of the extensively mapped S. cerevisiae genetic interaction network as an orthology-independent reference to guide the study of more complex species.

Ers1 links HP1 to RNAi

Pericentromeric heterochromatin formation is mediated by repressive histone H3 lysine 9 methylation (H3K9Me) and its recognition by HP1 proteins. Intriguingly, in many organisms, RNAi is coupled to this process through poorly understood mechanisms. In Schizosaccharomyces pombe, the H3-K9 methyltransferase Clr4 and the heterochromatin protein 1 (HP1) ortholog Swi6 are critical for RNAi, whereas RNAi stimulates H3K9Me. In addition to the endoribonuclease Dcr1, RNAi in S. pombe requires two interacting protein complexes, the RITS complex, which contains an Argonaute subunit, and the RDRC complex, which contains an RNA-dependent RNA polymerase subunit. We previously identified Ers1 (essential for RNAi-dependent silencing) as an orphan protein that genetically acts in the RNAi pathway. Using recombinant proteins, we show here that Ers1 directly and specifically interacts with HP1/Swi6. Two-hybrid assays indicate that Ers1 also directly interacts with several RNAi factors. Consistent with these interactions, Ers1 associates in vivo with the RITS complex, the RDRC complex, and Dcr1, and it promotes interactions between these factors. Ers1, like Swi6, is also required for RNAi complexes to associate with pericentromeric noncoding RNAs. Overexpression of Ers1 results in a dominant-negative phenotype that can be specifically suppressed by increasing levels of the RDRC subunit Hrr1 or of Dcr1, further supporting a functional role for Ers1 in promoting the assembly of the RNAi machinery. Through the interactions described here, Ers1 may promote RNAi by tethering the corresponding enzyme complexes to HP1-coated chromatin, thereby placing them in proximity to the nascent noncoding RNA substrate.

Topology of functional networks predicts physical binding of proteins

MOTIVATION

It has been recognized that the topology of molecular networks provides information about the certainty and nature of individual interactions. Thus, network motifs have been used for predicting missing links in biological networks and for removing false positives. However, various different measures can be inferred from the structure of a given network and their predictive power varies depending on the task at hand.

RESULTS

Herein, we present a systematic assessment of seven different network features extracted from the topology of functional genetic networks and we quantify their ability to classify interactions into different types of physical protein associations. Using machine learning, we combine features based on network topology with non-network features and compare their importance of the classification of interactions. We demonstrate the utility of network features based on human and budding yeast networks; we show that network features can distinguish different sub-types of physical protein associations and we apply the framework to fission yeast, which has a much sparser known physical interactome than the other two species. Our analysis shows that network features are at least as predictive for the tasks we tested as non-network features. However, feature importance varies between species owing to different topological characteristics of the networks. The application to fission yeast shows that small maps of physical interactomes can be extended based on functional networks, which are often more readily available.

AVAILABILITY AND IMPLEMENTATION

The R-code for computing the network features is available from www.cellularnetworks.org

Dynamic epistasis for different alleles of the same gene

Epistasis refers to the phenomenon in which phenotypic consequences caused by mutation of one gene depend on one or more mutations at another gene. Epistasis is critical for understanding many genetic and evolutionary processes, including pathway organization, evolution of sexual reproduction, mutational load, ploidy, genomic complexity, speciation, and the origin of life. Nevertheless, current understandings for the genome-wide distribution of epistasis are mostly inferred from interactions among one mutant type per gene, whereas how epistatic interaction partners change dynamically for different mutant alleles of the same gene is largely unknown. Here we address this issue by combining predictions from flux balance analysis and data from a recently published high-throughput experiment. Our results show that different alleles can epistatically interact with very different gene sets. Furthermore, between two random mutant alleles of the same gene, the chance for the allele with more severe mutational consequence to develop a higher percentage of negative epistasis than the other allele is 50~70% in eukaryotic organisms, but only 20~30% in bacteria and archaea. We developed a population genetics model that predicts that the observed distribution for the sign of epistasis can speed up the process of purging deleterious mutations in eukaryotic organisms. Our results indicate that epistasis among genes can be dynamically rewired at the genome level, and call on future efforts to revisit theories that can integrate epistatic dynamics among genes in biological systems.

Heterochromatin protein 1 homologue Swi6 acts in concert with Ers1 to regulate RNAi-directed heterochromatin assembly

In fission yeast, the RNAi pathway is required for centromeric heterochromatin assembly. siRNAs derived from centromeric transcripts are incorporated into the RNA-induced transcriptional silencing (RITS) complex and direct it to nascent homologous transcripts. The RNA-induced transcriptional silencing-bound nascent transcripts further recruit the RNA-directed RNA polymerase complex (RDRC) to promote dsRNA synthesis and siRNA production. Heterochromatin coated with Swi6/Heterochromain Protein 1 is then formed following recruitment of chromatin modification machinery. Swi6 is also required for the upstream production of siRNA, although the mechanism for this has remained obscure. Here, we demonstrate that Swi6 recruits RDRC to heterochromatin through Ers1, an RNAi factor intermediate. An ers1(+) mutant allele (ers1-C62) was identified in a genetic screen for mutants that alleviate centromeric silencing, and this phenotype was suppressed by overexpression of either the Hrr1 RDRC subunit or Clr4 histone H3-K9 methyltransferase. Ers1 physically interacts with Hrr1, and loss of Ers1 impairs RDRC centromeric localization. Although Ers1 failed to bind Clr4, a direct interaction with Swi6 was detected, and centromeric localization of Swi6 was enhanced by Clr4 overexpression in ers1-C62 cells. Consistent with this, deletion of swi6(+) reduced centromeric localization of Ers1 and RDRC. Moreover, tethering of Ers1 or Hrr1 to centromeric heterochromatin partially bypassed Swi6 function. These findings demonstrate an alternative mechanism for RDRC recruitment and explain the essential role of Swi6/Heterochromain Protein 1 in RNAi-directed heterochromatin assembly.

Towards understanding how molecular networks evolve in plants

Residing beneath the phenotypic landscape of a plant are intricate and dynamic networks of genes and proteins. As evolution operates on phenotypes, we expect its forces to shape somehow these underlying molecular networks. In this review, we discuss progress being made to elucidate the nature of these forces and their impact on the composition and structure of molecular networks. We also outline current limitations and open questions facing the broader field of plant network analysis.

Predicting the fission yeast protein interaction network

A systems-level understanding of biological processes and information flow requires the mapping of cellular component interactions, among which protein-protein interactions are particularly important. Fission yeast (Schizosaccharomyces pombe) is a valuable model organism for which no systematic protein-interaction data are available. We exploited gene and protein properties, global genome regulation datasets, and conservation of interactions between budding and fission yeast to predict fission yeast protein interactions in silico. We have extensively tested our method in three ways: first, by predicting with 70-80% accuracy a selected high-confidence test set; second, by recapitulating interactions between members of the well-characterized SAGA co-activator complex; and third, by verifying predicted interactions of the Cbf11 transcription factor using mass spectrometry of TAP-purified protein complexes. Given the importance of the pathway in cell physiology and human disease, we explore the predicted sub-networks centered on the Tor1/2 kinases. Moreover, we predict the histidine kinases Mak1/2/3 to be vital hubs in the fission yeast stress response network, and we suggest interactors of argonaute 1, the principal component of the siRNA-mediated gene silencing pathway, lost in budding yeast but preserved in S. pombe. Of the new high-quality interactions that were discovered after we started this work, 73% were found in our predictions. Even though any predicted interactome is imperfect, the protein network presented here can provide a valuable basis to explore biological processes and to guide wet-lab experiments in fission yeast and beyond. Our predicted protein interactions are freely available through PInt, an online resource on our website (www.bahlerlab.info/PInt).

Systematic detection of epistatic interactions based on allele pair frequencies

Epistatic genetic interactions are key for understanding the genetic contribution to complex traits. Epistasis is always defined with respect to some trait such as growth rate or fitness. Whereas most existing epistasis screens explicitly test for a trait, it is also possible to implicitly test for fitness traits by searching for the over- or under-representation of allele pairs in a given population. Such analysis of imbalanced allele pair frequencies of distant loci has not been exploited yet on a genome-wide scale, mostly due to statistical difficulties such as the multiple testing problem. We propose a new approach called Imbalanced Allele Pair frequencies (ImAP) for inferring epistatic interactions that is exclusively based on DNA sequence information. Our approach is based on genome-wide SNP data sampled from a population with known family structure. We make use of genotype information of parent-child trios and inspect 3×3 contingency tables for detecting pairs of alleles from different genomic positions that are over- or under-represented in the population. We also developed a simulation setup which mimics the pedigree structure by simultaneously assuming independence of the markers. When applied to mouse SNP data, our method detected 168 imbalanced allele pairs, which is substantially more than in simulations assuming no interactions. We could validate a significant number of the interactions with external data, and we found that interacting loci are enriched for genes involved in developmental processes.

Characterization of the ptr5+ gene involved in nuclear mRNA export in fission yeast

To analyze the mechanisms of mRNA export from the nucleus to the cytoplasm, we have isolated eleven mutants, ptr [poly(A)(+) RNA transport] 1 to 11, which accumulate poly(A)(+) RNA in the nucleus at a nonpermissive temperature in Schizosaccharomyces pombe. Of those, the ptr5-1 mutant shows dots- or a ring-like accumulation of poly(A)(+) RNA at the nuclear periphery after shifting to the nonpermissive temperature. We cloned the ptr5(+) gene and found that it encodes a component of the nuclear pore complex (NPC), nucleoporin 85 (Nup85). The ptr5-1 mutant shows no defects in protein transport, suggesting the specific involvement of Ptr5p/Nup85p in nuclear mRNA export in S. pombe. We identified Seh1p, a nucleoporin interacting with Nup85p, an mRNA-binding protein Mlo3p, and Sac3p, a component of the TREX-2 complex involved in coupling of nuclear mRNA export with transcription, as multi-copy suppressors for the ptr5-1 mutation. In addition, we found that the ptr5-1 mutation is synthetically lethal with a mutation of the mRNA export factor Rae1p, and that the double mutant exaggerates defective nuclear mRNA export, suggesting that Ptr5p/Nup85p is involved in nuclear mRNA export through Rae1p. Interestingly, the ptr5-1 mutation also showed synthetic effects with several prp pre-mRNA splicing mutations, suggesting a functional linkage between the NPCs and the splicing apparatus in the yeast nucleus.

DNA-RNA hybrid formation mediates RNAi-directed heterochromatin formation

Certain noncoding RNAs (ncRNAs) implicated in the regulation of chromatin structure associate with chromatin. During the formation of RNAi-directed heterochromatin in fission yeast, ncRNAs transcribed from heterochromatin are thought to recruit the RNAi machinery to chromatin for the formation of heterochromatin; however, the molecular details of this association are not clear. Here, using RNA immunoprecipitation assay, we showed that the heterochromatic ncRNA was associated with chromatin via the formation of a DNA-RNA hybrid and bound to the RNA-induced transcriptional silencing (RITS) complex. The presence of DNA-RNA hybrid in the cell was also confirmed by immunofluorescence analysis using anti-DNA-RNA hybrid antibody. Over-expression and depletion of RNase H in vivo decreased and increased the amount of DNA-RNA hybrid formed, respectively, and both disturbed heterochromatin. Moreover, DNA-RNA hybrid was formed on, and over-expression of RNase H inhibited the formation of, artificial heterochromatin induced by tethering of RITS to mRNA. These results indicate that heterochromatic ncRNAs are retained on chromatin via the formation of DNA-RNA hybrids and provide a platform for the RNAi-directed heterochromatin assembly and suggest that DNA-RNA hybrid formation plays a role in chromatic ncRNA function.

Near-neutrality, robustness, and epigenetics

The nearly neutral theory emphasizes the interaction of drift and weak selection in evolution. With progress of genome biology, the applicability of the nearly neutral theory has expanded. The genome-wide analyses of synonymous and nonsynonymous substitutions at protein-coding regions show prevalence of very weak selection. Many patterns of evolution of gene regulation are also in agreement with the nearly neutral prediction. Our consideration on near-neutrality expands in relation to the progress on molecular understanding of robustness and epigenetics. Both are bridges to link genotypes with phenotypes and important for understanding how weak selection and drift interact in the evolution of complex systems.

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.

Exploring the shallow end; estimating information content in transcriptomics studies

Transcriptomics is a major platform to study organismal biology. The advent of new parallel sequencing technologies has opened up a new avenue of transcriptomics with ever deeper and deeper sequencing to identify and quantify each and every transcript in a sample. However, this may not be the best usage of the parallel sequencing technology for all transcriptomics experiments. I utilized the Shannon Entropy approach to estimate the information contained within a transcriptomics experiment and tested the ability of shallow RNAseq to capture the majority of this information. This analysis showed that it was possible to capture nearly all of the network or genomic information present in a variety of transcriptomics experiments using a subset of the most abundant 5000 transcripts or less within any given sample. Thus, it appears that it should be possible and affordable to conduct large scale factorial analysis with a high degree of replication using parallel sequencing technologies.

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.

Comparative interaction networks: bridging genotype to phenotype

Over the past decade, biomedical research has witnessed an exponential increase in the throughput of the characterization of biological systems. Here we review the recent progress in large-scale methods to determine protein-protein, genetic and chemical-genetic interaction networks. We discuss some of the limitations and advantages of the different methods and give examples of how these networks are being used to study the evolutionary process. Comparative studies have revealed that different types of protein-protein interactions diverge at different rates with high conservation of co-complex membership but rapid divergence of more promiscuous interactions like those that mediate post-translational modifications. These evolutionary trends have consistent genetic consequences with highly conserved epistatic interactions within complex subunits but faster divergence of epistatic interactions across complexes or pathways. Finally, we discuss how these evolutionary observations are being used to interpret cross-species chemical-genetic studies and how they might shape therapeutic strategies. Together, these interaction networks offer us an unprecedented level of detail into how genotypes are translated to phenotypes, and we envision that they will be increasingly useful in the interpretation of genetic and phenotypic variation occurring within populations as well as the rational design of combinatorial therapeutics.

The RecQ4 orthologue Hrq1 is critical for DNA interstrand cross-link repair and genome stability in fission yeast

Of the five human RecQ family helicases, RecQ4, BLM, and WRN suppress distinct genome instability-linked diseases with severe phenotypes, often with indeterminate etiologies. Here, we functionally define Hrq1, a novel orthologue of RecQ4 from fission yeast. Biochemical analysis of Hrq1 reveals a DEAH box- and ATP-dependent 3'-5' helicase activity on various DNA substrates, including bubbles but not blunt duplexes, characteristic of the RecQ family. Cells lacking Hrq1 suffer spontaneous genomic instability and, consequently, require homologous recombination repair and the DNA damage checkpoint for viability. Hrq1 supports the nucleotide excision repair of DNA damage caused by the chemotherapeutic agent cisplatin and, in certain genetic contexts, UV light. Genetic epistasis analyses reveal that Hrq1 acts parallel to the PCNA/Ubc13/Mms2-dependent postreplication repair (PRR) pathway. Thus, in hrq1Δ cells, lesions are channeled through the PRR pathway, yielding hyper-recombinant and mutator phenotypes; analogous defects may underlie the genetic instability and diseases associated with RecQ4 dysfunction.

The Transition of Poised RNA Polymerase II to an Actively Elongating State Is a "Complex" Affair

The initial discovery of the occupancy of RNA polymerase II at certain genes prior to their transcriptional activation occurred a quarter century ago in Drosophila. The preloading of these poised complexes in this inactive state is now apparent in many different organisms across the evolutionary spectrum and occurs at a broad and diverse set of genes. In this paper, we discuss the genetic and biochemical efforts in S. cerevisiae to describe the conversion of these poised transcription complexes to the active state for productive elongation. The accumulated evidence demonstrates that a multitude of coactivators and chromatin remodeling complexes are essential for this transition.

Inferring mechanisms of compensation from E-MAP and SGA data using local search algorithms for max cut

A new method based on a mathematically natural local search framework for max cut is developed to uncover functionally coherent module and BPM motifs in high-throughput genetic interaction data. Unlike previous methods, which also consider physical protein-protein interaction data, our method utilizes genetic interaction data only; this becomes increasingly important as high-throughput genetic interaction data is becoming available in settings where less is known about physical interaction data. We compare modules and BPMs obtained to previous methods and across different datasets. Despite needing no physical interaction information, the BPMs produced by our method are competitive with previous methods. Biological findings include a suggested global role for the prefoldin complex and a SWR subcomplex in pathway buffering in the budding yeast interactome.

Biological interaction networks are conserved at the module level

Background

Orthologous genes are highly conserved between closely related species and biological systems often utilize the same genes across different organisms. However, while sequence similarity often implies functional similarity, interaction data is not well conserved even for proteins with high sequence similarity. Several recent studies comparing high throughput data including expression, protein-protein, protein-DNA, and genetic interactions between close species show conservation at a much lower rate than expected.

Results

In this work we collected comprehensive high-throughput interaction datasets for four model organisms (S. cerevisiae, S. pombe, C. elegans, and D. melanogaster) and carried out systematic analyses in order to explain the apparent lower conservation of interaction data when compared to the conservation of sequence data. We first showed that several previously proposed hypotheses only provide a limited explanation for such lower conservation rates. We combined all interaction evidences into an integrated network for each species and identified functional modules from these integrated networks. We then demonstrate that interactions that are part of functional modules are conserved at much higher rates than previous reports in the literature, while interactions that connect between distinct functional modules are conserved at lower rates.

Conclusions

We show that conservation is maintained between species, but mainly at the module level. Our results indicate that interactions within modules are much more likely to be conserved than interactions between proteins in different modules. This provides a network based explanation to the observed conservation rates that can also help explain why so many biological processes are well conserved despite the lower levels of conservation for the interactions of proteins participating in these processes.

Accompanying website: http://www.sb.cs.cmu.edu/CrossSP

Assembling global maps of cellular function through integrative analysis of physical and genetic networks

To take full advantage of high-throughput genetic and physical interaction mapping projects, the raw interactions must first be assembled into models of cell structure and function. PanGIA (for physical and genetic interaction alignment) is a plug-in for the bioinformatics platform Cytoscape, designed to integrate physical and genetic interactions into hierarchical module maps. PanGIA identifies 'modules' as sets of proteins whose physical and genetic interaction data matches that of known protein complexes. Higher-order functional cooperativity and redundancy is identified by enrichment for genetic interactions across modules. This protocol begins with importing interaction networks into Cytoscape, followed by filtering and basic network visualization. Next, PanGIA is used to infer a set of modules and their functional inter-relationships. This module map is visualized in a number of intuitive ways, and modules are tested for functional enrichment and overlap with known complexes. The full protocol can be completed between 10 and 30 min, depending on the size of the data set being analyzed.

Exploring the conservation of synthetic lethal genetic interaction networks

High-throughput studies have enabled the large-scale mapping of synthetic lethal genetic interaction networks in the budding yeast Saccharomyces cerevisiae (S. cerevisiae). Recently, complementary high-throughput methods have been developed to map genetic interactions in the fission yeast Schizosaccharomyces pombe (S. pombe), enabling comparative analyses of genetic interaction networks between S. pombe and S. cerevisiae, two species separated by hundreds of millions of years of evolution. The resultant data has providing our first view of a possible core genetic interaction network shared between two distantly related eukaryotes, and identified numerous species-specific interactions that may contribute to the unique biology of these two different organisms. These and other results suggest that comparative interactomic studies will provide novel insights into the structure of genetic interaction networks.

Drosophila RNAi screening in a postgenomic world

Drosophila melanogaster has a long history as a model organism with several unique features that make it an ideal research tool for the study of the relationship between genotype and phenotype. Importantly fundamental genetic principles as well as key human disease genes have been uncovered through the use of Drosophila. The contribution of the fruit fly to science and medicine continues in the postgenomic era as cell-based Drosophila RNAi screens are a cost-effective and scalable enabling technology that can be used to quantify the contribution of different genes to diverse cellular processes. Drosophila high-throughput screens can also be used as integral part of systems-level approaches to describe the architecture and dynamics of cellular networks.

Impact of epistasis and pleiotropy on evolutionary adaptation

Evolutionary adaptation is often likened to climbing a hill or peak. While this process is simple for fitness landscapes where mutations are independent, the interaction between mutations (epistasis) as well as mutations at loci that affect more than one trait (pleiotropy) are crucial in complex and realistic fitness landscapes. We investigate the impact of epistasis and pleiotropy on adaptive evolution by studying the evolution of a population of asexual haploid organisms (haplotypes) in a model of N interacting loci, where each locus interacts with K other loci. We use a quantitative measure of the magnitude of epistatic interactions between substitutions, and find that it is an increasing function of K. When haplotypes adapt at high mutation rates, more epistatic pairs of substitutions are observed on the line of descent than expected. The highest fitness is attained in landscapes with an intermediate amount of ruggedness that balance the higher fitness potential of interacting genes with their concomitant decreased evolvability. Our findings imply that the synergism between loci that interact epistatically is crucial for evolving genetic modules with high fitness, while too much ruggedness stalls the adaptive process.

Molecular mechanisms of epistasis within and between genes

'Disease-causing' mutations do not cause disease in all individuals. One possible important reason for this is that the outcome of a mutation can depend upon other genetic variants in a genome. These epistatic interactions between mutations occur both within and between molecules, and studies in model organisms show that they are extremely prevalent. However, epistatic interactions are still poorly understood at the molecular level, and consequently difficult to predict de novo. Here I provide an overview of our current understanding of the molecular mechanisms that can cause epistasis, and areas where more research is needed. A more complete understanding of epistasis will be vital for making accurate predictions about the phenotypes of individuals.

Towards genome-scale signalling network reconstructions

Biological signalling networks allow living organisms to issue an integrated response to current conditions and make limited predictions about future environmental changes. Small-scale dynamic models of signalling cascades, including mitogen-activated protein kinase cascades, have been developed to generate hypotheses about signal transduction. Owing to technical limitations, these models and the hypotheses they generate have focused on a limited subset of signalling molecules. Now that we can simultaneously measure a substantial portion of the molecular components of a cell, we can begin to develop and test systems-level models of cellular signalling and regulatory processes, therefore gaining insights into the 'thought' processes of a cell.

Cross-species chemogenomic profiling reveals evolutionarily conserved drug mode of action

Chemogenomic screens were performed in both budding and fission yeasts, allowing for a cross-species comparison of drug–gene interaction networks.

Drug–module interactions were more conserved than individual drug–gene interactions.

Combination of data from both species can improve drug–module predictions and helps identify a compound's mode of action.

Understanding the molecular effects of chemical compounds in living cells is an important step toward rational therapeutics. Drug discovery aims to find compounds that will target a specific pathway or pathogen with minimal side effects. However, even when an effective drug is found, its mode of action (MoA) is typically not well understood. The lack of knowledge regarding a drug's MoA makes the drug discovery process slow and rational therapeutics incredibly difficult. More recently, different high-throughput methods have been developed that attempt to discern how a compound exerts its effects in cells. One of these methods relies on measuring the growth of cells carrying different mutations in the presence of the compounds of interest, commonly referred to as chemogenomics (Wuster and Babu, 2008). The differential growth of the different mutants provides clues as to what the compounds target in the cell (Figure 2). For example, if a drug inhibits a branch in a vital two-branch pathway, then mutations in the second branch might result in cell death if the mutants are grown in the presence of the drug (Figure 2C). As these compound–mutant functional interactions are expected to be relatively rare, one can assume that the growth rate of a mutant–drug combination should generally be equal to the product of the growth rate of the untreated mutant with the growth rate of the drug-treated wild type. This expectation is defined as the neutral model and deviations from this provide a quantitative score that allow us to make informed predictions regarding a drug's MoA (Figure 2B;Parsons et al, 2006).

The availability of these high-throughput approaches now allows us to perform cross-species studies of functional interactions between compounds and genes. In this study, we have performed a quantitative analysis of compound–gene interactions for two fungal species (budding yeast (S. cerevisiae) and fission yeast (S. pombe)) that diverged from each other approximately 500–700 million years ago. A collection of 2957 compounds from the National Cancer Institute (NCI) were screened in both species for inhibition of wild-type cell growth. A total of 132 were found to be bioactive in both fungi and 9, along with 12 additional well-characterized drugs, were selected for subsequent screening. Mutant libraries of 727 and 438 gene deletions were used for S. cerevisiae and S. pombe, respectively, and these were selected based on availability of genetic interaction data from previous studies (Collins et al, 2007; Roguev et al, 2008; Fiedler et al, 2009) and contain an overlap of 190 one-to-one orthologs that can be directly compared. Deviations from the neutral expectation were quantified as drug–gene interactions scores (D-scores) for the 21 compounds against the deletion libraries. Replicates of both screens showed very high correlations (S. cerevisiae r=0.72, S. pombe r=0.76) and reproduced well previously known compound–gene interactions (Supplementary information). We then compared the D-scores for the 190 one-to-one orthologs present in the data set of both species. Despite the high reproducibility, we observed a very poor conservation of these compound–gene interaction scores across these species (r=0.13, Figure 4A).

Previous work had shown that, across these same species, genetic interactions within protein complexes were much more conserved than average genetic interactions (Roguev et al, 2008). Similarly we observed a higher cross-species conservation of the compound–module (complex or pathway) interactions than the overall compound–gene interactions. Specifically, the data derived from fission yeast were a poor predictor of S. cerevisaie drug–gene interactions, but a good predictor of budding yeast compound–module connections (Figure 4B). Also, a combined score from both species improved the prediction of compound–module interactions, above the accuracy observed with the S. cerevisae information alone, but this improvement was not observed for the prediction of drug–gene interactions (Figure 4B). Data from both species were used to predict drug–module interactions, and one specific interaction (compound NSC-207895 interaction with DNA repair complexes) was experimentally verified by showing that the compound activates the DNA damage repair pathway in three species (S. cerevisiae, S. pombe and H. sapiens).

To understand why the combination of chemogenomic data from two species might improve drug–module interaction predictions, we also analyzed previously published cross-species genetic–interaction data. We observed a significant correlation between the conservation of drug–gene and gene–gene interactions among the one-to-one orthologs (r=0.28, P-value=0.0078). Additionally, the strongest interactions of benomyl (a microtubule inhibitor) were to complexes that also had strong and conserved genetic interactions with microtubules (Figure 4C). We hypothesize that a significant number of the compound–gene interactions obtained from chemogenomic studies are not direct interactions with the physical target of the compounds, but include many indirect interactions that genetically interact with the main target(s). This would explain why the compound interaction networks show similar evolutionary patterns as the genetic interactions networks.

In summary, these results shed some light on the interplay between the evolution of genetic networks and the evolution of drug response. Understanding how genetic variability across different species might result in different sensitivity to drugs should improve our capacity to design treatments. Concretely, we hope that this line of research might one day help us create drugs and drug combinations that specifically affect a pathogen or diseased tissue, but not the host.

We present a cross-species chemogenomic screening platform using libraries of haploid deletion mutants from two yeast species, Saccharomyces cerevisiae and Schizosaccharomyces pombe. We screened a set of compounds of known and unknown mode of action (MoA) and derived quantitative drug scores (or D-scores), identifying mutants that are either sensitive or resistant to particular compounds. We found that compound–functional module relationships are more conserved than individual compound–gene interactions between these two species. Furthermore, we observed that combining data from both species allows for more accurate prediction of MoA. Finally, using this platform, we identified a novel small molecule that acts as a DNA damaging agent and demonstrate that its MoA is conserved in human cells.