Genetic Interaction Profiles Research Articles

Abstract A23: Multi-parametric genetic interactions map dynamic genetic network rewiring upon anti-proliferative treatment

Abstract Signaling pathways are often characterized as rather static networks whose outcome is the result of state changes of pathway components, e.g. by protein phosphorylation or other post-translational modifications. These state changes along signaling networks have been systematically studied using, for example proteomics methods. However it remained unknown how networks rewire under the influence of external stimuli, such as anti-cancer drug treatment. To analyze dynamic rewiring of a signaling network, we performed an arrayed high-throughput co-RNAi screen in a Drosophila melanogaster cell line (Dmel-2). Therin, we assessed statistic genetic interactions measured by 26 880 pairwise RNAi experiments under MEK1/2 inhibitor treatment over the time course of 96 hours. As phenotypic readout we conducted high-content imaging of DNA, cytoskeleton and microtubule markers and extracted >150 cellular features from 4 423 680 images each representing a specific condition, and marker. Together these features precisely characterize ~100 000 alleviating and 160 000 aggravating synergistic effects resulting from combinatorial perturbations. While only 2 % of all interactions are explained by cell viability the vast majority of dynamic differential interactions is explained by other features. Correlation of genetic interaction profiles across those features allows us to precisely how genes change pathway affiliation under different conditions. Our results show that, among others, key signaling nodes e.g. ERK1/2 or the Mediator complex build different connections within genetic networks depending on the environmental conditions and reveal yet unknown synthetic interactions. Using the confidence we gain from time resolved measurements of different cellular features we could identify numerous interactions which could resolve mechanisms of resistance to MEK inhibitor driven treatments and reveal potential new therapeutic targets. Citation Format: Florian Heigwer, Christian Scheeder, Thilo Miersch, Claudia Blass, Michael Boutros. Multi-parametric genetic interactions map dynamic genetic network rewiring upon anti-proliferative treatment [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr A23.

Learning directed acyclic graphs from large-scale genomics data

In this paper, we consider the problem of learning the genetic interaction map, i.e., the topology of a directed acyclic graph (DAG) of genetic interactions from noisy double-knockout (DK) data. Based on a set of well-established biological interaction models, we detect and classify the interactions between genes. We propose a novel linear integer optimization program called the Genetic-Interactions-Detector (GENIE) to identify the complex biological dependencies among genes and to compute the DAG topology that matches the DK measurements best. Furthermore, we extend the GENIE program by incorporating genetic interaction profile (GI-profile) data to further enhance the detection performance. In addition, we propose a sequential scalability technique for large sets of genes under study, in order to provide statistically significant results for real measurement data. Finally, we show via numeric simulations that the GENIE program and the GI-profile data extended GENIE (GI-GENIE) program clearly outperform the conventional techniques and present real data results for our proposed sequential scalability technique.

Interrogation of \u03b3-tubulin alleles using high-resolution fitness measurements reveals a distinct cytoplasmic function in spindle alignment

γ-Tubulin has a well-established role in nucleating the assembly of microtubules, yet how phosphorylation regulates its activity remains unclear. Here, we use a time-resolved, fitness-based SGA approach to compare two γ-tubulin alleles, and find that the genetic interaction profile of γtub-Y362E is enriched in spindle positioning and cell polarity genes relative to that of γtub-Y445D, which is enriched in genes involved in spindle assembly and stability. In γtub-Y362E cells, we find a defect in spindle alignment and an increase in the number of astral microtubules at both spindle poles. Our results suggest that the γtub-Y362E allele is a separation-of-function mutation that reveals a role for γ-tubulin phospho-regulation in spindle alignment. We propose that phosphorylation of the evolutionarily conserved Y362 residue of budding yeast γ-tubulin contributes to regulating the number of astral microtubules associated with spindle poles, and promoting efficient pre-anaphase spindle alignment.

A Functional Link Between Bir1 and the Saccharomyces cerevisiae Ctf19 Kinetochore Complex Revealed Through Quantitative Fitness Analysis.

The chromosomal passenger complex (CPC) is a key regulator of eukaryotic cell division, consisting of the protein kinase Aurora B/Ipl1 in association with its activator (INCENP/Sli15) and two additional proteins (Survivin/Bir1 and Borealin/Nbl1). Here, we report a genome-wide genetic interaction screen in Saccharomyces cerevisiae using the bir1-17 mutant, identifying through quantitative fitness analysis deletion mutations that act as enhancers and suppressors. Gene knockouts affecting the Ctf19 kinetochore complex were identified as the strongest enhancers of bir1-17, while mutations affecting the large ribosomal subunit or the mRNA nonsense-mediated decay pathway caused strong phenotypic suppression. Thus, cells lacking a functional Ctf19 complex become highly dependent on Bir1 function and vice versa. The negative genetic interaction profiles of bir1-17 and the cohesin mutant mcd1-1 showed considerable overlap, underlining the strong functional connection between sister chromatid cohesion and chromosome biorientation. Loss of some Ctf19 components, such as Iml3 or Chl4, impacted differentially on bir1-17 compared with mutations affecting other CPC components: despite the synthetic lethality shown by either iml3∆ or chl4∆ in combination with bir1-17, neither gene knockout showed any genetic interaction with either ipl1-321 or sli15-3. Our data therefore imply a specific functional connection between the Ctf19 complex and Bir1 that is not shared with Ipl1.

Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data

BackgroundMolecular interaction data at proteomic and genetic levels provide physical and functional insights into a molecular biosystem and are helpful for the construction of pathway structures complementarily. Despite advances in inferring biological pathways using genetic interaction data, there still exists weakness in developed models, such as, activity pathway networks (APN), when integrating the data from proteomic and genetic levels. It is necessary to develop new methods to infer pathway structure by both of interaction data.ResultsWe utilized probabilistic graphical model to develop a new method that integrates genetic interaction and protein interaction data and infers exquisitely detailed pathway structure. We modeled the pathway network as Bayesian network and applied this model to infer pathways for the coherent subsets of the global genetic interaction profiles, and the available data set of endoplasmic reticulum genes. The protein interaction data were derived from the BioGRID database. Our method can accurately reconstruct known cellular pathway structures, including SWR complex, ER-Associated Degradation (ERAD) pathway, N-Glycan biosynthesis pathway, Elongator complex, Retromer complex, and Urmylation pathway. By comparing N-Glycan biosynthesis pathway and Urmylation pathway identified from our approach with that from APN, we found that our method is able to overcome its weakness (certain edges are inexplicable). According to underlying protein interaction network, we defined a simple scoring function that only adopts genetic interaction information to avoid the balance difficulty in the APN. Using the effective stochastic simulation algorithm, the performance of our proposed method is significantly high.ConclusionWe developed a new method based on Bayesian network to infer detailed pathway structures from interaction data at proteomic and genetic levels. The results indicate that the developed method performs better in predicting signaling pathways than previously described models.

Functional annotation of chemical libraries across diverse biological processes.

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells to a compound, revealing chemical-genetic interactions that can elucidate a compound’s mode of action. We developed a highly parallel and unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized, diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode sequencing protocol, enabling assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened 7 different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

Genetic Interaction Score (S-Score) Calculation, Clustering, and Visualization of Genetic Interaction Profiles for Yeast.

This protocol describes computational analysis of genetic interaction screens, ranging from data capture (plate imaging) to downstream analyses. Plate imaging approaches using both digital camera and office flatbed scanners are included, along with a protocol for the extraction of colony size measurements from the resulting images. A commonly used genetic interaction scoring method, calculation of the S-score, is discussed. These methods require minimal computer skills, but some familiarity with MATLAB and Linux/Unix is a plus. Finally, an outline for using clustering and visualization software for analysis of resulting data sets is provided.

CRISPR-mediated genetic interaction profiling identifies RNA binding proteins controlling metazoan fitness.

Genetic interaction screens have aided our understanding of complex genetic traits, diseases, and biological pathways. However, approaches for synthetic genetic analysis with null-alleles in metazoans have not been feasible. Here, we present a CRISPR/Cas9-based Synthetic Genetic Interaction (CRISPR-SGI) approach enabling systematic double-mutant generation. Applying this technique in Caenorhabditis elegans, we comprehensively screened interactions within a set of 14 conserved RNA binding protein genes, generating all possible single and double mutants. Many double mutants displayed fitness defects, revealing synthetic interactions. For one interaction between the MBNL1/2 ortholog mbl-1 and the ELAVL ortholog exc-7, double mutants displayed a severely shortened lifespan. Both genes are required for regulating hundreds of transcripts and isoforms, and both may play a critical role in lifespan extension through insulin signaling. Thus, CRISPR-SGI reveals a rich genetic interaction landscape between RNA binding proteins in maintaining organismal health, and will serve as a paradigm applicable to other biological questions.

Genetic wiring maps of single-cell protein states reveal an off-switch for GPCR signalling.

As key executers of biological functions, the activity and abundance of proteins are subjected to extensive regulation. Deciphering the genetic architecture underlying this regulation is critical for understanding cellular signalling events and responses to environmental cues. Using random mutagenesis in haploid human cells, we apply a sensitive approach to directly couple genomic mutations to protein measurements in individual cells. Here we use this to examine a suite of cellular processes, such as transcriptional induction, regulation of protein abundance and splicing, signalling cascades (mitogen-activated protein kinase (MAPK), G-protein-coupled receptor (GPCR), protein kinase B (AKT), interferon, and Wingless and Int-related protein (WNT) pathways) and epigenetic modifications (histone crotonylation and methylation). This scalable, sequencing-based procedure elucidates the genetic landscapes that control protein states, identifying genes that cause very narrow phenotypic effects and genes that lead to broad phenotypic consequences. The resulting genetic wiring map identifies the E3-ligase substrate adaptor KCTD5 (ref. 1) as a negative regulator of the AKT pathway, a key signalling cascade frequently deregulated in cancer. KCTD5-deficient cells show elevated levels of phospho-AKT at S473 that could not be attributed to effects on canonical pathway components. To reveal the genetic requirements for this phenotype, we iteratively analysed the regulatory network linked to AKT activity in the knockout background. This genetic modifier screen exposes suppressors of the KCTD5 phenotype and mechanistically demonstrates that KCTD5 acts as an off-switch for GPCR signalling by triggering proteolysis of Gβγ heterodimers dissociated from the Gα subunit. Although biological networks have previously been constructed on the basis of gene expression, protein-protein associations, or genetic interaction profiles, we foresee that the approach described here will enable the generation of a comprehensive genetic wiring map for human cells on the basis of quantitative protein states.

Polymorphisms in MicroRNA Binding Sites Predict Colorectal Cancer Survival.

Background: MicroRNAs (miRNAs) mediate negative regulation of target genes through base pairing, and aberrant miRNA expression has been described in cancers. We hypothesized that single nucleotide polymorphisms (SNPs) within miRNA target sites might influence clinical outcomes in patients with colorectal cancer.Methods: Sixteen common SNPs within miRNA target sites were identified, and the association between these SNPs and overall survival was assessed in colorectal cancer patients using Kaplan-Meier analysis, Cox regression model, and survival tree analysis.Results: Survival tree analysis identified a higher-order genetic interaction profile consisting of the RPS6KB1 rs1051424 and ZNF839 rs11704 that was significantly associated with overall survival. The 5-year survival rates were 74.6%, 62.7%, and 57.1% for the low-, medium-, and high-risk genetic profiles, respectively (P = 0.006). The genetic interaction profile remained significant even after adjusting for potential risk factors. Additional in silico analysis provided evidence that rs1051424 and rs11704 affect RPS6KB1 and ZNF839 expressions, which in turn is significantly correlated with prognosis in colorectal cancer.Conclusion: Our results suggest that the genetic interaction profiles among SNPs within miRNA target sites might be prognostic markers for colorectal cancer survival.

Functional characterisation of long intergenic non-coding RNAs through genetic interaction profiling in Saccharomyces cerevisiae.

BackgroundTranscriptome studies have revealed that many eukaryotic genomes are pervasively transcribed producing numerous long non-coding RNAs (lncRNAs). However, only a few lncRNAs have been ascribed a cellular role thus far, with most regulating the expression of adjacent genes. Even less lncRNAs have been annotated as essential hence implying that the majority may be functionally redundant. Therefore, the function of lncRNAs could be illuminated through systematic analysis of their synthetic genetic interactions (GIs).ResultsHere, we employ synthetic genetic array (SGA) in Saccharomyces cerevisiae to identify GIs between long intergenic non-coding RNAs (lincRNAs) and protein-coding genes. We first validate this approach by demonstrating that the telomerase RNA TLC1 displays a GI network that corresponds to its well-described function in telomere length maintenance. We subsequently performed SGA screens on a set of uncharacterised lincRNAs and uncover their connection to diverse cellular processes. One of these lincRNAs, SUT457, exhibits a GI profile associating it to telomere organisation and we consistently demonstrate that SUT457 is required for telomeric overhang homeostasis through an Exo1-dependent pathway. Furthermore, the GI profile of SUT457 is distinct from that of its neighbouring genes suggesting a function independent to its genomic location. Accordingly, we show that ectopic expression of this lincRNA suppresses telomeric overhang accumulation in sut457Δ cells assigning a trans-acting role for SUT457 in telomere biology.ConclusionsOverall, our work proposes that systematic application of this genetic approach could determine the functional significance of individual lncRNAs in yeast and other complex organisms.Electronic supplementary materialThe online version of this article (doi:10.1186/s12915-016-0325-7) contains supplementary material, which is available to authorized users.

A global genetic interaction network maps a wiring diagram of cellular function

We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell.

Genetic interaction of P2X7 receptor and VEGFR-2 polymorphisms identifies a favorable prognostic profile in prostate cancer patients.

VEGFR-2 and P2X7 receptor (P2X7R) have been described to stimulate the angiogenesis and inflammatory processes of prostate cancer. The present study has been performed to investigate the genetic interactions among VEGFR-2 and P2X7R SNPs and their correlation with overall survival (OS) in a population of metastatic prostate cancer patients. Analyses were performed on germline DNA obtained from blood samples and SNPs were investigated by real-time PCR technique. The survival dimensionality reduction (SDR) methodology was applied to investigate the genetic interaction between SNPs. One hundred patients were enrolled. The SDR software provided two genetic interaction profiles consisting of the combination between specific VEGFR-2 (rs2071559, rs11133360) and P2X7R (rs3751143, rs208294) genotypes. The median OS was 126 months (95% CI, 115.94-152.96) and 65.65 months (95% CI, 52.95-76.53) for the favorable and the unfavorable genetic profile, respectively (p < 0.0001). The genetic statistical interaction between VEGFR-2 (rs2071559, rs11133360) and P2X7R (rs3751143, rs208294) genotypes may identify a population of prostate cancer patients with a better prognosis.

DNA repair defects ascribed to pby1 are caused by disruption of Holliday junction resolvase Mus81-Mms4.

PBY1 continues to be linked with DNA repair through functional genomics studies in yeast. Using the yeast knockout (YKO) strain collection, high-throughput genetic interaction screens have identified a large set of negative interactions between PBY1 and genes involved in genome stability. In drug sensitivity screens, the YKO collection pby1Δ strain exhibits a sensitivity profile typical for genes involved in DNA replication and repair. We show that these findings are not related to loss of Pby1. On the basis of genetic interaction profile similarity, we pinpoint disruption of Holliday junction resolvase Mus81-Mms4 as the mutation responsible for DNA repair phenotypes currently ascribed to pby1. The finding that Pby1 is not a DNA repair factor reconciles discrepancies in the data available for PBY1, and indirectly supports a role for Pby1 in mRNA metabolism. Data that has been collected using the YKO collection pby1Δ strain confirms and expands the chemical-genetic interactome of MUS81-MMS4.

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

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

Cdc1 removes the ethanolamine phosphate of the first mannose of GPI anchors and thereby facilitates the integration of GPI proteins into the yeast cell wall.

Temperature-sensitive cdc1(ts) mutants are reported to stop the cell cycle upon a shift to 30°C in early G2, that is, as small budded cells having completed DNA replication but unable to duplicate the spindle pole body. A recent report showed that PGAP5, a human homologue of CDC1, acts as a phosphodiesterase removing an ethanolamine phosphate (EtN-P) from mannose 2 of the glycosylphosphatidylinositol (GPI) anchor, thus permitting efficient endoplasmic reticulum (ER)-to-Golgi transport of GPI proteins. We find that the essential CDC1 gene can be deleted in mcd4∆ cells, which do not attach EtN-P to mannose 1 of the GPI anchor, suggesting that Cdc1 removes the EtN-P added by Mcd4. Cdc1-314(ts) mutants do not accumulate GPI proteins in the ER but have a partial secretion block later in the secretory pathway. Growth tests and the genetic interaction profile of cdc1-314(ts) pinpoint a distinct cell wall defect. Osmotic support restores GPI protein secretion and actin polarization but not growth. Cell walls of cdc1-314(ts) mutants contain large amounts of GPI proteins that are easily released by β-glucanases and not attached to cell wall β1,6-glucans and that retain their original GPI anchor lipid. This suggests that the presumed transglycosidases Dfg5 and Dcw1 of cdc1-314(ts) transfer GPI proteins to cell wall β1,6-glucans inefficiently.

Conditional Genetic Interactions ofRTT107,SLX4, andHRQ1Reveal Dynamic Networks upon DNA Damage inS. cerevisiae

The DNA damage response (DDR) is a dynamic process that is crucial for protecting the cell from challenges to genome integrity. Although many genome-wide studies in Saccharomyces cerevisiae have identified genes that contribute to resistance to DNA-damaging agents, more work is needed to elucidate the changes in genetic interaction networks in response to DNA lesions. Here we used conditional epistatic miniarray profiling to analyze the genetic interaction networks of the DDR genes RTT107, SLX4, and HRQ1 under three DNA-damaging conditions: camptothecin, hydroxyurea, and methyl methanesulfonate. Rtt107 and its interaction partner Slx4 are targets of the checkpoint kinase Mec1, which is central to the DDR-signaling cascades. Hrq1 recently was identified as a novel member of the RecQ helicase family in S. cerevisiae but is still poorly characterized. The conditional genetic networks that we generated revealed functional insights into all three genes and showed that there were varied responses to different DNA damaging agents. We observed that RTT107 had more genetic interactions under camptothecin conditions than SLX4 or HRQ1, suggesting that Rtt107 has an important role in response to this type of DNA lesion. Although RTT107 and SLX4 function together, they also had many distinct genetic interactions. In particular, RTT107 and SLX4 showed contrasting genetic interactions for a few genes, which we validated with independently constructed strains. Interestingly, HRQ1 had a genetic interaction profile that correlated with that of SLX4 and both were enriched for very similar gene ontology terms, suggesting that they function together in the DDR.

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.

Functional Divergence for Every Paralog

Because genes can be constrained by selection at more than one phenotypic level, the relaxation of constraints following gene duplication allows for functional divergence (FD) along multiple phenotypic axes. Many studies have generated individual measures of FD, but the profile of FD between paralogs across levels of phenotypic space remains largely uncharted. We evaluate paralog pairs that originated via the yeast whole-genome duplication (ohnolog pairs) at three distinct phenotypic levels (properties of proteins, gene expression, and overall organismal growth) using eight complementary measures of FD (protein: evolutionary rates, radical amino acid substitutions, and domain architecture; gene expression: expression differences in a single species and condition, across species in a single condition, and in a single species across conditions; and organismal: genetic interaction profiles and growth profiles in multiple conditions). We find that the majority of ohnolog pairs show FD by multiple phenotypic measures. Within each phenotypic level, measures of FD are strongly correlated but are generally weakly correlated between levels, suggesting that functional constraints exerted on genes from distinct phenotypic levels are largely decoupled. Our results suggest that redundancy is a rare functional fate for retained paralogs and that FD cannot be fully captured by measures at any single phenotypic level.

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

BackgroundThe 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.MethodsHigh-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.ResultsThis 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.ConclusionOur 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.