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

Saturated Transposon Analysis in Yeast as a one-step method to quantify the fitness effects of gene disruptions on a genome-wide scale

Transposon insertion site sequencing (TIS) is a powerful tool that has significantly advanced our knowledge of functional genomics. For example, TIS has been used to identify essential genes of Saccharomyces cerevisiae, screen for antibiotic resistance genes in Klebsiella pneumoniae and determine the set of genes required for virulence of Mycobacterium tuberculosis. While providing valuable insights, these applications of TIS focus on (conditional) gene essentiality and neglect possibly interesting but subtle differences in the importance of genes for fitness. Notably, it has been demonstrated that data obtained from TIS experiments can be used for fitness quantification and the construction of genetic interaction maps, but this potential is only sporadically exploited. Here, we present a method to quantify the fitness of gene disruption mutants using data obtained from a TIS screen developed for the yeast Saccharomyces cerevisiae called SATAY. We show that the mean read count per transposon insertion site provides a metric for fitness that is robust across biological and technical replicate experiments. Importantly, the ability to resolve differences between gene disruption mutants with low fitness depends crucially on the inclusion of insertion sites that are not observed in the sequencing data to estimate the mean. While our method provides reproducible results between replicate SATAY datasets, the obtained fitness distribution differs substantially from those obtained using other techniques. It is currently unclear whether these inconsistencies are due to biological or technical differences between the methods. We end with suggestions for modifications of the SATAY procedure that could improve the resolution of the fitness estimates. Our analysis indicates that increasing the sequencing depth does very little to reduce the uncertainty in the estimates, while replacing the PCR amplification with methods that avoid or reduce the number of amplification cycles will likely be most effective in reducing noise.

Arrayed CRISPRi library to suppress genes required for Schizosaccharomyces pombe viability

The fission yeast, Schizosaccharomyces pombe, is an excellent eukaryote model organism for studying essential biological processes. Its genome contains ∼1,200 genes essential for cell viability, most of which are evolutionarily conserved. To study these essential genes, resources enabling conditional perturbation of target genes are required. Here, we constructed comprehensive arrayed libraries of plasmids and strains to knock down essential genes in S. pombe using dCas9-mediated CRISPRi. These libraries cover ∼98% of all essential genes in fission yeast. We estimate that in ∼60% of these strains, transcription of a target gene was repressed so efficiently that cell proliferation was significantly inhibited. To demonstrate the usefulness of these libraries, we performed metabolic analyses with knockdown strains and revealed flexible interaction among metabolic pathways. Libraries established in this study enable comprehensive functional analyses of essential genes in S. pombe and will facilitate the understanding of essential biological processes in eukaryotes.

A multilineage screen identifies actionable synthetic lethal interactions in human cancers

Cancers are driven by alterations in diverse genes, creating dependencies that can be therapeutically targeted. However, many genetic dependencies have proven inconsistent across tumors. Here we describe SCHEMATIC, a strategy to identify a core network of highly penetrant, actionable genetic interactions. First, fundamental cellular processes are perturbed by systematic combinatorial knockouts across tumor lineages, identifying 1,805 synthetic lethal interactions (95% unreported). Interactions are then analyzed by hierarchical pooling, revealing that half segregate reliably by tissue type or biomarker status (51%) and a substantial minority are penetrant across lineages (34%). Interactions converge on 49 multigene systems, including MAPK signaling and BAF transcriptional regulatory complexes, which become essential on disruption of polymerases. Some 266 interactions translate to robust biomarkers of drug sensitivity, including frequent genetic alterations in the KDM5C/6A histone demethylases, which sensitize to inhibition of TIPARP (PARP7). SCHEMATIC offers a context-aware, data-driven approach to match genetic alterations to targeted therapies.

D-SPIN constructs gene regulatory network models from multiplexed scRNA-seq data revealing organizing principles of cellular perturbation response

Gene regulatory networks within cells modulate the expression of the genome in response to signals and changing environmental conditions. Reconstructions of gene regulatory networks can reveal the information processing and control principles used by cells to maintain homeostasis and execute cell-state transitions. Here, we introduce a computational framework, D-SPIN, that generates quantitative models of gene regulatory networks from single-cell mRNA-seq datasets collected across thousands of distinct perturbation conditions. D-SPIN models the cell as a collection of interacting gene-expression programs, and constructs a probabilistic model to infer regulatory interactions between gene-expression programs and external perturbations. Using large Perturb-seq and drug-response datasets, we demonstrate that D-SPIN models reveal the organization of cellular pathways, sub-functions of macromolecular complexes, and the logic of cellular regulation of transcription, translation, metabolism, and protein degradation in response to gene knockdown perturbations. D-SPIN can also be applied to dissect drug response mechanisms in heterogeneous cell populations, elucidating how combinations of immunomodulatory drugs can induce novel cell states through additive recruitment of gene expression programs. D-SPIN provides a computational framework for constructing interpretable models of gene-regulatory networks to reveal principles of cellular information processing and physiological control.

NRN1 epistasis with BDNF and CACNA1C: mediation effects on symptom severity through neuroanatomical changes in schizophrenia

The expression of Neuritin-1 (NRN1), a neurotrophic factor crucial for neurodevelopment and synaptic plasticity, is enhanced by the Brain Derived Neurotrophic Factor (BDNF). Although the receptor of NRN1 remains unclear, it is suggested that NRN1's activation of the insulin receptor (IR) pathway promotes the transcription of the calcium voltage-gated channel subunit alpha1 C (CACNA1C). These three genes have been independently associated with schizophrenia (SZ) risk, symptomatology, and brain differences. However, research on how they synergistically modulate these phenotypes is scarce. We aimed to study whether the genetic epistasis between these genes affects the risk and clinical presentation of the disorder via its effect on brain structure. First, we tested the epistatic effect of NRN1 and BDNF or CACNA1C on (i) the risk for SZ, (ii) clinical symptoms severity and functionality (onset, PANSS, CGI and GAF), and (iii) brain cortical structure (thickness, surface area and volume measures estimated using FreeSurfer) in a sample of 86 SZ patients and 89 healthy subjects. Second, we explored whether those brain clusters influenced by epistatic effects mediate the clinical profiles. Although we did not find a direct epistatic impact on the risk, our data unveiled significant effects on the disorder's clinical presentation. Specifically, the NRN1-rs10484320 x BDNF-rs6265 interplay influenced PANSS general psychopathology, and the NRN1-rs4960155 x CACNA1C-rs1006737 interaction affected GAF scores. Moreover, several interactions between NRN1 SNPs and BDNF-rs6265 significantly influenced the surface area and cortical volume of the frontal, parietal, and temporal brain regions within patients. The NRN1-rs10484320 x BDNF-rs6265 epistasis in the left lateral orbitofrontal cortex fully mediated the effect on PANSS general psychopathology. Our study not only adds clinical significance to the well-described molecular relationship between NRN1 and BDNF but also underscores the utility of deconstructing SZ into biologically validated brain-imaging markers to explore their mediation role in the path from genetics to complex clinical manifestation.

In Silico Analysis of Protein-Protein Interactions of Putative Endoplasmic Reticulum Metallopeptidase 1 in Schizosaccharomyces pombe

Ermp1 is a putative metalloprotease from Schizosaccharomyces pombe and a member of the Fxna peptidases. Although their function is unknown, orthologous proteins from rats and humans have been associated with the maturation of ovarian follicles and increased ER stress. This study focuses on proposing the first prediction of PPI by comparison of the interologues between humans and yeasts, as well as the molecular docking and dynamics of the M28 domain of Ermp1 with possible target proteins. As results, 45 proteins are proposed that could interact with the metalloprotease. Most of these proteins are related to the transport of Ca2+ and the metabolism of amino acids and proteins. Docking and molecular dynamics suggest that the M28 domain of Ermp1 could hydrolyze leucine and methionine residues of Amk2, Ypt5 and Pex12. These results could support future experimental investigations of other Fxna peptidases, such as human ERMP1.

A method for mining condition-specific co-expressed genes in Camellia sinensis based on k-means clustering

BACKGROUND

As one of the world's most important beverage crops, tea plants (Camellia sinensis) are renowned for their unique flavors and numerous beneficial secondary metabolites, attracting researchers to investigate the formation of tea quality. With the increasing availability of transcriptome data on tea plants in public databases, conducting large-scale co-expression analyses has become feasible to meet the demand for functional characterization of tea plant genes. However, as the multidimensional noise increases, larger-scale co-expression analyses are not always effective. Analyzing a subset of samples generated by effectively downsampling and reorganizing the global sample set often leads to more accurate results in co-expression analysis. Meanwhile, global-based co-expression analyses are more likely to overlook condition-specific gene interactions, which may be more important and worthy of exploration and research.

RESULTS

Here, we employed the k-means clustering method to organize and classify the global samples of tea plants, resulting in clustered samples. Metadata annotations were then performed on these clustered samples to determine the "conditions" represented by each cluster. Subsequently, we conducted gene co-expression network analysis (WGCNA) separately on the global samples and the clustered samples, resulting in global modules and cluster-specific modules. Comparative analyses of global modules and cluster-specific modules have demonstrated that cluster-specific modules exhibit higher accuracy in co-expression analysis. To measure the degree of condition specificity of genes within condition-specific clusters, we introduced the correlation difference value (CDV). By incorporating the CDV into co-expression analyses, we can assess the condition specificity of genes. This approach proved instrumental in identifying a series of high CDV transcription factor encoding genes upregulated during sustained cold treatment in Camellia sinensis leaves and buds, and pinpointing a pair of genes that participate in the antioxidant defense system of tea plants under sustained cold stress.

CONCLUSIONS

To summarize, downsampling and reorganizing the sample set improved the accuracy of co-expression analysis. Cluster-specific modules were more accurate in capturing condition-specific gene interactions. The introduction of CDV allowed for the assessment of condition specificity in gene co-expression analyses. Using this approach, we identified a series of high CDV transcription factor encoding genes related to sustained cold stress in Camellia sinensis. This study highlights the importance of considering condition specificity in co-expression analysis and provides insights into the regulation of the cold stress in Camellia sinensis.

Genetic Interactions in Various Environmental Conditions in Caenorhabditis elegans

Although it is well known that epistasis plays an important role in many evolutionary processes (e.g., speciation, evolution of sex), our knowledge on the frequency and prevalent sign of epistatic interactions is mainly limited to unicellular organisms or cell cultures of multicellular organisms. This is even more pronounced in regard to how the environment can influence genetic interactions. To broaden our knowledge in that respect we studied gene-gene interactions in a whole multicellular organism, Caenorhabditis elegans. We screened over one thousand gene interactions, each one in standard laboratory conditions, and under three different stressors: heat shock, oxidative stress, and genotoxic stress. Depending on the condition, between 7% and 22% of gene pairs showed significant genetic interactions and an overall sign of epistasis changed depending on the condition. Sign epistasis was quite common, but reciprocal sign epistasis was extremally rare. One interaction was common to all conditions, whereas 78% of interactions were specific to only one environment. Although epistatic interactions are quite common, their impact on evolutionary processes will strongly depend on environmental factors.

A Normalization Protocol Reduces Edge Effect in High-Throughput Analyses of Hydroxyurea Hypersensitivity in Fission Yeast

Edge effect denotes better growth of microbial organisms situated at the edge of the solid agar media. Although the precise reason underlying edge effect is unresolved, it is generally attributed to greater nutrient availability with less competing neighbors at the edge. Nonetheless, edge effect constitutes an unavoidable confounding factor that results in misinterpretation of cell fitness, especially in high-throughput screening experiments widely employed for genome-wide investigation using microbial gene knockout or mutant libraries. Here, we visualize edge effect in high-throughput high-density pinning arrays and report a normalization approach based on colony growth rate to quantify drug (hydroxyurea)-hypersensitivity in fission yeast strains. This normalization procedure improved the accuracy of fitness measurement by compensating cell growth rate discrepancy at different locations on the plate and reducing false-positive and -negative frequencies. Our work thus provides a simple and coding-free solution for a struggling problem in robotics-based high-throughput screening experiments.

Mapping the Genetic Interaction Network of PARP inhibitor Response

Genetic interactions have long informed our understanding of the coordinated proteins and pathways that respond to DNA damage in mammalian cells, but systematic interrogation of the genetic network underlying that system has yet to be achieved. Towards this goal, we measured 147,153 pairwise interactions among genes implicated in PARP inhibitor (PARPi) response. Evaluating genetic interactions at this scale, with and without exposure to PARPi, revealed hierarchical organization of the pathways and complexes that maintain genome stability during normal growth and defined changes that occur upon accumulation of DNA lesions due to cytotoxic doses of PARPi. We uncovered unexpected relationships among DNA repair genes, including context-specific buffering interactions between the minimally characterized AUNIP and BRCA1-A complex genes. Our work thus establishes a foundation for mapping differential genetic interactions in mammalian cells and provides a comprehensive resource for future studies of DNA repair and PARP inhibitors.

Transcriptional Regulation Technology for Gene Perturbation in Fission Yeast

Isolation and introduction of genetic mutations is the primary approach to characterize gene functions in model yeasts. Although this approach has proven very powerful, it is not applicable to all genes in these organisms. For example, introducing defective mutations into essential genes causes lethality upon loss of function. To circumvent this difficulty, conditional and partial repression of target transcription is possible. While transcriptional regulation techniques, such as promoter replacement and 3' untranslated region (3'UTR) disruption, are available for yeast systems, CRISPR-Cas-based technologies have provided additional options. This review summarizes these gene perturbation technologies, including recent advances in methods based on CRISPR-Cas systems for Schizosaccharomyces pombe. We discuss how biological resources afforded by CRISPRi can promote fission yeast genetics.

Targeting Mutant Dicer Tumorigenesis in Pleuropulmonary Blastoma via Inhibition of RNA Polymerase I

DICER1 mutations predispose to increased risk for various cancers, particularly pleuropulmonary blastoma (PPB), the commonest lung malignancy of childhood. There is a paucity of directly actionable molecular targets as these tumors are driven by loss-of-function mutations of DICER1. Therapeutic development for PPB is further limited by a lack of biologically and physiologically-representative disease models. Given recent evidence of Dicer's role as a haploinsufficient tumor suppressor regulating RNA polymerase I (Pol I), Pol I inhibition could abrogate mutant Dicer-mediated accumulation of stalled polymerases to trigger apoptosis. Hence, we developed a novel sub-pleural orthotopic PPB patient-derived xenograft (PDX) model that retained both RNase IIIa and IIIb hotspot mutations and recapitulated the cardiorespiratory physiology of intra-thoracic disease, and with it evaluated the tolerability and efficacy of first-in-class Pol I inhibitor CX-5461. In PDX tumors, CX-5461 significantly reduced H3K9 di-methylation and increased nuclear p53 expression, within 24 hours' exposure. Following treatment at the maximum tolerated dosing regimen (12 doses, 30mg/kg), tumors were smaller and less hemorrhagic than controls, with significantly decreased cellular proliferation, and increased apoptosis. As demonstrated in a novel intra-thoracic tumor model of PPB, Pol I inhibition with CX-5461 could be a tolerable and clinically-feasible therapeutic strategy for mutant Dicer tumors, inducing anti-tumor effects by decreasing H3K9 methylation and enhancing p53-mediated apoptosis.

A multiplexed, three-dimensional pooling and next-generation sequencing strategy for creating barcoded mutant arrays: construction of a Schizosaccharomyces pombe transposon insertion library

Arrayed libraries of defined mutants have been used to elucidate gene function in the post-genomic era. Yeast haploid gene deletion libraries have pioneered this effort, but are costly to construct, do not reveal phenotypes that may occur with partial gene function and lack essential genes required for growth. We therefore devised an efficient method to construct a library of barcoded insertion mutants with a wider range of phenotypes that can be generalized to other organisms or collections of DNA samples. We developed a novel but simple three-dimensional pooling and multiplexed sequencing approach that leveraged sequence information to reduce the number of required sequencing reactions by orders of magnitude, and were able to identify the barcode sequences and DNA insertion sites of 4391 Schizosaccharomyces pombe insertion mutations with only 40 sequencing preparations. The insertion mutations are in the genes and untranslated regions of nonessential, essential and noncoding RNA genes, and produced a wider range of phenotypes compared to the cognate deletion mutants, including novel phenotypes. This mutant library represents both a proof of principle for an efficient method to produce novel mutant libraries and a valuable resource for the S. pombe research community.

Proteasome-dependent truncation of the negative heterochromatin regulator Epe1 mediates antifungal resistance

Epe1 histone demethylase restricts H3K9-methylation-dependent heterochromatin, preventing it from spreading over, and silencing, gene-containing regions in fission yeast. External stress induces an adaptive response allowing heterochromatin island formation that confers resistance on surviving wild-type lineages. Here we investigate the mechanism by which Epe1 is regulated in response to stress. Exposure to caffeine or antifungals results in Epe1 ubiquitylation and proteasome-dependent removal of the N-terminal 150 residues from Epe1, generating truncated Epe1 (tEpe1) which accumulates in the cytoplasm. Constitutive tEpe1 expression increases H3K9 methylation over several chromosomal regions, reducing expression of underlying genes and enhancing resistance. Reciprocally, constitutive non-cleavable Epe1 expression decreases resistance. tEpe1-mediated resistance requires a functional JmjC demethylase domain. Moreover, caffeine-induced Epe1-to-tEpe1 cleavage is dependent on an intact cell integrity MAP kinase stress signaling pathway, mutations in which alter resistance. Thus, environmental changes elicit a mechanism that curtails the function of this key epigenetic modifier, allowing heterochromatin to reprogram gene expression, thereby bestowing resistance to some cells within a population. H3K9me-heterochromatin components are conserved in human and crop-plant fungal pathogens for which a limited number of antifungals exist. Our findings reveal how transient heterochromatin-dependent antifungal resistant epimutations develop and thus inform on how they might be countered.

From systems to structure - using genetic data to model protein structures

Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyse both physical and functional consequences of sequence changes at systems-wide and mechanistic scales. To achieve a systems view, protein interaction networks map which proteins physically interact, while genetic interaction networks inform on the phenotypic consequences of perturbing these protein interactions. Until recently, understanding the molecular mechanisms that underlie these interactions often required biophysical methods to determine the structures of the proteins involved. The past decade has seen the emergence of new approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical-genetic interaction mapping that enable modelling of the structures of individual proteins or protein complexes. Here, we review the emerging use of large-scale genetic datasets and deep learning approaches to model protein structures and their interactions, and discuss the integration of structural data from different sources.

ProMoCell and ProModb: Web services for analyzing interaction-based functionally localized protein modules in a cell

The modular organization of a cell which can be determined by its interaction network allows us to understand a mesh of cooperation among the functional modules. Therefore, cellular-level identification of functional modules aids in understanding the functional and structural characteristics of the biological network of a cell and also assists in determining or comprehending the evolutionary signal. We develop ProMoCell that performs real-time Web scraping for generating clusters of the cellular level functional units of an organism. ProMoCell constructs the Protein Locality Graphs and clusters the cellular level functional units of an organism by utilizing experimentally verified data from various online sources. Also, we develop ProModb, a database service that houses precomputed whole-cell protein-protein interaction network-based functional modules of an organism using ProMoCell. Our Web service is entirely synchronized with the KEGG pathway database and allows users to generate spatially localized protein modules for any organism belonging to the KEGG genome using its real-time Web scraping characteristics. Hence, the server will host as many organisms as is maintained by the KEGG database. Our Web services provide the users a comprehensive and integrated tool for an efficient browsing and extraction of the spatial locality-based protein locality graph and the functional modules constructed by gathering experimental data from several interaction databases and pathway maps. We believe that our Web services will be beneficial in pharmacological research, where a novel research domain called modular pharmacology has initiated the study on the diagnosis, prevention, and treatment of deadly diseases using functional modules.

Chl1 coordinates with H3K9 methyltransferase Clr4 to reduce the accumulation of RNA-DNA hybrids and maintain genome stability

A genome-wide analysis in Schizosaccharomyces pombe indicated that double-deletion mutants of Chl1 and histone H3K9 methyltransferase complex factors are synthetically sick. Here, we show that loss of Chl1 increases the accumulation of RNA-DNA hybrids at pericentromeric dg and dh repeats in the absence of the H3K9 methyltransferase Clr4, which leads to genome instability, including more severe defects in chromosome segregation and increased chromatin accessibility. Localization of Chl1 at pericentromeric regions depends on a subunit of replication protein A (RPA), Ssb1. In wild-type (WT) cells, transcriptionally repressed heterochromatin prevents the formation of RNA-DNA hybrids. When Clr4 is deleted, dg and dh repeats are highly transcribed. Then Ssb1 associates with the displaced single-stranded DNA (ssDNA) and recruits Chl1 to resolve the RNA-DNA hybrids. Together, our data suggest that Chl1 coordinates with Clr4 to eliminate RNA-DNA hybrids, which contributes to the maintenance of genome integrity.

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Double mutant of Chl1 and Chl1 leads to the accumulation of RNA-DNA hybrids

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RNA-DNA hybrids at pericentromeric regions affect genome stability and cell viability

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Ssb1 recruits Chl1 to unwind RNA-DNA hybrids in the absence of Clr4

Genetic interaction of the histone chaperone hip1 + with double strand break repair genes in Schizosaccharomyces pombe

Schizosaccharomyces pombe hip1 + (human HIRA) is a histone chaperone and transcription factor involved in establishment of the centromeric chromatin and chromosome segregation, regulation of histone transcription, and cellular response to stress. We carried out a double mutant genetic screen of Δhip1 and mutations in double strand break repair pathway. We find that hip1 + functions after the MRN complex which initiates resection of blunt double strand break ends but before recruitment of the DNA damage repair machinery. Further, deletion of hip1 + partially suppresses sensitivity to DNA damaging agents of mutations in genes involved in Break Induced Replication (BIR), one mechanism of rescue of stalled or collapses replication forks ( rad51 + , cdc27 + ). Δhip1 also suppresses mutations in two checkpoint genes ( cds1 + , rad3 + ) on hydroxyurea a drug that stalls replication forks. Our results show that hip1 + forms complex interactions with the DNA double strand break repair genes and may be involved in facilitating communication between damage sensors and downstream factors.

Arf6 anchors Cdr2 nodes at the cell cortex to control cell size at division

Fission yeast cells prevent mitotic entry until a threshold cell surface area is reached. The protein kinase Cdr2 contributes to this size control system by forming multiprotein nodes that inhibit Wee1 at the medial cell cortex. Cdr2 node anchoring at the cell cortex is not fully understood. Through a genomic screen, we identified the conserved GTPase Arf6 as a component of Cdr2 signaling. Cells lacking Arf6 failed to divide at a threshold surface area and instead shifted to volume-based divisions at increased overall size. Arf6 stably localized to Cdr2 nodes in its GTP-bound but not GDP-bound state, and its guanine nucleotide exchange factor (GEF), Syt22, was required for both Arf6 node localization and proper size at division. In arf6Δ mutants, Cdr2 nodes detached from the membrane and exhibited increased dynamics. These defects were enhanced when arf6Δ was combined with other node mutants. Our work identifies a regulated anchor for Cdr2 nodes that is required for cells to sense surface area.

Discovery of putative tumor suppressors from CRISPR screens reveals rewired lipid metabolism in acute myeloid leukemia cells

CRISPR knockout fitness screens in cancer cell lines reveal many genes whose loss of function causes cell death or loss of fitness or, more rarely, the opposite phenotype of faster proliferation. Here we demonstrate a systematic approach to identify these proliferation suppressors, which are highly enriched for tumor suppressor genes, and define a network of 145 such genes in 22 modules. One module contains several elements of the glycerolipid biosynthesis pathway and operates exclusively in a subset of acute myeloid leukemia cell lines. The proliferation suppressor activity of genes involved in the synthesis of saturated fatty acids, coupled with a more severe loss of fitness phenotype for genes in the desaturation pathway, suggests that these cells operate at the limit of their carrying capacity for saturated fatty acids, which we confirm biochemically. Overexpression of this module is associated with a survival advantage in juvenile leukemias, suggesting a clinically relevant subtype.

CRISPR-based knockout screens in cancer cells have suggested the existence of proliferation suppressor genes (PSG). Here, the authors develop an approach to systematically identify them, and reveal a PSG module involved in fatty acid synthesis and tumour suppression in acute myeloid leukemia cell lines.

Genetic landscape of T cells identifies synthetic lethality for T-ALL

To capture the global gene network regulating the differentiation of immature T cells in an unbiased manner, large-scale forward genetic screens in zebrafish were conducted and combined with genetic interaction analysis. After ENU mutagenesis, genetic lesions associated with failure of T cell development were identified by meiotic recombination mapping, positional cloning, and whole genome sequencing. Recessive genetic variants in 33 genes were identified and confirmed as causative by additional experiments. The mutations affected T cell development but did not perturb the development of an unrelated cell type, growth hormone-expressing somatotrophs, providing an important measure of cell-type specificity of the genetic variants. The structure of the genetic network encompassing the identified components was established by a subsequent genetic interaction analysis, which identified many instances of positive (alleviating) and negative (synthetic) genetic interactions. Several examples of synthetic lethality were subsequently phenocopied using combinations of small molecule inhibitors. These drugs not only interfered with normal T cell development, but also elicited remission in a model of T cell acute lymphoblastic leukaemia. Our findings illustrate how genetic interaction data obtained in the context of entire organisms can be exploited for targeted interference with specific cell types and their malignant derivatives.

IMCC: A Novel Quantitative Approach Revealing Variation of Global Modular Map and Local Inter-Module Coordination Among Differential Drug's Targeted Cerebral Ischemic Networks

Stroke is a common disease characterized by multiple genetic dysfunctions. In this complex disease, detecting the strength of inter-module coordination (genetic community interaction) and subsequent modular rewiring is essential to characterize the reactive biosystematic variation (biosystematic perturbation) brought by multiple-target drugs, whose effects are achieved by hitting on a series of targets (target profile) jointly. Here, a quantitative approach for inter-module coordination and its transition, named as IMCC, was developed. Applying IMCC to mouse cerebral ischemia–related gene microarray, we investigated a holistic view of modular map and its rewiring from ischemic stroke to drugs (baicalin, BA; ursodeoxycholic acid, UA; and jasminoidin, JA) perturbation states and locally identified the cooperative pathological module pair and its dissection. Our result suggested the global modular map in cerebral ischemia exhibited a characteristic “core–periphery” architecture, and this architecture was rewired by the effective drugs heterogeneously: BA and UA converged modules into an intensively connected integrity, whereas JA diverged partial modules and widened the remaining inter-module paths. Locally, the PMP dissociation brought by drugs contributed to the reversion of the pathological condition: the focus of the cellular function shift from survival after nervous system injury into development and repair, including neurotrophin regulation, hormone releasing, and chemokine signaling activation. The core targets and mechanisms were validated by in vivo experiments. Overall, our result highlights the holistic inter-module coordination rearrangement rather than a target or a single module that brings phenotype alteration. This strategy may lead to systematically explore detailed variation of inter-module pharmacological action mode of multiple-target drugs, which is the principal problem of module pharmacology for network-based drug discovery.

Repression of a large number of genes requires interplay between homologous recombination and HIRA

During homologous recombination, Dbl2 protein is required for localisation of Fbh1, an F-box helicase that efficiently dismantles Rad51-DNA filaments. RNA-seq analysis of dbl2Δ transcriptome showed that the dbl2 deletion results in upregulation of more than 500 loci in Schizosaccharomyces pombe. Compared with the loci with no change in expression, the misregulated loci in dbl2Δ are closer to long terminal and long tandem repeats. Furthermore, the misregulated loci overlap with antisense transcripts, retrotransposons, meiotic genes and genes located in subtelomeric regions. A comparison of the expression profiles revealed that Dbl2 represses the same type of genes as the HIRA histone chaperone complex. Although dbl2 deletion does not alleviate centromeric or telomeric silencing, it suppresses the silencing defect at the outer centromere caused by deletion of hip1 and slm9 genes encoding subunits of the HIRA complex. Moreover, our analyses revealed that cells lacking dbl2 show a slight increase of nucleosomes at transcription start sites and increased levels of methylated histone H3 (H3K9me2) at centromeres, subtelomeres, rDNA regions and long terminal repeats. Finally, we show that other proteins involved in homologous recombination, such as Fbh1, Rad51, Mus81 and Rad54, participate in the same gene repression pathway.

LI Detector: a framework for sensitive colony-based screens regardless of the distribution of fitness effects

Microbial growth characteristics have long been used to investigate fundamental questions of biology. Colony-based high-throughput screens enable parallel fitness estimation of thousands of individual strains using colony growth as a proxy for fitness. However, fitness estimation is complicated by spatial biases affecting colony growth, including uneven nutrient distribution, agar surface irregularities, and batch effects. Analytical methods that have been developed to correct for these spatial biases rely on the following assumptions: (1) that fitness effects are normally distributed, and (2) that most genetic perturbations lead to minor changes in fitness. Although reasonable for many applications, these assumptions are not always warranted and can limit the ability to detect small fitness effects. Beneficial fitness effects, in particular, are notoriously difficult to detect under these assumptions. Here, we developed the linear interpolation-based detector (LI Detector) framework to enable sensitive colony-based screening without making prior assumptions about the underlying distribution of fitness effects. The LI Detector uses a grid of reference colonies to assign a relative fitness value to every colony on the plate. We show that the LI Detector is effective in correcting for spatial biases and equally sensitive toward increase and decrease in fitness. LI Detector offers a tunable system that allows the user to identify small fitness effects with unprecedented sensitivity and specificity. LI Detector can be utilized to develop and refine gene-gene and gene-environment interaction networks of colony-forming organisms, including yeast, by increasing the range of fitness effects that can be reliably detected.

Emergence and propagation of epistasis in metabolic networks

Epistasis is often used to probe functional relationships between genes, and it plays an important role in evolution. However, we lack theory to understand how functional relationships at the molecular level translate into epistasis at the level of whole-organism phenotypes, such as fitness. Here, I derive two rules for how epistasis between mutations with small effects propagates from lower- to higher-level phenotypes in a hierarchical metabolic network with first-order kinetics and how such epistasis depends on topology. Most importantly, weak epistasis at a lower level may be distorted as it propagates to higher levels. Computational analyses show that epistasis in more realistic models likely follows similar, albeit more complex, patterns. These results suggest that pairwise inter-gene epistasis should be common, and it should generically depend on the genetic background and environment. Furthermore, the epistasis coefficients measured for high-level phenotypes may not be sufficient to fully infer the underlying functional relationships.

Machine Learning to Identify Gene Interactions from High-Throughput Mutant Crosses

Advances in molecular genetics through high-throughput gene mutagenesis and genetic crossing have enabled gene interaction mapping across whole genomes. Detecting gene interactions in even small microbial genomes relies on measuring growth phenotypes in thousands of crossed strains followed by statistical analysis to compare single and double mutants. The preferred computational approach is to use a multiplicative model that factors phenotype scores of single gene mutants to identify gene interactions in double mutants. Here we present how machine learning models that consider the characteristics of the phenotypic data improve on the classical multiplicative model. Importantly, machine learning improves the selection of cutoff values to identify gene interactions from phenotypic scores.

Matrix (factorization) reloaded: flexible methods for imputing genetic interactions with cross-species and side information

MOTIVATION

Mapping genetic interactions (GIs) can reveal important insights into cellular function and has potential translational applications. There has been great progress in developing high-throughput experimental systems for measuring GIs (e.g. with double knockouts) as well as in defining computational methods for inferring (imputing) unknown interactions. However, existing computational methods for imputation have largely been developed for and applied in baker's yeast, even as experimental systems have begun to allow measurements in other contexts. Importantly, existing methods face a number of limitations in requiring specific side information and with respect to computational cost. Further, few have addressed how GIs can be imputed when data are scarce.

RESULTS

In this article, we address these limitations by presenting a new imputation framework, called Extensible Matrix Factorization (EMF). EMF is a framework of composable models that flexibly exploit cross-species information in the form of GI data across multiple species, and arbitrary side information in the form of kernels (e.g. from protein-protein interaction networks). We perform a rigorous set of experiments on these models in matched GI datasets from baker's and fission yeast. These include the first such experiments on genome-scale GI datasets in multiple species in the same study. We find that EMF models that exploit side and cross-species information improve imputation, especially in data-scarce settings. Further, we show that EMF outperforms the state-of-the-art deep learning method, even when using strictly less data, and incurs orders of magnitude less computational cost.

AVAILABILITY

Implementations of models and experiments are available at: https://github.com/lrgr/EMF.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genes affecting the extension of chronological lifespan in Schizosaccharomyces pombe (fission yeast)

So far, more than 70 genes involved in the chronological lifespan (CLS) of Schizosaccharomyces pombe (fission yeast) have been reported. In this mini‐review, we arrange and summarize these genes based on the reported genetic interactions between them and the physical interactions between their products. We describe the signal transduction pathways that affect CLS in S. pombe: target of rapamycin complex 1, cAMP‐dependent protein kinase, Sty1, and Pmk1 pathways have important functions in the regulation of CLS extension. Furthermore, the Php transcription complex, Ecl1 family proteins, cyclin Clg1, and the cyclin‐dependent kinase Pef1 are important for the regulation of CLS extension in S. pombe. Most of the known genes involved in CLS extension are related to these pathways and genes. In this review, we focus on the individual genes regulating CLS extension in S. pombe and discuss the interactions among them.

Key Genes and Genetic Interactions of Plant-Pathogen Functional Modules in Poplar Infected by Marssonina brunnea

Marssonina brunnea, the causative pathogen of Marssonina leaf spot of poplars (MLSP), devastates poplar plantations by forming black spots on leaves and defoliating trees. Although MLSP has been studied for over 30 years, the key genes that function during M. brunnea infection and their effects on plant growth are poorly understood. Here, we used multigene association studies to investigate the effects of key genes in the plant-pathogen interaction pathway, as revealed by transcriptome analysis, on photosynthesis and growth in a natural population of 435 Populus tomentosa individuals. By analyzing transcriptomic changes during three stages of infection, we detected 628 transcription factor genes among the 7,611 differentially expressed genes that might be associated with basal defense responses. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed that transcriptomic changes across different stages of infection lead to the reprogramming of metabolic processes possibly related to defense activation. We identified 29,399 common single-nucleotide polymorphisms (SNPs) within 221 full-length genes in plant-pathogen interaction pathways that were significantly associated with photosynthetic and growth traits. We also detected 4,460 significant epistatic pairs associated with stomatal conductance, tree diameter, and tree height. Epistasis analysis uncovered significant interactions between 2,561 SNP-SNP pairs from different functional modules in the plant-pathogen interaction pathway, revealing possible genetic interactions. This analysis revealed many key genes that function during M. brunnea infection and their potential roles in mediating photosynthesis and plant growth, shedding light on genetic interactions between functional modules in the plant-pathogen interaction pathway.

Integrative analysis of large-scale loss-of-function screens identifies robust cancer-associated genetic interactions

Genetic interactions, including synthetic lethal effects, can now be systematically identified in cancer cell lines using high-throughput genetic perturbation screens. Despite this advance, few genetic interactions have been reproduced across multiple studies and many appear highly context-specific. Here, by developing a new computational approach, we identified 220 robust driver-gene associated genetic interactions that can be reproduced across independent experiments and across non-overlapping cell line panels. Analysis of these interactions demonstrated that: (i) oncogene addiction effects are more robust than oncogene-related synthetic lethal effects; and (ii) robust genetic interactions are enriched among gene pairs whose protein products physically interact. Exploiting the latter observation, we used a protein-protein interaction network to identify robust synthetic lethal effects associated with passenger gene alterations and validated two new synthetic lethal effects. Our results suggest that protein-protein interaction networks can be used to prioritise therapeutic targets that will be more robust to tumour heterogeneity.

Aligning functional network constraint to evolutionary outcomes

BACKGROUND

Functional constraint through genomic architecture is suggested to be an important dimension of genome evolution, but quantitative evidence for this idea is rare. In this contribution, existing evidence and discussions on genomic architecture as constraint for convergent evolution, rapid adaptation, and genic adaptation are summarized into alternative, testable hypotheses. Network architecture statistics from protein-protein interaction networks are then used to calculate differences in evolutionary outcomes on the example of genomic evolution in yeast, and the results are used to evaluate statistical support for these longstanding hypotheses.

RESULTS

A discriminant function analysis lent statistical support to classifying the yeast interactome into hub, intermediate and peripheral nodes based on network neighborhood connectivity, betweenness centrality, and average shortest path length. Quantitative support for the existence of genomic architecture as a mechanistic basis for evolutionary constraint is then revealed through utilizing these statistical parameters of the protein-protein interaction network in combination with estimators of protein evolution.

CONCLUSIONS

As functional genetic networks are becoming increasingly available, it will now be possible to evaluate functional genetic network constraint against variables describing complex phenotypes and environments, for better understanding of commonly observed deterministic patterns of evolution in non-model organisms. The hypothesis framework and methodological approach outlined herein may help to quantify the extrinsic versus intrinsic dimensions of evolutionary constraint, and result in a better understanding of how fast, effectively, or deterministically organisms adapt.

Genetic interaction profiles of regulatory kinases differ between environmental conditions and cellular states

Cell growth and quiescence in eukaryotic cells is controlled by an evolutionarily conserved network of signaling pathways. Signal transduction networks operate to modulate a wide range of cellular processes and physiological properties when cells exit proliferative growth and initiate a quiescent state. How signaling networks function to respond to diverse signals that result in cell cycle exit and establishment of a quiescent state is poorly understood. Here, we studied the function of signaling pathways in quiescent cells using global genetic interaction mapping in the model eukaryotic cell, Saccharomyces cerevisiae (budding yeast). We performed pooled analysis of genotypes using molecular barcode sequencing (Bar-seq) to test the role of ~4,000 gene deletion mutants and ~12,000 pairwise interactions between all non-essential genes and the protein kinase genes TOR1, RIM15, and PHO85 in three different nutrient-restricted conditions in both proliferative and quiescent cells. We detect up to 10-fold more genetic interactions in quiescent cells than proliferative cells. We find that both individual gene effects and genetic interaction profiles vary depending on the specific pro-quiescence signal. The master regulator of quiescence, RIM15, shows distinct genetic interaction profiles in response to different starvation signals. However, vacuole-related functions show consistent genetic interactions with RIM15 in response to different starvation signals, suggesting that RIM15 integrates diverse signals to maintain protein homeostasis in quiescent cells. Our study expands genome-wide genetic interaction profiling to additional conditions, and phenotypes, and highlights the conditional dependence of epistasis.

A Quantitative Genetic Interaction Map of HIV Infection

We have developed a platform for quantitative genetic interaction mapping using viral infectivity as a functional readout and constructed a viral host-dependency epistasis map (vE-MAP) of 356 human genes linked to HIV function, comprising >63,000 pairwise genetic perturbations. The vE-MAP provides an expansive view of the genetic dependencies underlying HIV infection and can be used to identify drug targets and study viral mutations. We found that the RNA deadenylase complex, CNOT, is a central player in the vE-MAP and show that knockout of CNOT1, 10, and 11 suppressed HIV infection in primary T cells by upregulating innate immunity pathways. This phenotype was rescued by deletion of IRF7, a transcription factor regulating interferon-stimulated genes, revealing a previously unrecognized host signaling pathway involved in HIV infection. The vE-MAP represents a generic platform that can be used to study the global effects of how different pathogens hijack and rewire the host during infection.

Genetic interaction networks mediate individual statin drug response in Saccharomyces cerevisiae

Eukaryotic genetic interaction networks (GINs) are extensively described in the Saccharomyces cerevisiae S288C model using deletion libraries, yet being limited to this one genetic background, not informative to individual drug response. Here we created deletion libraries in three additional genetic backgrounds. Statin response was probed with five queries against four genetic backgrounds. The 20 resultant GINs representing drug-gene and gene-gene interactions were not conserved by functional enrichment, hierarchical clustering, and topology-based community partitioning. An unfolded protein response (UPR) community exhibited genetic background variation including different betweenness genes that were network bottlenecks, and we experimentally validated this UPR community via measurements of the UPR that were differentially activated and regulated in statin-resistant strains relative to the statin-sensitive S288C background. These network analyses by topology and function provide insight into the complexity of drug response influenced by genetic background.

Recent insights into the genotype-phenotype relationship from massively parallel genetic assays

With the molecular revolution in Biology, a mechanistic understanding of the genotype-phenotype relationship became possible. Recently, advances in DNA synthesis and sequencing have enabled the development of deep mutational scanning assays, capable of scoring comprehensive libraries of genotypes for fitness and a variety of phenotypes in massively parallel fashion. The resulting empirical genotype-fitness maps pave the way to predictive models, potentially accelerating our ability to anticipate the behaviour of pathogen and cancerous cell populations from sequencing data. Besides from cellular fitness, phenotypes of direct application in industry (e.g. enzyme activity) and medicine (e.g. antibody binding) can be quantified and even selected directly by these assays. This review discusses the technological basis of and recent developments in massively parallel genetics, along with the trends it is uncovering in the genotype-phenotype relationship (distribution of mutation effects, epistasis), their possible mechanistic bases and future directions for advancing towards the goal of predictive genetics.

Changes in gene expression predictably shift and switch genetic interactions

Non-additive interactions between mutations occur extensively and also change across conditions, making genetic prediction a difficult challenge. To better understand the plasticity of genetic interactions (epistasis), we combine mutations in a single protein performing a single function (a transcriptional repressor inhibiting a target gene). Even in this minimal system, genetic interactions switch from positive (suppressive) to negative (enhancing) as the expression of the gene changes. These seemingly complicated changes can be predicted using a mathematical model that propagates the effects of mutations on protein folding to the cellular phenotype. More generally, changes in gene expression should be expected to alter the effects of mutations and how they interact whenever the relationship between expression and a phenotype is nonlinear, which is the case for most genes. These results have important implications for understanding genotype-phenotype maps and illustrate how changes in genetic interactions can often—but not always—be predicted by hierarchical mechanistic models.

Non-additive genetic interactions are plastic and can complicate genetic prediction. Here, using deep mutagenesis of the lambda repressor, Li et al. reveal that changes in gene expression can alter the strength and direction of genetic interactions between mutations in many genes and develop mathematical models for predicting them.

The TOR pathway modulates cytoophidium formation in Schizosaccharomyces pombe

CTP synthase (CTPS) has been demonstrated to form evolutionarily-conserved filamentous structures termed cytoophidia whose exact cellular functions remain unclear, but they may play a role in intracellular compartmentalization. We have previously shown that the mammalian target of rapamycin complex 1 (mTORC1)-S6K1 pathway mediates cytoophidium assembly in mammalian cells. Here, using the fission yeast Schizosaccharomyces pombe as a model of a unicellular eukaryote, we demonstrate that the target of rapamycin (TOR)-signaling pathway regulates cytoophidium formation (from the S. pombe CTPS ortholog Cts1) also in S. pombe Conducting a systematic analysis of all viable single TOR subunit-knockout mutants and of several major downstream effector proteins, we found that Cts1 cytoophidia are significantly shortened and often dissociate when TOR is defective. We also found that the activities of the downstream effector kinases of the TORC1 pathway, Sck1, Sck2, and Psk1 S6, as well as of the S6K/AGC kinase Gad8, the major downstream effector kinase of the TORC2 pathway, are necessary for proper cytoophidium filament formation. Interestingly, we observed that the Crf1 transcriptional corepressor for ribosomal genes is a strong effector of Cts1 filamentation. Our findings connect TOR signaling, a major pathway required for cell growth, with the compartmentalization of the essential nucleotide synthesis enzyme CTPS, and we uncover differences in the regulation of its filamentation among higher multicellular and unicellular eukaryotic systems.

Network dynamics of the yeast methyltransferome

Sulfur assimilation and the biosynthesis of methionine, cysteine and S-adenosylmethionine (SAM) are critical to life. As a cofactor, SAM is required for the activity of most methyltransferases (MTases) and as such has broad impact on diverse cellular processes. Assigning function to MTases remains a challenge however, as many MTases are partially redundant, they often have multiple cellular roles and these activities can be condition-dependent. To address these challenges, we performed a systematic synthetic genetic analysis of all pairwise MTase double mutations in normal and stress conditions (16°C, 37°C, and LiCl) resulting in an unbiased comprehensive overview of the complexity and plasticity of the methyltransferome. Based on this network, we performed biochemical analysis of members of the histone H3K4 COMPASS complex and the phospholipid methyltransferase OPI3 to reveal a new role for a phospholipid methyltransferase in mediating histone methylation (H3K4) which underscores a potential link between lipid homeostasis and histone methylation. Our findings provide a valuable resource to study methyltransferase function, the dynamics of the methyltransferome, genetic crosstalk between biological processes and the dynamics of the methyltransferome in response to cellular stress.

Functional protein representations from biological networks enable diverse cross-species inference

Transferring knowledge between species is key for many biological applications, but is complicated by divergent and convergent evolution. Many current approaches for this problem leverage sequence and interaction network data to transfer knowledge across species, exemplified by network alignment methods. While these techniques do well, they are limited in scope, creating metrics to address one specific problem or task. We take a different approach by creating an environment where multiple knowledge transfer tasks can be performed using the same protein representations. Specifically, our kernel-based method, MUNK, integrates sequence and network structure to create functional protein representations, embedding proteins from different species in the same vector space. First we show proteins in different species that are close in MUNK-space are functionally similar. Next, we use these representations to share knowledge of synthetic lethal interactions between species. Importantly, we find that the results using MUNK-representations are at least as accurate as existing algorithms for these tasks. Finally, we generalize the notion of a phenolog ('orthologous phenotype') to use functionally similar proteins (i.e. those with similar representations). We demonstrate the utility of this broadened notion by using it to identify known phenologs and novel non-obvious ones supported by current research.

The Causes and Consequences of Genetic Interactions (Epistasis)

The same mutation can have different effects in different individuals. One important reason for this is that the outcome of a mutation can depend on the genetic context in which it occurs. This dependency is known as epistasis. In recent years, there has been a concerted effort to quantify the extent of pairwise and higher-order genetic interactions between mutations through deep mutagenesis of proteins and RNAs. This research has revealed two major components of epistasis: nonspecific genetic interactions caused by nonlinearities in genotype-to-phenotype maps, and specific interactions between particular mutations. Here, we provide an overview of our current understanding of the mechanisms causing epistasis at the molecular level, the consequences of genetic interactions for evolution and genetic prediction, and the applications of epistasis for understanding biology and determining macromolecular structures.

iSeq 2.0: A Modular and Interchangeable Toolkit for Interaction Screening in Yeast

We developed a flexible toolkit for combinatorial screening in Saccharomyces cerevisiae, which generates large libraries of cells, each uniquely barcoded to mark a combination of DNA elements. This interaction sequencing platform (iSeq 2.0) includes genomic landing pads that assemble combinations through sequential integration of plasmids or yeast mating, 15 barcoded plasmid libraries containing split selectable markers (URA3AI, KanMXAI, HphMXAI, and NatMXAI), and an array of ∼24,000 "double-barcoder" strains that can make existing yeast libraries iSeq compatible. Various DNA elements are compatible with iSeq: DNA introduced on integrating plasmids, engineered genomic modifications, or entire genetic backgrounds. DNA element libraries are modular and interchangeable, and any two libraries can be combined, making iSeq capable of performing many new combinatorial screens by short-read sequencing.

Context-dependent genetic interactions in cancer

Genetic co-dependencies have been found in many contexts, from processes during the development of organisms to many diseases in man, including cancer. Genetic interactions - and in particular synthetic lethal phenotypes - have provided fundamental insights into the genetic architecture of cells and identified potential new opportunities for therapeutic interventions. However, recent studies also demonstrated that genetic interactions are highly context dependent and synthetic lethal interactions in one tumor context might not be translatable to others. Therefore, to better define and understand contexts will be a key challenge for future studies to fully exploit genetic interaction networks for target identification and cancer therapy. In this review, we summarize recent developments in mapping context-specific genetic interaction networks with a particular focus on conceptual and experimental advances in the past years. We then discuss genetic and environmental contexts that influence genetic interaction networks. Finally, we outline challenges of putting genetic interaction networks into context and give an outlook on future directions.

Using BEAN-counter to quantify genetic interactions from multiplexed barcode sequencing experiments

The construction of genome-wide mutant collections has enabled high-throughput, high-dimensional quantitative characterization of gene and chemical function, particularly via genetic and chemical-genetic interaction experiments. As the throughput of such experiments increases with improvements in sequencing technology and sample multiplexing, appropriate tools must be developed to handle the large volume of data produced. Here, we describe how to apply our approach to high-throughput, fitness-based profiling of pooled mutant yeast collections using the BEAN-counter software pipeline (Barcoded Experiment Analysis for Next-generation sequencing) for analysis. The software has also successfully processed data from Schizosaccharomyces pombe, Escherichia coli, and Zymomonas mobilis mutant collections. We provide general recommendations for the design of large-scale, multiplexed barcode sequencing experiments. The procedure outlined here was used to score interactions for ~4 million chemical-by-mutant combinations in our recently published chemical-genetic interaction screen of nearly 14,000 chemical compounds across seven diverse compound collections. Here we selected a representative subset of these data on which to demonstrate our analysis pipeline. BEAN-counter is open source, written in Python, and freely available for academic use. Users should be proficient at the command line; advanced users who wish to analyze larger datasets with hundreds or more conditions should also be familiar with concepts in analysis of high-throughput biological data. BEAN-counter encapsulates the knowledge we have accumulated from, and successfully applied to, our multiplexed, pooled barcode sequencing experiments. This protocol will be useful to those interested in generating their own high-dimensional, quantitative characterizations of gene or chemical function in a high-throughput manner.

Global Genetic Networks and the Genotype-to-Phenotype Relationship

Genetic interactions identify combinations of genetic variants that impinge on phenotype. With whole-genome sequence information available for thousands of individuals within a species, a major outstanding issue concerns the interpretation of allelic combinations of genes underlying inherited traits. In this Review, we discuss how large-scale analyses in model systems have illuminated the general principles and phenotypic impact of genetic interactions. We focus on studies in budding yeast, including the mapping of a global genetic network. We emphasize how information gained from work in yeast translates to other systems, and how a global genetic network not only annotates gene function but also provides new insights into the genotype-to-phenotype relationship.

Evolution of resilience in protein interactomes across the tree of life

Phenotype robustness to environmental fluctuations is a common biological phenomenon. Although most phenotypes involve multiple proteins that interact with each other, the basic principles of how such interactome networks respond to environmental unpredictability and change during evolution are largely unknown. Here we study interactomes of 1,840 species across the tree of life involving a total of 8,762,166 protein-protein interactions. Our study focuses on the resilience of interactomes to network failures and finds that interactomes become more resilient during evolution, meaning that interactomes become more robust to network failures over time. In bacteria, we find that a more resilient interactome is in turn associated with the greater ability of the organism to survive in a more complex, variable, and competitive environment. We find that at the protein family level proteins exhibit a coordinated rewiring of interactions over time and that a resilient interactome arises through gradual change of the network topology. Our findings have implications for understanding molecular network structure in the context of both evolution and environment.

Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions

Chemical-genetic interactions–observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes–contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes.

Understanding how chemical compounds affect biological systems is of paramount importance as pharmaceutical companies strive to develop life-saving medicines, governments seek to regulate the safety of consumer products and agrichemicals, and basic scientists continue to study the fundamental inner workings of biological organisms. One powerful approach to characterize the effects of chemical compounds in living cells is chemical-genetic interaction screening. Using this approach, a collection of cells–each with a different defined genetic perturbation–is tested for sensitivity or resistance to the presence of a compound, resulting in a quantitative profile describing the functional effects of that compound on the cells. The work presented here describes our efforts to integrate compounds’ chemical-genetic interaction profiles with reference genetic interaction profiles containing information on gene function to predict the cellular processes perturbed by the compounds. We focused on specifically developing a method that could scale to perform these functional predictions for large collections of thousands of screened compounds and robustly control the false discovery rate. With chemical-genetic and genetic interaction screens now underway in multiple species including human cells, the method described here can be generally applied to enable the characterization of compounds’ effects across the tree of life.

Synthetic Lethality and Cancer - Penetrance as the Major Barrier

Synthetic lethality has long been proposed as an approach for targeting genetic defects in tumours. Despite a decade of screening efforts, relatively few robust synthetic lethal targets have been identified. Improved genetic perturbation techniques, including CRISPR/Cas9 gene editing, have resulted in renewed enthusiasm for searching for synthetic lethal effects in cancer. An implicit assumption behind this enthusiasm is that the lack of reproducibly identified targets can be attributed to limitations of RNAi technologies. We argue here that a bigger hurdle is that most synthetic lethal interactions (SLIs) are not highly penetrant, in other words they are not robust to the extensive molecular heterogeneity seen in tumours. We outline strategies for identifying and prioritising SLIs that are most likely to be highly penetrant.

Synthetic Lethal Networks for Precision Oncology: Promises and Pitfalls

Synthetic lethal interactions, in which the simultaneous loss of function of two genes produces a lethal phenotype, are being explored as a means to therapeutically exploit cancer-specific vulnerabilities and expand the scope of precision oncology. Currently, three Food and Drug Administration-approved drugs work by targeting the synthetic lethal interaction between BRCA1/2 and PARP. This review examines additional efforts to discover networks of synthetic lethal interactions and discusses both challenges and opportunities regarding the translation of new synthetic lethal interactions into the clinic.

Sfh1, an essential component of the RSC chromatin remodeling complex, maintains genome integrity in fission yeast

Abp1 is a fission yeast CENP-B homologue that contributes to centromere function, silencing at pericentromeric heterochromatin and silencing of retrotransposons. We identified the sfh1 gene, encoding a core subunit of the fission yeast chromatin remodeling complex RSC as an Abp1-interacting protein. Because sfh1 is essential for growth, we isolated temperature-sensitive sfh1 mutants. These mutants showed defects in centromere functions, reflected by sensitivity to an inhibitor of spindle formation and minichromosome instability. Sfh1 localized at both kinetochore and pericentromeric heterochromatin regions. Although sfh1 mutations had minor effect on silencing at these regions, they decreased the levels of cohesin on centromeric heterochromatin. Sfh1 also localized at a retrotransposon, Tf2, in a partly Abp1-dependent manner, and assisted in silencing of Tf2 by Abp1 probably in the same pathway as a histone chaperon, HIRA, which is also known to involve in Tf2 repression. Furthermore, sfh1 mutants were sensitive to several DNA-damaging treatments (HU, MMS, UV and X-ray). Increase in spontaneous foci of Rad22, a recombination Mediator protein Rad52 homologue, in sfh1 mutant suggests that RSC functions in homologous recombination repair of double-stranded break downstream of the Rad22 recruitment. These results indicate that RSC plays multiple roles in the maintenance of genome integrity.