Systematic pathway analysis using high-resolution fitness profiling of combinatorial gene deletions

A gain-of-function mutation in zinc cluster transcription factor Rob1 drives Candida albicans adaptive growth in the cystic fibrosis lung environment

Candida albicans chronically colonizes the respiratory tract of patients with Cystic Fibrosis (CF). It competes with CF-associated pathogens (e.g. Pseudomonas aeruginosa) and contributes to disease severity. We hypothesize that C. albicans undergoes specific adaptation mechanisms that explain its persistence in the CF lung environment. To identify the underlying genetic and phenotypic determinants, we serially recovered 146 C. albicans clinical isolates over a period of 30 months from the sputum of 25 antifungal-naive CF patients. Multilocus sequence typing analyses revealed that most patients were individually colonized with genetically close strains, facilitating comparative analyses between serial isolates. We strikingly observed differential ability to filament and form monospecies and dual-species biofilms with P. aeruginosa among 18 serial isolates sharing the same diploid sequence type, recovered within one year from a pediatric patient. Whole genome sequencing revealed that their genomes were highly heterozygous and similar to each other, displaying a highly clonal subpopulation structure. Data mining identified 34 non-synonymous heterozygous SNPs in 19 open reading frames differentiating the hyperfilamentous and strong biofilm-former strains from the remaining isolates. Among these, we detected a glycine-to-glutamate substitution at position 299 (G299E) in the deduced amino acid sequence of the zinc cluster transcription factor ROB1 (ROB1G299E), encoding a major regulator of filamentous growth and biofilm formation. Introduction of the G299E heterozygous mutation in a co-isolated weak biofilm-former CF strain was sufficient to confer hyperfilamentous growth, increased expression of hyphal-specific genes, increased monospecies biofilm formation and increased survival in dual-species biofilms formed with P. aeruginosa, indicating that ROB1G299E is a gain-of-function mutation. Disruption of ROB1 in a hyperfilamentous isolate carrying the ROB1G299E allele abolished hyperfilamentation and biofilm formation. Our study links a single heterozygous mutation to the ability of C. albicans to better survive during the interaction with other CF-associated microbes and illuminates how adaptive traits emerge in microbial pathogens to persistently colonize and/or infect the CF-patient airways.

Synergistic enhancement of the mouse Pramex1 and Pramel1 in repressing retinoic acid (RA) signaling during gametogenesis

BACKGROUND

PRAME constitutes one of the largest multi-copy gene families in Eutherians, encoding cancer-testis antigens (CTAs) with leucine-rich repeats (LRR) domains, highly expressed in cancer cells and gametogenic germ cells. This study aims to elucidate genetic interactions between two members, Pramex1 and Pramel1, in the mouse Prame family during gametogenesis using a gene knockout approach.

RESULT

Single-gene knockout (sKO) of either Pramex1 or Pramel1 resulted in approximately 7% of abnormal seminiferous tubules, characterized by a Sertoli-cell only (SCO) phenotype, impacting sperm count and fecundity significantly. Remarkably, sKO female mice displayed normal reproductive functions. In contrast, Pramex1/Pramel1 double knockout (dKO) mice exhibited reduced fecundity in both sexes. In dKO females, ovarian primary follicle count decreased by 50% compared to sKO and WT mice, correlating with a 50% fecundity decrease. This suggested compensatory roles during oogenesis in Pramex1 or Pramel1 sKO females. Conversely, dKO males showed an 18% frequency of SCO tubules, increased apoptotic germ cells, and decreased undifferentiated spermatogonia compared to sKO and WT testes. Western blot analysis with PRAMEX1- or PRAMEL1-specific antibodies on sKO testes revealed compensatory upregulation of each protein (30-50%) in response to the other gene's deletion. Double KO males exhibited more severe defects in sperm count and litter size, surpassing Pramex1 and Pramel1 sKO accumulative effects, indicating a synergistic enhancement interaction during spermatogenesis. Additional experiments administering trans-retinoic acid (RA) and its inhibitor (WIN18,446) in sKO, dKO, and WT mice suggested that PRAMEX1 and PRAMEL1 synergistically repress the RA signaling pathway during spermatogenesis.

CONCLUSION

Data from sKO and dKO mice unveil a synergistic interaction via the RA signaling pathway between Pramex1 and Pramel1 genes during gametogenesis. This discovery sets the stage for investigating interactions among other members within the Prame family, advancing our understanding of multi-copy gene families involved in germ cell formation and function.

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.

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.

Leveraging the genetic correlation between traits improves the detection of epistasis in genome-wide association studies

Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this study, we present the "multivariate MArginal ePIstasis Test" (mvMAPIT)-a multioutcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact-thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search-based methods. Our proposed mvMAPIT builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate mvMAPIT as a multivariate linear mixed model and develop a multitrait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized genome-wide association studies. With simulations, we illustrate the benefits of mvMAPIT over univariate (or single-trait) epistatic mapping strategies. We also apply mvMAPIT framework to protein sequence data from two broadly neutralizing anti-influenza antibodies and approximately 2,000 heterogeneous stock of mice from the Wellcome Trust Centre for Human Genetics. The mvMAPIT R package can be downloaded at https://github.com/lcrawlab/mvMAPIT.

Escherichia coli Nissle 1917 Antagonizes Candida albicans Growth and Protects Intestinal Cells from C. albicans-Mediated Damage

Candida albicans is a pathobiont of the gastrointestinal tract. It can contribute to the diversity of the gut microbiome without causing harmful effects. When the immune system is compromised, C. albicans can damage intestinal cells and cause invasive disease. We hypothesize that a therapeutic approach against C. albicans infections can rely on the antimicrobial properties of probiotic bacteria. We investigated the impact of the probiotic strain Escherichia coli Nissle 1917 (EcN) on C. albicans growth and its ability to cause damage to intestinal cells. In co-culture kinetic assays, C. albicans abundance gradually decreased over time compared with C. albicans abundance in the absence of EcN. Quantification of C. albicans survival suggests that EcN exerts a fungicidal activity. Cell-free supernatants (CFS) collected from C. albicans-EcN co-culture mildly altered C. albicans growth, suggesting the involvement of an EcN-released compound. Using a model of co-culture in the presence of human intestinal epithelial cells, we further show that EcN prevents C. albicans from damaging enterocytes both distantly and through direct contact. Consistently, both C. albicans's filamentous growth and microcolony formation were altered by EcN. Taken together, our study proposes that probiotic-strain EcN can be exploited for future therapeutic approaches against C. albicans infections.

A global genetic interaction network by single-cell imaging and machine learning

Cellular and organismal phenotypes are controlled by complex gene regulatory networks. However, reference maps of gene function are still scarce across different organisms. Here, we generated synthetic genetic interaction and cell morphology profiles of more than 6,800 genes in cultured Drosophila cells. The resulting map of genetic interactions was used for machine learning-based gene function discovery, assigning functions to genes in 47 modules. Furthermore, we devised Cytoclass as a method to dissect genetic interactions for discrete cell states at the single-cell resolution. This approach identified an interaction of Cdk2 and the Cop9 signalosome complex, triggering senescence-associated secretory phenotypes and immunogenic conversion in hemocytic cells. Together, our data constitute a genome-scale resource of functional gene profiles to uncover the mechanisms underlying genetic interactions and their plasticity at the single-cell level.

An orthologous gene coevolution network provides insight into eukaryotic cellular and genomic structure and function

The evolutionary rates of functionally related genes often covary. We present a gene coevolution network inferred from examining nearly 3 million orthologous gene pairs from 332 budding yeast species spanning ~400 million years of evolution. Network modules provide insight into cellular and genomic structure and function. Examination of the phenotypic impact of network perturbation using deletion mutant data from the baker's yeast Saccharomyces cerevisiae, which were obtained from previously published studies, suggests that fitness in diverse environments is affected by orthologous gene neighborhood and connectivity. Mapping the network onto the chromosomes of S. cerevisiae and Candida albicans revealed that coevolving orthologous genes are not physically clustered in either species; rather, they are often located on different chromosomes or far apart on the same chromosome. The coevolution network captures the hierarchy of cellular structure and function, provides a roadmap for genotype-to-phenotype discovery, and portrays the genome as a linked ensemble of genes.

Quantitative-enhancer-FACS-seq (QeFS) reveals epistatic interactions among motifs within transcriptional enhancers in developing Drosophila tissue

Understanding the contributions of transcription factor DNA binding sites to transcriptional enhancers is a significant challenge. We developed Quantitative enhancer-FACS-Seq for highly parallel quantification of enhancer activities from a genomically integrated reporter in Drosophila melanogaster embryos. We investigate the contributions of the DNA binding motifs of four poorly characterized TFs to the activities of twelve embryonic mesodermal enhancers. We measure quantitative changes in enhancer activity and discover a range of epistatic interactions among the motifs, both synergistic and alleviating. We find that understanding the regulatory consequences of TF binding motifs requires that they be investigated in combination across enhancer contexts.

Investigation of RNA metabolism through large-scale genetic interaction profiling in yeast

Gene deletion and gene expression alteration can lead to growth defects that are amplified or reduced when a second mutation is present in the same cells. We performed 154 genetic interaction mapping (GIM) screens with query mutants related with RNA metabolism and estimated the growth rates of about 700 000 double mutant Saccharomyces cerevisiae strains. The tested targets included the gene deletion collection and 900 strains in which essential genes were affected by mRNA destabilization (DAmP). To analyze the results, we developed RECAP, a strategy that validates genetic interaction profiles by comparison with gene co-citation frequency, and identified links between 1471 genes and 117 biological processes. In addition to these large-scale results, we validated both enhancement and suppression of slow growth measured for specific RNA-related pathways. Thus, negative genetic interactions identified a role for the OCA inositol polyphosphate hydrolase complex in mRNA translation initiation. By analysis of suppressors, we found that Puf4, a Pumilio family RNA binding protein, inhibits ribosomal protein Rpl9 function, by acting on a conserved UGUAcauUA motif located downstream the stop codon of the RPL9B mRNA. Altogether, the results and their analysis should represent a useful resource for discovery of gene function in yeast.

Actin-Related Protein 6 (Arp6) Influences Double-Strand Break Repair in Yeast

DNA double-strand breaks (DSBs) are the most deleterious form of DNA damage and are repaired through non-homologous end-joining (NHEJ) or homologous recombination (HR). Repair initiation, regulation and communication with signaling pathways require several histone-modifying and chromatin-remodeling complexes. In budding yeast, this involves three primary complexes: INO80-C, which is primarily associated with HR, SWR1-C, which promotes NHEJ, and RSC-C, which is involved in both pathways as well as the general DNA damage response. Here we identify ARP6 as a factor involved in DSB repair through an RSC-C-related pathway. The loss of ARP6 significantly reduces the NHEJ repair efficiency of linearized plasmids with cohesive ends, impairs the repair of chromosomal breaks, and sensitizes cells to DNA-damaging agents. Genetic interaction analysis indicates that ARP6, MRE11 and RSC-C function within the same pathway, and the overexpression of ARP6 rescues rsc2∆ and mre11∆ sensitivity to DNA-damaging agents. Double mutants of ARP6, and members of the INO80 and SWR1 complexes, cause a significant reduction in repair efficiency, suggesting that ARP6 functions independently of SWR1-C and INO80-C. These findings support a novel role for ARP6 in DSB repair that is independent of the SWR1 chromatin remodeling complex, through an apparent RSC-C and MRE11-associated DNA repair pathway.

Systematic Prediction of Antifungal Drug Synergy by Chemogenomic Screening in Saccharomyces cerevisiae

Since the earliest days of using natural remedies, combining therapies for disease treatment has been standard practice. Combination treatments exhibit synergistic effects, broadly defined as a greater-than-additive effect of two or more therapeutic agents. Clinicians often use their experience and expertise to tailor such combinations to maximize the therapeutic effect. Although understanding and predicting biophysical underpinnings of synergy have benefitted from high-throughput screening and computational studies, one challenge is how to best design and analyze the results of synergy studies, especially because the number of possible combinations to test quickly becomes unmanageable. Nevertheless, the benefits of such studies are clear-by combining multiple drugs in the treatment of infectious disease and cancer, for instance, one can lessen host toxicity and simultaneously reduce the likelihood of resistance to treatment. This study introduces a new approach to characterize drug synergy, in which we extend the widely validated chemogenomic HIP-HOP assay to drug combinations; this assay involves parallel screening of comprehensive collections of barcoded deletion mutants. We identify a class of "combination-specific sensitive strains" that introduces mechanisms for the synergies we observe and further suggest focused follow-up studies.

Encapsulation of ribozymes inside model protocells leads to faster evolutionary adaptation

Functional biomolecules, such as RNA, encapsulated inside a protocellular membrane are believed to have comprised a very early, critical stage in the evolution of life, since membrane vesicles allow selective permeability and create a unit of selection enabling cooperative phenotypes. The biophysical environment inside a protocell would differ fundamentally from bulk solution due to the microscopic confinement. However, the effect of the encapsulated environment on ribozyme evolution has not been previously studied experimentally. Here, we examine the effect of encapsulation inside model protocells on the self-aminoacylation activity of tens of thousands of RNA sequences using a high-throughput sequencing assay. We find that encapsulation of these ribozymes generally increases their activity, giving encapsulated sequences an advantage over nonencapsulated sequences in an amphiphile-rich environment. In addition, highly active ribozymes benefit disproportionately more from encapsulation. The asymmetry in fitness gain broadens the distribution of fitness in the system. Consistent with Fisher's fundamental theorem of natural selection, encapsulation therefore leads to faster adaptation when the RNAs are encapsulated inside a protocell during in vitro selection. Thus, protocells would not only provide a compartmentalization function but also promote activity and evolutionary adaptation during the origin of life.

Environmental robustness of the global yeast genetic interaction network

Phenotypes associated with genetic variants can be altered by interactions with other genetic variants (GxG), with the environment (GxE), or both (GxGxE). Yeast genetic interactions have been mapped on a global scale, but the environmental influence on the plasticity of genetic networks has not been examined systematically. To assess environmental rewiring of genetic networks, we examined 14 diverse conditions and scored 30,000 functionally representative yeast gene pairs for dynamic, differential interactions. Different conditions revealed novel differential interactions, which often uncovered functional connections between distantly related gene pairs. However, the majority of observed genetic interactions remained unchanged in different conditions, suggesting that the global yeast genetic interaction network is robust to environmental perturbation and captures the fundamental functional architecture of a eukaryotic cell.

Ethylzingerone, a Novel Compound with Antifungal Activity

Preservatives increase the shelf life of cosmetic products by preventing growth of contaminating microbes, including bacteria and fungi. In recent years, the Scientific Committee on Consumer Safety (SCCS) has recommended the ban or restricted use of a number of preservatives due to safety concerns. Here, we characterize the antifungal activity of ethylzingerone (hydroxyethoxyphenyl butanone [HEPB]), an SCCS-approved new preservative for use in rinse-off, oral care, and leave-on cosmetic products. We show that HEPB significantly inhibits growth of Candida albicans, Candida glabrata, and Saccharomyces cerevisiae, acting fungicidally against C. albicans Using transcript profiling experiments, we found that the C. albicans transcriptome responded to HEPB exposure by increasing the expression of genes involved in amino acid biosynthesis while activating pathways involved in chemical detoxification/oxidative stress response. Comparative analyses revealed that C. albicans phenotypic and transcriptomic responses to HEPB treatment were distinguishable from those of two widely used preservatives, triclosan and methylparaben. Chemogenomic analyses, using a barcoded S. cerevisiae nonessential mutant library, revealed that HEPB antifungal activity strongly interfered with the biosynthesis of aromatic amino acids. The trp1Δ mutants in S. cerevisiae and C. albicans were particularly sensitive to HEPB treatment, a phenotype rescued by exogenous addition of tryptophan to the growth medium, providing a direct link between HEPB mode of action and tryptophan availability. Collectively, our study sheds light on the antifungal activity of HEPB, a new molecule with safe properties for use as a preservative in the cosmetic industry, and exemplifies the powerful use of functional genomics to illuminate the mode of action of antimicrobial agents.

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.

A self-consistent probabilistic formulation for inference of interactions

Large molecular interaction networks are nowadays assembled in biomedical researches along with important technological advances. Diverse interaction measures, for which input solely consisting of the incidence of causal-factors, with the corresponding outcome of an inquired effect, are formulated without an obvious mathematical unity. Consequently, conceptual and practical ambivalences arise. We identify here a probabilistic requirement consistent with that input, and find, by the rules of probability theory, that it leads to a model multiplicative in the complement of the effect. Important practical properties are revealed along these theoretical derivations, that has not been noticed before.

Chromatin regulatory genes differentially interact in networks to facilitate distinct GAL1 activity and noise profiles

Controlling chromatin state constitutes a major regulatory step in gene expression regulation across eukaryotes. While global cellular features or processes are naturally impacted by chromatin state alterations, little is known about how chromatin regulatory genes interact in networks to dictate downstream phenotypes. Using the activity of the canonical galactose network in yeast as a model, here, we measured the impact of the disruption of key chromatin regulatory genes on downstream gene expression, genetic noise and fitness. Using Trichostatin A and nicotinamide, we characterized how drug-based modulation of global histone deacetylase activity affected these phenotypes. Performing epistasis analysis, we discovered phenotype-specific genetic interaction networks of chromatin regulators. Our work provides comprehensive insights into how the galactose network activity is affected by protein interaction networks formed by chromatin regulators.

Genome Profiling for Aflatoxin B1 Resistance in Saccharomyces cerevisiae Reveals a Role for the CSM2/SHU Complex in Tolerance of Aflatoxin B1-Associated DNA Damage

Exposure to the mycotoxin aflatoxin B1 (AFB₁) strongly correlates with hepatocellular carcinoma (HCC). P450 enzymes convert AFB₁ into a highly reactive epoxide that forms unstable 8,9-dihydro-8-(N7-guanyl)-9-hydroxyaflatoxin B1 (AFB₁-N ⁷-Gua) DNA adducts, which convert to stable mutagenic AFB₁ formamidopyrimidine (FAPY) DNA adducts. In CYP1A2-expressing budding yeast, AFB₁ is a weak mutagen but a potent recombinagen. However, few genes have been identified that confer AFB₁ resistance. Here, we profiled the yeast genome for AFB₁ resistance. We introduced the human CYP1A2 into ∼90% of the diploid deletion library, and pooled samples from CYP1A2-expressing libraries and the original library were exposed to 50 μM AFB₁ for 20 hs. By using next generation sequencing (NGS) to count molecular barcodes, we initially identified 86 genes from the CYP1A2-expressing libraries, of which 79 were confirmed to confer AFB₁ resistance. While functionally diverse genes, including those that function in proteolysis, actin reorganization, and tRNA modification, were identified, those that function in postreplication DNA repair and encode proteins that bind to DNA damage were over-represented, compared to the yeast genome, at large. DNA metabolism genes also included those functioning in checkpoint recovery and replication fork maintenance, emphasizing the potency of the mycotoxin to trigger replication stress. Among genes involved in postreplication repair, we observed that CSM2, a member of the CSM2 (SHU) complex, functioned in AFB₁-associated sister chromatid recombination while suppressing AFB₁-associated mutations. These studies thus broaden the number of AFB₁ resistance genes and have elucidated a mechanism of error-free bypass of AFB₁-associated DNA adducts.

A genetic interaction map centered on cohesin reveals auxiliary factors involved in sister chromatid cohesion in S. cerevisiae

Eukaryotic chromosomes are replicated in interphase and the two newly duplicated sister chromatids are held together by the cohesin complex and several cohesin auxiliary factors. Sister chromatid cohesion is essential for accurate chromosome segregation during mitosis, yet has also been implicated in other processes, including DNA damage repair, transcription and DNA replication. To assess how cohesin and associated factors functionally interconnect and coordinate with other cellular processes, we systematically mapped the genetic interactions of 17 cohesin genes centered on quantitative growth measurements of >52,000 gene pairs in the budding yeast Saccharomyces cerevisiae Integration of synthetic genetic interactions unveiled a cohesin functional map that constitutes 373 genetic interactions, revealing novel functional connections with post-replication repair, microtubule organization and protein folding. Accordingly, we show that the microtubule-associated protein Irc15 and the prefoldin complex members Gim3, Gim4 and Yke2 are new factors involved in sister chromatid cohesion. Our genetic interaction map thus provides a unique resource for further identification and functional interrogation of cohesin proteins. Since mutations in cohesin proteins have been associated with cohesinopathies and cancer, it may also help in identifying cohesin interactions relevant in disease etiology.

An Optimized Competitive-Aging Method Reveals Gene-Drug Interactions Underlying the Chronological Lifespan of Saccharomyces cerevisiae

The chronological lifespan of budding yeast is a model of aging and age-related diseases. This paradigm has recently allowed genome-wide screening of genetic factors underlying post-mitotic viability in a simple unicellular system, which underscores its potential to provide a comprehensive view of the aging process. However, results from different large-scale studies show little overlap and typically lack quantitative resolution to derive interactions among different aging factors. We previously introduced a sensitive, parallelizable approach to measure the chronological-lifespan effects of gene deletions based on the competitive aging of fluorescence-labeled strains. Here, we present a thorough description of the method, including an improved multiple-regression model to estimate the association between death rates and fluorescent signals, which accounts for possible differences in growth rate and experimental batch effects. We illustrate the experimental procedure-from data acquisition to calculation of relative survivorship-for ten deletion strains with known lifespan phenotypes, which is achieved with high technical replicability. We apply our method to screen for gene-drug interactions in an array of yeast deletion strains, which reveals a functional link between protein glycosylation and lifespan extension by metformin. Competitive-aging screening coupled to multiple-regression modeling provides a powerful, straight-forward way to identify aging factors in yeast and their interactions with pharmacological interventions.

Correcting an instance of synthetic lethality with a pro-survival sequence

A human cDNA encoding the LIM domain containing 194 amino acid cysteine and glycine rich protein 3 (CSRP3) was identified as a BAX suppressor in yeast and a pro-survival sequence that abrogated copper mediated regulated cell death (RCD). Yeast lacks a CSRP3 orthologue but it has four LIM sequences, namely RGA1, RGA2, LRG1 and PXL1. These are known regulators of stress responses yet their roles in RCD remain unknown. Given that LIMs interact with other LIMs, we ruled out the possibility that overexpressed yeast LIMs alone could prevent RCD and that CSRP3 functions by acting as a dominant regulator of yeast LIMs. Of interest was the discovery that even though yeast cells lacking the LIM encoding PXL1 had no overt growth defect, it was nevertheless supersensitive to the effects of sublethal levels of copper. Heterologous expression of human CSPR3 as well as the pro-survival 14-3-3 sequence corrected this copper supersensitivity. These results show that the pxl1∆-copper synthetic lethality is likely due to the induction of RCD. This differs from the prevailing model in which synthetic lethality occurs because of specific defects generated by the combined loss of two overlapping but non-essential functions.

Genetic interactions derived from high-throughput phenotyping of 6589 yeast cell cycle mutants

Over the last 30 years, computational biologists have developed increasingly realistic mathematical models of the regulatory networks controlling the division of eukaryotic cells. These models capture data resulting from two complementary experimental approaches: low-throughput experiments aimed at extensively characterizing the functions of small numbers of genes, and large-scale genetic interaction screens that provide a systems-level perspective on the cell division process. The former is insufficient to capture the interconnectivity of the genetic control network, while the latter is fraught with irreproducibility issues. Here, we describe a hybrid approach in which the 630 genetic interactions between 36 cell-cycle genes are quantitatively estimated by high-throughput phenotyping with an unprecedented number of biological replicates. Using this approach, we identify a subset of high-confidence genetic interactions, which we use to refine a previously published mathematical model of the cell cycle. We also present a quantitative dataset of the growth rate of these mutants under six different media conditions in order to inform future cell cycle models.

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.

mSphere of Influence: Comprehensive Genetic Analysis

Jason M. Peters works in the fields of antibiotic resistance and biofuel production. In this mSphere of Influence article, he reflects on how the paper "A global genetic interaction network maps a wiring diagram of cellular function" by Costanzo et al. (Science 353:aaf1420, 2016, https://doi.org/10.1126/science.aaf1420) has impacted his work by highlighting the power of gene networks to uncover new biology.

The human Shu complex functions with PDS5B and SPIDR to promote homologous recombination

RAD51 plays a central role in homologous recombination during double-strand break repair and in replication fork dynamics. Misregulation of RAD51 is associated with genetic instability and cancer. RAD51 is regulated by many accessory proteins including the highly conserved Shu complex. Here, we report the function of the human Shu complex during replication to regulate RAD51 recruitment to DNA repair foci and, secondly, during replication fork restart following replication fork stalling. Deletion of the Shu complex members, SWS1 and SWSAP1, using CRISPR/Cas9, renders cells specifically sensitive to the replication fork stalling and collapse caused by methyl methanesulfonate and mitomycin C exposure, a delayed and reduced RAD51 response, and fewer sister chromatid exchanges. Our additional analysis identified SPIDR and PDS5B as novel Shu complex interacting partners and genetically function in the same pathway upon DNA damage. Collectively, our study uncovers a protein complex, which consists of SWS1, SWSAP1, SPIDR and PDS5B, involved in DNA repair and provides insight into Shu complex function and composition.

Highly Combinatorial Genetic Interaction Analysis Reveals a Multi-Drug Transporter Influence Network

Many traits are complex, depending non-additively on variant combinations. Even in model systems, such as the yeast S. cerevisiae, carrying out the high-order variant-combination testing needed to dissect complex traits remains a daunting challenge. Here, we describe “X-gene” genetic analysis (XGA), a strategy for engineering and profiling highly combinatorial gene perturbations. We demonstrate XGA on yeast ABC transporters by engineering 5,353 strains, each deleted for a random subset of 16 transporters, and profiling each strain’s resistance to 16 compounds. XGA yielded 85,648 genotype-to-resistance observations, revealing high-order genetic interactions for 13 of the 16 transporters studied. Neural networks yielded intuitive functional models and guided exploration of fluconazole resistance, which was influenced non-additively by five genes. Together, our results showed that highly combinatorial genetic perturbation can functionally dissect complex traits, supporting pursuit of analogous strategies in human cells and other model systems.

Celaj et al. introduce “X-gene” genetic analysis (XGA), a strategy for modeling complex systems by engineering and profiling highly combinatorial genetic perturbations. They apply XGA to 16 yeast ABC transporters, revealing many high-order genetic interactions. Neural network models yielded intuitive functional models and illuminated an ABC transporter influence network, supporting application of XGA to other organisms and processes.

Improving Chromosome Synthesis with a Semiquantitative Phenotypic Assay and Refined Assembly Strategy

Recent advances in DNA synthesis technology have made it possible to rewrite the entire genome of an organism. The major hurdles in this process are efficiently identifying and fixing the defect-inducing sequences (or "bugs") during rewriting. Here, we describe a high-throughput, semiquantitative phenotype assay for evaluating the fitness of synthetic yeast and identifying potential bugs. Growth curves were measured under a carefully chosen set of testing conditions. Statistical analysis revealed strains with subtle defects relative to the wild type, which were targeted for debugging. The effectiveness of the assay was demonstrated by phenotypic profiling of all intermediate synthetic strains of the synthetic yeast chromosome XII. Subsequently, the assay was applied during the process of constructing another synthetic chromosome. Furthermore, we designed an efficient chromosome assembly strategy that integrates iterative megachunk construction with CRISPR/Cas9-mediated assembly of synthetic segments. Together, the semiquantitative assay and refined assembly strategy could greatly facilitate synthetic genomics projects by improving efficiency during both debugging and construction.

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.

Deletion of GLX3 in Candida albicans affects temperature tolerance, biofilm formation and virulence

Candida albicans is a predominant cause of fungal infections in mucosal tissues as well as life-threatening bloodstream infections in immunocompromised patients. Within the human body, C. albicans is mostly embedded in biofilms, which provides increased resistance to antifungal drugs. The glyoxalase Glx3 is an abundant proteomic component of the biofilm extracellular matrix. Here, we document phenotypic studies of a glx3Δ null mutant concerning its role in biofilm formation, filamentation, antifungal drug resistance, cell wall integrity and virulence. First, consistent with its function as glyoxalase, the glx3 null mutant showed impaired growth on media containing glycerol as the carbon source and in the presence of low concentrations of hydrogen peroxide. Importantly, the glx3Δ mutant showed decreased fitness at 37°C and formed less biofilm as compared to wild type and a reintegrant strain. At the permissive temperature of 28°C, the glx3Δ mutant showed impaired filamentation as well as increased sensitivity to Calcofluor white, Congo red, sodium dodecyl sulfate and zymolyase, indicating subtle alterations in wall architecture even though gross quantitative compositional changes were not detected. Interestingly, and consistent with its impaired filamentation, biofilm formation and growth at 37°C, the glx3Δ mutant is avirulent. Our results underline the role of Glx3 in fungal pathogenesis and the involvement of the fungal wall in this process.

Epistasis between antibiotic tolerance, persistence, and resistance mutations

Understanding the evolution of microorganisms under antibiotic treatments is a burning issue. Typically, several resistance mutations can accumulate under antibiotic treatment, and the way in which resistance mutations interact, i.e., epistasis, has been extensively studied. We recently showed that the evolution of antibiotic resistance in Escherichia coli is facilitated by the early appearance of tolerance mutations. In contrast to resistance, which reduces the effectiveness of the drug concentration, tolerance increases resilience to antibiotic treatment duration in a nonspecific way, for example when bacteria transiently arrest their growth. Both result in increased survival under antibiotics, but the interaction between resistance and tolerance mutations has not been studied. Here, we extend our analysis to include the evolution of a different type of tolerance and a different antibiotic class and measure experimentally the epistasis between tolerance and resistance mutations. We derive the expected model for the effect of tolerance and resistance mutations on the dynamics of survival under antibiotic treatment. We find that the interaction between resistance and tolerance mutations is synergistic in strains evolved under intermittent antibiotic treatment. We extend our analysis to mutations that result in antibiotic persistence, i.e., to tolerance that is conferred only on a subpopulation of cells. We show that even when this population heterogeneity is included in our analysis, a synergistic interaction between antibiotic persistence and resistance mutations remains. We expect our general framework for the epistasis in killing conditions to be relevant for other systems as well, such as bacteria exposed to phages or cancer cells under treatment.

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.

Dissecting PCNA function with a systematically designed mutant library in yeast

Proliferating cell nuclear antigen (PCNA), encoded by POL30 in Saccharomyces cerevisiae, is a key component of DNA metabolism. Here, a library consisting of 304 PCNA mutants was designed and constructed to probe the contribution of each residue to the biological function of PCNA. Five regions with elevated sensitivity to DNA damaging reagents were identified using high-throughput phenotype screening. Using a series of genetic and biochemical analyses, we demonstrated that one particular mutant, K168A, has defects in the DNA damage tolerance (DDT) pathway by disrupting the interaction between PCNA and Rad5. Subsequent domain analysis showed that the PCNA-Rad5 interaction is a prerequisite for the function of Rad5 in DDT. Our study not only provides a resource in the form of a library of versatile mutants to study the functions of PCNA, but also reveals a key residue on PCNA (K168) which highlights the importance of the PCNA-Rad5 interaction in the template switching (TS) pathway.

The ability of transcription factors to differentially regulate gene expression is a crucial component of the mechanism underlying inversion, a frequently observed genetic interaction pattern

Genetic interactions, a phenomenon whereby combinations of mutations lead to unexpected effects, reflect how cellular processes are wired and play an important role in complex genetic diseases. Understanding the molecular basis of genetic interactions is crucial for deciphering pathway organization as well as understanding the relationship between genetic variation and disease. Several hypothetical molecular mechanisms have been linked to different genetic interaction types. However, differences in genetic interaction patterns and their underlying mechanisms have not yet been compared systematically between different functional gene classes. Here, differences in the occurrence and types of genetic interactions are compared for two classes, gene-specific transcription factors (GSTFs) and signaling genes (kinases and phosphatases). Genome-wide gene expression data for 63 single and double deletion mutants in baker's yeast reveals that the two most common genetic interaction patterns are buffering and inversion. Buffering is typically associated with redundancy and is well understood. In inversion, genes show opposite behavior in the double mutant compared to the corresponding single mutants. The underlying mechanism is poorly understood. Although both classes show buffering and inversion patterns, the prevalence of inversion is much stronger in GSTFs. To decipher potential mechanisms, a Petri Net modeling approach was employed, where genes are represented as nodes and relationships between genes as edges. This allowed over 9 million possible three and four node models to be exhaustively enumerated. The models show that a quantitative difference in interaction strength is a strict requirement for obtaining inversion. In addition, this difference is frequently accompanied with a second gene that shows buffering. Taken together, these results provide a mechanistic explanation for inversion. Furthermore, the ability of transcription factors to differentially regulate expression of their targets provides a likely explanation why inversion is more prevalent for GSTFs compared to kinases and phosphatases.

A Yeast System for Discovering Optogenetic Inhibitors of Eukaryotic Translation Initiation

The precise spatiotemporal regulation of protein synthesis is essential for many complex biological processes such as memory formation, embryonic development, and tumor formation. Current methods used to study protein synthesis offer only a limited degree of spatiotemporal control. Optogenetic methods, in contrast, offer the prospect of controlling protein synthesis noninvasively within minutes and with a spatial scale as small as a single synapse. Here, we present a hybrid yeast system where growth depends on the activity of human eukaryotic initiation factor 4E (eIF4E) that is suitable for screening optogenetic designs for the down-regulation of protein synthesis. We used this system to screen a diverse initial panel of 15 constructs designed to couple a light switchable domain (PYP, RsLOV, AsLOV, Dronpa) to 4EBP2 (eukaryotic initiation factor 4E binding protein 2), a native inhibitor of translation initiation. We identified cLIPS1 (circularly permuted LOV inhibitor of protein synthesis 1), a fusion of a segment of 4EBP2 and a circularly permuted version of the LOV2 domain from Avena sativa, as a photoactivated inhibitor of translation. Adapting the screen for higher throughput, we tested small libraries of cLIPS1 variants and found cLIPS2, a construct with an improved degree of optical control. We show that these constructs can both inhibit translation in yeast harboring a human eIF4E in vivo, and bind human eIF4E in vitro in a light-dependent manner. This hybrid yeast system thus provides a convenient way for discovering optogenetic constructs that can regulate human eIF4E-dependent translation initiation in a mechanistically defined 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.

Improved discovery of genetic interactions using CRISPRiSeq across multiple environments

Large-scale genetic interaction (GI) screens in yeast have been invaluable for our understanding of molecular systems biology and for characterizing novel gene function. Owing in part to the high costs and long experiment times required, a preponderance of GI data has been generated in a single environmental condition. However, an unknown fraction of GIs may be specific to other conditions. Here, we developed a pooled-growth CRISPRi-based sequencing assay for GIs, CRISPRiSeq, which increases throughput such that GIs can be easily assayed across multiple growth conditions. We assayed the fitness of approximately 17,000 strains encompassing approximately 7700 pairwise interactions in five conditions and found that the additional conditions increased the number of GIs detected nearly threefold over the number detected in rich media alone. In addition, we found that condition-specific GIs are prevalent and improved the power to functionally classify genes. Finally, we found new links during respiratory growth between members of the Ras nutrient-sensing pathway and both the COG complex and a gene of unknown function. Our results highlight the potential of conditional GI screens to improve our understanding of cellular genetic networks.

Time-resolved mapping of genetic interactions to model rewiring of signaling pathways

Context-dependent changes in genetic interactions are an important feature of cellular pathways and their varying responses under different environmental conditions. However, methodological frameworks to investigate the plasticity of genetic interaction networks over time or in response to external stresses are largely lacking. To analyze the plasticity of genetic interactions, we performed a combinatorial RNAi screen in Drosophila cells at multiple time points and after pharmacological inhibition of Ras signaling activity. Using an image-based morphology assay to capture a broad range of phenotypes, we assessed the effect of 12768 pairwise RNAi perturbations in six different conditions. We found that genetic interactions form in different trajectories and developed an algorithm, termed MODIFI, to analyze how genetic interactions rewire over time. Using this framework, we identified more statistically significant interactions compared to end-point assays and further observed several examples of context-dependent crosstalk between signaling pathways such as an interaction between Ras and Rel which is dependent on MEK activity.

Editorial note

This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

Phosphorylation of Histone H4T80 Triggers DNA Damage Checkpoint Recovery

In response to genotoxic stress, cells activate a signaling cascade known as the DNA damage checkpoint (DDC) that leads to a temporary cell cycle arrest and activation of DNA repair mechanisms. Because persistent DDC activation compromises cell viability, this process must be tightly regulated. However, despite its importance, the mechanisms regulating DDC recovery are not completely understood. Here, we identify a DNA-damage-regulated histone modification in Saccharomyces cerevisiae, phosphorylation of H4 threonine 80 (H4T80ph), and show that it triggers checkpoint inactivation. H4T80ph is critical for cell survival to DNA damage, and its absence causes impaired DDC recovery and persistent cell cycle arrest. We show that, in response to genotoxic stress, p21-activated kinase Cla4 phosphorylates H4T80 to recruit Rtt107 to sites of DNA damage. Rtt107 displaces the checkpoint adaptor Rad9, thereby interrupting the checkpoint-signaling cascade. Collectively, our results indicate that H4T80ph regulates DDC recovery.

Systematic gene overexpression in Candida albicans identifies a regulator of early adaptation to the mammalian gut

Candida albicans is part of the human gastrointestinal (GI) microbiota. To better understand how C. albicans efficiently establishes GI colonisation, we competitively challenged growth of 572 signature-tagged strains (~10% genome coverage), each conditionally overexpressing a single gene, in the murine gut. We identified CRZ2, a transcription factor whose overexpression and deletion respectively increased and decreased early GI colonisation. Using clues from genome-wide expression and gene-set enrichment analyses, we found that the optimal activity of Crz2p occurs under hypoxia at 37°C, as evidenced by both phenotypic and transcriptomic analyses following CRZ2 genetic perturbation. Consistent with early colonisation of the GI tract, we show that CRZ2 overexpression confers resistance to acidic pH and bile salts, suggesting an adaptation to the upper sections of the gut. Genome-wide location analyses revealed that Crz2p directly modulates the expression of many mannosyltransferase- and cell-wall protein-encoding genes, suggesting a link with cell-wall function. We show that CRZ2 overexpression alters cell-wall phosphomannan abundance and increases sensitivity to tunicamycin, suggesting a role in protein glycosylation. Our study reflects the powerful use of gene overexpression as a complementary approach to gene deletion to identify relevant biological pathways involved in C. albicans interaction with the host environment.

Network Rewiring in Cancer: Applications to Melanoma Cell Lines and the Cancer Genome Atlas Patients

Genes do not work in isolation, but rather as part of networks that have many feedback and redundancy mechanisms. Studying the properties of genetic networks and how individual genes contribute to overall network functions can provide insight into genetically-mediated disease processes. Most analytical techniques assume a network topology based on normal state networks. However, gene perturbations often lead to the rewiring of relevant networks and impact relationships among other genes. We apply a suite of analysis methodologies to assess the degree of transcriptional network rewiring observed in different sets of melanoma cell lines using whole genome gene expression microarray profiles. We assess evidence for network rewiring in melanoma patient tumor samples using RNA-sequence data available from The Cancer Genome Atlas. We make a distinction between “unsupervised” and “supervised” network-based methods and contrast their use in identifying consistent differences in networks between subsets of cell lines and tumor samples. We find that different genes play more central roles within subsets of genes within a broader network and hence are likely to be better drug targets in a disease state. Ultimately, we argue that our results have important implications for understanding the molecular pathology of melanoma as well as the choice of treatments to combat that pathology.

Mapping DNA damage-dependent genetic interactions in yeast via party mating and barcode fusion genetics

Condition‐dependent genetic interactions can reveal functional relationships between genes that are not evident under standard culture conditions. State‐of‐the‐art yeast genetic interaction mapping, which relies on robotic manipulation of arrays of double‐mutant strains, does not scale readily to multi‐condition studies. Here, we describe barcode fusion genetics to map genetic interactions (BFG‐GI), by which double‐mutant strains generated via en masse “party” mating can also be monitored en masse for growth to detect genetic interactions. By using site‐specific recombination to fuse two DNA barcodes, each representing a specific gene deletion, BFG‐GI enables multiplexed quantitative tracking of double mutants via next‐generation sequencing. We applied BFG‐GI to a matrix of DNA repair genes under nine different conditions, including methyl methanesulfonate (MMS), 4‐nitroquinoline 1‐oxide (4NQO), bleomycin, zeocin, and three other DNA‐damaging environments. BFG‐GI recapitulated known genetic interactions and yielded new condition‐dependent genetic interactions. We validated and further explored a subnetwork of condition‐dependent genetic interactions involving MAG1,SLX4, and genes encoding the Shu complex, and inferred that loss of the Shu complex leads to an increase in the activation of the checkpoint protein kinase Rad53.

Dynamics in Epistasis Analysis

Finding regulatory relationships between genes, including the direction and nature of influence between them, is a fundamental challenge in the field of molecular genetics. One classical approach to this problem is epistasis analysis. Broadly speaking, epistasis analysis infers the regulatory relationships between a pair of genes in a genetic pathway by considering the patterns of change in an observable trait resulting from single and double deletion of genes. While classical epistasis analysis has yielded deep insights on numerous genetic pathways, it is not without limitations. Here, we explore the possibility of dynamic epistasis analysis, in which, in addition to performing genetic perturbations of a pathway, we drive the pathway by a time-varying upstream signal. We explore the theoretical power of dynamical epistasis analysis by conducting an identifiability analysis of Boolean models of genetic pathways, comparing static and dynamic approaches. We find that even relatively simple input dynamics greatly increases the power of epistasis analysis to discriminate alternative network structures. Further, we explore the question of experiment design, and show that a subset of short time-varying signals, which we call dynamic primitives, allow maximum discriminative power with a reduced number of experiments.

Putting together and taking apart: assembly and disassembly of the Rad51 nucleoprotein filament in DNA repair and genome stability

Homologous recombination is a key mechanism providing both genome stability and genetic diversity in all living organisms. Recombinases play a central role in this pathway: multiple protein subunits of Rad51 or its orthologues bind single-stranded DNA to form a nucleoprotein filament which is essential for initiating recombination events. Multiple factors are involved in the regulation of this step, both positively and negatively. In this review, we discuss Rad51 nucleoprotein assembly and disassembly, how it is regulated and what functional significance it has in genome maintenance.

Genetic Evidence for Roles of Yeast Mitotic Cyclins at Single-Stranded Gaps Created by DNA Replication

Paused or stalled replication forks are major threats to genome integrity; unraveling the complex pathways that contribute to fork stability and restart is crucial. Experimentally, fork stalling is induced by growing the cells in presence of hydroxyurea (HU), which depletes the pool of deoxynucleotide triphosphates (dNTPs) and slows down replication progression in yeast. Here, I report an epistasis analysis, based on sensitivity to HU, between CLB2, the principal mitotic cyclin gene in Saccharomyces cerevisiae, and genes involved in fork stability and recombination. clb2Δ cells are not sensitive to HU, but the strong synergistic effect of clb2Δ with most genes tested indicates, unexpectedly, that CLB2 has an important role in DNA replication, in the stability and restart of stalled forks, and in pathways dependent on and independent of homologous recombination. Results indicate that CLB2 functions in parallel with the SGS1 helicase and EXO1 exonuclease to allow proper Rad51 recombination, but also regulates a combined Sgs1–Exo1 activity in a pathway dependent on Mec1 and Rad53 checkpoint protein kinases. The data argue that Mec1 regulates Clb2 to prevent a deleterious Sgs1–Exo1 activity at paused or stalled forks, whereas Rad53 checkpoint activation regulates Clb2 to allow a necessary Sgs1–Exo1 activity at stalled or collapsed forks. Altogether, this study indicates that Clb2 regulates the activity of numerous nucleases at single-stranded gaps created by DNA replication. A model is proposed for the function and regulation of Clb2 at stalled forks. These data provide new perspectives on the role of mitotic cyclins at the end of S phase.

Dual gene activation and knockout screen reveals directional dependencies in genetic networks

Understanding the direction of information flow is essential for characterizing how genetic networks affect phenotypes. However, methods to find genetic interactions largely fail to reveal directional dependencies. We combine two orthogonal Cas9 proteins from Streptococcus pyogenes and Staphylococcus aureus to carry out a dual screen in which one gene is activated while a second gene is deleted in the same cell. We analyse the quantitative effects of activation and knockout to calculate genetic interaction and directionality scores for each gene pair. Based on the results from over 100,000 perturbed gene pairs, we reconstruct a directional dependency network for human K562 leukemia cells and demonstrate how our approach allows the determination of directionality in activating genetic interactions. Our interaction network connects previously uncharacterised genes to well-studied pathways and identifies targets relevant for therapeutic intervention.

Characterization of Peroxisomal Regulation Networks

Peroxisome proliferation involves signal recognition and computation by molecular networks that direct molecular events of gene expression, metabolism, membrane biogenesis, organelle proliferation, protein import, and organelle inheritance. Peroxisome biogenesis in yeast has served as a model system for exploring the regulatory networks controlling this process. Yeast is an outstanding model system to develop tools and approaches to study molecular networks and cellular responses and because the mechanisms of peroxisome biogenesis and key aspects of the transcriptional regulatory networks are remarkably conserved from yeast to humans. In this chapter, we focus on the complex regulatory networks that respond to environmental cues leading to peroxisome assembly and the molecular events of organelle assembly. Ultimately, understanding the mechanisms of the entire peroxisome biogenesis program holds promise for predictive modeling approaches and for guiding rational intervention strategies that could treat human conditions associated with peroxisome function.

A CRISPR-Cas9-based gene drive platform for genetic interaction analysis in Candida albicans

Candida albicans is the leading cause of fungal infections; yet, complex genetic interaction analysis remains cumbersome in this diploid pathogen. Here, we developed a CRISPR-Cas9-based 'gene drive array' platform to facilitate efficient genetic analysis in C. albicans. In our system, a modified DNA donor molecule acts as a selfish genetic element, replaces the targeted site and propagates to replace additional wild-type loci. Using mating-competent C. albicans haploids, each carrying a different gene drive disabling a gene of interest, we are able to create diploid strains that are homozygous double-deletion mutants. We generate double-gene deletion libraries to demonstrate this technology, targeting antifungal efflux and biofilm adhesion factors. We screen these libraries to identify virulence regulators and determine how genetic networks shift under diverse conditions. This platform transforms our ability to perform genetic interaction analysis in C. albicans and is readily extended to other fungal pathogens.

A framework for exhaustively mapping functional missense variants

Although we now routinely sequence human genomes, we can confidently identify only a fraction of the sequence variants that have a functional impact. Here, we developed a deep mutational scanning framework that produces exhaustive maps for human missense variants by combining random codon mutagenesis and multiplexed functional variation assays with computational imputation and refinement. We applied this framework to four proteins corresponding to six human genes: UBE2I (encoding SUMO E2 conjugase), SUMO1 (small ubiquitin‐like modifier), TPK1 (thiamin pyrophosphokinase), and CALM1/2/3 (three genes encoding the protein calmodulin). The resulting maps recapitulate known protein features and confidently identify pathogenic variation. Assays potentially amenable to deep mutational scanning are already available for 57% of human disease genes, suggesting that DMS could ultimately map functional variation for all human disease genes.