A global analysis of genetic interactions in Caenorhabditis elegans

Pleiotropy, epistasis and the genetic architecture of quantitative traits

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

SLOAD: a comprehensive database of cancer-specific synthetic lethal interactions for precision cancer therapy via multi-omics analysis

Synthetic lethality has been widely concerned because of its potential role in cancer treatment, which can be harnessed to selectively kill cancer cells via identifying inactive genes in a specific cancer type and further targeting the corresponding synthetic lethal partners. Herein, to obtain cancer-specific synthetic lethal interactions, we aimed to predict genetic interactions via a pan-cancer analysis from multiple molecular levels using random forest and then develop a user-friendly database. First, based on collected public gene pairs with synthetic lethal interactions, candidate gene pairs were analyzed via integrating multi-omics data, mainly including DNA mutation, copy number variation, methylation and mRNA expression data. Then, integrated features were used to predict cancer-specific synthetic lethal interactions using random forest. Finally, SLOAD (http://www.tmliang.cn/SLOAD) was constructed via integrating these findings, which was a user-friendly database for data searching, browsing, downloading and analyzing. These results can provide candidate cancer-specific synthetic lethal interactions, which will contribute to drug designing in cancer treatment that can promote therapy strategies based on the principle of synthetic lethality.

Database URL http://www.tmliang.cn/SLOAD/

SIN-3 functions through multi-protein interaction to regulate apoptosis, autophagy, and longevity in Caenorhabditis elegans

SIN3/HDAC is a multi-protein complex that acts as a regulatory unit and functions as a co-repressor/co-activator and a general transcription factor. SIN3 acts as a scaffold in the complex, binding directly to HDAC1/2 and other proteins and plays crucial roles in regulating apoptosis, differentiation, cell proliferation, development, and cell cycle. However, its exact mechanism of action remains elusive. Using the Caenorhabditis elegans (C. elegans) model, we can surpass the challenges posed by the functional redundancy of SIN3 isoforms. In this regard, we have previously demonstrated the role of SIN-3 in uncoupling autophagy and longevity in C. elegans. In order to understand the mechanism of action of SIN3 in these processes, we carried out a comparative analysis of the SIN3 protein interactome from model organisms of different phyla. We identified conserved, expanded, and contracted gene classes. The C. elegans SIN-3 interactome -revealed the presence of  well-known proteins, such as DAF-16, SIR-2.1, SGK-1, and AKT-1/2, involved in autophagy, apoptosis, and longevity. Overall, our analyses propose  potential mechanisms by which SIN3 participates in multiple biological processes and their conservation across species and identifies candidate genes for further experimental analysis.

The FGFR4 Homolog KIN-9 Regulates Lifespan and Stress Responses in Caenorhabditis elegans

Fibroblast growth factor receptors (FGFRs) regulate diverse biological processes in eukaryotes. The nematode Caenorhabditis elegans is a good animal model for studying the roles of FGFR signaling and its mechanism of regulation. In this study, we report that KIN-9 is an FGFR homolog in C. elegans that plays essential roles in aging and stress response maintenance. kin-9 was discovered as a target of miR-246, a microRNA that is positively regulated by the Axin family member pry-1. We found that animals lacking kin-9 function were long-lived and resistant to chemically induced stress. Furthermore, they showed a reduced expression of endoplasmic reticulum unfolded protein response (ER-UPR) pathway genes, suggesting that kin-9 is required to maintain a normal ER-UPR. The analysis of GFP reporter-based expression in transgenic animals revealed that KIN-9 is localized in the intestine. Overall, our findings demonstrate that kin-9 is regulated by miR-246 and may function downstream of pry-1. This study prompts future investigations to understand the mechanism of miRNA-mediated FGFR function in maintaining aging and stress response processes.

Mapping the multiscale structure of biological systems

Biological systems are by nature multiscale, consisting of subsystems that factor into progressively smaller units in a deeply hierarchical structure. At any level of the hierarchy, an ever-increasing diversity of technologies can be applied to characterize the corresponding biological units and their relations, resulting in large networks of physical or functional proximities-e.g., proximities of amino acids within a protein, of proteins within a complex, or of cell types within a tissue. Here, we review general concepts and progress in using network proximity measures as a basis for creation of multiscale hierarchical maps of biological systems. We discuss the functionalization of these maps to create predictive models, including those useful in translation of genotype to phenotype, along with strategies for model visualization and challenges faced by multiscale modeling in the near future. Collectively, these approaches enable a unified hierarchical approach to biological data, with application from the molecular to the macroscopic.

Genetic interaction analysis in microbial pathogens: unravelling networks of pathogenesis, antimicrobial susceptibility and host interactions

Genetic interaction (GI) analysis is a powerful genetic strategy that analyzes the fitness and phenotypes of single- and double-gene mutant cells in order to dissect the epistatic interactions between genes, categorize genes into biological pathways, and characterize genes of unknown function. GI analysis has been extensively employed in model organisms for foundational, systems-level assessment of the epistatic interactions between genes. More recently, GI analysis has been applied to microbial pathogens and has been instrumental for the study of clinically important infectious organisms. Here, we review recent advances in systems-level GI analysis of diverse microbial pathogens, including bacterial and fungal species. We focus on important applications of GI analysis across pathogens, including GI analysis as a means to decipher complex genetic networks regulating microbial virulence, antimicrobial drug resistance and host-pathogen dynamics, and GI analysis as an approach to uncover novel targets for combination antimicrobial therapeutics. Together, this review bridges our understanding of GI analysis and complex genetic networks, with applications to diverse microbial pathogens, to further our understanding of virulence, the use of antimicrobial therapeutics and host-pathogen interactions. .

Genome-wide RNAi screen for regulators of UPRmt in Caenorhabditis elegans mutants with defects in mitochondrial fusion

Mitochondrial dynamics plays an important role in mitochondrial quality control and the adaptation of metabolic activity in response to environmental changes. The disruption of mitochondrial dynamics has detrimental consequences for mitochondrial and cellular homeostasis and leads to the activation of the mitochondrial unfolded protein response (UPRmt), a quality control mechanism that adjusts cellular metabolism and restores homeostasis. To identify genes involved in the induction of UPRmt in response to a block in mitochondrial fusion, we performed a genome-wide RNAi screen in Caenorhabditis elegans mutants lacking the gene fzo-1, which encodes the ortholog of mammalian Mitofusin, and identified 299 suppressors and 86 enhancers. Approximately 90% of these 385 genes are conserved in humans, and one third of the conserved genes have been implicated in human disease. Furthermore, many have roles in developmental processes, which suggests that mitochondrial function and the response to stress are defined during development and maintained throughout life. Our dataset primarily contains mitochondrial enhancers and non-mitochondrial suppressors of UPRmt, indicating that the maintenance of mitochondrial homeostasis has evolved as a critical cellular function, which, when disrupted, can be compensated for by many different cellular processes. Analysis of the subsets 'non-mitochondrial enhancers' and 'mitochondrial suppressors' suggests that organellar contact sites, especially between the ER and mitochondria, are of importance for mitochondrial homeostasis. In addition, we identified several genes involved in IP3 signaling that modulate UPRmt in fzo-1 mutants and found a potential link between pre-mRNA splicing and UPRmt activation.

The conserved transmembrane protein TMEM-39 coordinates with COPII to promote collagen secretion and regulate ER stress response

Dysregulation of collagen production and secretion contributes to aging and tissue fibrosis of major organs. How procollagen proteins in the endoplasmic reticulum (ER) route as specialized cargos for secretion remains to be fully elucidated. Here, we report that TMEM39, an ER-localized transmembrane protein, regulates production and secretory cargo trafficking of procollagen. We identify the C. elegans ortholog TMEM-39 from an unbiased RNAi screen and show that deficiency of tmem-39 leads to striking defects in cuticle collagen production and constitutively high ER stress response. RNAi knockdown of the tmem-39 ortholog in Drosophila causes similar defects in collagen secretion from fat body cells. The cytosolic domain of human TMEM39A binds to Sec23A, a vesicle coat protein that drives collagen secretion and vesicular trafficking. TMEM-39 regulation of collagen secretion is independent of ER stress response and autophagy. We propose that the roles of TMEM-39 in collagen secretion and ER homeostasis are likely evolutionarily conserved.

As the most abundant protein in animals, collagen plays diverse roles and its dysregulation impacts aging and many fibrotic disorders. It is important to understand how premature collagen proteins in the ER are processed and secreted, as many other aspects of collagen regulation have been elucidated in mechanistic detail. In this paper, we have characterized a novel conserved family of TMEM39 proteins, including human TMEM39A and C. elegans tmem-39 that regulates ER stress response and collagen secretion. Human TMEM39A directly interacts with SEC23A, a core component of the COPII vesicle coating complex responsible for vesicular cargo secretion to the Golgi apparatus. The function of TMEM-39 proteins in collagen secretion appears highly conserved and independent to the ER stress response and the autophagy pathway. Our results provide insights into functions and mechanisms of TMEM39 proteins in collagen secretion and suggest it as a plausible target for tissue fibrotic diseases.

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.

The Ancestral Caenorhabditis elegans Cuticle Suppresses rol-1

Genetic background commonly modifies the effects of mutations. We discovered that worms mutant for the canonical rol-1 gene, identified by Brenner in 1974, do not roll in the genetic background of the wild strain CB4856. Using linkage mapping, association analysis and gene editing, we determined that N2 carries an insertion in the collagen gene col-182 that acts as a recessive enhancer of rol-1 rolling. From population and comparative genomics, we infer the insertion is derived in N2 and related laboratory lines, likely arising during the domestication of Caenorhabditis elegans, and breaking a conserved protein. The ancestral version of col-182 also modifies the phenotypes of four other classical cuticle mutant alleles, and the effects of natural genetic variation on worm shape and locomotion. These results underscore the importance of genetic background and the serendipity of Brenner's choice of strain.

Genetic Modifiers and Rare Mendelian Disease

Despite advances in high-throughput sequencing that have revolutionized the discovery of gene defects in rare Mendelian diseases, there are still gaps in translating individual genome variation to observed phenotypic outcomes. While we continue to improve genomics approaches to identify primary disease-causing variants, it is evident that no genetic variant acts alone. In other words, some other variants in the genome (genetic modifiers) may alleviate (suppress) or exacerbate (enhance) the severity of the disease, resulting in the variability of phenotypic outcomes. Thus, to truly understand the disease, we need to consider how the disease-causing variants interact with the rest of the genome in an individual. Here, we review the current state-of-the-field in the identification of genetic modifiers in rare Mendelian diseases and discuss the potential for future approaches that could bridge the existing gap.

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.

Broadly conserved roles of TMEM131 family proteins in intracellular collagen assembly and secretory cargo trafficking

Collagen is the most abundant protein in animals. Its dysregulation contributes to aging and many human disorders, including pathological tissue fibrosis in major organs. How premature collagen proteins in the endoplasmic reticulum (ER) assemble and route for secretion remains molecularly undefined. From an RNA interference screen, we identified an uncharacterized Caenorhabditis elegans gene tmem-131, deficiency of which impairs collagen production and activates ER stress response. We find that amino termini of human TMEM131 contain bacterial PapD chaperone-like domains, which recruit premature collagen monomers for proper assembly and secretion. Carboxy termini of TMEM131 interact with TRAPPC8, a component of the TRAPP tethering complex, to drive collagen cargo trafficking from ER to the Golgi. We provide evidence that previously undescribed roles of TMEM131 in collagen recruitment and secretion are evolutionarily conserved in C. elegans, Drosophila, and humans.

Genome-wide synthetic lethal CRISPR screen identifies FIS1 as a genetic interactor of ALS-linked C9ORF72

Mutations in the C9ORF72 gene are the most common cause of amyotrophic lateral sclerosis (ALS). Both toxic gain of function and loss of function pathogenic mechanisms have been proposed. Accruing evidence from mouse knockout studies point to a role for C9ORF72 as a regulator of immune function. To provide further insight into its cellular function, we performed a genome-wide synthetic lethal CRISPR screen in human myeloid cells lacking C9ORF72. We discovered a strong synthetic lethal genetic interaction between C9ORF72 and FIS1, which encodes a mitochondrial membrane protein involved in mitochondrial fission and mitophagy. Mass spectrometry experiments revealed that in C9ORF72 knockout cells, FIS1 strongly bound to a class of immune regulators that activate the receptor for advanced glycation end (RAGE) products and trigger inflammatory cascades. These findings present a novel genetic interactor for C9ORF72 and suggest a compensatory role for FIS1 in suppressing inflammatory signaling in the absence of C9ORF72.

Worming into the Uncharacterized Human Proteome

Using neXtProt release 2019-01-11, we manually curated a list of 1837 functionally uncharacterized human proteins. Using OrthoList 2, we found that 270 of them have homologues in Caenorhabditis elegans, including 60 with a one-to-one orthology relationship. According to annotations extracted from WormBase, the vast majority of these 60 worm genes have RNAi experimental data or mutant alleles, but manual inspection shows that only 15% have phenotypes that could be interpreted in terms of a specific function. One third of the worm orthologs have protein-protein interaction data, and two of these interactions are conserved in humans. The combination of phenotypic, protein-protein interaction, and gene expression data provides functional hypotheses for 8 uncharacterized human proteins. Experimental validation in human or orthologs is necessary before they can be considered for annotation.

Function, dynamics and evolution of network motif modules in integrated gene regulatory networks of worm and plant

Gene regulatory networks (GRNs) consist of different molecular interactions that closely work together to establish proper gene expression in time and space. Especially in higher eukaryotes, many questions remain on how these interactions collectively coordinate gene regulation. We study high quality GRNs consisting of undirected protein–protein, genetic and homologous interactions, and directed protein–DNA, regulatory and miRNA–mRNA interactions in the worm Caenorhabditis elegans and the plant Arabidopsis thaliana. Our data-integration framework integrates interactions in composite network motifs, clusters these in biologically relevant, higher-order topological network motif modules, overlays these with gene expression profiles and discovers novel connections between modules and regulators. Similar modules exist in the integrated GRNs of worm and plant. We show how experimental or computational methodologies underlying a certain data type impact network topology. Through phylogenetic decomposition, we found that proteins of worm and plant tend to functionally interact with proteins of a similar age, while at the regulatory level TFs favor same age, but also older target genes. Despite some influence of the duplication mode difference, we also observe at the motif and module level for both species a preference for age homogeneity for undirected and age heterogeneity for directed interactions. This leads to a model where novel genes are added together to the GRNs in a specific biological functional context, regulated by one or more TFs that also target older genes in the GRNs. Overall, we detected topological, functional and evolutionary properties of GRNs that are potentially universal in all species.

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.

Ancient Function of Teneurins in Tissue Organization and Neuronal Guidance in the Nematode Caenorhabditis elegans

The nematode Caenorhabditis elegans expresses the ten-1 gene that encodes teneurin. TEN-1 protein is expressed throughout the life of C. elegans. The loss of ten-1 function results in embryonic and larval lethality, highlighting its importance for fundamental processes during development. TEN-1 is expressed in the epidermis and neurons. Defects in neuronal pathfinding and epidermal closure are characteristic of ten-1 loss-of-function mutations. The molecular mechanisms of TEN-1 function in neurite outgrowth, neuronal pathfinding, and dendritic morphology in C. elegans are largely unknown. Its genetic redundancy with the extracellular matrix receptors integrin and dystroglycan and genetic interactions with several basement membrane components suggest a role for TEN-1 in the maintenance of basement membrane integrity, which is essential for neuronal guidance. Identification of the lat-1 gene in C. elegans, which encodes latrophilin, as an interaction partner of ten-1 provides further mechanistic insights into TEN-1 function in neuronal development. However, receptor-ligand interactions between LAT-1 and TEN-1 remain to be experimentally proven. The present review discusses the function of teneurin in C. elegans, with a focus on its involvement in the formation of receptor signaling complexes and neuronal networks.

Modular epistasis and the compensatory evolution of gene deletion mutants

Screens for epistatic interactions have long been used to characterize functional relationships corresponding to protein complexes, metabolic pathways, and other functional modules. Although epistasis between adaptive mutations is also common in laboratory evolution experiments, the functional basis for these interactions is less well characterized. Here, we quantify the extent to which gene function (as determined by a genome-wide screen for epistasis among deletion mutants) influences the rate and genetic basis of compensatory adaptation in a set of 37 gene deletion mutants nested within 16 functional modules. We find that functional module has predictive power: mutants with deletions in the same module tend to adapt more similarly, on average, than those with deletions in different modules. At the same time, initial fitness also plays a role: independent of the specific functional modules involved, adaptive mutations tend to be systematically more beneficial in less-fit genetic backgrounds, consistent with a general pattern of diminishing returns epistasis. We measured epistatic interactions between initial gene deletion mutations and the mutations that accumulate during compensatory adaptation and found a general trend towards positive epistasis (i.e. mutations tend to be more beneficial in the background in which they arose). In two functional modules, epistatic interactions between the initial gene deletions and the mutations in their descendant lines caused evolutionary entrenchment, indicating an intimate functional relationship. Our results suggest that genotypes with similar epistatic interactions with gene deletion mutations will also have similar epistatic interactions with adaptive mutations, meaning that genome scale maps of epistasis between gene deletion mutations can be predictive of evolutionary dynamics.

The effects of mutations often depend on the presence or absence of other mutations. This phenomenon, known as epistasis, has been used extensively to infer functional associations between genes. For example, genes that participate in the same functional module will often show a characteristic pattern of positive epistasis where the knockout of one gene will mask the deleterious effects of knockouts in the other genes. In the context of adaptation, epistasis can cause the outcomes of evolution to depend strongly on the initial genotype. Although studies have found that epistasis is common in laboratory populations, we do not know the extent to which the patterns of epistasis that reveal functional associations overlap with the patterns of epistasis that are important in evolution. Here, by comparing evolution in strains with gene deletions in different functional modules, we quantify the effect of functional epistasis on evolutionary outcomes. We find that mutants with deletions in the same module have more similar evolutionary outcomes, on average, than mutants with deletions in different modules. This suggests that screens for epistasis between gene deletion mutations will not only reveal functional interactions between those genes but may also predict evolutionary dynamics.

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.

Integrating genetic and protein-protein interaction networks maps a functional wiring diagram of a cell

Systematic experimental approaches have led to construction of comprehensive genetic and protein-protein interaction networks for the budding yeast, Saccharomyces cerevisiae. Genetic interactions capture functional relationships between genes using phenotypic readouts, while protein-protein interactions identify physical connections between gene products. These complementary, and largely non-overlapping, networks provide a global view of the functional architecture of a cell, revealing general organizing principles, many of which appear to be evolutionarily conserved. Here, we focus on insights derived from the integration of large-scale genetic and protein-protein interaction networks, highlighting principles that apply to both unicellular and more complex systems, including human cells. Network integration reveals fundamental connections involving key functional modules of eukaryotic cells, defining a core network of cellular function, which could be elaborated to explore cell-type specificity in metazoans.

Genetic background effects in quantitative genetics: gene-by-system interactions

Proper cell function depends on networks of proteins that interact physically and functionally to carry out physiological processes. Thus, it seems logical that the impact of sequence variation in one protein could be significantly influenced by genetic variants at other loci in a genome. Nonetheless, the importance of such genetic interactions, known as epistasis, in explaining phenotypic variation remains a matter of debate in genetics. Recent work from our lab revealed that genes implicated from an association study of toxin tolerance in Saccharomyces cerevisiae show extensive interactions with the genetic background: most implicated genes, regardless of allele, are important for toxin tolerance in only one of two tested strains. The prevalence of background effects in our study adds to other reports of widespread genetic-background interactions in model organisms. We suggest that these effects represent many-way interactions with myriad features of the cellular system that vary across classes of individuals. Such gene-by-system interactions may influence diverse traits and require new modeling approaches to accurately represent genotype-phenotype relationships across individuals.

Toward an integrated map of genetic interactions in cancer cells

Cancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and create vulnerabilities for potential therapeutic exploitation. To identify genotype-dependent vulnerabilities, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework to integrate CRISPR/Cas9 screens originating from different libraries building on approaches pioneered for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cells combining functional data with information on genetic variants to explore more than 2.1 million gene-background relationships. In addition to known dependencies, we identified new genotype-specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities identified GANAB and PRKCSH as new positive regulators of Wnt/β-catenin signaling. By clustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data are included.

High-Throughput Quantitative Genetic Interaction Mapping in the Fission Yeast Schizosaccharomyces pombe

Epistasis mapping, in which the phenotype that emerges from combining pairs of mutations is measured quantitatively, is a powerful tool for unbiased study of gene function. When performed at a large scale, this approach has been used to assign function to previously uncharacterized genes, define functional modules and pathways, and study their cross talk. These experiments rely heavily on methods for rapid sampling of binary combinations of mutant alleles by systematic generation of a series of double mutants. Epistasis mapping technologies now exist in various model systems. Here we provide an overview of different epistasis mapping technologies, including the pombe epistasis mapper (PEM) system designed for the collection of quantitative genetic interaction data in fission yeast Schizosaccharomyces pombe Comprising a series of high-throughput selection steps for generation and characterization of double mutants, the PEM system has provided insight into a wide range of biological processes as well as facilitated evolutionary analysis of genetic interactomes across different species.

Using genetic buffering relationships identified in fission yeast to reveal susceptibilities in cells lacking hamartin or tuberin function

Tuberous sclerosis complex is an autosomal dominant disorder characterized by benign tumors arising from the abnormal activation of mTOR signaling in cells lacking TSC1 (hamartin) or TSC2 (tuberin) activity. To expand the genetic framework surrounding this group of growth regulators, we utilized the model eukaryote Schizosaccharomyces pombe to uncover and characterize genes that buffer the phenotypic effects of mutations in the orthologous tsc1 or tsc2 loci. Our study identified two genes: fft3 (encoding a DNA helicase) and ypa1 (encoding a peptidyle-prolyl cis/trans isomerase). While the deletion of fft3 or ypa1 has little effect in wild-type fission yeast cells, their loss in tsc1Δ or tsc2Δ backgrounds results in severe growth inhibition. These data suggest that the inhibition of Ypa1p or Fft3p might represent an 'Achilles' heel' of cells defective in hamartin/tuberin function. Furthermore, we demonstrate that the interaction between tsc1/tsc2 and ypa1 can be rescued through treatment with the mTOR inhibitor, torin-1, and that ypa1Δ cells are resistant to the glycolytic inhibitor, 2-deoxyglucose. This identifies ypa1 as a novel upstream regulator of mTOR and suggests that the effects of ypa1 loss, together with mTOR activation, combine to result in a cellular maladaptation in energy metabolism that is profoundly inhibitory to growth.

Embracing Complex Associations in Common Traits: Critical Considerations for Precision Medicine

Genome-wide association studies (GWAS) have identified numerous loci associated with human phenotypes. This approach, however, does not consider the richly diverse and complex environment with which humans interact throughout the life course, nor does it allow for interrelationships between genetic loci and across traits. As we move toward making precision medicine a reality, whereby we make predictions about disease risk based on genomic profiles, we need to identify improved predictive models of the relationship between genome and phenome. Methods that embrace pleiotropy (the effect of one locus on more than one trait), and gene-environment (G×E) and gene-gene (G×G) interactions, will further unveil the impact of alterations in biological pathways and identify genes that are only involved with disease in the context of the environment. This valuable information can be used to assess personal risk and choose the most appropriate medical interventions based on the genotype and environment of an individual, the whole premise of precision medicine.

Computational Approaches to Identify Genetic Interactions for Cancer Therapeutics

The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Here we describe how genetic interactions are being therapeutically exploited to identify novel targeted treatments for cancer. We discuss the current methodologies that use 'omics data to identify genetic interactions, in particular focusing on synthetic sickness lethality (SSL) and synthetic dosage lethality (SDL). We describe the experimental and computational approaches undertaken both in humans and model organisms to identify these interactions. Finally we discuss some of the identified targets with licensed drugs, inhibitors in clinical trials or with compounds under development.

Ras/MAPK Modifier Loci Revealed by eQTL in Caenorhabditis elegans

The oncogenic Ras/MAPK pathway is evolutionarily conserved across metazoans. Yet, almost all our knowledge on this pathway comes from studies using single genetic backgrounds, whereas mutational effects can be highly background dependent. Therefore, we lack insight in the interplay between genetic backgrounds and the Ras/MAPK-signaling pathway. Here, we used a Caenorhabditis elegans RIL population containing a gain-of-function mutation in the Ras/MAPK-pathway gene let-60 and measured how gene expression regulation is affected by this mutation. We mapped eQTL and found that the majority (∼73%) of the 1516 detected cis-eQTL were not specific for the let-60 mutation, whereas most (∼76%) of the 898 detected trans-eQTL were associated with the let-60 mutation. We detected six eQTL trans-bands specific for the interaction between the genetic background and the mutation, one of which colocalized with the polymorphic Ras/MAPK modifier amx-2. Comparison between transgenic lines expressing allelic variants of amx-2 showed the involvement of amx-2 in 79% of the trans-eQTL for genes mapping to this trans-band. Together, our results have revealed hidden loci affecting Ras/MAPK signaling using sensitized backgrounds in C. elegans. These loci harbor putative polymorphic modifier genes that would not have been detected using mutant screens in single genetic backgrounds.

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

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

Epistatic Networks Jointly Influence Phenotypes Related to Metabolic Disease and Gene Expression in Diversity Outbred Mice

In this study, Tyler et al. analyzed the complex genetic architecture of metabolic disease-related traits using the Diversity Outbred mouse population

Genetic studies of multidimensional phenotypes can potentially link genetic variation, gene expression, and physiological data to create multi-scale models of complex traits. The challenge of reducing these data to specific hypotheses has become increasingly acute with the advent of genome-scale data resources. Multi-parent populations derived from model organisms provide a resource for developing methods to understand this complexity. In this study, we simultaneously modeled body composition, serum biomarkers, and liver transcript abundances from 474 Diversity Outbred mice. This population contained both sexes and two dietary cohorts. Transcript data were reduced to functional gene modules with weighted gene coexpression network analysis (WGCNA), which were used as summary phenotypes representing enriched biological processes. These module phenotypes were jointly analyzed with body composition and serum biomarkers in a combined analysis of pleiotropy and epistasis (CAPE), which inferred networks of epistatic interactions between quantitative trait loci that affect one or more traits. This network frequently mapped interactions between alleles of different ancestries, providing evidence of both genetic synergy and redundancy between haplotypes. Furthermore, a number of loci interacted with sex and diet to yield sex-specific genetic effects and alleles that potentially protect individuals from the effects of a high-fat diet. Although the epistatic interactions explained small amounts of trait variance, the combination of directional interactions, allelic specificity, and high genomic resolution provided context to generate hypotheses for the roles of specific genes in complex traits. Our approach moves beyond the cataloging of single loci to infer genetic networks that map genetic etiology by simultaneously modeling all phenotypes.

A strategy to apply quantitative epistasis analysis on developmental traits

BACKGROUND

Genetic interactions are keys to understand complex traits and evolution. Epistasis analysis is an effective method to map genetic interactions. Large-scale quantitative epistasis analysis has been well established for single cells. However, there is a substantial lack of such studies in multicellular organisms and their complex phenotypes such as development. Here we present a method to extend quantitative epistasis analysis to developmental traits.

METHODS

In the nematode Caenorhabditis elegans, we applied RNA interference on mutants to inactivate two genes, used an imaging system to quantitatively measure phenotypes, and developed a set of statistical methods to extract genetic interactions from phenotypic measurement.

RESULTS

Using two different C. elegans developmental phenotypes, body length and sex ratio, as examples, we showed that this method could accommodate various metazoan phenotypes with performances comparable to those methods in single cell growth studies. Comparing with qualitative observations, this method of quantitative epistasis enabled detection of new interactions involving subtle phenotypes. For example, several sex-ratio genes were found to interact with brc-1 and brd-1, the orthologs of the human breast cancer genes BRCA1 and BARD1, respectively. We confirmed the brc-1 interactions with the following genes in DNA damage response: C34F6.1, him-3 (ortholog of HORMAD1, HORMAD2), sdc-1, and set-2 (ortholog of SETD1A, SETD1B, KMT2C, KMT2D), validating the effectiveness of our method in detecting genetic interactions.

CONCLUSIONS

We developed a reliable, high-throughput method for quantitative epistasis analysis of developmental phenotypes.

High-content behavioral profiling reveals neuronal genetic network modulating Drosophila larval locomotor program

Background

Two key questions in understanding the genetic control of behaviors are: what genes are involved and how these genes interact. To answer these questions at a systems level, we conducted high-content profiling of Drosophila larval locomotor behaviors for over 100 genotypes.

Results

We studied 69 genes whose C. elegans orthologs were neuronal signalling genes with significant locomotor phenotypes, and conducted RNAi with ubiquitous, pan-neuronal, or motor-neuronal Gal4 drivers. Inactivation of 42 genes, including the nicotinic acetylcholine receptors nAChRα1 and nAChRα3, in the neurons caused significant movement defects. Bioinformatic analysis suggested 81 interactions among these genes based on phenotypic pattern similarities. Comparing the worm and fly data sets, we found that these genes were highly conserved in having neuronal expressions and locomotor phenotypes. However, the genetic interactions were not conserved for ubiquitous profiles, and may be mildly conserved for the neuronal profiles. Unexpectedly, our data also revealed a possible motor-neuronal control of body size, because inactivation of Rdl and Gαo in the motor neurons reduced the larval body size. Overall, these data established a framework for further exploring the genetic control of Drosophila larval locomotion.

Conclusions

High content, quantitative phenotyping of larval locomotor behaviours provides a framework for system-level understanding of the gene networks underlying such behaviours.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-017-0513-7) contains supplementary material, which is available to authorized users.

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

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

Nestedness across biological scales

Biological networks pervade nature. They describe systems throughout all levels of biological organization, from molecules regulating metabolism to species interactions that shape ecosystem dynamics. The network thinking revealed recurrent organizational patterns in complex biological systems, such as the formation of semi-independent groups of connected elements (modularity) and non-random distributions of interactions among elements. Other structural patterns, such as nestedness, have been primarily assessed in ecological networks formed by two non-overlapping sets of elements; information on its occurrence on other levels of organization is lacking. Nestedness occurs when interactions of less connected elements form proper subsets of the interactions of more connected elements. Only recently these properties began to be appreciated in one-mode networks (where all elements can interact) which describe a much wider variety of biological phenomena. Here, we compute nestedness in a diverse collection of one-mode networked systems from six different levels of biological organization depicting gene and protein interactions, complex phenotypes, animal societies, metapopulations, food webs and vertebrate metacommunities. Our findings suggest that nestedness emerge independently of interaction type or biological scale and reveal that disparate systems can share nested organization features characterized by inclusive subsets of interacting elements with decreasing connectedness. We primarily explore the implications of a nested structure for each of these studied systems, then theorize on how nested networks are assembled. We hypothesize that nestedness emerges across scales due to processes that, although system-dependent, may share a general compromise between two features: specificity (the number of interactions the elements of the system can have) and affinity (how these elements can be connected to each other). Our findings suggesting occurrence of nestedness throughout biological scales can stimulate the debate on how pervasive nestedness may be in nature, while the theoretical emergent principles can aid further research on commonalities of biological networks.

A double-mutant collection targeting MAP kinase related genes in Arabidopsis for studying genetic interactions

Mitogen-activated protein kinase cascades are conserved in all eukaryotes. In Arabidopsis thaliana there are approximately 80 genes encoding MAP kinase kinase kinases (MAP3K), 10 genes encoding MAP kinase kinases (MAP2K), and 20 genes encoding MAP kinases (MAPK). Reverse genetic analysis has failed to reveal abnormal phenotypes for a majority of these genes. One strategy for uncovering gene function when single-mutant lines do not produce an informative phenotype is to perform a systematic genetic interaction screen whereby double-mutants are created from a large library of single-mutant lines. Here we describe a new collection of 275 double-mutant lines derived from a library of single-mutants targeting genes related to MAP kinase signaling. To facilitate this study, we developed a high-throughput double-mutant generating pipeline using a system for growing Arabidopsis seedlings in 96-well plates. A quantitative root growth assay was used to screen for evidence of genetic interactions in this double-mutant collection. Our screen revealed four genetic interactions, all of which caused synthetic enhancement of the root growth defects observed in a MAP kinase 4 (MPK4) single-mutant line. Seeds for this double-mutant collection are publicly available through the Arabidopsis Biological Resource Center. Scientists interested in diverse biological processes can now screen this double-mutant collection under a wide range of growth conditions in order to search for additional genetic interactions that may provide new insights into MAP kinase signaling.

An extended set of yeast-based functional assays accurately identifies human disease mutations

We can now routinely identify coding variants within individual human genomes. A pressing challenge is to determine which variants disrupt the function of disease-associated genes. Both experimental and computational methods exist to predict pathogenicity of human genetic variation. However, a systematic performance comparison between them has been lacking. Therefore, we developed and exploited a panel of 26 yeast-based functional complementation assays to measure the impact of 179 variants (101 disease- and 78 non-disease-associated variants) from 22 human disease genes. Using the resulting reference standard, we show that experimental functional assays in a 1-billion-year diverged model organism can identify pathogenic alleles with significantly higher precision and specificity than current computational methods.

Resistance to gray leaf spot of maize: genetic architecture and mechanisms elucidated through nested association mapping and near-isogenic line analysis

Gray leaf spot (GLS), caused by Cercospora zeae-maydis and Cercospora zeina, is one of the most important diseases of maize worldwide. The pathogen has a necrotrophic lifestyle and no major genes are known for GLS. Quantitative resistance, although poorly understood, is important for GLS management. We used genetic mapping to refine understanding of the genetic architecture of GLS resistance and to develop hypotheses regarding the mechanisms underlying quantitative disease resistance (QDR) loci. Nested association mapping (NAM) was used to identify 16 quantitative trait loci (QTL) for QDR to GLS, including seven novel QTL, each of which demonstrated allelic series with significant effects above and below the magnitude of the B73 reference allele. Alleles at three QTL, qGLS1.04, qGLS2.09, and qGLS4.05, conferred disease reductions of greater than 10%. Interactions between loci were detected for three pairs of loci, including an interaction between iqGLS4.05 and qGLS7.03. Near-isogenic lines (NILs) were developed to confirm and fine-map three of the 16 QTL, and to develop hypotheses regarding mechanisms of resistance. qGLS1.04 was fine-mapped from an interval of 27.0 Mb to two intervals of 6.5 Mb and 5.2 Mb, consistent with the hypothesis that multiple genes underlie highly significant QTL identified by NAM. qGLS2.09, which was also associated with maturity (days to anthesis) and with resistance to southern leaf blight, was narrowed to a 4-Mb interval. The distance between major leaf veins was strongly associated with resistance to GLS at qGLS4.05. NILs for qGLS1.04 were treated with the C. zeae-maydis toxin cercosporin to test the role of host-specific toxin in QDR. Cercosporin exposure increased expression of a putative flavin-monooxygenase (FMO) gene, a candidate detoxification-related gene underlying qGLS1.04. This integrated approach to confirming QTL and characterizing the potential underlying mechanisms advances the understanding of QDR and will facilitate the development of resistant varieties.

RNA Interference in Caenorhabditis elegans

RNAi has become an essential tool in C. elegans research. This unit describes procedures for RNAi in C. elegans by microinjecting with dsRNA, feeding with bacteria expressing dsRNA, and soaking in dsRNA solution, as well as high-throughput methods for RNAi-based screens.

Genetic Interaction Scoring Procedure for Bacterial Species

A genetic interaction occurs when the phenotype of an organism carrying two mutant genes differs from what should have been observed given their independent influence. Such unexpected outcome indicates a mechanistic connection between the perturbed genes, providing a key source of functional information about the cell. Large-scale screening for genetic interactions involves measuring phenotypes of single and double mutants, which for microorganisms is usually done by automated analysis of images of ordered colonies. Obtaining accurate colony sizes, and using them to identify genetic interactions from such screens remains a challenging and time-consuming task. Here, we outline steps to compute genetic interaction scores in E. coli by measuring colony sizes from plate images, performing normalisation, and quantifying the strength of the effect.

Conserved nutrient sensor O-GlcNAc transferase is integral to C. elegans pathogen-specific immunity

Discriminating pathogenic bacteria from bacteria used as a food source is key to Caenorhabidits elegans immunity. Using mutants defective in the enzymes of O-linked N-acetylglucosamine (O-GlcNAc) cycling, we examined the role of this nutrient-sensing pathway in the C. elegans innate immune response. Genetic analysis showed that deletion of O-GlcNAc transferase (ogt-1) yielded animals hypersensitive to the human pathogen S. aureus but not to P. aeruginosa. Genetic interaction studies revealed that nutrient-responsive OGT-1 acts through the conserved β-catenin (BAR-1) pathway and in concert with p38 MAPK (PMK-1) to modulate the immune response to S. aureus. Moreover, whole genome transcriptional profiling revealed that O-GlcNAc cycling mutants exhibited deregulation of unique stress- and immune-responsive genes. The participation of nutrient sensor OGT-1 in an immunity module evolutionarily conserved from C. elegans to humans reveals an unexplored nexus between nutrient availability and a pathogen-specific immune response.

Building high-resolution synthetic lethal networks: a 'Google map' of the cancer cell

The most commonly used therapies for cancer involve delivering high doses of radiation or toxic chemicals to the patient that also cause substantial damage to normal tissue. To overcome this, researchers have recently resorted to a basic biological concept called 'synthetic lethality' (SL) that takes advantage of interactions between gene pairs. The identification of SL interactions is of considerable therapeutic interest because if a particular gene is SL with a tumor-causing mutation, then the targeting that gene carries therapeutic advantages. Mapping these interactions in the context of human cancer cells could hold the key to effective, targeted cancer treatments. In this review, we cover the recent advances that aim to identify these SL interactions using unbiased genetic screens.

ZTF-8 interacts with the 9-1-1 complex and is required for DNA damage response and double-strand break repair in the C. elegans germline

Germline mutations in DNA repair genes are linked to tumor progression. Furthermore, failure in either activating a DNA damage checkpoint or repairing programmed meiotic double-strand breaks (DSBs) can impair chromosome segregation. Therefore, understanding the molecular basis for DNA damage response (DDR) and DSB repair (DSBR) within the germline is highly important. Here we define ZTF-8, a previously uncharacterized protein conserved from worms to humans, as a novel factor involved in the repair of both mitotic and meiotic DSBs as well as in meiotic DNA damage checkpoint activation in the C. elegans germline. ztf-8 mutants exhibit specific sensitivity to γ-irradiation and hydroxyurea, mitotic nuclear arrest at S-phase accompanied by activation of the ATL-1 and CHK-1 DNA damage checkpoint kinases, as well as accumulation of both mitotic and meiotic recombination intermediates, indicating that ZTF-8 functions in DSBR. However, impaired meiotic DSBR progression partially fails to trigger the CEP-1/p53-dependent DNA damage checkpoint in late pachytene, also supporting a role for ZTF-8 in meiotic DDR. ZTF-8 partially co-localizes with the 9-1-1 DDR complex and interacts with MRT-2/Rad1, a component of this complex. The human RHINO protein rescues the phenotypes observed in ztf-8 mutants, suggesting functional conservation across species. We propose that ZTF-8 is involved in promoting repair at stalled replication forks and meiotic DSBs by transducing DNA damage checkpoint signaling via the 9-1-1 pathway. Our findings define a conserved function for ZTF-8/RHINO in promoting genomic stability in the germline.

A functional genomic screen for evolutionarily conserved genes required for lifespan and immunity in germline-deficient C. elegans

The reproductive system regulates lifespan in insects, nematodes and vertebrates. In Caenorhabditis elegans removal of germline increases lifespan by 60% which is dependent upon insulin signaling, nuclear hormone signaling, autophagy and fat metabolism and their microRNA-regulators. Germline-deficient C. elegans are also more resistant to various bacterial pathogens but the underlying molecular mechanisms are largely unknown. Firstly, we demonstrate that previously identified genes that regulate the extended lifespan of germline-deficient C. elegans (daf-2, daf-16, daf-12, tcer-1, mir-7.1 and nhr-80) are also essential for resistance to the pathogenic bacterium Xenorhabdus nematophila. We then use a novel unbiased approach combining laser cell ablation, whole genome microarrays, RNAi screening and exposure to X. nematophila to generate a comprehensive genome-wide catalog of genes potentially required for increased lifespan and innate immunity in germline-deficient C. elegans. We find 3,440 genes to be upregulated in C. elegans germline-deficient animals in a gonad dependent manner, which are significantly enriched for genes involved in insulin signaling, fatty acid desaturation, translation elongation and proteasome complex function. Using RNAi against a subset of 150 candidate genes selected from the microarray results, we show that the upregulated genes such as transcription factor DAF-16/FOXO, the PTEN homolog lipid phosphatase DAF-18 and several components of the proteasome complex (rpn-6.1, rpn-7, rpn-9, rpn-10, rpt-6, pbs-3 and pbs-6) are essential for both lifespan and immunity of germline deficient animals. We also identify a novel role for genes including par-5 and T12G3.6 in both lifespan-extension and increased survival on X. nematophila. From an evolutionary perspective, most of the genes differentially expressed in germline deficient C. elegans also show a conserved expression pattern in germline deficient Pristionchus pacificus, a nematode species that diverged from C. elegans 250-400 MYA.

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

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

Combining growth-promoting genes leads to positive epistasis in Arabidopsis thaliana

Several genes positively influence final leaf size in Arabidopsis when mutated or overexpressed. The connections between these growth regulators are still poorly understood although such knowledge would further contribute to understand the processes driving leaf growth. In this study, we performed a combinatorial screen with 13 transgenic Arabidopsis lines with an increased leaf size. We found that from 61 analyzed combinations, 39% showed an additional increase in leaf size and most resulted from a positive epistasis on growth. Similar to what is found in other organisms in which such an epistasis assay was performed, only few genes were highly connected in synergistic combinations as we observed a positive epistasis in the majority of the combinations with samba, BRI1(OE) or SAUR19(OE). Furthermore, positive epistasis was found with combinations of genes with a similar mode of action, but also with genes which affect distinct processes, such as cell proliferation and cell expansion.DOI: http://dx.doi.org/10.7554/eLife.02252.001.

The Caenorhabditis elegans Myc-Mondo/Mad complexes integrate diverse longevity signals

The Myc family of transcription factors regulates a variety of biological processes, including the cell cycle, growth, proliferation, metabolism, and apoptosis. In Caenorhabditis elegans, the "Myc interaction network" consists of two opposing heterodimeric complexes with antagonistic functions in transcriptional control: the Myc-Mondo:Mlx transcriptional activation complex and the Mad:Max transcriptional repression complex. In C. elegans, Mondo, Mlx, Mad, and Max are encoded by mml-1, mxl-2, mdl-1, and mxl-1, respectively. Here we show a similar antagonistic role for the C. elegans Myc-Mondo and Mad complexes in longevity control. Loss of mml-1 or mxl-2 shortens C. elegans lifespan. In contrast, loss of mdl-1 or mxl-1 increases longevity, dependent upon MML-1:MXL-2. The MML-1:MXL-2 and MDL-1:MXL-1 complexes function in both the insulin signaling and dietary restriction pathways. Furthermore, decreased insulin-like/IGF-1 signaling (ILS) or conditions of dietary restriction increase the accumulation of MML-1, consistent with the notion that the Myc family members function as sensors of metabolic status. Additionally, we find that Myc family members are regulated by distinct mechanisms, which would allow for integrated control of gene expression from diverse signals of metabolic status. We compared putative target genes based on ChIP-sequencing data in the modENCODE project and found significant overlap in genomic DNA binding between the major effectors of ILS (DAF-16/FoxO), DR (PHA-4/FoxA), and Myc family (MDL-1/Mad/Mxd) at common target genes, which suggests that diverse signals of metabolic status converge on overlapping transcriptional programs that influence aging. Consistent with this, there is over-enrichment at these common targets for genes that function in lifespan, stress response, and carbohydrate metabolism. Additionally, we find that Myc family members are also involved in stress response and the maintenance of protein homeostasis. Collectively, these findings indicate that Myc family members integrate diverse signals of metabolic status, to coordinate overlapping metabolic and cytoprotective transcriptional programs that determine the progression of aging.