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Environmental robustness of the global yeast genetic interaction network


A phenotype can be affected by genes interacting with other genes, the environment, or both other genes and the environment (a differential interaction). To better understand how these interactions function in yeast, Costanzo et al. mapped gene-gene interactions using single- and double-mutant deletions and temperature-sensitive alleles under 14 environmental conditions. Many deleted or temperature-sensitive nonessential genes affected yeast fitness both positively and negatively under at least one of the environmental conditions tested. In these cases, up to 24% of yeast genes were affected. A minority of these differential interactions point to previously unknown genetic connections across functional networks, informing on how genetic architecture responds to environmental variation. Science , this issue p. [eabf8424][1] ### INTRODUCTION Genetic interactions are identified when variants in different genes combine to generate an unusual phenotype compared with the expected combined effect of the corresponding individual variants. For example, a synthetic lethal genetic interaction occurs when two mutations, neither of which is lethal on their own, combine to generate a lethal double-mutant phenotype. Although there are millions of possible gene-gene combinations for any eukaryotic cell, only a rare subset of gene pairs will display a genetic interaction. Digenic, or gene-by-gene (GxG), interactions appear to underlie key aspects of biology, including the relationship between genotype and phenotype. Environmental conditions can modulate the phenotype associated with genetic variants, giving rise to gene-by-environment (GxE) interactions, when a single variant phenotype is modified, or gene-by-gene-by-environment (GxGxE) interactions when a genetic interaction is changed. ### RATIONALE A global genetic interaction network has been mapped for the budding yeast Saccharomyces cerevisiae , identifying thousands of connections that often occur between functionally related genes. Because the global genetic network was mapped in a specific reference condition, the potential for different environmental conditions to rewire the network remains unclear. Automated yeast genetics, combined with knowledge of a reference map, enables quantification of the extent to which new environmental conditions either modulate known genetic interactions or generate novel genetic interactions to influence the genetic landscape of a cell. ### RESULTS We tested ~4000 yeast single mutants for GxE interactions across 14 diverse environments, including an alternative carbon source, osmotic and genotoxic stress, and treatment with 11 bioactive compounds targeting distinct yeast bioprocesses. To quantify GxGxE interactions, we constructed ~30,000 different double mutants, involving genes annotated to all major yeast bioprocesses, and we scored them for genetic interactions. The plasticity of the network is revealed by differential genetic interactions, which occur when a genetic interaction observed in a particular condition deviates from that scored in the control reference network. Although ~10,000 differential interactions were discovered across all 14 conditions, we observed ~60% fewer differential interactions per condition as compared with genetic interactions in the reference condition, indicating that GxGxE interactions are rare relative to GxG interactions. On average, a single environmental perturbation modulated ~14% of the reference genetic interactions and revealed a smaller subset of ~7% novel differential interactions. Whereas GxG genetic interactions tend to connect pairs of genes that share a close functional relationship, novel differential GxGxE interactions mediate weaker connections between gene pairs with diverse roles. ### CONCLUSION Our general findings reveal how environmental conditions modulate the yeast global genetic interaction network, allowing us to assess the plasticity of genetic networks and the extent to which mapping genetic interactions in different environments can expand a reference network. Although different environments have the potential to reveal novel interactions and uncover previously unidentified but weaker functional connections between genes, the vast majority of genetic interactions remained unchanged in different conditions, suggesting that the global yeast genetic interaction network is largely robust to environmental perturbation. ![Figure][2] Systematic analysis of environmental impact on the global yeast genetic interaction network. (Top left) Mapping GxE interactions and (bottom left) GxGxE differential interactions reveals (top right) the environmental robustness of the global yeast genetic interaction network, (bottom right) highlighting new and distant functional connections associated with novel differential interactions. 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. [1]: /lookup/doi/10.1126/science.abf8424 [2]: pending:yes

#324: Embodied Interactions: from Robotics to Dance, with Kim Baraka


In this episode, our interviewer Lauren Klein speaks with Kim Baraka about his PhD research to enable robots to engage in social interactions, including interactions with children with Autism Spectrum Disorder. Baraka discusses how robots can plan their actions across multiple modalities when interacting with humans, and how models from psychology can inform this process. He also tells us about his passion for dance, and how dance may serve as a testbed for embodied intelligence within Human-Robot Interaction. Kim Baraka is a postdoctoral researcher in the Socially Intelligent Machines Lab at the University of Texas at Austin, and an upcoming Assistant Professor in the Department of Computer Science at Vrije Universiteit Amsterdam, where he will be part of the Social Artificial Intelligence Group. Baraka recently graduated with a dual PhD in Robotics from Carnegie Mellon University (CMU) in Pittsburgh, USA, and the Instituto Superior Técnico (IST) in Lisbon, Portugal.

Prediction of Antibiotic Interactions Using Descriptors Derived from Molecular Structure. - PubMed - NCBI


Combination antibiotic therapies are clinically important in the fight against bacterial infections. However, the search space of drug combinations is large, making the identification of effective combinations a challenging task. Here, we present a computational framework that uses substructure profiles derived from the molecular structures of drugs and predicts antibiotic interactions. Using a previously published data set of 153 drug pairs, we showed that substructure profiles are useful in predicting synergy. We experimentally measured the interaction of 123 new drug pairs, as a prospective validation set for our approach, and identified 37 new synergistic pairs.

Capturing dynamic protein interactions


Protein-protein interactions form the molecular basis for organismal development and function (1, 2). In cells, protein interactions are dynamic and subject to spatiotemporal regulations that are specific to the cell type and cell cycle phase. Mutations that abolish or rewire protein-protein interaction networks (the interactome) are often detrimental and manifest in developmental anomalies and diseases (3, 4). Recent advances in quantitative proteomics offer snapshots of cell type–specific proteomes, but scientific understanding of how protein-protein interactions vary between physiological and disease conditions is limited.

Exploring whole-genome duplicate gene retention with complex genetic interaction analysis


Gene duplication within an organism is a relatively common event during evolution. However, we cannot predict the fate of the duplicated genes: Will they be lost, evolve, or overlap in function within an organismal lineage or species? Kuzmin et al. explored the fate of duplicated gene function within the yeast Saccharomyces cerevisiae (see the Perspective by Ehrenreich). They examined how experimental deletions of one or two duplicated genes (paralogs) affected yeast fitness and were able to determine which genes have likely evolved new essential functions and which retained functional overlap, a condition the authors refer to as entanglement. On the basis of these results, they propose how entanglement affects the evolutionary trajectory of gene duplications. Science , this issue p. [eaaz5667][1]; see also p. [1424][2] ### INTRODUCTION Whole-genome duplication (WGD) events are pervasive in eukaryotes, shaping the genomes of simple single-celled organisms, such as yeast, as well as those of more complex metazoans, including humans. Most duplicated genes are eliminated after WGD because one copy accumulates deleterious mutations, leading to its loss. However, a significant proportion of duplicates persists, and factors that result in duplicate gene retention are poorly understood but critical for understanding the evolutionary forces that shape genomes. ### RATIONALE Quantifying the functional divergence of paralog pairs is of particular interest because of the strong selection against functional redundancy. Negative genetic interactions identify functional relationships between genes and provide a means to directly capture the functional relationship between duplicated genes. Genetic interactions occur when the phenotype associated with a combination of mutations in two or more different genes deviates from the expected combined effect of the individual mutations. A negative genetic interaction refers to a combination of mutations that generates a stronger fitness defect than expected, such as synthetic lethality. Here, we used systematic analysis of digenic and trigenic interaction profiles to assess the functional relationship of retained duplicated genes. ### RESULTS To map both digenic and trigenic interactions of duplicated genes, we profiled query strains carrying single-deletion mutations and the corresponding double-deletion mutations for 240 different dispensable paralog pairs originating from the yeast WGD event. In total, we tested ~550,000 double and ~260,000 triple mutants for genetic interactions, and identified ~4700 negative digenic interactions and ~2500 negative trigenic interactions. We quantified the trigenic interaction fraction, defined as the ratio of negative trigenic interactions to the total number of interactions associated with the paralog pair. The distribution of the resulting trigenic interaction fractions was distinctly bimodal, with two-thirds of paralogs exhibiting a low trigenic interaction fraction (diverged paralogs) and one-third showing a high trigenic interaction fraction (functionally redundant paralogs). Paralogs with a high trigenic interaction fraction showed a relatively low asymmetry in their number of digenic interactions, low rates of protein sequence divergence, and a negative digenic interaction within the gene pair. We correlated position-specific evolutionary rate patterns between paralogs to assess constraints acting on their evolutionary trajectories. Paralogs with a high trigenic interaction fraction showed more correlated evolutionary rate patterns and thus were more evolutionarily constrained than paralogs with a low trigenic interaction fraction. Computational simulations that modeled duplicate gene evolution revealed that as the extent of the initial entanglement (overlap of functions) of paralogs increased, so did the range of functional redundancy at steady state. Thus, the bimodal distribution of the trigenic interaction fraction may reflect that some paralogs diverged, primarily evolving distinct functions without redundancy, while others converged to an evolutionary steady state with substantial redundancy due to their structural and functional entanglement. ### CONCLUSION We propose that the evolutionary fate of a duplicated gene is dictated by an interplay of structural and functional entanglement. Paralog pairs with high levels of entanglement are more likely to revert to a singleton state. In contrast, unconstrained paralogs will tend to partition their functions and adopt divergent roles. Intermediately entangled paralog pairs may partition or expand nonoverlapping functions while also retaining some common, overlapping functions, such that they can both adopt paralog-specific roles and maintain functional redundancy at an evolutionary steady state. ![Figure][3] Complex genetic interaction analysis of duplicated genes. The trigenic interaction fraction, which incorporates digenic and trigenic interactions, captures the functional relationship of duplicated genes and follows a bimodal distribution. Paralogs with a high trigenic interaction fraction are under evolutionary constraints reflecting their structural and functional entanglement. Whole-genome duplication has played a central role in the genome evolution of many organisms, including the human genome. Most duplicated genes are eliminated, and factors that influence the retention of persisting duplicates remain poorly understood. We describe a systematic complex genetic interaction analysis with yeast paralogs derived from the whole-genome duplication event. Mapping of digenic interactions for a deletion mutant of each paralog, and of trigenic interactions for the double mutant, provides insight into their roles and a quantitative measure of their functional redundancy. Trigenic interaction analysis distinguishes two classes of paralogs: a more functionally divergent subset and another that retained more functional overlap. Gene feature analysis and modeling suggest that evolutionary trajectories of duplicated genes are dictated by combined functional and structural entanglement factors. [1]: /lookup/doi/10.1126/science.aaz5667 [2]: /lookup/doi/10.1126/science.abc1796 [3]: pending:yes