Predicting genetic interactions from Boolean models of biological networks

The network representation of the cell fate decision model (Calzoneet al., 2010) is used to generate a genetic interaction network for the apoptosis phenotype. Most genetic interactions are epistatic, single nonmonotonic, and additive (Dreeset al., 2005).

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Mapping causal genes and genetic interactions for agronomic traits using a large F2 population in rice

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab318 ◽

2021 ◽

Author(s):

Laibao Feng ◽

Aimin Ma ◽

Bo Song ◽

Sibin Yu ◽

Xiaoquan Qi

Keyword(s):

Agronomic Traits ◽

Genetic Interaction ◽

Single Gene ◽

Interaction Network ◽

Genetic Interactions ◽

Crop Breeding ◽

F2 Population ◽

Genetic Interaction Network ◽

Causal Genes ◽

Genetic Mechanisms

Abstract Dissecting the genetic mechanisms underlying agronomic traits is of great importance for crop breeding. Agronomic traits are usually controlled by multiple quantitative trait loci (QTLs) and genetic interactions, and mapping the underlying causal genes is still labor-intensive and time-consuming. Here, we present a genetic tool for directly targeting the specific causal genes by using a single-gene resolution linkage map that was constructed from 3756 F2 rice plants via targeted sequencing technology and Tukey-Kramer multiple comparisons test. Three large- and moderate-effect QTLs, qHD6-2, qGL3-1, and qGW5-2, were successfully mapped to their specific causal genes, Hd1, GS3, and GW5, respectively. A complex genetic interaction network containing 30 QTL–QTL interactions was constructed, revealing that the alternative allele of hub QTL, qHD6-2, can hide or release the genetic contributions of the alleles at interacting loci. Moreover, arranging genetic interactions in the models lead to more accurate phenotypic predictions. These results provide a community resource and new feasible strategy for deciphering the genetic mechanisms of complex agronomic traits and accelerating crop breeding.

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Predicting Drug Synergy and Antagonism from Genetic Interaction Neighborhoods

10.1101/050567 ◽

2016 ◽

Author(s):

Jonathan H. Young ◽

Edward M. Marcotte

Keyword(s):

Target Genes ◽

Genetic Interaction ◽

Model Organism ◽

Interaction Network ◽

Genetic Interactions ◽

Yeast Saccharomyces Cerevisiae ◽

Drug Synergy ◽

Graph Metrics ◽

Genetic Interaction Network ◽

Close Proximity

AbstractAlthough drug combinations have proven efficacious in a variety of diseases, the design of such regimens often involves extensive experimental screening due to the myriad choice of drugs and doses. To address these challenges, we utilize the budding yeast Saccharomyces cerevisiae as a model organism to evaluate whether drug synergy or antagonism is mediated through genetic interactions between their target genes. Specifically, we hypothesize that if the inhibition targets of one chemical compound are in close proximity to those of a second compound in a genetic interaction network, then the compound pair will exhibit synergy or antagonism. Graph metrics are employed to make precise the notion of proximity in a network. Knowledge of genetic interactions and small-molecule targets are compiled through literature sources and curated databases, with predictions validated according to experimentally determined gold standards. Finally, we test whether genetic interactions propagate through networks according to a “guilt-by-association” framework. Our results suggest that close proximity between the target genes of one drug and those of another drug does not strongly predict synergy or antagonism. In addition, we find that the extent to which the growth of a double gene mutant deviates from expectation is moderately anti-correlated with their distance in a genetic interaction network.

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Predicting genetic interactions from Boolean models of biological networks

10.1101/018507 ◽

2015 ◽

Cited By ~ 1

Author(s):

Laurence Calzone ◽

Emmanuel Barillot ◽

Andrei Zinovyev

Keyword(s):

Biological Networks ◽

Genetic Interaction ◽

Initial Conditions ◽

Genetic Interactions ◽

Interaction Networks ◽

Stochastic Simulations ◽

Model Organisms ◽

Loss Of Function ◽

Boolean Models ◽

Computational Tools

Genetic interaction can be defined as a deviation of the phenotypic quantitative effect of a double gene mutation from the effect predicted from single mutations using a simple (e.g., multiplicative or linear additive) statistical model. Experimentally characterized genetic interaction networks in model organisms provide important insights into relationships between different biological functions. We describe a computational methodology allowing to systematically and quantitatively characterize a Boolean mathematical model of a biological network in terms of genetic interactions between all loss of function and gain of function mutations with respect to all model phenotypes or outputs. We use the probabilistic framework defined in MaBoSS software, based on continuous time Markov chains and stochastic simulations. In addition, we suggest several computational tools for studying the distribution of double mutants in the space of model phenotype probabilities. We demonstrate this methodology on three published models for each of which we derive the genetic interaction networks and analyze their properties. We classify the obtained interactions according to their class of epistasis, dependence on the chosen initial conditions and phenotype. The use of this methodology for validating mathematical models from experimental data and designing new experiments is discussed.

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Predictability of Genetic Interactions from Functional Gene Modules

10.1101/049627 ◽

2016 ◽

Author(s):

Jonathan H. Young ◽

Edward M. Marcotte

Keyword(s):

Web Application ◽

Genetic Interaction ◽

Homo Sapiens ◽

Interaction Network ◽

Genetic Interactions ◽

Functional Relationships ◽

Therapy Targets ◽

Genetic Interaction Network ◽

Gene Level ◽

Interaction Partners

AbstractCharacterizing genetic interactions is crucial to understanding cellular and organismal response to gene-level perturbations. Such knowledge can inform the selection of candidate disease therapy targets. Yet experimentally determining whether genes interact is technically non-trivial and time-consuming. High-fidelity prediction of different classes of genetic interactions in multiple organisms would substantially alleviate this experimental burden. Under the hypothesis that functionally-related genes tend to share common genetic interaction partners, we evaluate a computational approach to predict genetic interactions in Homo sapiens, Drosophila melanogaster, and Saccharomyces cerevisiae. By leveraging knowledge of functional relationships between genes, we cross-validate predictions on known genetic interactions and observe high-predictive power of multiple classes of genetic interactions in all three organisms. Additionally, our method suggests high-confidence candidate interaction pairs that can be directly experimentally tested. A web application is provided for users to query genes for predicted novel genetic interaction partners. Finally, by subsampling the known yeast genetic interaction network, we found that novel genetic interactions are predictable even when knowledge of currently known interactions is minimal.

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Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interaction networks

10.1101/111252 ◽

2017 ◽

Cited By ~ 3

Author(s):

Scott W. Simpkins ◽

Justin Nelson ◽

Raamesh Deshpande ◽

Sheena C. Li ◽

Jeff S. Piotrowski ◽

...

Keyword(s):

Biological Process ◽

Genetic Interaction ◽

Interaction Network ◽

Chemical Compounds ◽

Genetic Interactions ◽

Interaction Networks ◽

Biological Processes ◽

Functional Information ◽

Chemical Genetic ◽

Genetic Interaction Network

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

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Exploring a local genetic interaction network using evolutionary replay experiments

10.1101/2021.01.27.428500 ◽

2021 ◽

Author(s):

Ryan C. Vignogna ◽

Sean W. Buskirk ◽

Gregory I. Lang

Keyword(s):

Experimental Evolution ◽

Gene Deletion ◽

Genetic Interaction ◽

Interaction Network ◽

Genetic Interactions ◽

Loss Of Function ◽

Positive Allele ◽

Genetic Interaction Network ◽

Allele Specific ◽

Yeast Genetic

ABSTRACTUnderstanding how genes interact is a central challenge in biology. Experimental evolution provides a useful, but underutilized, tool for identifying genetic interactions, particularly those that involve non-loss-of-function mutations or mutations in essential genes. We previously identified a strong positive genetic interaction between specific mutations in KEL1 (P344T) and HSL7 (A695fs) that arose in an experimentally-evolved Saccharomyces cerevisiae population. Because this genetic interaction is not phenocopied by gene deletion, it was previously unknown. Using “evolutionary replay” experiments we identified additional mutations that have positive genetic interactions with the kel1-P344T mutation. We replayed the evolution of this population 672 times from six timepoints. We identified 30 populations where the kel1-P344T mutation reached high frequency. We performed whole-genome sequencing on these populations to identify genes in which mutations arose specifically in the kel1-P344T background. We reconstructed mutations in the ancestral and kel1-P344T backgrounds to validate positive genetic interactions. We identify several genetic interactors with KEL1, we validate these interactions by reconstruction experiments, and we show these interactions are not recapitulated by loss-of-function mutations. Our results demonstrate the power of experimental evolution to identify genetic interactions that are positive, allele specific, and not readily detected by other methods, and sheds light on a previously under-explored region of the yeast genetic interaction network.

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Functional Dissection of the NuA4 Histone Acetyltransferase Reveals Its Role as a Genetic Hub and that Eaf1 Is Essential for Complex Integrity

Molecular and Cellular Biology ◽

10.1128/mcb.01653-07 ◽

2008 ◽

Vol 28 (7) ◽

pp. 2244-2256 ◽

Cited By ~ 72

Author(s):

Leslie Mitchell ◽

Jean-Philippe Lambert ◽

Maria Gerdes ◽

Ashraf S. Al-Madhoun ◽

Ilona S. Skerjanc ◽

...

Keyword(s):

Histone Acetyltransferase ◽

Genetic Interaction ◽

Complex Structure ◽

Interaction Network ◽

Genetic Interactions ◽

Histone H4 ◽

Diverse Range ◽

Cellular Processes ◽

Genetic Interaction Network ◽

Integrative Study

ABSTRACT The Saccharomyces cerevisiae NuA4 histone acetyltransferase complex catalyzes the acetylation of histone H4 and the histone variant Htz1 to regulate key cellular events, including transcription, DNA repair, and faithful chromosome segregation. To further investigate the cellular processes impacted by NuA4, we exploited the nonessential subunits of the complex to build an extensive NuA4 genetic-interaction network map. The map reveals that NuA4 is a genetic hub whose function buffers a diverse range of cellular processes, many not previously linked to the complex, including Golgi complex-to-vacuole vesicle-mediated transport. Further, we probe the role that nonessential subunits play in NuA4 complex integrity. We find that most nonessential subunits have little impact on NuA4 complex integrity and display between 12 and 42 genetic interactions. In contrast, the deletion of EAF1 causes the collapse of the NuA4 complex and displays 148 genetic interactions. Our study indicates that Eaf1 plays a crucial function in NuA4 complex integrity. Further, we determine that Eaf5 and Eaf7 form a subcomplex, which reflects their similar genetic interaction profiles and phenotypes. Our integrative study demonstrates that genetic interaction maps are valuable in dissecting complex structure and provides insight into why the human NuA4 complex, Tip60, has been associated with a diverse range of pathologies.

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