Detecting genetic interactions using parallel evolution in experimental populations

Eukaryotic genomes contain thousands of genes organized into complex and interconnected genetic interaction networks. Most of our understanding of how genetic variation affects these networks comes from quantitative-trait loci mapping and from the systematic analysis of double-deletion (or knockdown) mutants, primarily in the yeast Saccharomyces cerevisiae . Evolve and re-sequence experiments are an alternative approach for identifying novel functional variants and genetic interactions, particularly between non-loss-of-function mutations. These experiments leverage natural selection to obtain genotypes with functionally important variants and positive genetic interactions. However, no systematic methods for detecting genetic interactions in these data are yet available. Here, we introduce a computational method based on the idea that variants in genes that interact will co-occur in evolved genotypes more often than expected by chance. We apply this method to a previously published yeast experimental evolution dataset. We find that genetic targets of selection are distributed non-uniformly among evolved genotypes, indicating that genetic interactions had a significant effect on evolutionary trajectories. We identify individual gene pairs with a statistically significant genetic interaction score. The strongest interaction is between genes TRK1 and PHO84 , genes that have not been reported to interact in previous systematic studies. Our work demonstrates that leveraging parallelism in experimental evolution is useful for identifying genetic interactions that have escaped detection by other methods. This article is part of the theme issue ‘Convergent evolution in the genomics era: new insights and directions’.

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

10.1101/646810 ◽

2019 ◽

Cited By ~ 1

Author(s):

Christopher J. Lord ◽

Niall Quinn ◽

Colm J. Ryan

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Genetic Interaction ◽

Interaction Network ◽

Genetic Interactions ◽

Molecular Heterogeneity ◽

Loss Of Function ◽

Driver Genes ◽

Lethal Effects ◽

Synthetic Lethal

AbstractGenetic interactions, such as synthetic lethal effects, can now be systematically identified in cancer cell lines using high-throughput genetic perturbation screens. Despite this advance, few genetic interactions have been reproduced across multiple studies and many appear highly context-specific. Understanding which genetic interactions are robust in the face of the molecular heterogeneity observed in tumours and what factors influence this robustness could streamline the identification of therapeutic targets. Here, we develop a computational approach to identify robust genetic interactions that can be reproduced across independent experiments and across non-overlapping cell line panels. We used this approach to evaluate >140,000 potential genetic interactions involving cancer driver genes and identified 1,520 that are significant in at least one study but only 220 that reproduce across multiple studies. Analysis of these interactions demonstrated that: (i) oncogene addiction effects are more robust than oncogene-related synthetic lethal effects; and (ii) robust genetic interactions in cancer are enriched for gene pairs whose protein products physically interact. This suggests that protein-protein interactions can be used not only to understand the mechanistic basis of genetic interaction effects, but also to prioritise robust targets for further development. To explore the utility of this approach, we used a protein-protein interaction network to guide the search for robust synthetic lethal interactions associated with passenger gene alterations and validated two novel robust synthetic lethalities.

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Regulatory kinase genetic interaction profiles differ between environmental conditions and cellular states

10.1101/735720 ◽

2019 ◽

Author(s):

Siyu Sun ◽

Anastasia Baryshnikova ◽

Nathan Brandt ◽

David Gresham

Keyword(s):

Signaling Pathways ◽

Genetic Interaction ◽

Eukaryotic Cell ◽

Pooled Analysis ◽

Genetic Interactions ◽

Quiescent State ◽

Gene Effects ◽

Individual Gene ◽

Quiescent Cells ◽

Wide Range

AbstractCell growth and quiescence in eukaryotic cells is controlled by an evolutionarily conserved network of signaling pathways. Signal transduction networks operate to modulate a wide range of cellular processes and physiological properties when cells exit proliferative growth and initiate a quiescent state. How signaling networks function to respond to diverse signals that result in cell cycle exit and establishment of a quiescent state is poorly understood. Here, we studied the function of signaling pathways in quiescent cells using global genetic interaction mapping in the model eukaryotic cell, Saccharomyces cerevisiae (budding yeast). We performed pooled analysis of genotypes using molecular barcode sequencing to test the role of ∼3,900 gene deletion mutants and ∼11,700 pairwise interactions between all non-essential genes and the protein kinases TOR1, RIM15, PHO85 in three different nutrient-restricted conditions in both proliferative and quiescent cells. We detect nearly five-fold more genetic interactions in quiescent cells compared to proliferative cells. We find that both individual gene effects and genetic interaction profiles vary depending on the specific pro-quiescence signal. The master regulator of quiescence, RIM15 shows distinct genetic interaction profiles in response to different starvation signals. However, vacuole-related functions show consistent genetic interactions with RIM15 in response to different starvation signals suggesting that RIM15 integrates diverse signals to maintain protein homeostasis in quiescent cells. Our study expands genome-wide genetic interaction profiling to additional conditions, and phenotypes, highlighting the conditional dependence of epistasis.

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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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A systematic analysis of genetic interactions and their underlying biology in childhood cancer

Communications Biology ◽

10.1038/s42003-021-02647-4 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Josephine T. Daub ◽

Saman Amini ◽

Denise J. E. Kersjes ◽

Xiaotu Ma ◽

Natalie Jäger ◽

...

Keyword(s):

Childhood Cancer ◽

Genetic Interaction ◽

Synthetic Lethality ◽

Child Death ◽

Developed Countries ◽

Genetic Interactions ◽

Cancer Development ◽

Systematic Analysis ◽

Treatment Research ◽

Interaction Map

AbstractChildhood cancer is a major cause of child death in developed countries. Genetic interactions between mutated genes play an important role in cancer development. They can be detected by searching for pairs of mutated genes that co-occur more (or less) often than expected. Co-occurrence suggests a cooperative role in cancer development, while mutual exclusivity points to synthetic lethality, a phenomenon of interest in cancer treatment research. Little is known about genetic interactions in childhood cancer. We apply a statistical pipeline to detect genetic interactions in a combined dataset comprising over 2,500 tumors from 23 cancer types. The resulting genetic interaction map of childhood cancers comprises 15 co-occurring and 27 mutually exclusive candidates. The biological explanation of most candidates points to either tumor subtype, pathway epistasis or cooperation while synthetic lethality plays a much smaller role. Thus, other explanations beyond synthetic lethality should be considered when interpreting genetic interaction test results.

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Genetic Interactions Effects of Cardiovascular Disorder Using Computational Models: A Review

Current Biotechnology ◽

10.2174/2211550109999201008125800 ◽

2020 ◽

Vol 9 (3) ◽

pp. 177-191

Author(s):

Sridharan Priya ◽

Radha K. Manavalan

Keyword(s):

Coronary Artery Disease ◽

Coronary Artery ◽

Cardiovascular Diseases ◽

Computational Models ◽

Genetic Interaction ◽

Association Studies ◽

Genetic Interactions ◽

Computational Techniques ◽

Genome Wide Association Studies ◽

Artery Disease

Background: The diseases in the heart and blood vessels such as heart attack, Coronary Artery Disease, Myocardial Infarction (MI), High Blood Pressure, and Obesity, are generally referred to as Cardiovascular Diseases (CVD). The risk factors of CVD include gender, age, cholesterol/ LDL, family history, hypertension, smoking, and genetic and environmental factors. Genome- Wide Association Studies (GWAS) focus on identifying the genetic interactions and genetic architectures of CVD. Objective: Genetic interactions or Epistasis infer the interactions between two or more genes where one gene masks the traits of another gene and increases the susceptibility of CVD. To identify the Epistasis relationship through biological or laboratory methods needs an enormous workforce and more cost. Hence, this paper presents the review of various statistical and Machine learning approaches so far proposed to detect genetic interaction effects for the identification of various Cardiovascular diseases such as Coronary Artery Disease (CAD), MI, Hypertension, HDL and Lipid phenotypes data, and Body Mass Index dataset. Conclusion: This study reveals that various computational models identified the candidate genes such as AGT, PAI-1, ACE, PTPN22, MTHR, FAM107B, ZNF107, PON1, PON2, GTF2E1, ADGRB3, and FTO, which play a major role in genetic interactions for the causes of CVDs. The benefits, limitations, and issues of the various computational techniques for the evolution of epistasis responsible for cardiovascular diseases are exhibited.

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Emerging Role of ODC1 in Neurodevelopmental Disorders and Brain Development

Genes ◽

10.3390/genes12040470 ◽

2021 ◽

Vol 12 (4) ◽

pp. 470

Author(s):

Jeremy W. Prokop ◽

Caleb P. Bupp ◽

Austin Frisch ◽

Stephanie M. Bilinovich ◽

Daniel B. Campbell ◽

...

Keyword(s):

Brain Development ◽

Neural Progenitor Cells ◽

Neurodevelopmental Disorder ◽

Fetal Brain ◽

Missense Variant ◽

Neural Progenitor ◽

Loss Of Function ◽

Gain Of Function ◽

Systematic Analysis ◽

Function Expression

Ornithine decarboxylase 1 (ODC1 gene) has been linked through gain-of-function variants to a rare disease featuring developmental delay, alopecia, macrocephaly, and structural brain anomalies. ODC1 has been linked to additional diseases like cancer, with growing evidence for neurological contributions to schizophrenia, mood disorders, anxiety, epilepsy, learning, and suicidal behavior. The evidence of ODC1 connection to neural disorders highlights the need for a systematic analysis of ODC1 genotype-to-phenotype associations. An analysis of variants from ClinVar, Geno2MP, TOPMed, gnomAD, and COSMIC revealed an intellectual disability and seizure connected loss-of-function variant, ODC G84R (rs138359527, NC_000002.12:g.10444500C > T). The missense variant is found in ~1% of South Asian individuals and results in 2.5-fold decrease in enzyme function. Expression quantitative trait loci (eQTLs) reveal multiple functionally annotated, non-coding variants regulating ODC1 that associate with psychiatric/neurological phenotypes. Further dissection of RNA-Seq during fetal brain development and within cerebral organoids showed an association of ODC1 expression with cell proliferation of neural progenitor cells, suggesting gain-of-function variants with neural over-proliferation and loss-of-function variants with neural depletion. The linkage from the expression data of ODC1 in early neural progenitor proliferation to phenotypes of neurodevelopmental delay and to the connection of polyamine metabolites in brain function establish ODC1 as a bona fide neurodevelopmental disorder gene.

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Multiple Pathways of LRRK2-G2019S/Rab10 Interaction in Dopaminergic Neurons

Journal of Parkinson s Disease ◽

10.3233/jpd-202421 ◽

2021 ◽

pp. 1-16

Author(s):

Alison Fellgett ◽

C. Adam Middleton ◽

Jack Munns ◽

Chris Ugbode ◽

David Jaciuch ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Dopaminergic Neurons ◽

Genetic Interaction ◽

In Vitro Assays ◽

Rab Proteins ◽

Circadian Activity ◽

Loss Of Function ◽

Lrrk2 G2019s

Background: Inherited mutations in the LRRK2 protein are the common causes of Parkinson’s disease, but the mechanisms by which increased kinase activity of mutant LRRK2 leads to pathological events remain to be determined. In vitro assays (heterologous cell culture, phospho-protein mass spectrometry) suggest that several Rab proteins might be directly phosphorylated by LRRK2-G2019S. An in vivo screen of Rab expression in dopaminergic neurons in young adult Drosophila demonstrated a strong genetic interaction between LRRK2-G2019S and Rab10. Objective: To determine if Rab10 is necessary for LRRK2-induced pathophysiological responses in the neurons that control movement, vision, circadian activity, and memory. These four systems were chosen because they are modulated by dopaminergic neurons in both humans and flies. Methods: LRRK2-G2019S was expressed in Drosophila dopaminergic neurons and the effects of Rab10 depletion on Proboscis Extension, retinal neurophysiology, circadian activity pattern (‘sleep’), and courtship memory determined in aged flies. Results: Rab10 loss-of-function rescued LRRK2-G2019S induced bradykinesia and retinal signaling deficits. Rab10 knock-down, however, did not rescue the marked sleep phenotype which results from dopaminergic LRRK2-G2019S. Courtship memory is not affected by LRRK2, but is markedly improved by Rab10 depletion. Anatomically, both LRRK2-G2019S and Rab10 are seen in the cytoplasm and at the synaptic endings of dopaminergic neurons. Conclusion: We conclude that, in Drosophila dopaminergic neurons, Rab10 is involved in some, but not all, LRRK2-induced behavioral deficits. Therefore, variations in Rab expression may contribute to susceptibility of different dopaminergic nuclei to neurodegeneration seen in people with Parkinson’s disease.

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Stress Mediating Genes in Aging, Health, and Longevity Traits: Effects of Multiple Interactions

Innovation in Aging ◽

10.1093/geroni/igaa057.915 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 286-286

Author(s):

Anatoliy Yashin ◽

Dequing Wu ◽

Konstantin Arbeev ◽

Arseniy Yashkin ◽

Galina Gorbunova ◽

...

Keyword(s):

Strong Interaction ◽

Genetic Interaction ◽

Interaction Effects ◽

Genetic Interactions ◽

Risk Scores ◽

Data Sets ◽

Interaction Patterns ◽

Polygenic Risk ◽

Drug Candidates ◽

Aging Health

Abstract Persistent stress of external or internal origin accelerates aging, increases risk of aging related health disorders, and shortens lifespan. Stressors activate stress response genes, and their products collectively influence traits. The variability of stressors and responses to them contribute to trait heterogeneity, which may cause the failure of clinical trials for drug candidates. The objectives of this paper are: to address the heterogeneity issue; to evaluate collective interaction effects of genetic factors on Alzheimer’s disease (AD) and longevity using HRS data; to identify differences and similarities in patterns of genetic interactions within two genders; and to compare AD related genetic interaction patterns in HRS and LOADFS data. To reach these objectives we: selected candidate genes from stress related pathways affecting AD/longevity; implemented logistic regression model with interaction term to evaluate effects of SNP-pairs on these traits for males and females; constructed the novel interaction polygenic risk scores for SNPs, which showed strong interaction potential, and evaluated effects of these scores on AD/longevity; and compared patterns of genetic interactions within the two genders and within two datasets. We found there were many genes involved in highly significant interactions that were the same and that were different within the two genders. The effects of interaction polygenic risk scores on AD were strong and highly statistically significant. These conclusions were confirmed in analyses of interaction effects on longevity trait using HRS data. Comparison of HRS to LOADFS data showed that many genes had strong interaction effects on AD in both data sets.

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Genome-wide mapping of unexplored essential regions in the Saccharomyces cerevisiae genome: evidence for hidden synthetic lethal combinations in a genetic interaction network

Nucleic Acids Research ◽

10.1093/nar/gku576 ◽

2014 ◽

Vol 42 (15) ◽

pp. 9838-9853 ◽

Cited By ~ 6

Author(s):

Saeed Kaboli ◽

Takuya Yamakawa ◽

Keisuke Sunada ◽

Tao Takagaki ◽

Yu Sasano ◽

...

Keyword(s):

Saccharomyces Cerevisiae ◽

Genetic Interaction ◽

Interaction Network ◽

Essential Genes ◽

Genetic Interactions ◽

Lethal Gene ◽

Synthetic Lethal ◽

Genome Wide ◽

A Genome ◽

Wide Scale

Abstract Despite systematic approaches to mapping networks of genetic interactions in Saccharomyces cerevisiae, exploration of genetic interactions on a genome-wide scale has been limited. The S. cerevisiae haploid genome has 110 regions that are longer than 10 kb but harbor only non-essential genes. Here, we attempted to delete these regions by PCR-mediated chromosomal deletion technology (PCD), which enables chromosomal segments to be deleted by a one-step transformation. Thirty-three of the 110 regions could be deleted, but the remaining 77 regions could not. To determine whether the 77 undeletable regions are essential, we successfully converted 67 of them to mini-chromosomes marked with URA3 using PCR-mediated chromosome splitting technology and conducted a mitotic loss assay of the mini-chromosomes. Fifty-six of the 67 regions were found to be essential for cell growth, and 49 of these carried co-lethal gene pair(s) that were not previously been detected by synthetic genetic array analysis. This result implies that regions harboring only non-essential genes contain unidentified synthetic lethal combinations at an unexpectedly high frequency, revealing a novel landscape of genetic interactions in the S. cerevisiae genome. Furthermore, this study indicates that segmental deletion might be exploited for not only revealing genome function but also breeding stress-tolerant strains.

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