scholarly journals Hierarchical organization of the human cell from a cancer coessentiality network

2018 ◽  
Author(s):  
Eiru Kim ◽  
Merve Dede ◽  
Walter F. Lenoir ◽  
Gang Wang ◽  
Sanjana Srinivasan ◽  
...  
Keyword(s):  
Cell Lines ◽  
Gene Function ◽  
Genetic Interaction ◽  
Genetic Interactions ◽  
Hierarchical Organization ◽  
Model Organisms ◽  
Function Modules ◽  
Strong Inference ◽  
Highly Correlated ◽  
Insight Into

AbstractGenetic interactions mediate the emergence of phenotype from genotype. Systematic survey of genetic interactions in yeast showed that genes operating in the same biological process have highly correlated genetic interaction profiles, and this observation has been exploited to infer gene function in model organisms. Systematic surveys of digenic perturbations in human cells are also highly informative, but are not scalable, even with CRISPR-mediated methods. As an alternative, we developed an indirect method of deriving functional interactions. We show that genes having correlated knockout fitness profiles across diverse, non-isogenic cell lines are analogous to genes having correlated genetic interaction profiles across isogenic query strains, and similarly implies shared biological function. We constructed a network of genes with correlated fitness profiles across 400 CRISPR knockout screens in cancer cell lines into a “coessentiality network,” with up to 500-fold enrichment for co-functional gene pairs, enabling strong inference of human gene function. Modules in the network are connected in a layered web that gives insight into the hierarchical organization of the cell.

scholarly journals A network of human functional gene interactions from knockout fitness screens in cancer cells

2019 ◽  
Vol 2 (2) ◽  
pp. e201800278 ◽  
Author(s):  
Eiru Kim ◽  
Merve Dede ◽  
Walter F Lenoir ◽  
Gang Wang ◽  
Sanjana Srinivasan ◽  
...  
Keyword(s):  
Cell Lines ◽  
Gene Function ◽  
Genetic Interaction ◽  
Genetic Interactions ◽  
Functional Gene ◽  
Model Organisms ◽  
Modular Organization ◽  
Gene Interactions ◽  
Strong Inference ◽  
Highly Correlated

Genetic interactions mediate the emergence of phenotype from genotype. The systematic survey of genetic interactions in yeast showed that genes operating in the same biological process have highly correlated genetic interaction profiles, and this observation has been exploited to infer gene function in model organisms. Such assays of digenic perturbations in human cells are also highly informative, but are not scalable, even with CRISPR-mediated methods. As an alternative, we developed an indirect method of deriving functional interactions. We show that genes having correlated knockout fitness profiles across diverse, non-isogenic cell lines are analogous to genes having correlated genetic interaction profiles across isogenic query strains and similarly imply shared biological function. We constructed a network of genes with correlated fitness profiles across 276 high-quality CRISPR knockout screens in cancer cell lines into a “coessentiality network,” with up to 500-fold enrichment for co-functional gene pairs, enabling strong inference of gene function and highlighting the modular organization of the cell.

scholarly journals Systematic characterization and genetic interaction analysis of adhesins in Candida albicans virulence

2020 ◽  
Author(s):  
Sierra Rosiana ◽  
Liyang Zhang ◽  
Grace H. Kim ◽  
Alexey V. Revtovich ◽  
Arjun Sukumaran ◽  
...  
Keyword(s):  
Candida Albicans ◽  
Genetic Interaction ◽  
Interaction Analysis ◽  
Genetic Interactions ◽  
Fungal Virulence ◽  
C Elegans ◽  
Adhesin Proteins ◽  
Insight Into

AbstractCandida albicans is a microbial fungus that exists as a commensal member of the human microbiome and an opportunistic pathogen. Cell surface-associated adhesin proteins play a crucial role in C. albicans’ ability to undergo cellular morphogenesis, develop robust biofilms, colonize, and cause infection in a host. However, a comprehensive analysis of the role and relationships between these adhesins has not been explored. We previously established a CRISPR-based platform for efficient generation of single- and double-gene deletions in C. albicans, which was used to construct a library of 144 mutants, comprising 12 unique adhesin genes deleted singly, or in every possible combination of double deletions. Here, we exploit this adhesin mutant library to explore the role of adhesin proteins in C. albicans virulence. We perform a comprehensive, high-throughput screen of this library, using Caenorhabditis elegans as a simplified model host system, which identified mutants critical for virulence and significant genetic interactions. We perform follow-up analysis to assess the ability of high- and low-virulence strains to undergo cellular morphogenesis and form biofilms in vitro, as well as to colonize the C. elegans host. We further perform genetic interaction analysis to identify novel significant negative genetic interactions between adhesin mutants, whereby combinatorial perturbation of these genes significantly impairs virulence, more than expected based on virulence of the single mutant constituent strains. Together, this yields important new insight into the role of adhesins, singly and in combinations, in mediating diverse facets of virulence of this critical fungal pathogen.SummaryCandida albicans is a human fungal pathogen and cause of life-threatening systemic infections. Cell surface-associated adhesins play a central role in this pathogen’s ability to establish infection. Here, we provide a comprehensive analysis of adhesin factors, and their role in fungal virulence. Exploiting a high-throughput workflow, we screened an adhesin mutant library using C. elegans as a simple model host, and identified mutants and genetic interactions involved in virulence. We found that adhesin mutants are impaired in in vitro pathogenicity, irrespective of their virulence. Together, this work provides new insight into the role of adhesin factors in mediating fungal virulence.

scholarly journals Towards an Integrated Map of Genetic Interactions in Cancer Cells

2017 ◽  
Author(s):  
Benedikt Rauscher ◽  
Florian Heigwer ◽  
Luisa Henkel ◽  
Thomas Hielscher ◽  
Oksana Voloshanenko ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Genetic Variants ◽  
Genetic Interaction ◽  
Human Cancer ◽  
Genetic Network ◽  
Genetic Interactions ◽  
Model Organisms ◽  
Genetic Screens ◽  
New Genotype ◽  
Analytical Approaches

ABSTRACTCancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and, as a side effect, create new vulnerabilities for potential therapeutic exploitation. To systematically identify genotype-dependent vulnerabilities and synthetic lethal interactions, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework that integrates CRISPR/Cas9 screens originating from many different libraries and laboratories to build genetic interaction maps. It builds on analytical approaches that were established for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cell lines combining functional data with information on genetic variants to explore the relationships of more than 2.1 million gene-background relationships. In addition to known dependencies, our analysis identified new genotype-specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities associated with aberrant Wnt/β-catenin signaling identifiedGANABandPRKCSHas 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 is included.

scholarly journals Predicting genetic interactions from Boolean models of biological networks

2015 ◽  
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.

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

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.

scholarly journals Monosomy 3 Is Linked to Resistance to MEK Inhibitors in Uveal Melanoma

2021 ◽  
Vol 22 (13) ◽  
pp. 6727
Author(s):  
Svenja Mergener ◽  
Jens T. Siveke ◽  
Samuel Peña-Llopis
Keyword(s):  
Cell Lines ◽  
Uveal Melanoma ◽  
Survival Data ◽  
Translation Initiation Factor ◽  
The Cancer Genome Atlas ◽  
Initiation Factor ◽  
Chromosome 3 ◽  
Mek Inhibitors ◽  
Mek Inhibition ◽  
Insight Into

The use of MEK inhibitors in the therapy of uveal melanoma (UM) has been investigated widely but has failed to show benefits in clinical trials due to fast acquisition of resistance. In this study, we investigated a variety of therapeutic compounds in primary-derived uveal melanoma cell lines and found monosomy of chromosome 3 (M3) and mutations in BAP1 to be associated with higher resistance to MEK inhibition. However, reconstitution of BAP1 in a BAP1-deficient UM cell line was unable to restore sensitivity to MEK inhibition. We then compared UM tumors from The Cancer Genome Atlas (TCGA) with mutations in BAP1 with tumors with wild-type BAP1. Principal component analysis (PCA) clearly differentiated both groups of tumors, which displayed disparate overall and progression-free survival data. Further analysis provided insight into differential expression of genes involved in signaling pathways, suggesting that the downregulation of the eukaryotic translation initiation factor 2A (EIF2A) observed in UM tumors with BAP1 mutations and M3 UM cell lines might lead to a decrease in ribosome biogenesis while inducing an adaptive response to stress. Taken together, our study links loss of chromosome 3 with decreased sensitivity to MEK inhibition and gives insight into possible related mechanisms, whose understanding is fundamental to overcome resistance in this aggressive tumor.

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

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.

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

2014 ◽  
Vol 42 (15) ◽  
pp. 9838-9853 ◽  
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.

scholarly journals p-adic numbers encode complex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hao Hua ◽  
Ludger Hovestadt
Keyword(s):  
Complex Networks ◽  
Random Graph ◽  
Genetic Interaction ◽  
Hierarchical Structures ◽  
Number System ◽  
Hierarchical Organization ◽  
Natural Representation ◽  
Multimodal Distributions ◽  
Number Sequences ◽  
Er Model

AbstractThe Erdős-Rényi (ER) random graph G(n, p) analytically characterizes the behaviors in complex networks. However, attempts to fit real-world observations need more sophisticated structures (e.g., multilayer networks), rules (e.g., Achlioptas processes), and projections onto geometric, social, or geographic spaces. The p-adic number system offers a natural representation of hierarchical organization of complex networks. The p-adic random graph interprets n as the cardinality of a set of p-adic numbers. Constructing a vast space of hierarchical structures is equivalent for combining number sequences. Although the giant component is vital in dynamic evolution of networks, the structure of multiple big components is also essential. Fitting the sizes of the few largest components to empirical data was rarely demonstrated. The p-adic ultrametric enables the ER model to simulate multiple big components from the observations of genetic interaction networks, social networks, and epidemics. Community structures lead to multimodal distributions of the big component sizes in networks, which have important implications in intervention of spreading processes.

scholarly journals Transgenic Epigenetics: Using Transgenic Organisms to Examine Epigenetic Phenomena

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Lori A. McEachern
Keyword(s):  
Molecular Mechanisms ◽  
Model Organism ◽  
Evolutionary Conservation ◽  
Model Organisms ◽  
Valuable Insight ◽  
Epigenetic Control ◽  
Transgenic Organisms ◽  
Epigenetic Analysis ◽  
Insight Into ◽  
Epigenetic Processes

Non-model organisms are generally more difficult and/or time consuming to work with than model organisms. In addition, epigenetic analysis of model organisms is facilitated by well-established protocols, and commercially-available reagents and kits that may not be available for, or previously tested on, non-model organisms. Given the evolutionary conservation and widespread nature of many epigenetic mechanisms, a powerful method to analyze epigenetic phenomena from non-model organisms would be to use transgenic model organisms containing an epigenetic region of interest from the non-model. Interestingly, while transgenic Drosophila and mice have provided significant insight into the molecular mechanisms and evolutionary conservation of the epigenetic processes that target epigenetic control regions in other model organisms, this method has so far been under-exploited for non-model organism epigenetic analysis. This paper details several experiments that have examined the epigenetic processes of genomic imprinting and paramutation, by transferring an epigenetic control region from one model organism to another. These cross-species experiments demonstrate that valuable insight into both the molecular mechanisms and evolutionary conservation of epigenetic processes may be obtained via transgenic experiments, which can then be used to guide further investigations and experiments in the species of interest.

Sign in / Sign up

Export Citation Format

Share Document