Transcriptional Derepression Uncovers Cryptic Higher-Order Genetic Interactions

Darwinian fitness is a central concept in evolutionary biology. In practice, however, it is hardly possible to measure fitness for all genotypes in a natural population. Here, we present quantitative tools to make inferences about epistatic gene interactions when the fitness landscape is only incompletely determined due to imprecise measurements or missing observations. We demonstrate that genetic interactions can often be inferred from fitness rank orders, where all genotypes are ordered according to fitness, and even from partial fitness orders. We provide a complete characterization of rank orders that imply higher order epistasis. Our theory applies to all common types of gene interactions and facilitates comprehensive investigations of diverse genetic interactions. We analyzed various genetic systems comprising HIV-1, the malaria-causing parasite Plasmodium vivax, the fungus Aspergillus niger, and the TEM-family of β-lactamase associated with antibiotic resistance. For all systems, our approach revealed higher order interactions among mutations.

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Higher Order Genetic Interactions and Complex Trait Variation

Encyclopedia of Life Sciences ◽

10.1002/9780470015902.a0026374 ◽

2016 ◽

pp. 1-5

Author(s):

Takeshi Matsui ◽

Ian M Ehrenreich

Keyword(s):

Complex Trait ◽

Higher Order ◽

Genetic Interactions ◽

Trait Variation

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Pairwise and higher-order genetic interactions during the evolution of a tRNA

Nature ◽

10.1038/s41586-018-0170-7 ◽

2018 ◽

Vol 558 (7708) ◽

pp. 117-121 ◽

Cited By ~ 49

Author(s):

Júlia Domingo ◽

Guillaume Diss ◽

Ben Lehner

Keyword(s):

Higher Order ◽

Genetic Interactions

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The Causes and Consequences of Genetic Interactions (Epistasis)

Annual Review of Genomics and Human Genetics ◽

10.1146/annurev-genom-083118-014857 ◽

2019 ◽

Vol 20 (1) ◽

pp. 433-460 ◽

Cited By ~ 40

Author(s):

Júlia Domingo ◽

Pablo Baeza-Centurion ◽

Ben Lehner

Keyword(s):

Molecular Level ◽

Higher Order ◽

Genetic Interactions ◽

Current Understanding ◽

Specific Interactions ◽

Concerted Effort ◽

Genetic Prediction ◽

Genetic Context

The same mutation can have different effects in different individuals. One important reason for this is that the outcome of a mutation can depend on the genetic context in which it occurs. This dependency is known as epistasis. In recent years, there has been a concerted effort to quantify the extent of pairwise and higher-order genetic interactions between mutations through deep mutagenesis of proteins and RNAs. This research has revealed two major components of epistasis: nonspecific genetic interactions caused by nonlinearities in genotype-to-phenotype maps, and specific interactions between particular mutations. Here, we provide an overview of our current understanding of the mechanisms causing epistasis at the molecular level, the consequences of genetic interactions for evolution and genetic prediction, and the applications of epistasis for understanding biology and determining macromolecular structures.

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An Alternative, Cas12a-based CRISPR Interference System for Mycobacteria

10.1101/2021.01.25.427309 ◽

2021 ◽

Author(s):

Neil Fleck ◽

Christoph Grundner

Keyword(s):

Mycobacterium Tuberculosis ◽

Higher Order ◽

Genetic Interactions ◽

Gene Repression ◽

Crispr Interference ◽

Interference System ◽

Multiple Genes ◽

Technical Challenges

ABSTRACTThe introduction of CRISPR interference (CRISPRi) has made gene repression in mycobacteria much more efficient, but technical challenges of the prototypical Cas9-based platform, for example in multigene regulation, remain. Here, we introduce an alternative CRSPRi platform that uses the minimal Cas12a enzyme in combination with synthetic CRISPR arrays. This system is simple, tunable, and can regulate multiple genes simultaneously, providing a new tool to probe higher-order genetic interactions in mycobacteria including Mycobacterium tuberculosis (Mtb).

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Inferring Genetic Interactions From Comparative Fitness Data

10.1101/137372 ◽

2017 ◽

Cited By ~ 1

Author(s):

Kristina Crona ◽

Alex Gavryushkin ◽

Devin Greene ◽

Niko Beerenwinkel

Keyword(s):

Evolutionary Biology ◽

Fitness Landscape ◽

Higher Order ◽

Complete Characterization ◽

Genetic Interactions ◽

Gene Interactions ◽

Rank Orders ◽

Darwinian Fitness ◽

Hiv 1

AbstractDarwinian fitness is a central concept in evolutionary biology. In practice, however, it is hardly possible to measure fitness for all genotypes in a natural population. Here, we present quantitative tools to make inferences about epistatic gene interactions when the fitness landscape is only incompletely determined due to imprecise measurements or missing observations. We demonstrate that genetic interactions can often be inferred from fitness rank orders, where all genotypes are ordered according to fitness, and even from partial fitness orders. We provide a complete characterization of rank orders that imply higher order epistasis. Our theory applies to all common types of gene interactions and facilitates comprehensive investigations of diverse genetic interactions. We analyzed various genetic systems comprising HIV-1, the malaria-causing parasite Plasmodium vivax, the fungus Aspergillus niger, and the TEM-family of β-lactamase associated with antibiotic resistance. For all systems, our approach revealed higher order interactions among mutations.

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DANGO: Predicting higher-order genetic interactions

10.1101/2020.11.26.400739 ◽

2020 ◽

Author(s):

Ruochi Zhang ◽

Jianzhu Ma ◽

Jian Ma

Keyword(s):

Genetic Interaction ◽

Phenotypic Diversity ◽

Search Space ◽

Higher Order ◽

Genetic Interactions ◽

Combinatorial Search ◽

Pairwise Interactions ◽

Phenotypic Variations ◽

Almost All

AbstractHigher-order genetic interactions, which have profound impact on phenotypic variations, remain poorly characterized. Almost all studies to date have primarily reported pairwise interactions because it is dauntingly difficult to design high-throughput genetic screenings of the large combinatorial search space for higher-order interactions. Here, we develop an algorithm named Dango, based on a self-attention hypergraph neural network, to effectively predict the higher-order genetic interaction for a group of genes. As a proof-of-concept, we make comprehensive prediction of >400 million trigenic interactions in the yeast S. cerevisiae, significantly expanding the quantitative characterization of trigenic interactions. We find that Dango can accurately predict trigenic interactions that reveal both known and new biological functions related to cell growth. The predicted trigenic interactions can also serve as powerful genetic markers to predict growth response to many distinct conditions. Dango enables unveiling a more complete map of complex genetic interactions that impinge upon phenotypic diversity.

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Higher-order genetic interactions

Nature Methods ◽

10.1038/s41592-018-0063-6 ◽

2018 ◽

Vol 15 (7) ◽

pp. 481-481

Author(s):

Nicole Rusk

Keyword(s):

Higher Order ◽

Genetic Interactions

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Dual systems for all: Higher-order, role-based relational reasoning as a uniquely derived feature of human cognition

Behavioral and Brain Sciences ◽

10.1017/s0140525x19000451 ◽

2019 ◽

Vol 42 ◽

Author(s):

Daniel J. Povinelli ◽

Gabrielle C. Glorioso ◽

Shannon L. Kuznar ◽

Mateja Pavlic

Keyword(s):

Temporal Reasoning ◽

Higher Order ◽

Important Case ◽

Human Cognition ◽

Relational Reasoning ◽

Dual Systems ◽

First Order ◽

Reasoning System ◽

Role Based

Abstract Hoerl and McCormack demonstrate that although animals possess a sophisticated temporal updating system, there is no evidence that they also possess a temporal reasoning system. This important case study is directly related to the broader claim that although animals are manifestly capable of first-order (perceptually-based) relational reasoning, they lack the capacity for higher-order, role-based relational reasoning. We argue this distinction applies to all domains of cognition.

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