Dataset Information

Changes in gene expression predictably shift and switch genetic interactions

ABSTRACT: An important goal in disease genetics and evolutionary biology is to understand how mutations combine together to alter phenotypes and fitness. Non-additive genetic (epistatic) interactions between mutations occur extensively within and between genes, which makes accurate genetic prediction a difficult challenge. Moreover, for unclear reasons, the interactions between mutations change quite extensively across conditions, cell types, and species, with important consequences for both evolution and precision medicine such as the exploitation of synthetic lethality in cancer. To better understand the plasticity of genetic interactions, we reduced the problem to a minimal system where we combined mutations within a single protein performing a single cellular function. The only perturbation to the system was a change in the expression level of the mutated gene itself. Even in this minimal system, the interactions between mutations were highly plastic, with interactions changing in both magnitude and sign when the expression level was altered. Mathematical modelling revealed the cause of this as the non-linear relationship between the concentration of the protein and the cellular phenotype.These non-linearities are widespread in biology and transform expression level-independent effects of mutations on protein folding and stability into mutation outcomes and interactions that shift and switch as gene expression changes. This plasticity of mutation effects and genetic interactions has important implications for human disease and evolutionary theory.

ORGANISM(S): synthetic construct

PROVIDER: GSE122806 | GEO | 2019/07/31

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Background: The impact of genetic interaction networks on evolution is a fundamental issue. Previous studies have demonstrated that the topology of the network is determined by the properties of the cellular machinery. Functionally related genes frequently interact with one another, and they establish modules, e.g., modules of protein complexes and biochemical pathways. In this study, we experimentally tested the hypothesis that compensatory evolutionary modifications, such as mutations and transcriptional changes, occur frequently in genes from perturbed modules of genetically interacting genes. Results: Using haploid strains of Saccharomyces cerevisiae deletion mutants as a model, we investigated two modules lacking COG7 or NUP133, which are evolutionarily conserved genes with many compensatory interactions. We performed laboratory evolution experiments with strains bearing these mutations in two genetic backgrounds (with or without additional deletion of MSH2), subjecting them to continuous culture in a non-limiting minimal medium. Next, the evolved yeast populations were characterized through whole-genome sequencing and transcriptome analyses. No obvious compensatory changes resulting from inactivation of genes already included in modules were identified. The supposedly compensatory inactivation of genes in the evolved strains was only rarely observed to be in accordance with the established fitness effect of the genetic interaction network. In fact, a substantial majority of the gene inactivations were predicted to be neutral. Similarly, transcriptome changes during continuous culture mostly signified adaptation to growth conditions rather than compensation of the absence of COG7, NUP133 or MSH2 genes. Conclusions: Our findings demonstrate that the genetic interactions and modular structure of the network described in other studies have very limited effects on the evolutionary trajectory observed on the genomic and transcriptomic levels, following gene deletion of module elements and upon our experimental conditions. This observation indicates that the modular structure of the cellular machinery has no impact on compensatory evolution in the short term.

Project description:Local adaptation and its underlying molecular basis has long been a key focus in evolutionary biology. There has recently been increased interest in the evolutionary role of plasticity and the molecular mechanisms underlying local adaptation. Using transcriptome analysis, we assessed differences in gene expression profiles for three brown trout (Salmo trutta) populations, one resident and two anadromous, experiencing different temperature regimes in the wild. The study was based on an F2 generation raised in a common garden setting. A previous study of the F1 generation revealed different reaction norms and significantly higher QST than FST among populations for two early life-history traits. In the present study we investigated if similar reaction norm patterns were present at the transcriptome level. Eggs from the three populations were incubated at two temperatures (5 and 8 degrees C) representing conditions encountered in the local environments. Global gene expression for fry at the stage of first feeding was analysed using a 32k cDNA microarray. The results revealed differences in gene expression between populations and temperatures and population × temperature interactions, the latter indicating locally adapted reaction norms. Moreover, the reaction norms paralleled those observed previously at early life-history traits. We were able to identify 90 cDNA clones among the genes with an interaction effect that were differently expressed between the ecologically divergent populations. These included genes involved in immune- and stress response. We observed less plasticity in the resident as compared to the anadromous populations, possibly reflecting that the degree of environmental heterogeneity encountered by individuals throughout their life cycle will select for variable level of phenotypic plasticity at the transcriptome level. Our study demonstrates the usefulness of transcriptome approaches to identify genes with different temperature reaction norms. The responses observed suggest that populations may vary in their susceptibility to climate change.

Dataset Information

Changes in gene expression predictably shift and switch genetic interactions

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets