Evidence map›Paper›PMID 33749796›Full record

ArticleMolecular biology and evolution2021

Exploring a Local Genetic Interaction Network Using Evolutionary Replay Experiments.

Ryan C Vignogna, Sean W Buskirk, Gregory I Lang

Abstract readValidation Study
In one paragraph

Article in Molecular biology and evolution, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ryan C VignognaDepartment of Biological Sciences, Lehigh University, Bethlehem, PA, USA.ORCID 0000-0001-5943-6464
Sean W BuskirkDepartment of Biological Sciences, Lehigh University, Bethlehem, PA, USA.ORCID 0000-0003-1213-8130
Gregory I LangDepartment of Biological Sciences, Lehigh University, Bethlehem, PA, USA.ORCID 0000-0002-7931-0428

Funding

Mapping genetic interactions between growth-promoting mutations in yeastR01GM127420 · NIGMS · LEHIGH UNIVERSITY · PI LANG, GREGORY I · 2018 to 2022
$1.7M
NIGMS NIH HHS R01 GM127420
6 · The paper itself

Abstract

Understanding 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, shedding light on an underexplored region of the yeast genetic interaction network.

Indexed as

Biological EvolutionGene Regulatory NetworksModels, GeneticMutationSaccharomyces cerevisiaeSelection, Geneticexperimental evolutiongenetic interactionsyeast

Identifiers

PMID33749796
PMCPMC8321538

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.