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High-throughput functional analysis of natural variants in yeast

View ORCID ProfileChiann-Ling C. Yeh, View ORCID ProfileAndreas Tsouris, View ORCID ProfileJoseph Schacherer, View ORCID ProfileMaitreya J. Dunham
doi: https://doi.org/10.1101/2021.02.26.433108
Chiann-Ling C. Yeh
1Department of Genome Sciences, University of Washington, Seattle, Washington 98195
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Andreas Tsouris
2Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
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Joseph Schacherer
2Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
3Institut Universitaire de France (IUF), Paris, France
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Maitreya J. Dunham
1Department of Genome Sciences, University of Washington, Seattle, Washington 98195
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  • For correspondence: maitreya@uw.edu
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Abstract

How natural variation affects phenotype is difficult to determine given our incomplete ability to deduce the functional impact of the polymorphisms detected in a population. Although current computational and experimental tools can predict and measure allele function, there has previously been no assay that does so in a high-throughput manner while also representing haplotypes derived from wild populations. Here, we present such an assay that measures the fitness of hundreds of natural alleles of a given gene without site-directed mutagenesis or DNA synthesis. With a large collection of diverse Saccharomyces cerevisiae natural isolates, we piloted this technique using the gene SUL1, which encodes a high-affinity sulfate permease that, at increased copy number, can improve the fitness of cells grown in sulfate-limited media. We cloned and barcoded all alleles from a collection of over 1000 natural isolates en masse and matched barcodes with their respective variants using PacBio long-read sequencing and a novel error-correction algorithm. We then transformed the reference S288C strain with this library and used barcode sequencing to track growth ability in sulfate limitation of lineages carrying each allele. We show that this approach allows us to measure the fitness conferred by each allele and stratify functional and nonfunctional alleles. Additionally, we pinpoint which polymorphisms in both coding and noncoding regions are detrimental to fitness or are of small effect and result in intermediate phenotypes. Integrating these results with a phylogenetic tree, we observe how often loss-of-function occurs and whether or not there is an evolutionary pattern to our observable phenotypic results. This approach is easily applicable to other genes. Our results complement classic genotype-phenotype mapping strategies and demonstrate a high-throughput approach for understanding the effects of polymorphisms across an entire species which can greatly propel future investigations into quantitative traits.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/dunhamlab/SUL1_natural_variants

  • https://www.ncbi.nlm.nih.gov/bioproject/PRJNA681436

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 26, 2021.
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High-throughput functional analysis of natural variants in yeast
Chiann-Ling C. Yeh, Andreas Tsouris, Joseph Schacherer, Maitreya J. Dunham
bioRxiv 2021.02.26.433108; doi: https://doi.org/10.1101/2021.02.26.433108
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High-throughput functional analysis of natural variants in yeast
Chiann-Ling C. Yeh, Andreas Tsouris, Joseph Schacherer, Maitreya J. Dunham
bioRxiv 2021.02.26.433108; doi: https://doi.org/10.1101/2021.02.26.433108

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