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On the (un-)predictability of a large intragenic fitness landscape

View ORCID ProfileClaudia Bank, View ORCID ProfileSebastian Matuszewski, Ryan T. Hietpas, Jeffrey Jensen
doi: https://doi.org/10.1101/048769
Claudia Bank
aInstituto Gulbenkian de Ciência, 2780-156 Oeiras, Portugal
bSchool of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
cSwiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
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Sebastian Matuszewski
bSchool of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
cSwiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
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Ryan T. Hietpas
dEli Lilly and Company, Indianapolis, IN 46225
eDepartment of Biochemistry & Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01605
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Jeffrey Jensen
bSchool of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
cSwiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
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  • For correspondence: jeffrey.jensen@epfl.ch
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Abstract

The study of fitness landscapes, which aims at mapping genotypes to fitness, is receiving ever-increasing attention. Novel experimental approaches combined with NGS methods enable accurate and extensive studies of the fitness effects of mutations – allowing us to test theoretical predictions and improve our understanding of the shape of the true underlying fitness landscape, and its implications for the predictability and repeatability of evolution.

Here, we present a uniquely large multi-allelic fitness landscape comprised of 640 engineered mutants that represent all possible combinations of 13 amino-acid changing mutations at six sites in the heat-shock protein Hsp90 in Saccharomyces cerevisiae under elevated salinity. Despite a prevalent pattern of negative epistasis in the landscape, we find that the global fitness peak is reached via four positively epistatic mutations. Combining traditional and extending recently proposed theoretical and statistical approaches, we quantify features of the global multi-allelic fitness landscape. Using subsets of this data, we demonstrate that extrapolation beyond a known part of the landscape is difficult owing to both local ruggedness and amino-acid specific epistatic hotspots, and that inference is additionally confounded by the non-random choice of mutations for experimental fitness landscapes.

Author Summary The study of fitness landscapes is fundamentally concerned with understanding the relative roles of stochastic and deterministic processes in adaptive evolution. Here, the authors present a uniquely large and complete multi-allelic intragenic fitness landscape of 640 systematically engineered mutations in yeast Hsp90. Using a combination of traditional and recently proposed theoretical approaches, they study the accessibility of the global fitness peak, and the potential for predictability of the fitness landscape topology. They report local ruggedness of the landscape and the existence of epistatic hotspot mutations, which together make extrapolation and hence predictability inherently difficult, if mutation-specific information is not considered.

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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 April 15, 2016.
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On the (un-)predictability of a large intragenic fitness landscape
Claudia Bank, Sebastian Matuszewski, Ryan T. Hietpas, Jeffrey Jensen
bioRxiv 048769; doi: https://doi.org/10.1101/048769
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On the (un-)predictability of a large intragenic fitness landscape
Claudia Bank, Sebastian Matuszewski, Ryan T. Hietpas, Jeffrey Jensen
bioRxiv 048769; doi: https://doi.org/10.1101/048769

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