Fitness landscape of a dynamic RNA structure.

RNA structures are dynamic. As a consequence, mutational effects can be hard to rationalize with reference to a single static native structure. We reasoned that deep mutational scanning experiments, which couple molecular function to fitness, should capture mutational effects across multiple conform...

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Main Authors: Valerie W C Soo, Jacob B Swadling, Andre J Faure, Tobias Warnecke
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2021-02-01
Series:PLoS Genetics
Online Access:https://doi.org/10.1371/journal.pgen.1009353
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spelling doaj-d33c6671e55f46499cd5307506c2944d2021-06-25T04:30:40ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042021-02-01172e100935310.1371/journal.pgen.1009353Fitness landscape of a dynamic RNA structure.Valerie W C SooJacob B SwadlingAndre J FaureTobias WarneckeRNA structures are dynamic. As a consequence, mutational effects can be hard to rationalize with reference to a single static native structure. We reasoned that deep mutational scanning experiments, which couple molecular function to fitness, should capture mutational effects across multiple conformational states simultaneously. Here, we provide a proof-of-principle that this is indeed the case, using the self-splicing group I intron from Tetrahymena thermophila as a model system. We comprehensively mutagenized two 4-bp segments of the intron. These segments first come together to form the P1 extension (P1ex) helix at the 5' splice site. Following cleavage at the 5' splice site, the two halves of the helix dissociate to allow formation of an alternative helix (P10) at the 3' splice site. Using an in vivo reporter system that couples splicing activity to fitness in E. coli, we demonstrate that fitness is driven jointly by constraints on P1ex and P10 formation. We further show that patterns of epistasis can be used to infer the presence of intramolecular pleiotropy. Using a machine learning approach that allows quantification of mutational effects in a genotype-specific manner, we demonstrate that the fitness landscape can be deconvoluted to implicate P1ex or P10 as the effective genetic background in which molecular fitness is compromised or enhanced. Our results highlight deep mutational scanning as a tool to study alternative conformational states, with the capacity to provide critical insights into the structure, evolution and evolvability of RNAs as dynamic ensembles. Our findings also suggest that, in the future, deep mutational scanning approaches might help reverse-engineer multiple alternative or successive conformations from a single fitness landscape.https://doi.org/10.1371/journal.pgen.1009353
collection DOAJ
language English
format Article
sources DOAJ
author Valerie W C Soo
Jacob B Swadling
Andre J Faure
Tobias Warnecke
spellingShingle Valerie W C Soo
Jacob B Swadling
Andre J Faure
Tobias Warnecke
Fitness landscape of a dynamic RNA structure.
PLoS Genetics
author_facet Valerie W C Soo
Jacob B Swadling
Andre J Faure
Tobias Warnecke
author_sort Valerie W C Soo
title Fitness landscape of a dynamic RNA structure.
title_short Fitness landscape of a dynamic RNA structure.
title_full Fitness landscape of a dynamic RNA structure.
title_fullStr Fitness landscape of a dynamic RNA structure.
title_full_unstemmed Fitness landscape of a dynamic RNA structure.
title_sort fitness landscape of a dynamic rna structure.
publisher Public Library of Science (PLoS)
series PLoS Genetics
issn 1553-7390
1553-7404
publishDate 2021-02-01
description RNA structures are dynamic. As a consequence, mutational effects can be hard to rationalize with reference to a single static native structure. We reasoned that deep mutational scanning experiments, which couple molecular function to fitness, should capture mutational effects across multiple conformational states simultaneously. Here, we provide a proof-of-principle that this is indeed the case, using the self-splicing group I intron from Tetrahymena thermophila as a model system. We comprehensively mutagenized two 4-bp segments of the intron. These segments first come together to form the P1 extension (P1ex) helix at the 5' splice site. Following cleavage at the 5' splice site, the two halves of the helix dissociate to allow formation of an alternative helix (P10) at the 3' splice site. Using an in vivo reporter system that couples splicing activity to fitness in E. coli, we demonstrate that fitness is driven jointly by constraints on P1ex and P10 formation. We further show that patterns of epistasis can be used to infer the presence of intramolecular pleiotropy. Using a machine learning approach that allows quantification of mutational effects in a genotype-specific manner, we demonstrate that the fitness landscape can be deconvoluted to implicate P1ex or P10 as the effective genetic background in which molecular fitness is compromised or enhanced. Our results highlight deep mutational scanning as a tool to study alternative conformational states, with the capacity to provide critical insights into the structure, evolution and evolvability of RNAs as dynamic ensembles. Our findings also suggest that, in the future, deep mutational scanning approaches might help reverse-engineer multiple alternative or successive conformations from a single fitness landscape.
url https://doi.org/10.1371/journal.pgen.1009353
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