Robustness and innovation in synthetic genotype networks

Genotype networks are sets of genotypes connected by small mutational changes that share the same phenotype. They facilitate evolutionary innovation by enabling the exploration of different neighborhoods in genotype space. Genotype networks, first suggested by theoretical models, have been empirical...

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Detalhes bibliográficos
Autores: Santos Moreno, Javier, Tasiudi, Eve, Kusumawardhani, Hadiastri, Stelling, Joerg, Schaerli, Yolanda
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Recursos:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/57933
Acesso em linha:http://hdl.handle.net/10230/57933
http://dx.doi.org/10.1038/s41467-023-38033-3
Access Level:acceso abierto
Palavra-chave:Evolvability
Regulatory networks
Robustness
Synthetic biology
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spelling Robustness and innovation in synthetic genotype networksSantos Moreno, JavierTasiudi, EveKusumawardhani, HadiastriStelling, JoergSchaerli, YolandaEvolvabilityRegulatory networksRobustnessSynthetic biologyGenotype networks are sets of genotypes connected by small mutational changes that share the same phenotype. They facilitate evolutionary innovation by enabling the exploration of different neighborhoods in genotype space. Genotype networks, first suggested by theoretical models, have been empirically confirmed for proteins and RNAs. Comparative studies also support their existence for gene regulatory networks (GRNs), but direct experimental evidence is lacking. Here, we report the construction of three interconnected genotype networks of synthetic GRNs producing three distinct phenotypes in Escherichia coli. Our synthetic GRNs contain three nodes regulating each other by CRISPR interference and governing the expression of fluorescent reporters. The genotype networks, composed of over twenty different synthetic GRNs, provide robustness in face of mutations while enabling transitions to innovative phenotypes. Through realistic mathematical modeling, we quantify robustness and evolvability for the complete genotype-phenotype map and link these features mechanistically to GRN motifs. Our work thereby exemplifies how GRN evolution along genotype networks might be driving evolutionary innovation.Nature Research202320232023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/57933http://dx.doi.org/10.1038/s41467-023-38033-3reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésNat Commun. 2023;14:2454© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/ licenses/by/4.0/http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/579332026-06-12T07:21:37Z
dc.title.none.fl_str_mv Robustness and innovation in synthetic genotype networks
title Robustness and innovation in synthetic genotype networks
spellingShingle Robustness and innovation in synthetic genotype networks
Santos Moreno, Javier
Evolvability
Regulatory networks
Robustness
Synthetic biology
title_short Robustness and innovation in synthetic genotype networks
title_full Robustness and innovation in synthetic genotype networks
title_fullStr Robustness and innovation in synthetic genotype networks
title_full_unstemmed Robustness and innovation in synthetic genotype networks
title_sort Robustness and innovation in synthetic genotype networks
dc.creator.none.fl_str_mv Santos Moreno, Javier
Tasiudi, Eve
Kusumawardhani, Hadiastri
Stelling, Joerg
Schaerli, Yolanda
author Santos Moreno, Javier
author_facet Santos Moreno, Javier
Tasiudi, Eve
Kusumawardhani, Hadiastri
Stelling, Joerg
Schaerli, Yolanda
author_role author
author2 Tasiudi, Eve
Kusumawardhani, Hadiastri
Stelling, Joerg
Schaerli, Yolanda
author2_role author
author
author
author
dc.subject.none.fl_str_mv Evolvability
Regulatory networks
Robustness
Synthetic biology
topic Evolvability
Regulatory networks
Robustness
Synthetic biology
description Genotype networks are sets of genotypes connected by small mutational changes that share the same phenotype. They facilitate evolutionary innovation by enabling the exploration of different neighborhoods in genotype space. Genotype networks, first suggested by theoretical models, have been empirically confirmed for proteins and RNAs. Comparative studies also support their existence for gene regulatory networks (GRNs), but direct experimental evidence is lacking. Here, we report the construction of three interconnected genotype networks of synthetic GRNs producing three distinct phenotypes in Escherichia coli. Our synthetic GRNs contain three nodes regulating each other by CRISPR interference and governing the expression of fluorescent reporters. The genotype networks, composed of over twenty different synthetic GRNs, provide robustness in face of mutations while enabling transitions to innovative phenotypes. Through realistic mathematical modeling, we quantify robustness and evolvability for the complete genotype-phenotype map and link these features mechanistically to GRN motifs. Our work thereby exemplifies how GRN evolution along genotype networks might be driving evolutionary innovation.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/57933
http://dx.doi.org/10.1038/s41467-023-38033-3
url http://hdl.handle.net/10230/57933
http://dx.doi.org/10.1038/s41467-023-38033-3
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Nat Commun. 2023;14:2454
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Nature Research
publisher.none.fl_str_mv Nature Research
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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