Cargando…

Yeast-based evolutionary modeling of androgen receptor mutations and natural selection

Cancer progression is associated with the evolutionary accumulation of genetic mutations that are biologically significant. Mutations of the androgen receptor (AR) are associated with the development of prostate cancer (PCa) by responding to non-androgenic hormones, and the lack of annotations in th...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Haoran, Zhang, Lu, Chen, Shaoyong, Yao, Mingdong, Ma, Zhenyi, Yuan, Yingjin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9718406/
https://www.ncbi.nlm.nih.gov/pubmed/36459502
http://dx.doi.org/10.1371/journal.pgen.1010518
_version_ 1784843085783498752
author Zhang, Haoran
Zhang, Lu
Chen, Shaoyong
Yao, Mingdong
Ma, Zhenyi
Yuan, Yingjin
author_facet Zhang, Haoran
Zhang, Lu
Chen, Shaoyong
Yao, Mingdong
Ma, Zhenyi
Yuan, Yingjin
author_sort Zhang, Haoran
collection PubMed
description Cancer progression is associated with the evolutionary accumulation of genetic mutations that are biologically significant. Mutations of the androgen receptor (AR) are associated with the development of prostate cancer (PCa) by responding to non-androgenic hormones, and the lack of annotations in their responsiveness to hormone ligands remains a daunting challenge. Here, we have used a yeast reporter system to quickly evaluate the responsiveness of all fifty clinical AR mutations to a variety of steroidal ligands including dihydrotestosterone (DHT), 17β-estradiol (E2), progesterone (PROG), and cyproterone acetate (CPA). Based on an AR-driven reporter that synthesizes histidine, a basic amino acid required for yeast survival and propagation, the yeast reporter system enabling clonal selection was further empowered by combining with a random DNA mutagenesis library to simulate the natural evolution of AR gene under the selective pressures of steroidal ligands. In a time-frame of 1–2 weeks, 19 AR mutants were identified, in which 11 AR mutants were validated for activation by tested steroidal compounds. The high efficiency of our artificial evolution strategy was further evidenced by a sequential selection that enabled the discovery of multipoint AR mutations and evolution directions under the pressure of steroidal ligands. In summary, our designer yeast is a portable reporter module that can be readily adapted to streamline high-throughput AR-compound screening, used as a PCa clinical reference, and combined with additional bioassay systems to further extend its potential.
format Online
Article
Text
id pubmed-9718406
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-97184062022-12-03 Yeast-based evolutionary modeling of androgen receptor mutations and natural selection Zhang, Haoran Zhang, Lu Chen, Shaoyong Yao, Mingdong Ma, Zhenyi Yuan, Yingjin PLoS Genet Research Article Cancer progression is associated with the evolutionary accumulation of genetic mutations that are biologically significant. Mutations of the androgen receptor (AR) are associated with the development of prostate cancer (PCa) by responding to non-androgenic hormones, and the lack of annotations in their responsiveness to hormone ligands remains a daunting challenge. Here, we have used a yeast reporter system to quickly evaluate the responsiveness of all fifty clinical AR mutations to a variety of steroidal ligands including dihydrotestosterone (DHT), 17β-estradiol (E2), progesterone (PROG), and cyproterone acetate (CPA). Based on an AR-driven reporter that synthesizes histidine, a basic amino acid required for yeast survival and propagation, the yeast reporter system enabling clonal selection was further empowered by combining with a random DNA mutagenesis library to simulate the natural evolution of AR gene under the selective pressures of steroidal ligands. In a time-frame of 1–2 weeks, 19 AR mutants were identified, in which 11 AR mutants were validated for activation by tested steroidal compounds. The high efficiency of our artificial evolution strategy was further evidenced by a sequential selection that enabled the discovery of multipoint AR mutations and evolution directions under the pressure of steroidal ligands. In summary, our designer yeast is a portable reporter module that can be readily adapted to streamline high-throughput AR-compound screening, used as a PCa clinical reference, and combined with additional bioassay systems to further extend its potential. Public Library of Science 2022-12-02 /pmc/articles/PMC9718406/ /pubmed/36459502 http://dx.doi.org/10.1371/journal.pgen.1010518 Text en © 2022 Zhang et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zhang, Haoran
Zhang, Lu
Chen, Shaoyong
Yao, Mingdong
Ma, Zhenyi
Yuan, Yingjin
Yeast-based evolutionary modeling of androgen receptor mutations and natural selection
title Yeast-based evolutionary modeling of androgen receptor mutations and natural selection
title_full Yeast-based evolutionary modeling of androgen receptor mutations and natural selection
title_fullStr Yeast-based evolutionary modeling of androgen receptor mutations and natural selection
title_full_unstemmed Yeast-based evolutionary modeling of androgen receptor mutations and natural selection
title_short Yeast-based evolutionary modeling of androgen receptor mutations and natural selection
title_sort yeast-based evolutionary modeling of androgen receptor mutations and natural selection
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9718406/
https://www.ncbi.nlm.nih.gov/pubmed/36459502
http://dx.doi.org/10.1371/journal.pgen.1010518
work_keys_str_mv AT zhanghaoran yeastbasedevolutionarymodelingofandrogenreceptormutationsandnaturalselection
AT zhanglu yeastbasedevolutionarymodelingofandrogenreceptormutationsandnaturalselection
AT chenshaoyong yeastbasedevolutionarymodelingofandrogenreceptormutationsandnaturalselection
AT yaomingdong yeastbasedevolutionarymodelingofandrogenreceptormutationsandnaturalselection
AT mazhenyi yeastbasedevolutionarymodelingofandrogenreceptormutationsandnaturalselection
AT yuanyingjin yeastbasedevolutionarymodelingofandrogenreceptormutationsandnaturalselection