Evolutionary approach for improved proton pumping activity of heterologous rhodopsin expressed in Escherichia coli.

Engineering Error-prone PCR Escherichia coli Proton pumping Rhodopsin Screening

Journal

Journal of bioscience and bioengineering
ISSN: 1347-4421
Titre abrégé: J Biosci Bioeng
Pays: Japan
ID NLM: 100888800

Informations de publication

Date de publication:
Dec 2022
Historique:
received: 31 05 2022
revised: 29 07 2022
accepted: 17 08 2022
pubmed: 29 9 2022
medline: 15 12 2022
entrez: 28 9 2022
Statut: ppublish

Résumé

A light-driven ATP regeneration system using rhodopsin has been utilized as a method to improve the production of useful substances by microorganisms. To enable the industrial use of this system, the proton pumping rate of rhodopsin needs to be enhanced. Nonetheless, a method for this enhancement has not been established. In this study, we attempted to develop an evolutionary engineering method to improve the proton-pumping activity of rhodopsins. We first introduced random mutations into delta-rhodopsin (dR) from Haloterrigena turkmenica using error-prone PCR to generate approximately 7000 Escherichia coli strains carrying the mutant dR genes. Rhodopsin-expressing E. coli with enhanced proton pumping activity have significantly increased survival rates in prolonged saline water. Considering this, we enriched the mutant E. coli cells with higher proton pumping rates by selecting populations able to survive starvation under 50 μmol m

Identifiants

pubmed: 36171161
pii: S1389-1723(22)00231-6
doi: 10.1016/j.jbiosc.2022.08.006
pii:
doi:

Substances chimiques

Rhodopsin 9009-81-8
Protons 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

484-490

Informations de copyright

Copyright © 2022 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Auteurs

Kensuke Otsuka (K)

Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan.

Taisuke Seike (T)

Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan.

Yoshihiro Toya (Y)

Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan.

Jun Ishii (J)

Engineering Biology Research Center, Kobe University, 1-1 Rokkodai, Nada, Kobe, Hyogo 657-8501, Japan; Graduate School of Science, Technology and Innovation, Kobe University, 1-1 Rokkodai, Nada, Kobe, Hyogo 657-8501, Japan.

Yoko Hirono-Hara (Y)

Department of Environmental and Life Sciences, School of Food and Nutritional Sciences, University of Shizuoka, 52-1 Yada, Suruga, Shizuoka 422-8526, Japan.

Kiyotaka Y Hara (KY)

Department of Environmental and Life Sciences, School of Food and Nutritional Sciences, University of Shizuoka, 52-1 Yada, Suruga, Shizuoka 422-8526, Japan; Graduate Division of Nutritional and Environmental Sciences, University of Shizuoka, 52-1 Yada, Suruga, Shizuoka 422-8526, Japan.

Fumio Matsuda (F)

Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan. Electronic address: fmatsuda@ist.osaka-u.ac.jp.

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Classifications MeSH