Engineered bacterial microcompartments: apps for programming metabolism.


Journal

Current opinion in biotechnology
ISSN: 1879-0429
Titre abrégé: Curr Opin Biotechnol
Pays: England
ID NLM: 9100492

Informations de publication

Date de publication:
10 2020
Historique:
received: 25 02 2020
revised: 04 05 2020
accepted: 06 05 2020
pubmed: 20 6 2020
medline: 2 3 2021
entrez: 20 6 2020
Statut: ppublish

Résumé

Bacterial Microcompartments (BMCs) are used by diverse bacteria to compartmentalize enzymatic reactions, functioning analogously to the organelles of eukaryotes. The bounding membrane and encapsulated components are composed entirely of protein, which makes them ideal targets for modification by genetic engineering. In contrast to viruses, in which generally only one protein forms the capsid, the shells of BMCs consist of a variety of shell proteins, each a potential unit of selection. Despite their differences in permeability, the shell proteins are surprisingly interchangeable. Recent developments have shown that they are also highly amenable to engineered modifications which poise them for a variety of biotechnological applications. Given their modular structure, with a module defined as a semi-autonomous functional unit, BMCs can be considered apps for programming metabolism that can be de-bugged by adaptive evolution.

Identifiants

pubmed: 32554213
pii: S0958-1669(20)30056-2
doi: 10.1016/j.copbio.2020.05.001
pmc: PMC7719235
mid: NIHMS1650389
pii:
doi:

Substances chimiques

Bacterial Proteins 0

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S. Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

225-232

Subventions

Organisme : NIAID NIH HHS
ID : R01 AI114975
Pays : United States

Informations de copyright

Copyright © 2020 Elsevier Ltd. All rights reserved.

Références

Nat Genet. 2005 Dec;37(12):1372-5
pubmed: 16311593
Appl Environ Microbiol. 2014 Apr;80(7):2193-205
pubmed: 24487526
J Exp Bot. 2017 Jun 1;68(14):3841-3855
pubmed: 28419380
Plant Physiol. 2016 Mar;170(3):1868-77
pubmed: 26792123
Arch Mikrobiol. 1956;24(2):147-62
pubmed: 13327992
Nat Rev Genet. 2020 Mar;21(3):151-170
pubmed: 31780816
Nat Rev Microbiol. 2019 Dec;17(12):725-741
pubmed: 31548653
Nature. 2001 Feb 22;409(6823):1102-9
pubmed: 11234024
J Am Chem Soc. 2016 Apr 27;138(16):5262-70
pubmed: 26704697
Plant Cell. 2015 Sep;27(9):2637-44
pubmed: 26320224
Microb Cell Fact. 2017 Apr 26;16(1):71
pubmed: 28446197
J Mol Biol. 2009 Sep 18;392(2):319-33
pubmed: 19328811
Microbiol Mol Biol Rev. 2013 Sep;77(3):357-79
pubmed: 24006469
Nano Lett. 2018 Nov 14;18(11):7030-7037
pubmed: 30346795
Cell. 2016 Jun 30;166(1):115-25
pubmed: 27345370
J Bacteriol. 2019 Feb 11;201(5):
pubmed: 30510145
Environ Microbiol. 2016 Sep;18(9):2886-98
pubmed: 26404097
ACS Synth Biol. 2017 Nov 17;6(11):2145-2156
pubmed: 28826205
J Biol Chem. 2012 May 18;287(21):17729-36
pubmed: 22461622
Microbiologyopen. 2020 May;9(5):e1010
pubmed: 32053746
Front Genet. 2019 Jul 04;10:636
pubmed: 31333721
PLoS One. 2019 Oct 11;14(10):e0223877
pubmed: 31603944
Plant Physiol. 2019 Nov;181(3):1050-1058
pubmed: 31501298
Biotechnol J. 2017 Mar;12(3):
pubmed: 28105684
Cell. 2013 Nov 21;155(5):1131-40
pubmed: 24267892
Structure. 2019 May 7;27(5):749-763.e4
pubmed: 30833088
J Biol Chem. 2018 Dec 21;293(51):19909-19918
pubmed: 30361441
Nat Commun. 2020 Jan 20;11(1):388
pubmed: 31959751
Lett Appl Microbiol. 2006 Oct;43(4):430-5
pubmed: 16965375
Science. 2008 Feb 22;319(5866):1083-6
pubmed: 18292340
J Biol Chem. 2008 May 23;283(21):14366-75
pubmed: 18332146
J Biol Chem. 2015 Oct 2;290(40):24519-33
pubmed: 26283792
Plant Physiol. 2019 Jan;179(1):156-167
pubmed: 30389783
Science. 2005 Aug 5;309(5736):936-8
pubmed: 16081736
Mol Cell. 2010 Apr 23;38(2):305-15
pubmed: 20417607
Appl Microbiol Biotechnol. 2011 Feb;89(3):739-46
pubmed: 20953603
Eng Life Sci. 2016 Sep 08;17(1):36-46
pubmed: 32624727
PLoS Comput Biol. 2014 Oct 23;10(10):e1003898
pubmed: 25340524
J Bacteriol. 1999 Sep;181(17):5317-29
pubmed: 10464203
New Phytol. 2020 Jan;225(2):793-806
pubmed: 31518434
Sci Rep. 2017 Feb 16;7:42757
pubmed: 28202954
Science. 2017 Jun 23;356(6344):1293-1297
pubmed: 28642439
Nature. 1999 Dec 2;402(6761 Suppl):C47-52
pubmed: 10591225
ACS Synth Biol. 2014 Jul 18;3(7):454-465
pubmed: 24933391
Metab Eng. 2016 Jul;36:48-56
pubmed: 26969252
J Mol Biol. 2014 May 29;426(11):2217-28
pubmed: 24631000
ACS Synth Biol. 2017 Oct 20;6(10):1880-1891
pubmed: 28585808
Commun Integr Biol. 2015 Jun 23;8(3):e1039755
pubmed: 26478774
Nano Lett. 2020 Jan 8;20(1):208-217
pubmed: 31747755
Nat Rev Microbiol. 2018 May;16(5):277-290
pubmed: 29503457
FEMS Microbiol Lett. 2017 Oct 2;364(18):
pubmed: 28934381
Metab Eng. 2019 Jul;54:286-291
pubmed: 31075444
Photosynth Res. 2016 Aug;129(2):147-57
pubmed: 27344651
Nat Commun. 2018 Aug 24;9(1):3413
pubmed: 30143644
Curr Opin Plant Biol. 2016 Jun;31:66-75
pubmed: 27060669
J Bacteriol. 1999 Oct;181(19):5967-75
pubmed: 10498708
PLoS Biol. 2016 Mar 09;14(3):e1002399
pubmed: 26959993
Nat Commun. 2018 Jul 23;9(1):2881
pubmed: 30038362
PLoS One. 2013;8(1):e54337
pubmed: 23382892

Auteurs

Cheryl A Kerfeld (CA)

MSU-DOE Plant Research Laboratory and Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, MI, USA; Environmental Genomics and Systems Biology and Molecular Biophysics and Integrated Bioimaging Divisions, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA. Electronic address: ckerfeld@lbl.gov.

Markus Sutter (M)

MSU-DOE Plant Research Laboratory and Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, MI, USA; Environmental Genomics and Systems Biology and Molecular Biophysics and Integrated Bioimaging Divisions, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA.

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