An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals

Pablo Carbonell; Adrian J. Jervis; Christopher J. Robinson; Cunyu Yan; Mark Dunstan; Neil Swainston; Maria Vinaxia; Katherine A. Hollywood; Andrew Currin; Nicholas J.W. Rattray; Sandra Taylor; Reynard Speiss; Rehana Sung; Alan R. Williams; Donal Fellows; Natalie J. Stanford; Paul Mulherin; Rosalind  Le Feuvre; Perdita Barran; Royston Goodacre; Nicholas J. Turner; Carole Goble; George Guoqiang Chen; Douglas B. Kell; Jason Mickelfield; Reiner Breitling; Eriko Takano; Jean-Loup Faulon; Nigel S. Scrutton

doi:10.1038/s42003-018-0076-9

An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals

Pablo Carbonell, Adrian J. Jervis, Christopher J. Robinson, Cunyu Yan, Mark Dunstan, Neil Swainston, Maria Vinaxia, Katherine A. Hollywood, Andrew Currin, Nicholas J.W. Rattray, Sandra Taylor, Reynard Speiss, Rehana Sung, Alan R. Williams, Donal Fellows, Natalie J. Stanford, Paul Mulherin, Rosalind Le Feuvre, Perdita Barran, Royston GoodacreNicholas J. Turner, Carole Goble, George Guoqiang Chen, Douglas B. Kell, Jason Mickelfield, Reiner Breitling, Eriko Takano, Jean-Loup Faulon, Nigel S. Scrutton

Strathclyde Institute Of Pharmacy And Biomedical Sciences

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Abstract

The microbial production of fine chemicals provides a promising biosustainable manufacturing solution that has led to the successful production of a growing catalog of natural products and high-value chemicals. However, development at industrial levels has been hindered by the large resource investments required. Here we present an integrated Design–Build-Test–Learn (DBTL) pipeline for the discovery and optimization of biosynthetic pathways, which is designed to be compound agnostic and automated throughout. We initially applied the pipeline for the production of the flavonoid (2S)-pinocembrin in Escherichia coli, to demonstrate rapid iterative DBTL cycling with automation at every stage. In this case, application of two DBTL cycles successfully established a production pathway improved by 500-fold, with competitive titers up to 88 mg L−1. The further application of the pipeline to optimize an alkaloids pathway demonstrates how it could facilitate the rapid optimization of microbial strains for production of any chemical compound of interest.

Original language	English
Article number	66
Number of pages	10
Journal	Communications Biology
Volume	1
DOIs	https://doi.org/10.1038/s42003-018-0076-9
Publication status	Published - 8 Jun 2018

Keywords

microbial production
Design–Build-Test–Learn (DBTL) pipeline
biosynthetic pathways

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10.1038/s42003-018-0076-9Licence: CC BY 4.0

Carbonell-etal-CB-2018-An-automated-Design-Build-Test-Learn-pipeline-for-enhanced-microbial-productionFinal published version, 1.34 MBLicence: CC BY 4.0

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Carbonell, P., Jervis, A. J., Robinson, C. J., Yan, C., Dunstan, M., Swainston, N., Vinaxia, M., Hollywood, K. A., Currin, A., Rattray, N. J. W., Taylor, S., Speiss, R., Sung, R., Williams, A. R., Fellows, D., Stanford, N. J., Mulherin, P., Le Feuvre, R., Barran, P., ... Scrutton, N. S. (2018). An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals. Communications Biology, 1, Article 66. https://doi.org/10.1038/s42003-018-0076-9

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abstract = "The microbial production of fine chemicals provides a promising biosustainable manufacturing solution that has led to the successful production of a growing catalog of natural products and high-value chemicals. However, development at industrial levels has been hindered by the large resource investments required. Here we present an integrated Design–Build-Test–Learn (DBTL) pipeline for the discovery and optimization of biosynthetic pathways, which is designed to be compound agnostic and automated throughout. We initially applied the pipeline for the production of the flavonoid (2S)-pinocembrin in Escherichia coli, to demonstrate rapid iterative DBTL cycling with automation at every stage. In this case, application of two DBTL cycles successfully established a production pathway improved by 500-fold, with competitive titers up to 88 mg L−1. The further application of the pipeline to optimize an alkaloids pathway demonstrates how it could facilitate the rapid optimization of microbial strains for production of any chemical compound of interest.",

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author = "Pablo Carbonell and Jervis, {Adrian J.} and Robinson, {Christopher J.} and Cunyu Yan and Mark Dunstan and Neil Swainston and Maria Vinaxia and Hollywood, {Katherine A.} and Andrew Currin and Rattray, {Nicholas J.W.} and Sandra Taylor and Reynard Speiss and Rehana Sung and Williams, {Alan R.} and Donal Fellows and Stanford, {Natalie J.} and Paul Mulherin and {Le Feuvre}, Rosalind and Perdita Barran and Royston Goodacre and Turner, {Nicholas J.} and Carole Goble and Chen, {George Guoqiang} and Kell, {Douglas B.} and Jason Mickelfield and Reiner Breitling and Eriko Takano and Jean-Loup Faulon and Scrutton, {Nigel S.}",

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Carbonell, P, Jervis, AJ, Robinson, CJ, Yan, C, Dunstan, M, Swainston, N, Vinaxia, M, Hollywood, KA, Currin, A, Rattray, NJW, Taylor, S, Speiss, R, Sung, R, Williams, AR, Fellows, D, Stanford, NJ, Mulherin, P, Le Feuvre, R, Barran, P, Goodacre, R, Turner, NJ, Goble, C, Chen, GG, Kell, DB, Mickelfield, J, Breitling, R, Takano, E, Faulon, J-L & Scrutton, NS 2018, 'An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals', Communications Biology, vol. 1, 66. https://doi.org/10.1038/s42003-018-0076-9

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AU - Carbonell, Pablo

AU - Jervis, Adrian J.

AU - Robinson, Christopher J.

AU - Yan, Cunyu

AU - Dunstan, Mark

AU - Swainston, Neil

AU - Vinaxia, Maria

AU - Hollywood, Katherine A.

AU - Currin, Andrew

AU - Rattray, Nicholas J.W.

AU - Taylor, Sandra

AU - Speiss, Reynard

AU - Sung, Rehana

AU - Williams, Alan R.

AU - Fellows, Donal

AU - Stanford, Natalie J.

AU - Mulherin, Paul

AU - Le Feuvre, Rosalind

AU - Barran, Perdita

AU - Goodacre, Royston

AU - Turner, Nicholas J.

AU - Goble, Carole

AU - Chen, George Guoqiang

AU - Kell, Douglas B.

AU - Mickelfield, Jason

AU - Breitling, Reiner

AU - Takano, Eriko

AU - Faulon, Jean-Loup

AU - Scrutton, Nigel S.

PY - 2018/6/8

Y1 - 2018/6/8

N2 - The microbial production of fine chemicals provides a promising biosustainable manufacturing solution that has led to the successful production of a growing catalog of natural products and high-value chemicals. However, development at industrial levels has been hindered by the large resource investments required. Here we present an integrated Design–Build-Test–Learn (DBTL) pipeline for the discovery and optimization of biosynthetic pathways, which is designed to be compound agnostic and automated throughout. We initially applied the pipeline for the production of the flavonoid (2S)-pinocembrin in Escherichia coli, to demonstrate rapid iterative DBTL cycling with automation at every stage. In this case, application of two DBTL cycles successfully established a production pathway improved by 500-fold, with competitive titers up to 88 mg L−1. The further application of the pipeline to optimize an alkaloids pathway demonstrates how it could facilitate the rapid optimization of microbial strains for production of any chemical compound of interest.

AB - The microbial production of fine chemicals provides a promising biosustainable manufacturing solution that has led to the successful production of a growing catalog of natural products and high-value chemicals. However, development at industrial levels has been hindered by the large resource investments required. Here we present an integrated Design–Build-Test–Learn (DBTL) pipeline for the discovery and optimization of biosynthetic pathways, which is designed to be compound agnostic and automated throughout. We initially applied the pipeline for the production of the flavonoid (2S)-pinocembrin in Escherichia coli, to demonstrate rapid iterative DBTL cycling with automation at every stage. In this case, application of two DBTL cycles successfully established a production pathway improved by 500-fold, with competitive titers up to 88 mg L−1. The further application of the pipeline to optimize an alkaloids pathway demonstrates how it could facilitate the rapid optimization of microbial strains for production of any chemical compound of interest.

KW - microbial production

KW - Design–Build-Test–Learn (DBTL) pipeline

KW - biosynthetic pathways

UR - https://www.nature.com/commsbio/

U2 - 10.1038/s42003-018-0076-9

DO - 10.1038/s42003-018-0076-9

M3 - Article

SN - 2399-3642

VL - 1

JO - Communications Biology

JF - Communications Biology

M1 - 66

ER -

An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals

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