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

Pablo Carbonell; Adrian Jervis; Christopher Robinson; Cunyu Yan; Mark Dunstan; Neil Swainston; Maria Vinaixa; Katherine Hollywood; Andrew Currin; Nicholas Rattray; Sandra Taylor; Reynard Spiess; Rehana Sung; Alan R Williams; Donal Fellows; Natalie Stanford; Paul Mulherin; Rosalind Le Feuvre; Perdita Barran; Royston Goodacre; Nicholas Turner; Carole Goble; George Guoqiang Chen; Douglas Kell; Jason Micklefield; Rainer Breitling; Eriko Takano; Jean-Loup Faulon; Nigel 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 Jervis
, Christopher Robinson
, Cunyu Yan
, Mark Dunstan
, Neil Swainston
, Maria Vinaixa
, Katherine Hollywood
, Andrew Currin
, Nicholas Rattray
, Sandra Taylor
, Reynard Spiess
, Rehana Sung
, Alan R Williams
, Donal Fellows
, Natalie Stanford
, Paul Mulherin
, Rosalind Le Feuvre
, Perdita Barran
, Royston Goodacre

Nicholas Turner, Carole Goble, George Guoqiang Chen, Douglas Kell, Jason Micklefield, Rainer Breitling, Eriko Takano, Jean-Loup Faulon, Nigel Scrutton

Research output: Contribution to journal › Article › peer-review

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
Pages (from-to)	1-10
Number of pages	10
Journal	Communications Biology
Volume	1
Early online date	8 Jun 2018
DOIs	https://doi.org/10.1038/s42003-018-0076-9
Publication status	Published - 8 Jun 2018

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Research Highlights, Institutes and Platforms

Manchester Institute of Biotechnology

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10.1038/s42003-018-0076-9

Cite this

Carbonell, P., Jervis, A., Robinson, C., Yan, C., Dunstan, M., Swainston, N., Vinaixa, M., Hollywood, K., Currin, A., Rattray, N., Taylor, S., Spiess, R., Sung, R., Williams, A. R., Fellows, D., Stanford, N., Mulherin, P., Le Feuvre, R., Barran, P., ... Scrutton, N. (2018). An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals. Communications Biology, 1, 1-10. 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{\textendash}Build-Test{\textendash}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.",

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

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doi = "10.1038/s42003-018-0076-9",

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Carbonell, P, Jervis, A, Robinson, C, Yan, C, Dunstan, M, Swainston, N, Vinaixa, M, Hollywood, K, Currin, A, Rattray, N, Taylor, S, Spiess, R, Sung, R, Williams, AR, Fellows, D, Stanford, N, Mulherin, P, Le Feuvre, R, Barran, P, Goodacre, R, Turner, N , Goble, C, Guoqiang Chen, G, Kell, D, Micklefield, J , Breitling, R , Takano, E, Faulon, J-L & Scrutton, N 2018, 'An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals', Communications Biology, vol. 1, 66, pp. 1-10. https://doi.org/10.1038/s42003-018-0076-9

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AU - Carbonell, Pablo

AU - Jervis, Adrian

AU - Robinson, Christopher

AU - Yan, Cunyu

AU - Dunstan, Mark

AU - Swainston, Neil

AU - Vinaixa, Maria

AU - Hollywood, Katherine

AU - Currin, Andrew

AU - Rattray, Nicholas

AU - Taylor, Sandra

AU - Spiess, Reynard

AU - Sung, Rehana

AU - Williams, Alan R

AU - Fellows, Donal

AU - Stanford, Natalie

AU - Mulherin, Paul

AU - Le Feuvre, Rosalind

AU - Barran, Perdita

AU - Goodacre, Royston

AU - Turner, Nicholas

AU - Goble, Carole

AU - Guoqiang Chen, George

AU - Kell, Douglas

AU - Micklefield, Jason

AU - Breitling, Rainer

AU - Takano, Eriko

AU - Faulon, Jean-Loup

AU - Scrutton, Nigel

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.

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DO - 10.1038/s42003-018-0076-9

M3 - Article

SN - 2399-3642

VL - 1

SP - 1

EP - 10

JO - Communications Biology

JF - Communications Biology

M1 - 66

ER -

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

Abstract

UN SDGs

Research Highlights, Institutes and Platforms

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eScience Lab

Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals

Biochemical and Biophysical Sciences Core Facility

Cite this

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

Abstract

UN SDGs

Research Highlights, Institutes and Platforms

Access to Document

Other files and links

Fingerprint

Projects

eScience Lab

Manchester Synthetic Biology Research Centre for Fine and Speciality Chemicals

Equipment

Biochemical and Biophysical Sciences Core Facility

Cite this