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

Research output: Contribution to journal › Article

26 Citations (Scopus)

11 Downloads (Pure)

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

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Keywords

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

Cite this

Carbonell, P., Jervis, A. J., Robinson, C. J., Yan, C., Dunstan, M., Swainston, N., ... Scrutton, N. S. (2018). An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals. Communications Biology, 1, [66]. https://doi.org/10.1038/s42003-018-0076-9

Carbonell, Pablo ; Jervis, Adrian J. ; Robinson, Christopher J. ; Yan, Cunyu ; Dunstan, Mark ; Swainston, Neil ; Vinaxia, Maria ; Hollywood, Katherine A. ; Currin, Andrew ; Rattray, Nicholas J.W. ; Taylor, Sandra ; Speiss, Reynard ; Sung, Rehana ; Williams, Alan R. ; Fellows, Donal ; Stanford, Natalie J. ; Mulherin, Paul ; Le Feuvre, Rosalind ; Barran, Perdita ; Goodacre, Royston ; Turner, Nicholas J. ; Goble, Carole ; Chen, George Guoqiang ; Kell, Douglas B. ; Mickelfield, Jason ; Breitling, Reiner ; Takano, Eriko ; Faulon, Jean-Loup ; Scrutton, Nigel S. / An automated Design-Build-Test-Learn pipeline for enhanced microbial production of fine chemicals. In: Communications Biology. 2018 ; Vol. 1.

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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

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

In: Communications Biology, Vol. 1, 66, 08.06.2018.

Research output: Contribution to journal › Article

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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.

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