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

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

doi:10.1038/s42003-018-0076-9

Pablo Carbonell, Christopher J Robinson + Show 27 more

Open Access

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.

Highlights

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
Genes and regulatory parts are combined in silico into large combinatorial libraries of pathway designs, which are statistically reduced using design of experiments (DoE) to smaller representative libraries
Our publicly available custom software produces assembly recipes and robotics worklists to enable automated ligase cycling reaction[16] for pathway assembly (Supplementary Data 1 and 2), and all DNA part designs and plasmid assemblies are simultaneously deposited in a JBEI-Inventory of Composable Elements (ICE) repository[17,18], providing unique IDs for sample tracking

Summary

Introduction

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. 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. The pipeline is designed to be agnostic regarding the target compound and runs from the in silico selection of candidate enzymes, through automated parts design, statistically guided and robot-assisted pathway assembly, rapid testing and rationalized redesign, providing an iterative DBTL cycle underpinned by computational and laboratory automation. This is a major step forward toward automating the DBTL cycle to develop biomanufacturing solutions for industrial chemical production

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