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.

The terpenoids constitute one of the largest and most diverse classes of natural compounds with applications as pharmaceuticals, flavorings and fragrances, pesticides and biofuels. Synthetic biology is ideally placed to create new routes to this chemical diversity and facilitation of new compound discovery. The C10 monoterpenoids display a huge structural diversity produced from a single substrate, geranyl diphosphate, by a family of monoterpene cyclases and synthases (mTC/S). Here we employ a library of mTC/S in a single ‘plug and play’ platform system for the production of over 30 different monoterpenoids in Escherichia coli by fermentation on glucose. These products include several compounds never before produced in engineered microbes demonstrating the power of this approach to rapidly create routes to structural diversity.

The field of synthetic biology aims to make the design of biological systems predictable, shrinking the huge design space to practical numbers for testing. When designing microbial cell factories, most optimization efforts have focused on enzyme and strain selection/ engineering, pathway regulation, and process development. In silico tools for the predictive design of bacterial ribosome binding sites (RBSs) and RBS libraries now allow translational tuning of biochemical pathways; however, methods for predicting optimal RBS combinations in multigene pathways are desirable. Here we present the implementation of machine learning algorithms to model the RBS sequence−phenotype relationship from representative subsets of large combinatorial RBS libraries allowing the accurate prediction of optimal high-producers. Applied to a recombinant monoterpenoid production pathway in Escherichia coli, our approach was able to boost production titers by over 60% when screening under 3% of a library. To facilitate library screening, a multiwell plate fermentation procedure was developed, allowing increased screening throughput with sufficient resolution to discriminate between high and low producers. High producers from one library did not translate during scale-up, but the reduced screening requirements allowed rapid rescreening at the larger scale. This methodology is potentially compatible with any biochemical pathway and provides a powerful tool toward predictive design of bacterial production chassis.

The first bacterial N-linked glycosylation system was discovered in Campylobacter jejuni, and the key enzyme involved in the coupling of glycan to asparagine residues within the acceptor sequon of the glycoprotein is the oligosaccharyltransferase PglB. Emerging genome sequence data have revealed that pglB orthologues are present in a subset of species from the Deltaproteobacteria and Epsilonproteobacteria, including three Helicobacter species: H. pullorum, H. canadensis, and H. winghamensis. In contrast to C. jejuni, in which a single pglB gene is located within a larger gene cluster encoding the enzymes required for the biosynthesis of the N-linked glycan, these Helicobacter species contain two unrelated pglB genes (pglB1 and pglB2), neither of which is located within a larger locus involved in protein glycosylation. In complementation experiments, the H. pullorum PglB1 protein, but not PglB2, was able to transfer C. jejuni N-linked glycan onto an acceptor protein in Escherichia coli. Analysis of the characterized C. jejuni N-glycosylation system with an in vitro oligosaccharyltransferase assay followed by matrix-assisted laser desorption ionization (MALDI) mass spectrometry demonstrated the utility of this approach, and when applied to H. pullorum, PglB1-dependent N glycosylation with a linear pentasaccharide was observed. This reaction required an acidic residue at the ؊2 position of the N-glycosylation sequon, as for C. jejuni. Attempted insertional knockout mutagenesis of the H. pullorum pglB2 gene was unsuccessful, suggesting that it is essential. These first data on N-linked glycosylation in a second bacterial species demonstrate the similarities to, and fundamental differences from, the well-studied C. jejuni system.

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