key: cord-0021069-i71myfds
authors: Orsi, Enrico; Claassens, Nico J.; Nikel, Pablo I.; Lindner, Steffen N.
title: Growth-coupled selection of synthetic modules to accelerate cell factory development
date: 2021-09-06
journal: Nat Commun
DOI: 10.1038/s41467-021-25665-6
sha: f9da32cf7e1527cf1c945a9d21b64d4e726d31fc
doc_id: 21069
cord_uid: i71myfds

Synthetic biology has brought about a conceptual shift in our ability to redesign microbial metabolic networks. Combining metabolic pathway-modularization with growth-coupled selection schemes is a powerful tool that enables deep rewiring of the cell factories’ biochemistry for rational bioproduction.

are typically followed up by screening strain performances; such traditional approaches have been extensively reviewed elsewhere 8 . In this study, we discuss the rationale behind (re-)emerging strategies for improving cell factories and propose exploiting metabolic versatility to advance the state-of-the-art.

Synthetic metabolism: modularity meets growth-coupled design We advocate that metabolic fluxes can be rewired by employing rationally designed selection strains as an alternative to traditional metabolic engineering approaches. In literature, such an approach is usually described as "growth-coupled selection" 9 or "growthbased selection" 10 . Here, metabolism is strategically interrupted by gene deletions and growth under restrictive conditions is exclusively rescued upon flux through the target enzyme or pathway 11, 12 . When maintaining such a selective pressure, selection strains can also evolve, thereby increasing flux capacity through the enzyme(s) of interest. In other words, combination of rational design, growth-coupled selection, and ALE will pave the way for screening and improving enzyme and pathway variants, which can be transformed into a microbial production platform. The coupling of metabolite biosynthesis to growth has been harnessed as a successful bioproduction strategy already for very long, especially in anaerobic fermentations. In the last two decades, design of knockout strains to couple production of new molecules to growth has been extensively explored, both by modeling and experimental approaches 13, 14 . Yet, growth-coupled selection of pathway modules is still a relatively underrated approach for metabolic engineering.

Modularity is the enabling concept for the design and harnessing of selection strains. Following the synthetic biology approach for pathway engineering, we can divide metabolic routes into functional modules consisting of at least one enzymatic activity 15, 16 . Then, to assess these modules in vivo, we can test and optimize them into dedicated microbial selection strains. When no module is present, these strains depend on supplementation of additional nutrients for synthesizing one or more biomass precursors 17 (Fig. 1a) . Modular selection strains are designed as such that, when removing external nutrient additions, synthesis of biomass building blocks relies solely on the activity of the tested module 18 (Fig. 1b) . The advantage of this approach is that the module's functionality is directly coupled to biomass formation 11 . Therefore, growth monitoring can be used to estimate the relative performance of different module variants in terms of the following: (i) rate, which can be approximated by assessing the growth rate of the selection strains, and (ii) yield, for which the highest biomass concentration of the selection strain can serve as proxy (Fig. 1c) .

The stringency on the strain's metabolic selection can be increased by introducing additional gene deletions or manipulating the incubation conditions, e.g., by changing or removing carbon substrates that feed different nodes in the biochemical network. This influences the concentration of biomass precursors, i.e., affecting maximal yield, and hence the flux the module needs to support 11, 17 . As explained above, ALE can further improve the module's functionality. A successful example of the step-by-step use of multiple selection strains with increasing stringency was the establishment of the complete formate assimilatory reductive glycine pathway in two separate microbial chassis 19, 20 .

In summary, by carefully "slicing" the metabolism into functional blocks to construct dedicated selection strains, multiple platforms can be generated for testing and tinkering with a variety of metabolic modules. Enzyme or pathway libraries can be assessed within this context, replenishing carbon in the essential metabolic nodes excluded by the gene deletions. Moreover, with this strategy "latent" enzymatic activities present in native host can be awakened [21] [22] [23] [24] , as well as new-to-nature ones could in principle be discovered 25 (Fig. 1b) . Using growth as an in vivo readout, optical density becomes a straightforward means to functionally assess module candidates (Fig. 1c ). In the next section, we discuss how this fast and simple monitoring tool can be used as an analytical step for high-throughput enzyme or pathway screenings and engineering.

Accelerating strain optimization using growth-coupled design While entering its third decade of life as an applied discipline 26 , synthetic biology claims an evident contribution to biomanufacturing 27, 28 . The current SynBio paradigm for rational strain engineering is defined by the "design-build-test-learn" (DBTL) cycle 7 . By iterating DBTL cycles, microbial strains are improved in their TRY values. Product formation must be measured, and many research endeavors have significantly improved the accessibility and the rapidity of high-throughput, -omics pipelines for metabolites determination 29, 30 . However, the type and number of samples to prepare, analyze, and interpret can render the Fig. 1 Schematic representation of the metabolic network of growth-coupled selection strains and their role as proxy for determining rate and yields. Black circles represent essential intermediates that act as biomass precursors. a Selection strain, where gene deletions "slice" metabolism and microbial growth becomes dependent on the supplementation of external substrates (green boxes). b Possible use of selection strains for testing different types of metabolic modules. This representation condenses all module types that can (independently) rescue growth. Each arrow color represents a particular type of module that can be screened via this approach: (i) novel assimilation route (brown); (ii) metabolic bypass for avoiding a rate-limiting step (light blue); (iii) exploration of latent promiscuous enzymatic activities (purple); and (iv) (synthetic) pathway that also produces a compound of interest (red). c Module activities will result in growth of the selection strain; monitoring biomass formation provides information related to rates and yields. Fig. 2 The classical "design-build-test-learn" (DBTL) pipeline (top) compared to the "growth selection-based" DBTL cycle proposed in this study (bottom). The "design" and "build" phases are supported by the same technologies in both cycles. Nevertheless, the novel pipeline changes in the "test" and "learn" phases, which become faster in their execution due to the lack of -omics analyses (shorter red and purple arrows). In case of necessity, the new cycle can host adaptive laboratory evolution within its "test" phase (longer red arrow). Created with BioRender.com. NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-021-25665-6 COMMENT NATURE COMMUNICATIONS | (2021) 12:5295 | https://doi.org/10.1038/s41467-021-25665-6 | www.nature.com/naturecommunications analytic step a bottleneck in some DBTL cycles 31, 32 . Only in very few cases product formation is measurable via readily available, cost-effective readouts, such as, e.g., colorimetry 33 .

If bioproduction can be coupled to the growth of a selection strain (Fig. 1b) , any "simple" measurement of biomass formation will be a readout of product synthesis. More generally, any selection strain investigated via growth-coupled schemes could represent a cost-competitive platform to rapidly test a module's performances. For such a pipeline, the DBTL paradigm can be simplified accordingly (Fig. 2) . The "design" phase may now also consist of in silico planning of the gene deletions to implement in one or more selection strains. Moreover, at this stage, one can choose whether test a library of module variants or mutate the DNA target sequence of the inserted pathway genes e.g., via errorprone PCR, multiplex automated genome engineering (MAGE), or clustered regularly interspaced short palindromic repeats (CRISPR)-Cas technologies 34 . In the "build" phase, the selection strain will be generated and the module(s) of interest (and variants) will be plugged into the strain. In the "test" phase, the different strains will be cultivated under one or more selective conditions, and biomass formation will be used as a proxy for in vivo quantification of modules' rates and yields (Fig. 1c) . Then, in the "learn" phase, growth data acquisition and interpretation will determine whether the results obtained are satisfactory, i.e., whether the modules tested are performing well enough or whether a new cycle must be initiated. If a library of variants is tested, gene sequencing at the end of the cultivation will help to identify the best-performing module variant. In case the tested module underperformed, the designer could decide to modify the module's architecture or the stringency of the selection schemes 17 .

A key advantage of this novel pipeline is that, in case of need, the "test" phase can be adapted into a short-or long-term ALE to increase the module's capacity. This can be performed with the very same selection strains. Once a DBTL cycle ends with evolution, the "learn" phase will include whole genome sequencing of the improved strain. This will allow to understand the pattern of mutations, which can be retro-engineered individually or in combination in a new DBTL cycle to assess their role in improving the growth phenotype.

We argue that this adapted "growth selection-based" DBTL concept (Fig. 2) improves the state-of-the-art by avoiding the current "test" phase bottleneck of some high-throughput strain engineering endeavors (i.e., -omics analyses), while still providing straightforward and useful data about the performance of biological parts. Moreover, it is a standard process that can be adapted to different needs, such as testing novel assimilation routes, alternatives for rate-limiting enzymes in the central metabolism, or synthetic production pathways (Fig. 1) . One example of application of this DBTL pipeline is illustrated by an E. coli NADPH-"auxotroph" strain, which was created for in vivo testing of NADPH regenerating reactions 35 . Such a platform served for identifying mutated formate dehydrogenases with NADP specificity 36 . If a full production pathway is strictly connected to growth, its growth-coupled selection can also be a useful analytic tool for determining the best-performing pathway variants and enhancing productivity. Yet, the proposed selection-based DBTL approach can also be employed in case the full production pathway cannot be coupled to growth. In fact, important key steps upstream of the production pathway itself (e.g., rate-limiting steps or alternative substrate assimilation pathways) could be optimized by using this pipeline. One exemplary study that used such growth-coupled approach for upstream pathway optimization tackled the production of N-hexanol in E. coli 37 . To optimize the upper part of the pathway towards C6-precursors formation, the authors coupled NAD + regeneration under anaerobic conditions to the formation of hexaonic acid. This served as a base to implement the whole pathway towards n-hexanol. In summary, use of growth-coupled selection could optimize production by performing early DBTL cycles and later continue with classical product screening-based DBTL cycles. This approach demonstrated to be beneficial, as the number of variants to be screened via the later classic DBTL could be limited.

An alternative approach for improving cell factories Biofoundries are high-throughput infrastructures used for analysis and optimization of biological systems via DBTL cycles 32, 38 . In a biofoundry, rapid prototyping and optimization generally focuses on the production pathway of a cell factory. However, due to their value as high-throughput infrastructure, they are finding alternative applications to the original DBTL pipeline for biomanufacturing 39 . Because of its technical simplicity, growthcoupled selection is an interesting setup for streamlining the DBTL method in biofoundries. As an intellectual exercise, here we describe how the biofoundry pipeline could be adapted based on this very approach (Fig. 2) . For this purpose, biofoundries need to be properly equipped to characterize growth of many strains in parallel (i.e., using micro-titer plates or droplet microfluidics 40 ) or allow for the selection of the fastest growers in direct competition cultures (e.g., in controlled bioreactors). Such a pipeline can be employed for screening from single enzymes up to entire pathways. We argue that one can follow this method for the assessment of candidate modules needed for substrate assimilation, as well as testing alternatives for debottlenecking rate-limiting enzymatic steps within the central carbon metabolism. A complementary utilization of such infrastructures could include selecting microbial platforms that are naturally endowed with production traits of interest (e.g., synthesis of an added value compound at high titer) and then engineering alternative assimilation routes for abundant, renewable substrates (e.g., CO 2 and H 2 , formate, or methanol). Alternative uses of these infrastructures could include testing libraries of enzyme variants that are rate limiting in central carbon metabolism or introducing synthetic pathways that better connect key metabolic nodes important for microbial fitness. In conclusion, the use of growthcoupled selection in combination with pathway modularization and evolution represents an accessible, highly valuable opportunity for expanding the boundaries of industrial biotechnology by creating superior cell factories for novel bioprocesses. 

Conversion of Escherichia coli to generate all biomass carbon from CO 2

Converting Escherichia coli to a synthetic methylotroph growing solely on methanol

The industrial yeast Pichia pastoris is converted from a heterotroph into an autotroph capable of growth on CO 2

Design principles of autocatalytic cycles constrain enzyme kinetics and force low substrate saturation at flux branch points

Construction and evolution of an Escherichia coli strain relying on nonoxidative glycolysis for sugar catabolism

Functional implementation of a linear glycolysis for sugar catabolism in Pseudomonas putida

Engineering cellular metabolism

Engineering carboxylic acid reductase (CAR) through a whole-cell growth-coupled NADPH recycling strategy

Development of a high-throughput, in vivo selection platform for NADPH-dependent reactions based on redox balance principles

An engineering approach for rewiring microbial metabolism

Synthetic methanol and formate assimilation via modular engineering and selection strategies

Growth-coupled overproduction is feasible for almost all metabolites in five major production organisms

Metabolic engineering of Escherichia coli for direct production of 1,4-butanediol

Reengineering metaphysics: modularity, parthood, and evolvability in metabolic engineering

Synthetic biology and metabolic engineering

Design and engineering of E. coli metabolic sensor strains with a wide sensitivity range for glycerate

In vivo assimilation of onecarbon via a synthetic reductive glycine pathway in Escherichia coli

Growth of E. coli on formate and methanol via the reductive glycine pathway

Replacing the Calvin cycle with the reductive glycine pathway in Cupriavidus necator

Change in cofactor specificity of oxidoreductases by adaptive evolution of an Escherichia coli NADPH-auxotrophic strain

Setting the stage for evolution of a new enzyme

Underground isoleucine biosynthesis pathways in E. coli

Awakening a latent carbon fixation cycle in Escherichia coli

Synthetic metabolism: metabolic engineering meets enzyme design

The second decade of synthetic biology

A comprehensive metabolic map for production of bio-based chemicals

Synthetic biology 2020-2030: six commercially-available products that are changing our world

Frontiers of highthroughput metabolomics

Automated 'cells-to-peptides' sample preparation workflow for high-throughput, quantitative proteomic assays of microbes

Advances in analytical tools for high throughput strain engineering

Engineering biological systems using automated biofoundries

Towards a fully automated algorithm driven platform for biosystems design

Synthetic evolution

NADPH-auxotrophic E. coli: a sensor strain for testing in vivo regeneration of NADPH

In vivo selection for formate dehydrogenases with high efficiency and specificity toward NADP +

A selection platform for carbon chain elongation using the CoA-dependent pathway to produce linear higher alcohols

Building a global alliance of biofoundries

A role for biofoundries in rapid development and validation of automated SARS-CoV-2 clinical diagnostics

Droplet microfluidics for synthetic biology

We thank Charlie Cotton 

The authors declare no competing interests.

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