Multiplexed Engineering in Biology

Engineering biology and automation-Replicability as a design principle

Applications in engineering biology increasingly share the need to run operations on very large numbers of biological samples. This is a direct consequence of the application of good engineering practices, the limited predictive power of current computational models and the desire to investigate very large design spaces in order to solve the hard, important problems the discipline promises to solve. Automation has been proposed as a key component for running large numbers of operations on biological samples. This is because it is strongly associated with higher throughput, and with higher replicability (thanks to the reduction of human input). The authors focus on replicability and make the point that, far from being an additional burden for automation efforts, replicability should be considered central to the design of the automated pipelines processing biological samples at scale-as trialled in biofoundries. There cannot be successful automation without effective error control. Design principles for an IT infrastructure that supports replicability are presented. Finally, the authors conclude with some perspectives regarding the evolution of automation in engineering biology. In particular, they speculate that the integration of hardware and software will show rapid progress, and offer users a degree of control and abstraction of the robotic infrastructure on a level significantly greater than experienced today.

Reprogramming cellular metabolism to increase the efficiency of microbial cell factories

Recent studies are increasingly focusing on advanced biotechnological tools, self-adjusting smart microorganisms, and artificial intelligent networks, to engineer microorganisms with various functions. Microbial cell factories are a vital platform for improving the bioproduction of medicines, biofuels, and biomaterials from renewable carbon sources. However, these processes are significantly affected by cellular metabolism, and boosting the efficiency of microbial cell factories remains a challenge. In this review, we present a strategy for reprogramming cellular metabolism to enhance the efficiency of microbial cell factories for chemical biosynthesis, which improves our understanding of microbial physiology and metabolic control. Current methods are mainly focused on synthetic pathways, metabolic resources, and cell performance. This review highlights the potential biotechnological strategy to reprogram cellular metabolism and provide novel guidance for designing more intelligent industrial microbes with broader applications in this growing field.

Physical Laboratory Automation in Synthetic Biology

Synthetic Biology has overcome many of the early challenges facing the field and is entering a systems era characterized by adoption of Design-Build-Test-Learn (DBTL) approaches. The need for automation and standardization to enable reproducible, scalable, and translatable research has become increasingly accepted in recent years, and many of the hardware and software tools needed to address these challenges are now in place or under development. However, the lack of connectivity between DBTL modules and barriers to access and adoption remain significant challenges to realizing the full potential of lab automation. In this review, we characterize and classify the state of automation in synthetic biology with a focus on the physical automation of experimental workflows. Though fully autonomous scientific discovery is likely a long way off, impressive progress has been made toward automating critical elements of experimentation by combining intelligent hardware and software tools. It is worth questioning whether total automation that removes humans entirely from the loop should be the ultimate goal, and considerations for appropriate automation versus total automation are discussed in this light while emphasizing areas where further development is needed in both contexts.

Biosensor for branched-chain amino acid metabolism in yeast and applications in isobutanol and isopentanol production

Branched-chain amino acid (BCAA) metabolism fulfills numerous physiological roles and can be harnessed to produce valuable chemicals. However, the lack of eukaryotic biosensors specific for BCAA-derived products has limited the ability to develop high-throughput screens for strain engineering and metabolic studies. Here, we harness the transcriptional regulator Leu3p from Saccharomyces cerevisiae to develop a genetically encoded biosensor for BCAA metabolism. In one configuration, we use the biosensor to monitor yeast production of isobutanol, an alcohol derived from valine degradation. Small modifications allow us to redeploy Leu3p in another biosensor configuration that monitors production of the leucine-derived alcohol, isopentanol. These biosensor configurations are effective at isolating high-producing strains and identifying enzymes with enhanced activity from screens for branched-chain higher alcohol (BCHA) biosynthesis in mitochondria as well as cytosol. Furthermore, this biosensor has the potential to assist in metabolic studies involving BCAA pathways, and offers a blueprint to develop biosensors for other products derived from BCAA metabolism.

There are a lack of eukaryotic biosensors specific for branched-chain amino acid (BCAA)-derived products. Here the authors report a genetically encoded biosensor for BCAA metabolism based on the Leu3p transcriptional regulator; they use this to monitor yeast production of isobutanol and isopentanol.

High-Efficiency Multi-site Genomic Editing (HEMSE) Made Easy

The ability to engineer bacterial genomes in an efficient way is crucial for many bio-related technologies. Single-stranded (ss) DNA recombineering technology allows to introduce mutations within bacterial genomes in a very simple and straightforward way. This technology was initially developed for E. coli but was later extended to other organisms of interest, including the environmentally and metabolically versatile Pseudomonas putida. The technology is based on three pillars: (1) adoption of a phage recombinase that works effectively in the target strain, (2) ease of introduction of short ssDNA oligonucleotide that carries the mutation into the bacterial cells at stake and (3) momentary suppression of the endogenous mismatch repair (MMR) through transient expression of a dominant negative mutL allele. In this way, the recombinase protects the ssDNA and stimulates recombination, while MutL_E36K^PP temporarily inhibits the endogenous MMR system, thereby allowing the introduction of virtually any possible type of genomic edits. In this chapter, a protocol is detailed for easily performing recombineering experiments aimed at entering single and multiple changes in the chromosome of P. putida. This was made by implementing the workflow named High-Efficiency Multi-site genomic Editing (HEMSE), which delivers simultaneous mutations with a simple and effective protocol.

Automated Rational Strain Construction Based on High-Throughput Conjugation

Molecular cloning is the core of synthetic biology, as it comprises the assembly of DNA and its expression in target hosts. At present, however, cloning is most often a manual, time-consuming, and repetitive process that highly benefits from automation. The automation of a complete rational cloning procedure, i.e., from DNA creation to expression in the target host, involves the integration of different operations and machines. Examples of such workflows are sparse, especially when the design is rational (i.e., the DNA sequence design is fixed and not based on randomized libraries) and the target host is less genetically tractable (e.g., not sensitive to heat-shock transformation). In this study, an automated workflow for the rational construction of plasmids and their subsequent conjugative transfer into the biotechnological platform organism Corynebacterium glutamicum is presented. The whole workflow is accompanied by a custom-made software tool. As an application example, a rationally designed library of transcription factor-biosensors based on the regulator Lrp was constructed and characterized. A sensor with an improved dynamic range was obtained, and insights from the screening provided evidence for a dual regulator function of C. glutamicum Lrp.

Streamlining the Analysis of Dynamic 13C-Labeling Patterns for the Metabolic Engineering of Corynebacterium glutamicum as l-Histidine Production Host

Today’s possibilities of genome editing easily create plentitudes of strain mutants that need to be experimentally qualified for configuring the next steps of strain engineering. The application of design-build-test-learn cycles requires the identification of distinct metabolic engineering targets as design inputs for subsequent optimization rounds. Here, we present the pool influx kinetics (PIK) approach that identifies promising metabolic engineering targets by pairwise comparison of up- and downstream ¹³C labeling dynamics with respect to a metabolite of interest. Showcasing the complex l-histidine production with engineered Corynebacterium glutamicuml-histidine-on-glucose yields could be improved to 8.6 ± 0.1 mol% by PIK analysis, starting from a base strain. Amplification of purA, purB, purH, and formyl recycling was identified as key targets only analyzing the signal transduction kinetics mirrored in the PIK values.

Role of Digital Microfluidics in Enabling Access to Laboratory Automation and Making Biology Programmable

Digital microfluidics (DMF) is a liquid handling technique that has been demonstrated to automate biological experimentation in a low-cost, rapid, and programmable manner. This review discusses the role of DMF as a "digital bioconverter"-a tool to connect the digital aspects of the design-build-learn cycle with the physical execution of experiments. Several applications are reviewed to demonstrate the utility of DMF as a digital bioconverter, namely, genetic engineering, sample preparation for sequencing and mass spectrometry, and enzyme-, immuno-, and cell-based screening assays. These applications show that DMF has great potential in the role of a centralized execution platform in a fully integrated pipeline for the production of novel organisms and biomolecules. In this paper, we discuss how the function of a DMF device within such a pipeline is highly dependent on integration with different sensing techniques and methodologies from machine learning and big data. In addition to that, we examine how the capacity of DMF can in some cases be limited by known technical and operational challenges and how consolidated efforts in overcoming these challenges will be key to the development of DMF as a major enabling technology in the computer-aided biology framework.

Cell-Free Protein Synthesis as a Prototyping Platform for Mammalian Synthetic Biology

The field of mammalian synthetic biology is expanding quickly, and technologies for engineering large synthetic gene circuits are increasingly accessible. However, for mammalian cell engineering, traditional tissue culture methods are slow and cumbersome, and are not suited for high-throughput characterization measurements. Here we have utilized mammalian cell-free protein synthesis (CFPS) assays using HeLa cell extracts and liquid handling automation as an alternative to tissue culture and flow cytometry-based measurements. Our CFPS assays take a few hours, and we have established optimized protocols for small-volume reactions using automated acoustic liquid handling technology. As a proof-of-concept, we characterized diverse types of genetic regulation in CFPS, including T7 constitutive promoter variants, internal ribosomal entry sites (IRES) constitutive translation-initiation sequence variants, CRISPR/dCas9-mediated transcription repression, and L7Ae-mediated translation repression. Our data shows simple regulatory elements for use in mammalian cells can be quickly prototyped in a CFPS model system.

Multiplexed genome engineering by Cas12a and CRISPR arrays encoded on single transcripts

The ability to modify multiple genetic elements simultaneously would help to elucidate and control the gene interactions and networks underlying complex cellular functions. However, current genome engineering technologies are limited in both the number and the type of perturbations that can be performed simultaneously. Here, we demonstrate that both Cas12a and a clustered regularly interspaced short palindromic repeat (CRISPR) array can be encoded in a single transcript by adding a stabilizer tertiary RNA structure. By leveraging this system, we illustrate constitutive, conditional, inducible, orthogonal and multiplexed genome engineering of endogenous targets using up to 25 individual CRISPR RNAs delivered on a single plasmid. Our method provides a powerful platform to investigate and orchestrate the sophisticated genetic programs underlying complex cell behaviors.

Simultaneous quantification of multiple bacterial metabolites using surface-enhanced Raman scattering

We combine liquid–liquid extraction, SERS detection and partial least squares analysis for simultaneous quantification of bacterial metabolites in E. coli supernatant.

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

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.

Pablo Carbonell et al. present an automated pipeline for the discovery and optimization of biosynthetic pathways for microbial production of fine chemicals. They apply their pipeline to the production of the flavonoid (2S)-pinocembrin in Escherichia coli and show improvement of the pathway by 500-fold.

Lighting up yeast cell factories by transcription factor-based biosensors

Our ability to rewire cellular metabolism for the sustainable production of chemicals, fuels and therapeutics based on microbial cell factories has advanced rapidly during the last two decades. Especially the speed and precision by which microbial genomes can be engineered now allow for more advanced designs to be implemented and tested. However, compared to the methods developed for engineering cell factories, the methods developed for testing the performance of newly engineered cell factories in high throughput are lagging far behind, which consequently impacts the overall biomanufacturing process. For this purpose, there is a need to develop new techniques for screening and selection of best-performing cell factory designs in multiplex. Here we review the current status of the sourcing, design and engineering of biosensors derived from allosterically regulated transcription factors applied to the biotechnology work-horse budding yeast Saccharomyces cerevisiae. We conclude by providing a perspective on the most important challenges and opportunities lying ahead in order to harness the full potential of biosensor development for increasing both the throughput of cell factory development and robustness of overall bioprocesses.

Synthetic addiction extends the productive life time of engineered Escherichia coli populations

Bioproduction of chemicals offers a sustainable alternative to petrochemical synthesis routes by using genetically engineered microorganisms to convert waste and simple substrates into higher-value products. However, efficient high-yield production commonly introduces a metabolic burden that selects for subpopulations of nonproducing cells in large fermentations. To postpone such detrimental evolution, we have synthetically addicted production cells to production by carefully linking signals of product presence to expression of nonconditionally essential genes. We addict Escherichia coli cells to their engineered biosynthesis of mevalonic acid by fine-tuned control of essential genes using a product-responsive transcription factor. Over the course of a long-term fermentation equivalent to industrial 200-m³ bioreactors such addicted cells remained productive, unlike the control, in which evolution fully terminated production.

Bio-production of chemicals is an important driver of the societal transition toward sustainability. However, fermentations with heavily engineered production organisms can be challenging to scale to industrial volumes. Such fermentations are subject to evolutionary pressures that select for a wide range of genetic variants that disrupt the biosynthetic capacity of the engineered organism. Synthetic product addiction that couples high-yield production of a desired metabolite to expression of nonconditionally essential genes could offer a solution to this problem by selectively favoring cells with biosynthetic capacity in the population without constraining the medium. We constructed such synthetic product addiction by controlling the expression of two nonconditionally essential genes with a mevalonic acid biosensor. The product-addicted production organism retained high-yield mevalonic acid production through 95 generations of cultivation, corresponding to the number of cell generations required for >200-m³ industrial-scale production, at which time the nonaddicted strain completely abolished production. Using deep DNA sequencing, we find that the product-addicted populations do not accumulate genetic variants that compromise biosynthetic capacity, highlighting how synthetic networks can be designed to control genetic population heterogeneity. Such synthetic redesign of evolutionary forces with endogenous processes may be a promising concept for realizing complex cellular designs required for sustainable bio-manufacturing.

Rational Design of Mouse Models for Cancer Research

The laboratory mouse is widely considered as a valid and affordable model organism to study human disease. Attempts to improve the relevance of murine models for the investigation of human pathologies led to the development of various genetically engineered, xenograft and humanized mouse models. Nevertheless, most preclinical studies in mice suffer from insufficient predictive value when compared with cancer biology and therapy response of human patients. We propose an innovative strategy to improve the predictive power of preclinical cancer models. Combining (i) genomic, tissue engineering and regenerative medicine approaches for rational design of mouse models with (ii) rapid prototyping and computational benchmarking against human clinical data will enable fast and nonbiased validation of newly generated models.

Interfacing Neural Network Components and Nucleic Acids

Translating neural activity into nucleic acid modifications in a controlled manner harbors unique advantages for basic neurobiology and bioengineering. It would allow for a new generation of biological computers that store output in ultra-compact and long-lived DNA and enable the investigation of animal nervous systems at unprecedented scales. Furthermore, by exploiting the ability of DNA to precisely influence neuronal activity and structure, it could be possible to more effectively create cellular therapy approaches for psychiatric diseases that are currently difficult to treat.

Hybridization and adaptive evolution of diverse Saccharomyces species for cellulosic biofuel production

BACKGROUND

Lignocellulosic biomass is a common resource across the globe, and its fermentation offers a promising option for generating renewable liquid transportation fuels. The deconstruction of lignocellulosic biomass releases sugars that can be fermented by microbes, but these processes also produce fermentation inhibitors, such as aromatic acids and aldehydes. Several research projects have investigated lignocellulosic biomass fermentation by the baker's yeast Saccharomyces cerevisiae. Most projects have taken synthetic biological approaches or have explored naturally occurring diversity in S. cerevisiae to enhance stress tolerance, xylose consumption, or ethanol production. Despite these efforts, improved strains with new properties are needed. In other industrial processes, such as wine and beer fermentation, interspecies hybrids have combined important traits from multiple species, suggesting that interspecies hybridization may also offer potential for biofuel research.

RESULTS

To investigate the efficacy of this approach for traits relevant to lignocellulosic biofuel production, we generated synthetic hybrids by crossing engineered xylose-fermenting strains of S. cerevisiae with wild strains from various Saccharomyces species. These interspecies hybrids retained important parental traits, such as xylose consumption and stress tolerance, while displaying intermediate kinetic parameters and, in some cases, heterosis (hybrid vigor). Next, we exposed them to adaptive evolution in ammonia fiber expansion-pretreated corn stover hydrolysate and recovered strains with improved fermentative traits. Genome sequencing showed that the genomes of these evolved synthetic hybrids underwent rearrangements, duplications, and deletions. To determine whether the genus Saccharomyces contains additional untapped potential, we screened a genetically diverse collection of more than 500 wild, non-engineered Saccharomyces isolates and uncovered a wide range of capabilities for traits relevant to cellulosic biofuel production. Notably, Saccharomyces mikatae strains have high innate tolerance to hydrolysate toxins, while some Saccharomyces species have a robust native capacity to consume xylose.

CONCLUSIONS

This research demonstrates that hybridization is a viable method to combine industrially relevant traits from diverse yeast species and that members of the genus Saccharomyces beyond S. cerevisiae may offer advantageous genes and traits of interest to the lignocellulosic biofuel industry.

Quantification of a bacterial secondary metabolite by SERS combined with SLM extraction for bioprocess monitoring

The yield of a bacterial secondary metabolite was quantified using SERS-based sensing combined with a SLM μfluidic device enabling sample extraction and enrichment.

Quantifying complexity in metabolic engineering using the LASER database

We previously introduced the LASER database (Learning Assisted Strain EngineeRing, https://bitbucket.org/jdwinkler/laser_release) (Winkler et al. 2015) to serve as a platform for understanding past and present metabolic engineering practices. Over the past year, LASER has been expanded by 50% to include over 600 engineered strains from 450 papers, including their growth conditions, genetic modifications, and other information in an easily searchable format. Here, we present the results of our efforts to use LASER as a means for defining the complexity of a metabolic engineering "design". We evaluate two complexity metrics based on the concepts of construction difficulty and novelty. No correlation is observed between expected product yield and complexity, allowing minimization of complexity without a performance trade-off. We envision the use of such complexity metrics to filter and prioritize designs prior to implementation of metabolic engineering efforts, thereby potentially reducing the time, labor, and expenses of large-scale projects. Possible future developments based on an expanding LASER database are then discussed.

Tailor-made transcriptional biosensors for optimizing microbial cell factories

Monitoring cellular behavior and eventually properly adapting cellular processes is key to handle the enormous complexity of today's metabolic engineering questions. Hence, transcriptional biosensors bear the potential to augment and accelerate current metabolic engineering strategies, catalyzing vital advances in industrial biotechnology. The development of such transcriptional biosensors typically starts with exploring nature's richness. Hence, in a first part, the transcriptional biosensor architecture and the various modi operandi are briefly discussed, as well as experimental and computational methods and relevant ontologies to search for natural transcription factors and their corresponding binding sites. In the second part of this review, various engineering approaches are reviewed to tune the main characteristics of these (natural) transcriptional biosensors, i.e., the response curve and ligand specificity, in view of specific industrial biotechnology applications, which is illustrated using success stories of transcriptional biosensor engineering.

System-level genome editing in microbes

The release of the first complete microbial genome sequences at the end of the past century opened the way for functional genomics and systems-biology to uncover the genetic basis of various phenotypes. The surge of available sequence data facilitated the development of novel genome editing techniques for system-level analytical studies. Recombineering allowed unprecedented throughput and efficiency in microbial genome editing and the recent discovery and widespread use of RNA-guided endonucleases offered several further perspectives: (i) previously recalcitrant species became editable, (ii) the efficiency of recombineering could be elevated, and as a result (iii) diverse genomic libraries could be generated more effectively. Supporting recombineering by RNA-guided endonucleases has led to success stories in metabolic engineering, but their use for system-level analysis is mostly unexplored. For the full exploitation of opportunities that are offered by the genome editing proficiency, future development of large scale analytical procedures is also vitally needed.

Bioinformatics for the synthetic biology of natural products: integrating across the Design-Build-Test cycle

Covering: 2000 to 2016Progress in synthetic biology is enabled by powerful bioinformatics tools allowing the integration of the design, build and test stages of the biological engineering cycle. In this review we illustrate how this integration can be achieved, with a particular focus on natural products discovery and production. Bioinformatics tools for the DESIGN and BUILD stages include tools for the selection, synthesis, assembly and optimization of parts (enzymes and regulatory elements), devices (pathways) and systems (chassis). TEST tools include those for screening, identification and quantification of metabolites for rapid prototyping. The main advantages and limitations of these tools as well as their interoperability capabilities are highlighted.

Biosensor-based engineering of biosynthetic pathways

Biosynthetic pathways provide an enzymatic route from inexpensive renewable resources to valuable metabolic products such as pharmaceuticals and plastics. Designing these pathways is challenging due to the complexities of biology. Advances in the design and construction of genetic variants has enabled billions of cells, each possessing a slightly different metabolic design, to be rapidly generated. However, our ability to measure the quality of these designs lags by several orders of magnitude. Recent research has enabled cells to report their own success in chemical production through the use of genetically encoded biosensors. A new engineering discipline is emerging around the creation and application of biosensors. Biosensors, implemented in selections and screens to identify productive cells, are paving the way for a new era of biotechnological progress.