Multiplexed tracking of combinatorial genomic mutations in engineered cell populations

High-throughput genetic engineering tools for regulating gene expression in a microbial cell factory

With the rapid advances in biotechnological tools and strategies, microbial cell factory-constructing strategies have been established for the production of value-added compounds. However, optimizing the tradeoff between the biomass, yield, and titer remains a challenge in microbial production. Gene regulation is necessary to optimize and control metabolic fluxes in microorganisms for high-production performance. Various high-throughput genetic engineering tools have been developed for achieving rational gene regulation and genetic perturbation, diversifying the cellular phenotype and enhancing bioproduction performance. In this paper, we review the current high-throughput genetic engineering tools for gene regulation. In particular, technological approaches used in a diverse range of genetic tools for constructing microbial cell factories are introduced, and representative applications of these tools are presented. Finally, the prospects for high-throughput genetic engineering tools for gene regulation are discussed.

Efficient retroelement-mediated DNA writing in bacteria

The ability to efficiently and dynamically change information stored in genomes would enable powerful strategies for studying cell biology and controlling cellular phenotypes. Current recombineering-mediated DNA writing platforms in bacteria are limited to specific laboratory conditions, often suffer from suboptimal editing efficiencies, and are not suitable for in situ applications. To overcome these limitations, we engineered a retroelement-mediated DNA writing system that enables efficient and precise editing of bacterial genomes without the requirement for target-specific elements or selection. We demonstrate that this DNA writing platform enables a broad range of applications, including efficient, scarless, and cis-element-independent editing of targeted microbial genomes within complex communities, the high-throughput mapping of spatial information and cellular interactions into DNA memory, and the continuous evolution of cellular traits.

Directed Evolution: Methodologies and Applications

Directed evolution aims to expedite the natural evolution process of biological molecules and systems in a test tube through iterative rounds of gene diversifications and library screening/selection. It has become one of the most powerful and widespread tools for engineering improved or novel functions in proteins, metabolic pathways, and even whole genomes. This review describes the commonly used gene diversification strategies, screening/selection methods, and recently developed continuous evolution strategies for directed evolution. Moreover, we highlight some representative applications of directed evolution in engineering nucleic acids, proteins, pathways, genetic circuits, viruses, and whole cells. Finally, we discuss the challenges and future perspectives in directed evolution.

Investigation of Bar-seq as a method to study population dynamics of Saccharomyces cerevisiae deletion library during bioreactor cultivation

Background

Despite the latest advancements in metabolic engineering for genome editing and characterization of host performance, the successful development of robust cell factories used for industrial bioprocesses and accurate prediction of the behavior of microbial systems, especially when shifting from laboratory-scale to industrial conditions, remains challenging. To increase the probability of success of a scale-up process, data obtained from thoroughly performed studies mirroring cellular responses to typical large-scale stimuli may be used to derive crucial information to better understand potential implications of large-scale cultivation on strain performance. This study assesses the feasibility to employ a barcoded yeast deletion library to assess genome-wide strain fitness across a simulated industrial fermentation regime and aims to understand the genetic basis of changes in strain physiology during industrial fermentation, and the corresponding roles these genes play in strain performance.

Results

We find that mutant population diversity is maintained through multiple seed trains, enabling large scale fermentation selective pressures to act upon the community. We identify specific deletion mutants that were enriched in all processes tested in this study, independent of the cultivation conditions, which include MCK1, RIM11, MRK1, and YGK3 that all encode homologues of mammalian glycogen synthase kinase 3 (GSK-3). Ecological analysis of beta diversity between all samples revealed significant population divergence over time and showed feed specific consequences of population structure. Further, we show that significant changes in the population diversity during fed-batch cultivations reflect the presence of significant stresses. Our observations indicate that, for this yeast deletion collection, the selection of the feeding scheme which affects the accumulation of the fermentative by-product ethanol impacts the diversity of the mutant pool to a higher degree as compared to the pH of the culture broth. The mutants that were lost during the time of most extreme population selection suggest that specific biological processes may be required to cope with these specific stresses.

Conclusions

Our results demonstrate the feasibility of Bar-seq to assess fermentation associated stresses in yeast populations under industrial conditions and to understand critical stages of a scale-up process where variability emerges, and selection pressure gets imposed. Overall our work highlights a promising avenue to identify genetic loci and biological stress responses required for fitness under industrial conditions.

Unlocking nature's biosynthetic potential by directed genome evolution

Microorganisms have been increasingly explored as microbial cell factories for production of fuels, chemicals, drugs, and materials. Among the various metabolic engineering strategies, directed genome evolution has emerged as one of the most powerful tools to unlock the full biosynthetic potential of microorganisms. Here we summarize the directed genome evolution strategies that have been developed in recent years, including adaptive laboratory evolution and various targeted genome-scale engineering strategies, and discuss their applications in basic and applied biological research.

Highly Combinatorial Genetic Interaction Analysis Reveals a Multi-Drug Transporter Influence Network

Many traits are complex, depending non-additively on variant combinations. Even in model systems, such as the yeast S. cerevisiae, carrying out the high-order variant-combination testing needed to dissect complex traits remains a daunting challenge. Here, we describe “X-gene” genetic analysis (XGA), a strategy for engineering and profiling highly combinatorial gene perturbations. We demonstrate XGA on yeast ABC transporters by engineering 5,353 strains, each deleted for a random subset of 16 transporters, and profiling each strain’s resistance to 16 compounds. XGA yielded 85,648 genotype-to-resistance observations, revealing high-order genetic interactions for 13 of the 16 transporters studied. Neural networks yielded intuitive functional models and guided exploration of fluconazole resistance, which was influenced non-additively by five genes. Together, our results showed that highly combinatorial genetic perturbation can functionally dissect complex traits, supporting pursuit of analogous strategies in human cells and other model systems.

Celaj et al. introduce “X-gene” genetic analysis (XGA), a strategy for modeling complex systems by engineering and profiling highly combinatorial genetic perturbations. They apply XGA to 16 yeast ABC transporters, revealing many high-order genetic interactions. Neural network models yielded intuitive functional models and illuminated an ABC transporter influence network, supporting application of XGA to other organisms and processes.

A versatile platform strain for high-fidelity multiplex genome editing

Precision genome editing accelerates the discovery of the genetic determinants of phenotype and the engineering of novel behaviors in organisms. Advances in DNA synthesis and recombineering have enabled high-throughput engineering of genetic circuits and biosynthetic pathways via directed mutagenesis of bacterial chromosomes. However, the highest recombination efficiencies have to date been reported in persistent mutator strains, which suffer from reduced genomic fidelity. The absence of inducible transcriptional regulators in these strains also prevents concurrent control of genome engineering tools and engineered functions. Here, we introduce a new recombineering platform strain, BioDesignER, which incorporates (i) a refactored λ-Red recombination system that reduces toxicity and accelerates multi-cycle recombination, (ii) genetic modifications that boost recombination efficiency, and (iii) four independent inducible regulators to control engineered functions. These modifications resulted in single-cycle recombineering efficiencies of up to 25% with a 7-fold increase in recombineering fidelity compared to the widely used recombineering strain EcNR2. To facilitate genome engineering in BioDesignER, we have curated eight context­-neutral genomic loci, termed Safe Sites, for stable gene expression and consistent recombination efficiency. BioDesignER is a platform to develop and optimize engineered cellular functions and can serve as a model to implement comparable recombination and regulatory systems in other bacteria.

Recent insights into the genotype-phenotype relationship from massively parallel genetic assays

With the molecular revolution in Biology, a mechanistic understanding of the genotype-phenotype relationship became possible. Recently, advances in DNA synthesis and sequencing have enabled the development of deep mutational scanning assays, capable of scoring comprehensive libraries of genotypes for fitness and a variety of phenotypes in massively parallel fashion. The resulting empirical genotype-fitness maps pave the way to predictive models, potentially accelerating our ability to anticipate the behaviour of pathogen and cancerous cell populations from sequencing data. Besides from cellular fitness, phenotypes of direct application in industry (e.g. enzyme activity) and medicine (e.g. antibody binding) can be quantified and even selected directly by these assays. This review discusses the technological basis of and recent developments in massively parallel genetics, along with the trends it is uncovering in the genotype-phenotype relationship (distribution of mutation effects, epistasis), their possible mechanistic bases and future directions for advancing towards the goal of predictive genetics.

Genomic Deoxyxylulose Phosphate Reductoisomerase (DXR) Mutations Conferring Resistance to the Antimalarial Drug Fosmidomycin in E. coli

Sequence to activity mapping technologies are rapidly developing, enabling the generation and isolation of mutations conferring novel phenotypes. Here we used the CRISPR enabled trackable genome engineering (CREATE) technology to investigate the inhibition of the essential ispC gene in its native genomic context in Escherichia coli. We created a full saturation library of 33 sites proximal to the ligand binding pocket and challenged this library with the antimalarial drug fosmidomycin, which targets the ispC gene product, DXR. This selection is especially challenging since it is relatively weak in E. coli, with multiple naturally occurring pathways for resistance. We identified several previously unreported mutations that confer fosmidomycin resistance, in highly conserved sites that also exist in pathogens including the malaria-inducing Plasmodium falciparum. This approach may have implications for the isolation of resistance-conferring mutations and may affect the design of future generations of fosmidomycin-based drugs.

Efficient Genome Editing of Magnetospirillum magneticum AMB-1 by CRISPR-Cas9 System for Analyzing Magnetotactic Behavior

Magnetotactic bacteria (MTB) are a diverse group of microorganisms capable of using geomagnetic fields for navigation. This magnetotactic behavior can help microorganisms move toward favorable habitats for optimal growth and reproduction. A comprehensive understanding of the magnetotactic mechanism at molecular levels requires highly efficient genomic editing tools, which remain underdeveloped in MTB. Here, we adapted an engineered CRISPR-Cas9 system for efficient inactivation of genes in a widely used MTB model strain, Magnetospirillum magneticum AMB-1. By combining a nuclease-deficient Cas9 (dCas9) and single-guide RNA (sgRNA), a CRISPR interference system was successfully developed to repress amb0994 expression. Furthermore, we constructed an in-frame deletion mutant of amb0994 by developing a CRISPR-Cas9 system. This mutant produces normal magnetosomes; however, its response to abrupt magnetic field reversals is faster than wild-type strain. This behavioral difference is probably a consequence of altered flagella function, as suggested with our dynamics simulation study by modeling M. magneticum AMB-1 cell as an ellipsoid. These data indicate that, Amb0994 is involved in the cellular response to magnetic torque changes via controlling flagella. In summary, this study, besides contributing to a better understanding of magnetotaxis mechanism, demonstrated the CRISPR-(d)Cas9 system as a useful genetic tool for efficient genome editing in MTB.

Synthetic biology advances and applications in the biotechnology industry: a perspective

Synthetic biology is a logical extension of what has been called recombinant DNA (rDNA) technology or genetic engineering since the 1970s. As rDNA technology has been the driver for the development of a thriving biotechnology industry today, starting with the commercialization of biosynthetic human insulin in the early 1980s, synthetic biology has the potential to take the industry to new heights in the coming years. Synthetic biology advances have been driven by dramatic cost reductions in DNA sequencing and DNA synthesis; by the development of sophisticated tools for genome editing, such as CRISPR/Cas9; and by advances in informatics, computational tools, and infrastructure to facilitate and scale analysis and design. Synthetic biology approaches have already been applied to the metabolic engineering of microorganisms for the production of industrially important chemicals and for the engineering of human cells to treat medical disorders. It also shows great promise to accelerate the discovery and development of novel secondary metabolites from microorganisms through traditional, engineered, and combinatorial biosynthesis. We anticipate that synthetic biology will continue to have broadening impacts on the biotechnology industry to address ongoing issues of human health, world food supply, renewable energy, and industrial chemicals and enzymes.

Upconversion Nanoparticles-Encoded Hydrogel Microbeads-Based Multiplexed Protein Detection

Fluorescently encoded microbeads are in demand for multiplexed applications in different fields. Compared to organic dye-based commercially available Luminex's xMAP technology, upconversion nanoparticles (UCNPs) are better alternatives due to their large anti-Stokes shift, photostability, nil background, and single wavelength excitation. Here, we developed a new multiplexed detection system using UCNPs for encoding poly(ethylene glycol) diacrylate (PEGDA) microbeads as well as for labeling reporter antibody. However, to prepare UCNPs-encoded microbeads, currently used swelling-based encapsulation leads to non-uniformity, which is undesirable for fluorescence-based multiplexing. Hence, we utilized droplet microfluidics to obtain encoded microbeads of uniform size, shape, and UCNPs distribution inside. Additionally, PEGDA microbeads lack functionality for probe antibodies conjugation on their surface. Methods to functionalize the surface of PEGDA microbeads (acrylic acid incorporation, polydopamine coating) reported thus far quench the fluorescence of UCNPs. Here, PEGDA microbeads surface was coated with silica followed by carboxyl modification without compromising the fluorescence intensity of UCNPs. In this study, droplet microfluidics-assisted UCNPs-encoded microbeads of uniform shape, size, and fluorescence were prepared. Multiple color codes were generated by mixing UCNPs emitting red and green colors at different ratios prior to encapsulation. UCNPs emitting blue color were used to label the reporter antibody. Probe antibodies were covalently immobilized on red UCNPs-encoded microbeads for specific capture of human serum albumin (HSA) as a model protein. The system was also demonstrated for multiplexed detection of both human C-reactive protein (hCRP) and HSA protein by immobilizing anti-hCRP antibodies on green UCNPs.

Enhanced guide-RNA design and targeting analysis for precise CRISPR genome editing of single and consortia of industrially relevant and non-model organisms

Motivation

Genetic diversity of non-model organisms offers a repertoire of unique phenotypic features for exploration and cultivation for synthetic biology and metabolic engineering applications. To realize this enormous potential, it is critical to have an efficient genome editing tool for rapid strain engineering of these organisms to perform novel programmed functions.

Results

To accommodate the use of CRISPR/Cas systems for genome editing across organisms, we have developed a novel method, named CRISPR Associated Software for Pathway Engineering and Research (CASPER), for identifying on- and off-targets with enhanced predictability coupled with an analysis of non-unique (repeated) targets to assist in editing any organism with various endonucleases. Utilizing CASPER, we demonstrated a modest 2.4% and significant 30.2% improvement (F-test, P < 0.05) over the conventional methods for predicting on- and off-target activities, respectively. Further we used CASPER to develop novel applications in genome editing: multitargeting analysis (i.e. simultaneous multiple-site modification on a target genome with a sole guide-RNA requirement) and multispecies population analysis (i.e. guide-RNA design for genome editing across a consortium of organisms). Our analysis on a selection of industrially relevant organisms revealed a number of non-unique target sites associated with genes and transposable elements that can be used as potential sites for multitargeting. The analysis also identified shared and unshared targets that enable genome editing of single or multiple genomes in a consortium of interest. We envision CASPER as a useful platform to enhance the precise CRISPR genome editing for metabolic engineering and synthetic biology applications.

Availability and implementation

https://github.com/TrinhLab/CASPER.

Contact

ctrinh@utk.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Genome-wide analysis of E. coli cell-gene interactions

BACKGROUND

The pursuit of standardization and reliability in synthetic biology has achieved, in recent years, a number of advances in the design of more predictable genetic parts for biological circuits. However, even with the development of high-throughput screening methods and whole-cell models, it is still not possible to predict reliably how a synthetic genetic construct interacts with all cellular endogenous systems. This study presents a genome-wide analysis of how the expression of synthetic genes is affected by systematic perturbations of cellular functions. We found that most perturbations modulate expression indirectly through an effect on cell size, putting forward the existence of a generic Size-Expression interaction in the model prokaryote Escherichia coli.

RESULTS

The Size-Expression interaction was quantified by inserting a dual fluorescent reporter gene construct into each of the 3822 single-gene deletion strains comprised in the KEIO collection. Cellular size was measured for single cells via flow cytometry. Regression analyses were used to discriminate between expression-specific and gene-specific effects. Functions of the deleted genes broadly mapped onto three systems with distinct primary influence on the Size-Expression map. Perturbations in the Division and Biosynthesis (DB) system led to a large-cell and high-expression phenotype. In contrast, disruptions of the Membrane and Motility (MM) system caused small-cell and low-expression phenotypes. The Energy, Protein synthesis and Ribosome (EPR) system was predominantly associated with smaller cells and positive feedback on ribosome function.

CONCLUSIONS

Feedback between cell growth and gene expression is widespread across cell systems. Even though most gene disruptions proximally affect one component of the Size-Expression interaction, the effect therefore ultimately propagates to both. More specifically, we describe the dual impact of growth on cell size and gene expression through cell division and ribosomal content. Finally, we elucidate aspects of the tight control between swarming, gene expression and cell growth. This work provides foundations for a systematic understanding of feedbacks between genetic and physiological systems.

Precise quantification of translation inhibition by mRNA structures that overlap with the ribosomal footprint in N-terminal coding sequences

A mRNA's translation rate is controlled by several sequence determinants, including the presence of RNA structures within the N-terminal regions of its coding sequences. However, the physical rules that govern when such mRNA structures will inhibit translation remain unclear. Here, we introduced systematically designed RNA hairpins into the N-terminal coding region of a reporter protein with steadily increasing distances from the start codon, followed by characterization of their mRNA and expression levels in Escherichia coli. We found that the mRNAs' translation rates were repressed, by up to 530-fold, when mRNA structures overlapped with the ribosome's footprint. In contrast, when the mRNA structure was located outside the ribosome's footprint, translation was repressed by <2-fold. By combining our measurements with biophysical modeling, we determined that the ribosomal footprint extends 13 nucleotides into the N-terminal coding region and, when a mRNA structure overlaps or partially overlaps with the ribosomal footprint, the free energy to unfold only the overlapping structure controlled the extent of translation repression. Overall, our results provide precise quantification of the rules governing translation initiation at N-terminal coding regions, improving the predictive design of post-transcriptional regulatory elements that regulate translation rate.

Optimizing complex phenotypes through model-guided multiplex genome engineering

We present a method for identifying genomic modifications that optimize a complex phenotype through multiplex genome engineering and predictive modeling. We apply our method to identify six single nucleotide mutations that recover 59% of the fitness defect exhibited by the 63-codon E. coli strain C321.∆A. By introducing targeted combinations of changes in multiplex we generate rich genotypic and phenotypic diversity and characterize clones using whole-genome sequencing and doubling time measurements. Regularized multivariate linear regression accurately quantifies individual allelic effects and overcomes bias from hitchhiking mutations and context-dependence of genome editing efficiency that would confound other strategies.

Quantitative Tracking of Combinatorially Engineered Populations with Multiplexed Binary Assemblies

Advances in synthetic biology and genomics have enabled full-scale genome engineering efforts on laboratory time scales. However, the absence of sufficient approaches for mapping engineered genomes at system-wide scales onto performance has limited the adoption of more sophisticated algorithms for engineering complex biological systems. Here we report on the development and application of a robust approach to quantitatively map combinatorially engineered populations at scales up to several dozen target sites. This approach works by assembling genome engineered sites with cell-specific barcodes into a format compatible with high-throughput sequencing technologies. This approach, called barcoded-TRACE (bTRACE) was applied to assess E. coli populations engineered by recursive multiplex recombineering across both 6-target sites and 31-target sites. The 31-target library was then tracked throughout growth selections in the presence and absence of isopentenol (a potential next-generation biofuel). We also use the resolution of bTRACE to compare the influence of technical and biological noise on genome engineering efforts.

Genetic interaction mapping with microfluidic-based single cell sequencing

Genetic interaction mapping is useful for understanding the molecular basis of cellular decision making, but elucidating interactions genome-wide is challenging due to the massive number of gene combinations that must be tested. Here, we demonstrate a simple approach to thoroughly map genetic interactions in bacteria using microfluidic-based single cell sequencing. Using single cell PCR in droplets, we link distinct genetic information into single DNA sequences that can be decoded by next generation sequencing. Our approach is scalable and theoretically enables the pooling of entire interaction libraries to interrogate multiple pairwise genetic interactions in a single culture. The speed, ease, and low-cost of our approach makes genetic interaction mapping viable for routine characterization, allowing the interaction network to be used as a universal read out for a variety of biology experiments, and for the elucidation of interaction networks in non-model organisms.

OptSSeq: High-Throughput Sequencing Readout of Growth Enrichment Defines Optimal Gene Expression Elements for Homoethanologenesis

The optimization of synthetic pathways is a central challenge in metabolic engineering. OptSSeq (Optimization by Selection and Sequencing) is one approach to this challenge. OptSSeq couples selection of optimal enzyme expression levels linked to cell growth rate with high-throughput sequencing to track enrichment of gene expression elements (promoters and ribosome-binding sites) from a combinatorial library. OptSSeq yields information on both optimal and suboptimal enzyme levels, and helps identify constraints that limit maximal product formation. Here we report a proof-of-concept implementation of OptSSeq using homoethanologenesis, a two-step pathway consisting of pyruvate decarboxylase (Pdc) and alcohol dehydrogenase (Adh) that converts pyruvate to ethanol and is naturally optimized in the bacterium Zymomonas mobilis. We used OptSSeq to determine optimal gene expression elements and enzyme levels for Z. mobilis Pdc, AdhA, and AdhB expressed in Escherichia coli. By varying both expression signals and gene order, we identified an optimal solution using only Pdc and AdhB. We resolved current uncertainty about the functions of the Fe²⁺-dependent AdhB and Zn²⁺-dependent AdhA by showing that AdhB is preferred over AdhA for rapid growth in both E. coli and Z. mobilis. Finally, by comparing predictions of growth-linked metabolic flux to enzyme synthesis costs, we established that optimal E. coli homoethanologenesis was achieved by our best pdc-adhB expression cassette and that the remaining constraints lie in the E. coli metabolic network or inefficient Pdc or AdhB function in E. coli. OptSSeq is a general tool for synthetic biology to tune enzyme levels in any pathway whose optimal function can be linked to cell growth or survival.

Genome-wide mapping of mutations at single-nucleotide resolution for protein, metabolic and genome engineering

Improvements in DNA synthesis and sequencing have underpinned comprehensive assessment of gene function in bacteria and eukaryotes. Genome-wide analyses require high-throughput methods to generate mutations and analyze their phenotypes, but approaches to date have been unable to efficiently link the effects of mutations in coding regions or promoter elements in a highly parallel fashion. We report that CRISPR-Cas9 gene editing in combination with massively parallel oligomer synthesis can enable trackable editing on a genome-wide scale. Our method, CRISPR-enabled trackable genome engineering (CREATE), links each guide RNA to homologous repair cassettes that both edit loci and function as barcodes to track genotype-phenotype relationships. We apply CREATE to site saturation mutagenesis for protein engineering, reconstruction of adaptive laboratory evolution experiments, and identification of stress tolerance and antibiotic resistance genes in bacteria. We provide preliminary evidence that CREATE will work in yeast. We also provide a webtool to design multiplex CREATE libraries.

A CRISPR-Cas9 Assisted Non-Homologous End-Joining Strategy for One-step Engineering of Bacterial Genome

Homologous recombination-mediated genome engineering has been broadly applied in prokaryotes with high efficiency and accuracy. However, this method is limited in realizing larger-scale genome editing with numerous genes or large DNA fragments because of the relatively complicated procedure for DNA editing template construction. Here, we describe a CRISPR-Cas9 assisted non-homologous end-joining (CA-NHEJ) strategy for the rapid and efficient inactivation of bacterial gene (s) in a homologous recombination-independent manner and without the use of selective marker. Our study show that CA-NHEJ can be used to delete large chromosomal DNA fragments in a single step that does not require homologous DNA template. It is thus a novel and powerful tool for bacterial genomes reducing and possesses the potential for accelerating the genome evolution.

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.

High-throughput evaluation of synthetic metabolic pathways

A central challenge in the field of metabolic engineering is the efficient identification of a metabolic pathway genotype that maximizes specific productivity over a robust range of process conditions. Here we review current methods for optimizing specific productivity of metabolic pathways in living cells. New tools for library generation, computational analysis of pathway sequence-flux space, and high-throughput screening and selection techniques are discussed.

Biobased organic acids production by metabolically engineered microorganisms

Bio-based production of organic acids via microbial fermentation has been traditionally used in food industry. With the recent desire to develop more sustainable bioprocesses for production of fuels, chemicals and materials, the market for microbial production of organic acids has been further expanded as organic acids constitute a key group among top building block chemicals that can be produced from renewable resources. Here we review the current status for production of citric acid and lactic acid, and we highlight the use of modern metabolic engineering technologies to develop high performance microbes for production of succinic acid and 3-hydroxypropionic acid. Also, the key limitations and challenges in microbial organic acids production are discussed.

Genome-Wide Tuning of Protein Expression Levels to Rapidly Engineer Microbial Traits

The reliable engineering of biological systems requires quantitative mapping of predictable and context-independent expression over a broad range of protein expression levels. However, current techniques for modifying expression levels are cumbersome and are not amenable to high-throughput approaches. Here we present major improvements to current techniques through the design and construction of E. coli genome-wide libraries using synthetic DNA cassettes that can tune expression over a ∼10(4) range. The cassettes also contain molecular barcodes that are optimized for next-generation sequencing, enabling rapid and quantitative tracking of alleles that have the highest fitness advantage. We show these libraries can be used to determine which genes and expression levels confer greater fitness to E. coli under different growth conditions.

Genome scale engineering techniques for metabolic engineering

Metabolic engineering has expanded from a focus on designs requiring a small number of genetic modifications to increasingly complex designs driven by advances in genome-scale engineering technologies. Metabolic engineering has been generally defined by the use of iterative cycles of rational genome modifications, strain analysis and characterization, and a synthesis step that fuels additional hypothesis generation. This cycle mirrors the Design-Build-Test-Learn cycle followed throughout various engineering fields that has recently become a defining aspect of synthetic biology. This review will attempt to summarize recent genome-scale design, build, test, and learn technologies and relate their use to a range of metabolic engineering applications.

Complex systems in metabolic engineering

Metabolic engineers manipulate intricate biological networks to build efficient biological machines. The inherent complexity of this task, derived from the extensive and often unknown interconnectivity between and within these networks, often prevents researchers from achieving desired performance. Other fields have developed methods to tackle the issue of complexity for their unique subset of engineering problems, but to date, there has not been extensive and comprehensive examination of how metabolic engineers use existing tools to ameliorate this effect on their own research projects. In this review, we examine how complexity affects engineering at the protein, pathway, and genome levels within an organism, and the tools for handling these issues to achieve high-performing strain designs. Quantitative complexity metrics and their applications to metabolic engineering versus traditional engineering fields are also discussed. We conclude by predicting how metabolic engineering practices may advance in light of an explicit consideration of design complexity.

White biotechnology: State of the art strategies for the development of biocatalysts for biorefining

White biotechnology is a term that is now often used to describe the implementation of biotechnology in the industrial sphere. Biocatalysts (enzymes and microorganisms) are the key tools of white biotechnology, which is considered to be one of the key technological drivers for the growing bioeconomy. Biocatalysts are already present in sectors such as the chemical and agro-food industries, and are used to manufacture products as diverse as antibiotics, paper pulp, bread or advanced polymers. This review proposes an original and global overview of highly complementary fields of biotechnology at both enzyme and microorganism level. A certain number of state of the art approaches that are now being used to improve the industrial fitness of biocatalysts particularly focused on the biorefinery sector are presented. The first part deals with the technologies that underpin the development of industrial biocatalysts, notably the discovery of new enzymes and enzyme improvement using directed evolution techniques. The second part describes the toolbox available by the cell engineer to shape the metabolism of microorganisms. And finally the last part focuses on the 'omic' technologies that are vital for understanding and guide microbial engineering toward more efficient microbial biocatalysts. Altogether, these techniques and strategies will undoubtedly help to achieve the challenging task of developing consolidated bioprocessing (i.e. CBP) readily available for industrial purpose.

The LASER database: Formalizing design rules for metabolic engineering

The ability of metabolic engineers to conceptualize, implement, and evaluate strain designs has dramatically increased in the last decade. Unlike other engineering fields, no centralized, open-access, and easily searched repository exists for cataloging these designs and the lessons learned from their construction and evaluation. To address this issue, we have developed a repository for metabolic engineering strain designs, known as LASER (Learning Assisted Strain EngineeRing, laser.colorado.edu) and a formal standard for disseminating designs to metabolic engineers. Curation of every available genetically-defined E. coli and S. cerevisiae strain from 310 metabolic engineering papers published over the last 21 years yields a total of 417 designs containing a total of 2661 genetic modifications. This collection has been deposited in LASER and represents the known bibliome of genetically defined and tested metabolic engineering designs in the academic literature. Properties of LASER designs and the analysis pipeline are examined to provide insight into LASER capabilities. Several future research directions utilizing LASER capabilities are discussed to highlight the potential of the LASER database for metabolic engineering.