Computer-aided design for metabolic engineering

Improved production of Taxol® precursors in S. cerevisiae using combinatorial in silico design and metabolic engineering

BACKGROUND

Integrated metabolic engineering approaches that combine system and synthetic biology tools enable the efficient design of microbial cell factories for synthesizing high-value products. In this study, we utilized in silico design algorithms on the yeast genome-scale model to predict genomic modifications that could enhance the production of early-step Taxol® in engineered Saccharomyces cerevisiae cells.

RESULTS

Using constraint-based reconstruction and analysis (COBRA) methods, we narrowed down the solution set of genomic modification candidates. We screened 17 genomic modifications, including nine gene deletions and eight gene overexpressions, through wet-lab studies to determine their impact on taxadiene production, the first metabolite in the Taxol® biosynthetic pathway. Under different cultivation conditions, most single genomic modifications resulted in increased taxadiene production. The strain named KM32, which contained four overexpressed genes (ILV2, TRR1, ADE13, and ECM31) involved in branched-chain amino acid biosynthesis, the thioredoxin system, de novo purine synthesis, and the pantothenate pathway, respectively, exhibited the best performance. KM32 achieved a 50% increase in taxadiene production, reaching 215 mg/L. Furthermore, KM32 produced the highest reported yields of taxa-4(20),11-dien-5α-ol (T5α-ol) at 43.65 mg/L and taxa-4(20),11-dien-5-α-yl acetate (T5αAc) at 26.2 mg/L among early-step Taxol® metabolites in S. cerevisiae.

CONCLUSIONS

This study highlights the effectiveness of computational and integrated approaches in identifying promising genomic modifications that can enhance the performance of yeast cell factories. By employing in silico design algorithms and wet-lab screening, we successfully improved taxadiene production in engineered S. cerevisiae strains. The best-performing strain, KM32, achieved substantial increases in taxadiene as well as production of T5α-ol and T5αAc. These findings emphasize the importance of using systematic and integrated strategies to develop efficient yeast cell factories, providing potential implications for the industrial production of high-value isoprenoids like Taxol®.

Bio-conversion of CO2 into biofuels and other value-added chemicals via metabolic engineering

Carbon dioxide (CO₂) occurs naturally in the atmosphere as a trace gas, which is produced naturally as well as by anthropogenic activities. CO₂ is a readily available source of carbon that in principle can be used as a raw material for the synthesis of valuable products. The autotrophic organisms are naturally equipped to convert CO₂ into biomass by obtaining energy from sunlight or inorganic electron donors. This autotrophic CO₂ fixation has been exploited in biotechnology, and microbial cell factories have been metabolically engineered to convert CO₂ into biofuels and other value-added bio-based chemicals. A variety of metabolic engineering efforts for CO₂ fixation ranging from basic copy, paste, and fine-tuning approaches to engineering and testing of novel synthetic CO₂ fixing pathways have been demonstrated. In this paper, we review the current advances and innovations in metabolic engineering for bio-conversion of CO₂ into bio biofuels and other value-added bio-based chemicals.

Personalized Metabolic Analysis of Diseases

The metabolic wiring of patient cells is altered drastically in many diseases, including cancer. Understanding the nature of such changes may pave the way for new therapeutic opportunities as well as the development of personalized treatment strategies for patients. In this paper, we propose an algorithm called Metabolitics, which allows systems-level analysis of changes in the biochemical network of cells in disease states. It enables the study of a disease at both reaction- and pathway-level granularities for a detailed and summarized view of disease etiology. Metabolitics employs flux variability analysis with a dynamically built objective function based on biofluid metabolomics measurements in a personalized manner. Moreover, Metabolitics builds supervised classification models to discriminate between patients and healthy subjects based on the computed metabolic network changes. The use of Metabolitics is demonstrated for three distinct diseases, namely, breast cancer, Crohn's disease, and colorectal cancer. Our results show that the constructed supervised learning models successfully differentiate patients from healthy individuals by an average f1-score of 88 percent. Besides, in addition to the confirmation of previously reported breast cancer-associated pathways, we discovered that Biotin Metabolism along with Arginine and Proline Metabolism is subject to a significant increase in flux capacity, which have not been reported before.

Automated engineering of synthetic metabolic pathways for efficient biomanufacturing

Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.

Computer-Aided Whole-Cell Design: Taking a Holistic Approach by Integrating Synthetic With Systems Biology

Computer-aided design (CAD) for synthetic biology promises to accelerate the rational and robust engineering of biological systems. It requires both detailed and quantitative mathematical and experimental models of the processes to (re)design biology, and software and tools for genetic engineering and DNA assembly. Ultimately, the increased precision in the design phase will have a dramatic impact on the production of designer cells and organisms with bespoke functions and increased modularity. CAD strategies require quantitative models of cells that can capture multiscale processes and link genotypes to phenotypes. Here, we present a perspective on how whole-cell, multiscale models could transform design-build-test-learn cycles in synthetic biology. We show how these models could significantly aid in the design and learn phases while reducing experimental testing by presenting case studies spanning from genome minimization to cell-free systems. We also discuss several challenges for the realization of our vision. The possibility to describe and build whole-cells in silico offers an opportunity to develop increasingly automatized, precise and accessible CAD tools and strategies.

Synthetic biology tools for engineering Yarrowia lipolytica

The non-conventional oleaginous yeast Yarrowia lipolytica shows great industrial promise. It naturally produces certain compounds of interest but can also artificially generate non-native metabolites, thanks to an engineering process made possible by the significant expansion of a dedicated genetic toolbox. In this review, we present recently developed synthetic biology tools that facilitate the manipulation of Y. lipolytica, including 1) DNA assembly techniques, 2) DNA parts for constructing expression cassettes, 3) genome-editing techniques, and 4) computational tools.

PlantSEED enables automated annotation and reconstruction of plant primary metabolism with improved compartmentalization and comparative consistency

Genome-scale metabolic reconstructions help us to understand and engineer metabolism. Next-generation sequencing technologies are delivering genomes and transcriptomes for an ever-widening range of plants. While such omic data can, in principle, be used to compare metabolic reconstructions in different species, organs and environmental conditions, these comparisons require a standardized framework for the reconstruction of metabolic networks from transcript data. We previously introduced PlantSEED as a framework covering primary metabolism for 10 species. We have now expanded PlantSEED to include 39 species and provide tools that enable automated annotation and metabolic reconstruction from transcriptome data. The algorithm for automated annotation in PlantSEED propagates annotations using a set of signature k-mers (short amino acid sequences characteristic of particular proteins) that identify metabolic enzymes with an accuracy of about 97%. PlantSEED reconstructions are built from a curated template that includes consistent compartmentalization for more than 100 primary metabolic subsystems. Together, the annotation and reconstruction algorithms produce reconstructions without gaps and with more accurate compartmentalization than existing resources. These tools are available via the PlantSEED web interface at http://modelseed.org, which enables users to upload, annotate and reconstruct from private transcript data and simulate metabolic activity under various conditions using flux balance analysis. We demonstrate the ability to compare these metabolic reconstructions with a case study involving growth on several nitrogen sources in roots of four species.

Pathway Design, Engineering, and Optimization

The microbial metabolic versatility found in nature has inspired scientists to create microorganisms capable of producing value-added compounds. Many endeavors have been made to transfer and/or combine pathways, existing or even engineered enzymes with new function to tractable microorganisms to generate new metabolic routes for drug, biofuel, and specialty chemical production. However, the success of these pathways can be impeded by different complications from an inherent failure of the pathway to cell perturbations. Pursuing ways to overcome these shortcomings, a wide variety of strategies have been developed. This chapter will review the computational algorithms and experimental tools used to design efficient metabolic routes, and construct and optimize biochemical pathways to produce chemicals of high interest.

A review of computational tools for design and reconstruction of metabolic pathways

Metabolic pathways reflect an organism's chemical repertoire and hence their elucidation and design have been a primary goal in metabolic engineering. Various computational methods have been developed to design novel metabolic pathways while taking into account several prerequisites such as pathway stoichiometry, thermodynamics, host compatibility, and enzyme availability. The choice of the method is often determined by the nature of the metabolites of interest and preferred host organism, along with computational complexity and availability of software tools. In this paper, we review different computational approaches used to design metabolic pathways based on the reaction network representation of the database (i.e., graph or stoichiometric matrix) and the search algorithm (i.e., graph search, flux balance analysis, or retrosynthetic search). We also put forth a systematic workflow that can be implemented in projects requiring pathway design and highlight current limitations and obstacles in computational pathway design.

Opportunities and challenges for the sustainable production of structurally complex diterpenoids in recombinant microbial systems

With over 50.000 identified compounds terpenes are the largest and most structurally diverse group of natural products. They are ubiquitous in bacteria, plants, animals and fungi, conducting several biological functions such as cell wall components or defense mechanisms. Industrial applications entail among others pharmaceuticals, food additives, vitamins, fragrances, fuels and fuel additives. Central building blocks of all terpenes are the isoprenoid compounds isopentenyl diphosphate and dimethylallyl diphosphate. Bacteria like Escherichia coli harbor a native metabolic pathway for these isoprenoids that is quite amenable for genetic engineering. Together with recombinant terpene biosynthesis modules, they are very suitable hosts for heterologous production of high value terpenes. Yet, in contrast to the number of extracted and characterized terpenes, little is known about the specific biosynthetic enzymes that are involved especially in the formation of highly functionalized compounds. Novel approaches discussed in this review include metabolic engineering as well as site-directed mutagenesis to expand the natural terpene landscape. Focusing mainly on the validation of successful integration of engineered biosynthetic pathways into optimized terpene producing Escherichia coli, this review shall give an insight in recent progresses regarding manipulation of mostly diterpene synthases.

A Method for Finding Metabolic Pathways Using Atomic Group Tracking

A fundamental computational problem in metabolic engineering is to find pathways between compounds. Pathfinding methods using atom tracking have been widely used to find biochemically relevant pathways. However, these methods require the user to define the atoms to be tracked. This may lead to failing to predict the pathways that do not conserve the user-defined atoms. In this work, we propose a pathfinding method called AGPathFinder to find biochemically relevant metabolic pathways between two given compounds. In AGPathFinder, we find alternative pathways by tracking the movement of atomic groups through metabolic networks and use combined information of reaction thermodynamics and compound similarity to guide the search towards more feasible pathways and better performance. The experimental results show that atomic group tracking enables our method to find pathways without the need of defining the atoms to be tracked, avoid hub metabolites, and obtain biochemically meaningful pathways. Our results also demonstrate that atomic group tracking, when incorporated with combined information of reaction thermodynamics and compound similarity, improves the quality of the found pathways. In most cases, the average compound inclusion accuracy and reaction inclusion accuracy for the top resulting pathways of our method are around 0.90 and 0.70, respectively, which are better than those of the existing methods. Additionally, AGPathFinder provides the information of thermodynamic feasibility and compound similarity for the resulting pathways.

Production of C2-C4 diols from renewable bioresources: new metabolic pathways and metabolic engineering strategies

C2-C4 diols classically derived from fossil resource are very important bulk chemicals which have been used in a wide range of areas, including solvents, fuels, polymers, cosmetics, and pharmaceuticals. Production of C2-C4 diols from renewable resources has received significant interest in consideration of the reducing fossil resource and the increasing environmental issues. While bioproduction of certain diols like 1,3-propanediol has been commercialized in recent years, biosynthesis of many other important C2-C4 diol isomers is highly challenging due to the lack of natural synthesis pathways. Recent advances in synthetic biology have enabled the de novo design of completely new pathways to non-natural molecules from renewable feedstocks. In this study, we review recent advances in bioproduction of C2-C4 diols, focusing on new metabolic pathways and metabolic engineering strategies being developed. We also discuss the challenges and future trends toward the development of economically competitive processes for bio-based diol production.

Plant synthetic biology for molecular engineering of signalling and development

Molecular genetic studies of model plants in the past few decades have identified many key genes and pathways controlling development, metabolism and environmental responses. Recent technological and informatics advances have led to unprecedented volumes of data that may uncover underlying principles of plants as biological systems. The newly emerged discipline of synthetic biology and related molecular engineering approaches is built on this strong foundation. Today, plant regulatory pathways can be reconstituted in heterologous organisms to identify and manipulate parameters influencing signalling outputs. Moreover, regulatory circuits that include receptors, ligands, signal transduction components, epigenetic machinery and molecular motors can be engineered and introduced into plants to create novel traits in a predictive manner. Here, we provide a brief history of plant synthetic biology and significant recent examples of this approach, focusing on how knowledge generated by the reference plant Arabidopsis thaliana has contributed to the rapid rise of this new discipline, and discuss potential future directions.

Synthetic biology for pharmaceutical drug discovery

Synthetic biology (SB) is an emerging discipline, which is slowly reorienting the field of drug discovery. For thousands of years, living organisms such as plants were the major source of human medicines. The difficulty in resynthesizing natural products, however, often turned pharmaceutical industries away from this rich source for human medicine. More recently, progress on transformation through genetic manipulation of biosynthetic units in microorganisms has opened the possibility of in-depth exploration of the large chemical space of natural products derivatives. Success of SB in drug synthesis culminated with the bioproduction of artemisinin by microorganisms, a tour de force in protein and metabolic engineering. Today, synthetic cells are not only used as biofactories but also used as cell-based screening platforms for both target-based and phenotypic-based approaches. Engineered genetic circuits in synthetic cells are also used to decipher disease mechanisms or drug mechanism of actions and to study cell-cell communication within bacteria consortia. This review presents latest developments of SB in the field of drug discovery, including some challenging issues such as drug resistance and drug toxicity.

Discovery and resupply of pharmacologically active plant-derived natural products: A review

Medicinal plants have historically proven their value as a source of molecules with therapeutic potential, and nowadays still represent an important pool for the identification of novel drug leads. In the past decades, pharmaceutical industry focused mainly on libraries of synthetic compounds as drug discovery source. They are comparably easy to produce and resupply, and demonstrate good compatibility with established high throughput screening (HTS) platforms. However, at the same time there has been a declining trend in the number of new drugs reaching the market, raising renewed scientific interest in drug discovery from natural sources, despite of its known challenges. In this survey, a brief outline of historical development is provided together with a comprehensive overview of used approaches and recent developments relevant to plant-derived natural product drug discovery. Associated challenges and major strengths of natural product-based drug discovery are critically discussed. A snapshot of the advanced plant-derived natural products that are currently in actively recruiting clinical trials is also presented. Importantly, the transition of a natural compound from a "screening hit" through a "drug lead" to a "marketed drug" is associated with increasingly challenging demands for compound amount, which often cannot be met by re-isolation from the respective plant sources. In this regard, existing alternatives for resupply are also discussed, including different biotechnology approaches and total organic synthesis. While the intrinsic complexity of natural product-based drug discovery necessitates highly integrated interdisciplinary approaches, the reviewed scientific developments, recent technological advances, and research trends clearly indicate that natural products will be among the most important sources of new drugs also in the future.

Recent applications of synthetic biology tools for yeast metabolic engineering

The last 20 years of metabolic engineering has enabled bio-based production of fuels and chemicals from renewable carbon sources using cost-effective bioprocesses. Much of this work has been accomplished using engineered microorganisms that act as chemical factories. Although the time required to engineer microbial chemical factories has steadily decreased, improvement is still needed. Through the development of synthetic biology tools for key microbial hosts, it should be possible to further decrease the development times and improve the reliability of the resulting microorganism. Together with continuous decreases in price and improvements in DNA synthesis, assembly and sequencing, synthetic biology tools will rationalize time-consuming strain engineering, improve control of metabolic fluxes, and diversify screening assays for cellular metabolism. This review outlines some recently developed synthetic biology tools and their application to improve production of chemicals and fuels in yeast. Finally, we provide a perspective for the challenges that lie ahead.

Improving collaboration by standardization efforts in systems biology

Collaborative genome-scale reconstruction endeavors of metabolic networks would not be possible without a common, standardized formal representation of these systems. The ability to precisely define biological building blocks together with their dynamic behavior has even been considered a prerequisite for upcoming synthetic biology approaches. Driven by the requirements of such ambitious research goals, standardization itself has become an active field of research on nearly all levels of granularity in biology. In addition to the originally envisaged exchange of computational models and tool interoperability, new standards have been suggested for an unambiguous graphical display of biological phenomena, to annotate, archive, as well as to rank models, and to describe execution and the outcomes of simulation experiments. The spectrum now even covers the interaction of entire neurons in the brain, three-dimensional motions, and the description of pharmacometric studies. Thereby, the mathematical description of systems and approaches for their (repeated) simulation are clearly separated from each other and also from their graphical representation. Minimum information definitions constitute guidelines and common operation protocols in order to ensure reproducibility of findings and a unified knowledge representation. Central database infrastructures have been established that provide the scientific community with persistent links from model annotations to online resources. A rich variety of open-source software tools thrives for all data formats, often supporting a multitude of programing languages. Regular meetings and workshops of developers and users lead to continuous improvement and ongoing development of these standardization efforts. This article gives a brief overview about the current state of the growing number of operation protocols, mark-up languages, graphical descriptions, and fundamental software support with relevance to systems biology.