13C Metabolic Flux Analysis for Systematic Metabolic Engineering of S. cerevisiae for Overproduction of Fatty Acids

Optimization of a hybrid bacterial/Arabidopsis thaliana fatty acid synthase system II in Saccharomyces cerevisiae

Fatty acids are produced by eukaryotes like baker's yeast Saccharomyces cerevisiae mainly using a large multifunctional type I fatty acid synthase (FASI) where seven catalytic steps and a carrier domain are shared between one or two protein subunits. While this system may offer efficiency in catalysis, only a narrow range of fatty acids are produced. Prokaryotes, chloroplasts and mitochondria rely instead on a FAS type II (FASII) where each catalytic step is carried out by a monofunctional enzyme encoded by a separate gene. FASII is more flexible and capable of producing a wider range of fatty acid structures, such as the direct production of unsaturated fatty acids. An efficient FASII in the preferred industrial organism S. cerevisiae could provide a platform for developing sustainable production of specialized fatty acids. We functionally replaced either yeast FASI genes (FAS1 or FAS2) with a FASII consisting of nine genes from Escherichia coli (acpP, acpS and fab -A, -B, -D, -F, -G, -H, -Z) as well as three from Arabidopsis thaliana (MOD1, FATA1 and FATB). The genes were expressed from an autonomously replicating multicopy vector assembled using the Yeast Pathway Kit for in-vivo assembly in yeast. Two rounds of adaptation led to a strain with a maximum growth rate (μmax) of 0.19 h^-1 without exogenous fatty acids, twice the growth rate previously reported for a comparable strain. Additional copies of the MOD1 or fabH genes resulted in cultures with higher final cell densities and three times higher lipid content compared to the control.

BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13C Metabolic Flux Analysis (13C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in "non-gaussian" situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P-13C MOMA and P-13C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty.

Creation of Environmentally Friendly Super "Dinitrotoluene Scavenger" Plants

Pervasive environmental contamination due to the uncontrolled dispersal of 2,4-dinitrotoluene (2,4-DNT) represents a substantial global health risk, demanding urgent intervention for the removal of this detrimental compound from affected sites and the promotion of ecological restoration. Conventional methodologies, however, are energy-intensive, susceptible to secondary pollution, and may inadvertently increase carbon emissions. In this study, a 2,4-DNT degradation module is designed, assembled, and validated in rice plants. Consequently, the modified rice plants acquire the ability to counteract the phytotoxicity of 2,4-DNT. The most significant finding of this study is that these modified rice plants can completely degrade 2,4-DNT into innocuous substances and subsequently introduce them into the tricarboxylic acid cycle. Further, research reveals that the modified rice plants enable the rapid phytoremediation of 2,4-DNT-contaminated soil. This innovative, eco-friendly phytoremediation approach for dinitrotoluene-contaminated soil and water demonstrates significant potential across diverse regions, substantially contributing to carbon neutrality and sustainable development objectives by repurposing carbon and energy from organic contaminants.

Overexpression of Citrate-Malate Carrier Promoted Lipid Accumulation in Oleaginous Filamentous Fungus Mortierella alpina

The mitochondrial citrate-malate carrier is responsible for the transport of citrate and malate between the mitochondria and cytosol, ensuring citrate supply substrate for fatty acid synthesis. In this study, we investigated the overexpression of the citrate-malate carrier coded by three genes (MaCT1/MaCT2/MaTCT) in Mortierella alpina to enhance lipid accumulation. Our results showed that the overexpression of MaCT1, MaCT2, and MaTCT increased the fatty acid content by up to 21.7, 29.5, and 12.8%, respectively, compared with the control strain, but had no effect on the growth. Among them, the MaCT2-overexpressing strain performed the best, and its total fatty acid yield was increased by 51.6% compared to the control. Furthermore, the relative transcription level of MaCT2 indeed increased significantly in the recombinant strains. These findings are beneficial to understanding the citrate transport system and improve the industrial applications of the oleaginous filamentous fungus M. alpina.

Focus and Insights into the Synthetic Biology-Mediated Chassis of Economically Important Fungi for the Production of High-Value Metabolites

Substantial progress has been achieved and knowledge gaps addressed in synthetic biology-mediated engineering of biological organisms to produce high-value metabolites. Bio-based products from fungi are extensively explored in the present era, attributed to their emerging importance in the industrial sector, healthcare, and food applications. The edible group of fungi and multiple fungal strains defines attractive biological resources for high-value metabolites comprising food additives, pigments, dyes, industrial chemicals, and antibiotics, including other compounds. In this direction, synthetic biology-mediated genetic chassis of fungal strains to enhance/add value to novel chemical entities of biological origin is opening new avenues in fungal biotechnology. While substantial success has been achieved in the genetic manipulation of economically viable fungi (including Saccharomyces cerevisiae) in the production of metabolites of socio-economic relevance, knowledge gaps/obstacles in fungal biology and engineering need to be remedied for complete exploitation of valuable fungal strains. Herein, the thematic article discusses the novel attributes of bio-based products from fungi and the creation of high-value engineered fungal strains to promote yield, bio-functionality, and value-addition of the metabolites of socio-economic value. Efforts have been made to discuss the existing limitations in fungal chassis and how the advances in synthetic biology provide a plausible solution.

Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale

The parameterization of kinetic models requires measurement of fluxes and/or metabolite levels for a base strain and a few genetic perturbations thereof. Unlike stoichiometric models that are mostly invariant to the specific strain, it remains unclear whether kinetic models constructed for different strains of the same species have similar or significantly different kinetic parameters. This important question underpins the applicability range and prediction limits of kinetic reconstructions. To this end, herein we parameterize two separate large-scale kinetic models using K-FIT with genome-wide coverage corresponding to two distinct strains of Saccharomyces cerevisiae: CEN.PK 113-7D strain (model k-sacce306-CENPK), and growth-deficient BY4741 (isogenic to S288c; model k-sacce306-BY4741). The metabolic network for each model contains 306 reactions, 230 metabolites, and 119 substrate-level regulatory interactions. The two models (for CEN.PK and BY4741) recapitulate, within one standard deviation, 77% and 75% of the fitted dataset fluxes, respectively, determined by ¹³C metabolic flux analysis for wild-type and eight single-gene knockout mutants of each strain. Strain-specific kinetic parameterization results indicate that key enzymes in the TCA cycle, glycolysis, and arginine and proline metabolism drive the metabolic differences between these two strains of S. cerevisiae. Our results suggest that although kinetic models cannot be readily used across strains as stoichiometric models, they can capture species-specific information through the kinetic parameterization process.

Current advancements in systems and synthetic biology studies of Saccharomyces cerevisiae

Saccharomyces cerevisiae has a long-standing history of biotechnological applications even before the dawn of modern biotechnology. The field is undergoing accelerated advancement with the recent systems and synthetic biology approaches. In this review, we highlight the recent findings in the field with a focus on omics studies of S. cerevisiae to investigate its stress tolerance in different industries. The latest advancements in S. cerevisiae systems and synthetic biology approaches for the development of genome-scale metabolic models (GEMs) and molecular tools such as multiplex Cas9, Cas12a, Cpf1, and Csy4 genome editing tools, modular expression cassette with optimal transcription factors, promoters, and terminator libraries as well as metabolic engineering. Omics data analysis is key to the identification of exploitable native genes/proteins/pathways in S. cerevisiae with the optimization of heterologous pathway implementation and fermentation conditions. Through systems and synthetic biology, various heterologous compound productions that require non-native biosynthetic pathways in a cell factory have been established via different strategies of metabolic engineering integrated with machine learning.

Recent advances in machine learning applications in metabolic engineering

Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations.

Metabolic Engineering: Methodologies and Applications

Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.

Metabolomics-driven strain improvement: A mini review

In recent years, mass spectrometry-based metabolomics has been established as a powerful and versatile technique for studying cellular metabolism by comprehensive analysis of metabolites in the cell. Although there are many scientific reports on the use of metabolomics for the elucidation of mechanism and physiological changes occurring in the cell, there are surprisingly very few reports on its use for the identification of rate-limiting steps in a synthetic biological system that can lead to the actual improvement of the host organism. In this mini review, we discuss different strategies for improving strain performance using metabolomics data and compare the application of metabolomics-driven strain improvement techniques in different host microorganisms. Finally, we highlight several success stories on the use of metabolomics-driven strain improvement strategies, which led to significant bioproductivity improvements.

Metabolic response of Lactobacillus acidophilus exposed to amoxicillin

Drug-induced diarrhea is a common adverse drug reaction, especially the one caused by the widespread use of antibiotics. The reduction of probiotics is one reason for intestinal disorders induced by an oral antibiotic. However, the intrinsic mechanism of drug-induced diarrhea is still unknown. In this study, we used metabolomics methods to explore the effects of the classic oral antibiotic, amoxicillin, on the growth and metabolism of Lactobacillus acidophilus, while scanning electron microscopy (SEM) and 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assays were employed to evaluate changes in cell activity and morphology. The results showed that cell viability gradually decreased, while the degree of cell wall rupture increased, with increasing amoxicillin concentrations. A non-targeted metabolomics analysis identified 13 potential biomarkers associated with 9 metabolic pathways. The data showed that arginine and proline metabolism, nicotinate and nicotinamide metabolism, pyrimidine metabolism, glycine, serine and threonine metabolism, beta-alanine metabolism, glycerolipid metabolism, tryptophan metabolism, steroid hormone biosynthesis, and histidine metabolism may be involved in the different effects exerted by amoxicillin on L. acidophilus. This study provides potential targets for screening probiotics regulators and lays a theoretical foundation for the elucidation of their mechanisms.

Metabolism and strategies for enhanced supply of acetyl-CoA in Saccharomyces cerevisiae

Acetyl-CoA is a kind of important cofactor that is involved in many metabolic pathways. It serves as the precursor for many interesting commercial products, such as terpenes, flavonoids and anthraquinones. However, the insufficient supply of acetyl-CoA limits biosynthesis of its derived compounds in the intracellular. In this review, we outlined metabolic pathways involved in the catabolism and anabolism of acetyl-CoA, as well as some important derived products. We examined several strategies for the enhanced supply of acetyl-CoA, and provided insight into pathways that generate acetyl-CoA to balance metabolism, which can be harnessed to improve the titer, yield and productivities of interesting products in Saccharomyces cerevisiae and other eukaryotic microorganisms. We believe that peroxisomal fatty acid β-oxidation could be an attractive strategy for enhancing the supply of acetyl-CoA.

Microbial production of advanced biofuels

Concerns over climate change have necessitated a rethinking of our transportation infrastructure. One possible alternative to carbon-polluting fossil fuels is biofuels produced by engineered microorganisms that use a renewable carbon source. Two biofuels, ethanol and biodiesel, have made inroads in displacing petroleum-based fuels, but their uptake has been limited by the amounts that can be used in conventional engines and by their cost. Advanced biofuels that mimic petroleum-based fuels are not limited by the amounts that can be used in existing transportation infrastructure but have had limited uptake due to costs. In this Review, we discuss engineering metabolic pathways to produce advanced biofuels, challenges with substrate and product toxicity with regard to host microorganisms and methods to engineer tolerance, and the use of functional genomics and machine learning approaches to produce advanced biofuels and prospects for reducing their costs.

Advances in developing metabolically engineered microbial platforms to produce fourth-generation biofuels and high-value biochemicals

Producing bio-based chemicals is imperative to establish an eco-friendly circular bioeconomy. However, the compromised titer of these biochemicals hampers their commercial implementation. Advances in genetic engineering tools have enabled researchers to develop robust strains producing desired titers of the next-generation biofuels and biochemicals. The native and non-native pathways have been extensively engineered in various host strains via pathway reconstruction and metabolic flux redirection of lipid metabolism and central carbon metabolism to produce myriad biomolecules including alcohols, isoprenoids, hydrocarbons, fatty-acids, and their derivatives. This review has briefly covered the research efforts made during the previous decade to produce advanced biofuels and biochemicals through engineered microbial platforms along with the engineering approaches employed. The efficiency of the various techniques along with their shortcomings is also covered to provide a comprehensive overview of the progress and future directions to achieve higher titer of fourth-generation biofuels and biochemicals while keeping environmental sustainability intact.

The Role of Metabolic Engineering Technologies for the Production of Fatty Acids in Yeast

Simple Summary

Metabolic engineering involves the sustainable production of high-value products. E. coli and yeast, in particular, are used for such processes. Using metabolic engineering, the biosynthetic pathways of these cells are altered to obtain a high production of desired products. Fatty acids (FAs) and their derivatives are products produced using metabolic engineering. However, classical methods used for engineering yeast metabolic pathways for the production of fatty acids and their derivatives face problems such as the low supply of key precursors and product tolerance. This review introduces the different ways FAs are being produced in E. coli and yeast and the genetic manipulations for enhanced production of FAs. The review also summarizes the latest techniques (i.e., CRISPR–Cas and synthetic biology) for developing FA-producing yeast cell factories.

Abstract

Metabolic engineering is a cutting-edge field that aims to produce simple, readily available, and inexpensive biomolecules by applying different genetic engineering and molecular biology techniques. Fatty acids (FAs) play an important role in determining the physicochemical properties of membrane lipids and are precursors of biofuels. Microbial production of FAs and FA-derived biofuels has several advantages in terms of sustainability and cost. Conventional yeast Saccharomyces cerevisiae is one of the models used for FA synthesis. Several genetic manipulations have been performed to enhance the citrate accumulation and its conversation into acetyl-CoA, a precursor for FA synthesis. Success has been achieved in producing different chemicals, including FAs and their derivatives, through metabolic engineering. However, several hurdles such as slow growth rate, low oleaginicity, and cytotoxicity are still need to be resolved. More robust research needs to be conducted on developing microbes capable of resisting diverse environments, chemicals, and cost-effective feed requirements. Redesigning microbes to produce FAs with cutting-edge synthetic biology and CRISPR techniques can solve these problems. Here, we reviewed the technological progression of metabolic engineering techniques and genetic studies conducted on S. cerevisiae, making it suitable as a model organism and a great candidate for the production of biomolecules, especially FAs.

Correcting for Naturally Occurring Mass Isotopologue Abundances in Stable-Isotope Tracing Experiments with PolyMID

Stable-isotope tracing is a method to measure intracellular metabolic pathway utilization by feeding a cellular system a stable-isotope-labeled tracer nutrient. The power of the method to resolve differential pathway utilization is derived from the enrichment of metabolites in heavy isotopes that are synthesized from the tracer nutrient. However, the readout is complicated by the presence of naturally occurring heavy isotopes that are not derived from the tracer nutrient. Herein we present an algorithm, and a tool that applies it (PolyMID-Correct, part of the PolyMID software package), to computationally remove the influence of naturally occurring heavy isotopes. The algorithm is applicable to stable-isotope tracing data collected on low- and high- mass resolution mass spectrometers. PolyMID-Correct is open source and available under an MIT license.

Role of the mitochondrial citrate-oxoglutarate carrier in lipid accumulation in the oleaginous fungus Mortierella alpina

OBJECTIVES

The transport of citrate from the mitochondria to the cytoplasm is essential during lipid accumulation. This study aimed to explore the role of mitochondrial citrate-oxoglutarate carrier in lipid accumulation in the oleaginous fungus Mortierella alpina.

RESULTS

Homologous MaYHM (the gene encoding the mitochondrial citrate-oxoglutarate carrier) was overexpressed in M. alpina. The fatty acid content of MaYHM-overexpressing recombinant strains was increased by up to 30% compared with the control. Moreover, the intracellular α-ketoglutarate level in recombinant strains was increased by 2.2 fold, together with a 23-35% decrease in NAD⁺-isocitrate dehydrogenase activity compared with the control. The overexpression of MaYHM altered the metabolic flux in the glutamate dehydrogenase shunt and 4-aminobutyric acid shunt during metabolic reprogramming, supplying more carbon to synthesize fatty acids.

CONCLUSIONS

Overexpression of MaYHM resulted in more efflux of citrate from mitochondria to the cytoplasm and enhanced lipid accumulation. These findings provide new perspectives for the improvement of industrial lipid production in M. alpina.

Recent advances in systems and synthetic biology approaches for developing novel cell-factories in non-conventional yeasts

Microbial bioproduction of chemicals, proteins, and primary metabolites from cheap carbon sources is currently an advancing area in industrial research. The model yeast, Saccharomyces cerevisiae, is a well-established biorefinery host that has been used extensively for commercial manufacturing of bioethanol from myriad carbon sources. However, its Crabtree-positive nature often limits the use of this organism for the biosynthesis of commercial molecules that do not belong in the fermentative pathway. To avoid extensive strain engineering of S. cerevisiae for the production of metabolites other than ethanol, non-conventional yeasts can be selected as hosts based on their natural capacity to produce desired commodity chemicals. Non-conventional yeasts like Kluyveromyces marxianus, K. lactis, Yarrowia lipolytica, Pichia pastoris, Scheffersomyces stipitis, Hansenula polymorpha, and Rhodotorula toruloides have been considered as potential industrial eukaryotic hosts owing to their desirable phenotypes such as thermotolerance, assimilation of a wide range of carbon sources, as well as ability to secrete high titers of protein and lipid. However, the advanced metabolic engineering efforts in these organisms are still lacking due to the limited availability of systems and synthetic biology methods like in silico models, well-characterised genetic parts, and optimized genome engineering tools. This review provides an insight into the recent advances and challenges of systems and synthetic biology as well as metabolic engineering endeavours towards the commercial usage of non-conventional yeasts. Particularly, the approaches in emerging non-conventional yeasts for the production of enzymes, therapeutic proteins, lipids, and metabolites for commercial applications are extensively discussed here. Various attempts to address current limitations in designing novel cell factories have been highlighted that include the advances in the fields of genome-scale metabolic model reconstruction, flux balance analysis, 'omics'-data integration into models, genome-editing toolkit development, and rewiring of cellular metabolisms for desired chemical production. Additionally, the understanding of metabolic networks using ¹³C-labelling experiments as well as the utilization of metabolomics in deciphering intracellular fluxes and reactions have also been discussed here. Application of cutting-edge nuclease-based genome editing platforms like CRISPR/Cas9, and its optimization towards efficient strain engineering in non-conventional yeasts have also been described. Additionally, the impact of the advances in promising non-conventional yeasts for efficient commercial molecule synthesis has been meticulously reviewed. In the future, a cohesive approach involving systems and synthetic biology will help in widening the horizon of the use of unexplored non-conventional yeast species towards industrial biotechnology.

Setting Up an Automated Biomanufacturing Laboratory

Laboratory automation is a key enabling technology for genetic engineering that can lead to higher throughput, more efficient and accurate experiments, better data management and analysis, decrease in the DBT (Design, Build, and Test) cycle turnaround, increase of reproducibility, and savings in lab resources. Choosing the correct framework among so many options available in terms of software, hardware, and skills needed to operate them is crucial for the success of any automation project. This chapter explores the multiple aspects to be considered for the solid development of a biofoundry project including available software and hardware tools, resources, strategies, partnerships, and collaborations in the field needed to speed up the translation of research results to solve important society problems.

Expressing a cytosolic pyruvate dehydrogenase complex to increase free fatty acid production in Saccharomyces cerevisiae

BACKGROUND

Saccharomyces cerevisiae is being exploited as a cell factory to produce fatty acids and their derivatives as biofuels. Previous studies found that both precursor supply and fatty acid metabolism deregulation are essential for enhanced fatty acid synthesis. A bacterial pyruvate dehydrogenase (PDH) complex expressed in the yeast cytosol was reported to enable production of cytosolic acetyl-CoA with lower energy cost and no toxic intermediate.

RESULTS

Overexpression of the PDH complex significantly increased cell growth, ethanol consumption and reduced glycerol accumulation. Furthermore, to optimize the redox imbalance in production of fatty acids from glucose, two endogenous NAD⁺-dependent glycerol-3-phosphate dehydrogenases were deleted, and a heterologous NADP⁺-dependent glyceraldehyde-3-phosphate dehydrogenase was introduced. The best fatty acid producing strain PDH7 with engineering of precursor and co-factor metabolism could produce 840.5 mg/L free fatty acids (FFAs) in shake flask, which was 83.2% higher than the control strain YJZ08. Profile analysis of free fatty acid suggested the cytosolic PDH complex mainly resulted in the increases of unsaturated fatty acids (C16:1 and C18:1).

CONCLUSIONS

We demonstrated that cytosolic PDH pathway enabled more efficient acetyl-CoA provision with the lower ATP cost, and improved FFA production. Together with engineering of the redox factor rebalance, the cytosolic PDH pathway could achieve high level of FFA production at similar levels of other best acetyl-CoA producing pathways.

Efficient System Wide Metabolic Pathway Comparisons in Multiple Microbes Using Genome to KEGG Orthology (G2KO) Pipeline Tool

Comparison of system-wide metabolic pathways among microbes provides valuable insights of organisms' metabolic capabilities that can further assist in rationally screening organisms in silico for various applications. In this work, we present a much needed, efficient and user-friendly Genome to KEGG Orthology (G2KO) pipeline tool that facilitates efficient comparison of system wide metabolic networks of multiple organisms simultaneously. The optimized strategy primarily involves automatic retrieval of the KEGG Orthology (KO) identifiers of user defined organisms from the KEGG database followed by overlaying and visualization of the metabolic genes using the KEGG Mapper reconstruct pathway tool. We demonstrate the applicability of G2KO via two case studies in which we processed 24,314 genes across 15 organisms, mapped on to 530 reference pathways in KEGG, while focusing on pathways of interest. First, an in-silico designing of synthetic microbial consortia towards bioprocessing of cellulose to valuable products by comparing the cellulose degradation and fermentative pathways of microbes was undertaken. Second, we comprehensively compared the amino acid biosynthetic pathways of multiple microbes and demonstrated the potential of G2KO as an efficient tool for metabolic studies. We envisage the tool will find immensely useful to the metabolic engineers as well as systems biologists. The tool's web-server, along with tutorial is publicly available at https://faculty.iitmandi.ac.in/~shyam/tools/g2ko/g2ko.cgi . Also, standalone tool can be downloaded freely from https://sourceforge.net/projects/g2ko/ , and from the supplementary.

Model-assisted identification of metabolic engineering strategies for Jatropha curcas lipid pathways

Efficient approaches to increase plant lipid production are necessary to meet current industrial demands for this important resource. While Jatropha curcas cell culture can be used for in vitro lipid production, scaling up the system for industrial applications requires an understanding of how growth conditions affect lipid metabolism and yield. Here we present a bottom-up metabolic reconstruction of J. curcas supported with labeling experiments and biomass characterization under three growth conditions. We show that the metabolic model can accurately predict growth and distribution of fluxes in cell cultures and use these findings to pinpoint energy expenditures that affect lipid biosynthesis and metabolism. In addition, by using constraint-based modeling approaches we identify network reactions whose joint manipulation optimizes lipid production. The proposed model and computational analyses provide a stepping stone for future rational optimization of other agronomically relevant traits in J. curcas.

Engineering Saccharomyces cerevisiae cells for production of fatty acid-derived biofuels and chemicals

The yeast Saccharomyces cerevisiae is a widely used cell factory for the production of fuels and chemicals, in particular ethanol, a biofuel produced in large quantities. With a need for high-energy-density fuels for jets and heavy trucks, there is, however, much interest in the biobased production of hydrocarbons that can be derived from fatty acids. Fatty acids also serve as precursors to a number of oleochemicals and hence provide interesting platform chemicals. Here, we review the recent strategies applied to metabolic engineering of S. cerevisiae for the production of fatty acid-derived biofuels and for improvement of the titre, rate and yield (TRY). This includes, for instance, redirection of the flux towards fatty acids through engineering of the central carbon metabolism, balancing the redox power and varying the chain length of fatty acids by enzyme engineering. We also discuss the challenges that currently hinder further TRY improvements and the potential solutions in order to meet the requirements for commercial application.

Genome-Scale 13C Fluxomics Modeling for Metabolic Engineering of Saccharomyces cerevisiae

Synthetic biology is a rapidly developing field that pursues the application of engineering principles and development approaches to biological engineering. Synthetic biology is poised to change the way biology is practiced, and has important practical applications: for example, building genetically engineered organisms to produce biofuels, medicines, and other chemicals. Traditionally, synthetic biology has focused on manipulating a few genes (e.g., in a single pathway or genetic circuit), but its combination with systems biology holds the promise of creating new cellular architectures and constructing complex biological systems from the ground up. Enabling this merge of synthetic and systems biology will require greater predictive capability for modeling the behavior of cellular systems, and more comprehensive data sets for building and calibrating these models. The so-called "-omics" data sets can now be generated via high throughput techniques in the form of genomic, proteomic, transcriptomic, and metabolomic information on the engineered biological system. Of particular interest with respect to the engineering of microbes capable of producing biofuels and other chemicals economically and at scale are metabolomic datasets, and their insights into intracellular metabolic fluxes. Metabolic fluxes provide a rapid and easy to understand picture of how carbon and energy flow throughout the cell. Here, we present a detailed guide to performing metabolic flux analysis and modeling using the open source JBEI Quantitative Metabolic Modeling (jQMM) library. This library allows the user to transform metabolomics data in the form of isotope labeling data from a ¹³C labeling experiment into a determination of cellular fluxes that can be used to develop genetic engineering strategies for metabolic engineering.The jQMM library presents a complete toolbox for performing a range of different tasks of interest in metabolic engineering. Various different types of flux analysis and modeling can be performed such as flux balance analysis, ¹³C metabolic flux analysis, and two-scale ¹³C metabolic flux analysis (2S-¹³C MFA). 2S-¹³C MFA is a novel method that determines genome-scale fluxes without the need of every single carbon transition in the metabolic network. In addition to several other capabilities, the jQMM library can make model based predictions for how various genetic engineering strategies can be incorporated toward bioengineering goals: it can predict the effects of reaction knockouts on metabolism using both the MoMA and ROOM methodologies. In this chapter, we will illustrate the use of the jQMM library through a step-by-step demonstration of flux determination and knockout prediction in a complex eukaryotic model organism: Saccharomyces cerevisiae (S. cerevisiae). Included with this chapter is a digital Jupyter Notebook file that provides a computable appendix showing a self-contained example of jQMM usage, which can be changed to fit the user's specific needs. As an open source software project, users can modify and extend the code base to make improvements at will, allowing them to share their development work and contribute back to the jQMM modeling community.

Advances in metabolic flux analysis toward genome-scale profiling of higher organisms

Methodological and technological advances have recently paved the way for metabolic flux profiling in higher organisms, like plants. However, in comparison with omics technologies, flux profiling has yet to provide comprehensive differential flux maps at a genome-scale and in different cell types, tissues, and organs. Here we highlight the recent advances in technologies to gather metabolic labeling patterns and flux profiling approaches. We provide an opinion of how recent local flux profiling approaches can be used in conjunction with the constraint-based modeling framework to arrive at genome-scale flux maps. In addition, we point at approaches which use metabolomics data without introduction of label to predict either non-steady state fluxes in a time-series experiment or flux changes in different experimental scenarios. The combination of these developments allows an experimentally feasible approach for flux-based large-scale systems biology studies.

Towards a sustainable bio-based economy: Redirecting primary metabolism to new products with plant synthetic biology

Humans have domesticated many plant species as indispensable sources of food, materials, and medicines. The dawning era of synthetic biology represents a means to further refine, redesign, and engineer crops to meet various societal and industrial needs. Current and future endeavors will utilize plants as the foundation of a bio-based economy through the photosynthetic production of carbohydrate feedstocks for the microbial fermentation of biofuels and bioproducts, with the end goal of decreasing our dependence on petrochemicals. As our technological capabilities improve, metabolic engineering efforts may expand the utility of plants beyond sugar feedstocks through the direct production of target compounds, including pharmaceuticals, renewable fuels, and commodity chemicals. However, relatively little work has been done to fully realize the potential in redirecting central carbon metabolism in plants for the engineering of novel bioproducts. Although our ability to rationally engineer and manipulate plant metabolism is in its infancy, I highlight some of the opportunities and challenges in applying synthetic biology towards engineering plant primary metabolism.

A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data

New synthetic biology capabilities hold the promise of dramatically improving our ability to engineer biological systems. However, a fundamental hurdle in realizing this potential is our inability to accurately predict biological behavior after modifying the corresponding genotype. Kinetic models have traditionally been used to predict pathway dynamics in bioengineered systems, but they take significant time to develop, and rely heavily on domain expertise. Here, we show that the combination of machine learning and abundant multiomics data (proteomics and metabolomics) can be used to effectively predict pathway dynamics in an automated fashion. The new method outperforms a classical kinetic model, and produces qualitative and quantitative predictions that can be used to productively guide bioengineering efforts. This method systematically leverages arbitrary amounts of new data to improve predictions, and does not assume any particular interactions, but rather implicitly chooses the most predictive ones.

Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis

Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13C Metabolic Flux Analysis (13C MFA) and Two-Scale 13C Metabolic Flux Analysis (2S-13C MFA) are two techniques used to determine such fluxes. Both operate on the simplifying approximation that metabolic flux from peripheral metabolism into central “core” carbon metabolism is minimal, and can be omitted when modeling isotopic labeling in core metabolism. The validity of this “two-scale” or “bow tie” approximation is supported both by the ability to accurately model experimental isotopic labeling data, and by experimentally verified metabolic engineering predictions using these methods. However, the boundaries of core metabolism that satisfy this approximation can vary across species, and across cell culture conditions. Here, we present a set of algorithms that (1) systematically calculate flux bounds for any specified “core” of a genome-scale model so as to satisfy the bow tie approximation and (2) automatically identify an updated set of core reactions that can satisfy this approximation more efficiently. First, we leverage linear programming to simultaneously identify the lowest fluxes from peripheral metabolism into core metabolism compatible with the observed growth rate and extracellular metabolite exchange fluxes. Second, we use Simulated Annealing to identify an updated set of core reactions that allow for a minimum of fluxes into core metabolism to satisfy these experimental constraints. Together, these methods accelerate and automate the identification of a biologically reasonable set of core reactions for use with 13C MFA or 2S-13C MFA, as well as provide for a substantially lower set of flux bounds for fluxes into the core as compared with previous methods. We provide an open source Python implementation of these algorithms at https://github.com/JBEI/limitfluxtocore.

Two-Scale 13C Metabolic Flux Analysis for Metabolic Engineering

Accelerating the Design-Build-Test-Learn (DBTL) cycle in synthetic biology is critical to achieving rapid and facile bioengineering of organisms for the production of, e.g., biofuels and other chemicals. The Learn phase involves using data obtained from the Test phase to inform the next Design phase. As part of the Learn phase, mathematical models of metabolic fluxes give a mechanistic level of comprehension to cellular metabolism, isolating the principle drivers of metabolic behavior from the peripheral ones, and directing future experimental designs and engineering methodologies. Furthermore, the measurement of intracellular metabolic fluxes is specifically noteworthy as providing a rapid and easy-to-understand picture of how carbon and energy flow throughout the cell. Here, we present a detailed guide to performing metabolic flux analysis in the Learn phase of the DBTL cycle, where we show how one can take the isotope labeling data from a ¹³C labeling experiment and immediately turn it into a determination of cellular fluxes that points in the direction of genetic engineering strategies that will advance the metabolic engineering process.For our modeling purposes we use the Joint BioEnergy Institute (JBEI) Quantitative Metabolic Modeling (jQMM) library, which provides an open-source, python-based framework for modeling internal metabolic fluxes and making actionable predictions on how to modify cellular metabolism for specific bioengineering goals. It presents a complete toolbox for performing different types of flux analysis such as Flux Balance Analysis, ¹³C Metabolic Flux Analysis, and it introduces the capability to use ¹³C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale ¹³C Metabolic Flux Analysis (2S-¹³C MFA) [1]. In addition to several other capabilities, the jQMM is also able to predict the effects of knockouts using the MoMA and ROOM methodologies. The use of the jQMM library is illustrated through a step-by-step demonstration, which is also contained in a digital Jupyter Notebook format that enhances reproducibility and provides the capability to be adopted to the user's specific needs. As an open-source software project, users can modify and extend the code base and make improvements at will, providing a base for future modeling efforts.

Engineering high-level production of fatty alcohols by Saccharomyces cerevisiae from lignocellulosic feedstocks

Fatty alcohols in the C12-C18 range are used in personal care products, lubricants, and potentially biofuels. These compounds can be produced from the fatty acid pathway by a fatty acid reductase (FAR), yet yields from the preferred industrial host Saccharomyces cerevisiae remain under 2% of the theoretical maximum from glucose. Here we improved titer and yield of fatty alcohols using an approach involving quantitative analysis of protein levels and metabolic flux, engineering enzyme level and localization, pull-push-block engineering of carbon flux, and cofactor balancing. We compared four heterologous FARs, finding highest activity and endoplasmic reticulum localization from a Mus musculus FAR. After screening an additional twenty-one single-gene edits, we identified increasing FAR expression; deleting competing reactions encoded by DGA1, HFD1, and ADH6; overexpressing a mutant acetyl-CoA carboxylase; limiting NADPH and carbon usage by the glutamate dehydrogenase encoded by GDH1; and overexpressing the Δ9-desaturase encoded by OLE1 as successful strategies to improve titer. Our final strain produced 1.2g/L fatty alcohols in shake flasks, and 6.0g/L in fed-batch fermentation, corresponding to ~ 20% of the maximum theoretical yield from glucose, the highest titers and yields reported to date in S. cerevisiae. We further demonstrate high-level production from lignocellulosic feedstocks derived from ionic-liquid treated switchgrass and sorghum, reaching 0.7g/L in shake flasks. Altogether, our work represents progress towards efficient and renewable microbial production of fatty acid-derived products.

Flux-Enabled Exploration of the Role of Sip1 in Galactose Yeast Metabolism

¹³C metabolic flux analysis (¹³C MFA) is an important systems biology technique that has been used to investigate microbial metabolism for decades. The heterotrimer Snf1 kinase complex plays a key role in the preference Saccharomyces cerevisiae exhibits for glucose over galactose, a phenomenon known as glucose repression or carbon catabolite repression. The SIP1 gene, encoding a part of this complex, has received little attention, presumably, because its knockout lacks a growth phenotype. We present a fluxomic investigation of the relative effects of the presence of galactose in classically glucose-repressing media and/or knockout of SIP1 using a multi-scale variant of ¹³C MFA known as 2-Scale ¹³C metabolic flux analysis (2S-¹³C MFA). In this study, all strains have the galactose metabolism deactivated (gal1Δ background) so as to be able to separate the metabolic effects purely related to glucose repression from those arising from galactose metabolism. The resulting flux profiles reveal that the presence of galactose in classically glucose-repressing conditions, for a CEN.PK113-7D gal1Δ background, results in a substantial decrease in pentose phosphate pathway (PPP) flux and increased flow from cytosolic pyruvate and malate through the mitochondria toward cytosolic branched-chain amino acid biosynthesis. These fluxomic redistributions are accompanied by a higher maximum specific growth rate, both seemingly in violation of glucose repression. Deletion of SIP1 in the CEN.PK113-7D gal1Δ cells grown in mixed glucose/galactose medium results in a further increase. Knockout of this gene in cells grown in glucose-only medium results in no change in growth rate and a corresponding decrease in glucose and ethanol exchange fluxes and flux through pathways involved in aspartate/threonine biosynthesis. Glucose repression appears to be violated at a 1/10 ratio of galactose-to-glucose. Based on the scientific literature, we may have conducted our experiments near a critical sugar ratio that is known to allow galactose to enter the cell. Additionally, we report a number of fluxomic changes associated with these growth rate increases and unexpected flux profile redistributions resulting from deletion of SIP1 in glucose-only medium.