Metabolic Engineering Experiments Research Articles

Isolation and characterization of sub-plastidial fractions from carotenoid rich fruits.

Carotenoid biosynthesis and sequestration in higher plants occurs in the plastid organelle. Among diverse germplasm collections displaying natural variation for carotenoids and outputs from metabolic engineering experiments it has become clear that plastid type and numbers can have important implications on the quantitative composition of carotenoids accumulating. Therefore, it is important to characterize these organelles to fully evaluate the potential of the germplasm to enhance carotenoids and create nutrient dense fruits and vegetables. In this article the procedures used to isolate sub-plastidial structures from carotenoid-rich Solanaceae fruits (tomato and Capsicum) are described.

The Design-Build-Test-Learn cycle for metabolic engineering of Streptomycetes.

Streptomycetes are producers of a wide range of specialized metabolites of great medicinal and industrial importance, such as antibiotics, antifungals, or pesticides. Having been the drivers of the golden age of antibiotics in the 1950s and 1960s, technological advancements over the last two decades have revealed that very little of their biosynthetic potential has been exploited so far. Given the great need for new antibiotics due to the emerging antimicrobial resistance crisis, as well as the urgent need for sustainable biobased production of complex molecules, there is a great renewed interest in exploring and engineering the biosynthetic potential of streptomycetes. Here, we describe the Design-Build-Test-Learn (DBTL) cycle for metabolic engineering experiments in streptomycetes and how it can be used for the discovery and production of novel specialized metabolites.

Screening and Growth Characterization of Non-conventional Yeasts in a Hemicellulosic Hydrolysate.

Lignocellulosic biomass is an attractive raw material for the sustainable production of chemicals and materials using microbial cell factories. Most of the existing bioprocesses focus on second-generation ethanol production using genetically modified Saccharomyces cerevisiae, however, this microorganism is naturally unable to consume xylose. Moreover, extensive metabolic engineering has to be carried out to achieve high production levels of industrially relevant building blocks. Hence, the use of non-Saccharomyces species, or non-conventional yeasts, bearing native metabolic routes, allows conversion of a wide range of substrates into different products, and higher tolerance to inhibitors improves the efficiency of biorefineries. In this study, nine non-conventional yeast strains were selected and screened on a diluted hemicellulosic hydrolysate from Birch. Kluyveromyces marxianus CBS 6556, Scheffersomyces stipitis CBS 5773, Lipomyces starkeyi DSM 70295, and Rhodotorula toruloides CCT 7815 were selected for further characterization, where their growth and substrate consumption patterns were analyzed under industrially relevant substrate concentrations and controlled environmental conditions in bioreactors. K. marxianus CBS 6556 performed poorly under higher hydrolysate concentrations, although this yeast was determined among the fastest-growing yeasts on diluted hydrolysate. S. stipitis CBS 5773 demonstrated a low growth and biomass production while consuming glucose, while during the xylose-phase, the specific growth and sugar co-consumption rates were among the highest of this study (0.17 h–1 and 0.37 g/gdw*h, respectively). L. starkeyi DSM 70295 and R. toruloides CCT 7815 were the fastest to consume the provided sugars at high hydrolysate conditions, finishing them within 54 and 30 h, respectively. R. toruloides CCT 7815 performed the best of all four studied strains and tested conditions, showing the highest specific growth (0.23 h–1), substrate co-consumption (0.73 ± 0.02 g/gdw*h), and xylose consumption (0.22 g/gdw*h) rates. Furthermore, R. toruloides CCT 7815 was able to produce 10.95 ± 1.37 gL–1 and 1.72 ± 0.04 mgL–1 of lipids and carotenoids, respectively, under non-optimized cultivation conditions. The study provides novel information on selecting suitable host strains for biorefinery processes, provides detailed information on substrate consumption patterns, and pinpoints to bottlenecks possible to address using metabolic engineering or adaptive evolution experiments.

Development of new dominant selectable markers for the nonconventional yeasts Ogataea polymorpha and Candida famata.

Nonconventional yeast Candida famata and Ogataea polymorpha are interesting organisms for basic and applied studies. O. polymorpha is methylotrophic thermotolerant yeast capable of xylose alcoholic fermentation whereas C. famata is capable of riboflavin overproduction. Still, the new tools for molecular research of these species are needed. The aim of this study was to develop the new dominant selective markers for C. famata and O. polymorpha usable in metabolic engineering experiments. In this work, the BSD gene from Aspergillus terreus coding for blasticidin S deaminase, O. polymorpha AUR1 gene required for sphingolipid synthesis and IMH3 gene, which encodes IMP dehydrogenase, were tested as the new dominant selective marker genes. Our results showed that AUR1 and IMH3 genes could be used as dominant selective markers for O. polymorpha with frequencies of transformation of 40 and 20 transformants per microgram of DNA, respectively. The IMH3 gene was successfully used as the marker for construction of O. polymorpha strains with increased ethanol production from xylose due to overexpression of TAL1, TKL1 and AOX1 genes. The BSD gene from A. terreus, conferring resistance to blasticidin, was found to be efficient for selection of C. famata transformants.

Arabidopsis: the original plant chassis organism.

Arabidopsis thaliana (thale cress) has a past, current, and future role in the era of synthetic biology. Arabidopsis is one of the most well-studied plants with a wealth of genomics, genetics, and biochemical resources available for the metabolic engineer and synthetic biologist. Here we discuss the tools and resources that enable the identification of target genes and pathways in Arabidopsis and heterologous expression in this model plant. While there are numerous examples of engineering Arabidopsis for decreased lignin, increased seed oil, increased vitamins, and environmental remediation, this plant has provided biochemical tools for introducing Arabidopsis genes, pathways, and/or regulatory elements into other plants and microorganisms. Arabidopsis is not a vegetative or oilseed crop, but it is as an excellent model chassis for proof-of-concept metabolic engineering and synthetic biology experiments in plants.

Metabolic modelling and flux analysis of microorganisms from the Atacama Desert used in biotechnological processes

Metabolic modelling is a useful tool that enables the rational design of metabolic engineering experiments and the study of the unique capabilities of biotechnologically important microorganisms. The extreme abiotic conditions of the Atacama Desert have selected microbial diversity with exceptional characteristics that can be applied in the mining industry for bioleaching processes and for production of specialised metabolites with antimicrobial, antifungal, antiviral, antitumoral, among other activities. In this review we summarise the scientific data available of the use of metabolic modelling and flux analysis to improve the performance of Atacama Desert microorganisms in biotechnological applications.

Book Review: Recent Advances in Yeast Metabolic Engineering

This book has been published to present in great detail the recent advances of yeast metabolic engineering, with a special focus on tools, technologies, assays, pathway engineering, selection, optimization, and screening. This book is written in an easy to understand language by eminent scientists in the field of metabolic engineering. It is a timely compilation of the recent advances and the future scope of the field. This book is an excellent and informative for yeast metabolic engineering experiments, as the editor has made an impressive compilation of almost of all the methods for the manipulation of yeast strains, and the consequent development of strains for industrial purposes with great simplicity. The central idea behind the book is to highlight the number of important aspects of metabolic engineering in a way that can help future investigators, researchers, students, and stakeholders to perform research with ease.

Synthetic Core Promoters as Universal Parts for Fine-Tuning Expression in Different Yeast Species

Synthetic biology and metabolic engineering experiments frequently require the fine-tuning of gene expression to balance and optimize protein levels of regulators or metabolic enzymes. A key concept of synthetic biology is the development of modular parts that can be used in different contexts. Here, we have applied a computational multifactor design approach to generate de novo synthetic core promoters and 5′ untranslated regions (UTRs) for yeast cells. In contrast to upstream cis-regulatory modules (CRMs), core promoters are typically not subject to specific regulation, making them ideal engineering targets for gene expression fine-tuning. 112 synthetic core promoter sequences were designed on the basis of the sequence/function relationship of natural core promoters, nucleosome occupancy and the presence of short motifs. The synthetic core promoters were fused to the Pichia pastoris AOX1 CRM, and the resulting activity spanned more than a 200-fold range (0.3% to 70.6% of the wild type AOX1 level). The top-ten synthetic core promoters with highest activity were fused to six additional CRMs (three in P. pastoris and three in Saccharomyces cerevisiae). Inducible CRM constructs showed significantly higher activity than constitutive CRMs, reaching up to 176% of natural core promoters. Comparing the activity of the same synthetic core promoters fused to different CRMs revealed high correlations only for CRMs within the same organism. These data suggest that modularity is maintained to some extent but only within the same organism. Due to the conserved role of eukaryotic core promoters, this rational design concept may be transferred to other organisms as a generic engineering tool.

Design and Use of a Low Cost, Automated Morbidostat for Adaptive Evolution of Bacteria Under Antibiotic Drug Selection.

We describe a low cost, configurable morbidostat for characterizing the evolutionary pathway of antibiotic resistance. The morbidostat is a bacterial culture device that continuously monitors bacterial growth and dynamically adjusts the drug concentration to constantly challenge the bacteria as they evolve to acquire drug resistance. The device features a working volume of ~10 ml and is fully automated and equipped with optical density measurement and micro-pumps for medium and drug delivery. To validate the platform, we measured the stepwise acquisition of trimethoprim resistance in Escherichia coli MG 1655, and integrated the device with a multiplexed microfluidic platform to investigate cell morphology and antibiotic susceptibility. The approach can be up-scaled to laboratory studies of antibiotic drug resistance, and is extendible to adaptive evolution for strain improvements in metabolic engineering and other bacterial culture experiments.

Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation.

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolic network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. The defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.

Transcript abundance on its own cannot be used to infer fluxes in central metabolism.

An attempt has been made to define the extent to which metabolic flux in central plant metabolism is reflected by changes in the transcriptome and metabolome, based on an analysis of in vitro cultured immature embryos of two oilseed rape (Brassica napus) accessions which contrast for seed lipid accumulation. Metabolic flux analysis (MFA) was used to constrain a flux balance metabolic model which included 671 biochemical and transport reactions within the central metabolism. This highly confident flux information was eventually used for comparative analysis of flux vs. transcript (metabolite). Metabolite profiling succeeded in identifying 79 intermediates within the central metabolism, some of which differed quantitatively between the two accessions and displayed a significant shift corresponding to flux. An RNA-Seq based transcriptome analysis revealed a large number of genes which were differentially transcribed in the two accessions, including some enzymes/proteins active in major metabolic pathways. With a few exceptions, differential activity in the major pathways (glycolysis, TCA cycle, amino acid, and fatty acid synthesis) was not reflected in contrasting abundances of the relevant transcripts. The conclusion was that transcript abundance on its own cannot be used to infer metabolic activity/fluxes in central plant metabolism. This limitation needs to be borne in mind in evaluating transcriptome data and designing metabolic engineering experiments.

In silico analysis of bioethanol production from glucose/xylose mixtures during fed-batch fermentation of co-culture and mono-culture systems

This study presents a detailed in silico analysis of bioethanol production from glucose/xylose mixtures of various compositions by fed-batch co-culture and mono-culture fermentation of specialized microbes. The mono-culture consists of recombinant Saccharomyces cerevisise that can metabolize both hexose and pentose sugars while the co-culture system consists of substrate-selective microbes. Dynamic flux balance models based on available genome-scale reconstructions of the microorganisms have been used to analyze bioethanol production in fed-batch culture with constant feed rates and the maximization of ethanol productivity is addressed by computing optimal aerobic-anaerobic switching times. The simulation results clearly point to the superior performance of fed-batch fermentation of microbial co-culture against fed-batch fermentation of mono-culture for bioethanol production from glucose/xylose mixtures. A set of potential genetic engineering strategies for enhancement of S. cerevisiae and Escherichia coli strains performance have been identified. Such in silico predictions using genome-scale models provide valuable guidance for conducting in vivo metabolic engineering experiments.

A translation-coupling DNA cassette for monitoring protein translation in Escherichia coli

A major challenge to using heterologous expression in metabolic engineering experiments is the inability to quickly dissect experiments that have failed at the stage of translating mRNA. While many methods of detecting proteins exist, methods that detect untagged proteins at low levels are limited. Here, we describe a method to quickly determine whether Escherichia coli is capable of expressing the product of any target gene by coupling translation of a target gene to a detectable response gene. A translational coupling cassette was designed to encode a mRNA sequence that forms a secondary structure in the absence of translation and contains the translational start sequence of a detectable response gene. The translational coupling method was successfully tested with fluorescent proteins and antibiotic resistance markers. Only when the target gene was fully translated was the response observed. Further characterization demonstrated that translational coupling functions at both low and high levels of expression and that the response signal is proportional to the amount of target gene product. The translational coupling system was used to determine that a large multi-domain enzyme was not actively translated in E. coli, to isolate the translation problems to the C-terminal domains, and to optimize conditions for expressing a codon-optimized sequence variant.

Evolutionary and cellular webs in benzylisoquinoline alkaloid biosynthesis

Alkaloids are a group of approximately 12,000 low molecular weight and nitrogenous secondary metabolites found in 20% of plant species. Their potent biological activity suggests that alkaloids function as defense compounds. Benzylisoquinoline alkaloids (BIAs) are derived from tyrosine and are diversified by an intricate biochemical network of intramolecular coupling, reduction, methylation, hydroxylation, and other reactions to generate the estimated 2500 known structures. Several BIAs are used directly as pharmaceuticals or serve as precursors for the synthesis of semi-synthetic drugs. Plants remain the only economical source for the production of compounds such as morphine and codeine owing to their chemical complexity, which makes de novo synthesis challenging and costly. Much research has been directed toward understanding the biosynthesis of the BIAs and manipulating source plants to increase production of key products and pathway intermediates. However, metabolic engineering experiments often yield unexpected results demonstrating the need for an improved perspective on the biochemistry, regulation, and cell biology of BIA pathways. This review summarizes recent advances in the establishment of predictive metabolic engineering within the context of plant alkaloid biosynthesis.

Genome‐scale analysis of Mannheimia succiniciproducens metabolism

Mannheimia succiniciproducens MBEL55E isolated from bovine rumen is a capnophilic gram-negative bacterium that efficiently produces succinic acid, an industrially important four carbon dicarboxylic acid. In order to design a metabolically engineered strain which is capable of producing succinic acid with high yield and productivity, it is essential to optimize the whole metabolism at the systems level. Consequently, in silico modeling and simulation of the genome-scale metabolic network was employed for genome-scale analysis and efficient design of metabolic engineering experiments. The genome-scale metabolic network of M. succiniciproducens consisting of 686 reactions and 519 metabolites was constructed based on reannotation and validation experiments. With the reconstructed model, the network structure and key metabolic characteristics allowing highly efficient production of succinic acid were deciphered; these include strong PEP carboxylation, branched TCA cycle, relative weak pyruvate formation, the lack of glyoxylate shunt, and non-PTS for glucose uptake. Constraints-based flux analyses were then carried out under various environmental and genetic conditions to validate the genome-scale metabolic model and to decipher the altered metabolic characteristics. Predictions based on constraints-based flux analysis were mostly in excellent agreement with the experimental data. In silico knockout studies allowed prediction of new metabolic engineering strategies for the enhanced production of succinic acid. This genome-scale in silico model can serve as a platform for the systematic prediction of physiological responses of M. succiniciproducens to various environmental and genetic perturbations and consequently for designing rational strategies for strain improvement.

Understanding carotenoid metabolism as a necessity for genetic engineering of crop plants

As a proof of concept, the qualitative and quantitative engineering of carotenoid formation has been achieved in crop plants. Successful reports in tomato, potato, rice, and canola all describe the enhancement of carotenoid with nutritional value, while in model systems such as tobacco and Arabidopsis the engineering of carotenoid to confer abiotic stress has been described. For all the successful applications there have been many examples of unintended/unpredicted phenotypes and results. Typically this has resided from our lack of understanding of carotenoid formation and its regulation. In the present article, we will review advances in carotenoid formation and its regulation to illustrate how metabolic engineering experiments have shed light on regulatory mechanisms.