Integrated pathway mining and selection of an artificial CYP79-mediated bypass to improve benzylisoquinoline alkaloid biosynthesis.

BACKGROUND: Computational mining of useful enzymes and biosynthesis pathways is a powerful strategy for metabolic engineering. Through systematic exploration of all conceivable combinations of enzyme reactions, including both known compounds and those inferred from the chemical structures of established reactions, we can uncover previously undiscovered enzymatic processes. The application of the novel alternative pathways enables us to improve microbial bioproduction by bypassing or reinforcing metabolic bottlenecks. Benzylisoquinoline alkaloids (BIAs) are a diverse group of plant-derived compounds with important pharmaceutical properties. BIA biosynthesis has developed into a prime example of metabolic engineering and microbial bioproduction. The early bottleneck of BIA production in Escherichia coli consists of 3,4-dihydroxyphenylacetaldehyde (DHPAA) production and conversion to tetrahydropapaveroline (THP). Previous studies have selected monoamine oxidase (MAO) and DHPAA synthase (DHPAAS) to produce DHPAA from dopamine and oxygen; however, both of these enzymes produce toxic hydrogen peroxide as a byproduct. RESULTS: In the current study, in silico pathway design is applied to relieve the bottleneck of DHPAA production in the synthetic BIA pathway. Specifically, the cytochrome P450 enzyme, tyrosine N-monooxygenase (CYP79), is identified to bypass the established MAO- and DHPAAS-mediated pathways in an alternative arylacetaldoxime route to DHPAA with a peroxide-independent mechanism. The application of this pathway is proposed to result in less formation of toxic byproducts, leading to improved production of reticuline (up to 60 mg/L at the flask scale) when compared with that from the conventional MAO pathway. CONCLUSIONS: This study showed improved reticuline production using the bypass pathway predicted by the M-path computational platform. Reticuline production in E. coli exceeded that of the conventional MAO-mediated pathway. The study provides a clear example of the integration of pathway mining and enzyme design in creating artificial metabolic pathways and suggests further potential applications of this strategy in metabolic engineering.

Development of a GIN11/FRT-based multiple-gene integration technique affording inhibitor-tolerant, hemicellulolytic, xylose-utilizing abilities to industrial Saccharomyces cerevisiae strains for ethanol production from undetoxified lignocellulosic hemicelluloses.

BACKGROUND: Bioethanol produced by the yeast Saccharomyces cerevisiae is currently one of the most promising alternatives to conventional transport fuels. Lignocellulosic hemicelluloses obtained after hydrothermal pretreatment are important feedstock for bioethanol production. However, hemicellulosic materials cannot be directly fermented by yeast: xylan backbone of hemicelluloses must first be hydrolyzed by heterologous hemicellulases to release xylose, and the yeast must then ferment xylose in the presence of fermentation inhibitors generated during the pretreatment. RESULTS: A GIN11/FRT-based multiple-gene integration system was developed for introducing multiple functions into the recombinant S. cerevisiae strains engineered with the xylose metabolic pathway. Antibiotic markers were efficiently recycled by a novel counter selection strategy using galactose-induced expression of both FLP recombinase gene and GIN11 flanked by FLP recombinase recognition target (FRT) sequences. Nine genes were functionally expressed in an industrial diploid strain of S. cerevisiae: endoxylanase gene from Trichoderma reesei, xylosidase gene from Aspergillus oryzae, ß-glucosidase gene from Aspergillus aculeatus, xylose reductase and xylitol dehydrogenase genes from Scheffersomyces stipitis, and XKS1, TAL1, FDH1 and ADH1 variant from S. cerevisiae. The genes were introduced using the homozygous integration system and afforded hemicellulolytic, xylose-assimilating and inhibitor-tolerant abilities to the strain. The engineered yeast strain demonstrated 2.7-fold higher ethanol titer from hemicellulosic material than a xylose-assimilating yeast strain. Furthermore, hemicellulolytic enzymes displayed on the yeast cell surface hydrolyzed hemicelluloses that were not hydrolyzed by a commercial enzyme, leading to increased sugar utilization for improved ethanol production. CONCLUSIONS: The multifunctional yeast strain, developed using a GIN11/FRT-based marker recycling system, achieved direct conversion of hemicellulosic biomass to ethanol without the addition of exogenous hemicellulolytic enzymes. No detoxification processes were required. The multiple-gene integration technique is a powerful approach for introducing and improving the biomass fermentation ability of industrial diploid S. cerevisiae strains.

Detoxification of furfural in Corynebacterium glutamicum under aerobic and anaerobic conditions.

The toxic fermentation inhibitors in lignocellulosic hydrolysates raise serious problems for the microbial production of fuels and chemicals. Furfural is considered to be one of the most toxic compounds among these inhibitors. Here, we describe the detoxification of furfural in Corynebacterium glutamicum ATCC13032 under both aerobic and anaerobic conditions. Under aerobic culture conditions, furfuryl alcohol and 2-furoic acid were produced as detoxification products of furfural. The ratio of the products varied depending on the initial furfural concentration. Neither furfuryl alcohol nor 2-furoic acid showed any toxic effect on cell growth, and both compounds were determined to be the end products of furfural degradation. Interestingly, unlike under aerobic conditions, most of the furfural was converted to furfuryl alcohol under anaerobic conditions, without affecting the glucose consumption rate. Both the NADH/NAD(+) and NADPH/NADP(+) ratio decreased in the accordance with furfural concentration under both aerobic and anaerobic conditions. These results indicate the presence of a single or multiple endogenous enzymes with broad and high affinity for furfural and co-factors in C. glutamicum ATCC13032.

Comprehensive metabolic profiling of Geotrichum candidum and comparison with Saccharomyces cerevisiae.

Geotrichum candidum is a dimorphic yeast used in cheese processing. To our knowledge, no major metabolites have been identified to date in G. candidum except for some amino acid and fatty acid metabolites. This has limited research on the commercial use of G. candidum. In this study, we aimed to analyze temporal changes in the intra- and extra-cellular metabolites of G. candidum and Saccharomyces cerevisiae cultured in YM medium as reference. As a result of metabolite analysis, it was observed that G. candidum tends to accumulate　pentose phosphate pathway compounds, which are involved in nucleic acid synthesis, after 48 h of cultivation when compared to S. cerevisiae. In addition, G. candidum accumulated higher amounts of the antioxidant glutathione in the medium than did S. cerevisiae. In addition, G. candidum accumulated large amounts of B vitamins such as pantothenic acid and nicotinic acid in the medium. Finally, we examined the potential of G. candidum as a host for the production of useful compounds such as pantothenic acid. When cultured in medium supplemented with the pantothenic acid precursor ß-alanine, G. candidum produced 12-fold higher amounts of pantothenic acid (30 µM) than that by S. cerevisiae. This study indicates that G. candidum accumulates various useful compounds that are dissimilar to those produced by S. cerevisiae. Furthermore, G. candidum has the potential to produce useful chemicals under appropriate culture conditions.

Flowering and expression of flowering-related genes under long-day conditions with light-emitting diodes.

The effects of light quality on flowering time were investigated in Gypsophila paniculata, which is a long-day cut flower, and with Arabidopsis under long-day conditions with light-emitting diodes (LEDs). Gypsophila paniculata plants were grown under natural daylight and flowering was controlled by long-day treatment with a weak LED light of a single color in the night. Flowering was promoted not by blue light, but by far-red light in G. paniculata, while flowering was promoted by both light colors in Arabidopsis. FT homologs of G. paniculata GpFT1 and GpFT2 were differentially expressed under long-day conditions with white light, suggesting that they play roles in flowering at different stages of reproductive development. GpFTs and FT gene expression was not induced by far-red light in G. paniculata or Arabidopsis. Instead, the expression of the SOC1 homolog of G. paniculata GpSOC1 and SOC1 was induced by far-red light in G. paniculata and Arabidopsis. Flowering was promoted by induction of FT and SOC1 expression with blue light in Arabidopsis, whereas GpFTs and GpSOC1 expression was low with blue light induction in G. paniculata. The relationship between flowering and the expression of FT and SOC1 in Arabidopsis was confirmed with ft and soc1 mutants. These results suggest that long-day conditions with far-red light promote flowering through SOC1 and its homologs, while the conditions with blue light do not promote flowering in G. paniculata, because of low expression of GpFTs and GpSOC1 in contrast to that in Arabidopsis.

An ion-pair free LC-MS/MS method for quantitative metabolite profiling of microbial bioproduction systems.

Data-driven engineering of microbes has been demonstrated for the sustainable production of high-performance chemicals. Metabolic profiling analysis is essential to increase the productivity of target compounds. However, improvement of comprehensive analysis methodologies is required for the high demands of metabolic engineering. Therefore, a liquid chromatography-tandem mass spectrometry (LC-MS/MS) based methodology was designed and applied to cover a wide target range with high precision. Ion-pair free separation of metabolites on a pentafluorophenyl propyl column enabled high-precision quantification of 113 metabolites. The method was further evaluated for high reproducibility and robustness. Target analytes consisted of primary metabolites and intermediate metabolites for microbial production of high-performance chemicals. 95 metabolites could be detected with high reproducibility of peak area (intraday data: CV<15%), and 53 metabolites could be sensitively determined within a wide dynamic linear range (3-4 orders of magnitude). The developed system was further applied to the metabolomic analysis of various prokaryotic and eukaryotic microorganisms. Differences due to culture media and metabolic phenotypes could be observed when comparing the metabolomes of conventional and non-conventional yeast. Furthermore, almost all Kluyveromyces marxianus metabolites could be detected with moderate reproducibility (CV<40%, among independent extractions), where 41 metabolites were detected with very high reproducibility (CV<15%). In addition, the accuracy was validated via a spike-and-recovery test,and 78 metabolites were detected with analyte recovery in the 80-120% range. Together these results establish ion-pair free metabolic profiling as a comprehensive and precise tool for data-driven bioengineering applications.

Mathematical Model for Small Size Time Series Data of Bacterial Secondary Metabolic Pathways.

Measuring the concentrations of metabolites and estimating the reaction rates of each reaction step consisting of metabolic pathways are significant for an improvement in microorganisms used in maximizing the production of materials. Although the reaction pathway must be identified for such an improvement, doing so is not easy. Numerous reaction steps have been reported; however, the actual reaction steps activated vary or change according to the conditions. Furthermore, to build mathematical models for a dynamical analysis, the reaction mechanisms and parameter values must be known; however, to date, sufficient information has yet to be published for many cases. In addition, experimental observations are expensive. A new mathematical approach that is applicable to small sample data, and that requires no detailed reaction information, is strongly needed. S-system is one such model that can use smaller samples than other ordinary differential equation models. We propose a simplified S-system to apply minimal quantities of samples for a dynamic analysis of the metabolic pathways. We applied the model to the phenyl lactate production pathway of Escherichia coli. The model obtained suggests that actually activated reaction steps and feedback are inhibitions within the pathway.

A Systematic Approach to Time-series Metabolite Profiling and RNA-seq Analysis of Chinese Hamster Ovary Cell Culture.

Chinese hamster ovary (CHO) cells are the primary host used for biopharmaceutical protein production. The engineering of CHO cells to produce higher amounts of biopharmaceuticals has been highly dependent on empirical approaches, but recent high-throughput "omics" methods are changing the situation in a rational manner. Omics data analyses using gene expression or metabolite profiling make it possible to identify key genes and metabolites in antibody production. Systematic omics approaches using different types of time-series data are expected to further enhance understanding of cellular behaviours and molecular networks for rational design of CHO cells. This study developed a systematic method for obtaining and analysing time-dependent intracellular and extracellular metabolite profiles, RNA-seq data (enzymatic mRNA levels) and cell counts from CHO cell cultures to capture an overall view of the CHO central metabolic pathway (CMP). We then calculated correlation coefficients among all the profiles and visualised the whole CMP by heatmap analysis and metabolic pathway mapping, to classify genes and metabolites together. This approach provides an efficient platform to identify key genes and metabolites in CHO cell culture.

Direct ethanol production from hemicellulosic materials of rice straw by use of an engineered yeast strain codisplaying three types of hemicellulolytic enzymes on the surface of xylose-utilizing Saccharomyces cerevisiae cells.

The cost of the lignocellulose-hydrolyzing enzymes used in the saccharification process of ethanol production from biomass accounts for a relatively high proportion of total processing costs. Cell surface engineering technology has facilitated a reduction in these costs by integrating saccharification and fermentation processes into a recombinant microbe strain expressing heterologous enzymes on the cell surface. We constructed a recombinant Saccharomyces cerevisiae that not only hydrolyzed hemicelluloses by codisplaying endoxylanase from Trichoderma reesei, ß-xylosidase from Aspergillus oryzae, and ß-glucosidase from Aspergillus aculeatus but that also assimilated xylose through the expression of xylose reductase and xylitol dehydrogenase from Pichia stipitis and xylulokinase from S. cerevisiae. The recombinant strain successfully produced ethanol from rice straw hydrolysate consisting of hemicellulosic material containing xylan, xylooligosaccharides, and cellooligosaccharides without requiring the addition of sugar-hydrolyzing enzymes or detoxication. The ethanol titer of the strain was 8.2g/l after 72h fermentation, which was approximately 2.5-fold higher than that of the control strain. The yield (grams of ethanol per gram of total sugars in rice straw hydrolysate consumed) was 0.41g/g, which corresponded to 82% of the theoretical yield. The cell surface-engineered strain was thus highly effective for consolidating the process of ethanol production from hemicellulosic materials.