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Metabolomic and Transcriptomic Analyses of Escherichia Coli for Efficient Fermentation of L-Fucose

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Metabolomic and Transcriptomic Analyses of Escherichia Coli for Efficient Fermentation of L-Fucose marine drugs Article Metabolomic and Transcriptomic Analyses of Escherichia coli for Efficient Fermentation of L-Fucose Jungyeon Kim 1, Yu Eun Cheong 1, Inho Jung 1,2 and Kyoung Heon Kim 1,* 1 Department of Biotechnology, Graduate School, Korea University, Seoul 02841, Korea; [email protected] (J.K.); [email protected] (Y.E.C.); [email protected] (I.J.) 2 Forest Product Analysis and Certification Division, Korea Forestry Promotion Institute, Seoul 07570, Korea * Correspondence: [email protected] Received: 29 December 2018; Accepted: 24 January 2019; Published: 29 January 2019 Abstract: L-Fucose, one of the major monomeric sugars in brown algae, possesses high potential for use in the large-scale production of bio-based products. Although fucose catabolic pathways have been enzymatically evaluated, the effects of fucose as a carbon source on intracellular metabolism in industrial microorganisms such as Escherichia coli are still not identified. To elucidate the effects of fucose on cellular metabolism and to find clues for efficient conversion of fucose into bio-based products, comparative metabolomic and transcriptomic analyses were performed on E. coli on L-fucose and on D-glucose as a control. When fucose was the carbon source for E. coli, integration of the two omics analyses revealed that excess gluconeogenesis and quorum sensing led to severe depletion of ATP, resulting in accumulation and export of fucose extracellularly. Therefore, metabolic engineering and optimization are needed for E. coil to more efficiently ferment fucose. This is the first multi-omics study investigating the effects of fucose on cellular metabolism in E. coli. These omics data and their biological interpretation could be used to assist metabolic engineering of E. coli producing bio-based products using fucose-containing brown macroalgae. Keywords: fucose; fermentation; metabolomics; transcriptomics; Escherichia coli; brown macroalgae 1. Introduction Owing to increasing concerns surrounding energy security and global warming, not only lignocellulose [1–3] but also macroalgae are considered as important biomass resources for the production of biofuels and bio-based products using microorganisms [4]. Macroalgae are widely used owing to their many advantages over terrestrial plants, since they do not require arable land or fertilizers during cultivation, and have high carbohydrate but low lignin content [4–6]. In macroalgae, alginate, laminarin, mannitol, and fucoidan are the major carbohydrates [7,8]. To convert these carbohydrates in brown macroalgae into biofuels and chemicals in a more efficient and economical manner, understanding the metabolism of the monomeric sugars of these carbohydrates in fermentative microorganisms is essential [1–3,8]. L-Fucose is the main monomeric sugar of fucoidan in brown macroalgae [8–10]. Although the current price of L-fucose is high [11], it has high potential to be used for production of bio-based products owing to its high abundance in diverse environments such as brown macroalgae and biofilms [10–12]. In addition, L-fucose is used as a substrate for the microbial production of 2-fucosyllactose and 3-fucosyllactose [13–15]. With such microbial conversion of fucose into bio-based products, it is important to understand the fermentation of fucose and its related cellular metabolism in industrial microbial hosts such as Escherichia coli [8,10,16]. Until now, metabolic enzymatic studies focusing on the kinase-dependent metabolic pathway involving the conversion of L-fucose into L-fuculose, L-fuculose-1-phosphate, and dihydroxyacetone phosphate (DHAP), have been performed Mar. Drugs 2019, 17, 82; doi:10.3390/md17020082 www.mdpi.com/journal/marinedrugs Mar. Drugs 2019, 17, 82 2 of 14 using E. coli [17–19]. However, there are no studies regarding the intracellular metabolism of E. coli utilizing L-fucose, which could provide the design principles of microbial engineering for more efficient fermentation and conversion of fucose into bio-based products. Until now, only two microorganisms, Lactobacillus rhamnosus GG and Sulfolobus solfataricus, have been cultured on fucose for comparative transcriptomic, proteomic, and metabolomics analyses [20,21]. However, since these microorganisms are not industrial fermentative hosts, such analyses need to be performed on industrial strains such as E. coli [22]. Omics technologies, such as transcriptomics, proteomics, and metabolomics, can be effectively used in combination in order to study cellular metabolism [20,21,23–25]. In particular, metabolomics can identify global changes in metabolites which are directly related to cellular phenotypes [26]. Furthermore, to understand metabolite profile changes at the molecular level, it is necessary to combine metabolomics with transcriptomics, which reveals gene expression levels [20,21,23–25]. This is because changes in metabolite profiles can result from either up-regulation or down-regulation of gene expression [27]. Eventually, this multi-omics approach, combining metabolomics and transcriptomics, could indicate metabolic bottlenecks and metabolic flows [20,21,23–25]. In this study, the effects of L-fucose fermentation on the global metabolic network of E. coli K12 MG1655, one of the representative microorganisms in industrial production of various chemicals [16], was elucidated using metabolomic and transcriptomic analyses, and for more efficient conversion of L-fucose into bio-based products, possible targets of metabolic engineering were revealed. For metabolome analysis, gas chromatography/time of flight-mass spectrometry (GC/TOF–MS) and ultra-high performance liquid chromatography (UHPLC)/Q–Exactive mass spectrometry (MS) were used. For transcriptome analysis, a HiSeq 2000 sequencing system was used. 2. Results 2.1. Comparison of Growth and Fermentation Product Profiles of E. coli on Fucose and Glucose Growth, substrate consumption, and production of main fermentation products were compared for E. coli cultured in M9 media containing fucose or glucose (Figure1). E. coli cells cultured in fucose media showed notably lower growth rates (µ = 0.121 h−1) than those cultured in glucose media (µ = 0.223 h−1) (Figure1A). Consumption of fucose and glucose (Figure1B) and production of acetic acid and 1,2-propanediol (1,2-PDO) were compared (Figure1C). E. coli cultured in fucose media produced two main fermentation products, namely acetic acid (0.66 g/L) and 1,2-PDO (1.82 g/L), after 24 h. In contrast, E. coli cultured in glucose media produced acetic acid (0.91 g/L) as the main fermentation product after 16 h. 2.2. Identification of Metabolites and Transcripts In this study, 102 intracellular metabolites (Table S1A) and 53 extracellular metabolites (Table S1B) were identified using GC/TOF–MS. Eight intracellular cofactors, acetyl-CoA, AMP, ADP, ATP, NAD+, NADH, NADP+, and NADPH, were identified by UHPLC and Q-Exactive MS (Table S1C). A total of 4414 transcripts were identified using the HiSeq 2000 platform (Table S2). 2.3. Comparison of Intracellular Metabolite Profiles of E. coli on Fucose and Glucose To investigate the effect of fucose fermentation on intracellular metabolism, 102 intracellular metabolites of E. coli cultured in fucose and glucose media were compared. To compare the intracellular metabolite profiles between the two carbon sources (fucose and glucose) at three different growth phases (lag, exponential, and stationary phases), multivariate statistical analysis, principal component analysis (PCA), was performed (Figure2). Mar. Drugs 2019, 17, 82 3 of 14 Mar. Drugs 2018, 16, x 3 of 14 FigureFigure 1. ComparisonComparison of of the the growth growth and and fe fermentationrmentation product product profiles profiles of of E.E. coli coli culturedcultured on on fucose andand glucose glucose as as the carbon source (mean ± SD):SD): ( (AA)) cell cell density density recorded recorded as as optical optical density density at at 600 600 nm nm (OD(OD600600);); (B (B) )concentrations concentrations of of substrate; substrate; and and (C (C) )by-products by-products (three (three independent independent replicates). replicates). 2.2. IdentificationA score plot of of Metabolites the PCA model and Transcripts using 102 intracellular metabolites showed the discrimination of metabolite profiles depending on different sugars and growth phases (Figure2A). The quality of the PCAIn model this study, with two 102 components intracellular was metabolites represented (Table by R S1A)2X (cumulative) and 53 extracellular of 0.558 and metabolitesQ2 (cumulative) (Table S1B)of 0.469. were The identified high values using of GC/TOF–MS.R2X and Q2 indicateEight intracellular goodness ofcofactors, the fit, and acetyl-CoA, predictive AMP, capability ADP, ofATP, the NAD+,PCA model, NADH, respectively. NADP+, and The NADPH, loading were plot ofidentified the PCA by model UHPLC with and the Q-Exactive two principal MS (Table components S1C). Ashowed total of the 4414 contribution transcripts of were each identified metabolite using to the the PCs HiSeq (Figure 20002B). platform The loading (Table values S2). of metabolites in the PCA models are listed in Table S3. 2.3. Comparison of Intracellular Metabolite Profiles of E. coli on Fucose and Glucose To investigate the effect of fucose fermentation on intracellular metabolism, 102 intracellular metabolites of E. coli cultured in fucose and glucose media were compared. To compare the intracellular metabolite profiles between the two carbon sources (fucose and glucose) at three different growth phases (lag, exponential,
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