A Metabolic Model of Lipomyces Starkeyi for Predicting Lipogenesis
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Zhou et al. Biotechnol Biofuels (2021) 14:148 https://doi.org/10.1186/s13068-021-01997-9 Biotechnology for Biofuels RESEARCH Open Access A metabolic model of Lipomyces starkeyi for predicting lipogenesis potential from diverse low-cost substrates Wei Zhou1†, Yanan Wang2†, Junlu Zhang1, Man Zhao1, Mou Tang1, Wenting Zhou1,3* and Zhiwei Gong1,3* Abstract Background: Lipomyces starkeyi has been widely regarded as a promising oleaginous yeast with broad industrial application prospects because of its wide substrate spectrum, good adaption to fermentation inhibitors, excellent fatty acid composition for high-quality biodiesel, and negligible lipid remobilization. However, the currently low experimental lipid yield of L. starkeyi prohibits its commercial success. Metabolic model is extremely valuable to com- prehend the complex biochemical processes and provide great guidance for strain modifcation to facilitate the lipid biosynthesis. Results: A small-scale metabolic model of L. starkeyi NRRL Y-11557 was constructed based on the genome annota- tion information. The theoretical lipid yields of glucose, cellobiose, xylose, glycerol, and acetic acid were calculated according to the fux balance analysis (FBA). The optimal fux distribution of the lipid synthesis showed that pentose phosphate pathway (PPP) independently met the necessity of NADPH for lipid synthesis, resulting in the relatively low lipid yields. Several targets (NADP-dependent oxidoreductases) benefcial for oleaginicity of L. starkeyi with signif- cantly higher theoretical lipid yields were compared and elucidated. The combined utilization of acetic acid and other carbon sources and a hypothetical reverse β-oxidation (RBO) pathway showed outstanding potential for improving the theoretical lipid yield. Conclusions: The lipid biosynthesis potential of L. starkeyi can be signifcantly improved through appropriate modif- cation of metabolic network, as well as combined utilization of carbon sources according to the metabolic model. The prediction and analysis provide valuable guidance to improve lipid production from various low-cost substrates. Keywords: Lipomyces starkeyi, Metabolic model, Flux balance analysis, Triacylglycerol, Theoretical lipid yield Background accumulate large amount of intracellular lipid under Microbial lipid has garnered much attention recently nutritional restriction, especially nitrogen starvation [2]. as it can be served as promising feedstock for edible Lipomyces starkeyi is an excellent lipid producer featur- oils, functional polyunsaturated fatty acids, oleochemi- ing wide substrate spectrum, good tolerance to fermen- cals, and biodiesel [1]. Oleaginous species routinely tation inhibitors, excellent fatty acid composition of lipid for high-quality biodiesel, and negligible lipid remobiliza- tion. A variety of low-cost materials including lignocellu- *Correspondence: [email protected]; [email protected] losic biomass, starch materials, biodiesel derived glycerol, †Wei Zhou and Yanan Wang contributed equally to this work volatile fatty acids, molasses, and sewage sludge have 1 School of Chemistry and Chemical Engineering, Wuhan University of Science and Technology, 947 Heping Road, Wuhan 430081, People’s been applied for lipid production by L. starkeyi [1, 3]. Republic of China Especially, lignocellulosic hydrolysates have been directly Full list of author information is available at the end of the article utilized for lipid production without detoxifcation by L. © The Author(s) 2021. 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The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Zhou et al. Biotechnol Biofuels (2021) 14:148 Page 2 of 17 starkeyi, which is of great signifcance for the commer- was performed to calculate the theoretical lipid yields cial success [4, 5]. L. starkeyi exhibits high robustness to of a variety of carbon sources originated from low-cost the major lignocellulosic inhibitors including acetic acid, substrates. Several targets (NADP-dependent oxidore- furfural, and 5-hydroxymethylfurfural (HMF) and these ductases) were evaluated for improving the potential of agents even could be metabolized by the yeast [5]. In lipid biosynthesis in L. starkeyi. Te strategy of combined addition, L. starkeyi scarcely consumes the cellular lipid utilization of carbon sources mixture was investigated for although the nutrients are completely exhausted com- improving the theoretical lipid yield. In addition, a hypo- pared with other oleaginous species, which is benefcial thetical reverse β-oxidation (RBO) pathway was added to for the preservation [6]. the model to estimate the lipid synthesis potential. Tis High efective genetic transformation system is cru- study provides valuable guidance and promising strategy cial for improving the oleaginicity of oleaginous yeasts. to signifcantly improve lipid accumulation capacity from Recently, a variety of genetic transformation methods low-cost substrates. including lithium acetate-mediated transformation, PEG-mediated spheroplast transformation, agrobac- Results and discussion terium-mediated transformation, and electroporation Construction of the small‑scale metabolic model of L. transformation have been established for L. starkeyi starkeyi [7–10]. A site-directed gene knockout strategy has been All the reactions and metabolites in the small-scale reported in L. starkeyi NRRL Y-11558 [11]. Te develop- metabolic model of L. starkeyi are summarized in the ment of synthetic biology approaches, coupled with the Additional fle 1: Tables S1 and S2, respectively. Te visu- omics technologies [12–14], has continuously deepened alization of the metabolic map of L. starkeyi is depicted the understanding of lipid metabolism of L. starkeyi [3]. in Fig. 2. Tis model contained 112 metabolites, 123 reac- Metabolic model has been widely used in many tions and 3 cell compartments including extracellular, felds including industrial biotechnology [15, 16]. Te cytoplasm, and mitochondria. Te metabolic pathways genome-scale metabolic model is convenient to predict included glycolysis, pentose phosphate pathway (PPP), biological capabilities and provide guidance for strain tricarboxylic acid cycle (TCA), glyoxylate cycle, pyruvate improvement. In recent years, a series of software have dehydrogenase bypass, fatty acid (FA) synthesis pathway, been developed to facilitate the automated and semi- and glycerolipid metabolism. Te model involved in 13 automated construction of metabolic model [17]. Inter- exchange reactions and 31 transport reactions. Te bio- estingly, genome-scale metabolic models of Yarrowia mass reaction was used to analyze whether the metabolic lipolytica, Rhodotorula toruloides, and Cutaneotrichos- model could normally generate biomass using a specifc poron oleaginosus have been established to systemati- substrate. Te model included the metabolism of 5 car- cally analyze the lipid metabolism [18–20]. Small-scale bon sources including glucose, cellobiose, xylose, glyc- metabolic model has been constructed in favor of some erol, and acetic acid. Te P/O ratios of the mitochondrial special purposes as the construction of the genome-scale NADH, mitochondrial FADH2, and cytoplasmic NADH metabolic model is very time-consuming and laborious. were 2.5, 1.5, and 1.5, respectively. For example, Bommareddy and co-workers constructed a small-scale metabolic model of Rhodosporidium toru- Prediction of lipid production potential from diferent loides to evaluate the lipid production potential of several carbon sources carbon sources [21]. A revised small-scale model con- Te theoretical lipid yields of glucose, cellobiose, xylose, taining 93 metabolites, 104 reactions, and 3 cell compart- glycerol, and acetic acid by L. starkeyi were calculated ments was reconstructed by Castañeda and co-workers and the results are shown in Table 2. Te optimal fux for more accurate prediction [22]. Tang and co-workers distribution for lipid production from glucose is shown in constructed a small-scale metabolic model of C. ole- Fig. 3a. It was clear that the FA and triacylglycerol (TAG) aginosum to evaluate the lipogenesis potential of chitin- synthesis pathways were very active, while the TCA cycle derived carbon sources [23]. were severely stagnant (Fig. 3a). Tis phenomenon was Glucose, xylose, cellobiose, glycerol, and acetic acid in line with the transcriptome analysis result reported by originated from a variety of low-cost substrates can be Pomraning and co-workers [13]. metabolized for lipogenesis by L. starkeyi (Fig. 1). How- It should be noted that abundant NADPH