BackgroundMortierella alpina is an oleaginous fungus used in the industrial scale production of arachidonic acid (ARA). In order to investigate the metabolic characteristics at a systems level and to explore potential strategies for enhanced lipid production, a genome-scale metabolic model of M. alpina was reconstructed.ResultsThis model included 1106 genes, 1854 reactions and 1732 metabolites. On minimal growth medium, 86 genes were identified as essential, whereas 49 essential genes were identified on yeast extract medium. A series of sequential desaturase and elongase catalysed steps are involved in the synthesis of polyunsaturated fatty acids (PUFAs) from acetyl-CoA precursors, with concomitant NADPH consumption, and these steps were investigated in this study. Oxygen is known to affect the degree of unsaturation of PUFAs, and robustness analysis determined that an oxygen uptake rate of 2.0 mmol gDW−1 h−1 was optimal for ARA accumulation. The flux of 53 reactions involving NADPH was significantly altered at different ARA levels. Of these, malic enzyme (ME) was confirmed as a key component in ARA production and NADPH generation. When using minimization of metabolic adjustment, a knock-out of ME led to a 38.28% decrease in ARA production.ConclusionsThe simulation results confirmed the model as a useful tool for future research on the metabolism of PUFAs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-014-0137-8) contains supplementary material, which is available to authorized users.

Microbial cell factories are widely used for the production of high-value chemicals. However, maximizing production titers is made difficult by the complicated regulatory mechanisms of these cell platforms. Here, k cat values were incorporated to construct an Escherichia coli enzyme-constrained model. The resulting ec_iML1515 model showed that the protein demand and protein synthesis rate were the key factors affecting lysine production. By optimizing the expression of the 20 topdemanded proteins, lysine titers reached 95.7 ± 0.7 g/L, with a 0.45 g/g glucose yield. Moreover, adjusting NH 4 + and dissolved oxygen levels to regulate the synthesis rate of energy metabolism-related proteins caused lysine titers and glucose yields to increase to 193.6 ± 1.8 g/L and 0.74 g/g, respectively. The ec_iML1515 model provides insight into how enzymes required for the biosynthesis of certain products are distributed between and within metabolic pathways. This information can be used to accurately predict and rationally design lysine production.

BackgroundSchizochytrium limacinum SR21 is a potential industrial strain for docosahexaenoic acid (DHA) production that contains more than 30–40 % DHA among its total fatty acids.MethodsTo resolve the DHA biosynthesis mechanism and improve DHA production at a systematic level, a genomescale metabolic model (GSMM), named iCY1170_DHA, which contains 1769 reactions, 1659 metabolites, and 1170 genes, was reconstructed.ResultsBased on genome annotation results and literature reports, a new DHA synthesis pathway based on a polyketide synthase (PKS) system was detected in S. limacinum. Similarly to conventional fatty acid synthesis, the biosynthesis of DHA via PKS requires abundant acetyl-CoA and NADPH. The in silico addition of malate and citrate led to increases of 24.5 % and 37.1 % in DHA production, respectively. Moreover, based on the results predicted by the model, six amino acids were shown to improve DHA production by experiment. Finally, 30 genes were identified as potential targets for DHA over-production using a Minimization of Metabolic Adjustment algorithm.ConclusionsThe reconstructed GSMM, iCY1170_DHA, could be used to elucidate the mechanism by which DHA is synthesized in S. limacinum and predict the requirements of abundant acetyl-CoA and NADPH for DHA production as well as the enhanced yields achieved via supplementation with six amino acids, malate, and citrate.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2042-y) contains supplementary material, which is available to authorized users.

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Biological network construction for Saccharomyces cerevisiae is a widely used approach for simulating phenotypes and designing cell factories. However, due to a complicated regulatory mechanism governing the translation of genotype to phenotype, precise prediction of phenotypes remains challenging. Here, we present WM_S288C, a computational whole-cell model that includes 15 cellular states and 26 cellular processes and which enables integrated analyses of physiological functions of Saccharomyces cerevisiae. Using WM_S288C to predict phenotypes of S. cerevisiae, the functions of 1140 essential genes were characterized and linked to phenotypes at five levels. During the cell cycle, the dynamic allocation of intracellular molecules could be tracked in real-time to simulate cell activities. Additionally, one-third of non-essential genes were identified to affect cell growth via regulating nucleotide concentrations. These results demonstrated the value of WM_S288C as a tool for understanding and investigating the phenotypes of S. cerevisiae.

Candida glabrata CCTCC M202019 as an industrial yeast strain that is widely used to produce α-oxocarboxylic acid. Strain M202019 has been proven to have a higher pyruvate-producing capacity than the reference strain CBS138. To characterize the genotype of the M202019 strain, we generated a draft sequence of its genome, which has a size of 12.1 Mbp and a GC content of 38.47%. Evidence accumulated during genome annotation suggests that strain M202019 has strong capacities for glucose transport and pyruvate biosynthesis, defects in pyruvate catabolism, as well as variations in genes involved in nutrient and dicarboxylic acid transport, oxidative phosphorylation, and other relevant aspects of carbon metabolism, which might promote pyruvate accumulation. In addition to differences in its central carbon metabolism, a genomic analysis revealed genetic differences in adhesion metabolism. Forty-nine adhesin-like proteins of strain M202019 were identified classified into seven subfamilies. Decreased amounts of adhesive proteins, and deletions or changes of low-complexity repeats and functional domains might lead to lower adhesion and reduced pathogenicity. Further virulence experiments validated the biological safety of strain M202019. Analysis of the C. glabrata CCTCC M202019 genome sequence provides useful insights into its genetic context, physical characteristics, and potential metabolic capacity.

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