Characterization of oil-producing yeast Lipomyces starkeyi on glycerol carbon source based on metabolomics and 13C-labeling

Metabolomics as a Prospective Tool for Soybean (Glycine max) Crop Improvement

Global demand for soybean and its products has stimulated research into the production of novel genotypes with higher yields, greater drought and disease tolerance, and shorter growth times. Genetic research may be the most effective way to continue developing high-performing cultivars with desirable agronomic features and improved nutritional content and seed performance. Metabolomics, which predicts the metabolic marker for plant performance under stressful conditions, is rapidly gaining interest in plant breeding and has emerged as a powerful tool for driving crop improvement. The development of increasingly sensitive, automated, and high-throughput analytical technologies, paired with improved bioinformatics and other omics techniques, has paved the way for wide characterization of genetic characteristics for crop improvement. The combination of chromatography (liquid and gas-based) with mass spectrometry has also proven to be an indisputable efficient platform for metabolomic studies, notably plant metabolic fingerprinting investigations. Nevertheless, there has been significant progress in the use of nuclear magnetic resonance (NMR), capillary electrophoresis, and Fourier-transform infrared spectroscopy (FTIR), each with its own set of benefits and drawbacks. Furthermore, utilizing multivariate analysis, principal components analysis (PCA), discriminant analysis, and projection to latent structures (PLS), it is possible to identify and differentiate various groups. The researched soybean varieties may be correctly classified by using the PCA and PLS multivariate analyses. As metabolomics is an effective method for evaluating and selecting wild specimens with desirable features for the breeding of improved new cultivars, plant breeders can benefit from the identification of metabolite biomarkers and key metabolic pathways to develop new genotypes with value-added features.

Effects of Hypoxia Stress on Growth, Root Respiration, and Metabolism of Phyllostachys praecox

Hypoxia affects plant growth, hormone content, various enzyme activities, cell structure, peroxide production, and metabolic level, therefore reducing crop yield. This study assessed the physiological, biochemical, and metabolic characteristics of Phyllostachys praecox. Results revealed that hypoxia stress treatment significantly inhibited plant growth. Leaf chlorophyll contents was initially improved and then reduced with plant growth time. Under hypoxia stress, the root activity significantly was reduced, leading to the decrease in the nutrient absorption and transport. Yet, with low oxygen concentration, the contents of ethanol, acetaldehyde, and lactic acid were improved. With hypoxia stress, phospholipids and amino acids were the main metabolites of Phyllostachys praecox. Glycosphospholipid metabolism is the key pathway in responding to hypoxia stress significantly (p < 0.05), and lysophosphatidlycholine (lysoPC) and phosphatidylcholines (PC) in the metabolites of this metabolic pathway were significantly enhanced. Our study reveals the mechanism of Phyllostachys praecox cell membrane responding to hypoxia stress based on molecular level. This is conducive to finding targeted solutions to improve the productivity of Phyllostachys praecox to better optimize a mulching approach in the bamboo forest.

A metabolic model of Lipomyces starkeyi for predicting lipogenesis potential from diverse low-cost substrates

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 comprehend the complex biochemical processes and provide great guidance for strain modification to facilitate the lipid biosynthesis.

RESULTS

A small-scale metabolic model of L. starkeyi NRRL Y-11557 was constructed based on the genome annotation information. The theoretical lipid yields of glucose, cellobiose, xylose, glycerol, and acetic acid were calculated according to the flux balance analysis (FBA). The optimal flux 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) beneficial for oleaginicity of L. starkeyi with significantly 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 significantly improved through appropriate modification 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.

13C-metabolic flux analysis in glycerol-assimilating strains of Saccharomyces cerevisiae

Glycerol is an attractive raw material for the production of useful chemicals using microbial cells. We previously identified metabolic engineering targets for the improvement of glycerol assimilation ability in Saccharomyces cerevisiae based on adaptive laboratory evolution (ALE) and transcriptome analysis of the evolved cells. We also successfully improved glycerol assimilation ability by the disruption of the RIM15 gene encoding a Greatwall protein kinase together with overexpression of the STL1 gene encoding the glycerol/H⁺ symporter. To understand glycerol assimilation metabolism in the evolved glycerol-assimilating strains and STL1-overexpressing RIM15 disruptant, we performed metabolic flux analysis using ¹³C-labeled glycerol. Significant differences in metabolic flux distributions between the strains obtained from the culture after 35 and 85 generations in ALE were not found, indicating that metabolic flux changes might occur in the early phase of ALE (i.e., before 35 generations at least). Similarly, metabolic flux distribution was not significantly changed by RIM15 gene disruption. However, fluxes for the lower part of glycolysis and the TCA cycle were larger and, as a result, flux for the pentose phosphate pathway was smaller in the STL1-overexpressing RIM15 disruptant than in the strain obtained from the culture after 85 generations in ALE. It could be effective to increase flux for the pentose phosphate pathway to improve the glycerol assimilation ability in S. cerevisiae.

Inference of Regulatory System for TAG Biosynthesis in Lipomyces starkeyi

Improving the bioproduction ability of efficient host microorganisms is a central aim in bioengineering. To control biosynthesis in living cells, the regulatory system of the whole biosynthetic pathway should be clearly understood. In this study, we applied our network modeling method to infer the regulatory system for triacylglyceride (TAG) biosynthesis in Lipomyces starkeyi, using factor analyses and structural equation modeling to construct a regulatory network model. By factor analysis, we classified 89 TAG biosynthesis-related genes into nine groups, which were considered different regulatory sub-systems. We constructed two different types of regulatory models. One is the regulatory model for oil productivity, and the other is the whole regulatory model for TAG biosynthesis. From the inferred oil productivity regulatory model, the well characterized genes DGA1 and ACL1 were detected as regulatory factors. Furthermore, we also found unknown feedback controls in oil productivity regulation. These regulation models suggest that the regulatory factor induction targets should be selected carefully. Within the whole regulatory model of TAG biosynthesis, some genes were detected as not related to TAG biosynthesis regulation. Using network modeling, we reveal that the regulatory system is helpful for the new era of bioengineering.

The assimilation of glycerol into lipid acyl chains and associated carbon backbones of Nannochloropsis salina varies under nitrogen replete and deplete conditions

Mixotrophic cultivation can increase microalgae productivity, yet the associated lipid metabolism remains mostly unknown. Stable isotope labeling was used to track assimilation of glycerol into the triacylglyceride (TAG) and membrane lipids of Nannochloropsis salina. In N-replete media, glycerol uptake and ¹³ C incorporation into acyl chains were, respectively, 6-fold and 12-fold higher than in N-deplete conditions. In N-replete cultures, 42% of the carbon in the consumed glycerol was assimilated into lipid acyl chains, mostly in membrane lipids rather than TAG. In N-deplete cultures, only 11% of the limited amount of consumed glycerol was fixed into lipid acyl chains. Labeled lipid-associated glycerol backbones were predominantly ¹³ C₃ labeled, suggesting that intact glycerol molecules were directly esterified with fatty acids/polar head groups. However, the presence of singly and doubly labeled lipid-bound glycerol species suggested that some glycerol also went through the central carbon metabolism before forming glycerol-3-phosphate destined for lipid esterification. ¹³ C incorporation was higher in the saturated and monounsaturated than the polyunsaturated acyl chains of TAG, indicating the flux of carbon from glycerol went first to de novo fatty acid synthesis before acyl editing reactions. The results demonstrate that nitrogen availability influences both glycerol consumption and utilization for lipid synthesis in Nannochloropsis, providing novel insights for developing mixotrophic cultivation strategies.