Search

Oleaginous yeasts utilize renewable resources to produce lipids, which benefits sustainable development, and it is of great interest to screen robust lipid producers. Curvibasidium sp. belongs to nonconventional yeast that are very limitedly studied. Here, two cold-adaptive strains of Curvibasidium sp., namely, Y230 and Y231, isolated from the medicinal lichen Usnea diffracta were investigated for their potential in lipid production. Genome mining of Curvibasidium sp. Y231 was performed, and the special features related to fatty acid biosynthesis were revealed. Glucose, xylose, and glycerol were tested as sole carbon sources for yeast cell growth and lipid production. The total lipid contents of Curvibasidium sp. Y230 and Y231 range from 38.43% to 54.62% of the cell dry cell weight at 20°C, and glucose is the optimal carbon source. These results indicate that the Curvibasidium sp. strains are promising for sustainable lipid production. Our study provides basis for exploration of lichen-derived strains for biotechnological applications, and also benefits utilization of other nonconventional yeasts for sustainable production based on genome-based studies.

Motivation: Gene deletion and overexpression are critical technologies for designing or improving the metabolic flux distribution of microbes. Some algorithms including flux balance analysis (FBA) and minimization of metabolic adjustment (MOMA) predict a flux distribution from a stoichiometric matrix in the mutants in which some metabolic genes are deleted or non-functional, but there are few algorithms that predict how a broad range of genetic modifications, such as over- and underexpression of metabolic genes, alters the phenotypes of the mutants at the metabolic flux level. Results: To overcome such existing limitations, we develop a novel algorithm that predicts the flux distribution of the mutants with a broad range of genetic modification, based on elementary mode analysis. It is denoted as genetic modification of flux (GMF), which couples two algorithms that we have developed: modified control effective flux (mCEF) and enzyme control flux (ECF). mCEF is proposed based on CEF to estimate the gene expression patterns in genetically modified mutants in terms of specific biological functions. GMF is demonstrated to predict the flux distribution of not only gene deletion mutants, but also the mutants with underexpressed and overexpressed genes in Escherichia coli and Corynebacterium glutamicum. This achieves breakthrough in the a priori flux prediction of a broad range of genetically modified mutants. Contact: kurata@bio.kyutech.ac.jp Supplementary information: Supplementary file and programs are available at Bioinformatics online or http://www.cadlive.jp.