High-throughput insertional mutagenesis reveals novel targets for enhancing lipid accumulation in Nannochloropsis oceanica
Ontology highlight
ABSTRACT: Oleaginous microalgae are considered a promising platform for the sustainable production of high-value lipids and biofuel feedstocks. However, current lipid yields are too low to allow for an economically feasible production process. Lipid yields could be enhanced by improving microalgal strains through genetic engineering. Strain improvement strategies for the industrially relevant genus Nannochloropsis have met limited success because most genes of this genus lack a functional annotation, hindering our understanding of lipid metabolism and its regulation. To gain fundamental insights and to provide targets for genetic engineering of lipid metabolism, the aim of this study was to discover novel genes that are associated with higher neutral lipid (NL) content in Nannochloropsis oceanica. Therefore, we constructed a random gene knockout (KO) insertional mutagenesis library of N. oceanica, and we screened it by five rounds of fluorescence-activated cell sorting to select high lipid mutant (HLM) strains. Several strains showed increased NL contents compared to the wild type under favorable growth conditions. By using an adapted cassette PCR strategy involving the type IIS restriction endonuclease MmeI, we traced the responsible genetic KO of the five most promising mutant strains. One particularly promising mutant strain (HLM23) was disrupted in gene NO06G03670, which encodes a putative APETALA2-like transcription factor. HLM23 was not affected in growth rate, had increase d photosynthetic performance and a NL content of 30% dry cell weight^(-1), a 40% increase compared to the wild type. RNA sequencing revealed a transcriptional upregulation of genes related to plastidial fatty acid biosynthesis, glycolysis and the Calvin–Benson–Bassham cycle in this mutant.
ORGANISM(S): Nannochloropsis oceanica strain IMET1
PROVIDER: GSE167058 | GEO | 2021/05/13
REPOSITORIES: GEO
ACCESS DATA