Genome sequencing, assembly, and annotation of the self-flocculating microalga Scenedesmus obliquus AS-6-11

Scenedesmus obliquus belongs to green microalgae and is widely used in aquaculture as feed, which is also explored for lipid production and bioremediation. However, genomic studies of this microalga have been very limited. Cell self-flocculation of microalgal cells can be used as a simple and economic method for harvesting biomass, and it is of great importance to perform genome-scale studies for the self-flocculating S. obliquus strains to promote their biotechnological applications.

We employed the Pacific Biosciences sequencing platform for sequencing the genome of the self-flocculating microalga S. obliquus AS-6-11, and used the MECAT software for de novo genome assembly. The estimated genome size of S. obliquus AS-6-11 is 172.3 Mbp with an N50 of 94,410 bp, and 31,964 protein-coding genes were identified. Gene Ontology (GO) and KEGG pathway analyses revealed 65 GO terms and 428 biosynthetic pathways. Comparing to the genome sequences of the well-studied green microalgae Chlamydomonas reinhardtii, Chlorella variabilis, Volvox carteri and Micractinium conductrix, the genome of S. obliquus AS-6-11 encodes more unique proteins, including one gene that encodes D-mannose binding lectin. Genes encoding the glycosylphosphatidylinositol (GPI)-anchored cell wall proteins, and proteins with fasciclin domains that are commonly found in cell wall proteins might be responsible for the self-flocculating phenotype, and were analyzed in detail. Four genes encoding both GPI-anchored cell wall proteins and fasciclin domain proteins are the most interesting targets for further studies.

The genome sequence of the self-flocculating microalgal S. obliquus AS-6-11 was annotated and analyzed. To our best knowledge, this is the first report on the in-depth annotation of the S. obliquus genome, and the results will facilitate functional genomic studies and metabolic engineering of this important microalga. The comparative genomic analysis here also provides new insights into the evolution of green microalgae. Furthermore, identification of the potential genes encoding self-flocculating proteins will benefit studies on the molecular mechanism underlying this phenotype for its better control and biotechnological applications as well.