High-throughput sequencing revealed differences of microbial community structure and diversity between healthy and diseased Caulerpa lentillifera.

ABSTRACT

BACKGROUND: Caulerpa lentillifera is one of the most important economic green macroalgae in the world. Increasing demand for consumption has led to the commercial cultivation of C. lentillifera in Japan and Vietnam in recent decades. Concomitant with the increase of C. lentillifera cultivation is a rise in disease. We hypothesise that epiphytes or other microorganisms outbreak at the C. lentillifera farm may be an important factor contributing to disease in C. lentillifera. The main aims are obtaining differences in the microbial community structure and diversity between healthy and diseased C. lentillifera and key epiphytes and other microorganisms affecting the differences through the results of high-throughput sequencing and bioinformatics analysis in the present study. RESULTS: A total of 14,050, 2479, and 941 operational taxonomic units (OTUs) were obtained from all samples using 16S rDNA, 18S rDNA, and internal transcribed spacer (ITS) high-throughput sequencing, respectively. 16S rDNA sequencing and 18S rDNA sequencing showed that microbial community diversity was higher in diseased C. lentillifera than in healthy C. lentillifera. Both PCoA results and UPGMA results indicated that the healthy and diseased algae samples have characteristically different microbial communities. The predominant prokaryotic phyla were Proteobacteria, Planctomycetes, Bacteroidetes, Cyanobacteria, Acidobacteria, Acidobacteria and Parcubacteria in all sequences. Chlorophyta was the most abundant eukaryotic phylum followed by Bacillariophyta based on 18S rDNA sequencing. Ascomycota was the dominant fungal phylum detected in healthy C. lentillifera based on ITS sequencing, whereas fungi was rare in diseased C. lentillifera, suggesting that Ascomycota was probably fungal endosymbiont in healthy C. lentillifera. There was a significantly higher abundance of Bacteroidetes, Cyanobacteria, Bacillariophyta, Ulvales and Tetraselmis in diseased C. lentillifera than in healthy C. lentillifera. Disease outbreaks significantly change carbohydrate metabolism, environmental information processing and genetic information processing of prokaryotic communities in C. lentillifera through predicted functional analyses using the Tax4Fun tool. CONCLUSIONS: Bacteroidetes, Cyanobacteria, Bacillariophyta, Ulvales and Tetraselmis outbreak at the C. lentillifera farm sites was an important factor contributing to disease in C. lentillifera.