Yarimizu Kyoko, Fujiyoshi So, Kawai Mikihiko, Acuña Jacquelinne J, Rilling Joaquin-Ignacio, Campos Marco, Vilugrón Jonnathan, Cameron Henry, Vergara Karen, Gajardo Gonzalo, Espinoza-González Oscar, Guzmán Leonardo, Nagai Satoshi, Riquelme Carlos, Jorquera Milko A, Maruyama Fumito
Office of Research and Academia-Government-Community Collaboration, Hiroshima University;
Office of Research and Academia-Government-Community Collaboration, Hiroshima University.
J Vis Exp. 2021 Aug 26(174). doi: 10.3791/62967.
Harmful algae blooms (HABs) monitoring has been implemented worldwide, and Chile, a country famous for its fisheries and aquaculture, has intensively used microscopic and toxin analyses for decades for this purpose. Molecular biological methods, such as high-throughput DNA sequencing and bacterial assemblage-based approaches, are just beginning to be introduced in Chilean HAB monitoring, and the procedures have not yet been standardized. Here, 16S rRNA and 18S rRNA metabarcoding analyses for monitoring Chilean HABs are introduced stepwise. According to a recent hypothesis, algal-bacterial mutualistic association plays a critical synergetic or antagonistic relationship accounting for bloom initiation, maintenance, and regression. Thus, monitoring HAB from algal-bacterial perspectives may provide a broader understanding of HAB mechanisms and the basis for early warning. Metabarcoding analysis is one of the best suited molecular-based tools for this purpose because it can detect massive algal-bacterial taxonomic information in a sample. The visual procedures of sampling to metabarcoding analysis herein provide specific instructions, aiming to reduce errors and collection of reliable data.
有害藻华(HABs)监测已在全球范围内展开,而以渔业和水产养殖闻名的智利,几十年来一直大量使用显微镜检查和毒素分析来进行此项工作。分子生物学方法,如高通量DNA测序和基于细菌群落的方法,才刚刚开始被引入智利的有害藻华监测中,且相关程序尚未标准化。在此,逐步介绍用于监测智利有害藻华的16S rRNA和18S rRNA宏条形码分析。根据最近的一个假说,藻类与细菌的共生关系在藻华的起始、维持和消退过程中起着关键的协同或拮抗作用。因此,从藻类与细菌的角度监测有害藻华可能会更全面地理解有害藻华的形成机制,并为早期预警提供依据。宏条形码分析是最适合用于此目的的基于分子的工具之一,因为它可以检测样本中大量的藻类和细菌分类信息。本文从采样到宏条形码分析的可视化程序提供了具体指导,旨在减少误差并收集可靠数据。