Mapping-based all-RNA-information sequencing analysis (ARIseq) pipeline simultaneously revealed taxonomic composition, gene expression, and their correlation in an acidic stream ecosystem
We developed a new pipeline for simultaneous analyses of both rRNA profile as a taxonomic marker and mRNA profile as a functional marker, to understand microbial ecosystems in natural environments. Our pipeline, named All-RNA-Information sequencing (ARIseq), comprises a high-throughput sequencing of reverse transcribed total RNA and several widely used computational tools, and generates quantitatively reliable information on both community structures and gene expression patterns, which were verified by quantitative PCR analyses in this study. Particularly, correlation network analysis in the pipeline can reveal microbial taxa and expressed genes that share patterns of dynamics among different time and/or geographical points. The pipeline is primarily mapping-based, using a public database for small subunit rRNA genes and obtained contigs as the reference database for protein-coding genes. We applied this pipeline to biofilm samples, as examples, collected from an acidic spring water stream in the Chyatsubomi-goke Park in Gunma prefecture, Japan. Our analyses revealed the predominance of iron and sulfur-oxidizing bacteria and Pinnularia diatoms, and also indicated that the distributions of the iron-sulfur-oxidizing bacterial consortium and the Pinnularia diatoms largely overlapped but showed distinct patterns. In addition, our analyses showed that the iron-oxidizing bacterial genus Acidithiobacillus and co-occurring Acidiphilium shared similar distribution pattern whereas another iron-oxidizing genus Leptospirillum exhibited a distinct pattern. Our pipeline enables researchers to more easily capture the outline of microbial ecosystems based on the taxonomic composition, protein-coding gene expression, and their correlations.