A comprehensive metatranscriptome analysis pipeline and its validation using human small intestine microbiota datasets

Abstract

Background: Next generation sequencing (NGS) technologies can be applied in complex microbial ecosystems for metatranscriptome analysis by employing direct cDNA sequencing, which is known as RNA sequencing (RNA-seq). RNA-seq generates large datasets of great complexity, the comprehensive interpretation of which requires a reliable bioinformatic pipeline. In this study, we focus on the development of such a metatranscriptome pipeline, which we validate using Illumina RNA-seq datasets derived from the small intestine microbiota of two individuals with an ileostomy. Results: The metatranscriptome pipeline developed here enabled effective removal of rRNA derived sequences, followed by confident assignment of the predicted function and taxonomic origin of the mRNA reads. Phylogenetic analysis of the small intestine metatranscriptome datasets revealed a strong similarity with the community composition profiles obtained from 16S rDNA and rRNA pyrosequencing, indicating considerable congruency between community composition (rDNA), and the taxonomic distribution of overall (rRNA) and specific (mRNA) activity among its microbial members. Reproducibility of the metatranscriptome sequencing approach was established by independent duplicate experiments. In addition, comparison of metatranscriptome analysis employing single-or paired-end sequencing methods indicated that the latter approach does not provide improved functional or phylogenetic insights. Metatranscriptome functional-mapping allowed the analysis of global, and genus specific activity of the microbiota, and illustrated the potential of these approaches to unravel syntrophic interactions in microbial ecosystems. Conclusions: A reliable pipeline for metatransciptome data analysis was developed and evaluated using RNA-seq datasets obtained for the human small intestine microbiota. The set-up of the pipeline is very generic and can be applied for (bacterial) metatranscriptome analysis in any chosen niche.

A Systematic Analysis of Biosynthetic Gene Clusters in the Human Microbiome Reveals a Common Family of Antibiotics

“…Another recent study correlated the prevalence of hepatic cancer in mice to deoxycholic acid, a secondary bile acid produced by certain members of the gut microbiota (Yoshimoto et al, 2013). A recent metatranscriptomic study showed the expression of genes matching the COG category “secondary metabolites biosynthesis, transport, and catabolism”, which is consistent with the possibility of small molecule production but could also indicate the expression of catabolic and/or transport genes unrelated to biosynthesis (Leimena et al, 2013). These examples raise the question of whether there exists a much larger set of bacterially produced molecules that mediate microbiota-host interactions.…”

Untangling the Effect of Fatty Acid Addition at Species Level Revealed Different Transcriptional Responses of the Biogas Microbial Community Members

“…Previous studies in relevant scientific fields reported contradictory findings; Leimena et al, 38 found a substantial similarity between results obtained from the metatranscriptome and the metagenome of gut microbiome, while other investigations on the same topic showed the presence of a specific fraction of species that are inactive. 8,39 Our analysis showed that the entire microbial community is active and thus the presence of GBs with an extremely low transcriptional activity was not evidenced (Figures 3 and 4).…”

Analysis of the metatranscriptome of microbial communities of an alkaline hot sulfur spring revealed different gene encoding pathway enzymes associated with energy metabolism

“…The functional aspect of the microbial communities in the natural environment is better elucidated by analyzing the mRNA profiles; hence, here, we applied the metatranscriptomic approach to investigate the functional gene encoding enzymes associated with the anaerobic metabolic processes of the hot spring microbiomes. Phylogenetic and functional assignments were obtained by directly aligning the mRNA reads to the complete NCBI prokaryote genome database that includes draft genomes (Leimena et al 2013).…”