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

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.

Comparative analysis of Lactobacillus plantarum WCFS1 transcriptomes by using DNA microarray and next-generation sequencing technologies.

RNA sequencing is starting to compete with the use of DNA microarrays for transcription analysis in eukaryotes as well as in prokaryotes. The application of RNA sequencing in prokaryotes requires additional steps in the RNA preparation procedure to increase the relative abundance of mRNA and cannot employ the poly(T)-primed approach in cDNA synthesis. In this study, we aimed to validate the use of RNA sequencing (direct cDNA sequencing and 3'-untranslated region [UTR] sequencing) using Lactobacillus plantarum WCFS1 as a model organism, employing its established microarray platform as a reference. A limited effect of mRNA enrichment on genome-wide transcript quantification was observed, and comparative transcriptome analyses were performed for L. plantarum WCFS1 grown in two different laboratory media. Microarray analyses and both RNA sequencing methods resulted in similar depths of analysis and generated similar fold-change ratios of differentially expressed genes. The highest overall correlation was found between microarray and direct cDNA sequencing-derived transcriptomes, while the 3'-UTR sequencing-derived transcriptome appeared to deviate the most. Overall, a high similarity between patterns of transcript abundance and fold-change levels of differentially expressed genes was detected by all three methods, indicating that the biological conclusions drawn from the transcriptome data were consistent among the three technologies.

Mining microbial metatranscriptomes for expression of antibiotic resistance genes under natural conditions.

Antibiotic resistance genes are found in a broad range of ecological niches associated with complex microbiota. Here we investigated if resistance genes are not only present, but also transcribed under natural conditions. Furthermore, we examined the potential for antibiotic production by assessing the expression of associated secondary metabolite biosynthesis gene clusters. Metatranscriptome datasets from intestinal microbiota of four human adults, one human infant, 15 mice and six pigs, of which only the latter have received antibiotics prior to the study, as well as from sea bacterioplankton, a marine sponge, forest soil and sub-seafloor sediment, were investigated. We found that resistance genes are expressed in all studied ecological niches, albeit with niche-specific differences in relative expression levels and diversity of transcripts. For example, in mice and human infant microbiota predominantly tetracycline resistance genes were expressed while in human adult microbiota the spectrum of expressed genes was more diverse, and also included ß-lactam, aminoglycoside and macrolide resistance genes. Resistance gene expression could result from the presence of natural antibiotics in the environment, although we could not link it to expression of corresponding secondary metabolites biosynthesis clusters. Alternatively, resistance gene expression could be constitutive, or these genes serve alternative roles besides antibiotic resistance.