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

Leimena MM, Ramiro-Garcia J, Davids M, van den Bogert B, Smidt H, Smid EJ, Boekhorst J, Zoetendal EG, Schaap PJ, Kleerebezem M - BMC Genomics (2013)

Bottom Line: 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.The set-up of the pipeline is very generic and can be applied for (bacterial) metatranscriptome analysis in any chosen niche.

View Article: PubMed Central - HTML - PubMed

Affiliation: TI Food and Nutrition (TIFN), P,O, Box 557, 6700 AN, Wageningen, The Netherlands.

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.

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Metabolic pathways mapping of dataset A and B. Metabolic pathways belonging to lipid, carbohydrate, energy, nucleotide, and amino acid metabolism were dominantly expressed in both datasets. The majority of the metabolic pathways overlapped between datasets A and B (red lines), while unique pathways for dataset A or B were indicated as green and blue lines, respectively. The line width indicates gene expression levels. Metabolic pathways were generated using iPath v2 based on KEGG annotation of the detected genes.
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Figure 5: Metabolic pathways mapping of dataset A and B. Metabolic pathways belonging to lipid, carbohydrate, energy, nucleotide, and amino acid metabolism were dominantly expressed in both datasets. The majority of the metabolic pathways overlapped between datasets A and B (red lines), while unique pathways for dataset A or B were indicated as green and blue lines, respectively. The line width indicates gene expression levels. Metabolic pathways were generated using iPath v2 based on KEGG annotation of the detected genes.

Mentions: Annotations using the KEGG database [41] were performed to enable effective metabolic pathway identification using the compatible iPath pathway mapping system [42], which is less well compatible with COG function assignments. Metabolic pathway mapping of the transcript profiles obtained from datasets A and A-rep gave an overall similar result (Additional file 9: Figure S7). Nevertheless, the higher resolution of dataset A allowed for the identification of pathways involved in secondary metabolite production and lipopolysaccharide biosynthesis (Additional file 9: Figure S7). As expected, identical pathway mapping results were obtained for datasets B-left and B-right of sample-B (data not shown). The metabolic mapping of the metatranscriptomic profile of datasets A and B also displayed a high degree of similarity (Figure 5). Both profiles revealed major similarity of pathways related to nucleotide, carbohydrate, amino acid, energy, and lipid metabolism, as well as cofactor and vitamin synthesis. Nevertheless, detailed analysis still allowed detection of differences of pathways related to oxidative phosphorylation and propanoate metabolism which were detected at a much higher level in dataset A, while pathways related to metabolism of specific amino acids were more abundant in dataset B (Figure 5). These differences may reflect ecosystem adaptations to environmental differences such as variation in the dietary composition of subject A and B.


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

Leimena MM, Ramiro-Garcia J, Davids M, van den Bogert B, Smidt H, Smid EJ, Boekhorst J, Zoetendal EG, Schaap PJ, Kleerebezem M - BMC Genomics (2013)

Metabolic pathways mapping of dataset A and B. Metabolic pathways belonging to lipid, carbohydrate, energy, nucleotide, and amino acid metabolism were dominantly expressed in both datasets. The majority of the metabolic pathways overlapped between datasets A and B (red lines), while unique pathways for dataset A or B were indicated as green and blue lines, respectively. The line width indicates gene expression levels. Metabolic pathways were generated using iPath v2 based on KEGG annotation of the detected genes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750648&req=5

Figure 5: Metabolic pathways mapping of dataset A and B. Metabolic pathways belonging to lipid, carbohydrate, energy, nucleotide, and amino acid metabolism were dominantly expressed in both datasets. The majority of the metabolic pathways overlapped between datasets A and B (red lines), while unique pathways for dataset A or B were indicated as green and blue lines, respectively. The line width indicates gene expression levels. Metabolic pathways were generated using iPath v2 based on KEGG annotation of the detected genes.
Mentions: Annotations using the KEGG database [41] were performed to enable effective metabolic pathway identification using the compatible iPath pathway mapping system [42], which is less well compatible with COG function assignments. Metabolic pathway mapping of the transcript profiles obtained from datasets A and A-rep gave an overall similar result (Additional file 9: Figure S7). Nevertheless, the higher resolution of dataset A allowed for the identification of pathways involved in secondary metabolite production and lipopolysaccharide biosynthesis (Additional file 9: Figure S7). As expected, identical pathway mapping results were obtained for datasets B-left and B-right of sample-B (data not shown). The metabolic mapping of the metatranscriptomic profile of datasets A and B also displayed a high degree of similarity (Figure 5). Both profiles revealed major similarity of pathways related to nucleotide, carbohydrate, amino acid, energy, and lipid metabolism, as well as cofactor and vitamin synthesis. Nevertheless, detailed analysis still allowed detection of differences of pathways related to oxidative phosphorylation and propanoate metabolism which were detected at a much higher level in dataset A, while pathways related to metabolism of specific amino acids were more abundant in dataset B (Figure 5). These differences may reflect ecosystem adaptations to environmental differences such as variation in the dietary composition of subject A and B.

Bottom Line: 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.The set-up of the pipeline is very generic and can be applied for (bacterial) metatranscriptome analysis in any chosen niche.

View Article: PubMed Central - HTML - PubMed

Affiliation: TI Food and Nutrition (TIFN), P,O, Box 557, 6700 AN, Wageningen, The Netherlands.

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.

Show MeSH