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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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Phylogenetic profiling of datasets A and B. Phylogenetic profiling of detected bacterial taxa for 16S rDNA and rRNA sequences obtained from pyrosequencing (a) and for mRNA reads obtained from Illumina sequencing (b). Both 16S and mRNA reads were classified into genus (colour key), or family (light grey), classified reads and the remaining unclassified reads (dark grey), based on the applied cut off (see methods). Only genera that contribute at least 2% to one of the profiles were represented. Separate phylogenetic profiling at genus level using 16S and mRNA reads of both datasets is presented in figure S4.
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Figure 2: Phylogenetic profiling of datasets A and B. Phylogenetic profiling of detected bacterial taxa for 16S rDNA and rRNA sequences obtained from pyrosequencing (a) and for mRNA reads obtained from Illumina sequencing (b). Both 16S and mRNA reads were classified into genus (colour key), or family (light grey), classified reads and the remaining unclassified reads (dark grey), based on the applied cut off (see methods). Only genera that contribute at least 2% to one of the profiles were represented. Separate phylogenetic profiling at genus level using 16S and mRNA reads of both datasets is presented in figure S4.

Mentions: According to simulation experiments (see Additional file 4 and Additional file 5: Figure S3A), minimum bit score thresholds of 148 and 110 can be used for phylogenetic assignments at genus- and family-level, both with > 80% confidence level. Respectively 73% and 50% of the mRNA reads of datasets A and B aligned with sequences in the NCBI prokaryote genome database with a bit score of 148 or higher. The phylogenetic distribution of these genus-assigned mRNA reads revealed that 70% and 34% of the mRNA reads were assigned to the genus Streptococcus in the RNA-seq datasets A and B, respectively. Both datasets also contained mRNA reads that were assigned to the genus Veillonella, which appeared to be more abundant in the dataset A (3%) as compared to dataset B (0.2%). Inversely, mRNA reads that were assigned to the Clostridium and Haemophilus genera were more abundant in dataset B (9% and 2.5%, respectively) compared to dataset A (2% and 1%, respectively) (Figure 2). Notably, Turicibacter-assigned mRNA reads were only encountered in the dataset B (3%). These observations illustrate the subject specificity of the small intestinal microbiota ecosystem activity profile. A similar conclusion was also reached in previous studies that described the human small intestine microbiota composition, revealing relatively consistent high abundances of Streptococcus spp. in different individuals and a more variable relative abundance of species belonging to the genera of Veillonella and Clostridium[4,26,27]. Highly similar phylogenetic distributions at genus-level were obtained for datasets A and A-rep, while the separate analysis of the B-left and B-right datasets generated a virtually identical phylogenetic profile (Figure 2; Additional file 6: Figure S4).


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)

Phylogenetic profiling of datasets A and B. Phylogenetic profiling of detected bacterial taxa for 16S rDNA and rRNA sequences obtained from pyrosequencing (a) and for mRNA reads obtained from Illumina sequencing (b). Both 16S and mRNA reads were classified into genus (colour key), or family (light grey), classified reads and the remaining unclassified reads (dark grey), based on the applied cut off (see methods). Only genera that contribute at least 2% to one of the profiles were represented. Separate phylogenetic profiling at genus level using 16S and mRNA reads of both datasets is presented in figure S4.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Phylogenetic profiling of datasets A and B. Phylogenetic profiling of detected bacterial taxa for 16S rDNA and rRNA sequences obtained from pyrosequencing (a) and for mRNA reads obtained from Illumina sequencing (b). Both 16S and mRNA reads were classified into genus (colour key), or family (light grey), classified reads and the remaining unclassified reads (dark grey), based on the applied cut off (see methods). Only genera that contribute at least 2% to one of the profiles were represented. Separate phylogenetic profiling at genus level using 16S and mRNA reads of both datasets is presented in figure S4.
Mentions: According to simulation experiments (see Additional file 4 and Additional file 5: Figure S3A), minimum bit score thresholds of 148 and 110 can be used for phylogenetic assignments at genus- and family-level, both with > 80% confidence level. Respectively 73% and 50% of the mRNA reads of datasets A and B aligned with sequences in the NCBI prokaryote genome database with a bit score of 148 or higher. The phylogenetic distribution of these genus-assigned mRNA reads revealed that 70% and 34% of the mRNA reads were assigned to the genus Streptococcus in the RNA-seq datasets A and B, respectively. Both datasets also contained mRNA reads that were assigned to the genus Veillonella, which appeared to be more abundant in the dataset A (3%) as compared to dataset B (0.2%). Inversely, mRNA reads that were assigned to the Clostridium and Haemophilus genera were more abundant in dataset B (9% and 2.5%, respectively) compared to dataset A (2% and 1%, respectively) (Figure 2). Notably, Turicibacter-assigned mRNA reads were only encountered in the dataset B (3%). These observations illustrate the subject specificity of the small intestinal microbiota ecosystem activity profile. A similar conclusion was also reached in previous studies that described the human small intestine microbiota composition, revealing relatively consistent high abundances of Streptococcus spp. in different individuals and a more variable relative abundance of species belonging to the genera of Veillonella and Clostridium[4,26,27]. Highly similar phylogenetic distributions at genus-level were obtained for datasets A and A-rep, while the separate analysis of the B-left and B-right datasets generated a virtually identical phylogenetic profile (Figure 2; Additional file 6: Figure S4).

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