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Strand-specific community RNA-seq reveals prevalent and dynamic antisense transcription in human gut microbiota.

Bao G, Wang M, Doak TG, Ye Y - Front Microbiol (2015)

Bottom Line: Metagenomics and other meta-omics approaches (including metatranscriptomics) provide insights into the composition and function of microbial communities living in different environments or animal hosts.Metatranscriptomics research provides an unprecedented opportunity to examine gene regulation for many microbial species simultaneously, and more importantly, for the majority that are unculturable microbial species, in their natural environments (or hosts).Current analyses of metatranscriptomic datasets focus on the detection of gene expression levels and the study of the relationship between changes of gene expression and changes of environment.

View Article: PubMed Central - PubMed

Affiliation: School of Informatics and Computing, Indiana University Bloomington, IN, USA.

ABSTRACT
Metagenomics and other meta-omics approaches (including metatranscriptomics) provide insights into the composition and function of microbial communities living in different environments or animal hosts. Metatranscriptomics research provides an unprecedented opportunity to examine gene regulation for many microbial species simultaneously, and more importantly, for the majority that are unculturable microbial species, in their natural environments (or hosts). Current analyses of metatranscriptomic datasets focus on the detection of gene expression levels and the study of the relationship between changes of gene expression and changes of environment. As a demonstration of utilizing metatranscriptomics beyond these common analyses, we developed a computational and statistical procedure to analyze the antisense transcripts in strand-specific metatranscriptomic datasets. Antisense RNAs encoded on the DNA strand opposite a gene's CDS have the potential to form extensive base-pairing interactions with the corresponding sense RNA, and can have important regulatory functions. Most studies of antisense RNAs in bacteria are rather recent, are mostly based on transcriptome analysis, and have been applied mainly to single bacterial species. Application of our approaches to human gut-associated metatranscriptomic datasets allowed us to survey antisense transcription for a large number of bacterial species associated with human beings. The ratio of protein coding genes with antisense transcription ranges from 0 to 35.8% (median = 10.0%) among 47 species. Our results show that antisense transcription is dynamic, varying between human individuals. Functional enrichment analysis revealed a preference of certain gene functions for antisense transcription, and transposase genes are among the most prominent ones (but we also observed antisense transcription in bacterial house-keeping genes).

No MeSH data available.


Read coverage plots for example genes in B. vulgatus. Genes are represented as arrows in the plots, and the read coverage curves are shown below the genes, with the coverage for sense and antisense reads shown in blue and purple, respectively. (A) Read coverage plot for an operon with four genes, shown as cyan arrows on the top: BVU_0219 is a putative aldo/keto reductase, BVU_0220 is a hypothetical protein, BVU_0221 is a putative fucose permease, and BVU_0222 is a putative sorbitol dehydrogenase. (B) Read coverage plot for BVU_3334 (and its neighboring genes): BVU_3334 is a putative transcriptional regulator, BVU_3333 is similar to a fructose-6-phosphate aldolase from E. coli, BVU_3332 is a putative ABC transporter permease, BVU_3335 is a hypotentical protein, and BVU_3336 is a putative glycosyl transferase.
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Figure 5: Read coverage plots for example genes in B. vulgatus. Genes are represented as arrows in the plots, and the read coverage curves are shown below the genes, with the coverage for sense and antisense reads shown in blue and purple, respectively. (A) Read coverage plot for an operon with four genes, shown as cyan arrows on the top: BVU_0219 is a putative aldo/keto reductase, BVU_0220 is a hypothetical protein, BVU_0221 is a putative fucose permease, and BVU_0222 is a putative sorbitol dehydrogenase. (B) Read coverage plot for BVU_3334 (and its neighboring genes): BVU_3334 is a putative transcriptional regulator, BVU_3333 is similar to a fructose-6-phosphate aldolase from E. coli, BVU_3332 is a putative ABC transporter permease, BVU_3335 is a hypotentical protein, and BVU_3336 is a putative glycosyl transferase.

Mentions: Not surprisingly, most of the strains we tested recruited many more sense than antisense reads, and tend to have more genes with sense transcription than genes with antisense transcription (such as B. vulgatus ATCC 8482, as shown in Figures 4A,B, Parabacteroides distasonis ATCC 8503 as shown in Figures 4C,D, and M. smithii ATCC 35061 as shown in Figures 4E,F). Our results are consistent with a previous study (Franzosa et al., 2014), showing that M. smithii is abundant and highly transcriptionally active (supported by huge numbers of RNA-seq reads) in five of the eight individuals (Figures 4E,F). But for these species, individual genes may still have significant antisense transcription or even have antisense transcription only; for examples, Figure 5A shows the read coverage plot for an operon in B. vulgatus (the operon information was extracted from the Database of Prokaryotic Operons; Mao et al., 2009), showing that all four genes in this operon have both sense and antisense transcription; and Figure 5B shows that gene BVU_3334 (which encodes for a putative transcriptional regulator) only has antisense reads.


Strand-specific community RNA-seq reveals prevalent and dynamic antisense transcription in human gut microbiota.

Bao G, Wang M, Doak TG, Ye Y - Front Microbiol (2015)

Read coverage plots for example genes in B. vulgatus. Genes are represented as arrows in the plots, and the read coverage curves are shown below the genes, with the coverage for sense and antisense reads shown in blue and purple, respectively. (A) Read coverage plot for an operon with four genes, shown as cyan arrows on the top: BVU_0219 is a putative aldo/keto reductase, BVU_0220 is a hypothetical protein, BVU_0221 is a putative fucose permease, and BVU_0222 is a putative sorbitol dehydrogenase. (B) Read coverage plot for BVU_3334 (and its neighboring genes): BVU_3334 is a putative transcriptional regulator, BVU_3333 is similar to a fructose-6-phosphate aldolase from E. coli, BVU_3332 is a putative ABC transporter permease, BVU_3335 is a hypotentical protein, and BVU_3336 is a putative glycosyl transferase.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4555090&req=5

Figure 5: Read coverage plots for example genes in B. vulgatus. Genes are represented as arrows in the plots, and the read coverage curves are shown below the genes, with the coverage for sense and antisense reads shown in blue and purple, respectively. (A) Read coverage plot for an operon with four genes, shown as cyan arrows on the top: BVU_0219 is a putative aldo/keto reductase, BVU_0220 is a hypothetical protein, BVU_0221 is a putative fucose permease, and BVU_0222 is a putative sorbitol dehydrogenase. (B) Read coverage plot for BVU_3334 (and its neighboring genes): BVU_3334 is a putative transcriptional regulator, BVU_3333 is similar to a fructose-6-phosphate aldolase from E. coli, BVU_3332 is a putative ABC transporter permease, BVU_3335 is a hypotentical protein, and BVU_3336 is a putative glycosyl transferase.
Mentions: Not surprisingly, most of the strains we tested recruited many more sense than antisense reads, and tend to have more genes with sense transcription than genes with antisense transcription (such as B. vulgatus ATCC 8482, as shown in Figures 4A,B, Parabacteroides distasonis ATCC 8503 as shown in Figures 4C,D, and M. smithii ATCC 35061 as shown in Figures 4E,F). Our results are consistent with a previous study (Franzosa et al., 2014), showing that M. smithii is abundant and highly transcriptionally active (supported by huge numbers of RNA-seq reads) in five of the eight individuals (Figures 4E,F). But for these species, individual genes may still have significant antisense transcription or even have antisense transcription only; for examples, Figure 5A shows the read coverage plot for an operon in B. vulgatus (the operon information was extracted from the Database of Prokaryotic Operons; Mao et al., 2009), showing that all four genes in this operon have both sense and antisense transcription; and Figure 5B shows that gene BVU_3334 (which encodes for a putative transcriptional regulator) only has antisense reads.

Bottom Line: Metagenomics and other meta-omics approaches (including metatranscriptomics) provide insights into the composition and function of microbial communities living in different environments or animal hosts.Metatranscriptomics research provides an unprecedented opportunity to examine gene regulation for many microbial species simultaneously, and more importantly, for the majority that are unculturable microbial species, in their natural environments (or hosts).Current analyses of metatranscriptomic datasets focus on the detection of gene expression levels and the study of the relationship between changes of gene expression and changes of environment.

View Article: PubMed Central - PubMed

Affiliation: School of Informatics and Computing, Indiana University Bloomington, IN, USA.

ABSTRACT
Metagenomics and other meta-omics approaches (including metatranscriptomics) provide insights into the composition and function of microbial communities living in different environments or animal hosts. Metatranscriptomics research provides an unprecedented opportunity to examine gene regulation for many microbial species simultaneously, and more importantly, for the majority that are unculturable microbial species, in their natural environments (or hosts). Current analyses of metatranscriptomic datasets focus on the detection of gene expression levels and the study of the relationship between changes of gene expression and changes of environment. As a demonstration of utilizing metatranscriptomics beyond these common analyses, we developed a computational and statistical procedure to analyze the antisense transcripts in strand-specific metatranscriptomic datasets. Antisense RNAs encoded on the DNA strand opposite a gene's CDS have the potential to form extensive base-pairing interactions with the corresponding sense RNA, and can have important regulatory functions. Most studies of antisense RNAs in bacteria are rather recent, are mostly based on transcriptome analysis, and have been applied mainly to single bacterial species. Application of our approaches to human gut-associated metatranscriptomic datasets allowed us to survey antisense transcription for a large number of bacterial species associated with human beings. The ratio of protein coding genes with antisense transcription ranges from 0 to 35.8% (median = 10.0%) among 47 species. Our results show that antisense transcription is dynamic, varying between human individuals. Functional enrichment analysis revealed a preference of certain gene functions for antisense transcription, and transposase genes are among the most prominent ones (but we also observed antisense transcription in bacterial house-keeping genes).

No MeSH data available.