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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.


Different Streptococcus species have different levels of antisense transcripts. The y-axis shows the ratio of genes with antisense transcription. The x-axis shows the different species; sang: S. anginosus C1051, sanc: S. anginosus C238, sif: S. infantarius CJ18, smut: S. mutans GS5, smj: S. mutans LJ23, smc: S. mutans NN2025, smu: S. mutans UA159, scp: S. parasanguinis ATCC 15912, scf: S. parasanguinis FW213, stf: S. salivarius 57 I, ssr: S. salivarius CCHSS3, stj: S. salivarius JIM8777, ssa: S. sanguinis SK36, stc: S. thermophilus CNRZ1066, stu: S. thermophilus JIM 8232, ste: S. thermophilus LMD 9, stl: S. thermophilus LMG 18311, stw: S. thermophilus MN ZLW 002, stn: S. thermophilus ND03. The boxplots for the different strains of the same species are shown in the same color.
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Figure 6: Different Streptococcus species have different levels of antisense transcripts. The y-axis shows the ratio of genes with antisense transcription. The x-axis shows the different species; sang: S. anginosus C1051, sanc: S. anginosus C238, sif: S. infantarius CJ18, smut: S. mutans GS5, smj: S. mutans LJ23, smc: S. mutans NN2025, smu: S. mutans UA159, scp: S. parasanguinis ATCC 15912, scf: S. parasanguinis FW213, stf: S. salivarius 57 I, ssr: S. salivarius CCHSS3, stj: S. salivarius JIM8777, ssa: S. sanguinis SK36, stc: S. thermophilus CNRZ1066, stu: S. thermophilus JIM 8232, ste: S. thermophilus LMD 9, stl: S. thermophilus LMG 18311, stw: S. thermophilus MN ZLW 002, stn: S. thermophilus ND03. The boxplots for the different strains of the same species are shown in the same color.

Mentions: Different species of the same genus showed various ratios of antisense transcripts. Figure 6 shows the ratio of genes with antisense transcription in different species of Streptococcus (one of the dominant genera in human gut microbiota) across the eight human individuals. Overall, Streptococcus species have relatively low antisense transcription: the median of the ratios of antisense reads is 1.1% and the median of the ratios of genes with antisense transcription is 4.4%. S. mutans and S. parasanguinis have the lowest ratio of genes with antisense transcription; other Staphylococcus species seem to have higher antisense transcription, but the ratios vary greatly across different individuals. Similar trends are observed in a plot that shows the ratios of antisense reads for these species (Supplementary Figure S1).


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)

Different Streptococcus species have different levels of antisense transcripts. The y-axis shows the ratio of genes with antisense transcription. The x-axis shows the different species; sang: S. anginosus C1051, sanc: S. anginosus C238, sif: S. infantarius CJ18, smut: S. mutans GS5, smj: S. mutans LJ23, smc: S. mutans NN2025, smu: S. mutans UA159, scp: S. parasanguinis ATCC 15912, scf: S. parasanguinis FW213, stf: S. salivarius 57 I, ssr: S. salivarius CCHSS3, stj: S. salivarius JIM8777, ssa: S. sanguinis SK36, stc: S. thermophilus CNRZ1066, stu: S. thermophilus JIM 8232, ste: S. thermophilus LMD 9, stl: S. thermophilus LMG 18311, stw: S. thermophilus MN ZLW 002, stn: S. thermophilus ND03. The boxplots for the different strains of the same species are shown in the same color.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
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Figure 6: Different Streptococcus species have different levels of antisense transcripts. The y-axis shows the ratio of genes with antisense transcription. The x-axis shows the different species; sang: S. anginosus C1051, sanc: S. anginosus C238, sif: S. infantarius CJ18, smut: S. mutans GS5, smj: S. mutans LJ23, smc: S. mutans NN2025, smu: S. mutans UA159, scp: S. parasanguinis ATCC 15912, scf: S. parasanguinis FW213, stf: S. salivarius 57 I, ssr: S. salivarius CCHSS3, stj: S. salivarius JIM8777, ssa: S. sanguinis SK36, stc: S. thermophilus CNRZ1066, stu: S. thermophilus JIM 8232, ste: S. thermophilus LMD 9, stl: S. thermophilus LMG 18311, stw: S. thermophilus MN ZLW 002, stn: S. thermophilus ND03. The boxplots for the different strains of the same species are shown in the same color.
Mentions: Different species of the same genus showed various ratios of antisense transcripts. Figure 6 shows the ratio of genes with antisense transcription in different species of Streptococcus (one of the dominant genera in human gut microbiota) across the eight human individuals. Overall, Streptococcus species have relatively low antisense transcription: the median of the ratios of antisense reads is 1.1% and the median of the ratios of genes with antisense transcription is 4.4%. S. mutans and S. parasanguinis have the lowest ratio of genes with antisense transcription; other Staphylococcus species seem to have higher antisense transcription, but the ratios vary greatly across different individuals. Similar trends are observed in a plot that shows the ratios of antisense reads for these species (Supplementary Figure S1).

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.