Literature DB >> 34410646

Usage of Metatranscriptomics to Understand Oral Disease.

Takayasu Watanabe1.   

Abstract

Metatranscriptomics is a method used to comprehensively capture bacterial activity within microbiota at the transcription level. It has become an alternative to the 16S rDNA sequencing, which uses only the 16S rRNA gene for predicting bacterial composition. By conducting metatranscriptomics, investigators can obtain substantial information about what types of genes are transcribed at the time of sampling and which bacterial taxa are responsible for their transcription. Here, I describe a protocol for metatranscriptomics for oral microbiota by using high-throughput sequencing technology. A remarkable feature of this protocol is that it uses the level of rRNA expression as the internal control for measuring transcriptional activity of each bacterial taxon. The normalized mRNA level is given by the mRNA/rRNA ratio, which indicates the extent of transcriptional activity.
© 2021. Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Metatranscriptome; Microbiota; RNA-seq; Transcriptional activity; mRNA; rRNA

Mesh:

Substances:

Year:  2021        PMID: 34410646     DOI: 10.1007/978-1-0716-1518-8_11

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  21 in total

Review 1.  Impact of 16S rRNA gene sequence analysis for identification of bacteria on clinical microbiology and infectious diseases.

Authors:  Jill E Clarridge
Journal:  Clin Microbiol Rev       Date:  2004-10       Impact factor: 26.132

Review 2.  A review of methods and databases for metagenomic classification and assembly.

Authors:  Florian P Breitwieser; Jennifer Lu; Steven L Salzberg
Journal:  Brief Bioinform       Date:  2019-07-19       Impact factor: 11.622

3.  Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses.

Authors:  Steven J Blazewicz; Romain L Barnard; Rebecca A Daly; Mary K Firestone
Journal:  ISME J       Date:  2013-07-04       Impact factor: 10.302

Review 4.  High-throughput bacterial genome sequencing: an embarrassment of choice, a world of opportunity.

Authors:  Nicholas J Loman; Chrystala Constantinidou; Jacqueline Z M Chan; Mihail Halachev; Martin Sergeant; Charles W Penn; Esther R Robinson; Mark J Pallen
Journal:  Nat Rev Microbiol       Date:  2012-08-06       Impact factor: 60.633

5.  Fast identification and removal of sequence contamination from genomic and metagenomic datasets.

Authors:  Robert Schmieder; Robert Edwards
Journal:  PLoS One       Date:  2011-03-09       Impact factor: 3.240

6.  Transcriptomics technologies.

Authors:  Rohan Lowe; Neil Shirley; Mark Bleackley; Stephen Dolan; Thomas Shafee
Journal:  PLoS Comput Biol       Date:  2017-05-18       Impact factor: 4.475

7.  Functional dysbiosis within dental plaque microbiota in cleft lip and palate patients.

Authors:  Kenta Funahashi; Takahiko Shiba; Takayasu Watanabe; Keiko Muramoto; Yasuo Takeuchi; Takuya Ogawa; Yuichi Izumi; Tsutomu Sekizaki; Ichiro Nakagawa; Keiji Moriyama
Journal:  Prog Orthod       Date:  2019-03-25       Impact factor: 2.750

8.  Trimmomatic: a flexible trimmer for Illumina sequence data.

Authors:  Anthony M Bolger; Marc Lohse; Bjoern Usadel
Journal:  Bioinformatics       Date:  2014-04-01       Impact factor: 6.937

9.  Distinct interacting core taxa in co-occurrence networks enable discrimination of polymicrobial oral diseases with similar symptoms.

Authors:  Takahiko Shiba; Takayasu Watanabe; Hirokazu Kachi; Tatsuro Koyanagi; Noriko Maruyama; Kazunori Murase; Yasuo Takeuchi; Fumito Maruyama; Yuichi Izumi; Ichiro Nakagawa
Journal:  Sci Rep       Date:  2016-08-08       Impact factor: 4.379

Review 10.  Capturing the genetic makeup of the active microbiome in situ.

Authors:  Esther Singer; Michael Wagner; Tanja Woyke
Journal:  ISME J       Date:  2017-06-02       Impact factor: 10.302

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