Literature DB >> 35896837

Organelle 16S rRNA amplicon sequencing enables profiling of active gut microbiota in murine model.

Dong Han1,2, Hongmin Zhen1, Xiaoyan Liu1, Justyna Zulewska3, Zhennai Yang4.   

Abstract

High-throughput sequencing of ribosomal RNA (rRNA) amplicons has served as a cornerstone in microbiome studies. Despite crucial implication of organelle 16S rRNA measurements to host gut microbial activities, genomic DNA (gDNA) was overwhelmingly targeted for amplicon sequencings. Although gDNA could be a reliable resource for gene existing validation, little information is revealed in regard to the activity of microorganisms owing to the limited changes gDNA undertaken in inactive, dormant, and dead bacteria. We applied both rRNA- and gDNA-derived sequencings on mouse cecal contents. Respective experimental designs were verified to be suitable for nucleic acid (NA) purification. Via benchmarking, mainstream 16S rRNA hypervariable region targets and reference databases were proven adequate for respective amplicon sequencing study. In phylogenetic studies, significant microbial composition differences were observed between two methods. Desulfovibrio spp. (an important group of anaerobic gut microorganisms that has caused analytical difficulties), Pediococcus spp., and Proteobacteria were drastically lower as represented by gDNA-derived compositions, while microbes like Firmicutes were higher as represented by gDNA-derived microbiome compositions. Also, using PICRUSt2 as an example, we illustrated that rRNA-derived sequencing might be more suitable for microbiome function predictions since pathways like sugar metabolism were lower as represented by rRNA-derived results. The findings of this study demonstrated that rRNA-derived amplicon sequencing could improve identification capability of specific gut microorganisms and might be more suitable for in silico microbiome function predictions. Therefore, rRNA-derived amplicon sequencings, preferably coupled with gDNA-derived ones, could be used as a capable tool to unveil active microbial components in host gut. KEY POINTS: • Conventional pipelines were adequate for the respective amplicon sequencing study • Groups, such as Desulfovibrio spp., were differently represented by two methods • Comparative amplicon sequencings could be useful in host active microbiota studies.
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Entities:  

Keywords:  16S rRNA; Active microorganism; Amplicon sequencing; Gut microbiota; High-throughput sequencing

Mesh:

Substances:

Year:  2022        PMID: 35896837     DOI: 10.1007/s00253-022-12083-x

Source DB:  PubMed          Journal:  Appl Microbiol Biotechnol        ISSN: 0175-7598            Impact factor:   5.560


  56 in total

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Authors:  A Bairoch
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria.

Authors:  Soumitesh Chakravorty; Danica Helb; Michele Burday; Nancy Connell; David Alland
Journal:  J Microbiol Methods       Date:  2007-02-22       Impact factor: 2.363

3.  Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.

Authors:  Evan Bolyen; Jai Ram Rideout; Matthew R Dillon; Nicholas A Bokulich; Christian C Abnet; Gabriel A Al-Ghalith; Harriet Alexander; Eric J Alm; Manimozhiyan Arumugam; Francesco Asnicar; Yang Bai; Jordan E Bisanz; Kyle Bittinger; Asker Brejnrod; Colin J Brislawn; C Titus Brown; Benjamin J Callahan; Andrés Mauricio Caraballo-Rodríguez; John Chase; Emily K Cope; Ricardo Da Silva; Christian Diener; Pieter C Dorrestein; Gavin M Douglas; Daniel M Durall; Claire Duvallet; Christian F Edwardson; Madeleine Ernst; Mehrbod Estaki; Jennifer Fouquier; Julia M Gauglitz; Sean M Gibbons; Deanna L Gibson; Antonio Gonzalez; Kestrel Gorlick; Jiarong Guo; Benjamin Hillmann; Susan Holmes; Hannes Holste; Curtis Huttenhower; Gavin A Huttley; Stefan Janssen; Alan K Jarmusch; Lingjing Jiang; Benjamin D Kaehler; Kyo Bin Kang; Christopher R Keefe; Paul Keim; Scott T Kelley; Dan Knights; Irina Koester; Tomasz Kosciolek; Jorden Kreps; Morgan G I Langille; Joslynn Lee; Ruth Ley; Yong-Xin Liu; Erikka Loftfield; Catherine Lozupone; Massoud Maher; Clarisse Marotz; Bryan D Martin; Daniel McDonald; Lauren J McIver; Alexey V Melnik; Jessica L Metcalf; Sydney C Morgan; Jamie T Morton; Ahmad Turan Naimey; Jose A Navas-Molina; Louis Felix Nothias; Stephanie B Orchanian; Talima Pearson; Samuel L Peoples; Daniel Petras; Mary Lai Preuss; Elmar Pruesse; Lasse Buur Rasmussen; Adam Rivers; Michael S Robeson; Patrick Rosenthal; Nicola Segata; Michael Shaffer; Arron Shiffer; Rashmi Sinha; Se Jin Song; John R Spear; Austin D Swafford; Luke R Thompson; Pedro J Torres; Pauline Trinh; Anupriya Tripathi; Peter J Turnbaugh; Sabah Ul-Hasan; Justin J J van der Hooft; Fernando Vargas; Yoshiki Vázquez-Baeza; Emily Vogtmann; Max von Hippel; William Walters; Yunhu Wan; Mingxun Wang; Jonathan Warren; Kyle C Weber; Charles H D Williamson; Amy D Willis; Zhenjiang Zech Xu; Jesse R Zaneveld; Yilong Zhang; Qiyun Zhu; Rob Knight; J Gregory Caporaso
Journal:  Nat Biotechnol       Date:  2019-08       Impact factor: 54.908

4.  EPA-ng: Massively Parallel Evolutionary Placement of Genetic Sequences.

Authors:  Pierre Barbera; Alexey M Kozlov; Lucas Czech; Benoit Morel; Diego Darriba; Tomáš Flouri; Alexandros Stamatakis
Journal:  Syst Biol       Date:  2019-03-01       Impact factor: 15.683

5.  DADA2: High-resolution sample inference from Illumina amplicon data.

Authors:  Benjamin J Callahan; Paul J McMurdie; Michael J Rosen; Andrew W Han; Amy Jo A Johnson; Susan P Holmes
Journal:  Nat Methods       Date:  2016-05-23       Impact factor: 28.547

6.  Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data.

Authors:  Kathrin P Aßhauer; Bernd Wemheuer; Rolf Daniel; Peter Meinicke
Journal:  Bioinformatics       Date:  2015-05-07       Impact factor: 6.937

7.  How fast-growing bacteria robustly tune their ribosome concentration to approximate growth-rate maximization.

Authors:  Evert Bosdriesz; Douwe Molenaar; Bas Teusink; Frank J Bruggeman
Journal:  FEBS J       Date:  2015-03-26       Impact factor: 5.542

8.  Exact sequence variants should replace operational taxonomic units in marker-gene data analysis.

Authors:  Benjamin J Callahan; Paul J McMurdie; Susan P Holmes
Journal:  ISME J       Date:  2017-07-21       Impact factor: 10.302

9.  Transient inability to manage proteobacteria promotes chronic gut inflammation in TLR5-deficient mice.

Authors:  Frederic A Carvalho; Omry Koren; Julia K Goodrich; Malin E V Johansson; Ilke Nalbantoglu; Jesse D Aitken; Yueju Su; Benoit Chassaing; William A Walters; Antonio González; Jose C Clemente; Tyler C Cullender; Nicolas Barnich; Arlette Darfeuille-Michaud; Matam Vijay-Kumar; Rob Knight; Ruth E Ley; Andrew T Gewirtz
Journal:  Cell Host Microbe       Date:  2012-08-02       Impact factor: 21.023

10.  Assessing similarities and disparities in the skin microbiota between wild and laboratory populations of house mice.

Authors:  Meriem Belheouane; Marie Vallier; Aleksa Čepić; Cecilia J Chung; Saleh Ibrahim; John F Baines
Journal:  ISME J       Date:  2020-06-09       Impact factor: 10.302

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