Literature DB >> 28842259

A cytometric approach to follow variation and dynamics of the salivary microbiota.

Susanna van Gelder1, Nicola Röhrig1, Florian Schattenberg2, Nicolas Cichocki2, Joachim Schumann2, Gerhard Schmalz1, Rainer Haak1, Dirk Ziebolz1, Susann Müller3.   

Abstract

Microbial flow cytometry is an established fast and economic technique for complex ecosystem studies and enables visualization of rapidly changing community structures by measuring characteristics of single microbial cells. Cytometric evaluation routines are available such as flowCyBar which are useful for automatic data processing. Here, a cytometric workflow was established which allows to routinely analyze salivary microbiomes on the example of ten oral healthy subjects. First, saliva was collected within a 3-month period, cytometrically analyzed and the evolution of the microbiomes followed as well as the calculation of their intra- and inter-subject similarity. Second, the respective microbiomes were stressed by exposition to high sugar or acid concentrations and immediate changes were recorded. Third, bactericide solutions were tested on their impact on the microbiomes. In all three set ups huge intra-individual variations in cytometric community structures were found to be largely absent, even under stress, while inter-individual diversity was obvious. The bacterial cell counts of saliva samples were found to vary between 3.0×107 and 6.2×108 cells per sample and subject in undisturbed environments. The application of the two bactericides did not cause noteworthy diversity changes but the loss in cell numbers by about 50% was high after treatment. Illumina® sequencing of whole microbiomes or sorted sub-microbiomes revealed typical phyla such as Firmicutes, Proteobacteria, Actinobacteria, Bacteroidetes and Fusobacteria. This approach is useful for fast monitoring of individual salivary microbiomes and automatic calculation of intra- and inter-individual dynamic changes and variability and opens insight into ecological principles leading to their sustainment in their individual environment.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Cell counting; Microbial diversity; Microbial flow cytometry; Mouth microbiome; Saliva

Mesh:

Substances:

Year:  2017        PMID: 28842259     DOI: 10.1016/j.ymeth.2017.08.009

Source DB:  PubMed          Journal:  Methods        ISSN: 1046-2023            Impact factor:   3.608


  6 in total

1.  Characterizing Microbiome Dynamics - Flow Cytometry Based Workflows from Pure Cultures to Natural Communities.

Authors:  Johannes Lambrecht; Florian Schattenberg; Hauke Harms; Susann Mueller
Journal:  J Vis Exp       Date:  2018-07-12       Impact factor: 1.355

2.  Oral Microbiota Display Profound Differential Metabolic Kinetics and Community Shifts upon Incubation with Sucrose, Trehalose, Kojibiose, and Xylitol.

Authors:  Stanley O Onyango; Nele De Clercq; Koen Beerens; John Van Camp; Tom Desmet; Tom Van de Wiele
Journal:  Appl Environ Microbiol       Date:  2020-08-03       Impact factor: 4.792

3.  Flow cytometry can reliably capture gut microbial composition in healthy adults as well as dysbiosis dynamics in patients with aggressive B-cell non-Hodgkin lymphoma.

Authors:  Maren Schmiester; René Maier; René Riedel; Pawel Durek; Marco Frentsch; Stefan Kolling; Mir-Farzin Mashreghi; Robert Jenq; Liangliang Zhang; Christine B Peterson; Lars Bullinger; Hyun-Dong Chang; Il-Kang Na
Journal:  Gut Microbes       Date:  2022 Jan-Dec

4.  The Simplified Human Intestinal Microbiota (SIHUMIx) Shows High Structural and Functional Resistance against Changing Transit Times in In Vitro Bioreactors.

Authors:  Stephanie Serena Schäpe; Jannike Lea Krause; Beatrice Engelmann; Katarina Fritz-Wallace; Florian Schattenberg; Zishu Liu; Susann Müller; Nico Jehmlich; Ulrike Rolle-Kampczyk; Gunda Herberth; Martin von Bergen
Journal:  Microorganisms       Date:  2019-12-03

5.  flowEMMi: an automated model-based clustering tool for microbial cytometric data.

Authors:  Joachim Ludwig; Christian Höner Zu Siederdissen; Zishu Liu; Peter F Stadler; Susann Müller
Journal:  BMC Bioinformatics       Date:  2019-12-09       Impact factor: 3.169

Review 6.  Computational Analysis of Microbial Flow Cytometry Data.

Authors:  Peter Rubbens; Ruben Props
Journal:  mSystems       Date:  2021-01-19       Impact factor: 6.496

  6 in total

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