BACKGROUND: Nurse researchers are well poised to study the connection of the microbiome to health and disease. Evaluating published microbiome results can assist with study design and hypothesis generation. OBJECTIVES: This article aims to present and define important analysis considerations in microbiome study planning and to identify genera shared across studies despite methodological differences. This methods article will highlight a workflow that the nurse scientist can use to combine and evaluate taxonomy tables for microbiome study or research proposal planning. METHODS: We compiled taxonomy tables from 13 published gut microbiome studies that had used Ion Torrent sequencing technology. We searched for studies that had amplified multiple hypervariable (V) regions of the 16S rRNA gene when sequencing the bacteria from healthy gut samples. RESULTS: We obtained 15 taxonomy tables from the 13 studies, comprised of samples from four continents and eight V regions. Methodology among studies was highly variable, including differences in V regions amplified, geographic location, and population demographics. Nevertheless, of the 354 total genera identified from the 15 data sets, 25 were shared in all V regions and the four continents. When relative abundance differences across the V regions were compared, Dorea and Roseburia were statistically different. Taxonomy tables from Asian subjects had increased average abundances of Prevotella and lowered abundances of Bacteroides compared with the European, North American, and South American study subjects. DISCUSSION: Evaluating taxonomy tables from previously published literature is essential for study planning. The genera found from different V regions and continents highlight geography and V region as important variables to consider in microbiome study design. The 25 shared genera across the various studies may represent genera commonly found in healthy gut microbiomes. Understanding the factors that may affect the results from a variety of microbiome studies will allow nurse scientists to plan research proposals in an informed manner. This work presents a valuable framework for future cross-study comparisons conducted across the globe.
BACKGROUND: Nurse researchers are well poised to study the connection of the microbiome to health and disease. Evaluating published microbiome results can assist with study design and hypothesis generation. OBJECTIVES: This article aims to present and define important analysis considerations in microbiome study planning and to identify genera shared across studies despite methodological differences. This methods article will highlight a workflow that the nurse scientist can use to combine and evaluate taxonomy tables for microbiome study or research proposal planning. METHODS: We compiled taxonomy tables from 13 published gut microbiome studies that had used Ion Torrent sequencing technology. We searched for studies that had amplified multiple hypervariable (V) regions of the 16S rRNA gene when sequencing the bacteria from healthy gut samples. RESULTS: We obtained 15 taxonomy tables from the 13 studies, comprised of samples from four continents and eight V regions. Methodology among studies was highly variable, including differences in V regions amplified, geographic location, and population demographics. Nevertheless, of the 354 total genera identified from the 15 data sets, 25 were shared in all V regions and the four continents. When relative abundance differences across the V regions were compared, Dorea and Roseburia were statistically different. Taxonomy tables from Asian subjects had increased average abundances of Prevotella and lowered abundances of Bacteroides compared with the European, North American, and South American study subjects. DISCUSSION: Evaluating taxonomy tables from previously published literature is essential for study planning. The genera found from different V regions and continents highlight geography and V region as important variables to consider in microbiome study design. The 25 shared genera across the various studies may represent genera commonly found in healthy gut microbiomes. Understanding the factors that may affect the results from a variety of microbiome studies will allow nurse scientists to plan research proposals in an informed manner. This work presents a valuable framework for future cross-study comparisons conducted across the globe.
Authors: Katherine A Maki; Ana F Diallo; Mark B Lockwood; Alexis T Franks; Stefan J Green; Paule V Joseph Journal: Biol Res Nurs Date: 2018-11-08 Impact factor: 2.522
Authors: David Taras; Rainer Simmering; Matthew D Collins; Paul A Lawson; Michael Blaut Journal: Int J Syst Evol Microbiol Date: 2002-03 Impact factor: 2.747
Authors: Sara Vieira-Silva; Jeroen Raes; Mireia Valles-Colomer; Gwen Falony; Youssef Darzi; Ettje F Tigchelaar; Jun Wang; Raul Y Tito; Carmen Schiweck; Alexander Kurilshikov; Marie Joossens; Cisca Wijmenga; Stephan Claes; Lukas Van Oudenhove; Alexandra Zhernakova Journal: Nat Microbiol Date: 2019-02-04 Impact factor: 17.745
Authors: Jennifer J Barb; Andrew J Oler; Hyung-Suk Kim; Natalia Chalmers; Gwenyth R Wallen; Ann Cashion; Peter J Munson; Nancy J Ames Journal: PLoS One Date: 2016-02-01 Impact factor: 3.240