Theresa Lüth1, Simon Graspeuntner2, Kay Neumann3, Laura Kirchhoff4, Antonia Masuch3, Susen Schaake1, Mariia Lupatsii4, Ronnie Tse1, Georg Griesinger3, Joanne Trinh1, Jan Rupp4,5. 1. Institute of Neurogenetics, University of Lübeck, Lübeck, Germany. 2. Department of Infectious Diseases and Microbiology, University of Lübeck, Lübeck, Germany. Simon.Graspeuntner@uksh.de. 3. Department of Gynaecological Endocrinology and Reproductive Medicine, University Hospital of Schleswig-Holstein, Campus Lübeck, Lübeck, Germany. 4. Department of Infectious Diseases and Microbiology, University of Lübeck, Lübeck, Germany. 5. German Center for Infection Research, Partner Site Hamburg-Lübeck-Borstel-Riems, Lübeck, Germany.
Abstract
PURPOSE: Subclinical alterations of the vaginal microbiome have been described to be associated with female infertility and may serve as predictors for failure of in vitro fertilization treatment. While large prospective studies to delineate the role of microbial composition are warranted, integrating microbiome information into clinical management depends on economical and practical feasibility, specifically on a short duration from sampling to final results. The currently most used method for microbiota analysis is either metagenomics sequencing or amplicon-based microbiota analysis using second-generation methods such as sequencing-by-synthesis approaches (Illumina), which is both expensive and time-consuming. Thus, additional approaches are warranted to accelerate the usability of the microbiome as a marker in clinical praxis. METHODS: Herein, we used a set of ten selected vaginal swabs from women undergoing assisted reproduction, comparing and performing critical optimization of nanopore-based microbiota analysis with the results from MiSeq-based data as a quality reference. RESULTS: The analyzed samples carried varying community compositions, as shown by amplicon-based analysis of the V3V4 region of the bacterial 16S rRNA gene by MiSeq sequencing. Using a stepwise procedure to optimize adaptation, we show that a close approximation of the microbial composition can be achieved within a reduced time frame and at a minimum of costs using nanopore sequencing. CONCLUSIONS: Our work highlights the potential of a nanopore-based methodical setup to support the feasibility of interventional studies and contribute to the development of microbiome-based clinical decision-making in assisted reproduction.
PURPOSE: Subclinical alterations of the vaginal microbiome have been described to be associated with female infertility and may serve as predictors for failure of in vitro fertilization treatment. While large prospective studies to delineate the role of microbial composition are warranted, integrating microbiome information into clinical management depends on economical and practical feasibility, specifically on a short duration from sampling to final results. The currently most used method for microbiota analysis is either metagenomics sequencing or amplicon-based microbiota analysis using second-generation methods such as sequencing-by-synthesis approaches (Illumina), which is both expensive and time-consuming. Thus, additional approaches are warranted to accelerate the usability of the microbiome as a marker in clinical praxis. METHODS: Herein, we used a set of ten selected vaginal swabs from women undergoing assisted reproduction, comparing and performing critical optimization of nanopore-based microbiota analysis with the results from MiSeq-based data as a quality reference. RESULTS: The analyzed samples carried varying community compositions, as shown by amplicon-based analysis of the V3V4 region of the bacterial 16S rRNA gene by MiSeq sequencing. Using a stepwise procedure to optimize adaptation, we show that a close approximation of the microbial composition can be achieved within a reduced time frame and at a minimum of costs using nanopore sequencing. CONCLUSIONS: Our work highlights the potential of a nanopore-based methodical setup to support the feasibility of interventional studies and contribute to the development of microbiome-based clinical decision-making in assisted reproduction.
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