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Multimodal Approach to Assessment of Fecal Microbiota Donors based on Three Complementary Methods
Methods of stool assessment are mostly focused on next-generation sequencing (NGS) or classical culturing, but only rarely both. We conducted a series of experiments using a multi-method approach to trace the stability of gut microbiota in various donors over time, to find the best method for the pr...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7409046/ https://www.ncbi.nlm.nih.gov/pubmed/32610522 http://dx.doi.org/10.3390/jcm9072036 |
Sumario: | Methods of stool assessment are mostly focused on next-generation sequencing (NGS) or classical culturing, but only rarely both. We conducted a series of experiments using a multi-method approach to trace the stability of gut microbiota in various donors over time, to find the best method for the proper selection of fecal donors and to find “super-donor” indicators. Ten consecutive stools donated by each of three donors were used for the experiments (30 stools in total). The experiments assessed bacterial viability measured by flow cytometry, stool culturing on different media and in various conditions, and NGS (90 samples in total). There were no statistically significant differences between live and dead cell numbers; however, we found a group of cells classified as not-dead-not-alive, which may be possibly important in selection of “good” donors. Donor C, being a regular stool donor, was characterized by the largest number of cultivable species (64). Cultivable core microbiota (shared by all donors) was composed of only 16 species. ANCOM analysis of NGS data highlighted particular genera to be more abundant in one donor vs. the others. There was a correlation between the not-dead-not-alive group found in flow cytometry and Anaeroplasma found by NGS, and we could distinguish a regular stool donor from the others. In this work, we showed that combining various methods of microbiota assessment gives more information than each method separately. |
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