Sequence variant analysis reveals poor correlations in microbial taxonomic abundance between humans and mice after gnotobiotic transfer
Transplanting human gut microbiotas into germ-free (GF) mice is a popular approach to disentangle cause-and-effect relationships between enteric microbes and disease. Algorithm development has enabled sequence variant (SV) identification from 16S rRNA gene sequence data. SV analyses can identify whi...
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Published in | The ISME Journal Vol. 14; no. 7; pp. 1809 - 1820 |
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Main Authors | , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
London
Nature Publishing Group UK
01.07.2020
Oxford University Press |
Subjects | |
Online Access | Get full text |
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Summary: | Transplanting human gut microbiotas into germ-free (GF) mice is a popular approach to disentangle cause-and-effect relationships between enteric microbes and disease. Algorithm development has enabled sequence variant (SV) identification from 16S rRNA gene sequence data. SV analyses can identify which donor taxa colonize recipient GF mice, and how SV abundance in humans is replicated in these mice. Fecal microbiotas from 8 human subjects were used to generate 77 slurries, which were transplanted into 153 GF mice. Strong correlations between fecal and slurry microbial communities were observed; however, only 42.15 ± 9.95% of SVs successfully transferred from the donor to the corresponding recipient mouse. Firmicutes had a particularly low transfer rate and SV abundance was poorly correlated between donor and recipient pairs. Our study confirms human fecal microbiotas colonize formerly GF mice, but the engrafted community only partially resembles the input human communities. Our findings emphasize the importance of reporting a standardized transfer rate and merit the exploration of other animal models or
in silico
tools to understand the relationships between human gut microbiotas and disease. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
ISSN: | 1751-7362 1751-7370 1751-7370 |
DOI: | 10.1038/s41396-020-0645-z |