Open AccessPattern extraction of structural responses of gut microbiota to rotavirus infection via multivariate statistical analysis of clone library data
Author(s) -
Zhang Meiling,
Zhang Menghui,
Zhang Chenhong,
Du Huimin,
Wei Guifang,
Pang Xiaoyan,
Zhou Haokui,
Liu Binbin,
Zhao Liping
Publication year - 2009
Publication title -
fems microbiology ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.377
H-Index - 155
eISSN - 1574-6941
pISSN - 0168-6496
DOI - 10.1111/j.1574-6941.2009.00694.x
Subject(s) - biology , bacteroides fragilis , rotavirus , bacteroides , clone (java method) , gut flora , microbiology and biotechnology , 16s ribosomal rna , ribosomal rna , feces , reoviridae , genetics , immunology , bacteria , gene , virus , antibiotics
This study developed a new statistical strategy for analyzing clone library data to observe whether there is a defined pattern in structural responses of gut microbiota to environmental perturbations. A large clone library of genus Bacteroides was constructed with fecal samples for each subject in rotavirus‐infected (Group R) and healthy children (Group H). In all, 665 clones of the 12 Group H subjects and 284 clones of the nine Group R subjects were sequenced and classified into 34 operational taxonomic units (OTUs) with a similarity cutoff at 98%. Partial least squares‐discriminant analysis was used to observe the change of the Bacteroides spp. composition caused by rotavirus infection and to identify the most relevant species contributing to this shift. It was revealed that H subjects and R subjects were well separated. Bacteroides vulgatus, Bacteroides stercoris and Bacteroides fragilis were identified as the most important discriminating OTUs between two groups. The increased abundance of B. fragilis and the decreased populations of B. vulgatus and B. stercoris in infected guts observed in this study were in agreement with previous culture‐based studies. The strategy developed in this work can be used to reveal patterns in structural responses of gut microbiota to environmental perturbations from large‐scale 16S rRNA gene‐based sequencing data.