Table 1.
Changes of hematocrit (Hct) and hemoglobin (Hb) content in BABL/c mouse (n = 6).
Fig 1.
Rarefaction curves of OTUs clustered at 97% sequence identity across the sample. The sample labeled with NC1, NC2, NC3, NC4, NC5 and NC6 correspond to six replicates of NC group; HC1, HC2, HC3, HC4, HC5 and HC6 correspond to six replicates of HC group.
Table 2.
Summary of HiSeq sequencing data.
Fig 2.
Non-Metric Multi-Dimensional Scaling (NMDS) plot (A) and ANOSIM analysis (B). NMDS is a simple method for visual interpretations to compare the overall structure of fecal microbiota between two samples while ANOSIM is used to statistically test the significant difference between groups.
Fig 3.
Relative abundance of bacterial phyla (A) in fecal (Top 10), and statistical significance between two groups at phylum, class and genus levels (B). Phylogenetic groups accounting for unclassified sequences are summarized in the artificial group ‘others’. Statistical differences between two groups were tested using t-test. Differences were considered significant at P < 0.05.
Fig 4.
Phenotypic prediction based on BugBase analysis.
BugBase is a bioinformatics tool that infers community-wide phenotypes, and predict phenotypic differences from 16S rRNA sequence data. BugBase identified that phenotypes associated with aerobic, anaerobic, facultatively anaerobic, potentially pathogenic, stress tolerance, mobile element, biofilms formation, gram negative bacteria and gram positive bacteria.
Fig 5.
Bacterial gene functions were predicted from 16S rRNA gene-based microbial compositions using the PICRUSt algorithm to make inferences from KEGG annotated databases.
Data from PICRUSt were imported into the statistical analysis of metagenomic profiles (STAMP) (version 2.1.3) package for statistical analysis and visualization. Differences were considered significant at P < 0.05 using t-test.