2.5. Statistics
All results are expressed as the mean ± SEM. Differences between means
were tested for statistical significance using a one-way analysis of
variance (ANOVA) and post-hoc least significance tests. Differences
between proportions were analyzed with the chi-squared test. All
statistical analyses were carried out with the GraphPad 8 software
package (GraphPad Software, Inc., La Jolla, CA, USA), with statistical
significance set at P< 0.05.
For microbiota evaluation, alpha diversity indices and bacterial
abundance data of the different groups were compared using
Kruskal–Wallis test followed by pairwise Mann–Whitney U comparison.
Resulting p-values were corrected by Bonferroni method. Analysis of
α-diversity was performed on the output normalized data, which were
evaluated using Mothur. LEfSE (linear discriminatory analysis (LDA)
effect size) (Version 1.0) was employed to identify biomarkers for both
species taxonomic analysis and functional pathways via calculation of
the linear discriminant analysis (LDA) score among different phenotype
groups. Principal coordinate analysis (PCoA) was performed to identify
principal coordinates and visualize β-diversity in complex
multidimensional data of bacteriomes from different groups of mice.
Differences in beta-diversity were tested by permutational multivariate
analysis of variance (PERMANOVA) using the web-based algorithm tool
Microbiome Analyst (Dhariwal, Chong,
Habib, King, Agellon & Xia, 2017;
Rodríguez-Nogales et al., 2015). The data
are expressed as the mean ± standard error of the mean (SEM).
Experimental data were analyzed in GraphPad Prism 8 (GraphPad Software,
Inc., La Jolla, CA, USA) by one-way or two-way ANOVA or Pearson
correlation. Data with P < 0.05 were considered statistically
significant.
Hierarchical clustering and heat maps depicting the metabolic
parameters, patterns of abundance and log values were constructed within
the “R” statistical software package (version 3.6.0;
https://www.r-project.org/) using the ”pheatmap”, “heatmap.2” and
“ggplots” packages. Spearman’s correlations of bacterial taxa with
metabolic parameters and KEGG metagenomic functions were calculated in
the “R” statistical software package (version 3.6.0;
https://www.r-project.org/). Co-occurrence networks between taxa and
functions were calculated by using the open-source software Gephi
(https://gephi.org/) to find differential associations caused by similar
alterations in the proportion of different taxa and their predicted
functions between different groups of mice. Modularity-based
co-occurrence networks were analyzed at a Spearman’s correlation cut off
0.7 and p-value < 0.01; the selected correlation data were
imported into the interactive platform, Gephi (version 0.9.2;
https://gephi.org), and the following modularity analyses and keystone
node identification were conducted within Gephi.