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.