2.9. DNA extraction and Illumina MiSeq sequencing
Faecal DNA was isolated as
described(Rodriguez-Nogales et al.,
2017). The resultant sequences were quality-filtered, clustered, and
taxonomically allocated against the SILVA database with 97% similarity
threshold using the QIIME software package (Version 1.9.1) (Knight Lab,
San Diego, CA, USA). The resulting abundance was used to compute the
total bacterial diversity in an equivalent manner.
DNA from fecal contents was isolated following the procedure described
by Rodríguez-Nogales et al .
2017(Rodriguez-Nogales et al., 2017).
Total DNA from faecal samples was PCR amplified using primers targeting
regions flanking the variable regions 4 to 5 of the bacterial 16S rRNA
gene (V4-5), gel purified, and analyzed using multiplexing on the
Illumina MiSeq machine. PCR reactions from the same samples were cleaned
and then normalized using the high-throughput Invitrogen SequalPrep
96-well Plate kit. Later, a library from the samples was made
fluorometrically to be quantified fluorometrically before sequencing.
The resulting sequences were completed, quality-filtered, clustered, and
taxonomically assigned on the basis of 97% similarity level against the
RDP (Ribosomal Database Project) by the QIIME software package (Version
1.9.1) (Knight Lab, San Diego, CA, USA). Sequences were selected to
estimate the total bacterial diversity of the DNA samples in a
comparable manner and were trimmed to remove barcodes, primers,
chimeras, plasmids, mitochondrial DNA and any non-16S bacterial reads
and sequences <150 bp.
Alpha diversity (α-diversity) indices and bacterial abundance data were
compared using Kruskal–Wallis test followed by pairwise Mann–Whitney U
comparison. Resulting p-values were corrected by the Bonferroni method.
Analysis of α-diversity was performed on the output normalized data,
which were evaluated using Mothur. The biomarkers for both species
taxonomic analysis and functional pathways via calculation of the linear
discriminant analysis (LDA) score among different phenotype groups were
calculated by LEfSe (linear discriminatory analysis (LDA) effect size)
(Version 1.0). 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 β-diversity were tested by permutational multivariate
analysis of variance (PERMANOVA) using the web-based algorithm tool
Microbiome Analyst (Dhariwal et al.,
2017). The data are expressed as the mean ± standard error of the mean
(SEM). Experimental data were analysed 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. Metabolic phenotypes were obtained by genera
classification according to their primary fermentation products as
acetate, butyrate, lactate, or other producers using Bergey’s Manual of
Systematic Bacteriology(Boone, Castenholz,
& Garrity, 2001). The genera with unknown or ambiguous fermentative
products were excluded. Major genera were classified according to the
dominant fermentation end-product(s).
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
“R”. 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
analysed at a Spearman’s correlation cut off 0.7 andp<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.