Statistical and computational analyses
Microbial pathways were analysed with reference to the UniPathway
database (23). The R languages’ vegan package was used for calculating
Shannon’s index within sample diversity, α-diversity (24), and
Bray-Curtis dissimilarity between microbiome and microbial pathway
sample pairs. Similarity between pairs of profiles were calculated as
1-d, where d is the associated Bray-Curtis dissimilarity. These
dissimilarity matrices were used to evaluate sample-to sample diversity,
β-diversity, between cohorts and timepoints using the permutational
multivariate analysis of variance (PERMANOVA) implemented in vegan’s
ADONIS function, as well as to visualize the relationship between sample
groups using the principal coordinate analysis implemented in R’s
phyloseq package (25). When PERMANOVA analysis gave a significant
result, the R’s DESeq2 package (26) with test and fitType parameters set
to “Wald” and “parametric”, and with pseudocount of 1 addition to
phylum-/genus-/species-level MetaPhlAn2 RPKM data, were used to identify
differentially-abundant microbiome taxa and microbial pathways. The
p-values following Wald test were corrected for multiple testing using
the Benjamini-Hochberg method.
Kruskal-Wallis test was employed to assess differences between groups at
the taxonomic pathway level. When a significant difference was obtained,
the Mann-Whitney U test with a Holm-Bonferroni correction was used for
post-hoc comparisons. Wilcoxon Signed-Rank test was used to test
differences in alpha-diversity over time using paired baseline and
longitudinal follow-up. Spearman correlations were used to determine the
relationship between microbiome abundance and clinical traits and
inflammatory biomarkers. All statistical tests performed were
two-tailed.