2.6 Core analysis, differentially abundant taxa and functional
prediction
Shared ASVs among sources of each site were visualized with the
“MicEco” package. Core communities were defined to facilitate the
interpretation of host and environmental microbiota. ASVs being present
in at least 70 % of samples were considered as core and rare ASVs were
those that were present in fewer than 30 % of samples (Björk et al.,
2018). All other ASVs were considered transient. Indicator ASVs were
identified with the multipatt function with the “indicspecies”
package (Cáceres et al., 2023). Differentially abundant ASVs between
sources were also identified with the ANOVA-Like Differential Gene
Expression Analysis (ALDEx2) with the “ALDEx2” package (Fernandes et
al., 2014).
Phylogenetic Investigation of Communities by Reconstruction of
Unobserved States (PICRUSt2) was used to predict physiological and
metabolic functions of the host and environment microbiota based on ASVs
generated from the QIIME2 DADA2 pipeline (Douglas et al., 2020; Langille
et al., 2013). This procedure predicts the relative abundance of
functional genes (expressed as Kegg Orthologs–KOs) in a 16S ASV
community from the phylogenetic conservation of these genes in all
currently sequenced and assembled prokaryotic genomes. Quality control
was implemented by computing weighted nearest sequenced taxon index
(NSTI) values of each ASV. NSTI evaluates the prediction accuracy of
PICRUSt because it reflects the average genetic distance (measured as
number of substitutions per site) between each ASV against a reference
genome (Douglas et al., 2020; Langille et al., 2013). NSTI values higher
than 2 were eliminated following the developer’s guidelines (Douglas et
al., 2020). PERMANOVA with 999 permutations was adopted to compare
functional pathways between sources and sites. Potential differentially
abundant functional MetaCyc pathways between sources were analysed by
ALDEx2. Those that were significantly differentially abundant (p
< 0.01) were then visualized with the “ComplexHeatmap”
package (Gu et al., 2016). All R packages mentioned were implemented in
RStudio ver. 1.2.5019. In order to support and facilitate scientific
reproducibility, all analyses performed were included in the script as
part of the supplementary materials.