Data analysis
After sequencing, reads were pre-processed for quality control by the DADA2 software to denoise, remove primers, de-replicate single-end sequences, remove chimaeras and exclude low quality reads [The most abundant features identified in the negative control were matched with those found in the analyzed BAL samples to exclude the presence of any contamination able to critically impact results [Data were first evaluated by the Kolmogorov–Smirnov test to verify their non-normal distribution. Following taxonomic analysis, differences in alpha and beta diversity for unpaired samples (standard vs. specialized nutrition at T7) were analyzed by using the QIIME 2 software. Alpha diversity analysis was based on the observed features (a measure of species richness based on ASVs abundance) and Shannon (a quantitative measure of both the number of species and the inequality between species abundances) indexes and was carried out using the Mann–Whitney test for unpaired samples. Beta diversity Principal Coordinates Analyses (PCoA) was also performed between the two nutrition groups using Bray–Curtis (a quantitative measure of community dissimilarity), unweighted (a qualitative measure of community dissimilarity incorporating phylogenetic relationships between the features) and weighted (a quantitative measure of community dissimilarity incorporating phylogenetic relationships between the features) UniFrac distances matrices by the QIIME 2 software [Compositional beta diversity analysis was performed by Robust Aitchison Principal Component Analysis with DEICODE through QIIME 2 software. This principal component analysis (PCA) is based on a compositional distance metric which can properly account for the relative changes of microbial taxa abundances. The Robust Aitchison PCA weights more heavily microbes displaying large fold changes across samples and can handle sparse data (zero values) through the use of matrix completion. It can also provide information on the taxonomic abundance changes responsible for sample clustering and can therefore identify the taxa driving the difference between sample groups [