Computational protocol: Body Mass Index and Sex Affect Diverse Microbial Niches within the Gut

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Protocol publication

[…] Genomic DNA was extracted from brush samples by using the QIAamp DNA Microbiome Kit (QIAGEN, Hilden, Germany). Collected samples were dislodged from brushes by vigorous agitation after adding 1 ml of phosphate buffered saline (PBS) to 15 mL tube. Subsequent steps were performed according to manufacturer’s recommendations. Briefly, depletion of host cells was performed by adding lysis buffer and benzonase to samples. Bacterial cells lysis was then achieved using Pathogen Lysis Tubes (containing large beads) and a lysis buffer in the TissueLyser LT instrument (QIAGEN). Lysates were transferred to QIAamp UCP Mini Columns and bound DNA was eluted in 50 μl of buffer. Bacterial genomic DNA in stool samples was extracted by using the Spin stool DNA kit (Stratec Molecular, Berlin, Germany), according to the manufacturer’s instructions. Briefly, after homogenizing fecal samples in the lysis buffer for inactivating DNases, Zirconia Beads II were added for a complete lysis of bacterial cells by using TissueLyser LT. Bacterial lysates were then mixed with InviAdsorb reagent, a step designed to remove PCR inhibitors. Bacterial DNA, bound to the membrane RTA Spin Filter, was eluted in 100 μl of buffer. Library preparation and 16S rRNA NGS were performed as previously reported, using the Illumina MiSeq platform (; ).Next-generation sequencing raw reads were processed merging read pairs by using PandaSeq software (“PAired-eND Assembler for DNA sequences”) () and quality-filtered using the “” utility of the QIIME suite (), filtering out sequences having more than 25% nucleotides with a phred score of 3 or less. Quality-filtered reads were analyzed with the standard QIIME pipeline. Sequences were grouped into OTUs (operational taxonomic units) by using UCLUST () with 97% similarity threshold and taxonomically classified against the 13.8 release of the Greengenes bacterial 16S rRNA database by RDP classifier () at 50% confidence. Singletons (i.e., OTUs having only 1 supporting read along the whole 80-samples dataset) were considered possible chimeras and thus discarded. Sequencing libraries for luminal microbiota were subsampled to at most 100,000 reads per sample.Raw sequence data have been deposited in NCBI Short-Reads Archive (SRA) under BioProject PRJNA401981. [...] Statistical analysis was performed using the statistical software Matlab (Natick, MA, United States) and R platform. When performing analysis on LAM and MAM population separately, obvious outlier samples were removed from the dataset. This included five luminal samples (F3, F12, F15, F21, and F25) and three mucosal samples (M10, M17, and M29), all characterized by a very low biodiversity, with very few bacterial groups accounting for the majority of their composition. Sample biodiversity (i.e., alpha diversity evaluation) was estimated according to different microbial diversity metrics (i.e., chao1, Shannon index, observed species and Faith’s phylogenetic distance). Inter-sample diversity (i.e., beta-diversity) was calculated using both weighted and unweighted Unifrac metrics () and Principal Coordinates Analysis (PCoA). Data separation was tested with a permutation test with pseudo F-ratios (function “adonis”) and the significant clustering of groups was evaluated with analysis of similarities (ANOSIM) in the “vegan” package (). Indicator species analysis was performed using the “indicspecies” package (). For relative abundance analysis, a Mann–Whitney U-test was used; a p-value < 0.05 was chosen as threshold for statistical significance.The relationships between differential OTUs were evaluated by Spearman’s rank correlation. Sequences alignment were performed by using the basic local alignment tool (BLAST) program (), from the National Center For Biotechnology Information BLAST website, against the “nr” database with default settings.Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) 1.0.0 () was applied to predict metagenome function from the 16S rRNA gene data; Bray–Curtis distances were used to determine similarity of samples based on metagenomic composition. Differences in the taxa and predicted molecular functions were analyzed by the linear discriminant analysis (LDA) effect size (LEfSe) () with default settings (Alpha value for the factorial Kruskal–Wallis test among classes = 0.05; Threshold on the logarithmic LDA score for discriminative features = 2.0). […]

Pipeline specifications

Software tools PANDAseq, QIIME, UCLUST, RDP Classifier, UniFrac, vegan, PICRUSt, LEfSe
Databases Greengenes
Applications Phylogenetics, Metagenomic sequencing analysis, 16S rRNA-seq analysis
Organisms Escherichia coli, Faecalibacterium prausnitzii, Homo sapiens, Bifidobacterium adolescentis
Chemicals Amino Acids, Oxygen, Lamivudine