Computational protocol: Deletion of the Toll-Like Receptor 5 Gene Per Se Does Not Determine the Gut Microbiome Profile That Induces Metabolic Syndrome: Environment Trumps Genotype

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Protocol publication

[…] Littermate mice were separately housed based on genotypes. To assess the gut microbiome, genomic DNA was extracted from the cecal contents of WT or TLR5KO2 mice (n = 10 per group) using ZR Fecal DNA Kit (ZYMO research corp. Cat#: D6010) and stored in -80 freezer until used for sequencing. Pyrosequencing of the 16S RNA gene was performed on microbiota DNA samples using the bacterial tag-encoded GS FLX-Titanium amplicon with primers 28f (5’- GAGTTTGATCNTGGCTCAG-3’) and 519r (5’-GTNTTACNGCGGCKGCTG-3’). The sequence data are uploaded into the Sequence Read Archive (SRA) ( and accessible through the accession number SRR3114140. Sequences were processed with the QIIME software package[]. Briefly, barcodes and primers were depleted and sequences with an average quality score of less than 30 were removed from the dataset. Sequences shorter than 200 base pairs, containing ambiguous base-pair designation or greater than 8 homopolymers were also removed to maintain sequencing quality and aligned to the V1-V3 region of bacterial 16S RNA gene using the SILVA reference alignment as a template[]. Chimeric sequences were removed using the UCHIME algorithm[]. A distance matrix was created with a threshold of 0.15 and was used to cluster sequences into operational taxonomic units (OTU) using the Mothur software package algorithm for average neighbor grouping with a cutoff of 95% sequence similarity. Finally, OTUs were classified into consensus taxonomies using the SILVA database. Within community diversity (α-diversity) was calculated using QIIME. Alpha rarefaction curve was generated using Chao estimator of species richness[] with ten sampling repetitions at each sampling depth. An even depth of 1,500 sequences per sample was used for calculation of richness and diversity indices. To compare microbial composition between samples, β-diversity was measured by calculating the weighted and unweighted Unifrac distances[] based on proportion of OTUs and phylogenetic relation between those OTUs. Principal coordinate analysis (PCoA) was applied on resulting distance matrices using PRIMER v6 software[]. Permutational multivariate analysis of variance (PERMANOVA)[] was used to calculate P-values and test for significant differences of β-diversity among treatment groups. Label permutations were used in PERMANOVA to estimate the distribution of test statistics under the null hypothesis that within-group distances are not significantly different from between-group distances. Both weighted and unweighted Unifrac distances were used to compute the test statistic and P-values to determine the significance of the statistic[]. [...] We used PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States, [], a bioinformatics tool that predicts gene family based on 16S gene surveys for bacterial community. For the analysis, OTUs were closed-reference picked against updated Greengenes database (reference) using QIIME v1.8 according to the online protocol [, ]. The resulting closed-reference OTU table was then input into the PICRUSt pipeline. The accuracy of metagenome predictions was judged by how closely related the microbes in a given sample are to microbes with sequenced genome representatives, as measured by Nearest Sequenced Taxon Index (NSTI), with lower values indicating a closer mean relationship[, ]. Analyzing PICRUSt predicted metagenomes was implemented in a graphical software package, STAMP (Statistical Analysis of Metagenomic Profiles, []. Principal coordinate analysis (PCoA) was applied on distance matric of resulting functional abundances using PRIMER v6 software[]. Permutational multivariate analysis of variance (PERMANOVA)[] was used to calculate P-values and test for significant differences of functional diversity among treatment groups. […]

Pipeline specifications

Software tools QIIME, UCHIME, mothur, UniFrac, PICRUSt, STAMP
Databases SRA
Applications Phylogenetics, Metagenomic sequencing analysis, 16S rRNA-seq analysis
Organisms Mus musculus
Diseases Cardiovascular Diseases, Diabetes Mellitus, Hyperlipidemias, Hypertension, Metabolic Diseases, Machado-Joseph Disease