Computational protocol: Dietary Heme Induces Gut Dysbiosis, Aggravates Colitis, and Potentiates the Development of Adenomas in Mice

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Protocol publication

[…] Pre-processing of sequence reads: Forward and reverse 16S rRNA gene sequences obtained from Illumina (available at the Sequence Read Archive SUB2963298) were aligned to each other using the Paired-End Read (PEAR) merger (). Clustering of reads into operational taxonomic units (OTUs): Using the Quantitative Insights into Microbial Ecology (QIIME; ) software, the merged sequences were aligned to the Greengenes database version 13.8 (i.e., “closed reference” approach to clustering) containing the sequences for OTUs devoid of chimeric sequences. We used the 97% similarity database to identify bacteria at the species level. OTUs were filtered to remove taxa present in only one sample and taxa with less than 100 reads across all samples. Samples were confirmed to have a minimum of 1000 reads each.α- and β-diversity analysis: The Shannon index of diversity was calculated using the R package vegan (). To investigate the level of differences between experimental groups (β-diversity or how taxa are shared between groups), we performed Principal Coordinate Analysis (PCoA) using the unweighted UniFrac distance (), with the R package phyloseq (), and analysis of variance (ANOVA) using distance matrices (Adonis) using the R package vegan ().Functional analysis: To infer sample metagenomes from the 16S rRNA gene analysis (i.e., infer the genes present in the microbiota population), we used the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States tool (PICRUSt) () with the Greengenes version 13.5 precalculated files for the Kyoto Encyclopedia of Genes and Genomes (KEGG) genes and pathways (). Since the Greengenes OTU database does not change from version 13.5 to 13.8, we used the results from the clustering described above. The PICRUSt results were then analyzed using linear discriminant analysis effect size (LEfSe) to identify microbial functions that were significantly different in their abundance between groups. FishTaco was used to link taxonomic and functional shifts in the microbiome (). The R programming environment () was used to generate the graphical outputs. […]

Pipeline specifications

Software tools PEAR, QIIME, UniFrac, phyloseq, PICRUSt, LEfSe
Databases SRA
Applications Phylogenetics, Metagenomic sequencing analysis, 16S rRNA-seq analysis
Diseases Adenoma, Colitis, Colorectal Neoplasms, Inflammatory Bowel Diseases, Drug-Related Side Effects and Adverse Reactions
Chemicals Azoxymethane, Heme, Iron