Computational protocol: Characterization of Microbial Dysbiosis and Metabolomic Changes in Dogs with Acute Diarrhea

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Protocol publication

[…] A subset of 6,900 sequences per sample was randomly selected to account for unequal sequencing depth across samples. Differences in microbial communities between healthy dogs, dogs with NHD, and dogs with AHD were analyzed using the phylogeny-based unweighted UniFrac distance metric, PCoA plots, and rarefaction curves generated by QIIME []. Rarefaction curves and PCoA plots show alpha (i.e., Chao 1, Shannon Index, and Observed Species) and beta (i.e., microbial community distance matrix) diversity, respectively. ANOSIM (Analysis of Similarity) within the software package PRIMER 6 (PRIMER-E Ltd., Luton, UK) was used to determine significant differences in microbial communities between healthy dogs and diseased dogs. To visualize the relative abundance of bacterial families for individual fecal samples, heat maps were generated in NCSS 2007 (NCSS, Kaysville, Utah).All datasets were tested for normality using the Shapiro-Wilk test (JMP 10, SAS software Inc.). Because most datasets did not meet the assumptions of normal distribution, comparisons between healthy and disease groups were determined using non-parametric Kruskal-Wallis tests (healthy dogs vs. dogs with NHD vs. dogs with AHD) or a Mann-Whitney U test (healthy dogs vs. dogs with acute diarrhea AD [dogs with NHD and dogs with AHD combined]). Taxa that were present in at least 70% of dogs (either healthy or diseased) were included in 454-pyrosequencing data analysis. The resulting p-values of the Kruskal-Wallis tests or Mann-Whitney U test were adjusted for multiple comparisons using the Benjamini & Hochberg’s False Discovery Rate (FDR), and an adjusted p<0.05 was considered statistically significant []. A Dunn’s post-test was used to determine which disease types were significantly different if applicable. [...] The software PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) was used to make functional gene content predictions based on 16S rRNA gene data present in the Greengenes database []. PICRUSt is freely available online in the Galaxy workflow framework and can also be used through the QIIME open-source pipeline [, ]. [...] Linear discriminant analysis effect size (LEfSe) was used to elucidate taxa and genes associated with healthy or diseased states. For bacterial groups, the LDA score threshold was set to > 3.5; for functional genes and their specific KEGG orthologs the LDA score threshold was set to > 2.5. LEfSe is freely available online in the Galaxy workflow framework [, ]. [...] Waters raw files were converted to netCDF format, and XCMS [] peak detection, retention time alignment, and feature grouping were performed on both the low and high collision energy channels (MS and MSe). The datasets were separated following alignment, normalized to total extracted ion signal, averaged by injection replicate, and the idMS/MS workflow described previously was applied to all significant features for generation of indiscriminant MS/MS (idMS/MS) spectra for library searching and compound identification []. For identification of spectra, recreated idMS/MS spectra were searched against the NIST ‘12 MS/MS database, Metlin, and Massbank. idMS/MS spectra which matched database MS/MS spectra were assigned a level 2 identification confidence []. For urine, concentrations of compound within each urine sample were normalized to urine creatinine concentration and expressed as ratios. Serum was normalized by volume. After data pre-processing, principle component analysis and univariate analysis was conducted using the web-based metabolomic data processing tool MetaboAnalyst 2.0 [, ]. […]

Pipeline specifications

Software tools UniFrac, QIIME, PICRUSt, Galaxy, LEfSe
Databases AHD
Applications Phylogenetics, Metagenomic sequencing analysis, 16S rRNA-seq analysis
Organisms Canis lupus familiaris
Diseases Multiple Sclerosis