Male C57BL/6 mice (7–9 weeks old) were purchased from Charles River (Canada) and maintained in the animal care facility at the University of Manitoba. The experimental protocol was approved by the University of Manitoba Animal Ethics Committee (15-010) and the research was conducted according to the Canadian Guidelines for Animal Research (Gauthier, 2002; Demers et al., 2006). Two groups of four and eight mice were studied, one receiving the vehicle solution and one receiving intra-rectal (i.r.) infusion of CST for 6 days. By using mice from the same sex, source, age, and keeping them in co-housed conditions while receiving the same food, the environmental effects on gut microbiota were minimized.

The CST (Human CgA_352−372: SSMKLSFRARAYGFRGPGPQL) (Mahata et al., 2010) was used (Biopeptide Co., Inc., San Diego, CA, USA), and the peptide was injected (i.r.) at 1.5 mg/per kg body weight per day for 6 days. Saline (0.9%) was injected in the control group. Mice were anesthetized using isoflurane (Abbott, Toronto, ON, Canada). PE-90 tubing (10 cm long; ClayAdam, Parisppany, NJ, USA), which was attached to a tuberculin syringe (BD, Mississauga, ON, Canada), was inserted 3.5 cm into the colon. The dose was determined according to our previous published study (Rabbi et al., 2014).

Weight loss, stool consistency, and bleeding were assessed daily to determine any possible physical changes in the mice as a result of CST treatment (Cooper et al., 1993). Scores were defined as follows: weight: 0, no loss; 1, 5–10%; 2, 10–15%; 3, 15–20%; and 4, 20% weight loss; stool: 0, normal; 2, loose stool; and 4, diarrhea; and bleeding: 0, no blood; 2, presence of blood; and 4, gross blood. Blood was assessed using the Hemoccult II test (Beckman Coulter, Oakville, ON, Canada).

Samples were collected 6 days post-treatment induction, after euthanasia under isoflurane (Abbot) anesthesia. The macroscopic score was determined on the sacrifice day based on stool consistency, rectal prolapse, and rectal and colonic bleeding. On the day of sacrifice, the colon was opened and approximately a 250 mg fecal sample was collected near the rectal opening. In addition, a portion of the colon tissue was collected within 5 cm from rectal opening. Approximately 50 mg of mucosa scrapings were collected from these colon tissue. All samples were collected in individual collector tubes from each animal and snap frozen in liquid nitrogen and preserved at −80°C until use.

Samples were homogenized at room temperature, and genomic DNA was extracted from mucosa scarpings using a ZR Tissue and Insect DNA extraction Kit (Zymo Research Corp., Orange, CA, USA). Fecal DNA extraction was performed using a ZR fecal DNA extraction kit (Zymo Research Corp., Orange, CA). Both DNA extraction kits had a bead-beating step to mechanically lyse microbial cells. DNA was quantified using a Nanodrop 2000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA). DNA samples were normalized to achieve a concentration of 20 ng/μl, and quality-checked by PCR amplification of 16S rRNA gene using primers 27F (5′-GAAGAGTTTGATCATGGCTCAG-3′) and 342R (5′-CTGCTGCCTCCCGTAG-3′) (Sepehri et al., 2007; Khafipour et al., 2009). Amplicons were verified by agarose gel electrophoresis.

Library construction and Illumina sequencing were performed as described by Derakhshani et al. (2016) Briefly, the V4 region of the 16S rRNA gene was targeted for PCR amplification using modified F515/R806 primers (Caporaso et al., 2012). A reverse PCR primer was indexed with 12-base Golay barcodes to allow for sample multiplexing. The PCR reaction for each sample was performed in duplicate and contained 1.0 μl of pre-normalized DNA, 1.0 μl each of forward and reverse primers (10 μM), 12 μl HPLC grade water (Fisher Scientific, Ottawa, ON, Canada) and 10 μl 5 Prime Hot MasterMix (5 Prime, Inc., Gaithersburg, MD, USA). Reactions consisted of an initial denaturing step at 94°C for 3 min followed by 35 amplification cycles at 94°C for 45 s, 50°C for 60 s and 72°C for 90 s; this was followed by an extension step at 72°C for 10 min in an Eppendorf Mastercycler (Eppendorf, Hamburg, Germany). PCR products were then purified using a ZR-96 DNA Clean-up Kit (ZYMO Research, Irvine, CA, USA) to remove primers, dNTPs and reaction components. The V4 library was then generated by pooling 200 ng of each sample, and quantified using Picogreen dsDNA (Invitrogen, Carlsbad, CA, USA). This was followed by multiple dilution steps using pre-chilled hybridization buffer (HT1) (Illumina, San Diego, CA, USA) to bring the pooled amplicons to a final concentration of 5 pM, and the concentration was measured through optical density using a Qubit 2.0 Fluorometer (Life technologies, Burlington, ON, Canada). Finally, 15% of the PhiX control library was spiked into the amplicon pool to improve the unbalanced and biased base composition, a known characteristic of low diversity 16S rRNA libraries. Customized sequencing primers for read1 (5′-TATGGTAATTGTGTGCCAGCMGCCGCGGTAA-3′), read2 (5′-AGTCAGTCAGCCGGACTACHVGGGTWTCTAAT-3′), and index read (5′-ATTAGAWACCCBDGTAGTCCGGCTGACTGACT-3′) were synthesized and purified using polyacrylamide gel electrophoresis (Integrated DNA Technologies, Coralville, IA, USA) and added to the MiSeq Reagent Kit V2 (300-cycle) (Illumina, CA, USA). The 150 paired-end sequencing reaction was performed on a MiSeq platform (Illumina, CA, USA) at the Gut Microbiome and Large Animal Biosecurity Laboratories, Department of Animal Science, University of Manitoba, Canada. The sequencing data are uploaded into the Sequence Read Archive (SRA) of NCBI (http://www.ncbi.nlm.nih.gov/sra) and are accessible through accession number SRR2830596.

Bioinformatics analyses were performed as described previously (Derakhshani et al., 2016). Briefly, the PANDAseq assembler (Masella et al., 2012) was used to merge overlapping paired-end Illumina fastq files. All the sequences with mismatches or ambiguous calls in the overlapping region were discarded. The output fastq file was then analyzed using downstream computational pipelines in the open source software package QIIME (Caporaso et al., 2010a). Chimeric reads were filtered using UCHIME (Edgar et al., 2011) and sequences were assigned to operational taxonomic units (OTU) using the QIIME implementation of UCLUST (Edgar, 2010) at the 97% pairwise identity threshold. Taxonomies were assigned to the representative sequence of each OTU using an RDP classifier (Wang et al., 2007) and aligned with the Greengenes (v. 13.5) core reference database (DeSantis et al., 2006) using PyNAST algorithms (Caporaso et al., 2010b). The phylogenetic tree was built with FastTree 2.1.3 (Price et al., 2010) for additional comparisons between microbial communities.

Within-community diversity (α-diversity) was calculated using QIIME and differences between control and CST groups were determined using SAS (SAS 9.3). An α rarefaction curve was generated using a Chao 1 estimator of species richness (Chao, 1984) with 10 sampling repetitions at each sampling depth. An even depth of ~15,700 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 (Lozupone and Knight, 2005) using QIIME default scripts. Principal coordinate analysis (PCoA) was applied on the resulting distance matrices to generate two-dimensional plots using PRIMER v6 software (Warwick and Clarke, 2006). Permutational multivariate analysis of variance of Bray-Curtis distance (PERMANOVA; Anderson, 2005) was used to calculate P-values and test for significant differences in β-diversity among treatment groups.

Partial least square discriminant analysis (PLS-DA; SIMCA P+ 13.0, Umetrics, Umea, Sweden) was performed on the genus data to identify the effects of treatments (Li et al., 2012; Derakhshani et al., 2016). The PLS-DA is a particular case of partial least square regression analysis in which Y is a set of variables describing categories of variables on X. In this case, X variables were the bacterial genera and the Y variables were observations of different treatment groups compared together. To avoid over-parameterization of the model, the variable influence on the projection (VIP) value was estimated for each genus, and genera with VIP < 0.50 were removed from the final model (Pérez-Enciso and Tenenhaus, 2003; Verhulst et al., 2011). R² estimate then was used to evaluate the goodness of fit and Q² estimate was used to evaluate the predictive value of the model. The PLS-regression coefficients were used to identify genera that were most characteristic of each treatment group and the results were visualized by PLS-DA loading scatter plots.

The open source software PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of unobserved States; v. 1.0.0-dev) was used to predict the functional capacity of microbiome using 16S rRNA gene sequencing data and Greengenes (v. 13.5) reference database (Langille et al., 2013). To make our open-reference picked OTUs compatible with PICRUSt, all de-novo OTUs were removed and only those that had matching Greengenes identifications were retained. The new OTU table was then used to generate metagenomic data after normalizing the data by copy numbers, and to derive relative Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway abundance (Langille et al., 2013). The KEGG data were analyzed using STAMP (STatistical Analysis of Metage- nomic Profiles; Parks and Beiko, 2010).

The SAS UNIVARIATE procedure (SAS 9.3) was used to test the normality of residuals for α biodiversity data. Non-normally distributed data were log transformed and then used to assess the effect of sampling using the SAS MIXED procedure. The SAS MIXED procedure was used, as described above, to test for significant changes in the proportions of different phyla among the groups of interest. Differences between groups were considered significant at P < 0.05 while trends were observed at P < 0.1.

Over the 6 days of treatment, daily i.r. infusion of the peptide did not show any effect on the weight, stool consistency, and presence of blood in the feces. The data are consistent with our previous findings (Rabbi et al., 2014; data not shown).

After sacrifice, we collected fecal and colon samples from a total of 12 mice. Among these, the control group received normal saline i.r and the others received CST i.r. (1.5 mg per kg body weight for 6 days). During the DNA extraction process, one colonic mucosa sample from the saline-treated group was discarded because of poor quality or purity, resulting in a total of 12 useable fecal samples and 11 useable colonic mucosa samples for Illumina sequencing. For fecal samples, a total of 328,085 sequences were generated. After quality-filtering steps, an average of 27,340 high quality sequences per sample was obtained. For colonic mucosa samples, a total of 207,123 sequences were generated. After quality-filtering steps, an average of 18,829 high-quality sequences per sample was obtained.

Bacterial richness and diversity from both fecal and colonic mucosa samples between control and CST-treated groups were calculated. However, no significant differences were observed in both fecal and colon samples (data not shown).

Bacterial communities from fecal samples of CST-treated mice clustered separately (P < 0.05) from controls suggesting that the treatment modified the fecal bacterial profile (Figure 1). However, there was no significant change in the bacterial community composition in colonic samples in CST-treated mice compared to controls (Figure 2).

In the fecal samples, a total of 10 phyla were identified, of which four phyla were considered to be abundant within the community (≥1%); these included Firmicutes, Bacteroidetes, Proteobacteria, and Deferribacteres. The other six phyla were in low abundance within the community (<1%) and included Actinobacteria, Cyanobacteria, Fibrobacteres, TM7, Tenericutes, and Verrucomicrobia (Table 1). Among the four abundant phyla, CST treatment increased the relative abundance of Bacteroidetes (P < 0.05) and decreased the Firmicutes proportion (P < 0.001) in the feces (Figure 3).

In the colonic mucosa samples, a total of 19 phyla were identified, of which four phyla were considered to be abundant within the community; these included Firmicutes, Bacteroidetes, Proteobacteria, and Deferribacteres. The other 15 phyla were in low abundance within the community, and included Acidobacteria, Actinobacteria, Armatimonadetes, Chlamydiae, Chlorobi, Cyanobacteria, Fibrobacteres, Lentisphaerae, OD1, OP3, Planctomycetes, Spirochaetes, TM7, Tenericutes, and Verrucomicrobia (Table 2). CST treatment had no significant impact on the relative abundance of bacterial phyla (Figure 4).

A total of 86 bacterial taxa were identified. While majority of taxa were classified at the genus or species levels, some were only classified at the phylum (P), class (C), order (O), or family (F) levels. Of the 86 taxa, 54 taxa were considered abundant within the community, while 32 were in low abundance. Results of the relative abundance of various genera with percentages of sequences ≥0.01% of community were analyzed using PLS-DA to identify bacteria that were most characteristic of CST or Control treatments. The PLS-DA analysis showed that the genera Prevotella, Bacteroides, Ovatus, Parabacteroidesdistarosis, Parabacteroides, and Dorea were positively associated with the CST treatment in the fecal samples (R² = 0.94, Q² = 0.57; Figure 5). In addition, members of Alpharoteobacteria (Class), Bacteroidales (Order), RF32 (Order), and YS2 (Order) also showed a positive association with CST treatment in the fecal samples (R² = 0.94, Q² = 0.57). A negative association with the members of Adlercreutzia, Allobaculum, Bacteroidaceae (Family), Clostridia (Class) and Ruminococcaceae (Family) were evident in the fecal samples collected from CST-treated mice (R² = 0.94, Q² = 0.57).

A hundred and seventy nine taxa were identified. Of these 84 taxa were considered abundant within the community, while 95 taxa were in low abundance. The relative abundance of various genera/taxa with sequence percentages ≥0.01% of community were analyzed using PLS-DA to identify bacteria that were most characteristic of the CST or control treatments. The PLS-DA analysis of the colonic mucosa samples showed that genera Bifidobacterium and Stenotrophomonas had a positive association with the CST treatment (R² = 0.32, Q² = 0.242; Figure 6). Members of Bacteroidales (Order), Chitinophagaceae (Family), Clostridiaceae (Family), Clostridiales (Order), Coriobacteriaceae (Family), Pseudomonadaceae (Family), Rikenellaceae (Family), Ruminococcaceae (Family), and YS2 (Order) also showed a positive association with the CST treatment in the colonic mucosa samples (R² = 0.32, Q² = 0.242; Figure 6).

To determine the functional KEGG pathways that could be associated with the observed microbial changes, we compared the functional pathways for the microbiota in the fecal and colonic mucosa samples from the CST-treated group with those of the control mice. Several metabolic pathways were determined. Subsystems or pathways that had a significant positive or negative correlation with CST treatment are shown in Figures 7, 8. In the fecal samples from CST-treated mice, chlorocyclohexane and chlorobenzene degradation were underrepresented (P = 0.015; Figure 7). However, nitrogen metabolism was enriched in the fecal samples from CST-treated mice (P = 0.033; Figure 7). In the colonic mucosa samples from CST treated mice, nicotinate and nicotinamide metabolism, cell division and ribosome biogenesis were enriched compared to the controls (P < 0.05; Figure 8).

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.