OTU table

QIIIME output showing relative abundance of bacterial taxa in stool from each mous

Impact of obesity and intestinal tumor presence on the fecal metabolome of mice.

<p>Low and high fat fed mice are compared in the first column (A,D,G). Low fat fed and genetically obese mice are compared in the second column (B,E,H). Mice with and without intestinal tumors are compared in the third column (C,F,I). The top row (A-C) shows heat maps of top 25 most significantly different metabolites for each comparison (p<0.05); color represents normalized metabolite concentration from low (blue) to high (red). The second row (D-F) shows discrimination of groups using partial least squares discriminate analysis. The third row (G-I) shows the metabolites most strongly influencing discrimination by the partial least squares discriminate analysis. The Variable Importance In Projection (VIP) score is the weighted sum of squares for the partial least-squares loadings with the amount of variance explained by each component taken into account.</p

Meta and tumor data

File includes genotype and treatment data as well as tumor, weight, MRI, glucose, insulin and colonic cytokine dat

Diet- and Genetically-Induced Obesity Differentially Affect the Fecal Microbiome and Metabolome in Apc^1638N Mice

<div><p>Obesity is a risk factor for colorectal cancer (CRC), and alterations in the colonic microbiome and metabolome may be mechanistically involved in this relationship. The relative contribution of diet and obesity <i>per se</i> are unclear. We compared the effect of diet- and genetically-induced obesity on the intestinal microbiome and metabolome in a mouse model of CRC. Apc^1638N mice were made obese by either high fat (HF) feeding or the presence of the Lepr^db/db (DbDb) mutation. Intestinal tumors were quantified and stool microbiome and metabolome were profiled. Genetic obesity, and to a lesser extent HF feeding, promoted intestinal tumorigenesis. Each induced distinct microbial patterns: taxa enriched in HF were mostly Firmicutes (6 of 8) while those enriched in DbDb were split between Firmicutes (7 of 12) and Proteobacteria (5 of 12). <i>Parabecteroides distasonis</i> was lower in tumor-bearing mice and its abundance was inversely associated with colonic Il1b production (p<0.05). HF and genetic obesity altered the abundance of 49 and 40 fecal metabolites respectively, with 5 in common. Of these 5, adenosine was also lower in obese and in tumor-bearing mice (p<0.05) and its concentration was inversely associated with colonic Il1b and Tnf production (p<0.05). HF and genetic obesity differentially alter the intestinal microbiome and metabolome. A depletion of adenosine and <i>P</i>.<i>distasonis</i> in tumor-bearing mice could play a mechanistic role in tumor formation. Adenosine and <i>P</i>. <i>distasonis</i> have previously been shown to be anti-inflammatory in the colon and we postulate their reduction could promote tumorigenesis by de-repressing inflammation.</p></div

Multivariate Association with Linear Models (MaAsLin) output.

<p>Model = Apc (Mut or Wt) x DbDb (Mut or Wt) x Diet (LF or HF) x Sex (M or F) x Tumors (number of tumors present). Abbreviations: Mut, mutant; Wt, wildtype; LF, low fat; HF, high fat; p_, phylum; c_, class; o_, order; f_, family; g_, genus; s_, species. N = 41 (includes WtWt mice). Taxa in bold were also identified to be associated with that trait (variable) in the LDA effect size analysis.</p><p>Multivariate Association with Linear Models (MaAsLin) output.</p

LDA effect size analysis of between group differences in stool bacterial abundances in Apc^1638N mice.

<p>A) Output showing effect size of 29 significantly enriched taxa in each group. Model = group x gender. B) Significant taxa plotted onto a cladogram. F, Firmicutes; P, Proteobacteria; B, Bacteroidetes; c_, class; o_, order; f_, family; g_genus, s_, species. * taxa that were also associated with that group in the MaASLin analysis. N = 29.</p

Diet composition.

<p>Diets: LF, Low fat. HF, High fat. BioServ (custom) catalog numbers F6654, and F6653 respectively.</p><p>Diet composition.</p

Effect of diet and genotype on body weight and tumor burden.

<p>A) Weight of <i>female</i> mice by group. * p<0.05 vs. LF, # p<0.05 vs. LF (all time points), α p<0.05 vs. HF (all time points). B) Weight of <i>male</i> mice by group. * p<0.05 vs. LF, # P<0.05 vs. LF (all time points), β p<0.05 vs. HF. C) Small intestinal tumor burden by group. p_trend <0.001 for tumor number and burden. Groups with different number are significantly different by post-test (p<0.05).</p

Impact of obesity and intestinal tumor presence on the fecal metabolome of mice.

<p>Low and high fat fed mice are compared in the first column (A,D,G). Low fat fed and genetically obese mice are compared in the second column (B,E,H). Mice with and without intestinal tumors are compared in the third column (C,F,I). The top row (A-C) shows heat maps of top 25 most significantly different metabolites for each comparison (p<0.05); color represents normalized metabolite concentration from low (blue) to high (red). The second row (D-F) shows discrimination of groups using partial least squares discriminate analysis. The third row (G-I) shows the metabolites most strongly influencing discrimination by the partial least squares discriminate analysis. The Variable Importance In Projection (VIP) score is the weighted sum of squares for the partial least-squares loadings with the amount of variance explained by each component taken into account.</p