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. 2022 Sep 28:13:1021325.
doi: 10.3389/fmicb.2022.1021325. eCollection 2022.

Integrative analysis of the mouse fecal microbiome and metabolome reveal dynamic phenotypes in the development of colorectal cancer

Jingjing Liu  1   2 , Mingyang Qi  1 , Chengchao Qiu  1 , Feng Wang  2 , Shaofei Xie  2 , Jian Zhao  1 , Jing Wu  3 , Xiaofeng Song  1
Affiliations

Integrative analysis of the mouse fecal microbiome and metabolome reveal dynamic phenotypes in the development of colorectal cancer

Jingjing Liu et al. Front Microbiol. .

Abstract

The gut microbiome and its interaction with host have been implicated as the causes and regulators of colorectal cancer (CRC) pathogenesis. However, few studies comprehensively investigate the compositions of gut bacteria and their interactions with host at the early inflammatory and cancerous stages of CRC. In this study, mouse fecal samples collected at inflammation and CRC were subjected to microbiome and metabolome analyses. The datasets were analyzed individually and integratedly using various bioinformatics approaches. Great variations in gut microbiota abundance and composition were observed in inflammation and CRC. The abundances of Bacteroides, S24-7_group_unidifineted, and Allobaculum were significantly changed in inflammation and CRC. The abundances of Bacteroides and Allobaculum were significantly different between inflammation and CRC. Furthermore, strong excluding and appealing microbial interactions were found in the gut microbiota. CRC and inflammation presented specific fecal metabolome profiling. Fecal metabolomic analysis led to the identification and quantification of 1,138 metabolites with 32 metabolites significantly changed in CRC and inflammation. 1,17-Heptadecanediol and 24,25,26,27-Tetranor-23-oxo-hydroxyvitamin D3 were potential biomarkers for CRC. 3α,7β,12α-Trihydroxy-6-oxo-5α-cholan-24-oic Acid and NNAL-N-glucuronide were potential biomarkers for inflammation. The significantly changed bacterial species and metabolites contribute to inflammation and CRC diagnosis. Integrated microbiome and metabolomic analysis correlated microbes with host metabolites, and the variated microbe-metabolite association in inflammation and CRC suggest that microbes facilitate tumorigenesis of CRC through interfering host metabolism.

Keywords: colorectal cancer; gut microbiota; inflammation; metabolome; microbiome.

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Conflict of interest statement

Authors JL, FW, and SX were employed by the Jiangsu Simcere Pharmaceutical Co., Ltd. The remaining 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.

Figures

FIGURE 1
FIGURE 1
Establishment of mouse colorectal cancer model. Timeline of chemical dosage in control (bottom) and experimental (top) mice.
FIGURE 2
FIGURE 2
Biochemical testing of established mouse colorectal cancer model. Inflammation level detected by LC-MS/MS (A) and MDA Assay Kit (B) in control and experimental mice from week 0 to week 8. (C) HE staining of colorectal tissue in control (left) and experiment (right) group. (D) Macroscopic colorectal tumors in experimental mice.
FIGURE 3
FIGURE 3
Diversity and abundancy analysis of gut microbiota. (A) Boxplots of Chao1 Richness Index. (B) Boxplots of Shannon Diversity Index. (C) Boxplots of Simpson Diversity Index. (D) PCoA plot of Group C, Group L, and Group H, showed a significant difference between health, colorectal inflammation, and colorectal cancer. (E) Relative abundance of microbial communities at phylum level. The relative abundance is defined as a percentage of the total microbial sequences in a sample. (F) Boxplots of significantly changed floras at phylum level. (G) Heat map of the 37 most abundant genera at genus level. (H) Boxplots of significantly changed floras at genus level. ns: p > 0.05, no significance. *p ≤ 0.05; **p ≤ 0.01.
FIGURE 4
FIGURE 4
Linear discriminant analysis (LDA) effect size (LEfSe) and correlation analysis of bacterial communities based on 16S rRNA gene sequences. (A) Taxonomic distribution of bacterial groups significant for inflammation and CRC. (B) Histogram of the LDA scores computed for differentially abundant bacterial taxa between health, inflammation and CRC. (C) Correlation analysis of the 30 most abundant genera in Group C, Group L, and Group H. *0.01 < p ≤ 0.05; **p ≤ 0.01.
FIGURE 5
FIGURE 5
Important discriminatory metabolites identified by clustering, correlation, and multivariate analysis in inflammation and CRC. (A) Hierarchical clustering analysis (HCA) for the significantly changed metabolites in inflammation. (B) Hierarchical clustering analysis (HCA) for significantly changed metabolites in CRC. (C) OPLS-DA analysis displaying the grouped discrimination of health, inflammation and CRC by the first two PCs.
FIGURE 6
FIGURE 6
Pathway enrichment and statistical significance of the unique or significantly changed metabolites in CRC (A), inflammation (B), and health (C). Represented metabolites engaged in the significantly changed metabolic pathways of CRC (D) and inflammation (E). (F) Represented unique metabolites detected in health mice.
FIGURE 7
FIGURE 7
Integrated correlation-based network analysis (Pearson’s correlation) of microbes and metabolites in health (A), inflammation (B), and CRC (C).
FIGURE 8
FIGURE 8
Metabolic biomarker analysis of CRC and inflammation. (A) ROC curve analyses based on random forests algorithms for biomarker analysis in CRC. (B) Top 15 metabolites discriminating CRC from health. (C) ROC curve analyses based on random forests algorithms for biomarker analysis in inflammation. (D) Top 15 metabolites discriminating inflammation from health. (E) Candidate metabolic biomarkers of CRC screened out by statistical analysis. (F) Candidate metabolic biomarkers of inflammation screened out by statistical analysis.
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