AUTHOR=Hong Yuntian , Chen Baoxiang , Zhai Xiang , Qian Qun , Gui Rui , Jiang Congqing TITLE=Integrated analysis of the gut microbiome and metabolome in a mouse model of inflammation-induced colorectal tumors JOURNAL=Frontiers in Microbiology VOLUME=13 YEAR=2023 URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2022.1082835 DOI=10.3389/fmicb.2022.1082835 ISSN=1664-302X ABSTRACT=

Colorectal cancer (CRC) is a common malignancy worldwide, and the gut microbiota and metabolites play an important role in its initiation and progression. In this study, we constructed a mouse model of inflammation-induced colorectal tumors, with fixed doses of azoxymethane/dextran sulfate sodium (AOM/DSS). We found that colorectal tumors only formed in some mice treated with certain concentrations of AOM/DSS (tumor group), whereas other mice did not develop tumors (non-tumor group). 16S rDNA amplicon sequencing and liquid chromatography-mass spectrometry (LC-MS)/MS analyses were performed to investigate the microbes and metabolites in the fecal samples. As a result, 1189 operational taxonomic units (OTUs) were obtained from the fecal samples, and the non-tumor group had a relatively higher OTU richness and diversity. Moreover, 53 different microbes were identified at the phylum and genus levels, including Proteobacteria, Cyanobacteria, and Prevotella. Furthermore, four bacterial taxa were obviously enriched in the non-tumor group, according to linear discriminant analysis scores (log10) > 4. The untargeted metabolomics analysis revealed significant differences between the fecal samples and metabolic phenotypes. Further, the heatmaps and volcano plots revealed 53 and 19 dysregulated metabolites between the groups, in positive and negative ion modes, respectively. Styrene degradation and amino sugar-nucleotide sugar metabolism pathways were significantly different in positive and negative ion modes, respectively. Moreover, a correlation analysis between the metabolome and microbiome was further conducted, which revealed the key microbiota and metabolites. In conclusion, we successfully established a tumor model using a certain dose of AOM/DSS and identified the differential intestinal microbiota and characteristic metabolites that might modulate tumorigenesis, thereby providing new concepts for the prevention and treatment of CRC.