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. 2020 Mar 24;6(1):14.
doi: 10.1038/s41522-020-0123-4.

Metaproteomics Characterizes Human Gut Microbiome Function in Colorectal Cancer

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Free PMC article

Metaproteomics Characterizes Human Gut Microbiome Function in Colorectal Cancer

Shuping Long et al. NPJ Biofilms Microbiomes. .
Free PMC article

Abstract

Pathogenesis of colorectal cancer (CRC) is associated with alterations in gut microbiome. Previous studies have focused on the changes of taxonomic abundances by metagenomics. Variations of the function of intestinal bacteria in CRC patients compared to healthy crowds remain largely unknown. Here we collected fecal samples from CRC patients and healthy volunteers and characterized their microbiome using quantitative metaproteomic method. We have identified and quantified 91,902 peptides, 30,062 gut microbial protein groups, and 195 genera of microbes. Among the proteins, 341 were found significantly different in abundance between the CRC patients and the healthy volunteers. Microbial proteins related to iron intake/transport; oxidative stress; and DNA replication, recombination, and repair were significantly alternated in abundance as a result of high local concentration of iron and high oxidative stress in the large intestine of CRC patients. Our study shows that metaproteomics can provide functional information on intestinal microflora that is of great value for pathogenesis research, and can help guide clinical diagnosis in the future.

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Metaproteomic characterization of the gut microbiome of CRC patients and healthy crowds.
a Experimental design and workflow. The numbers of protein groups (b), peptides (c), and genera (d) identified from the CRC patient group (P) and the healthy volunteer group (H). e Volcano plot indicating the differential proteins. Protein groups with P/H fold change (FC(P/H)) ≥ 2 and P value < 0.05 were colored red, while those with FC(P/H) ≤ 0.5 and P value < 0.05 were colored blue. Source data are provided as a Source Data file.
Fig. 2
Fig. 2. Taxonomic abundances of the gut microbiome of the CRC patients (P) and healthy crowds (H).
a Cladogram illustrating taxa (domain to family) abundances in the two groups. Colors indicate the fold change log2(P/H). Large circles indicate P value < 0.05. Information on the phylum, class, order, and family of the labeled numbers is given in Supplementary Data 6. b Bar charts showing the selected differential families, genera, and species between the CRC patients and the healthy controls. The bars show the average of 14 samples in each group. Error bars indicate standard deviation. Individual data points are overlaid as dots. Taxa are selected for presentation in a and/or b if significant differences of their abundance between CRC patients and healthy people have been reported in previous studies or are observed (P value < 0.05) in this work. The selected classes and families are highlighted in a with red or blue color of the taxa name. Source data are provided as a Source Data file.
Fig. 3
Fig. 3. Top 20 differential proteins between the CRC patients and the healthy controls.
a The 10 proteins with the largest fold change (FC(P/H)) (red) and the 10 proteins with the smallest FC(P/H) (blue). b Receiver operating characteristic (ROC) curve using the 20 proteins as potential candidate biomarkers for CRC diagnosis. Linear support vector machine (LSVM) was used as a classification method, and 100 rounds of Monte-Carlo cross-validation were performed to generate the ROC curve. Details are described in the “Methods” section. Source data are provided as a Source Data file.
Fig. 4
Fig. 4. Clusters of orthologous groups (COG) categories of the 341 differential proteins (P value < 0.05).
a Numbers of proteins in each COG category; red: more abundant in CRC patients, blue: more abundant in healthy crowds. b Bar plots of differential proteins in COG category L (DNA replication, recombination and repair); red: gut microbial proteins in CRC patients, blue: gut microbial proteins in healthy crowds. The bars show the average of 14 samples in each group. Error bars indicate standard deviation. Individual data points are overlaid as dots. Source data are provided as a Source Data file.
Fig. 5
Fig. 5. Quantitative comparison of proteins related to iron intake and transport and oxidative stress between the CRC patient group (red) and the healthy control group (blue).
a TonB-dependent receptors, b rubrerythrin family proteins, c nicotinamide adenine dinucleotide (NADH):flavin oxidoreductase/NADH oxidases, d superoxide dismutases (SODs). The bars show the average of 14 samples in each group. Error bars indicate standard deviation. Individual data points are overlaid as dots. Source data are provided as a Source Data file.
Fig. 6
Fig. 6. Diagram of gut microbiome in the pathogenesis of CRC.
High iron concentration in the intestinal lumen of CRC patients promotes the production of reactive oxygen species (ROS) and results in high oxidative stress. Concentrations of superoxide dismutases (SODs) in the intestinal bacteria are increased. The excessive ROS cause DNA damage of intestinal epithelial cells and probiotic bacteria, which can accelerate the progression of CRC.

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