Fecal microbiota, fecal metabolome, and colorectal cancer interrelations

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Abstract

Background and Aims Investigation of microbe-metabolite relationships in the gut is needed to understand and potentially reduce colorectal cancer (CRC) risk. Methods Microbiota and metabolomics profiling were performed on lyophilized feces from 42 CRC cases and 89 matched controls. Multivariable logistic regression was used to identify statistically independent associations with CRC. First principal coordinate-component pair (PCo1-PC1) and false discovery rate (0.05)-corrected P-values were calculated for 116,000 Pearson correlations between 530 metabolites and 220 microbes in a sex*case/control meta-analysis. Results Overall microbe-metabolite PCo1-PC1 was more strongly correlated in cases than in controls (Rho 0.606 vs 0.201, P = 0.01). CRC was independently associated with lower levels of Clostridia, Lachnospiraceae, p-aminobenzoate and conjugated linoleate, and with higher levels of Fusobacterium, Porphyromonas, p-hydroxy-benzaldehyde, and palmitoyl-sphingomyelin. Through postulated effects on cell shedding (palmitoyl-sphingomyelin), inflammation (conjugated linoleate), and innate immunity (p-aminobenzoate), metabolites mediated the CRC association with Fusobacterium and Porphyromonas by 29% and 34%, respectively. Overall, palmitoyl-sphingomyelin correlated directly with abundances of Enterobacteriaceae (Gammaproteobacteria), three Actinobacteria and five Firmicutes. Only Parabacteroides correlated inversely with palmitoyl-sphingomyelin. Other lipids correlated inversely with Alcaligenaceae (Betaproteobacteria). Six Bonferroni-significant correlations were found, including low indolepropionate and threnoylvaline with Actinobacteria and high erythronate and an uncharacterized metabolite with Enterobacteriaceae. Conclusions Feces from CRC cases had very strong microbe-metabolite correlations that were predominated by Enterobacteriaceae and Actinobacteria. Metabolites mediated a direct CRC association with Fusobacterium and Porphyromonas, but not an inverse association with Clostridia and Lachnospiraceae. This study identifies complex microbe-metabolite networks that may provide insights on neoplasia and targets for intervention.

Figures

  • Table 1. Selected characteristics of colorectal cancer cases and controls with fecal metabolite data.
  • Table 2. Multivariable logistic regression models of fecal microbes andmetabolites independently associated with colorectal cancer (CRC).*
  • Table 3. Pearson correlation coefficients for metabolites andmicrobes associated with colorectal cancer case status in multivariate analyses.*
  • Fig 1. Observed inverse (-log10) meta-analyzed P-values for associations between 530 fecal metabolites and 220 fecal microbes.Microbes are color coded by phylum (Actinobacteria, red; Bacteroidetes, yellow; Firmicutes, purple; Proteobacteria, cyan; Fusobacteria, black; other phyla, orange) and sorted by genus. Bonferroni and false discovery rate (FDR) 0.05 and 0.1 threshold lines are presented.
  • Fig 2. Heat map of the 100 strongest values of Pearson correlation coefficients of the residuals of all 530*220 fecal metabolite-microbe pairs. Asterisk (*) indicates correlation significant at false discovery rate (FDR) 0.2. Bars at the top are color coded by phylum, as in Fig 1 (Proteobacteria, cyan; Actinobacteria, red; Firmicutes, purple; Bacteroidetes, yellow). Clusters are arbitrarily labeled A, B, C, and D. Bars on the left are color coded by metabolite pathway, as indicated.

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Sinha, R., Ahn, J., Sampson, J. N., Shi, J., Yu, G., Xiong, X., … Goedert, J. J. (2016). Fecal microbiota, fecal metabolome, and colorectal cancer interrelations. PLoS ONE, 11(3). https://doi.org/10.1371/journal.pone.0152126

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