Colorectal carcinogenesis involves the overexpression of many immediate-early response genes associated with growth and inflammation, which significantly alters downstream protein synthesis and small-molecule metabolite production. We have performed a serum metabolic analysis to test the hypothesis that the distinct metabolite profiles of malignant tumors are reflected in biofluids. In this study, we have analyzed the serum metabolites from 64 colorectal cancer (CRC) patients and 65 healthy controls using gas chromatography time-of-flight mass spectrometry (GC-TOFMS) and Acquity ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (Acquity UPLC-QTOFMS). Orthogonal partial least-squares discriminate analysis (OPLS-DA) models generated from GC-TOFMS and UPLC-QTOFMS metabolic profile data showed robust discrimination from CRC patients and healthy controls. A total of 33 differential metabolites were identified using these two analytical platforms, five of which were detected in both instruments. These metabolites potentially reveal perturbation of glycolysis, arginine and proline metabolism, fatty acid metabolism and oleamide metabolism, associated with CRC morbidity. These results suggest that serum metabolic profiling has great potential in detecting CRC and helping to understand its underlying mechanisms.

Pu-erh tea displays cholesterol-lowering properties, but the underlying mechanism has not been elucidated. Theabrownin is one of the most active and abundant pigments in Pu-erh tea. Here, we show that theabrownin alters the gut microbiota in mice and humans, predominantly suppressing microbes associated with bile-salt hydrolase (BSH) activity. Theabrownin increases the levels of ileal conjugated bile acids (BAs) which, in turn, inhibit the intestinal FXR-FGF15 signaling pathway, resulting in increased hepatic production and fecal excretion of BAs, reduced hepatic cholesterol, and decreased lipogenesis. The inhibition of intestinal FXR-FGF15 signaling is accompanied by increased gene expression of enzymes in the alternative BA synthetic pathway, production of hepatic chenodeoxycholic acid, activation of hepatic FXR, and hepatic lipolysis. Our results shed light into the mechanisms behind the cholesterol- and lipid-lowering effects of Pu-erh tea, and suggest that decreased intestinal BSH microbes and/or decreased FXR-FGF15 signaling may be potential anti-hypercholesterolemia and anti-hyperlipidemia therapies.

Missing values exist widely in mass-spectrometry (MS) based metabolomics data. Various methods have been applied for handling missing values, but the selection can significantly affect following data analyses. Typically, there are three types of missing values, missing not at random (MNAR), missing at random (MAR), and missing completely at random (MCAR). Our study comprehensively compared eight imputation methods (zero, half minimum (HM), mean, median, random forest (RF), singular value decomposition (SVD), k-nearest neighbors (kNN), and quantile regression imputation of left-censored data (QRILC)) for different types of missing values using four metabolomics datasets. Normalized root mean squared error (NRMSE) and NRMSE-based sum of ranks (SOR) were applied to evaluate imputation accuracy. Principal component analysis (PCA)/partial least squares (PLS)-Procrustes analysis were used to evaluate the overall sample distribution. Student’s t-test followed by correlation analysis was conducted to evaluate the effects on univariate statistics. Our findings demonstrated that RF performed the best for MCAR/MAR and QRILC was the favored one for left-censored MNAR. Finally, we proposed a comprehensive strategy and developed a public-accessible web-tool for the application of missing value imputation in metabolomics (https://metabolomics.cc.hawaii.edu/software/MetImp/).