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.

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/).

After our serum metabonomic study of colorectal cancer (CRC) patients recently published in J. Proteome Res., we profiled urine metabolites from the same group of CRC patients (before and after surgical operation) and 63 age-matched healthy volunteers using gas chromatography-mass spectrometry (GC-MS) in conjunction with a multivariate statistics technique. A parallel metabonomic study on a 1,2-dimethylhydrazine (DMH)-treated Sprague-Dawley rat model was also performed to identify significantly altered metabolites associated with chemically induced precancerous colorectal lesion. The orthogonal partial least-squares-discriminant analysis (OPLS-DA) models of metabonomic results demonstrated good separations between CRC patients or DMH-induced model rats and their healthy counterparts. The significantly increased tryptophan metabolism, and disturbed tricarboxylic acid (TCA) cycle and the gut microflora metabolism were observed in both the CRC patients and the rat model. The urinary metabolite profile of postoperative CRC subjects altered significantly from that of the preoperative stage. The significantly down-regulated gut microflora metabolism and TCA cycle were observed in postoperative CRC subjects, presumably due to the colon flush involved in the surgical procedure and weakened physical conditions of the patients. The expression of 5-hydroxytryptophan significantly decreased in postsurgery samples, suggesting a recovered tryptophan metabolism toward healthy state. Abnormal histamine metabolism and glutamate metabolism were found only in the urine samples of CRC patients, and the abnormal polyamine metabolism was found only in the rat urine. This study assessed the important metabonomic variations in urine associated with CRC and, therefore, provided baseline information complementary to serum/plasma and tissue metabonomics for the complete elucidation of the underlying metabolic mechanisms of CRC.

SUMMARY Rapidly proliferating leukemic progenitor cells consume substantial glucose that may lead to glucose insufficiency in bone marrow. We show that acute myeloid leukemia (AML) cells are prone to fructose utilization with an upregulated fructose transporter GLUT5, compensating for glucose deficiency. Notably, AML patients with upregulated transcription of GLUT5-encoding gene SLC2A5 or increased fructose utilization have poor outcomes. Pharmacological blockage of fructose uptake ameliorates leukemic phenotypes and potentiates the cytotoxicity of antileukemic agent, Ara-C. In conclusion, this study highlights enhanced fructose utilization as a metabolic feature of AML and a potential therapeutic target.

Chronic stress is closely linked to clinical depression, which could be assessed by a chronic unpredictable mild stress (CUMS) animal model. We present here a GC/MS-based metabolic profiling approach to investigate neurochemical changes in the cerebral cortex, hippocampus, thalamus, and remaining brain tissues. Multi-criteria assessment for multivariate statistics could identify differential metabolites between the CUMS-model rats versus the healthy controls. This study demonstrates that the significantly perturbed metabolites mainly involving amino acids play an indispensable role in regulating neural activity in the brain. Therefore, results obtained from such metabolic profiling strategy potentially provide a unique perspective on molecular mechanisms of chronic stress.

Recent studies revealed strong evidence that branched-chain and aromatic amino acids (BCAAs and AAAs) are closely associated with the risk of developing type 2 diabetes in several Western countries. The aim of this study was to evaluate the potential role of BCAAs and AAAs in predicting the diabetes development in Chinese populations. The serum levels of valine, leucine, isoleucine, tyrosine, and phenylalanine were measured in a longitudinal and a cross sectional studies with a total of 429 Chinese participants at different stages of diabetes development, using an ultra-performance liquid chromatography triple quadruple mass spectrometry platform. The alterations of the five AAs in Chinese populations are well in accordance with previous reports. Early elevation of the five AAs and their combined score was closely associated with future development of diabetes, suggesting an important role of these metabolites as early markers of diabetes. On the other hand, the five AAs were not as good as existing clinical markers in differentiating diabetic patients from their healthy counterparts. Our findings verified the close correlation of BCAAs and AAAs with insulin resistance and future development of diabetes in Chinese populations and highlighted the predictive value of these markers for future development of diabetes.

Bile acids (BAs) are a group of important physiological agents for cholesterol metabolism, intestinal nutrient absorption, and biliary secretion of lipids, toxic metabolites, and xenobiotics. Extensive research in the last two decades has unveiled new functions of BAs as signaling molecules and metabolic regulators that modulate hepatic lipid, glucose, and energy homeostasis through the activation of nuclear receptors and G-protein-coupled receptor signaling in gut-liver metabolic axis involving host-gut microbial co-metabolism. Therefore, investigation of serum BA profiles, in healthy human male and female subjects with a wide range of age and body mass index (BMI), will provide important baseline information on the BA physiology as well as metabolic homeostasis among human subjects that are regulated by two sets of genome, host genome, and symbiotic microbiome. Previous reports on age- or gender-related changes on BA profiles in animals and human showed inconsistent results, and the information acquired from various studies was highly fragmentary. Here we profiled the serum BAs in a large population of healthy participants (n = 502) and examined the impact of age, gender, and BMI on serum BA concentrations and compositions using a targeted metabonomics approach with ultraperformance liquid chromatography triple-quadrupole mass spectrometry. We found that the BA profiles were dependent on gender, age, and BMI among study subjects. The total BAs were significantly higher in males than in females (p < 0.05) and higher in obese females than in lean females (p < 0.05). The difference in BA profiles between male and female subjects was decreased at age of 50-70 years, while the difference in BA profiles between lean and obese increased for subjects aged 50-70 years. The study provides a comprehensive understanding of the BA profiles in healthy subjects and highlights the need to take into account age, gender, and BMI differences when investigating pathophysiological changes of BAs resulting from gastrointestinal diseases.

BackgroundObesity is not a homogeneous condition across individuals since about 25–40% of obese individuals can maintain healthy status with no apparent signs of metabolic complications. The simple anthropometric measure of body mass index does not always reflect the biological effects of excessive body fat on health, thus additional molecular characterizations of obese phenotypes are needed to assess the risk of developing subsequent metabolic conditions at an individual level.MethodsTo better understand the associations of free fatty acids (FFAs) with metabolic phenotypes of obesity, we applied a targeted metabolomics approach to measure 40 serum FFAs from 452 individuals who participated in four independent studies, using an ultra-performance liquid chromatograph coupled to a Xevo G2 quadruple time-of-flight mass spectrometer.FindingsFFA levels were significantly elevated in overweight/obese subjects with diabetes compared to their healthy counterparts. We identified a group of unsaturated fatty acids (UFAs) that are closely correlated with metabolic status in two groups of obese individuals who underwent weight loss intervention and can predict the recurrence of diabetes at two years after metabolic surgery. Two UFAs, dihomo-gamma-linolenic acid and palmitoleic acid, were also able to predict the future development of metabolic syndrome (MS) in a group of obese subjects.InterpretationThese findings underscore the potential role of UFAs in the MS pathogenesis and also as important markers in predicting the risk of developing diabetes in obese individuals or diabetes remission after a metabolic surgery.