True ion pick (TIPick): a denoising and peak picking algorithm to extract ion signals from liquid chromatography/mass spectrometry data

Global untargeted and individual targeted plasma metabolomics of breast cancer recurrence modified by hormone receptors

BACKGROUND

Breast cancer is a heterogeneous and complex etiological disease. Understanding perturbations of circulating metabolites could improve prognosis.

METHODS

We recruited breast cancer patients from Kaohsiung Medical University (KMU) to perform untargeted (case-control design) and targeted (patient cohort) metabolomics analyses in the discovery and validation phases to evaluate interaction effects between clinical factors and plasma metabolites using multivariable Cox proportional hazards model.

RESULTS

In the discovery phase, partial least squares-discriminant analysis (PLS-DA) showed that plasma metabolites were significantly different between recurrent and non-recurrent breast cancer patients. Metabolite set enrichment analysis (MSEA) and metabolomic pathway analysis (MetPA) showed that valine, leucine, and isoleucine degradation was the significant pathway, and volcano plot showed significant ten upregulated and two downregulated metabolites between recurrent and non-recurrent cases. Combined with receiver operating characteristic (ROC) curve and biological significance, creatine, valine, methionine, and mannose were selected for the validation phase. In this patient cohort with 41 new-recurrent vs. 248 non-recurrent breast cancer cases, followed for 720.49 person-years, compared with low level of valine, high valine level was significantly negatively associated with recurrent breast cancer (aHR: 0.36, 95% CI: 0.18-0.72, P = 0.004), especially in ER-negative and PR-negative status. There were interaction effects between valine and ER (Pinteraction = 0.006) as well as PR (Pinteraction = 0.002) on recurrent breast cancer. After Bonferroni correction, stratification effects between valine and hormone receptors were still significant.

CONCLUSION

Our study revealed that plasma metabolites were significantly different between recurrent and non-recurrent patients, proposing therapeutic insights for breast cancer prognosis.

Extrapolation of cytotoxic masked effects in planar in vitro assays

The masking of specific effects in in vitro assays by cytotoxicity is a commonly known phenomenon. This may result in a partial or complete loss of effect signals. For common in vitro assays, approaches for identifying and quantifying cytotoxic masking are partly available. However, a quantification of cytotoxicity-affected signals is not possible. As an alternative, planar bioassays that combine high-performance thin layer chromatography with in vitro assays, such as the planar yeast estrogen screen (p-YES), might allow for a quantification of cytotoxically affected signals. Affected signals form a typical ring structure with a supressed or completely lacking centre that results in a double peak chromatogram. This study investigates whether these double peaks can be used for fitting a peak function to extrapolate the theoretical, unaffected signals. The precision of the modelling was evaluated for four individual peak functions, using 42 ideal, undistorted peaks from estrogenic model compounds in the p-YES. Modelled ED50-values from bisphenol A (BPA) experiments with cytotoxically disturbed signals were 13 times higher than for the apparent data without compensation for cytotoxicity (320 ± 63 ng versus 24 ± 17 ng). This finding has a high relevance for the modelling of mixture effects according to concentration addition that requires unaffected, complete dose-response relationships. Finally, we applied the approach to results of a p-YES assay on leachate samples of an elastomer material used in water engineering. In summary, the fitting approach enables the quantitative evaluation of cytotoxically affected signals in planar in vitro assays and also has applications for other fields of chemical analysis like distorted chromatography signals.

Combination of continuous wavelet transform and genetic algorithm-based Otsu for efficient mass spectrometry peak detection

Mass spectrometry (MS) data is susceptible to random noises and alternating baseline, posing great challenges to spectral peak detection, especially for weak peaks and overlapping peaks. Herein, an efficient peak detection algorithm combining continuous wavelet transform (CWT) and genetic algorithm-based threshold segmentation (denoted as WSTGA) for mass spectrometry was proposed. Firstly, Mexican Hat wavelet was selected as the mother wavelet by comparing the matching degree between the difference of Gaussian (DOG) and different wavelets. Subsequently, the ridges and valleys were identified from 2D wavelet coefficient matrix. Afterward, an improved threshold segmentation method, Otsu method based on genetic algorithm, was introduced to find optimal segmentation threshold and achieve better image segmentation, overcoming the deficiency of traditional Otsu method that cannot handle long-tailed unimodal histograms. Finally, the characteristic peaks were successfully identified by utilizing the ridge-valley lines in wavelet space and original spectrum. Receiver operating characteristic (ROC) curve, area under curve (AUC) and F₁ measure are used as criterions to evaluate performance of peak detection algorithms. Compared with multi-scale peak detection (MSPD) and CWT and image segmentation (CWT-IS) methods, all the results showed that WSTGA can achieve better peak detection. More importantly, the experimental results from MALDI-TOF spectra demonstrated that WSTGA can effectively detect more weak peaks and overlapping peaks while maintaining a lower false peak detection rate than MSPD and CWT-IS methods, indicating its great advantages in characteristic peak identification.

Identification of traumatic acid as a potential plasma biomarker for sarcopenia using a metabolomics-based approach

BACKGROUND

The pathogenesis of sarcopenia is complex and has not been well explored. Identifying biomarkers is a promising strategy for exploring the mechanism of sarcopenia. This study aimed to identify potential biomarkers of sarcopenia through a metabolomic analysis of plasma metabolites in elderly subjects (≥65 years of age) vs. younger adults (<65 years of age).

METHODS

Of the 168 candidates in the Comprehensive Geriatric Assessment and Frailty Study of Elderly Outpatients, 24 elderly subjects (≥65 years of age) with sarcopenia were age and sex matched with 24 elderly subjects without sarcopenia. In addition, 24 younger adults were recruited for comparison. Muscle strength, gait speed, and metabolic and inflammatory parameters, including plasma tumour necrosis factor-α, C-reactive protein, irisin, and growth differentiation factor 15 (GDF-15) levels were assessed. Metabolomic analysis was carried out using the plasma metabolites.

RESULTS

Seventy-two participants were enrolled, including 10 (41.6%) men and 14 (58.3%) women in both groups of elderly subjects. The median ages of elderly subjects with and without sarcopenia were 82 (range: 67-88) and 81.5 (range: 67-87) years, respectively. Among the 242 plasma metabolic peaks analysed among these three groups, traumatic acid was considered as a sarcopenia-related metabolite. The plasma traumatic acid signal intensity level was significantly higher in elderly subjects with sarcopenia than in elderly subjects without sarcopenia [591.5 (inter-quartile range, IQR: 491.5-664.5) vs. 430.0 (IQR: 261.0-599.5), P = 0.0063]. The plasma concentrations of traumatic acid were 15.8 (IQR: 11.5-21.7), 21.1 (IQR: 16.0-25.8), and 24.3 (IQR: 18.0-29.5) ppb in younger adults [age range: 23-37 years, 12 (50%) men], elderly subjects without sarcopenia, and elderly subjects with sarcopenia, respectively, thereby depicting an increasing tendency (P for trend = 0.034). This pattern was similar to that of GDF-15, a recognized sarcopenia-related factor. Plasma traumatic acid concentrations were also positively correlated with the presence of hypertension (r = 0.25, P = 0.034), glucose AC (r = 0.34, P = 0.0035), creatinine (r = 0.40, P = 0.0006), and GDF-15 levels (r = 0.25, P = 0.0376), but negatively correlated with the Modification of Diet in Renal Disease-simplify-glomerular filtration rate (r = -0.50, P < 0.0001). Similarly, plasma GDF-15 concentrations were associated with these factors.

CONCLUSIONS

Traumatic acid might represent a potential plasma biomarker of sarcopenia. However, further studies are needed to validate the results and investigate the underlying mechanisms.

Creation and filtering of a recurrent spectral library of CHO cell metabolites and media components

This paper reports the first implementation of a new type of mass spectral library for the analysis of Chinese hamster ovary (CHO) cell metabolites that allows users to quickly identify most compounds in any complex metabolite sample. We also describe an annotation methodology developed to filter out artifacts and low‐quality spectra from recurrent unidentified spectra of metabolites. CHO cells are commonly used to produce biological therapeutics. Metabolic profiles of CHO cells and media can be used to monitor process variability and look for markers that discriminate between batches of product. We have created a comprehensive library of both identified and unidentified metabolites derived from CHO cells that can be used in conjunction with tandem mass spectrometry to identify metabolites. In addition, we present a workflow that can be used for assigning confidence to a NIST MS/MS Library search match based on prior probability of general utility. The goal of our work is to annotate and identify (when possible), all liquid chromatography‐mass spectrometry generated metabolite ions as well as create automatable library building and identification pipelines for use by others in the field.

Investigating the Mechanistic Differences of Obesity-Inducing Lactobacillus kefiranofaciens M1 and Anti-obesity Lactobacillus mali APS1 by Microbolomics and Metabolomics

Many studies have investigated the anti-obesity effects of probiotics in animal models and humans. However, few studies have focused on the mechanisms of obesity-inducing probiotics. In a previous study, we demonstrated that specific bacterial strains isolated from kefir, Lactobacillus kefirnofaciens M1 and Lactobacillus mali APS1, possess obesity and anti-obesity effects, respectively, in high-fat diet (HFD)-induced obese mice. Thus, in the present study, we systematically investigated whether APS1 and M1 affect energy homeostasis and lipid metabolism in HFD-induced obese mice and how this might be achieved. We observed that the M1/APS1 intervention influenced fat accumulation by regulating adipogenesis and inflammation-related marker expression both in vitro and in a HFD induced C57BL/6J mice model. We also observed putative links between key taxa and possible metabolic processes of the gut microbiota. Notably, families Christensenellaceae and S24_7 were negatively correlated with body weight gain through increase in the essential esterized carnitine for energy expenditure. These results suggest the importance of specific probiotic interventions affecting leanness and obesity of subjects under a HFD, which are operated by modulating the tripartite relationship among the host, microbiota, and metabolites.

Urine metabolomics signatures in reversible cerebral vasoconstriction syndrome

Background

The pathophysiology of reversible cerebral vasoconstriction syndrome is unclear. An unbiased systems-based approach might help to illustrate the metabolite profiling and underlying pathophysiology.

Methods

Urine samples were collected from reversible cerebral vasoconstriction syndrome patients and matched controls recruited in Taipei Veterans General Hospital. 1H-Nuclear magnetic resonance was used to initially explore the metabolic profile, and liquid chromatography tandem mass spectrometry was then used to identify metabolic alterations in reversible cerebral vasoconstriction syndrome. Untargeted metabolite screening was randomly performed on 10 reversible cerebral vasoconstriction syndrome patients and 10 control subjects in the discovery phase. The selected untargeted metabolites were further validated on 47 reversible cerebral vasoconstriction syndrome patients during their ictal stage (with 40 of them having remission samples) and 47 controls in the replication phase.

Results and conclusion

Six metabolites-hippurate, citrate, 1,3,7-trimethyluric acid, ascorbic acid, D-glucurono-6,3-lactone, and D- threo-isocitric acid-with t-test derived p-value < 0.05 and VIP score >1, were identified as potential urine signatures that can well distinguish reversible cerebral vasoconstriction syndrome subjects at ictal stage from controls. Among them, citrate, hippurate, ascorbic acid, and D-glucurono-6,3-lactone were significantly lower, and 1,3,7-trimethyluric acid and D- threo-isocitric acid were higher in reversible cerebral vasoconstriction syndrome patients. Of these, four selected metabolites, citrate, D-glucurono-6,3-lactone, ascorbic acid, and 1,3,7-trimethyluric acid, returned to normal levels in remission. These metabolites are related to pathways associated with free radical scavenging, with the hub molecules being associated with endothelial dysfunction or sympathetic overactivity. Whether these metabolites and their implicated networks play a role in the pathogenesis of reversible cerebral vasoconstriction syndrome remains to be confirmed.

ASIC3-dependent metabolomics profiling of serum and urine in a mouse model of fibromyalgia

Fibromyalgia (FM) is characterized by chronic widespread pain. The pathogenesis of FM remains unclear. No specific biomarkers are available. Animal models of FM may provide an opportunity to explore potential biomarkers in a relative homogenous disease condition. Here, we probed the metabolomics profiles of serum and urine in a mouse model of FM induced by intermittent cold stress (ICS). We focused on the role of acid-sensing ion channel 3 (ASIC3) in the metabolomics profiling because ICS treatment induced chronic widespread muscle pain lasting for 1 month in wild-type (Asic3^+/+) but not Asic3-knockout (Asic3^−/−) mice. Serum and urine samples were collected from both genotypes at different ICS stages, including before ICS (basal level) and post-ICS at days 10 (middle phase, P10) and 40 (recovery phase, P40). Control naïve mice and ICS-induced FM mice differed in ¹H-NMR- and LC-MS-based metabolomics profiling. On pathway analysis, the leading regulated pathways in Asic3^+/+ mice were taurine and hypotaurine, cysteine and methionine, glycerophospholipid, and ascorbate and aldarate metabolisms, and the major pathways in Asic3^−/− mice involved amino acid-related metabolism. Finally, we developed an algorithm for the impactful metabolites in the FM model including cis-aconitate, kynurenate, taurine, pyroglutamic acid, pyrrolidonecarboxylic acid, and 4-methoxyphenylacetic acid in urine as well as carnitine, deoxycholic acid, lysoPC(16:0), lysoPC(20:3), oleoyl-L-carnitine, and trimethylamine N-oxide in serum. Asic3^−/− mice were impaired in only muscle allodynia development but not other pain symptoms in the ICS model, so the ASIC3-dependent metabolomics changes could be useful for developing diagnostic biomarkers specific to chronic widespread muscle pain, the core symptom of FM. Further pharmacological validations are needed to validate these metabolomics changes as potential biomarkers for FM diagnosis and/or treatment responses.

Metabolomics of Children and Adolescents Exposed to Industrial Carcinogenic Pollutants

Studies on metabolomes of carcinogenic pollutants among children and adolescents are limited. We aim to identify metabolic perturbations in 107 children and adolescents (aged 9-15) exposed to multiple carcinogens in a polluted area surrounding the largest petrochemical complex in Taiwan. We measured urinary concentrations of eight carcinogen exposure biomarkers (heavy metals and polycyclic aromatic hydrocarbons (PAHs) represented by 1-hydroxypyrene), and urinary oxidative stress biomarkers and serum acylcarnitines as biomarkers of early health effects. Serum metabolomics was analyzed using a liquid chromatography mass spectrometry-based method. Pathway analysis and "meet-in-the-middle" approach were applied to identify potential metabolites and biological mechanisms linking carcinogens exposure with early health effects. We found 10 potential metabolites possibly linking increased exposure to IARC group 1 carcinogens (As, Cd, Cr, Ni) and group 2 carcinogens (V, Hg, PAHs) with elevated oxidative stress and deregulated serum acylcarnitines, including inosine monophosphate and adenosine monophosphate (purine metabolism), malic acid and oxoglutaric acid (citrate cycle), carnitine (fatty acid metabolism), and pyroglutamic acid (glutathione metabolism). Purine metabolism was identified as the possible mechanism affected by children and adolescents' exposure to carcinogens. These findings contribute to understanding the health effects of childhood and adolescence exposure to multiple industrial carcinogens during critical periods of development.

Clinical association between the metabolite of healthy gut microbiota, 3-indolepropionic acid and chronic kidney disease

BACKGROUND & AIMS

Emerging evidence indicates that gut microbiota serves an important role in the development and progression of chronic kidney disease (CKD). Changes to the gut microbial flora can cause the generation of uremic toxins, which contribute to chronic kidney injury. The aim of the current study was to explore the clinical association between metabolites and CKD.

METHODS

Between August 2013 and January 2015, a two-phase case-control study was conducted to analyze the clinical association between metabolites and CKD in a community health program. The first phase of the study was a prospective case-control survey designed for comparing the differences in the metabolome profile of patients with (n = 10) and without (n = 10) estimated glomerular filtration rate (eGFR) rapid decline (a yearly decline >20%). The second phase of the study was a cross-sectional case-control study, which checked and compared the metabolites, indoxyl sulfate and p-cresol sulfate levels between healthy subjects (n = 144) and CKD patients (n = 140).

RESULTS

In the first phase of the study, it was revealed that IPA levels of patients with rapid renal function decline were significantly reduced compared with the control patients (n = 10 for each group). The second phase furthered checked and compared the IPA, indoxyl sulfate and p-cresol sulfate levels between healthy subjects (n = 144) and CKD patients (n = 140). The results showed that the average level of indoxyl sulfate (2738.2 vs. 541.0 ng/ml, P < 0.01) and p-cresol sulfate (1442.8 vs. 1394.6 ng/ml, P < 0.01) were significantly higher in the CKD patients, while the average level of IPA was significantly higher (49.8 vs. 34.7 ng/ml, P < 0.01) in the control patients.

CONCLUSIONS

Our results suggest that IPA might be an important biomarker and renal protector against the development of CKD.

Metabolome analysis for investigating host-gut microbiota interactions

Dysbiosis of the gut microbiome is associated with host health conditions. Many diseases have shown to have correlations with imbalanced microbiota, including obesity, inflammatory bowel disease, cancer, and even neurodegeneration disorders. Metabolomics studies targeting small molecule metabolites that impact the host metabolome and their biochemical functions have shown promise for studying host-gut microbiota interactions. Metabolome analysis determines the metabolites being discussed for their biological implications in host-gut microbiota interactions. To facilitate understanding the critical aspects of metabolome analysis, this article reviewed (1) the sample types used in host-gut microbiome studies; (2) mass spectrometry (MS)-based analytical methods and (3) useful tools for MS-based data processing/analysis. In addition to the most frequently used sample type, feces, we also discussed others biosamples, such as urine, plasma/serum, saliva, cerebrospinal fluid, exhaled breaths, and tissues, to better understand gut metabolite systemic effects on the whole organism. Gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and capillary electrophoresis-mass spectrometry (CE-MS), three powerful tools that can be utilized to study host-gut microbiota interactions, are included with examples of their applications. After obtaining big data from MS-based instruments, noise removal, peak detection, missing value imputation, and data analysis are all important steps for acquiring valid results in host-gut microbiome research. The information provided in this review will help new researchers aiming to join this field by providing a global view of the analytical aspects involved in gut microbiota-related metabolomics studies.

Postprandial Metabolomics Response to Various Cooking Oils in Humans

Lipids account for a high proportion of dietary calories, which greatly affect human health. As a result of differences in composition of fatty acid of individual cooking oils, certain biological effects of these oils may vary. This study aimed to compare postprandial metabolomic profiles of six commonly consumed cooking oils/fats. Adopting a switch-over experimental design ( n = 15), we carried out a human feeding study with six groups (control without oils, soybean oil, olive oil, palm oil, camellia oil, and tallow) and collected fasting and postprandial serum samples. The metabolomic profile was measured by ultra-high-pressure liquid chromatography-quadrupole time of flight. We observed significant differences between the control group and experimental groups for 33 serum metabolites (false discovery rate; p < 0.05), which take part in lipid digestion, fatty acid metabolism, metabolism of pyrimidines and pyrimidine nucleosides, amino acid metabolism, neurobiology, and antioxidation. Sparse partial least squares discriminant analysis revealed distinct metabolomics patterns between monounsaturated fatty acid (MUFA) and saturated fatty acid oils, between soybean oil, olive oil, and palm oil, and between two MUFA-rich oils (olive and camellia oils). The present metabolomics study suggests shared and distinct metabolisms of various cooking oils/fats.

A combination of Lactobacillus mali APS1 and dieting improved the efficacy of obesity treatment via manipulating gut microbiome in mice

The difficulty of long-term management has produced a high rate of failure for obesity patients. Therefore, improving the efficacy of current obesity treatment is a significant goal. We hypothesized that combining a probiotic Lactobacillus mali APS1 intervention with dieting could improve the efficacy of obesity and hepatic steatosis treatment compared to dieting alone. Mice were fed a high-fat diet for 6 weeks and then treated with: saline + normal diet and APS1 + normal diet (NDAPS1) for 3 weeks. NDAPS1 accelerated body weight loss and reduced caloric intake and fat accumulation. The fecal microbiome showed that accelerating weight loss by NDAPS1 resulted in restoring intestinal microbiota toward a pre-obese state, with alteration of specific changes in the obesity-associated bacteria. APS1 manipulated the gut microbiome's obesity-associated metabolites, followed by regulation of lipid metabolism, enhancement of energy expenditure and inhibition of appetite. The specific hepatic metabolites induced by the APS1-manipulated gut microbiome also contributed to the amelioration of hepatic steatosis. Our results highlighted a possible microbiome and metabolome that contributed to accelerating weight loss following treatment with a combination of APS1 and dieting and suggested that probiotics could serve as a potential therapy for modulating physiological function and downstream of the microbiota.

KPIC2: An Effective Framework for Mass Spectrometry-Based Metabolomics Using Pure Ion Chromatograms

Distilling accurate quantitation information on metabolites from liquid chromatography coupled with mass spectrometry (LC-MS) data sets is crucial for further statistical analysis and biomarker identification. However, it is still challenging due to the complexity of biological systems. The concept of pure ion chromatograms (PICs) is an effective way of extracting meaningful ions, but few toolboxes provide a full processing workflow for LC-MS data sets based on PICs. In this study, an integrated framework, KPIC2, has been developed for metabolomics studies, which can detect pure ions accurately, align PICs across samples, group PICs to identify isotope and potential adducts, fill missing peaks and do multivariate pattern recognition. To evaluate its performance, MM48, metabolomics quantitation, and Soybean seeds data sets have been analyzed using KPIC2, XCMS, and MZmine2. KPIC2 can extract more true ions with fewer detecting features, have good quantification ability on a metabolomics quantitation data set, and achieve satisfactory classification on a soybean seeds data set through kernel-based OPLS-DA and random forest. It is implemented in R programming language, and the software, user guide, as well as example scripts and data sets are available as an open source package at https://github.com/hcji/KPIC2 .

Automated Algorithm for Detection of Transient Adenosine Release

Spontaneous adenosine release events have been discovered in the brain that last only a few seconds. The identification of these adenosine events from fast-scan cyclic voltammetry (FSCV) data is difficult due to the random nature of adenosine release. In this study, we develop an algorithm that automatically identifies and characterizes adenosine transient features, including event time, concentration, and duration. Automating the data analysis reduces analysis time from 10 to 18 h to about 40 min per experiment. The algorithm identifies adenosine based on its two oxidation peaks, the time delay between them, and their current vs time peak ratios. In order to validate the program, four data sets from three independent researchers were analyzed by the algorithm and then compared to manual identification by an analyst. The algorithm resulted in 10 ± 4% false negatives and 9 ± 3% false positives. The specificity of the algorithm was verified by comparing calibration data for adenosine triphosphate (ATP), histamine, hydrogen peroxide, and pH changes and these analytes were not identified as adenosine. Stimulated histamine release in vivo was also not identified as adenosine. The code is modular in design and could be easily adjusted to detect features of spontaneous dopamine or other neurochemical transients in FSCV data.

Chromatographic peak reconstruction algorithm to improve qualitative and quantitative analysis of trace pesticide residues

RATIONALE

In order to improve analysis of analytes in trace amounts in a complex matrix, we developed a novel post-processing method, named Chromatographic Peak Reconstruction (CPR), to process the recorded data from gas chromatography/time-of-flight mass spectrometry (GC/TOFMS).

METHODS

For a trace ion, the relative deviation (δ) between the adjacent scanned mass-to-charge ratios (m/z) was found to be inversely proportional to its MS peak intensity. Based on this relationship, the thresholds of δ value within the specified intensity segments were estimated by the CPR and used to screen out the suspicious scan-points in the extracted ion chromatographic (EIC) peak. Then, the intensities of these suspicious scan-points were calibrated to reconstruct a new EIC peak.

RESULTS

In the qualitative analysis of 118 pesticides, 107 out of the test pesticides can be confirmed. The corrected response ratios of the qualitative ion (q) over the quantitative ion (Q), q/Q, became closer to their references. In the quantitative analysis of 10 test pesticides at 5 ppb, the relative errors of the calculated concentrations after using the CPR were below ±1.55%, down from ±2.29% without using the CPR.

CONCLUSIONS

The developed CPR showed great potential in the analysis of trace analytes in complex matrices. It was proved to be a helpful data processing method for the monitoring of trace pesticide residues. Copyright © 2016 John Wiley & Sons, Ltd.

Curcumin Induces Apoptosis of Colorectal Cancer Stem Cells by Coupling with CD44 Marker

This study investigated the effect of curcumin on colorectal cancer stem cells (CCSCs) and its possible mechanism. Comparison of the metabolic profiles of human adenomatous polyp (N = 61) and colorectal cancer (CRC) (N = 57) tissue found statistically significant differences (p < 0.05) in their composition of adenosine monophosphate (AMP), adenine, 5'-methythioadenosine, 3-hydroxybutyric acid, prostaglandin E2, threonine, and glutamine. Our cell culture model study found that curcumin treatment (50 μM for 48 h) did indeed increase apoptosis of CRC cells as well as of CCSCs, but at a significant level only in CD44(+) cells. Further metabolic profile studies of the CRC, CD44(+), and CD44(-) cells indicated that curcumin treatment increased glyceraldehyde and hydroxypropionic acid in CD44(-) cells but decreased glutamine content in both curcumin-treated CRC and CD44(+) cells. Based on our comparison of the metabolic profiles of human tissues and cancer cells, we suggest that curcumin might couple with CD44 and that curcumin-CD44(+) coupling at the cell membrane might have some blocking effect on the transport of glutamine into the cells, thus decreasing the glutamine content in the CD44(+) cells and inducing apoptosis.

Mass-Spectrometry-Based Serum Metabolomics of a C57BL/6J Mouse Model of High-Fat-Diet-Induced Non-alcoholic Fatty Liver Disease Development

Obesity, dyslipidemia, insulin resistance, oxidative stress, and inflammation are key clinical risk factors for the progression of non-alcoholic fatty liver disease (NAFLD). Currently, there is no comprehensive metabolic profile of a well-established animal model that effectively mimics the etiology and pathogenesis of NAFLD in humans. Here, we report the pathophysiological and metabolomic changes associated with NAFLD development in a C57BL/6J mouse model in which NAFLD was induced by feeding a high-fat diet (HFD) for 4, 8, 12, and 16 weeks. Serum metabolomic analysis was conducted using ultrahigh-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) and gas chromatography-mass spectrometry (GC-MS) to establish a metabolomic profile. Analysis of the metabolomic profile in combination with principal component analysis revealed marked differences in metabolites between the control and HFD group depending upon NAFLD severity. A total of 30 potential biomarkers were strongly associated with the development of NAFLD. Among these, 11 metabolites were mainly related to carbohydrate metabolism, hepatic biotransformation, collagen synthesis, and gut microbial metabolism, which are characteristics of obesity, as well as significantly increased serum glucose, total cholesterol, and hepatic triglyceride levels during the onset of NAFLD (4 weeks). At 8 weeks, 5 additional metabolites that are chiefly involved in perturbation of lipid metabolism and insulin secretion were found to be associated with hyperinsulinemia, hyperlipidemia, and hepatic steatosis in the mid-term of NAFLD progression. At the end of 12 and 16 weeks, 14 additional metabolites were predominantly correlated to abnormal bile acid synthesis, oxidative stress, and inflammation, representing hepatic inflammatory infiltration during NAFLD development. These results provide potential biomarkers for early risk assessment of NAFLD and further insights into NAFLD development.

The metabolome profiling and pathway analysis in metabolic healthy and abnormal obesity

OBJECTIVES

Mechanisms of the development of abnormal metabolic phenotypes among obese population are not yet clear. In this study, we aimed to screen metabolomes of both healthy and subjects with abnormal obesity to identify potential metabolic pathways that may regulate the different metabolic characteristics of obesity.

METHODS

We recruited subjects with body mass index (BMI) over 25 from the weight-loss clinic of a central hospital in Taiwan. Metabolic healthy obesity (MHO) is defined as without having any form of hyperglycemia, hypertension and dyslipidemia, while metabolic abnormal obesity (MAO) is defined as having one or more abnormal metabolic indexes. Serum-based metabolomic profiling using both liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry of 34 MHO and MAO individuals with matching age, sex and BMI was performed. Conditional logistic regression and partial least squares discriminant analysis were applied to identify significant metabolites between the two groups. Pathway enrichment and topology analyses were conducted to evaluate the regulated pathways.

RESULTS

A differential metabolite panel was identified to be significantly differed in MHO and MAO groups, including L-kynurenine, glycerophosphocholine (GPC), glycerol 1-phosphate, glycolic acid, tagatose, methyl palmitate and uric acid. Moreover, several metabolic pathways were relevant in distinguishing MHO from MAO groups, including fatty acid biosynthesis, phenylalanine metabolism, propanoate metabolism, and valine, leucine and isoleucine degradation.

CONCLUSION

Different metabolomic profiles and metabolic pathways are important for distinguishing between MHO and MAO groups. We have identified and discussed the key metabolites and pathways that may prove important in the regulation of metabolic traits among the obese, which could provide useful clues to study the underlying mechanisms of the development of abnormal metabolic phenotypes.

Current breathomics--a review on data pre-processing techniques and machine learning in metabolomics breath analysis

We define breathomics as the metabolomics study of exhaled air. It is a strongly emerging metabolomics research field that mainly focuses on health-related volatile organic compounds (VOCs). Since the amount of these compounds varies with health status, breathomics holds great promise to deliver non-invasive diagnostic tools. Thus, the main aim of breathomics is to find patterns of VOCs related to abnormal (for instance inflammatory) metabolic processes occurring in the human body. Recently, analytical methods for measuring VOCs in exhaled air with high resolution and high throughput have been extensively developed. Yet, the application of machine learning methods for fingerprinting VOC profiles in the breathomics is still in its infancy. Therefore, in this paper, we describe the current state of the art in data pre-processing and multivariate analysis of breathomics data. We start with the detailed pre-processing pipelines for breathomics data obtained from gas-chromatography mass spectrometry and an ion-mobility spectrometer coupled to multi-capillary columns. The outcome of data pre-processing is a matrix containing the relative abundances of a set of VOCs for a group of patients under different conditions (e.g. disease stage, treatment). Independently of the utilized analytical method, the most important question, 'which VOCs are discriminatory?', remains the same. Answers can be given by several modern machine learning techniques (multivariate statistics) and, therefore, are the focus of this paper. We demonstrate the advantages as well the drawbacks of such techniques. We aim to help the community to understand how to profit from a particular method. In parallel, we hope to make the community aware of the existing data fusion methods, as yet unresearched in breathomics.

Analysis of volatile organic compounds in exhaled breath by gas chromatography-mass spectrometry combined with chemometric analysis

Analysis of exhaled breath samples reveals the presence of many volatile organic compounds (VOCs). The VOC composition of the breath, the so-called breath profile, contains a variety of information including the health status and condition of the organism that produced the sample. Therefore, breath profiling can be used in diagnosing and monitoring disease and other characteristics of the organism, such as phenotype, diet, and exercise. Among various techniques available for breath analysis, GC-MS provides the most extensive information with regard to the qualitative and quantitative presence of VOCs in breath.