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Omphalocele and gastroschisis are the two most common congenital abdominal wall defects; however, no previous study has focused on gastrointestinal and hepatobiliary tract malformations in these two conditions. This study aimed to investigate the demographic characteristics, coexisting congenital gastrointestinal and hepatobiliary tract anomalies, hospital course, and outcomes of patients with gastroschisis and omphalocele.This is retrospective chart review of all patients admitted to one tertiary medical center in Taiwan between January 1, 2000 and June 30, 2020 with a diagnosis of gastroschisis or omphalocele. The medical records were reviewed to obtain demographic data regarding coexisting gastrointestinal and hepatobiliary tract anomalies and outcomes.Of the 51 patients included, 21 had gastroschisis and 30 had omphalocele. Gastroschisis was associated with a significantly younger maternal age and a higher incidence of small for gestational age. Of the 30 patients with omphalocele, twelve had associated gastrointestinal and hepatobiliary anomalies. Seven of the 21 patients with gastroschisis had gastrointestinal anomalies, and none had hepatobiliary anomalies. Among the omphalocele patients, three (10%) had documented malrotation, and one developed midgut volvulus. Among gastroschisis patients, four patients (19%) had malrotation, and two developed midgut volvulus. There were no statistically significant differences in postoperative complications or mortality rates between those with and without gastrointestinal/hepatobiliary tract anomalies.The diversity of coexisting gastrointestinal and hepatobiliary tract anomalies is higher in the omphalocele than in gastroschisis. In addition, we demonstrate that patients with gastroschisis or omphalocele have a higher rate of intestinal malrotation and midgut volvulus.

Motivation Liquid chromatography coupled with mass spectrometry (LC-MS) is widely used in metabolomics studies, while HILIC LC-MS is particularly suited for polar metabolites. Determining an optimized mobile phase and developing a proper liquid chromatography method tend to be laborious, time-consuming and empirical. Results We developed a containerized web tool providing a workflow to quickly determine the optimized mobile phase by batch-evaluating chromatography peaks for metabolomics LC-MS studies. A mass chromatographic quality value, an asymmetric factor, and the local maximum intensity of the extracted ion chromatogram were calculated to determine the number of peaks and peak retention time. The optimal mobile phase can be quickly determined by selecting the mobile phase that produces the largest number of resolved peaks. Moreover, the workflow enables one to automatically process the repeats by evaluating chromatography peaks and determining the retention time of large standards. This workflow was validated with 20 chemical standards and successfully constructed a reference library of 571 metabolites for the HILIC LC-MS platform. Availability and implementation MetaMOPE is freely available at https://metamope.cmdm.tw. Source code and installation instructions are available on GitHub: https://github.com/CMDM-Lab/MetaMOPE. Supplementary information Supplementary data are available at Bioinformatics Advances online.

Background: Over 30% of cancer survivors experience chronic fatigue. An alteration in energy metabolism is one of the hypothesized mechanisms for cancer-related fatigue (CRF). No studies have evaluated for changes in metabolic profiles in cancer survivors with CRF. The purpose of this pilot study was to evaluate for differences in metabolic profiles between fatigued and non-fatigued survivors of colorectal cancer (CRC). Methods: Survivors were recruited from the surgical outpatient department and the oncology clinic of a medical center in northern Taiwan. Fatigue was assessed using the Fatigue Symptom Inventory. Fasting blood samples were collected on the day the fatigue questionnaire was completed. Metabolomic profile analysis was performed using non-targeted, liquid chromatography/time-of-flight mass spectrometry. Fold change analyses, t-tests, and pathway analyses were performed to identify differences in metabolomic profiles between the fatigued and non-fatigued survivors. Results: Of the 56 CRC survivors in this study, 28.6% (n = 16) were in the fatigue group. Statistically significant differences in carnitine, L-norleucine, pyroglutamic acid, pyrrolidonecarboxylic acid, spermine, hydroxyoctanoic acid, and paraxanthine were found between the two fatigue groups. In addition, two pathways were enriched for these metabolites (i.e., glutathione metabolism, D-glutamine and D-glutamate metabolism). Conclusions: Findings from this pilot study provide preliminary evidence that two pathways that are involved with the regulation of ATP production and cellular energy (i.e., glutathione metabolism, D-glutamine and D-glutamate metabolism) are associated with fatigue in CRC survivors. If these findings are confirmed, they may provide new therapeutic targets to decrease fatigue in cancer survivors.

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.

Background There are limited studies on the lipidomics of children and adolescents exposed to multiple industrial pollutants. Objectives In this study, we aimed to investigate lipid profile perturbations in 99 children and adolescents (aged 9–15) who lived in a polluted area surrounding the largest petrochemical complex in Taiwan. Previous studies have reported increased risks of acute and chronic diseases including liver dysfunctions and chronic kidney disease (CKD) in residents living in this area. Methods We measured urinary concentrations of 11 metals and metalloids and polycyclic aromatic hydrocarbons (PAHs) metabolite 1-hydroxypyrene (1-OHP) as exposure biomarkers, and urinary oxidative stress biomarkers and serum acylcarnitines as early health effect biomarkers. The association between individual exposure biomarkers and early health effect biomarkers were analyzed using linear regression, while association of combined exposure biomarkers with four oxidative stress biomarkers and acylcarnitines were analyzed using weighted quantile sum (WQS) regression. Lipid profiles were analyzed using an untargeted liquid chromatography mass spectrometry-based technique. “Meet-in-the-middle” approach was applied to identify potential lipid features that linked multiple industrial pollutants exposure with early health effects. Results We identified 15 potential lipid features that linked elevated multiple industrial pollutants exposure with three increased oxidative stress biomarkers and eight deregulated serum acylcarnitines, including one lysophosphatidylcholines (LPCs), four phosphatidylcholines (PCs), and two sphingomyelins (SMs) that were up-regulated in high exposure group compared to low exposure group, and two LPCs, four PCs, and two phosphatidylinositols (PIs) down-regulated in high exposure group compared to low exposure group. Conclusion Our findings could provide information for understanding the health effects, including early indicators and biological mechanism identification, of children and adolescents exposed to multiple industrial pollutants during critical stages of development.

Supplemental_Tables_and_Figures_03192020 for A Pilot Study of Metabolomic Pathways Associated With Fatigue in Survivors of Colorectal Cancer by Yun-Jen Chou, Kord M. Kober, Ching-Hua Kuo, Kun-Huei Yeh, Tien-Chueh Kuo, Yufeng J. Tseng, Christine Miaskowski, Jin-Tung Liang and Shiow-Ching Shun in Biological Research For Nursing

Two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC/TOF-MS) is superior for chromatographic separation and provides great sensitivity for complex biological fluid analysis in metabolomics. However, GC×GC/TOF-MS data processing is currently limited to vendor software and typically requires several preprocessing steps. In this work, we implement a web-based platform, which we call GC2MS, to facilitate the application of recent advances in GC×GC/TOF-MS, especially for metabolomics studies. The core processing workflow of GC2MS consists of blob/peak detection, baseline correction, and blob alignment. GC2MS treats GC×GC/TOF-MS data as pictures and clusters the pixels as blobs according to the brightness of each pixel to generate a blob table. GC2MS then aligns the blobs of two GC×GC/TOF-MS data sets according to their distance and similarity. The blob distance and similarity are the Euclidean distance of the first and second retention times of two blobs and the Pearson's correlation coefficient of the two mass spectra, respectively. GC2MS also directly corrects the raw data baseline. The analytical performance of GC2MS was evaluated using GC×GC/TOF-MS data sets of Angelica sinensis compounds acquired under different experimental conditions and of human plasma samples. The results show that GC2MS is an easy-to-use tool for detecting peaks and correcting baselines, and GC2MS is able to align GC×GC/TOF-MS data sets acquired under different experimental conditions. GC2MS is freely accessible at http://gc2ms.web.cmdm.tw .

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.

BACKGROUND Noninvasive stress tests for the diagnosis of significant coronary arterial stenosis requiring intervention are not perfect. We investigated whether plasma metabolome during the oral glucose tolerance test (OGTT) can improve the diagnosis. METHODS A total of 117 subjects with positive stress test results who received coronary angiography were recruited. After excluding subjects with a history of myocardial infarction and subjects who did not receive OGTT, the 18 subjects without significant stenosis were selected as controls. Another 18 age- and sex-matched subjects with significant stenosis were selected as cases. Plasma metabolome from samples obtained in fasting, 30 and 120 min after OGTT was measured using liquid chromatography combined with time-of-flight mass spectrometry. RESULTS We found five metabolites which can identify patients with significant stenosis independent to clinical risk factors, including diabetes, hypertension, hypercholesterolaemia, smoking and history of percutaneous coronary intervention (all P

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. 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. 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. 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.

Hypoxia affects the tumor microenvironment and is considered important to metastasis progression and therapy resistance. Thus far, the majority of global analyses of tumor hypoxia responses have been limited to just a single omics level. Combining multiple omics data can broaden our understanding of tumor hypoxia. Here, we investigate the temporal change of the metabolite composition with gene expression data from literature to provide a more comprehensive insight into the system level in response to hypoxia. Nuclear magnetic resonance spectroscopy was used to perform metabolomic profiling on the MDA-MB-231 breast cancer cell line under hypoxic conditions. Multivariate statistical analysis revealed that the metabolic difference between hypoxia and normoxia was similar over 24 h, but became distinct over 48 h. Time dependent microarray data from the same cell line in the literature displayed different gene expressions under hypoxic and normoxic conditions mostly at 12 h or earlier. The direct metabolomic profiles show a large overlap with theoretical metabolic profiles deduced from previous transcriptomic studies. Consistent pathways are glycolysis/gluconeogenesis, pyruvate, purine and arginine and proline metabolism. Ten metabolic pathways revealed by metabolomics were not covered by the downstream of the known transcriptomic profiles, suggesting new metabolic phenotypes. These results confirm previous transcriptomics understanding and expand the knowledge from existing models on correlation and co-regulation between transcriptomic and metabolomics profiles, which demonstrates the power of integrated omics analysis.