PubMed

Related Articles Comparison of Bi- and Tri-Linear PLS Models for Variable Selection in Metabolomic Time-Series Experiments. Metabolites. 2019 May 09;9(5): Authors: Gao Q, Dragsted LO, Ebbels T Abstract Metabolomic studies with a time-series design are widely used for discovery and validation of biomarkers. In such studies, changes of metabolic profiles over time under different conditions (e.g., control and intervention) are compared, and metabolites responding differently between the conditions are identified as putative biomarkers. To incorporate time-series information into the variable (biomarker) selection in partial least squares regression (PLS) models, we created PLS models with different combinations of bilinear/trilinear X and group/time response dummy Y. In total, five PLS models were evaluated on two real datasets, and also on simulated datasets with varying characteristics (number of subjects, number of variables, inter-individual variability, intra-individual variability and number of time points). Variables showing specific temporal patterns observed visually and determined statistically were labelled as discriminating variables. Bootstrapped-VIP scores were calculated for variable selection and the variable selection performance of five PLS models were assessed based on their capacity to correctly select the discriminating variables. The results showed that the bilinear PLS model with group × time response as dummy Y provided the highest recall (true positive rate) of 83-95% with high precision, independent of most characteristics of the datasets. Trilinear PLS models tend to select a small number of variables with high precision but relatively high false negative rate (lower power). They are also less affected by the noise compared to bilinear PLS models. In datasets with high inter-individual variability, bilinear PLS models tend to provide higher recall while trilinear models tend to provide higher precision. Overall, we recommend bilinear PLS with group x time response Y for variable selection applications in metabolomics intervention time series studies. PMID: 31075899 [PubMed]

Related Articles Acute Tumor Transition Angle on Computed Tomography Predicts Chromosomal Instability Status of Primary Gastric Cancer: Radiogenomics Analysis from TCGA and Independent Validation. Cancers (Basel). 2019 May 09;11(5): Authors: Lai YC, Yeh TS, Wu RC, Tsai CK, Yang LY, Lin G, Kuo MD Abstract Chromosomal instability (CIN) of gastric cancer is correlated with distinct outcomes. This study aimed to investigate the role of computed tomography (CT) imaging traits in predicting the CIN status of gastric cancer. We screened 443 patients in the Cancer Genome Atlas gastric cancer cohort to filter 40 patients with complete CT imaging and genomic data as the training cohort. CT imaging traits were subjected to logistic regression to select independent predictors for the CIN status. For the validation cohort, we prospectively enrolled 18 gastric cancer patients for CT and tumor genomic analysis. The imaging predictors were tested in the validation cohort using receiver operating characteristic curve (ROC) analysis. Thirty patients (75%) in the training cohort and 9 patients (50%) in the validation cohort had CIN subtype gastric cancers. Smaller tumor diameter (p = 0.017) and acute tumor transition angle (p = 0.045) independently predict CIN status in the training cohort. In the validation cohort, acute tumor transition angle demonstrated the highest accuracy, sensitivity, and specificity of 88.9%, 88.9%, and 88.9%, respectively, and areas under ROC curve of 0.89. In conclusion, this pilot study showed acute tumor transition angle on CT images may predict the CIN status of gastric cancer. PMID: 31075839 [PubMed]

Related Articles Biotransformation profiles from a cohort of chronic fatigue women in response to a hepatic detoxification challenge. PLoS One. 2019;14(5):e0216298 Authors: Erasmus E, Steffens FE, van Reenen M, Vorster BC, Reinecke CJ Abstract Chronic fatigue, in its various manifestations, frequently co-occur with pain, sleep disturbances and depression and is a non-communicable condition which is rapidly becoming endemic worldwide. However, it is handicapped by a lack of objective definitions and diagnostic measures. This has prompted the World Health Organization to develop an international instrument whose intended purpose is to improve quality of life (QOL), with energy and fatigue as one domain of focus. To complement this objective, the interface between detoxification, the exposome, and xenobiotic-sensing by nuclear receptors that mediate induction of biotransformation-linked genes, is stimulating renewed attention to a rational development of strategies to identify the metabolic profiles in complex multifactorial conditions like fatigue. Here we present results from a seven-year study of a cohort of 576 female patients suffering from low to high levels of chronic fatigue, in which phase I and phase II biotransformation was assessed. The biotransformation profiles used were based on hepatic detoxification challenge tests through oral caffeine, acetaminophen and acetylsalicylic acid ingestion coupled with oxidative stress analyses. The interventions indicated normal phase I but increased phase II glucuronidation and glycination conjugation. Complementarity was indicated between a fatigue scale, medical symptoms and associated energy-related parameters by application of Chi-square Automatic Interaction Detector (CHAID) analysis. The presented study provides a cluster of data from which we propose that multidisciplinary inputs from the combination of a fatigue scale, medical symptoms and biotransformation profiles provide the rationale for the development of a comprehensive laboratory instrument for improved diagnostics and personalized interventions in patients with chronic fatigue with a view to improving their QOL. PMID: 31075116 [PubMed - in process]

Related Articles Impact of phytosterols on liver and distal colon metabolome in experimental murine colitis model: an explorative study. J Enzyme Inhib Med Chem. 2019 Dec;34(1):1041-1050 Authors: Iaccarino N, Amato J, Pagano B, Di Porzio A, Micucci M, Bolelli L, Aldini R, Novellino E, Budriesi R, Randazzo A Abstract Phytosterols are known to reduce plasma cholesterol levels and thereby reduce cardiovascular risk. Studies conducted on human and animal models have demonstrated that these compounds have also anti-inflammatory effects. Recently, an experimental colitis model (dextran sulphate sodium-induced) has shown that pre-treatment with phytosterols decreases infiltration of inflammatory cells and accelerates mucosal healing. This study aims to understand the mechanism underlying the colitis by analysing the end-products of the metabolism in distal colon and liver excised from the same mice used in the previous work. In particular, an unsupervised gas chromatography-mass spectrometry (GC-MS) and NMR based metabolomics approach was employed to identify the metabolic pathways perturbed by the dextran sodium sulphate (DSS) insult (i.e. Krebs cycle, carbohydrate, amino acids, and nucleotide metabolism). Interestingly, phytosterols were able to restore the homeostatic equilibrium of the hepatic and colonic metabolome. PMID: 31074304 [PubMed - in process]

Related Articles Human Fetal Skin Fibroblast Isolation and Expansion for Clinical Application. Methods Mol Biol. 2019 May 10;: Authors: Goodarzi P, Aghayan HR, Payab M, Larijani B, Alavi-Moghadam S, Sarvari M, Adibi H, Khatami F, Heravani NF, Hadavandkhani M, Arjmand B Abstract Cell therapy is one of the most hopeful technologies of regenerative medicine approaches. Among various cells, human skin fibroblasts have been progressively used for wound healing as cell-based therapy purposes. By increasing the age, the number of skin fibroblasts' abilities including cell migration, growth, collagen production, etc. decreases. Hence, use of the fetal source is more beneficent. In this respect, this chapter covers the manufacturing of human fetal skin-derived fibroblasts for clinical application. PMID: 31073862 [PubMed - as supplied by publisher]

Related Articles Innovative omics-based approaches for prioritisation and targeted isolation of natural products - new strategies for drug discovery. Nat Prod Rep. 2019 May 10;: Authors: Wolfender JL, Litaudon M, Touboul D, Queiroz EF Abstract Covering: 2013 to 2019The exploration of the chemical diversity of extracts from various biological sources has led to major drug discoveries. Over the past two decades, despite the introduction of advanced methodologies for natural product (NP) research (e.g., dereplication and high content screening), successful accounts of the validation of NPs as lead therapeutic candidates have been limited. In this context, one of the main challenges faced is related to working with crude natural extracts because of their complex composition and the inadequacies of classical bioguided isolation studies given the pace of high-throughput screening campaigns. In line with the development of metabolomics, genomics and chemometrics, significant advances in metabolite profiling have been achieved and have generated high-quality massive genome and metabolome data on natural extracts. The unambiguous identification of each individual NP in an extract using generic methods remains challenging. However, the establishment of structural links among NPs via molecular network analysis and the determination of common features of extract composition have provided invaluable information to the scientific community. In this context, new multi-informational-based profiling approaches integrating taxonomic and/or bioactivity data can hold promise for the discovery and development of new bioactive compounds and return NPs back to an exciting era of development. In this article, we examine recent studies that have the potential to improve the efficiency of NP prioritisation and to accelerate the targeted isolation of key NPs. Perspectives on the field's evolution are discussed. PMID: 31073562 [PubMed - as supplied by publisher]

Related Articles Discrimination of Citrus reticulata Blanco and Citrus reticulata 'Chachi' as well as the Citrus reticulata 'Chachi' within different storage years using ultra high performance liquid chromatography quadrupole/time-of-flight mass spectrometry based metabolomics approach. J Pharm Biomed Anal. 2019 Jul 15;171:218-231 Authors: Luo Y, Zeng W, Huang KE, Li DX, Chen W, Yu XQ, Ke XH Abstract Using ultra high performance liquid chromatography quadrupole/time-of-flight mass spectrometry (UPLC-QTOFMS) based metabolomics, we focused on developing a method for the comprehensive distinction between Citri Reticulatae Blanco Pericarpium(CRBP) and Citri Reticulatae Chachi Pericarpium (CRCP), as well as the CRCP within different storage years in this study. Through this, we hope to enhance Citri Reticulatae Pericarpium (CRP) Quality Control system. Using UNIFI software and an online database identified chemical components in the 3-30 years CRCP(40 batches) and CRBP (10 batches)samples, and multivariate statistical analysis methods and heat-map were applied to distinguish between CRCP and CRBP and CRCP in different storage years. The results showed that a total of 92 compounds were identified from CRCP and CRBP samples, most of which were flavonoids. Principal component analysis (PCA) and orthogonal partial least squares discrimination analysis (OPLS-DA) indicated that it can effectively distinguish between CRBP and CRCP and various storage years CRCP, and 19 metabolites were identified as potential markers for distinguishing between CRBP and CRCP, and 15 potential markers showed a higher level of CRCP than CRBP. At the same time, 31 metabolites were identified to distinguish CRCP in different storage years, metabolite levels increased in 3-10 years and decreased after 15-30 years. Therefore, this approach can effectively distinguish between CRCP and CRBP and CRCP with different storage years, and may also provide a feasible strategy for the certification of Chinese herbal medicines from different species and storage years. PMID: 31072532 [PubMed - in process]

Related Articles Integrated omics-based pathway analyses uncover CYP epoxygenase-associated networks as theranostic targets for metastatic triple negative breast cancer. J Exp Clin Cancer Res. 2019 May 09;38(1):187 Authors: Apaya MK, Shiau JY, Liao GS, Liang YJ, Chen CW, Yang HC, Chu CH, Yu JC, Shyur LF Abstract BACKGROUND: Current prognostic tools and targeted therapeutic approaches have limited value for metastatic triple negative breast cancer (TNBC). Building upon current knowledge, we hypothesized that epoxyeicosatrienoic acids (EETs) and related CYP450 epoxygenases may have differential roles in breast cancer signaling, and better understanding of which may uncover potential directions for molecular stratification and personalized therapy for TNBC patients. METHODS: We analyzed the oxylipin metabolome of paired tumors and adjacent normal mammary tissues from patients with pathologically confirmed breast cancer (N = 62). We used multivariate statistical analysis to identify important metabolite contributors and to determine the predictive power of tumor tissue metabolite clustering. In vitro functional assays using a panel of breast cancer cell lines were carried out to further confirm the crucial roles of endogenous and exogenous EETs in the metastasis transformation of TNBC cells. Deregulation of associated downstream signaling networks associated with EETs/CYPs was established using transcriptomics datasets from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). Comparative TNBC proteomics using the same tissue specimens subjected to oxylipin metabolomics analysis was used as validation set. RESULTS: Metabolite-by-metabolite comparison, tumor immunoreactivity, and gene expression analyses showed that CYP epoxygenases and arachidonic acid-epoxygenation products, EET metabolites, are strongly associated with TNBC metastasis. Notably, all the 4 EET isomers (5,6-, 8,9-, 11,12-, and 14,15-EET) was observed to profoundly drive the metastasis transformation of mesenchymal-like TNBC cells among the TNBC (basal- and mesenchymal-like), HER2-overexpressing and luminal breast cancer cell lines examined. Our pathway analysis revealed that, in hormone-positive breast cancer subtype, CYP epoxygenase overexpression is more related to immune cell-associated signaling, while EET-mediated Myc, Ras, MAPK, EGFR, HIF-1α, and NOD1/2 signaling are the molecular vulnerabilities of metastatic CYP epoxygenase-overexpressing TNBC tumors. CONCLUSIONS: This study suggests that categorizing breast tumors according to their EET metabolite ratio classifiers and CYP epoxygenase profiles may be useful for prognostic and therapeutic assessment. Modulation of CYP epoxygenase and EET-mediated signaling networks may offer an effective approach for personalized treatment of breast cancer, and may be an effective intervention option for metastatic TNBC patients. PMID: 31072371 [PubMed - in process]

Related Articles Plasma Biomarkers: Potent Screeners of Alzheimer's Disease. Am J Alzheimers Dis Other Demen. 2019 May 09;:1533317519848239 Authors: Naveed M, Mubeen S, Khan A, Ibrahim S, Meer B Abstract Alzheimer's disease (AD), a neurological disorder, is as a complex chronic disease of brain cell death that usher to cognitive decline and loss of memory. Its prevalence differs according to risk factors associated with it and necropsy performs vital role in its definite diagnosis. The stages of AD vary from preclinical to severe that proceeds to death of patient with no availability of treatment. Biomarker may be a biochemical change that can be recognized by different emerging technologies such as proteomics and metabolomics. Plasma biomarkers, 5-protein classifiers, are readily being used for the diagnosis of AD and can also predict its progression with a great accuracy, specificity, and sensitivity. In this review, upregulation or downregulation of few plasma proteins in patients with AD has also been discussed, when juxtaposed with control, and thus serves as potent biomarker in the diagnosis of AD. PMID: 31072117 [PubMed - as supplied by publisher]

Related Articles Plasma Metabolites Associated with Coffee Consumption: A Metabolomic Approach within the PREDIMED Study. Nutrients. 2019 May 08;11(5): Authors: Papandreou C, Hernández-Alonso P, Bulló M, Ruiz-Canela M, Yu E, Guasch-Ferré M, Toledo E, Dennis C, Deik A, Clish C, Razquin C, Corella D, Estruch R, Ros E, Fitó M, Arós F, Fiol M, Lapetra J, Ruano C, Liang L, Martínez-González MA, Hu FB, Salas-Salvadó J Abstract Few studies have examined the association of a wide range of metabolites with total and subtypes of coffee consumption. The aim of this study was to investigate associations of plasma metabolites with total, caffeinated, and decaffeinated coffee consumption. We also assessed the ability of metabolites to discriminate between coffee consumption categories. This is a cross-sectional analysis of 1664 participants from the PREDIMED study. Metabolites were semiquantitatively profiled using a multiplatform approach. Consumption of total coffee, caffeinated coffee and decaffeinated coffee was assessed by using a validated food frequency questionnaire. We assessed associations between 387 metabolite levels with total, caffeinated, or decaffeinated coffee consumption (≥50 mL coffee/day) using elastic net regression analysis. Ten-fold cross-validation analyses were used to estimate the discriminative accuracy of metabolites for total and subtypes of coffee. We identified different sets of metabolites associated with total coffee, caffeinated and decaffeinated coffee consumption. These metabolites consisted of lipid species (e.g., sphingomyelin, phosphatidylethanolamine, and phosphatidylcholine) or were derived from glycolysis (alpha-glycerophosphate) and polyphenol metabolism (hippurate). Other metabolites included caffeine, 5-acetylamino-6-amino-3-methyluracil, cotinine, kynurenic acid, glycocholate, lactate, and allantoin. The area under the curve (AUC) was 0.60 (95% CI 0.56-0.64), 0.78 (95% CI 0.75-0.81) and 0.52 (95% CI 0.49-0.55), in the multimetabolite model, for total, caffeinated, and decaffeinated coffee consumption, respectively. Our comprehensive metabolic analysis did not result in a new, reliable potential set of metabolites for coffee consumption. PMID: 31072000 [PubMed - in process]

Related Articles NMR-Based Metabolic Profiles of Intact Zebrafish Embryos Exposed to Aflatoxin B1 Recapitulates Hepatotoxicity and Supports Possible Neurotoxicity. Toxins (Basel). 2019 May 08;11(5): Authors: Zuberi Z, Eeza MNH, Matysik J, Berry JP, Alia A Abstract Aflatoxin B1 (AFB1) is a widespread contaminant of grains and other agricultural crops and is globally associated with both acute toxicity and carcinogenicity. In the present study, we utilized nuclear magnetic resonance (NMR), and specifically high-resolution magic angle spin (HRMAS) NMR, coupled to the zebrafish (Danio rerio) embryo toxicological model, to characterize metabolic profiles associated with exposure to AFB1. Exposure to AFB1 was associated with dose-dependent acute toxicity (i.e., lethality) and developmental deformities at micromolar (≤ 2 µM) concentrations. Toxicity of AFB1 was stage-dependent and specifically consistent, in this regard, with a role of the liver and phase I enzyme (i.e., cytochrome P450) bioactivation. Metabolic profiles of intact zebrafish embryos exposed to AFB1 were, furthermore, largely consistent with hepatotoxicity previously reported in mammalian systems including metabolites associated with cytotoxicity (i.e., loss of cellular membrane integrity), glutathione-based detoxification, and multiple pathways associated with the liver including amino acid, lipid, and carbohydrate (i.e., energy) metabolism. Taken together, these metabolic alterations enabled the proposal of an integrated model of the hepatotoxicity of AFB1 in the zebrafish embryo system. Interestingly, changes in amino acid neurotransmitters (i.e., Gly, Glu, and GABA), as a key modulator of neural development, supports a role in recently-reported neurobehavioral and neurodevelopmental effects of AFB1 in the zebrafish embryo model. The present study reinforces not only toxicological pathways of AFB1 (i.e., hepatotoxicity, neurotoxicity), but also multiple metabolites as potential biomarkers of exposure and toxicity. More generally, this underscores the capacity of NMR-based approaches, when coupled to animal models, as a powerful toxicometabolomics tool. PMID: 31071948 [PubMed - in process]

Related Articles Molecular phenomics and metagenomics of hepatic steatosis in non-diabetic obese women. Nat Med. 2018 07;24(7):1070-1080 Authors: Hoyles L, Fernández-Real JM, Federici M, Serino M, Abbott J, Charpentier J, Heymes C, Luque JL, Anthony E, Barton RH, Chilloux J, Myridakis A, Martinez-Gili L, Moreno-Navarrete JM, Benhamed F, Azalbert V, Blasco-Baque V, Puig J, Xifra G, Ricart W, Tomlinson C, Woodbridge M, Cardellini M, Davato F, Cardolini I, Porzio O, Gentileschi P, Lopez F, Foufelle F, Butcher SA, Holmes E, Nicholson JK, Postic C, Burcelin R, Dumas ME Abstract Hepatic steatosis is a multifactorial condition that is often observed in obese patients and is a prelude to non-alcoholic fatty liver disease. Here, we combine shotgun sequencing of fecal metagenomes with molecular phenomics (hepatic transcriptome and plasma and urine metabolomes) in two well-characterized cohorts of morbidly obese women recruited to the FLORINASH study. We reveal molecular networks linking the gut microbiome and the host phenome to hepatic steatosis. Patients with steatosis have low microbial gene richness and increased genetic potential for the processing of dietary lipids and endotoxin biosynthesis (notably from Proteobacteria), hepatic inflammation and dysregulation of aromatic and branched-chain amino acid metabolism. We demonstrated that fecal microbiota transplants and chronic treatment with phenylacetic acid, a microbial product of aromatic amino acid metabolism, successfully trigger steatosis and branched-chain amino acid metabolism. Molecular phenomic signatures were predictive (area under the curve = 87%) and consistent with the gut microbiome having an effect on the steatosis phenome (>75% shared variation) and, therefore, actionable via microbiome-based therapies. PMID: 29942096 [PubMed - indexed for MEDLINE]

29 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2019/05/10PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

18 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results: metabolomics These pubmed results were generated on 2019/05/09PubMed comprises more than millions of citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Related Articles Phloroglucinol-conjugated gold nanoparticles targeting mitochondrial membrane potential of human cervical (HeLa) cancer cell lines. Spectrochim Acta A Mol Biomol Spectrosc. 2019 Apr 22;219:450-456 Authors: Mahalakshmi M, Kumar P Abstract In recent, targeting mitochondria in cancer is considered to be a challenging task. This report illustrates preliminary findings from an investigation of the conjugation of gold nanoparticles with a bioactive natural compound, phloroglucinol targeting mitochondrial transmembrane potential of HeLa cancer cells. We systematically investigated the formation of gold-nano conjugates over precisely controlled reaction conditions. Their sharp features enable superior surface plasmon resonance, morphology, surface charge, and stability. We show that gold-nano conjugates scavenging free radicals and persuade cell death in HeLa cancer cells. We also show that gold-nano conjugates induce apoptosis by promoting mitochondrial transmembrane permeation via fluorescent microscopic studies. This work gives new insights into bridging metabolomics and nanotechnology into developing novel lead therapeutic molecules. PMID: 31063960 [PubMed - as supplied by publisher]

Related Articles Using a high-resolution mass spectrometry-based metabolomics strategy for comprehensively screening and identifying biomarkers of phthalate exposure: Method development and application. Environ Int. 2019 May 04;128:261-270 Authors: Hsu JF, Tien CP, Shih CL, Liao PM, Wong HI, Liao PC Abstract Di-(2-propylheptyl) phthalate (DPHP) is an alternative plasticizer that can replace other phthalates currently being scrutinized, and its use and production volumes are increasing. This study aimed to develop a high-resolution mass spectrometry (HRMS)-based metabolomics strategy to comprehensively screen urinary biomarkers of DPHP exposure and filter out potentially useful DPHP exposure markers for human exposure assessments. This strategy included three stages: screening of biomarkers, verification of dose-response relationships in laboratory animals, and application in human subjects. The multivariate data analysis method known as orthogonal partial least-squares discriminant analysis (OPLS-DA) was used to screen and find meaningful signals in an MS dataset generated from urine samples collected from DPHP-administered rats. Thirty-six MS signals were verified as exposure marker candidates by assessing dose-response relationships in an animal feeding study. A biotransformation product of DPHP, mono-(2-propyl-7-dihydroxy-heptyl) phthalate, was suggested as a DPHP exposure marker for general human exposure assessments after the human application study and chemical structure identification. Three previously oxidized DPHP biotransformation products might be suitable for human exposure assessments in high-level exposure groups but not in the general population due to their low sensitivity. PMID: 31063951 [PubMed - as supplied by publisher]

Related Articles Maternal short and medium chain fatty acids supply during early pregnancy improves embryo survival through enhancing progesterone synthesis in rats. J Nutr Biochem. 2019 Mar 29;69:98-107 Authors: Ye Q, Cai S, Wang S, Zeng X, Ye C, Chen M, Zeng X, Qiao S Abstract Exploring strategies to prevent miscarriage in women or early pregnancy loss in mammals is of great importance. Manipulating maternal lipid metabolism to maintain sufficient progesterone level is an effective way. To investigated the embryo loss and progesterone synthesis impacts of short and medium chain fatty acids on the lipid metabolism, pregnancy outcome and embryo implantation were investigated in rats fed the pregnancy diets supplemented without or with 0.1% sodium butyrate (SB), 0.1% sodium hexanoate (SH), or 0.1% sodium caprylate (SC) during the entire pregnancy and early pregnancy, respectively, followed with evaluation of potential mechanisms. Maternal SB, SH, or SC supply significantly improved live litter size and embryo implantation in rats. Serum progesterone, arachidonic acid, and phospholipid metabolites levels were significantly increased in response to maternal SB, SH, and SC supply. The expression of key genes involved in ovarian steroidogenesis and granulosa cell luteinization were elevated in ovaries and primary cultured granulosa cells, including cluster of differentiation 36 (CD36), steroidogenic acute regulatory protein (StAR), and cholesterol side-chain cleavage enzyme (CYP11A1). Additionally, the expression of lysophosphatidic acid receptor 3 (LPA3) and cyclooxygenase-2 (COX2) related with phospholipid metabolism were enhanced in uterus in vivo and in in vitro cultured uterine tissue. In conclusion, maternal SB, SH and SC supply reduced early pregnancy loss through modulating maternal phospholipid metabolism and ovarian progesterone synthesis in rats. Our results have important implications that short or medium chain fatty acids have the potential to prevent miscarriage in women or early pregnancy loss in mammals. PMID: 31063920 [PubMed - as supplied by publisher]

Related Articles Metabolomics in Stem Cell Biology Research. Methods Mol Biol. 2019;1975:321-330 Authors: Sun Z, Zhao J, Yu H, Zhang C, Li H, Zeng Z, Zhang J Abstract Stem cell research has been greatly facilitated by comprehensive and integrative multi-omics studies. As a unique approach of functional analysis, metabolomics measures many metabolites and activities of metabolic pathways which can directly indicate cellular energetic status, cell proliferation and fitness, and stem cell fate choices such as self-renewal versus differentiation. Here we describe the methods of applying metabolomics, 13C-labeled glucose and glutamine tracing with mouse embryonic stem cells (ES cells), metabolite analysis using mass spectrometry tools, and the following statistical and computational modeling analysis. Integration of these methods into the more common gene expression and epigenetics analysis toolbox will help to generate a more complete picture and in-depth understanding of one's stem cells of interest. PMID: 31062317 [PubMed - in process]

Related Articles Dynamic Network Modeling of Stem Cell Metabolism. Methods Mol Biol. 2019;1975:305-320 Authors: Shen F, Cheek C, Chandrasekaran S Abstract Stem cell metabolism is intrinsically tied to stem cell pluripotency and function. Yet, understanding metabolic rewiring in stem cells has been challenging due to the complex and highly interconnected nature of the metabolic network. Genome-scale metabolic network models are increasingly used to holistically model the metabolic behavior of various cells and tissues using transcriptomics data. However, these powerful approaches that model steady-state behavior have limited utility for studying dynamic stem cell state transitions. To address this complexity, we recently developed the dynamic flux activity (DFA) approach; DFA is a genome-scale modeling approach that uses time-course metabolic data to predict metabolic flux rewiring. This protocol outlines the steps for modeling steady-state and dynamic metabolic behavior using transcriptomics and time-course metabolomics data, respectively. Using data from naive and primed pluripotent stem cells, we demonstrate how we can use genome-scale modeling and DFA to comprehensively characterize the metabolic differences between these states. PMID: 31062316 [PubMed - in process]

Related Articles Application of Metabolomics to Osteoarthritis: from Basic Science to the Clinical Approach. Curr Rheumatol Rep. 2019 May 06;21(6):26 Authors: Showiheen SAA, Sun AR, Wu X, Crawford R, Xiao Y, Wellard RM, Prasadam I Abstract PURPOSE OF THE REVIEW: Osteoarthritis (OA) is a multifactorial and progressive disease affecting whole synovial joint. The extract pathogenic mechanisms and diagnostic biomarkers of OA remain unclear. In this article, we review the studies related to metabolomics of OA, discuss the biomarkers as a tool for early OA diagnosis. Furthermore, we examine the major studies on the application of metabolomics methodology in the complex context of OA and create a bridge from findings in basic science to their clinical utility. RECENT FINDINGS: Recently, the tissue metabolomics signature permits a view into transitional phases between the healthy and OA joint. Both nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry-based metabolomics approaches have been used to interrogate the metabolic alterations that may indicate the complex progression of OA. Specifically, studies on alterations pertaining to lipids, glucose, and amino acid metabolism have aided in the understanding of the complex pathogenesis of OA. The discovery of identified metabolites could be important for diagnosis and staging of OA, as well as for the assessment of efficacy of new drugs. PMID: 31062102 [PubMed - in process]