PubMed

Related Articles Comprehensive Circulatory Metabolomics in ME/CFS Reveals Disrupted Metabolism of Acyl Lipids and Steroids. Metabolites. 2020 Jan 14;10(1): Authors: Germain A, Barupal DK, Levine SM, Hanson MR Abstract The latest worldwide prevalence rate projects that over 65 million patients suffer from myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), an illness with known effects on the functioning of the immune and nervous systems. We performed an extensive metabolomics analysis on the plasma of 52 female subjects, equally sampled between controls and ME/CFS patients, which delivered data for about 1750 blood compounds spanning 20 super-pathways, subdivided into 113 sub-pathways. Statistical analysis combined with pathway enrichment analysis points to a few disrupted metabolic pathways containing many unexplored compounds. The most intriguing finding concerns acyl cholines, belonging to the fatty acid metabolism sub-pathway of lipids, for which all compounds are consistently reduced in two distinct ME/CFS patient cohorts. We compiled the extremely limited knowledge about these compounds and regard them as promising in the quest to explain many of the ME/CFS symptoms. Another class of lipids with far-reaching activity on virtually all organ systems are steroids; androgenic, progestin, and corticosteroids are broadly reduced in our patient cohort. We also report on lower dipeptides and elevated sphingolipids abundance in patients compared to controls. Disturbances in the metabolism of many of these molecules can be linked to the profound organ system symptoms endured by ME/CFS patients. PMID: 31947545 [PubMed]

Related Articles Early-Life Biomarkers for Psychosis Risk in Young People: Another Nail in the Coffin for Cartesian Dualism. Biol Psychiatry. 2019 07 01;86(1):2-3 Authors: Khandaker GM PMID: 31221245 [PubMed - indexed for MEDLINE]

32 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 2020/01/17PubMed 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.

20 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 2020/01/16PubMed 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.

19 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 2020/01/15PubMed 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.

32 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 2020/01/14PubMed 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 4,5 caffeoylquinic acid and scutellarin, identified by integrated metabolomics and proteomics approach as the active ingredients of Dengzhan Shengmai, act against chronic cerebral hypoperfusion by regulating glutamatergic and GABAergic synapses. Pharmacol Res. 2020 Jan 08;:104636 Authors: Sheng N, Zheng H, Li M, Li M, Wang Z, Peng Y, Yu H, Zhang J Abstract Dengzhan Shengmai (DZSM) is a proprietary Chinese medicine for remarkable curative effect as a treatment of cerebrovascular diseases, such as chronic cerebral hypoperfusion (CCH) and dementia based on evidence-based medicine, which have been widely used in the recovery period of ischemic cerebrovascular diseases. The purpose of this study was to investigate the active substances and mechanism of DZSM against CCH. Integrative metabolomic and proteomic studies were performed to investigate the neuroprotective effect of DZSM based on CCH model rats. The exposed components of DZSM in target brain tissue were analysed by a high-sensitivity HPLC-MS/MS method, and the exposed components were tested on a glutamate-induced neuronal excitatory damage cell model for the verification of active ingredients and mechanism of DZSM. Upon proteomic and metabolomic analysis, we observed a significant response in DZSM therapy from the interconnected neurotransmitter transport pathways including glutamatergic and GABAergic synapses. Additionally, DZSM had a significant regulatory effect on glutamate and GABA-related proteins including vGluT1 and vIAAT, suggested that DZSM could be involved in the vesicle transport of excitatory and inhibitory neurotransmitters in the pre-synaptic membrane. DZSM could also regulated the metabolism of arachidonic acid (AA), phospholipids, lysophospholipids and the expression of phospholipase A2 in post-synaptic membrane. The results of glutamate-induced neuronal excitatory injury cell model experiment for verification of active ingredients and mechanism of DZSM showed that there are five active ingredients, and among them, 4,5 caffeoylquinic acid (4,5-CQA) and scutellarin (SG) could simultaneously affect the GABAergic and glutamatergic synaptic metabolism as well as the related receptors, the NR2b subunit of NMDA and the α1 subunit of GABAA. The active ingredients of DZSM could regulate the over-expression of the NMDA receptor, enhance the expression of the GABAA receptor, resist glutamate-induced neuronal excitatory damage, and finally maintain the balance of excitatory and inhibitory synaptic metabolism dominated by glutamate and GABA. Furtherly, we compared the efficacy of DZSM, 4,5-CQA, SG and the synergistic effect of 4,5-CQA and SG, and the results showed that all the groups significantly improved cell viability compared with the model group (p < 0.001). The western blot results showed that DZSM, 4,5-CQA, SG and 4,5-CQA/SG co-administration groups could significantly regulate the expression of receptors (GABAA α1 and NR2b subunit of NMDA) and synaptic-related proteins, such as Sv2a, Syp, Slc17a7, bin1 and Prkca, respectively. These results proved DZSM and its active ingredients (4,5-CQA and SG) had the effect of regulating glutamatergic and GABAergic synapses. Finally, membrane potential FLIPR assay of 4,5-CQA and SG was used for GABRA1 activity test, and it was found that the two compounds could increase GABA-induced activation of GABRA1 receptor (GABA 10 µM) in a dose-dependent manner with EC50 value of 48.74 µM and 29.77 µM, respectively. Manual patch clamp method was used to record NMDA NR1/NR2B subtype currents, and scutellarin could cause around 10% blockade at 10 μM (p＜0.05 compared with the control group). These studies provided definitive clues of the mechanism for the neuroprotective effect of DZSM for CCH treatment and the active compounds regulating glutamatergic and GABAergic synapses. Additionally, 4,5-CQA and SG might be potential drugs for the treatment of neurodegenerative disease related to CCH. PMID: 31926275 [PubMed - as supplied by publisher]

Related Articles Identification of key metabolites during cisplatin-induced acute kidney injury using an HPLC-TOF/MS-based non-targeted urine and kidney metabolomics approach in rats. Toxicology. 2020 Jan 08;:152366 Authors: Qu X, Gao H, Sun J, Tao L, Zhang Y, Zhai J, Song Y, Hu T, Li Z Abstract Kidney injury is a major adverse effect of cisplatin use. Metabolomics has been used to characterize physiological or pathological conditions through identification of metabolites and characterization of the metabolic pathway. Metabolomics profiling could allow for identification of nephrotoxic mechanisms of cisplatin and identification of biomarkers of cisplatin-induced injury. In this study, we performed metabolomics analysis to characterize key changes in metabolite levels during cisplatin-induced acute kidney injury (AKI) in rats, and screened for sensitive biomarkers for early diagnosis using HPLC-TOF/MS. Rats were intraperitoneally injected with 7.5 mg/kg or 15 mg/kg of cisplatin, or normal saline, and 12 h urine and kidney samples were collected after 72 h. Serum biochemical parameters and kidney histological evaluations showed dose-dependent AKI in response to cisplatin. Metabolomics analysis showed that 37 and 35 endogenous metabolite levels changed in rat urine and kidneys, respectively. Seven key metabolic pathways were disrupted, including the tricarboxylic acid cycle (TCA cycle), phenylalanine, tyrosine, and tryptophan biosynthesis, phenylalanine metabolism, glycerophospholipid metabolism, taurine and hypotaurine metabolism, D-glutamine and D-glutamate metabolism, and nicotinate and nicotinamide metabolism. These pathways are involved in energy generation, and amino acid and lipid metabolism, and disruption of these pathways could contribute to oxidative stress injury, inflammation, and cell membrane damage. Furthermore, 11 sensitive metabolites in urine were screened as potential biomarkers of AKI. To validate these biomarkers, we quantified 4 off these biomarkers, and confirmed that levels of these metabolites were altered in urine of rats treated with CDDP. PMID: 31926187 [PubMed - as supplied by publisher]

Related Articles Anti-tumour immune response in GL261 glioblastoma generated by Temozolomide Immune-Enhancing Metronomic Schedule monitored with MRSI-based nosological images. NMR Biomed. 2020 Jan 11;:e4229 Authors: Wu S, Calero-Pérez P, Villamañan L, Arias-Ramos N, Pumarola M, Ortega-Martorell S, Julià-Sapé M, Arús C, Candiota AP Abstract Glioblastomas (GB) are brain tumours with poor prognosis even after aggressive therapy. Improvements in both therapeutic and follow-up strategies are urgently needed. In previous work we described an oscillatory pattern of response to Temozolomide (TMZ) using a standard administration protocol, detected through MRSI-based machine learning approaches. In the present work, we have introduced the Immune-Enhancing Metronomic Schedule (IMS) with an every 6-d TMZ administration at 60 mg/kg and investigated the consistence of such oscillatory behaviour. A total of n = 17 GL261 GB tumour-bearing C57BL/6j mice were studied with MRI/MRSI every 2 d, and the oscillatory behaviour (6.2 ± 1.5 d period from the TMZ administration day) was confirmed during response. Furthermore, IMS-TMZ produced significant improvement in mice survival (22.5 ± 3.0 d for controls vs 135.8 ± 78.2 for TMZ-treated), outperforming standard TMZ treatment. Histopathological correlation was investigated in selected tumour samples (n = 6) analyzing control and responding fields. Significant differences were found for CD3+ cells (lymphocytes, 3.3 ± 2.5 vs 4.8 ± 2.9, respectively) and Iba-1 immunostained area (microglia/macrophages, 16.8% ± 9.7% and 21.9% ± 11.4%, respectively). Unexpectedly, during IMS-TMZ treatment, tumours from some mice (n = 6) fully regressed and remained undetectable without further treatment for 1 mo. These animals were considered "cured" and a GL261 re-challenge experiment performed, with no tumour reappearance in five out of six cases. Heterogeneous therapy response outcomes were detected in tumour-bearing mice, and a selected group was investigated (n = 3 non-responders, n = 6 relapsing tumours, n = 3 controls). PD-L1 content was found ca. 3-fold increased in the relapsing group when comparing with control and non-responding groups, suggesting that increased lymphocyte inhibition could be associated to IMS-TMZ failure. Overall, data suggest that host immune response has a relevant role in therapy response/escape in GL261 tumours under IMS-TMZ therapy. This is associated to changes in the metabolomics pattern, oscillating every 6 d, in agreement with immune cycle length, which is being sampled by MRSI-derived nosological images. PMID: 31926117 [PubMed - as supplied by publisher]

Related Articles DESI-MSI and METASPACE indicates lipid abnormalities and altered mitochondrial membrane components in diabetic renal proximal tubules. Metabolomics. 2020 Jan 10;16(1):11 Authors: Zhang G, Zhang J, DeHoog RJ, Pennathur S, Anderton CR, Venkatachalam MA, Alexandrov T, Eberlin LS, Sharma K Abstract INTRODUCTION: Diabetic kidney disease (DKD) is the most prevalent complication in diabetic patients, which contributes to high morbidity and mortality. Urine and plasma metabolomics studies have been demonstrated to provide valuable insights for DKD. However, limited information on spatial distributions of metabolites in kidney tissues have been reported. OBJECTIVES: In this work, we employed an ambient desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) coupled to a novel bioinformatics platform (METASPACE) to characterize the metabolome in a mouse model of DKD. METHODS: DESI-MSI was performed for spatial untargeted metabolomics analysis in kidneys of mouse models (F1 C57BL/6J-Ins2Akita male mice at 17 weeks of age) of type 1 diabetes (T1D, n = 5) and heathy controls (n = 6). RESULTS: Multivariate analyses (i.e., PCA and PLS-DA (a 2000 permutation test: P < 0.001)) showed clearly separated clusters for the two groups of mice on the basis of 878 measured m/z's in kidney cortical tissues. Specifically, mice with T1D had increased relative abundances of pseudouridine, accumulation of free polyunsaturated fatty acids (PUFAs), and decreased relative abundances of cardiolipins in cortical proximal tubules when compared with healthy controls. CONCLUSION: Results from the current study support potential key roles of pseudouridine and cardiolipins for maintaining normal RNA structure and normal mitochondrial function, respectively, in cortical proximal tubules with DKD. DESI-MSI technology coupled with METASPACE could serve as powerful new tools to provide insight on fundamental pathways in DKD. PMID: 31925564 [PubMed - in process]

Related Articles Anabolic androgenic steroids exert a selective remodeling of the plasma lipidome that mirrors the decrease of the de novo lipogenesis in the liver. Metabolomics. 2020 Jan 10;16(1):12 Authors: Balgoma D, Zelleroth S, Grönbladh A, Hallberg M, Pettersson C, Hedeland M Abstract INTRODUCTION: The abuse of anabolic androgenic steroids (AASs) is a source of public concern because of their adverse effects. Supratherapeutic doses of AASs are known to be hepatotoxic and regulate the lipoproteins in plasma by modifying the metabolism of lipids in the liver, which is associated with metabolic diseases. However, the effect of AASs on the profile of lipids in plasma is unknown. OBJECTIVES: To describe the changes in the plasma lipidome exerted by AASs and to discuss these changes in the light of previous research about AASs and de novo lipogenesis in the liver. METHODS: We treated male Wistar rats with supratherapeutic doses of nandrolone decanoate and testosterone undecanoate. Subsequently, we isolated the blood plasma and performed lipidomics analysis by liquid chromatography-high resolution mass spectrometry. RESULTS: Lipid profiling revealed a decrease of sphingolipids and glycerolipids with palmitic, palmitoleic, stearic, and oleic acids. In addition, lipid profiling revealed an increase in free fatty acids and glycerophospholipids with odd-numbered chain fatty acids and/or arachidonic acid. CONCLUSION: The lipid profile presented herein reports the imprint of AASs on the plasma lipidome, which mirrors the downregulation of de novo lipogenesis in the liver. In a broader perspective, this profile will help to understand the influence of androgens on the lipid metabolism in future studies of diseases with dysregulated lipogenesis (e.g. type 2 diabetes, fatty liver disease, and hepatocellular carcinoma). PMID: 31925559 [PubMed - in process]

Related Articles Optimization of XCMS parameters for LC-MS metabolomics: an assessment of automated versus manual tuning and its effect on the final results. Metabolomics. 2020 Jan 10;16(1):14 Authors: Albóniga OE, González O, Alonso RM, Xu Y, Goodacre R Abstract INTRODUCTION: Several software packages containing diverse algorithms are available for processing Liquid Chromatography-Mass Spectrometry (LC-MS) chromatographic data and within these deconvolution packages different parameters settings can lead to different outcomes. XCMS is the most widely used peak picking and deconvolution software for metabolomics, but the parameter selection can be hard for inexpert users. To solve this issue, the automatic optimization tools such as Isotopologue Parameters Optimization (IPO) can be extremely helpful. OBJECTIVES: To evaluate the suitability of IPO as a tool for XCMS parameters optimization and compare the results with those manually obtained by an exhaustive examination of the LC-MS characteristics and performance. METHODS: Raw HPLC-TOF-MS data from two types of biological samples (liver and plasma) analysed in both positive and negative electrospray ionization modes from three groups of piglets were processed with XCMS using parameters optimized following two different approaches: IPO and Manual. The outcomes were compared to determine the advantages and disadvantages of using each method. RESULTS: IPO processing produced the higher number of repeatable (%RSD < 20) and significant features for all data sets and allowed the different piglet groups to be distinguished. Nevertheless, on multivariate level, similar clustering results were obtained by Principal Component Analysis (PCA) when applied to IPO and manual matrices. CONCLUSION: IPO is a useful optimization tool that helps in choosing the appropriate parameters. It works well on data with a good LC-MS performance but the lack of such adequate data can result in unrealistic parameter settings, which might require further investigation and manual tuning. On the contrary, manual selection criteria requires deeper knowledge on LC-MS, programming language and XCMS parameter interpretation, but allows a better fine-tuning of the parameters, and thus more robust deconvolution. PMID: 31925557 [PubMed - in process]

Related Articles The differential activation of metabolic pathways in leukemic cells depending on their genotype and micro-environmental stress. Metabolomics. 2020 Jan 10;16(1):13 Authors: Lo Presti C, Fauvelle F, Mondet J, Mossuz P Abstract INTRODUCTION: Acute myeloid leukemia (AML) is characterized by a set of malignant proliferations leading to an accumulation of blasts in the bone marrow and blood. The prognosis is pejorative due to the molecular complexity and pathways implicated in leukemogenesis. OBJECTIVES: Our research was focused on comparing the metabolic profiles of leukemic cells in basal culture and deprivation conditions to investigate their behaviors under metabolic stress. METHODS: We performed untargeted metabolomics using 1H HRMAS-NMR. Five human leukemic cell lines-KG1, K562, HEL, HL60 and OCIAML3-were studied in the basal and nutrient deprivation states. A multivariate analysis of the metabolic profile was performed to find over- or under- expressed metabolites in the different cell lines, depending on the experimental conditions. RESULTS: In the basal state, each leukemic cell line exhibited a specific metabolic signature related to the diversity of AML subtypes represented and their phenotypes. When cultured in a serum-free medium, they showed quick metabolic adaptation and continued to proliferate and survive despite the lack of nutrients. Low apoptosis was observed. Increased phosphocholine and glutathione was a common feature of all the observed cell lines, with the maximum increase in these metabolites at 24 h of culture, suggesting the involvement of lipid metabolism and oxidative stress regulators in the survival mechanism developed by the leukemic cells. CONCLUSIONS: Our study provides new insights into the metabolic mechanisms in leukemogenesis and suggests a hierarchy of metabolic pathways activated within leukemic cells, some dependent on their genotypes and others conserved among the subtypes but commonly induced under micro-environmental stress. PMID: 31925544 [PubMed - in process]

Related Articles Targeting of lipid metabolism with a metabolic inhibitor cocktail eradicates peritoneal metastases in ovarian cancer cells. Commun Biol. 2019 Jul 31;2(1):281 Authors: Chen RR, Yung MMH, Xuan Y, Zhan S, Leung LL, Liang RR, Leung THY, Yang H, Xu D, Sharma R, Chan KKL, Ngu SF, Ngan HYS, Chan DW Abstract Ovarian cancer is an intra-abdominal tumor in which the presence of ascites facilitates metastatic dissemination, and associated with poor prognosis. However, the significance of metabolic alterations in ovarian cancer cells in the ascites microenvironment remains unclear. Here we show ovarian cancer cells exhibited increased aggressiveness in ascites microenvironment via reprogramming of lipid metabolism. High lipid metabolic activities are found in ovarian cancer cells when cultured in the ascites microenvironment, indicating a metabolic shift from aerobic glycolysis to β-oxidation and lipogenesis. The reduced AMP-activated protein kinase (AMPK) activity due to the feedback effect of high energy production led to the activation of its downstream signaling, which in turn, enhanced the cancer growth. The combined treatment of low toxic AMPK activators, the transforming growth factor beta-activated kinase 1 (TAK1) and fatty acid synthase (FASN) inhibitors synergistically impair oncogenic augmentation of ovarian cancer. Collectively, targeting lipid metabolism signaling axis impede ovarian cancer peritoneal metastases. PMID: 31925109 [PubMed - in process]

Related Articles Combined network analysis and machine learning allows the prediction of metabolic pathways from tomato metabolomics data. Commun Biol. 2019 Jun 18;2(1):214 Authors: Toubiana D, Puzis R, Wen L, Sikron N, Kurmanbayeva A, Soltabayeva A, Del Mar Rubio Wilhelmi M, Sade N, Fait A, Sagi M, Blumwald E, Elovici Y Abstract The identification and understanding of metabolic pathways is a key aspect in crop improvement and drug design. The common approach for their detection is based on gene annotation and ontology. Correlation-based network analysis, where metabolites are arranged into network formation, is used as a complentary tool. Here, we demonstrate the detection of metabolic pathways based on correlation-based network analysis combined with machine-learning techniques. Metabolites of known tomato pathways, non-tomato pathways, and random sets of metabolites were mapped as subgraphs onto metabolite correlation networks of the tomato pericarp. Network features were computed for each subgraph, generating a machine-learning model. The model predicted the presence of the β-alanine-degradation-I, tryptophan-degradation-VII-via-indole-3-pyruvate (yet unknown to plants), the β-alanine-biosynthesis-III, and the melibiose-degradation pathway, although melibiose was not part of the networks. In vivo assays validated the presence of the melibiose-degradation pathway. For the remaining pathways only some of the genes encoding regulatory enzymes were detected. PMID: 31925015 [PubMed - in process]

Related Articles Non-enzymatic hydrogen sulfide production from cysteine in blood is catalyzed by iron and vitamin B6. Commun Biol. 2019 May 21;2(1):194 Authors: Yang J, Minkler P, Grove D, Wang R, Willard B, Dweik R, Hine C Abstract Hydrogen sulfide (H2S) plays important roles in metabolism and health. Its enzymatic generation from sulfur-containing amino acids (SAAs) is well characterized. However, the existence of non-enzymatic H2S production from SAAs, the chemical mechanism, and its biological implications remain unclear. Here we present non-enzymatic H2S production in vitro and in blood via a reaction specific for the SAA cysteine serving as substrate and requires coordinated catalysis by Vitamin B6, pyridoxal(phosphate), and iron under physiological conditions. An initial cysteine-aldimine is formed by nucleophilic attack of the cysteine amino group to the pyridoxal(phosphate) aldehyde group. Free or heme-bound iron drives the formation of a cysteine-quinonoid, thiol group elimination, and hydrolysis of the desulfurated aldimine back to pyridoxal(phosphate). The reaction ultimately produces pyruvate, NH3, and H2S. This work highlights enzymatic production is inducible and robust in select tissues, whereas iron-catalyzed production contributes underappreciated basal H2S systemically with pathophysiological implications in hemolytic, iron overload, and hemorrhagic disorders. PMID: 31924987 [PubMed - in process]

Related Articles Plant metabolism of nematode pheromones mediates plant-nematode interactions. Nat Commun. 2020 Jan 10;11(1):208 Authors: Manohar M, Tenjo-Castano F, Chen S, Zhang YK, Kumari A, Williamson VM, Wang X, Klessig DF, Schroeder FC Abstract Microorganisms and nematodes in the rhizosphere profoundly impact plant health, and small-molecule signaling is presumed to play a central role in plant rhizosphere interactions. However, the nature of the signals and underlying mechanisms are poorly understood. Here we show that the ascaroside ascr#18, a pheromone secreted by plant-parasitic nematodes, is metabolized by plants to generate chemical signals that repel nematodes and reduce infection. Comparative metabolomics of plant tissues and excretions revealed that ascr#18 is converted into shorter side-chained ascarosides that confer repellency. An Arabidopsis mutant defective in two peroxisomal acyl-CoA oxidases does not metabolize ascr#18 and does not repel nematodes, indicating that plants, like nematodes, employ conserved peroxisomal β-oxidation to edit ascarosides and change their message. Our results suggest that plant-editing of nematode pheromones serves as a defense mechanism that acts in parallel to conventional pattern-triggered immunity, demonstrating that plants may actively manipulate chemical signaling of soil organisms. PMID: 31924834 [PubMed - in process]

Related Articles Unraveling Asian Soybean Rust metabolomics using mass spectrometry and Molecular Networking approach. Sci Rep. 2020 Jan 10;10(1):138 Authors: Silva E, da Graça JP, Porto C, Martin do Prado R, Hoffmann-Campo CB, Meyer MC, de Oliveira Nunes E, Pilau EJ Abstract Asian Soybean Rust (ASR), caused by the biotrophic fungus Phakopsora pachyrhizi, is a devastating disease with an estimated crop yield loss of up to 90%. Yet, there is a nerf of information on the metabolic response of soybean plants to the pathogen Untargeted metabolomics and Global Natural Products Social Molecular Networking platform approach was used to explore soybean metabolome modulation to P. pachyrhizi infection. Soybean plants susceptible to ASR was inoculated with P. pachyrhizi spore suspension and non-inoculated plants were used as controls. Leaves from both groups were collected 14 days post-inoculation and extracted using different extractor solvent mixtures. The extracts were analyzed on an ultra-high performance liquid chromatography system coupled to high-definition electrospray ionization-mass spectrometry. There was a significant production of defense secondary metabolites (phenylpropanoids, terpenoids and flavonoids) when P. pachyrhizi infected soybean plants, such as putatively identified liquiritigenin, coumestrol, formononetin, pisatin, medicarpin, biochanin A, glyoceollidin I, glyoceollidin II, glyoceollin I, glyoceolidin II, glyoceolidin III, glyoceolidin IV, glyoceolidin VI. Primary metabolites (amino acids, peptides and lipids) also were putatively identified. This is the first report using untargeted metabolomics and GNPS-Molecular Networking approach to explore ASR in soybean plants. Our data provide insights into the potential role of some metabolites in the plant resistance to ASR, which could result in the development of resistant genotypes of soybean to P. pachyrhizi, and effective and specific products against the pathogen. PMID: 31924833 [PubMed - in process]

Related Articles Diagnosis of Bovine Respiratory Disease in feedlot cattle using blood 1H NMR metabolomics. Sci Rep. 2020 Jan 10;10(1):115 Authors: Blakebrough-Hall C, Dona A, D'occhio MJ, McMeniman J, González LA Abstract Current diagnosis methods for Bovine Respiratory Disease (BRD) in feedlots have a low diagnostic accuracy. The current study aimed to search for blood biomarkers of BRD using 1H NMR metabolomics and determine their accuracy in diagnosing BRD. Animals with visual signs of BRD (n = 149) and visually healthy (non-BRD; n = 148) were sampled for blood metabolomics analysis. Lung lesions indicative of BRD were scored at slaughter. Non-targeted 1H NMR metabolomics was used to develop predictive algorithms for disease classification using classification and regression trees. In the absence of a gold standard for BRD diagnosis, six reference diagnosis methods were used to define an animal as BRD or non-BRD. Sensitivity (Se) and specificity (Sp) were used to estimate diagnostic accuracy (Acc). Blood metabolomics demonstrated a high accuracy at diagnosing BRD when using visual signs of BRD (Acc = 0.85), however was less accurate at diagnosing BRD using rectal temperature (Acc = 0.65), lung auscultation score (Acc = 0.61) and lung lesions at slaughter as reference diagnosis methods (Acc = 0.71). Phenylalanine, lactate, hydroxybutyrate, tyrosine, citrate and leucine were identified as metabolites of importance in classifying animals as BRD or non-BRD. The blood metabolome classified BRD and non-BRD animals with high accuracy and shows potential for use as a BRD diagnosis tool. PMID: 31924818 [PubMed - in process]

Related Articles Machine-learning facilitates selection of a novel diagnostic panel of metabolites for the detection of heart failure. Sci Rep. 2020 Jan 10;10(1):130 Authors: Marcinkiewicz-Siemion M, Kaminski M, Ciborowski M, Ptaszynska-Kopczynska K, Szpakowicz A, Lisowska A, Jasiewicz M, Tarasiuk E, Kretowski A, Sobkowicz B, Kaminski KA Abstract The metabolic derangement is common in heart failure with reduced ejection fraction (HFrEF). The aim of the study was to check feasibility of the combined approach of untargeted metabolomics and machine learning to create a simple and potentially clinically useful diagnostic panel for HFrEF. The study included 67 chronic HFrEF patients (left ventricular ejection fraction-LVEF 24.3 ± 5.9%) and 39 controls without the disease. Fasting serum samples were fingerprinted by liquid chromatography-mass spectrometry. Feature selection based on random-forest models fitted to resampled data and followed by linear modelling, resulted in selection of eight metabolites (uric acid, two isomers of LPC 18:2, LPC 20:1, deoxycholic acid, docosahexaenoic acid and one unknown metabolite), demonstrating their predictive value in HFrEF. The accuracy of a model based on metabolites panel was comparable to BNP (0.85 vs 0.82), as verified on the test set. Selected metabolites correlated with clinical, echocardiographic and functional parameters. The combination of two innovative tools (metabolomics and machine-learning methods), both unrestrained by the gaps in the current knowledge, enables identification of a novel diagnostic panel. Its diagnostic value seems to be comparable to BNP. Large scale, multi-center studies using validated targeted methods are crucial to confirm clinical utility of proposed markers. PMID: 31924803 [PubMed - in process]