Publications by authors named "Dmitry Grapov"

41 Publications

Metabolomic profile of patients with left ventricular assist devices: a pilot study.

Ann Cardiothorac Surg 2021 Mar;10(2):240-247

Anesthesia and Intensive Care, IRCCS ISMETT, UPMC Italy, Palermo, Italy.

Background: Metabolomic profiling has important diagnostic and prognostic value in heart failure (HF). We investigated whether left ventricular assist device (LVAD) support has an impact on the metabolomic profile of chronic HF patients and if specific metabolic patterns are associated with the development of adverse events.

Methods: We applied untargeted metabolomics to detect and analyze molecules such as amino acids, sugars, fatty acids and other metabolites in plasma samples collected from thirty-three patients implanted with a continuous-flow LVAD. Data were analyzed at baseline, i.e., before implantation of the LVAD, and at long-term follow-up.

Results: Our results reveal significant changes in the metabolomic profile after LVAD implant compared to baseline. In detail, we observed a pre-implant reduction in amino acid metabolism (aminoacyl-tRNA biosynthesis) and increased galactose metabolism, which reversed over the course of support [median follow-up 187 days (63-334 days)]. These changes were associated with improved patient functional capacity driven by LVAD therapy, according to NYHA functional classification of HF (NYHA class I-II: pre-implant =0% of the patients; post-implant =97% of the patients; P<0.001). Moreover, patients who developed adverse thromboembolic events (n=4, 13%) showed a pre-operative metabolomic fingerprint mainly associated with alterations of fatty acid biosynthesis and mitochondrial beta-oxidation of short-chain saturated fatty acids.

Conclusions: Our data provide preliminary evidence that LVAD therapy is associated with changes in the metabolomic profile of HF and suggest the potential use of metabolomics as a new tool to stratify LVAD patients in regard to the risk of adverse events.
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http://dx.doi.org/10.21037/acs-2020-cfmcs-117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8033252PMC
March 2021

Deep metabolome: Applications of deep learning in metabolomics.

Comput Struct Biotechnol J 2020 1;18:2818-2825. Epub 2020 Oct 1.

Metabolomics and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand.

In the past few years, deep learning has been successfully applied to various omics data. However, the applications of deep learning in metabolomics are still relatively low compared to others omics. Currently, data pre-processing using convolutional neural network architecture appears to benefit the most from deep learning. Compound/structure identification and quantification using artificial neural network/deep learning performed relatively better than traditional machine learning techniques, whereas only marginally better results are observed in biological interpretations. Before deep learning can be effectively applied to metabolomics, several challenges should be addressed, including metabolome-specific deep learning architectures, dimensionality problems, and model evaluation regimes.
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http://dx.doi.org/10.1016/j.csbj.2020.09.033DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7575644PMC
October 2020

Impact of a weight loss and fitness intervention on exercise-associated plasma oxylipin patterns in obese, insulin-resistant, sedentary women.

Physiol Rep 2020 09;8(17):e14547

Arkansas Children's Nutrition Center, Little Rock, AR, USA.

Very little is known about how metabolic health status, insulin resistance or metabolic challenges modulate the endocannabinoid (eCB) or polyunsaturated fatty acid (PUFA)-derived oxylipin (OxL) lipid classes. To address these questions, plasma eCB and OxL concentrations were determined at rest, 10 and 20 min during an acute exercise bout (30 min total, ~45% of preintervention V̇O , ~63 W), and following 20 min recovery in overnight-fasted sedentary, obese, insulin-resistant women under controlled diet conditions. We hypothesized that increased fitness and insulin sensitivity following a ~14-week training and weight loss intervention would lead to significant changes in lipid signatures using an identical acute exercise protocol to preintervention. In the first 10 min of exercise, concentrations of a suite of OxL diols and hydroxyeicosatetraenoic acid (HETE) metabolites dropped significantly. There was no increase in 12,13-DiHOME, previously reported to increase with exercise and proposed to activate muscle fatty acid uptake and tissue metabolism. Following weight loss intervention, exercise-associated reductions were more pronounced for several linoleate and alpha-linolenate metabolites including DiHOMEs, DiHODEs, KODEs, and EpODEs, and fasting concentrations of 9,10-DiHODE, 12,13-DiHODE, and 9,10-DiHOME were reduced. These findings suggest that improved metabolic health modifies soluble epoxide hydrolase, cytochrome P450 epoxygenase (CYP), and lipoxygenase (LOX) systems. Acute exercise led to reductions for most eCB metabolites, with no evidence for concentration increases even at recovery. It is proposed that during submaximal aerobic exercise, nonoxidative fates of long-chain saturated, monounsaturated, and PUFAs are attenuated in tissues that are important contributors to the blood OxL and eCB pools.
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http://dx.doi.org/10.14814/phy2.14547DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7460071PMC
September 2020

Evaluating maize phenotypic variance, heritability, and yield relationships at multiple biological scales across agronomically relevant environments.

Plant Cell Environ 2020 04 5;43(4):880-902. Epub 2020 Feb 5.

Bayer Crop Science, Chesterfield, Missouri.

A challenge to improve an integrative phenotype, like yield, is the interaction between the broad range of possible molecular and physiological traits that contribute to yield and the multitude of potential environmental conditions in which they are expressed. This study collected data on 31 phenotypic traits, 83 annotated metabolites, and nearly 22,000 transcripts from a set of 57 diverse, commercially relevant maize hybrids across three years in central U.S. Corn Belt environments. Although variability in characteristics created a complex picture of how traits interact produce yield, phenotypic traits and gene expression were more consistent across environments, while metabolite levels showed low repeatability. Phenology traits, such as green leaf number and grain moisture and whole plant nitrogen content showed the most consistent correlation with yield. A machine learning predictive analysis of phenotypic traits revealed that ear traits, phenology, and root traits were most important to predicting yield. Analysis suggested little correlation between biomass traits and yield, suggesting there is more of a sink limitation to yield under the conditions studied here. This work suggests that continued improvement of maize yields requires a strong understanding of baseline variation of plant characteristics across commercially-relevant germplasm to drive strategies for consistently improving yield.
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http://dx.doi.org/10.1111/pce.13681DOI Listing
April 2020

Exercise plasma metabolomics and xenometabolomics in obese, sedentary, insulin-resistant women: impact of a fitness and weight loss intervention.

Am J Physiol Endocrinol Metab 2019 12 17;317(6):E999-E1014. Epub 2019 Sep 17.

Arkansas Children's Nutrition Center, Little Rock, Arkansas.

Insulin resistance has wide-ranging effects on metabolism, but there are knowledge gaps regarding the tissue origins of systemic metabolite patterns and how patterns are altered by fitness and metabolic health. To address these questions, plasma metabolite patterns were determined every 5 min during exercise (30 min, ∼45% of V̇o, ∼63 W) and recovery in overnight-fasted sedentary, obese, insulin-resistant women under controlled conditions of diet and physical activity. We hypothesized that improved fitness and insulin sensitivity following a ∼14-wk training and weight loss intervention would lead to fixed workload plasma metabolomics signatures reflective of metabolic health and muscle metabolism. Pattern analysis over the first 15 min of exercise, regardless of pre- versus postintervention status, highlighted anticipated increases in fatty acid tissue uptake and oxidation (e.g., reduced long-chain fatty acids), diminution of nonoxidative fates of glucose [e.g., lowered sorbitol-pathway metabolites and glycerol-3-galactoside (possible glycerolipid synthesis metabolite)], and enhanced tissue amino acid use (e.g., drops in amino acids; modest increase in urea). A novel observation was that exercise significantly increased several xenometabolites ("non-self" molecules, from microbes or foods), including benzoic acid-salicylic acid-salicylaldehyde, hexadecanol-octadecanol-dodecanol, and chlorogenic acid. In addition, many nonannotated metabolites changed with exercise. Although exercise itself strongly impacted the global metabolome, there were surprisingly few intervention-associated differences despite marked improvements in insulin sensitivity, fitness, and adiposity. These results and previously reported plasma acylcarnitine profiles support the principle that most metabolic changes during submaximal aerobic exercise are closely tethered to absolute ATP turnover rate (workload), regardless of fitness or metabolic health status.
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http://dx.doi.org/10.1152/ajpendo.00091.2019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6962502PMC
December 2019

Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine.

OMICS 2018 10 20;22(10):630-636. Epub 2018 Aug 20.

3 Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University , Bangkok, Thailand .

Machine learning (ML) is being ubiquitously incorporated into everyday products such as Internet search, email spam filters, product recommendations, image classification, and speech recognition. New approaches for highly integrated manufacturing and automation such as the Industry 4.0 and the Internet of things are also converging with ML methodologies. Many approaches incorporate complex artificial neural network architectures and are collectively referred to as deep learning (DL) applications. These methods have been shown capable of representing and learning predictable relationships in many diverse forms of data and hold promise for transforming the future of omics research and applications in precision medicine. Omics and electronic health record data pose considerable challenges for DL. This is due to many factors such as low signal to noise, analytical variance, and complex data integration requirements. However, DL models have already been shown capable of both improving the ease of data encoding and predictive model performance over alternative approaches. It may not be surprising that concepts encountered in DL share similarities with those observed in biological message relay systems such as gene, protein, and metabolite networks. This expert review examines the challenges and opportunities for DL at a systems and biological scale for a precision medicine readership.
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http://dx.doi.org/10.1089/omi.2018.0097DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6207407PMC
October 2018

Umbilical cord blood metabolomics reveal distinct signatures of dyslipidemia prior to bronchopulmonary dysplasia and pulmonary hypertension.

Am J Physiol Lung Cell Mol Physiol 2018 11 16;315(5):L870-L881. Epub 2018 Aug 16.

Department of Pediatrics, University of California, Davis Medical Center , Sacramento, California.

Pulmonary hypertension (PH) is a common consequence of bronchopulmonary dysplasia (BPD) and remains a primary contributor to increased morbidity and mortality among preterm infants. Unfortunately, at the present time, there are no reliable early predictive markers for BPD-associated PH. Considering its health consequences, understanding in utero perturbations that lead to the development of BPD and BPD-associated PH and identifying early predictive markers is of utmost importance. As part of the discovery phase, we applied a multiplatform metabolomics approach consisting of untargeted and targeted methodologies to screen for metabolic perturbations in umbilical cord blood (UCB) plasma from preterm infants that did ( n = 21; cases) or did not ( n = 21; controls) develop subsequent PH. A total of 1,656 features were detected, of which 407 were annotated by metabolite structures. PH-associated metabolic perturbations were characterized by reductions in major choline-containing phospholipids, such as phosphatidylcholines and sphingomyelins, indicating altered lipid metabolism. The reduction in UCB abundances of major choline-containing phospholipids was confirmed in an independent validation cohort consisting of UCB plasmas from 10 cases and 10 controls matched for gestational age and BPD status. Subanalyses in the discovery cohort indicated that elevations in the oxylipins PGE1, PGE2, PGF2a, 9- and 13-HOTE, 9- and 13-HODE, and 9- and 13-KODE were positively associated with BPD presence and severity. This expansive evaluation of cord blood plasma identifies compounds reflecting dyslipidemia and suggests altered metabolite provision associated with metabolic immaturity that differentiate subjects, both by BPD severity and PH development.
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http://dx.doi.org/10.1152/ajplung.00283.2017DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6295510PMC
November 2018

Enhanced field emission properties of carbon nanotube bundles confined in SiO pits.

Nanotechnology 2018 Feb;29(7):075205

Centre for Micro-/Nano-electronics (NOVITAS), School of Electrical and Electronics Engineering, Nanyang Technological University, 50 Nanyang Avenue 639798, Singapore.

It has been widely reported that carbon nanotubes (CNTs) exhibit superior field emission (FE) properties due to their high aspect ratios and unique structural properties. Among the various types of CNTs, random growth CNTs exhibit promising FE properties due to their reduced inter-tube screening effect. However, growing random growth CNTs on individual catalyst islands often results in spread out CNT bundles, which reduces overall field enhancement. In this study, significant improvement in FE properties in CNT bundles is demonstrated by confining them in microfabricated SiO pits. Growing CNT bundles in narrow (0.5 μm diameter and 2 μm height) SiO pits achieves FE current density of 1-1.4 A cm, which is much higher than for freestanding CNT bundles (76.9 mA cm). From the Fowler Nordheim plots, confined CNT bundles show a higher field enhancement factor. This improvement can be attributed to the reduced bundle diameter by SiO pit confinement, which yields bundles with higher aspect ratios. Combining the obtained outcomes, it can be conclusively summarized that confining CNTs in SiO pits yields higher FE current density due to the higher field enhancement of confined CNTs.
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http://dx.doi.org/10.1088/1361-6528/aaa1bbDOI Listing
February 2018

Omega-6 and omega-3 oxylipins are implicated in soybean oil-induced obesity in mice.

Sci Rep 2017 10 2;7(1):12488. Epub 2017 Oct 2.

Department of Cell Biology and Neuroscience, University of California, Riverside, CA, 92521, USA.

Soybean oil consumption is increasing worldwide and parallels a rise in obesity. Rich in unsaturated fats, especially linoleic acid, soybean oil is assumed to be healthy, and yet it induces obesity, diabetes, insulin resistance, and fatty liver in mice. Here, we show that the genetically modified soybean oil Plenish, which came on the U.S. market in 2014 and is low in linoleic acid, induces less obesity than conventional soybean oil in C57BL/6 male mice. Proteomic analysis of the liver reveals global differences in hepatic proteins when comparing diets rich in the two soybean oils, coconut oil, and a low-fat diet. Metabolomic analysis of the liver and plasma shows a positive correlation between obesity and hepatic C18 oxylipin metabolites of omega-6 (ω6) and omega-3 (ω3) fatty acids (linoleic and α-linolenic acid, respectively) in the cytochrome P450/soluble epoxide hydrolase pathway. While Plenish induced less insulin resistance than conventional soybean oil, it resulted in hepatomegaly and liver dysfunction as did olive oil, which has a similar fatty acid composition. These results implicate a new class of compounds in diet-induced obesity-C18 epoxide and diol oxylipins.
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http://dx.doi.org/10.1038/s41598-017-12624-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5624939PMC
October 2017

Enhanced Carbon Nanotubes Growth Using Nickel/Ferrocene-Hybridized Catalyst.

ACS Omega 2017 Sep 21;2(9):6063-6071. Epub 2017 Sep 21.

Micro- and Nanoelectronics Department, Belarusian State University of Informatics and Radioelectronics, vulica Pietrusia Broŭki 6, 220013 Minsk, Belarus.

Tall, crystalline carbon nanotubes (CNTs) are desired to successfully integrate them in various applications. As the crystallinity of CNTs improves with increasing growth temperatures, higher growth temperatures are required to obtain crystalline CNTs. However, in a typical chemical vapor deposition (CVD) process, CNT growth rate reduces when the growth temperature exceeds a specific level due to the degradation of the catalyst particles. In this study, we have demonstrated the improved catalytic activity of nickel/ferrocene-hybridized catalyst as compared to sole ferrocene catalyst. To demonstrate this, CNTs are grown on bare silicon (Si) as well as nickel (Ni) catalyst-deposited substrates using volatile catalyst source (ferrocene/xylene) CVD at the growth temperatures ranging from 790 to 880 °C. It was found that CNTs grown on bare Si substrate experience a reduction in height at growth temperature above 860 °C, whereas the CNTs grown on 10 nm Ni catalyst-deposited substrates experience continuous increase in height as the temperature increases from 790 to 880 °C. The enhancement in the height of CNTs by the addition of Ni catalyst is also demonstrated on 5, 20, and 30 nm Ni layers. The examination of CNTs using electron microscopy and Raman spectra shows that the additional Ni catalyst source improves the CNT growth rates and crystallinity, yielding taller CNTs with a high degree of structural crystallinity.
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http://dx.doi.org/10.1021/acsomega.7b00858DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6644565PMC
September 2017

The Human Serum Metabolome of Vitamin B-12 Deficiency and Repletion, and Associations with Neurological Function in Elderly Adults.

J Nutr 2017 Oct;147(10):1839-1849

USDA Agricultural Research Service, Western Human Nutrition Research Center, Davis, CA.

Background: The specific metabolomic perturbations that occur in vitamin B-12 deficiency, and their associations with neurological function, are not well characterized.

Objective: We sought to characterize the human serum metabolome in subclinical vitamin B-12 deficiency and repletion.

Methods: A before-and-after treatment study provided 1 injection of 10 mg vitamin B-12 (with 100 mg pyridoxine and 100 mg thiamin) to 27 community-dwelling elderly Chileans (∼74 y old) with vitamin B-12 deficiency, as evaluated with serum vitamin B-12, total plasma homocysteine (tHcy), methylmalonic acid (MMA), and holotranscobalamin. The combined indicator of vitamin B-12 status (cB-12) was computed. Targeted metabolites [166 acylcarnitines, amino acids, sugars, glycerophospholipids, and sphingolipids (liquid chromatography-tandem mass spectrometry)], and untargeted metabolites [247 chemical entities (gas chromatography time-of-flight mass spectrometry)] were measured at baseline and 4 mo after treatment. A peripheral nerve score was developed. Differences before and after treatment were examined. For targeted metabolomics, the data from 18 individuals with adequate vitamin B-12 status (selected from the same population) were added to the before-and-after treatment data set. Network visualizations and metabolic pathways are illustrated.

Results: The injection increased serum vitamin B-12, holotranscobalamin, and cB-12 (P < 0.001), and reduced tHcy and serum MMA (P < 0.001). Metabolomic changes from before to after treatment included increases (P < 0.001) in acylcarnitines, plasmalogens, and other phospholipids, whereas proline and other intermediaries of one-carbon metabolism-that is, methionine and cysteine-were reduced (P < 0.001). Direct significant correlations (P < 0.05 after the false discovery rate procedure) were identified between acylcarnitines, plasmalogens, phospholipids, lyso-phospholipids, and sphingomyelins compared with vitamin B-12 status and nerve function. Multiple connections were identified with primary metabolites (e.g., an inverse relation between vitamin B-12 markers and tryptophan, tyrosine, and pyruvic, succinic, and citric acids, and a direct correlation between the nerve score and arginine).

Conclusions: The human serum metabolome in vitamin B-12 deficiency and the changes that occur after supplementation are characterized. Metabolomics revealed connections between vitamin B-12 status and serum metabolic markers of mitochondrial function, myelin integrity, oxidative stress, and peripheral nerve function, including some previously implicated in Alzheimer and Parkinson diseases. This trial was registered at www.controlled-trials.com as ISRCTN02694183.
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http://dx.doi.org/10.3945/jn.117.248278DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5610547PMC
October 2017

Insulin induces a shift in lipid and primary carbon metabolites in a model of fasting-induced insulin resistance.

Metabolomics 2017 May 27;13(5). Epub 2017 Mar 27.

Molecular Cell Biology, School of Natural Sciences, University of California, Merced, USA.

Introduction: Prolonged fasting in northern elephant seals (NES) is characterized by a reliance on lipid metabolism, conservation of protein, and reduced plasma insulin. During early fasting, glucose infusion previously reduced plasma free fatty acids (FFA); however, during late-fasting, it induced an atypical elevation in FFA despite comparable increases in insulin during both periods suggestive of a dynamic shift in tissue responsiveness to glucose-stimulated insulin secretion.

Objective: To better assess the contribution of insulin to this fasting-associated shift in substrate metabolism.

Methods: We compared the responses of plasma metabolites (amino acids (AA), FFA, endocannabinoids (EC), and primary carbon metabolites (PCM)) to an insulin infusion (65 mU/kg) in early- and late-fasted NES pups (n = 5/group). Plasma samples were collected prior to infusion (T0) and at 10, 30, 60, and 120 min post-infusion, and underwent untargeted and targeted metabolomics analyses utilizing a variety of GC-MS and LC-MS technologies.

Results: In early fasting, the majority (72%) of metabolite trajectories return to baseline levels within 2 h, but not in late fasting indicative of an increase in tissue sensitivity to insulin. In late-fasting, increases in FFA and ketone pools, coupled with decreases in AA and PCM, indicate a shift toward lipolysis, beta-oxidation, ketone metabolism, and decreased protein catabolism. Conversely, insulin increased PCM AUC in late fasting suggesting that gluconeogenic pathways are activated. Insulin also decreased FFA AUC between early and late fasting suggesting that insulin suppresses triglyceride hydrolysis.

Conclusion: Naturally adapted tolerance to prolonged fasting in these mammals is likely accomplished by suppressing insulin levels and activity, providing novel insight on the evolution of insulin during a condition of temporary, reversible insulin resistance.
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http://dx.doi.org/10.1007/s11306-017-1186-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5526460PMC
May 2017

Review of emerging metabolomic tools and resources: 2015-2016.

Electrophoresis 2017 09 1;38(18):2257-2274. Epub 2017 Aug 1.

CDS Creative Data Solutions, Ballwin, MO, USA.

Data processing and analysis are major bottlenecks in high-throughput metabolomic experiments. Recent advancements in data acquisition platforms are driving trends toward increasing data size (e.g., petabyte scale) and complexity (multiple omic platforms). Improvements in data analysis software and in silico methods are similarly required to effectively utilize these advancements and link the acquired data with biological interpretations. Herein, we provide an overview of recently developed and freely available metabolomic tools, algorithms, databases, and data analysis frameworks. This overview of popular tools for MS and NMR-based metabolomics is organized into the following sections: data processing, annotation, analysis, and visualization. The following overview of newly developed tools helps to better inform researchers to support the emergence of metabolomics as an integral tool for the study of biochemistry, systems biology, environmental analysis, health, and personalized medicine.
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http://dx.doi.org/10.1002/elps.201700110DOI Listing
September 2017

Metabox: A Toolbox for Metabolomic Data Analysis, Interpretation and Integrative Exploration.

PLoS One 2017 31;12(1):e0171046. Epub 2017 Jan 31.

West Coast Metabolomics Center, Genome Center, University of California Davis, Davis, California, United States of America.

Similar to genomic and proteomic platforms, metabolomic data acquisition and analysis is becoming a routine approach for investigating biological systems. However, computational approaches for metabolomic data analysis and integration are still maturing. Metabox is a bioinformatics toolbox for deep phenotyping analytics that combines data processing, statistical analysis, functional analysis and integrative exploration of metabolomic data within proteomic and transcriptomic contexts. With the number of options provided in each analysis module, it also supports data analysis of other 'omic' families. The toolbox is an R-based web application, and it is freely available at http://kwanjeeraw.github.io/metabox/ under the GPL-3 license.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0171046PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5283729PMC
August 2017

Integrated Metabolomics and Proteomics Highlight Altered Nicotinamide- and Polyamine Pathways in Lung Adenocarcinoma.

Carcinogenesis 2017 03 3;38(3):271-280. Epub 2017 Jan 3.

Division of Hematology and Oncology, Department of Internal Medicine, School of Medicine, University of California, Davis Medical Center, Sacramento, California

Lung cancer is the leading cause of cancer mortality in the United States with non-small cell lung cancer (NSCLC) adenocarcinoma being the most common histological type. Early perturbations in cellular metabolism are a hallmark of cancer, but the extent of these changes in early stage lung adenocarcinoma remains largely unknown. In the current study, an integrated metabolomics and proteomics approach was utilized to characterize the biochemical and molecular alterations between malignant and matched control tissue from 27 subjects diagnosed with early stage lung adenocarcinoma. Differential analysis identified 71 metabolites and 1102 proteins that delineated tumor from control tissue. Integrated results indicated four major metabolic changes in early stage adenocarcinoma: (1) increased glycosylation and glutaminolysis; (2) elevated Nrf2 activation; (3) increase in nicotinic and nicotinamide salvaging pathways; and (4) elevated polyamine biosynthesis linked to differential regulation of the SAM/nicotinamide methyl-donor pathway. Genomic data from publicly available databases were included to strengthen proteomic findings. Our findings provide insight into the biochemical and molecular biological reprogramming that may accompanies early stage lung tumorigenesis and highlight potential therapeutic targets.
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http://dx.doi.org/10.1093/carcin/bgw205DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5862279PMC
March 2017

Proteomic profiling of lung adenocarcinoma indicates heightened DNA repair, antioxidant mechanisms and identifies LASP1 as a potential negative predictor of survival.

Clin Proteomics 2016 27;13:31. Epub 2016 Oct 27.

Division of Hematology and Oncology, Department of Internal Medicine, University of California, Davis Medical Center, 4501 X Street, Suite 3016, Sacramento, CA 95817 USA.

Background: Lung cancer is the leading cause of cancer mortality in the United States. Non-small cell lung cancer accounts for 85% of all lung cancers for which adenocarcinoma is the most common histological type. Management of lung cancer is hindered by high false-positive rates due to difficulty resolving between benign and malignant tumors. Better molecular analysis comparing malignant and non-malignant tissues will provide important evidence of the underlying biology contributing to tumorigenesis.

Methods: We utilized a proteomics approach to analyze 38 malignant and non-malignant paired tissue samples obtained from current or former smokers with early stage (Stage IA/IB) lung adenocarcinoma. Statistical mixed effects modeling and orthogonal partial least squares discriminant analysis were used to identify key cancer-associated perturbations in the adenocarcinoma proteome. Identified proteins were subsequently assessed against clinicopathological variables.

Results: Top cancer-associated protein alterations were characterized by: (1) elevations in APEX1, HYOU1 and PDIA4, indicative of increased DNA repair machinery and heightened anti-oxidant defense mechanisms; (2) increased LRPPRC, STOML2, COPG1 and EPRS, suggesting altered tumor metabolism and inflammation; (3) reductions in SPTB, SPTA1 and ANK1 implying dysregulation of membrane integrity; and (4) decreased SLCA41 suggesting altered pH regulation. Increased protein levels of HYOU1, EPRS and LASP1 in NSCLC adenocarcinoma was independently validated by tissue microarray immunohistochemistry. Immunohistochemistry for HYOU1 and EPRS indicated AUCs of 0.952 and 0.841, respectively, for classifying tissue as malignant. Increased LASP1 correlated with poor overall survival (HR 3.66 per unit increase; CI 1.37-9.78; p = 0.01).

Conclusion: These results reveal distinct proteomic changes associated with early stage lung adenocarcinoma that may be useful prognostic indicators and therapeutic targets.
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http://dx.doi.org/10.1186/s12014-016-9132-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5084393PMC
October 2016

Primary HCMV infection in pregnancy from classic data towards metabolomics: An exploratory analysis.

Clin Chim Acta 2016 Sep 8;460:23-32. Epub 2016 Jun 8.

Molecular Virology Unit, Microbiology and VirologyDepartment, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Policlinico San Matteo, Pavia, Italy.

Background: Human cytomegalovirus (HCMV) is one of the most frequent risk of viral infections during pregnancy. The aim of this study was to evaluate the metabolic profile in amniotic fluid (AF) samples obtained from HCMV-infected, and uninfected fetuses in order to elucidate changes in metabolic pathways during congenital HCMV infection and to recognize new potential diagnostic and/or prognostic biomarkers.

Methods: A retrospective cohort study was conducted on 63 pregnant women: 20 contracted primary HCMV infection during pregnancy and, subsequently, transmitted the virus to the fetus (transmitters); 20 contracted the infection without transmitting the virus to the fetus (non-transmitters); 23 who underwent amniocentesis for cytogenetic-based diagnosis were considered controls. Metabolomics analysis was performed by using the hyphenated technique Gas chromatography-mass spectrometry (GC-MS) followed by a multivariate statistical approach. Four PLS-DA models were generated: controls vs. transmitters; controls vs. non-transmitters; transmitters vs. non-transmitters; and asymptomatic infected vs. symptomatic infected newborns. Subsequently, these models were exploited for network mapping.

Results: Compared with controls, HCMV transmitters showed significantly increased levels in glutamine, glycine, serine, pyruvic acid, threonine, threonic acid, and cystine; conversely, unknown U1715 and U1804, glutamic acid, U1437, fructose, sugar-like A203003 and A203005, and tyrosine levels were found decreased. In non-transmitters, glutamine, serine, glycine, threonic acid, threonine, 1-monostearin, urea, and cystine were found increased, while sorbitol, unknown U1804, sugar-like A203003, U1751, xylitol, leucine and fructose were decreased. The comparison between transmitters and non-transmitters did not produce a statistically significant model. Unlike controls' profile, a common feature of HCMV infected subjects (transmitters and non-transmitters) was the activation of glutamine-glutamate and pyrimidine metabolic pathways. In addition, a clusterization for asymptomatic vs. symptomatic outcome was also observed due to alteration of fatty acids biosynthesis.

Conclusions: Metabolomics approach could highlight the significant modification of maternal and placental status during HCMV infection for both transmitter and non-transmitter subjects. A further separation was observed for asymptomatic vs. symptomatic HCMV congenital infections model. Therefore, metabolomics may be a promising tool to improve the accuracy of an early diagnosis, and the management of HCMV pregnancy-related infections.
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http://dx.doi.org/10.1016/j.cca.2016.06.005DOI Listing
September 2016

Metabolic changes associated with methionine stress sensitivity in MDA-MB-468 breast cancer cells.

Cancer Metab 2016 2;4. Epub 2016 May 2.

Department of Biological Chemistry, University of California, Irvine, Irvine, CA USA.

Background: The majority of cancer cells have a unique metabolic requirement for methionine that is not observed in normal, non-tumorigenic cells. This phenotype is described as "methionine dependence" or "methionine stress sensitivity" in which cancer cells are unable to proliferate when methionine has been replaced with its metabolic precursor, homocysteine, in cell culture growth media. We focus on the metabolic response to methionine stress in the triple negative breast cancer cell line MDA-MB-468 and its methionine insensitive derivative cell line MDA-MB-468res-R8.

Results: Using a variety of techniques including fluorescence lifetime imaging microscopy (FLIM) and extracellular flux assays, we identified a metabolic down-regulation of oxidative phosphorylation in both MDA-MB-468 and MDA-MB-468res-R8 cell types when cultured in homocysteine media. Untargeted metabolomics was performed by way of gas chromatography/time-of-flight mass spectrometry on both cell types cultured in homocysteine media over a period of 2 to 24 h. We determined unique metabolic responses between the two cell lines in specific pathways including methionine salvage, purine/pyrimidine synthesis, and the tricarboxylic acid cycle. Stable isotope tracer studies using deuterium-labeled homocysteine indicated a redirection of homocysteine metabolism toward the transsulfuration pathway and glutathione synthesis. This data corroborates with increased glutathione levels concomitant with increased levels of oxidized glutathione. Redirection of homocysteine flux resulted in reduced generation of methionine from homocysteine particularly in MDA-MB-468 cells. Consequently, synthesis of the important one-carbon donor S-adenosylmethionine (SAM) was decreased, perturbing the SAM to S-adenosylhomocysteine ratio in MDA-MB-468 cells, which is an indicator of the cellular methylation potential.

Conclusion: This study indicates a differential metabolic response between the methionine sensitive MDA-MB-468 cells and the methionine insensitive derivative cell line MDA-MB-468res-R8. Both cell lines appear to experience oxidative stress when methionine was replaced with its metabolic precursor homocysteine, forcing cells to redirect homocysteine metabolism toward the transsulfuration pathway to increase glutathione synthesis. The methionine stress resistant MDA-MB-468res-R8 cells responded to this cellular stress earlier than the methionine stress sensitive MDA-MB468 cells and coped better with metabolic demands. Additionally, it is evident that S-adenosylmethionine metabolism is dependent on methionine availability in cancer cells, which cannot be sufficiently supplied by homocysteine metabolism under these conditions.
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http://dx.doi.org/10.1186/s40170-016-0148-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4852440PMC
May 2016

Serum phosphatidylethanolamine levels distinguish benign from malignant solitary pulmonary nodules and represent a potential diagnostic biomarker for lung cancer.

Cancer Biomark 2016 Mar;16(4):609-17

Division of Hematology and Oncology, Department of Internal Medicine, School of Medicine, University of California, Davis Medical Center, Sacramento, CA, USA.

Background: Recent computed tomography (CT) screening trials showed that it is effective for early detection of lung cancer, but were plagued by high false positive rates. Additional blood biomarker tests designed to complement CT screening and reduce false positive rates are highly desirable.

Objective: Identify blood-based metabolite biomarkers for diagnosing lung cancer.

Mehtods: Serum samples from subjects participating in a CT screening trial were analyzed using untargeted GC-TOFMS and HILIC-qTOFMS-based metabolomics. Samples were acquired prior to diagnosis (pre-diagnostic, n= 17), at-diagnosis (n= 25) and post-diagnosis (n= 19) of lung cancer and from subjects with benign nodules (n= 29).

Results: Univariate analysis identified 40, 102 and 30 features which were significantly different between subjects with malignant (pre-, at- and post-diagnosis) solitary pulmonary nodules (SPNs) and benign SPNs, respectively. Ten metabolites were consistently different between subjects presenting malignant (pre- and at-diagnosis) or benign SPNs. Three of these 10 metabolites were phosphatidylethanolamines (PE) suggesting alterations in lipid metabolism. Accuracies of 77%, 83% and 78% in the pre-diagnosis group and 69%, 71% and 67% in the at-diagnosis group were determined for PE(34:2), PE(36:2) and PE(38:4), respectively.

Conclusions: This study demonstrates evidence of early metabolic alterations that can possibly distinguish malignant from benign SPNs. Further studies in larger pools of samples are warranted.
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http://dx.doi.org/10.3233/CBM-160602DOI Listing
March 2016

Genomic, Proteomic, and Metabolomic Data Integration Strategies.

Biomark Insights 2015 7;10(Suppl 4):1-6. Epub 2015 Sep 7.

CDS Creative Data Solutions, Ballwin, MO, USA.

Robust interpretation of experimental results measuring discreet biological domains remains a significant challenge in the face of complex biochemical regulation processes such as organismal versus tissue versus cellular metabolism, epigenetics, and protein post-translational modification. Integration of analyses carried out across multiple measurement or omic platforms is an emerging approach to help address these challenges. This review focuses on select methods and tools for the integration of metabolomic with genomic and proteomic data using a variety of approaches including biochemical pathway-, ontology-, network-, and empirical-correlation-based methods.
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http://dx.doi.org/10.4137/BMI.S29511DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4562606PMC
September 2015

Integrating Multiple Analytical Datasets to Compare Metabolite Profiles of Mouse Colonic-Cecal Contents and Feces.

Metabolites 2015 Sep 11;5(3):489-501. Epub 2015 Sep 11.

Grand Forks Human Nutrition Research Center, Agricultural Research Service, United States Department of Agriculture, Grand Forks, ND 58203, USA.

The pattern of metabolites produced by the gut microbiome comprises a phenotype indicative of the means by which that microbiome affects the gut. We characterized that phenotype in mice by conducting metabolomic analyses of the colonic-cecal contents, comparing that to the metabolite patterns of feces in order to determine the suitability of fecal specimens as proxies for assessing the metabolic impact of the gut microbiome. We detected a total of 270 low molecular weight metabolites in colonic-cecal contents and feces by gas chromatograph, time-of-flight mass spectrometry (GC-TOF) and ultra-high performance liquid chromatography, quadrapole time-of-flight mass spectrometry (UPLC-Q-TOF). Of that number, 251 (93%) were present in both types of specimen, representing almost all known biochemical pathways related to the amino acid, carbohydrate, energy, lipid, membrane transport, nucleotide, genetic information processing, and cancer-related metabolism. A total of 115 metabolites differed significantly in relative abundance between both colonic-cecal contents and feces. These data comprise the first characterization of relationships among metabolites present in the colonic-cecal contents and feces in a healthy mouse model, and shows that feces can be a useful proxy for assessing the pattern of metabolites to which the colonic mucosum is exposed.
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http://dx.doi.org/10.3390/metabo5030489DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4588808PMC
September 2015

Metabolomics and transcriptomics identify pathway differences between visceral and subcutaneous adipose tissue in colorectal cancer patients: the ColoCare study.

Am J Clin Nutr 2015 Aug 8;102(2):433-43. Epub 2015 Jul 8.

Division of Preventive Oncology, National Center for Tumor Diseases and German Cancer Research Center, Heidelberg, Germany; Cancer Prevention Program, Fred Hutchinson Cancer Research Center, Seattle, WA; and Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT

Background: Metabolic and transcriptomic differences between visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) compartments, particularly in the context of obesity, may play a role in colorectal carcinogenesis. We investigated the differential functions of their metabolic compositions.

Objectives: Biochemical differences between adipose tissues (VAT compared with SAT) in patients with colorectal carcinoma (CRC) were investigated by using mass spectrometry metabolomics and gene expression profiling. Metabolite compositions were compared between VAT, SAT, and serum metabolites. The relation between patients' tumor stage and metabolic profiles was assessed.

Design: Presurgery blood and paired VAT and SAT samples during tumor surgery were obtained from 59 CRC patients (tumor stages I-IV) of the ColoCare cohort. Gas chromatography time-of-flight mass spectrometry and liquid chromatography quadrupole time-of-flight mass spectrometry were used to measure 1065 metabolites in adipose tissue (333 identified compounds) and 1810 metabolites in serum (467 identified compounds). Adipose tissue gene expression was measured by using Illumina's HumanHT-12 Expression BeadChips.

Results: Compared with SAT, VAT displayed elevated markers of inflammatory lipid metabolism, free arachidonic acid, phospholipases (PLA2G10), and prostaglandin synthesis-related enzymes (PTGD/PTGS2S). Plasmalogen concentrations were lower in VAT than in SAT, which was supported by lower gene expression of FAR1, the rate-limiting enzyme for ether-lipid synthesis in VAT. Serum sphingomyelin concentrations were inversely correlated (P = 0.0001) with SAT adipose triglycerides. Logistic regression identified lipids in patients' adipose tissues, which were associated with CRC tumor stage.

Conclusions: As one of the first studies, we comprehensively assessed differences in metabolic, lipidomic, and transcriptomic profiles between paired human VAT and SAT and their association with CRC tumor stage. We identified markers of inflammation in VAT, which supports prior evidence regarding the role of visceral adiposity and cancer.
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http://dx.doi.org/10.3945/ajcn.114.103804DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4515859PMC
August 2015

Systemic alterations in the metabolome of diabetic NOD mice delineate increased oxidative stress accompanied by reduced inflammation and hypertriglyceremia.

Am J Physiol Endocrinol Metab 2015 Jun 7;308(11):E978-89. Epub 2015 Apr 7.

Department of Medicine, The University of Chicago, Chicago, Illinois

Nonobese diabetic (NOD) mice are a commonly used model of type 1 diabetes (T1D). However, not all animals will develop overt diabetes despite undergoing similar autoimmune insult. In this study, a comprehensive metabolomic approach, consisting of gas chromatography time-of-flight (GC-TOF) mass spectrometry (MS), ultra-high-performance liquid chromatography-accurate mass quadruple time-of-flight (UHPLC-qTOF) MS and targeted UHPLC-tandem mass spectrometry-based methodologies, was used to capture metabolic alterations in the metabolome and lipidome of plasma from NOD mice progressing or not progressing to T1D. Using this multi-platform approach, we identified >1,000 circulating lipids and metabolites in male and female progressor and nonprogressor animals (n = 71). Statistical and multivariate analyses were used to identify age- and sex-independent metabolic markers, which best differentiated metabolic profiles of progressors and nonprogressors. Key T1D-associated perturbations were related with 1) increases in oxidation products glucono-δ-lactone and galactonic acid and reductions in cysteine, methionine and threonic acid, suggesting increased oxidative stress; 2) reductions in circulating polyunsaturated fatty acids and lipid signaling mediators, most notably arachidonic acid (AA) and AA-derived eicosanoids, implying impaired states of systemic inflammation; 3) elevations in circulating triacylglyercides reflective of hypertriglyceridemia; and 4) reductions in major structural lipids, most notably lysophosphatidylcholines and phosphatidylcholines. Taken together, our results highlight the systemic perturbations that accompany a loss of glycemic control and development of overt T1D.
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http://dx.doi.org/10.1152/ajpendo.00019.2015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4451288PMC
June 2015

MetaMapR: pathway independent metabolomic network analysis incorporating unknowns.

Bioinformatics 2015 Aug 5;31(16):2757-60. Epub 2015 Apr 5.

National Institutes of Health West Coast Metabolomics Center, Genome Center, University of California Davis, Davis CA 95616, USA and King Abdulaziz University, Biochemistry Department, Jeddah, Saudi Arabia.

Unlabelled: Metabolic network mapping is a widely used approach for integration of metabolomic experimental results with biological domain knowledge. However, current approaches can be limited by biochemical domain or pathway knowledge which results in sparse disconnected graphs for real world metabolomic experiments. MetaMapR integrates enzymatic transformations with metabolite structural similarity, mass spectral similarity and empirical associations to generate richly connected metabolic networks. This open source, web-based or desktop software, written in the R programming language, leverages KEGG and PubChem databases to derive associations between metabolites even in cases where biochemical domain or molecular annotations are unknown. Network calculation is enhanced through an interface to the Chemical Translation System, which allows metabolite identifier translation between >200 common biochemical databases. Analysis results are presented as interactive visualizations or can be exported as high-quality graphics and numerical tables which can be imported into common network analysis and visualization tools.

Availability And Implementation: Freely available at http://dgrapov.github.io/MetaMapR/. Requires R and a modern web browser. Installation instructions, tutorials and application examples are available at http://dgrapov.github.io/MetaMapR/.

Contact: ofiehn@ucdavis.edu.
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http://dx.doi.org/10.1093/bioinformatics/btv194DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4528626PMC
August 2015

Diabetes Associated Metabolomic Perturbations in NOD Mice.

Metabolomics 2015 Apr;11(2):425-437

Department of Medicine, The University of Chicago, Chicago, Illinois.

Non-obese diabetic (NOD) mice are a widely-used model oftype1 diabetes (T1D). However, not all animals develop overt diabetes. This study examined the circulating metabolomic profiles of NOD mice progressing or not progressing to T1D. Total beta-cell mass was quantified in the intact pancreas using transgenic NOD mice expressinggreen fluorescent protein under the control of mouse insulin I promoter.While both progressor and non-progressor animals displayed lymphocyte infiltration and endoplasmic reticulum stress in the pancreas tissue;overt T1D did not develop until animals lost ~70% of the total beta-cell mass.Gas chromatography time of flight mass spectrometry (GC-TOF) was used to measure >470 circulating metabolites in male and female progressor and non-progressor animals (n=76) across a wide range of ages (neonates to >40-wk).Statistical and multivariate analyses were used to identify age and sex independent metabolic markers which best differentiated progressor and non-progressor animals' metabolic profiles. Key T1D-associated perturbations were related with: (1) increased plasma glucose and reduced 1,5-anhydroglucitol markers of glycemic control; (2) increased allantoin, gluconic acid and nitric oxide-derived saccharic acid markers of oxidative stress; (3) reduced lysine, an insulin secretagogue; (4) increased branched-chain amino acids, isoleucine and valine; (5) reduced unsaturated fatty acids including arachidonic acid; and (6)perturbations in urea cycle intermediates suggesting increased arginine-dependent NO synthesis. Together these findings highlight the strength of the unique approach of comparing progressor and non-progressor NOD mice to identify metabolic perturbations involved in T1D progression.
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http://dx.doi.org/10.1007/s11306-014-0706-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4351755PMC
April 2015

Metabolomic markers of altered nucleotide metabolism in early stage adenocarcinoma.

Cancer Prev Res (Phila) 2015 May 5;8(5):410-8. Epub 2015 Feb 5.

Division of Hematology and Oncology, Department of Internal Medicine, School of Medicine, University of California, Davis Medical Center, Sacramento, California.

Adenocarcinoma, a type of non-small cell lung cancer, is the most frequently diagnosed lung cancer and the leading cause of lung cancer mortality in the United States. It is well documented that biochemical changes occur early in the transition from normal to cancer cells, but the extent to which these alterations affect tumorigenesis in adenocarcinoma remains largely unknown. Herein, we describe the application of mass spectrometry and multivariate statistical analysis in one of the largest biomarker research studies to date aimed at distinguishing metabolic differences between malignant and nonmalignant lung tissue. Gas chromatography time-of-flight mass spectrometry was used to measure 462 metabolites in 39 malignant and nonmalignant lung tissue pairs from current or former smokers with early stage (stage IA-IB) adenocarcinoma. Statistical mixed effects models, orthogonal partial least squares discriminant analysis and network integration, were used to identify key cancer-associated metabolic perturbations in adenocarcinoma compared with nonmalignant tissue. Cancer-associated biochemical alterations were characterized by (i) decreased glucose levels, consistent with the Warburg effect, (ii) changes in cellular redox status highlighted by elevations in cysteine and antioxidants, alpha- and gamma-tocopherol, (iii) elevations in nucleotide metabolites 5,6-dihydrouracil and xanthine suggestive of increased dihydropyrimidine dehydrogenase and xanthine oxidoreductase activity, (iv) increased 5'-deoxy-5'-methylthioadenosine levels indicative of reduced purine salvage and increased de novo purine synthesis, and (v) coordinated elevations in glutamate and UDP-N-acetylglucosamine suggesting increased protein glycosylation. The present study revealed distinct metabolic perturbations associated with early stage lung adenocarcinoma, which may provide candidate molecular targets for personalizing therapeutic interventions and treatment efficacy monitoring.
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http://dx.doi.org/10.1158/1940-6207.CAPR-14-0329DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4618700PMC
May 2015

Gene expression profiling in pachyonychia congenita skin.

J Dermatol Sci 2015 Mar 14;77(3):156-65. Epub 2015 Jan 14.

TransDerm Inc., Santa Cruz, CA 95060, USA. Electronic address:

Background: Pachyonychia congenita (PC) is a skin disorder resulting from mutations in keratin (K) proteins including K6a, K6b, K16, and K17. One of the major symptoms is painful plantar keratoderma. The pathogenic sequelae resulting from the keratin mutations remain unclear.

Objective: To better understand PC pathogenesis.

Methods: RNA profiling was performed on biopsies taken from PC-involved and uninvolved plantar skin of seven genotyped PC patients (two K6a, one K6b, three K16, and one K17) as well as from control volunteers. Protein profiling was generated from tape-stripping samples.

Results: A comparison of PC-involved skin biopsies to adjacent uninvolved plantar skin identified 112 differentially-expressed mRNAs common to patient groups harboring K6 (i.e., both K6a and K6b) and K16 mutations. Among these mRNAs, 25 encode structural proteins including keratins, small proline-rich and late cornified envelope proteins, 20 are related to metabolism and 16 encode proteases, peptidases, and their inhibitors including kallikrein-related peptidases (KLKs), and serine protease inhibitors (SERPINs). mRNAs were also identified to be differentially expressed only in K6 (81) or K16 (141) patient samples. Furthermore, 13 mRNAs were identified that may be involved in pain including nociception and neuropathy. Protein profiling, comparing three K6a plantar tape-stripping samples to non-PC controls, showed changes in the PC corneocytes similar, but not identical, to the mRNA analysis.

Conclusion: Many differentially-expressed genes identified in PC-involved skin encode components critical for skin barrier homeostasis including keratinocyte proliferation, differentiation, cornification, and desquamation. The profiling data provide a foundation for unraveling the pathogenesis of PC and identifying targets for developing effective PC therapeutics.
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http://dx.doi.org/10.1016/j.jdermsci.2015.01.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4374015PMC
March 2015

Acute and chronic plasma metabolomic and liver transcriptomic stress effects in a mouse model with features of post-traumatic stress disorder.

PLoS One 2015 28;10(1):e0117092. Epub 2015 Jan 28.

US Army Center for Environmental Health Research, Fort Detrick, MD, United States of America.

Acute responses to intense stressors can give rise to post-traumatic stress disorder (PTSD). PTSD diagnostic criteria include trauma exposure history and self-reported symptoms. Individuals who meet PTSD diagnostic criteria often meet criteria for additional psychiatric diagnoses. Biomarkers promise to contribute to reliable phenotypes of PTSD and comorbidities by linking biological system alterations to behavioral symptoms. Here we have analyzed unbiased plasma metabolomics and other stress effects in a mouse model with behavioral features of PTSD. In this model, C57BL/6 mice are repeatedly exposed to a trained aggressor mouse (albino SJL) using a modified, resident-intruder, social defeat paradigm. Our recent studies using this model found that aggressor-exposed mice exhibited acute stress effects including changed behaviors, body weight gain, increased body temperature, as well as inflammatory and fibrotic histopathologies and transcriptomic changes of heart tissue. Some of these acute stress effects persisted, reminiscent of PTSD. Here we report elevated proteins in plasma that function in inflammation and responses to oxidative stress and damaged tissue at 24 hrs post-stressor. Additionally at this acute time point, transcriptomic analysis indicated liver inflammation. The unbiased metabolomics analysis showed altered metabolites in plasma at 24 hrs that only partially normalized toward control levels after stress-withdrawal for 1.5 or 4 wks. In particular, gut-derived metabolites were altered at 24 hrs post-stressor and remained altered up to 4 wks after stress-withdrawal. Also at the 4 wk time point, hyperlipidemia and suppressed metabolites of amino acids and carbohydrates in plasma coincided with transcriptomic indicators of altered liver metabolism (activated xenobiotic and lipid metabolism). Collectively, these system-wide sequelae to repeated intense stress suggest that the simultaneous perturbed functioning of multiple organ systems (e.g., brain, heart, intestine and liver) can interact to produce injuries that lead to chronic metabolic changes and disorders that have been associated with PTSD.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0117092PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4309402PMC
February 2016

Metabolomics in psoriatic disease: pilot study reveals metabolite differences in psoriasis and psoriatic arthritis.

F1000Res 2014 21;3:248. Epub 2014 Oct 21.

NIH West Coast Metabolomics Center, University of California Davis, Davis, CA, 95616, USA.

Importance: While "omics" studies have advanced our understanding of inflammatory skin diseases, metabolomics is mostly an unexplored field in dermatology.

Objective: We sought to elucidate the pathogenesis of psoriatic diseases by determining the differences in metabolomic profiles among psoriasis patients with or without psoriatic arthritis and healthy controls.

Design: We employed a global metabolomics approach to compare circulating metabolites from patients with psoriasis, psoriasis and psoriatic arthritis, and healthy controls.

Setting: Study participants were recruited from the general community and from the Psoriasis Clinic at the University of California Davis in United States.

Participants: We examined metabolomic profiles using blood serum samples from 30 patients age and gender matched into three groups: 10 patients with psoriasis, 10 patients with psoriasis and psoriatic arthritis and 10 control participants. Main outcome(s) and measures(s): Metabolite levels were measured calculating the mean peak intensities from gas chromatography time-of-flight mass spectrometry.

Results: Multivariate analyses of metabolomics profiles revealed altered serum metabolites among the study population. Compared to control patients, psoriasis patients had a higher level of alpha ketoglutaric acid (Pso: 288 ± 88;

Control: 209 ± 69; p=0.03), a lower level of asparagine (Pso: 5460 ± 980;

Control: 7260 ± 2100; p=0.02), and a lower level of glutamine (Pso: 86000 ± 20000;

Control: 111000 ± 27000; p=0.02). Compared to control patients, patients with psoriasis and psoriatic arthritis had increased levels of glucuronic acid (Pso + PsA: 638 ± 250;

Control: 347 ± 61; p=0.001). Compared to patients with psoriasis alone, patients with both psoriasis and psoriatic arthritis had a decreased level of alpha ketoglutaric acid (Pso + PsA: 186 ± 80; Pso: 288 ± 88; p=0.02) and an increased level of lignoceric acid (Pso + PsA: 442 ± 280; Pso: 214 ± 64; p=0.02).

Conclusions And Relevance: The metabolite differences help elucidate the pathogenesis of psoriasis and psoriatic arthritis and they may provide insights for therapeutic development.
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http://dx.doi.org/10.12688/f1000research.4709.1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4288418PMC
January 2015

High-dose simvastatin exhibits enhanced lipid-lowering effects relative to simvastatin/ezetimibe combination therapy.

Circ Cardiovasc Genet 2014 Dec;7(6):955-964

Department of Medical Biochemistry & Biophysics, Division of Physiological Chemistry II, Karolinska Institutet, Stockholm, Sweden.

Statins are the frontline in cholesterol reduction therapies; however, their use in combination with agents that possess complimentary mechanisms of action may achieve further reductions in low-density lipoprotein cholesterol. Thirty-nine patients were treated with either 80 mg simvastatin (n=20) or 10 mg simvastatin plus 10 mg ezetimibe (n=19) for 6 weeks. Dosing was designed to produce comparable low-density lipoprotein cholesterol reductions, while enabling assessment of potential simvastatin-associated pleiotropic effects. Baseline and post-treatment plasma were analyzed for lipid mediators (eg, eicosanoids and endocannabinoids) and structural lipids by liquid chromatography tandem mass spectrometry. After statistical analysis and orthogonal projections to latent structures multivariate modeling, no changes were observed in lipid mediator levels, whereas global structural lipids were reduced in response to both monotherapy (R(2)Y=0.74; Q(2)=0.66; cross-validated ANOVA P=7.0×10(-8)) and combination therapy (R(2)Y=0.67; Q(2)=0.54; cross-validated ANOVA P=2.6×10(-5)). Orthogonal projections to latent structures modeling identified a subset of 12 lipids that classified the 2 treatment groups after 6 weeks (R(2)Y=0.65; Q(2)=0.61; cross-validated ANOVA P=5.4×10(-8)). Decreases in the lipid species phosphatidylcholine (15:0/18:2) and hexosyl-ceramide (d18:1/24:0) were the strongest discriminators of low-density lipoprotein cholesterol reductions for both treatment groups (q<0.00005), whereas phosphatidylethanolamine (36:3e) contributed most to distinguishing treatment groups (q=0.017). Shifts in lipid composition were similar for high-dose simvastatin and simvastatin/ezetimibe combination therapy, but the magnitude of the reduction was linked to simvastatin dosage. Simvastatin therapy did not affect circulating levels of lipid mediators, suggesting that pleiotropic effects are not associated with eicosanoid production. Only high-dose simvastatin reduced the relative proportion of sphingomyelin and ceramide to phosphatidylcholine (q=0.008), suggesting a pleiotropic effect previously associated with a reduced risk of cardiovascular disease.
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http://dx.doi.org/10.1161/CIRCGENETICS.114.000606DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4270085PMC
December 2014