Lipid mediated plant immunity in susceptible and tolerant soybean cultivars in response to Phytophthora sojae colonization and infection.

BACKGROUND: Soybean is one of the most cultivated crops globally and a staple food for much of the world's population. The annual global crop losses due to infection by Phytophthora sojae is currently estimated at $20B USD, yet we have limited understanding of the role of lipid mediators in the adaptative strategies used by the host plant to limit infection. Since root is the initial site of this infection, we examined the infection process in soybean root infected with Phytophthora sojae using scanning electron microscopy to observe the changes in root morphology and a multi-modal lipidomics approach to investigate how soybean cultivars remodel their lipid mediators to successfully limit infection by Phytophthora sojae. RESULTS: The results reveal the presence of elevated biogenic crystals and more severe damaged cells in the root morphology of the infected susceptible cultivar compared to the infected tolerant cultivars. Furthermore, induced accumulation of stigmasterol was observed in the susceptible cultivar whereas, induced accumulation of phospholipids and glycerolipids occurred in tolerant cultivar. CONCLUSION: The altered lipidome reported in this study suggest diacylglycerol and phosphatidic acid mediated lipid signalling impacting phytosterol anabolism appears to be a strategy used by tolerant soybean cultivars to successfully limit infection and colonization by Phytophthora sojae.

Metabolomics of head and neck cancer in biofluids: an integrative systematic review.

INTRODUCTION: Head and neck cancer (HNC) is the fifth most common cancer globally. Diagnosis at early stages are critical to reduce mortality and improve functional and esthetic outcomes associated with HNC. Metabolomics is a promising approach for discovery of biomarkers and metabolic pathways for risk assessment and early detection of HNC. OBJECTIVES: To summarize and consolidate the available evidence on metabolomics and HNC in plasma/serum, saliva, and urine. METHODS: A systematic search of experimental research was executed using PubMed and Web of Science. Available data on areas under the curve was extracted. Metabolic pathway enrichment analysis were performed to identify metabolic pathways altered in HNC. Fifty-four studies were eligible for data extraction (33 performed in plasma/serum, 15 in saliva and 6 in urine). RESULTS: Metabolites with high discriminatory performance for detection of HNC included single metabolites and combination panels of several lysoPCs, pyroglutamate, glutamic acid, glucose, tartronic acid, arachidonic acid, norvaline, linoleic acid, propionate, acetone, acetate, choline, glutamate and others. The glucose-alanine cycle and the urea cycle were the most altered pathways in HNC, among other pathways (i.e. gluconeogenesis, glycine and serine metabolism, alanine metabolism, etc.). Specific metabolites that can potentially serve as complementary less- or non-invasive biomarkers, as well as metabolic pathways integrating the data from the available studies, are presented. CONCLUSION: The present work highlights utility of metabolite-based biomarkers for risk assessment, early detection, and prognostication of HNC, as well as facilitates incorporation of available metabolomics studies into multi-omics data integration and big data analytics for personalized health.

Adipocytes reprogram cancer cell metabolism by diverting glucose towards glycerol-3-phosphate thereby promoting metastasis.

In the tumor microenvironment, adipocytes function as an alternate fuel source for cancer cells. However, whether adipocytes influence macromolecular biosynthesis in cancer cells is unknown. Here we systematically characterized the bidirectional interaction between primary human adipocytes and ovarian cancer (OvCa) cells using multi-platform metabolomics, imaging mass spectrometry, isotope tracing and gene expression analysis. We report that, in OvCa cells co-cultured with adipocytes and in metastatic tumors, a part of the glucose from glycolysis is utilized for the biosynthesis of glycerol-3-phosphate (G3P). Normoxic HIF1α protein regulates the altered flow of glucose-derived carbons in cancer cells, resulting in increased glycerophospholipids and triacylglycerol synthesis. The knockdown of HIF1α or G3P acyltransferase 3 (a regulatory enzyme of glycerophospholipid synthesis) reduced metastasis in xenograft models of OvCa. In summary, we show that, in an adipose-rich tumor microenvironment, cancer cells generate G3P as a precursor for critical membrane and signaling components, thereby promoting metastasis. Targeting biosynthetic processes specific to adipose-rich tumor microenvironments might be an effective strategy against metastasis.

Phyto-oxylipin mediated plant immune response to colonization and infection in the soybean-Phytophthora sojae pathosystem.

Introduction: Food security is a major challenge to sustainably supply food to meet the demands of the ever-growing global population. Crop loss due to pathogens is a major concern to overcoming this global food security challenge. Soybean root and stem rot caused by Phytophthora sojae results in approximately 20B $US crop loss annually. Phyto-oxylipins are metabolites biosynthesized in the plants by oxidative transformation of polyunsaturated fatty acids through an array of diverging metabolic pathways and play an important role in plant development and defense against pathogen colonization and infection. Lipid mediated plant immunity is a very attractive target for developing long term resistance in many plants' disease pathosystem. However, little is known about the phyto-oxylipin's role in the successful strategies used by tolerant soybean cultivar to mitigate Phytophthora sojae infection. Methods: We used scanning electron microscopy to observe the alterations in root morphology and a targeted lipidomics approach using high resolution accurate mass tandem mass spectrometry to assess phyto-oxylipin anabolism at 48 h, 72 h and 96 h post infection. Results and discussion: We observed the presence of biogenic crystals and reinforced epidermal walls in the tolerant cultivar suggesting a mechanism for disease tolerance when compared with susceptible cultivar. Similarly, the unequivocally unique biomarkers implicated in oxylipin mediated plant immunity [10(E),12(Z)-13S-hydroxy-9(Z),11(E),15(Z)-octadecatrienoic acid, (Z)-12,13-dihydroxyoctadec-9-enoic acid, (9Z,11E)-13-Oxo-9,11-octadecadienoic acid, 15(Z)-9-oxo-octadecatrienoic acid, 10(E),12(E)-9-hydroperoxyoctadeca-10,12-dienoic acid, 12-oxophytodienoic acid and (12Z,15Z)-9, 10-dihydroxyoctadeca-12,15-dienoic acid] generated from intact oxidized lipid precursors were upregulated in tolerant soybean cultivar while downregulated in infected susceptible cultivar relative to non-inoculated controls at 48 h, 72 h and 96 h post infection by Phytophthora sojae, suggesting that these molecules may be a critical component of the defense strategies used in tolerant cultivar against Phytophthora sojae infection. Interestingly, microbial originated oxylipins, 12S-hydroperoxy-5(Z),8(Z),10(E),14(Z)-eicosatetraenoic acid and (4Z,7Z,10Z,13Z)-15-[3-[(Z)-pent-2-enyl]oxiran-2-yl]pentadeca-4,7,10,13-tetraenoic acid were upregulated only in infected susceptible cultivar but downregulated in infected tolerant cultivar. These microbial originated oxylipins are capable of modulating plant immune response to enhance virulence. This study demonstrated novel evidence for phyto-oxylipin metabolism in soybean cultivars during pathogen colonization and infection using the Phytophthora sojae-soybean pathosystem. This evidence may have potential applications in further elucidation and resolution of the role of phyto-oxylipin anabolism in soybean tolerance to Phytophthora sojae colonization and infection.

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

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.

Deep metabolome: Applications of deep learning in metabolomics.

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.

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

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 V̇O2peak , ~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.

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

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.

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

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 V̇o2peak, ∼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.

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

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.

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

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.

Enhanced field emission properties of carbon nanotube bundles confined in SiO2 pits.

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 SiO2 pits. Growing CNT bundles in narrow (0.5 µm diameter and 2 µm height) SiO2 pits achieves FE current density of 1-1.4 A cm-2, which is much higher than for freestanding CNT bundles (76.9 mA cm-2). 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 SiO2 pit confinement, which yields bundles with higher aspect ratios. Combining the obtained outcomes, it can be conclusively summarized that confining CNTs in SiO2 pits yields higher FE current density due to the higher field enhancement of confined CNTs.

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

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.

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

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.

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

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.

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

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.

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

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.

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

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.

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

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.

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

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.