A systematic review on metabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.

INTRODUCTION: Metabolomics is an emerging approach for early detection of cancer. Along with the development of metabolomics, high-throughput technologies and statistical learning, the integration of multiple biomarkers has significantly improved clinical diagnosis and management for patients. OBJECTIVES: In this study, we conducted a systematic review to examine recent advancements in the oncometabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for relevant studies published before September 2017. We examined the study designs, the metabolomics approaches, and the reporting methodological quality following PRISMA statement. RESULTS AND CONCLUSION: The included 25 studies primarily focused on the identification rather than the validation of predictive capacity of potential biomarkers. The sample size ranged from 10 to 8760. External validation of the biomarker panels was observed in nine studies. The diagnostic area under the curve ranged from 0.68 to 1.00 (sensitivity: 0.43-1.00, specificity: 0.73-1.00). The effects of patients' bio-parameters on metabolome alterations in a context-dependent manner have not been thoroughly elucidated. The most reported candidates were glutamic acid and histidine in seven studies, and glutamine and isoleucine in five studies, leading to the predominant enrichment of amino acid-related pathways. Notably, 46 metabolites were estimated in at least two studies. Specific challenges and potential pitfalls to provide better insights into future research directions were thoroughly discussed. Our investigation suggests that metabolomics is a robust approach that will improve the diagnostic assessment of pancreatic cancer. Further studies are warranted to validate their validity in multi-clinical settings.

Senescing human bone-marrow-derived clonal mesenchymal stem cells have altered lysophospholipid composition and functionality.

Mesenchymal stem cells (MSCs) have been used in a wide range of research and clinical studies because MSCs do not have any ethical issues and have the advantage of low carcinogenicity due to their limited proliferation. However, because only a small number of MSCs can be obtained from the bone marrow, ex vivo amplification is inevitably required. For that reason, this study was conducted to acquire the metabolic information to examine and control the changes in the activities and differentiation potency of MSCs during the ex vivo culture process. Endogenous metabolites of human bone-marrow-derived clonal MSCs (hcMSCs) during cellular senescence were profiled by ultraperformance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC/QTOFMS). To select significant metabolites, we used the linear mixed effects model having fixed effects for batch and time (passage) and random effects for metabolites, determining the mean using a t test and the standard deviation using an F test. We used structural analysis with representative standards and spectrum patterns with different collision energies to distinctly identify eight metabolites with altered expression during senescence as types of lysophosphatidylcholine (LPC) and lysophosphatidylethanolamine (LPE), such as LPC 16:0 and LPE 22:4. The present study revealed changes in endogenous metabolites and mechanisms due to senescence.

Evaluation of four different analytical tools to determine the regional origin of Gastrodia elata and Rehmannia glutinosa on the basis of metabolomics study.

Chemical profiles of medicinal plants could be dissimilar depending on the cultivation environments, which may influence their therapeutic efficacy. Accordingly, the regional origin of the medicinal plants should be authenticated for correct evaluation of their medicinal and market values. Metabolomics has been found very useful for discriminating the origin of many plants. Choosing the adequate analytical tool can be an essential procedure because different chemical profiles with different detection ranges will be produced according to the choice. In this study, four analytical tools, Fourier transform near­infrared spectroscopy (FT-NIR), 1H-nuclear magnetic resonance spectroscopy (1H­NMR), liquid chromatography-mass spectrometry (LC-MS), and gas chromatography-mass spectroscopy (GC-MS) were applied in parallel to the same samples of two popular medicinal plants (Gastrodia elata and Rehmannia glutinosa) cultivated either in Korea or China. The classification abilities of four discriminant models for each plant were evaluated based on the misclassification rate and Q2 obtained from principal component analysis (PCA) and orthogonal projection to latent structures-discriminant analysis (OPLS­DA), respectively. 1H-NMR and LC-MS, which were the best techniques for G. elata and R. glutinosa, respectively, were generally preferable for origin discrimination over the others. Reasoned by integrating all the results, 1H-NMR is the most prominent technique for discriminating the origins of two plants. Nonetheless, this study suggests that preliminary screening is essential to determine the most suitable analytical tool and statistical method, which will ensure the dependability of metabolomics-based discrimination.

Accurate determination of 11 representative phthalates and di(2-ethylhexyl) terephthalate in polyvinyl chloride using isotope dilution-gas chromatography/mass spectrometry.

Phthalates are mainly used as plasticizers in polyvinyl chloride (PVC). However, prolonged exposure to phthalates poses considerable risks to human health. Consequently, the utilization of phthalates in consumer products is subject to regulations, with a defined threshold of 0.1 %. In this study, we developed an accurate and simultaneous method for determination of 11 representative phthalates and a non-phthalate plasticizer (di(2-ethylhexyl) terephthalate, DEHT) in PVC as a higher-order reference method. Homogeneously prepared PVC samples, each containing approximately 0.1 % of the target plasticizer compounds, were analyzed using gas chromatography-mass spectrometry (GC-MS) with deuterium-labeled phthalates and DEHT. The developed method could effectively separate and quantify all target plasticizers without interference with each other and potential overlap between the isomeric forms of phthalates, di-isodecyl phthalate, and di-isononyl phthalate. The developed method has high-order metrological quality, exhibiting exceptional selectivity, accuracy, repeatability (≤ 2.17 %), reproducibility (≤ 2.16 %), and relative expanded uncertainty (≤ 5.6 %). This analytical method is thus suitable for accurately assessing the target plasticizer levels in PVC products for ensuring compliance with the established 0.1 % threshold. This method was successfully applied to quantify twelve distinct plasticizers in PVC products obtained from the Korean market, validating its effectiveness and reliability in real-world scenarios.

Accurate determination of four tetracycline residues in chicken meat by isotope dilution-liquid chromatography/tandem mass spectrometry.

An analytical method based on isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC‒MS/MS) was developed to accurately determine four representative tetracyclines (tetracycline, chlortetracycline, doxycycline, and oxytetracycline) in chicken meat. Tetracyclines are known to have a great tendency for epimerization and keto-enol tautomerism, which often provoke major challenges in their determination. Since this isomerization was found to be unavoidable during the whole chain of the current analysis, the total content (µg kg‒1) of individual tetracycline was quantified as a sum of each parent compound and its respective isomeric forms. Using this approach in combination with IDMS analysis, more consistent, accurate, and reproducible measurement results for the four tetracyclines in chicken meat were acquired. LC-MS/MS conditions and sample preparation processes were comprehensively optimized to minimize the chelating effect of tetracyclines and possible co-extracted interferences. Details of the sample preparation scheme, LC‒MS/MS detection, calculation equation, and method validation are described in this article. The method provided very good accuracy (97.7-102.6%) for all analytes across the concentration range of 10-200 µg kg‒1, with relative standard deviations for intra-day and inter-day precision of less than 4%. The limits of quantification were below 0.2 µg kg‒1, demonstrating the high sensitivity of the method. Furthermore, the measurement uncertainty was generally below 5.5%. Hence, the established method exhibits high-order metrological quality with superior performance over various existing methodologies. Moreover, this method can provide references for general food testing laboratories close to and far below the established maximum residue limits (100 µg kg‒1) for animal muscle tissues.

Effects of climatic factors on the prevalence of influenza virus infection in Cheonan, Korea.

Big data can be used to correlate diseases and climatic factors. The prevalence of influenza (flu) virus, accounting for a large proportion of respiratory infections, suggests that the effect of climate variables according to seasonal dynamics of influenza virus infections should be investigated. Here, trends in flu virus detection were analyzed using data from 9,010 tests performed between January 2012 and December 2018 at Dankook University Hospital, Cheonan, Korea. We compared the detection of the flu virus in Cheonan area and its association with climate change. The flu virus detection rate was 9.9% (894/9,010), and the detection rate was higher for flu virus A (FLUAV; 6.9%) than for flu virus B (FLUBV; 3.0%). Both FLUAV and FLUBV infections are considered an epidemic each year. We identified 43.1% (n = 385) and 35.0% (n = 313) infections in children aged < 10 years and adults aged > 60 years, respectively. The combination of these age groups encompassed 78.1% (n = 698/894) of the total data. Flu virus infections correlated with air temperature, relative humidity, vapor pressure, atmospheric pressure, particulate matter, and wind chill temperature (P < 0.001). However, the daily temperature range did not significantly correlate with the flu detection results. This is the first study to identify the relationship between long-term flu virus infection with temperature in the temperate region of Cheonan.

Age-specific influences of refractive error and illuminance on pupil diameter.

To assess the most influential factor for pupil diameter changes among age, illuminance, and refractive state and reestablish the optimal procedures for clinical applications based on refractive state and illuminance for different age groups. The study was an observational study (repeated measure study). Participants included 219 Korean adults aged 20 to 69 years. Pupil diameters were measured using a pupilometer under scotopic, mesopic-low, and mesopic-high lighting conditions. Factor interactions among age, illuminance, and refractive state were evaluated using mixed linear model and chi-square automated interaction detection. Illuminance mainly contributed to variations in pupil diameter of participants over 50 years, whereas the refractive state was the dominant controlling factor for the pupil variation in participants below 50 years. For more generalized application, the pupil diameter decreased with older age and brighter illuminance (P < .001, inverse correlation, all comparisons). The mean pupil diameter was significantly higher in myopes and emmetropes than in hyperopes (P < .001). Pupil diameter variation modeled using the mixed model confirmed age, illuminance, and refractive error as significant factors (P < .001). Accounting for the interactions among age, illuminance, and refractive error and establishing their hierarchical dominance can be generalized using the chi-square automated interaction detection method and mixed model. Promoting age-dependent consideration for both illuminance and refractive state is necessary when pupil diameters play significant roles in clinical and manufacturing circumstances.

Association between climatic factors and respiratory syncytial virus detection rates in Cheonan, Korea.

The use of big data may facilitate the recognition and interpretation of causal relationships between disease occurrence and climatic variables. This study examined the effects of various climatic variables on the seasonal epidemiology of respiratory syncytial virus (RSV) infections in the temperate climate of Korea. Trends in RSV detection were analyzed using 9010 samples tested between January 1, 2012, and December 31, 2018, at Dankook University Hospital in Cheonan, Korea. Seasonal patterns in RSV detection frequency were compared with local climatic variables during the same period. RSV detection rate of 12.8% (n = 1150/9010) was observed, which was higher for RSV-A (7.1%) than RSV-B (5.8%) and RSV-A and RSV-B alternated each year. Children < 1 year exhibited high infection rates with RSV-A (68.5%) and RSV-B (58.7%). RSV-A and RSV-B infection rates in children under 9 years old were 96.2% and 92.1%, respectively. RSV had a significant relationship with several climatic factors. Air temperature, wind chill temperature, and particulate matter concentration were lower on days with a higher frequency of RSV detection. In contrast, atmospheric pressure was higher on days with lower RSV detection. Although the detection rates for RSV-A and RSV-B increased on days with lower air temperatures, those for RSV-B also increased on days with lower wind chill temperatures. Our findings suggest that climatic variables affect the RSV detection rate among children under 10 years of age. The present data may help predict the time when prevention strategies may be the most effective.

Method development for accurate determination of eight polycyclic aromatic hydrocarbons in extruded high-impact polystyrene.

An analytical method was developed for the accurate determination of eight polycyclic aromatic hydrocarbons (PAHs) in extruded high-impact polystyrene (HIPS) as a higher-order reference method. Uncleaned HIPS matrix rendered the accurate quantitation impossible and hampered the repeatability of the gas chromatography/mass spectrometry (GC/MS) measurement. This led to a bias in the results and increased the measurement uncertainty. Extracts were sufficiently purified through a two-step process: (i) dissolution and precipitation and (ii) molecularly imprinted polymers-solid phase extraction for clean-up. Co-elution of indeno(1,2,3-cd)pyrene (IP), dibenz(a,h)anthracene (DBahA), and their 13C6-labeled isotopes resulted in a bias in the area ratios of IP/13C6-IP and DBahA/13C6-DBahA, thus increasing the measurement uncertainty. An optimized GC condition using an Rxi-PAH column improved the peak separation of IP and DBahA and their isotopes, thus improving the quality of the measurement. The optimized method was validated using PAH (0.36-0.45 mg/kg)-containing HIPS pellets. The optimized method had excellent repeatability (<2%) and reproducibility (<3%) for concentrations less than 0.5 mg/kg of the eight PAHs in HIPS. Using the optimized method, the relative expanded uncertainties for all the target PAHs were below 5% (with a 95% level of confidence).

Efficacy of Integrating a Novel 16-Gene Biomarker Panel and Intelligence Classifiers for Differential Diagnosis of Rheumatoid Arthritis and Osteoarthritis.

Introducing novel biomarkers for accurately detecting and differentiating rheumatoid arthritis (RA) and osteoarthritis (OA) using clinical samples is essential. In the current study, we searched for a novel data-driven gene signature of synovial tissues to differentiate RA from OA patients. Fifty-three RA, 41 OA, and 25 normal microarray-based transcriptome samples were utilized. The area under the curve random forests (RF) variable importance measurement was applied to seek the most influential differential genes between RA and OA. Five algorithms including RF, k-nearest neighbors (kNN), support vector machines (SVM), naïve-Bayes, and a tree-based method were employed for the classification. We found a 16-gene signature that could effectively differentiate RA from OA, including TMOD1, POP7, SGCA, KLRD1, ALOX5, RAB22A, ANK3, PTPN3, GZMK, CLU, GZMB, FBXL7, TNFRSF4, IL32, MXRA7, and CD8A. The externally validated accuracy of the RF model was 0.96 (sensitivity = 1.00, specificity = 0.90). Likewise, the accuracy of kNN, SVM, naïve-Bayes, and decision tree was 0.96, 0.96, 0.96, and 0.91, respectively. Functional meta-analysis exhibited the differential pathological processes of RA and OA; suggested promising targets for further mechanistic and therapeutic studies. In conclusion, the proposed genetic signature combined with sophisticated classification methods may improve the diagnosis and management of RA patients.

Optimized Mass Spectrometry-Based Metabolite Extraction and Analysis for the Geographical Discrimination of White Rice (Oryza sativa L.): A Method Comparison Study.

In this study, we examined the effects of different extraction methods for the GC-MS- and LC-MS-based metabolite profiling of white rice (Oryza sativa L.). In addition, the metabolite divergence of white rice cultivated in either Korea or China was also evaluated. The discrimination analysis of each extraction method for white rice from Korea and China and the corresponding discriminatory markers were estimated by unpaired t-test, principal component analysis, k-means cluster analysis, partial least-squares discriminant analysis (PLS-DA), and random forest (RF). According to the prediction parameters obtained from PLS-DA and RF classifiers as well as features that could be identified, the extraction method using 75% isopropanol heated at 100°C coupled with LC-MS analysis was confirmed to be superior to the other extraction methods. Noticeably, lysophospholipid concentrations were significantly different in white rice between Korea and China, and they are novel markers for geographical discrimination. In conclusion, our study suggests an optimized extraction and analysis method as well as novel markers for the geographical discrimination of white rice.

Non-destructive profiling of volatile organic compounds using HS-SPME/GC-MS and its application for the geographical discrimination of white rice.

The authenticity determination of white rice is crucial to prevent deceptive origin labeling and dishonest trading. However, a non-destructive and comprehensive method for rapidly discriminating the geographical origins of white rice between countries is still lacking. In the current study, we developed a volatile organic compound based geographical discrimination method using headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry (HS-SPME/GC-MS) to discriminate rice samples from Korea and China. A partial least squares discriminant analysis (PLS-DA) model exhibited a good classification of white rice between Korea and China (accuracy = 0.958, goodness of fit = 0.937, goodness of prediction = 0.831, and permutation test p-value = 0.043). Combining the PLS-DA based feature selection with the differentially expressed features from the unpaired t-test and significance analysis of microarrays, 12 discriminatory biomarkers were found. Among them, hexanal and 1-hexanol have been previously known to be associated with the cultivation environment and storage conditions. Other hydrocarbon biomarkers are novel, and their impact on rice production and storage remains to be elucidated. In conclusion, our findings highlight the ability to rapidly discriminate white rice from Korea and China. The developed method maybe useful for the authenticity and quality control of white rice.

The integration of multi-platform MS-based metabolomics and multivariate analysis for the geographical origin discrimination of Oryza sativa L.

For the authentication of white rice from different geographical origins, the selection of outstanding discrimination markers is essential. In this study, 80 commercial white rice samples were collected from local markets of Korea and China and discriminated by mass spectrometry-based untargeted metabolomics approaches. Additionally, the potential markers that belong to sugars & sugar alcohols, fatty acids, and phospholipids were examined using several multivariate analyses to measure their discrimination efficiencies. Unsupervised analyses, including principal component analysis and k-means clustering demonstrated the potential of the geographical classification of white rice between Korea and China by fatty acids and phospholipids. In addition, the accuracy, goodness-of-fit (R2), goodness-of-prediction (Q2), and permutation test p-value derived from phospholipid-based partial least squares-discriminant analysis were 1.000, 0.902, 0.870, and 0.001, respectively. Random Forests further consolidated the discrimination ability of phospholipids. Furthermore, an independent validation set containing 20 white rice samples also confirmed that phospholipids were the excellent discrimination markers for white rice between two countries. In conclusion, the proposed approach successfully highlighted phospholipids as the better discrimination markers than sugars & sugar alcohols and fatty acids in differentiating white rice between Korea and China.

Comparative study on metabolite level in tissue-specific human mesenchymal stem cells by an ultra-performance liquid chromatography quadrupole time of flight mass spectrometry.

Mesenchymal stem cells (MSCs) are a promising therapeutic option for cell-based therapy due to their immunomodulatory and regenerative properties. They can be isolated from various adult tissues, including bone marrow, fat, dental tissue, and glandular tissue. Although they share common characteristics, little is known about the biological differences between MSC populations derived from different tissues. In this study, we used MS to compare the endogenous metabolite level in the human MSCs originating from the bone marrow, adipose tissue, periodontal ligaments, and salivary glands. Using an optimized metabolomics technique, we verified that human MSCs exhibit differences in the endogenous metabolite level depending on their source material, while the multivariate analysis showed that 5 lysophosphatidylcholines and 3 lysophosphatidylethanolamines can serve as markers for the discrimination between MSC sources and may be related to differences in their differentiation capacity. These results may significantly contribute to further mechanistic studies on the MSCs and provide novel insights into the properties and optimal usage of MSCs from different tissues.

Combination of mass spectrometry-based targeted lipidomics and supervised machine learning algorithms in detecting adulterated admixtures of white rice.

The mixing of extraneous ingredients with original products is a common adulteration practice in food and herbal medicines. In particular, authenticity of white rice and its corresponding blended products has become a key issue in food industry. Accordingly, our current study aimed to develop and evaluate a novel discrimination method by combining targeted lipidomics with powerful supervised learning methods, and eventually introduce a platform to verify the authenticity of white rice. A total of 30 cultivars were collected, and 330 representative samples of white rice from Korea and China as well as seven mixing ratios were examined. Random forests (RF), support vector machines (SVM) with a radial basis function kernel, C5.0, model averaged neural network, and k-nearest neighbor classifiers were used for the classification. We achieved desired results, and the classifiers effectively differentiated white rice from Korea to blended samples with high prediction accuracy for the contamination ratio as low as five percent. In addition, RF and SVM classifiers were generally superior to and more robust than the other techniques. Our approach demonstrated that the relative differences in lysoGPLs can be successfully utilized to detect the adulterated mixing of white rice originating from different countries. In conclusion, the present study introduces a novel and high-throughput platform that can be applied to authenticate adulterated admixtures from original white rice samples.

Development and assessment of a lysophospholipid-based deep learning model to discriminate geographical origins of white rice.

Geographical origin determination of white rice has become the major issue of food industry. However, there is still lack of a high-throughput method for rapidly and reproducibly differentiating the geographical origins of commercial white rice. In this study, we developed a method that employed lipidomics and deep learning to discriminate white rice from Korea to China. A total of 126 white rice of 30 cultivars from different regions were utilized for the method development and validation. By using direct infusion-mass spectrometry-based targeted lipidomics, 17 lysoglycerophospholipids were simultaneously characterized within minutes per sample. Unsupervised data exploration showed a noticeable overlap of white rice between two countries. In addition, lysophosphatidylcholines (lysoPCs) were prominent in white rice from Korea while lysophosphatidylethanolamines (lysoPEs) were enriched in white rice from China. A deep learning prediction model was built using 2014 white rice and validated using two different batches of 2015 white rice. The model accurately discriminated white rice from two countries. Among 10 selected predictors, lysoPC(18:2), lysoPC(14:0), and lysoPE(16:0) were the three most important features. Random forest and gradient boosting machine models also worked well in this circumstance. In conclusion, this study provides an architecture for high-throughput classification of white rice from different geographical origins.

A rapid and reliable method for discriminating rice products from different regions using MCX-based solid-phase extraction and DI-MS/MS-based metabolomics approach.

The expansion of the global rice marketplace ultimately raises concerns about authenticity control. Several analytical methods for differentiating the geographical origin of rice have been developed, yet a high-throughput method is still in demand. In this study, we developed a rapid approach using direct infusion-mass spectrometry (DI-MS) to distinguish rice products from different countries. Specifically, the elimination of the matrix effect by a polytetrafluoroethylene (PTFE) filter, a mixed-mode cation exchange (MCX) solid-phase extraction (SPE) with 20% methanol, and an MCX SPE with 100% methanol were measured. Afterward, partial least squares discriminant analysis and random forests were applied to seek the optimal discrimination method. The results revealed that the combination of MCX SPE with 100% methanol and DI-MS in positive ion mode (accuracy=1.000, R2=0.916, Q2=0.720, B/W-based p-value=0.015) or the combination of MCX SPE with 20% methanol and targeted DI-MS/MS in positive ion mode (accuracy=1.000, R2=0.931, Q2=0.849, B/W-based p-value=0.002) showed the excellent discriminatory ability. Furthermore, differentially expressed metabolites including sodiated lysophosphatidylcholine, lysophosphatidylcholine, lysophosphatidylethanolamines and lysophosphatidylglycerol classes were found. In conclusion, our study provides a rapid and reliable platform for geographical discrimination of white rice and will contribute to the authenticity control of rice products.

Simultaneous Profiling of Lysoglycerophospholipids in Rice (Oryza sativa L.) Using Direct Infusion-Tandem Mass Spectrometry with Multiple Reaction Monitoring.

White rice is the final product after the hull and bran layers have been removed during the milling process. Although lysoglycerophospholipids (lysoGPLs) only occupy a small proportion in white rice, they are essential for evaluating rice authenticity and quality. In this study, we developed a high-throughput and targeted lipidomics approach that involved direct infusion-tandem mass spectrometry with multiple reaction monitoring to simultaneously profile lysoGPLs in white rice. The method is capable of characterizing 17 lysoGPLs within 1 min. In addition, unsupervised and supervised analyses exhibited a considerably large diversity of lysoGPL concentrations in white rice from different origins. In particular, a classification model was built using identified lysoGPLs that can differentiate white rice from Korea, China, and Japan. Among the discriminatory lysoGPLs, for the lysoPE(16:0) and lysoPE(18:2) compositions, there were relatively small within-group variations, and they were considerably different among the three countries. In conclusion, our proposed method provides a rapid, high-throughput, and comprehensive format for profiling lysoGPLs in rice samples.

Expeditious discrimination of four species of the Panax genus using direct infusion-MS/MS combined with multivariate statistical analysis.

A practical approach based on direct infusion-MS/MS (DI-MS/MS) was demonstrated for metabolomic classification of four species in the Panax genus. The species Panax ginseng, Panax notoginseng, Panax quinquefolius and Panax vietnamensis were analyzed to develop an efficient tool for authenticating ginseng. Four target ions (m/z 783.5, 945.5, 1107.5 and 1149.2) were selected from LC-MS screening results for DI-MS/MS analysis. The target ions served as classifiers of the four species. As a targeted analysis, DI-MS/MS provided the structural identities of the target ions, clear spectral data and high sensitivity in a shorter time than routine LC-MS analysis. Principal component analysis and partial least squares-discriminant analysis of the DI-MS/MS fingerprinting revealed distinct grouping of the data. The results were validated by cross-validation and a permutation test to examine the utility of the statistical models. The spectral intensities of each species were compared with one another using box plots, which allowed straightforward authentication of the Panax species. The proposed method showed improved efficiency over other current methods for discrimination of large quantities of plant material. Additionally, to the best of our knowledge, this is the first study in which DI-MS/MS has been used to classify plant samples.

Orotic acid induces hypertension associated with impaired endothelial nitric oxide synthesis.

Orotic acid (OA) is an intermediate of pyrimidine nucleotide biosynthesis. Hereditary deficiencies in some enzymes associated with pyrimidine synthesis or the urea cycle induce OA accumulation, resulting in orotic aciduria. A link between patients with orotic aciduria and hypertension has been reported; however, the molecular mechanisms remain elusive. In this study, to elucidate the role of OA in vascular insulin resistance, we investigated whether OA induced endothelial dysfunction and hypertension. OA inhibited insulin- or metformin-stimulated nitric oxide (NO) production and endothelial NO synthase (eNOS) phosphorylation in human umbilical vein endothelial cells. A decreased insulin response by OA was mediated by impairment of the insulin-stimulated phosphoinositide 3-kinase (PI3K)-protein kinase B (PKB/Akt) signaling pathway in cells overexpressing the p110-PI3K catalytic subunit. Impaired effects of metformin on eNOS phosphorylation and NO production were reversed in cells transfected with constitutively active AMP-activated protein kinase. Moreover, experimental induction of orotic aciduria in rats caused insulin resistance, measured as a 125% increase in the homeostasis model assessment, and hypertension, measured as a 25% increase in systolic blood pressure. OA increased the plasma concentration of endothelin-1 by 201% and significantly inhibited insulin- or metformin-induced vasodilation. A compromised insulin or metformin response on the Akt/eNOS and AMP-activated protein kinase/eNOS pathway was observed in aortic rings of OA-fed rats. Taken together, we showed that OA induces endothelial dysfunction by contributing to vascular and systemic insulin resistance that affects insulin- or metformin-induced NO production, leading to the development of hypertension.