Molecular signature of renal cell carcinoma by means of a multiplatform metabolomics analysis.

Renal cell carcinoma (RCC) is a disease with no specific diagnostic method or treatment. Thus, the evaluation of novel diagnostic tools or treatment possibilities is essential. In this study, a multiplatform untargeted metabolomics analysis of urine was applied to search for a metabolic pattern specific for RCC, which could enable comprehensive assessment of its biochemical background. Thirty patients with diagnosed RCC and 29 healthy volunteers were involved in the first stage of the study. Initially, the utility of the application of the selected approach was checked for RCC with no differentiation for cancer subtypes. In the second stage, this approach was used to study clear cell renal cell carcinoma (ccRCC) in 38 ccRCC patients and 38 healthy volunteers. Three complementary analytical platforms were used: reversed-phase liquid chromatography coupled with time-of-flight mass spectrometry (RP-HPLC-TOF/MS), capillary electrophoresis coupled with time-of-flight mass spectrometry (CE-TOF/MS), and gas chromatography triple quadrupole mass spectrometry (GC-QqQ/MS). As a result of urine sample analyses, two panels of metabolites specific for RCC and ccRCC were selected. Disruptions in amino acid, lipid, purine, and pyrimidine metabolism, the TCA cycle and energetic processes were observed. The most interesting differences were observed for modified nucleosides. This is the first time that the levels of these compounds were found to be changed in RCC and ccRCC patients, providing a framework for further studies. Moreover, the application of the CE-MS technique enabled the determination of statistically significant changes in symmetric dimethylarginine (SDMA) in RCC.

Untargeted Metabolomics Study of Three Matrices: Seminal Fluid, Urine, and Serum to Search the Potential Indicators of Prostate Cancer.

The simultaneous determination of metabolites from biological fluids may provide more accurate information about the current body condition. So far, the metabolomics approach has been successfully applied to study the mechanism of several disorders and to search for novel biomarkers. Urine and plasma are widely accepted matrices for the evaluation of several pathologies, while prostate cancer (CaP) development is still unknown. For this reason, an alternative matrix, the seminal fluid, was proposed to expand the knowledge about the CaP pathomechanism. The main aim of this study was to develop and optimize the sample preparation protocol to ensure the highest coverage of the metabolome of ejaculate samples. Parameters like the type and composition of the solvent mixture, time of extraction, and applied volume of the solvent were tested. The optimized method was applied for the untargeted metabolomics profiling of seminal fluid samples obtained from CaP patients. Moreover, urine and serum samples were also prepared for untargeted metabolomics analysis. Analyses were carried out with the use of two complementary analytical techniques: GC-EI-QqQ/MS and LC-ESI-TOF/MS. Finally, the metabolic signature of seminal fluid (n = 7), urine (n = 7), and plasma (n = 7) samples was compared. Furthermore, the hypothesis of the increased level of metabolites in ejaculate samples related to the CaP development was evaluated. The results indicated that the developed and optimized sample preparation protocol for seminal fluid may be successfully applied for metabolomics study. Untargeted analysis of ejaculate enabled to determine the following classes of compounds: fatty acids, sphingolipids, phospholipids, sugars, and their derivatives, as well as amino acids. Finally, a comparison of the three tested matrices was carried out. To our best knowledge, it is the first time when the metabolic profile of the three matrices, namely, urine, plasma, and seminal fluid, was compared. Based on the results, it can be pointed out that ejaculate comprises the metabolic signature of both matrices (polar compounds characteristic for urine, and non-polar ones present in plasma samples). Compared to plasma, semen samples revealed to have a similar profile; however, determined levels of metabolites were lower in case of ejaculate. In case of urine samples, compared to semen metabolic profiles, the levels of detected metabolites were decreased in the latter ones.

Pre- and Post-Resection Urine Metabolic Profiles of Bladder Cancer Patients: Results of Preliminary Studies on Time Series Metabolomics Analysis.

The incidence of bladder cancer (BCa) has remained high for many years. Nevertheless, its pathomechanism has not yet been fully understood and is still being studied. Therefore, multiplatform untargeted urinary metabolomics analysis has been performed in order to study differences in the metabolic profiles of urine samples collected at three time points: before transurethral resection of bladder tumor (TURBT), the day after the procedure and two weeks after TURBT. Collected samples were analyzed with the use of high-performance liquid chromatography hyphenated with time-of-flight mass spectrometry detection (HPLC-TOF/MS) and gas chromatography coupled with triple quadrupole mass spectrometry detection (GC-QqQ/MS, in a scan mode). Levels of metabolites selected in our previous study were assessed in order to confirm their potential to differentiate the healthy and diseased samples, regardless of the risk factors and individual characteristics. Hippuric acid, pentanedioic acid and uridine confirmed their potential for sample differentiation. Based on the results of statistical analysis for the paired samples (comparison of metabolic profiles of samples collected before TURBT and two weeks after), a set of metabolites belonging to nucleotide metabolism and methylation processes was also selected. Longitudinal studies proved to be useful for the evaluation of metabolic changes in bladder cancer.

Comprehensive Metabolic Signature of Renal Dysplasia in Children. A Multiplatform Metabolomics Concept.

Renal dysplasia is a severe congenital abnormality of the kidney parenchyma, which is an important cause of end-stage renal failure in childhood and early adulthood. The diagnosis of renal dysplasia relies on prenatal or postnatal ultrasounds as children show no specific clinical symptoms before chronic kidney disease develops. Prompt diagnosis is important in terms of early introduction of nephroprotection therapy and improved long-term prognosis. Metabolomics was applied to study children with renal dysplasia to provide insight into the changes in biochemical pathways underlying its pathology and in search of early indicators for facilitated diagnosis. The studied cohort consisted of 72 children, 39 with dysplastic kidneys and 33 healthy controls. All subjects underwent comprehensive urine metabolic profiling with the use of gas chromatography and liquid chromatography coupled to mass spectrometry, with two complementary separation modes of the latter. Univariate and multivariate statistical calculations identified a total of nineteen metabolites, differentiating the compared cohorts, independent of their estimated glomerular filtration rate. Seven acylcarnitines, xanthine, and glutamine were downregulated in the urine of renal dysplasia patients. Conversely, renal dysplasia was associated with higher urinary levels of dimethylguanosine, threonic acid or glyceric acid. This is the first metabolomic study of subjects with renal dysplasia. The authors define a characteristic urine metabolic signature in children with dysplastic kidneys, irrespective of renal function, linking the condition with altered fatty acid oxidation, amino acid and purine metabolisms.

GC-MS-based untargeted metabolomics of plasma and urine to evaluate metabolic changes in prostate cancer.

Prostate cancer (CaP) is a common cancer in men. Its late detection and inefficient diagnosis are a challenge for researchers who are currently searching for new cancer-related indicators that would facilitate better detectability of CaP and explain its pathogenesis. In the present preliminary study, endogenous volatile metabolites were detected in plasma and urine samples by using the metabolic fingerprinting approach. The analyses were performed using the GC-QqQ/MS technique in the scan mode. The detected and putatively identified metabolites were statistically analyzed using advanced univariate and multivariate statistical methods. Eleven urinary and three plasma metabolites were selected as statistically significant in patients with CaP as compared to those in healthy controls. Supervised methods such as logistic regression and quadratic support vector machine were applied to obtain the classification models. The accuracy, sensitivity, and specificity of the models were above 83%, 85%, and 81%, respectively. The putatively identified metabolites were associated with biochemical pathways such as tricarboxylic acid cycle, glycolysis, carbohydrate conversion, and steroidal lipid metabolism that are mainly involved in energy production for cell growth and proliferation.

Metabolomic Signature of Early Vascular Aging (EVA) in Hypertension.

Arterial stiffening is a hallmark of early vascular aging (EVA) syndrome and an independent predictor of cardiovascular morbidity and mortality. In this case-control study we sought to identify plasma metabolites associated with EVA syndrome in the setting of hypertension. An untargeted metabolomic approach was used to identify plasma metabolites in an age-, BMI-, and sex-matched groups of EVA (n = 79) and non-EVA (n = 73) individuals with hypertension. After raw data processing and filtration, 497 putative compounds were characterized, out of which 4 were identified as lysophosphaditylcholines (LPCs) [LPC (18:2), LPC (16:0), LPC (18:0), and LPC (18:1)]. A main finding of this study shows that identified LPCs were independently associated with EVA status. Although LPCs have been shown previously to be positively associated with inflammation and atherosclerosis, we observed that hypertensive individuals characterized by 4 down-regulated LPCs had 3.8 times higher risk of EVA compared to those with higher LPC levels (OR = 3.8, 95% CI 1.7-8.5, P < 0.001). Our results provide new insights into a metabolomic phenotype of vascular aging and warrants further investigation of negative association of LPCs with EVA status. This study suggests that LPCs are potential candidates to be considered for further evaluation and validation as predictors of EVA in patients with hypertension.

Urinary metabolomic signature of muscle-invasive bladder cancer: A multiplatform approach.

Bladder cancer (BCa) is ninth amongst the most common types of cancer in the human population worldwide. The statistics of incidence and mortality of BCa are alarming and the currently applied diagnostic methods are still not sensitive enough. This leads to a large number of undiagnosed BCa cases, usually among patients in the early stages of the disease. Despite the fact that many risk factors of BCa have been recognized, the pathomechanism of development of bladder cancer has not been fully explained yet. Therefore, in the present study, multiplatform urinary metabolomics has been implemented in order to scrutinize potential diagnostic indicators of BCa that might help to explain its pathomechanism and be potentially useful in diagnosis and determination of stage of the disease. Urine samples collected from muscle-invasive high grade BCa patients (n = 24) and healthy volunteers (n = 24) were matched in terms of most common BCa risk factors i.e. gender, age, BMI and smoking status. They were analyzed by high performance liquid chromatography coupled with time of flight mass spectrometry detection (HPLC-TOF/MS) using RP and HILIC chromatography, gas chromatography hyphenated with triple quadruple mass spectrometry detection (GC-QqQ/MS) in scan mode, and proton nuclear magnetic resonance (1H NMR). The six datasets obtained were submitted to univariate and multivariate statistical analyses. 17 metabolites significantly discriminated urinary profiles of BCa patients from urinary profiles of healthy volunteers. These metabolites are mainly involved in amino acid metabolism, pyrimidine and purine metabolism, as well as energy metabolism and might play a crucial role in the pathogenesis of BCa.

Multiplatform metabolomics provides insight into the molecular basis of chronic kidney disease.

Changes in metabolites composition can reflect currently present pathological processes in a living organism and constitute a basis for diagnosis and treatment improvements. Thus, the multiplatform metabolomics approach was applied for the investigation of molecular mechanisms of chronic kidney disease (CKD) progression. The high-performance liquid chromatography coupled with time-of-flight mass spectrometry (HPLC-TOF-MS) and gas chromatography coupled with triple quadrupole mass spectrometry (GC-QqQ/MS) serum metabolic fingerprinting followed by uni- and multivariate statistical analysis was carried out to determine metabolic pattern differentiating CKD patients and healthy controls. Furthermore, metabolites changes between stage 3 and 4 of the disease, as well as health status were investigated. The progression of the disease was found to be related to alterations in acylcarnitine, amino acid, lysophospholipid and carbohydrate metabolism. Elevated levels of serum acylcarnitines, sugar alcohols, and organic acids, as well as decreased levels of lysophospholipids, and amino acids, were found to be statistically significant for CKD progression. The obtained results confirm the utility of metabolomics approach as a tool for an explanation of molecular processes underlying CKD development.

Untargeted Metabolomics Provides Insight into the Mechanisms Underlying Resistant Hypertension.

BACKGROUND: Resistant hypertension (RH) affects about 15-20% of treated hypertensive patients worldwide. RH increases the risk of cardiovascular events such as myocardial infarction and stroke by 50%. The pathological mechanisms underlying resistance to treatment are still poorly understood. OBJECTIVE: The main goal of this pilot study was to determine and compare plasma metabolomic profiles in resistant and non-resistant hypertensive patients. METHODS: We applied untargeted metabolomic profiling in plasma samples collected from 69 subjects with RH and 81 subjects with controlled hypertension. To confirm patients' compliance to antihypertensive treatment, levels of selected drugs and their metabolites were determined in plasma samples with the LC-ESI-TOF/MS technique. RESULTS: The results showed no statistically significant differences in the administration of antihypertensive drug in the compared groups. We identified 19 up-regulated and 13 downregulated metabolites in the RH. CONCLUSION: The metabolites altered in RH are linked to oxidative stress and inflammation, endothelium dysfunction, vasoconstriction and cell proliferation. Our results may generate new hypothesis about RH development and progression.

Design of Experiments in metabolomics-related studies: An overview.

Nowadays, Design of Experiments (DoE) approach is a very popular methodology of planning and conducting experiments, where the effect of each tested factor on the studied responses is systematically examined and documented. The results obtained in such manner represent the design space more precisely than in the case of One-Variable-At-Time (OVAT) approach, leading to reliable and comprehensive results, while saving time and resources. Despite such a large increase of interest in this approach recently, its implementation in metabolomics research seems to be limited. Therefore, in this short overview, apart from summarizing some basic concepts of DoE, we wanted to provide a guideline for those who are about to plan metabolomics-related experiments. This overview is divided into four sections. In addition to the first section, which will introduce the history and basics of DoE, second part will provide concise description of the most popular experimental designs. Furthermore, third section will describe examples of DoE application in metabolomics and related studies. We will conclude with fourth section, providing you briefly with opportunities and trends in metabolomics research utilizing experimental design.

HPLC-MS/MS method for dexmedetomidine quantification with Design of Experiments approach: application to pediatric pharmacokinetic study.

AIM: The purpose of this work was to develop and validate a rapid and robust LC-MS/MS method for the determination of dexmedetomidine (DEX) in plasma, suitable for analysis of a large number of samples. METHOD: Systematic approach, Design of Experiments, was applied to optimize ESI source parameters and to evaluate method robustness, therefore, a rapid, stable and cost-effective assay was developed. The method was validated according to US FDA guidelines. LLOQ was determined at 5 pg/ml. The assay was linear over the examined concentration range (5-2500 pg/ml), Results: Experimental design approach was applied for optimization of ESI source parameters and evaluation of method robustness. The method was validated according to the US FDA guidelines. LLOQ was determined at 5 pg/ml. The assay was linear over the examined concentration range (R2 > 0.98). The accuracies, intra- and interday precisions were less than 15%. The stability data confirmed reliable behavior of DEX under tested conditions. CONCLUSION: Application of Design of Experiments approach allowed for fast and efficient analytical method development and validation as well as for reduced usage of chemicals necessary for regular method optimization. The proposed technique was applied to determination of DEX pharmacokinetics in pediatric patients undergoing long-term sedation in the intensive care unit.

Selective determination of cocaine and its metabolite benzoylecgonine in environmental samples by newly developed sorbent materials.

Sewage epidemiology, as compared to crime statistics, health, medical reports or population surveys, is becoming the most objective and realistic approach to estimate drug consumption and trends in local communities. In this study we proposed newly synthesized sorbent materials for selective extraction of cocaine and benzoylecgonine from wastewater samples. The molecular modeling calculations were conducted to provide the choice of proper template and functional monomer for synthesis of extraction materials. The physicochemical properties of synthesized sorbents were studied using various techniques. The newly developed sorbent materials were applied for selective extraction of cocaine and benzoylecgonine from wastewater samples collected from different wastewater treatment plants in Poland. The obtained recoveries values in wastewater samples were 83.6(±7.1)% and 72.1(±4.8)%, for cocaine and benzoylecgonine, respectively. The newly developed sorbents comprise an alternative to conventional ones, which are not entirely suitable for highly efficient purification of environmental samples due to the presence of contaminants.

New sorbent materials for selective extraction of cocaine and benzoylecgonine from human urine samples.

An increase in cocaine consumption has been observed in Europe during the last decade. Benzoylecgonine, as a main urinary metabolite of cocaine in human, is so far the most reliable marker of cocaine consumption. Determination of cocaine and its metabolite in complex biological samples as urine or blood, requires efficient and selective sample pretreatment. In this preliminary study, the newly synthesized sorbent materials were proposed for selective extraction of cocaine and benzoylecgonine from urine samples. Application of these sorbent media allowed to determine cocaine and benzoylecgonine in urine samples at the concentration level of 100ng/ml with good recovery values as 81.7%±6.6 and 73.8%±4.2, respectively. The newly synthesized materials provided efficient, inexpensive and selective extraction of both cocaine and benzoylecgonine from urine samples, which can consequently lead to an increase of the sensitivity of the current available screening diagnostic tests.

Metabolomics in cardiovascular diseases.

Cardiovascular diseases (CVDs) are the main cause of death globally. There is a need for the development of specific diagnostic methods, more effective therapeutic procedures as well as drugs, which can decrease the risk of deaths in the course of CVDs. For this reason, better understanding and explanation of molecular pathomechanisms of CVDs are essential. Metabolomics is focused on analysis of metabolites, small molecules which reflect the state of an organism in a certain point of time. Application of metabolomics approach in the investigation of molecular processes responsible for CVDs development may provide valuable information. In this article we overviewed recent reports employing application of untargeted and targeted metabolomic analyses in particular CVDs. Moreover, we focused on applications of various analytical platforms and metabolomics approaches which may contribute to the explanation of the pathomechanisms of different cardiovascular diseases.

Urine metabolic fingerprinting using LC-MS and GC-MS reveals metabolite changes in prostate cancer: A pilot study.

Prostate cancer (CaP) is a leading cause of cancer deaths in men worldwide. The alarming statistics, the currently applied biomarkers are still not enough specific and selective. In addition, pathogenesis of CaP development is not totally understood. Therefore, in the present work, metabolomics study related to urinary metabolic fingerprinting analyses has been performed in order to scrutinize potential biomarkers that could help in explaining the pathomechanism of the disease and be potentially useful in its diagnosis and prognosis. Urine samples from CaP patients and healthy volunteers were analyzed with the use of high performance liquid chromatography coupled with time of flight mass spectrometry detection (HPLC-TOF/MS) in positive and negative polarity as well as gas chromatography hyphenated with triple quadruple mass spectrometry detection (GC-QqQ/MS) in a scan mode. The obtained data sets were statistically analyzed using univariate and multivariate statistical analyses. The Principal Component Analysis (PCA) was used to check systems' stability and possible outliers, whereas Partial Least Squares Discriminant Analysis (PLS-DA) was performed for evaluation of quality of the model as well as its predictive ability using statistically significant metabolites. The subsequent identification of selected metabolites using NIST library and commonly available databases allows for creation of a list of putative biomarkers and related biochemical pathways they are involved in. The selected pathways, like urea and tricarboxylic acid cycle, amino acid and purine metabolism, can play crucial role in pathogenesis of prostate cancer disease.