Michal Ciborowski

Metabolomics with LC-QTOF-MS Permits the Prediction of Disease Stage in Aortic Abdominal Aneurysm Based on Plasma Metabolic Fingerprint

Abdominal aortic aneurysm (AAA) is a permanent and localized aortic dilation, defined as aortic diameter ≥3 cm. It is an asymptomatic but potentially fatal condition because progressive enlargement of the abdominal aorta is spontaneously evolving towards rupture. Biomarkers may help to explain pathological processes of AAA expansion, and allow us to find novel therapeutic strategies or to determine the efficiency of current therapies. Metabolomics seems to be a good approach to find biomarkers of AAA. In this study, plasma samples of patients with large AAA, small AAA, and controls were fingerprinted with LC-QTOF-MS. Statistical analysis was used to compare metabolic fingerprints and select metabolites that showed a significant change. Results presented here reveal that LC-QTOF-MS based fingerprinting of plasma from AAA patients is a very good technique to distinguish small AAA, large AAA, and controls. With the use of validated PLS-DA models it was possible to classify patients according to the disease stage and predict properly the stage of additional AAA patients. Identified metabolites indicate a role for sphingolipids, lysophospholipids, cholesterol metabolites, and acylcarnitines in the development and progression of AAA. Moreover, guanidinosuccinic acid, which mimics nitric oxide in terms of its vasodilatory action, was found as a strong marker of large AAA.

Metabolomic Approach with LC-QTOF to Study the Effect of a Nutraceutical Treatment on Urine of Diabetic Rats

The rat treated with streptozotocin has been proposed as the most appropriate model of systemic oxidative stress for studying antioxidant therapies. In that sense, rosemary extracts have long been recognized as having antioxidant properties, and folic acid may be able to improve endothelial progenitor cell function. A mixture containing both has been tested as a possible nutraceutical to improve health complications in diabetes. We have developed the methodology to evaluate metabolic changes in the urine of streptozotocin-induced diabetic rats after supplementing their diet with rosemary extract obtained with supercritical fluids (SFE) containing 10% folic acid in an acute but short-term study. It has been done with a metabolomics approach using LC-QTOF as an analytical tool. About 20 endogenous metabolites have been identified by databases and MS/MS showing statistically significant changes. Among them, several amino acids and their metabolites point to changes due to the effect of the gut microbiota. In addition, the comparison between control and streptozotocin-diabetic rats has permitted the showing of some metabolic coincidences between type 1 diabetes and other (possible) autoimmune diseases such as autism and/or Crohns disease, and the nutraceutical intervention has succeeded in inducing changes in such biomarkers.

From numbers to a biological sense: How the strategy chosen for metabolomics data treatment may affect final results. A practical example based on urine fingerprints obtained by LC-MS.

Application of high‐throughput technologies in metabolomics studies increases the quantity of data obtained, which in turn imposes several problems during data analysis. Correctly and clearly addressed biological question and comprehensive knowledge about data structure and properties are definitely necessary to select proper chemometric tools. However, there is a broad range of chemometric tools available for use with metabolomics data, which makes this choice challenging. Precisely performed data treatment enables valuable extraction of information and its proper interpretation. The effect of an error made at an early stage will be enhanced throughout the later stages, which in combination with other errors made at each step can accumulate and significantly affect the data interpretation. Moreover, adequate application of these tools may help not only to detect, but sometimes also to correct, biological, analytical, or methodological errors, which may affect truthfulness of obtained results. This report presents steps and tools used for LC‐MS based metabolomics data extraction, reduction, and visualization. Following such steps as data reprocessing, data pretreatment, data treatment, and data revision, authors want to show how to extract valuable information and how to avoid misinterpretation of results obtained. The purpose of this work was to emphasize problematic characteristics of metabolomics data and the necessity for their attentive and precise treatment. The dataset used to illustrate metabolomics data properties and to illustrate major data treatment challenges was obtained utilizing an animal model of control and diabetic rats, both with and without rosemary treatment. Urine samples were fingerprinted employing LC‐QTOF‐MS.

Metabolites secreted by human atherothrombotic aneurysms revealed through a metabolomic approach.

Abdominal aortic aneurysm (AAA) is perma-nent and localized dilation of the abdominal aorta. Intraluminal thrombus (ILT) is involved in evolution and rupture of AAA. Complex biological processes associated with AAA include oxidative stress, proteolysis, neovascularization, aortic inflammation, cell death, and extracellular matrix breakdown. Biomarkers of growth and AAA rupture could give a more nuanced indication for surgery, unveil novel pathogenic pathways, and open possibilities for pharmacological inhibition of growth. Differential analysis of metabolites released by normal and pathological arteries in culture may help to find molecules that have a high probability of later being found in plasma and start signaling processes or be useful diagnostic/prognostic markers. We used a LC-QTOF-MS metabolomic approach to analyze metabolites released by human ILT (divided into luminal and abluminal layers), aneurysm wall (AW), and healthy wall (HW). Statistical analysis was used to compare luminal with abluminal ILT layer, ILT with AW, and AW with HW to select the metabolites exchanged between tissue and external medium. Identified compounds are related to inflammation and oxidative stress and indicate the possible role of fatty acid amides in AAA. Some metabolites (e.g., hippuric acid) had not been previously associated to aneurysm, others (fatty acid amides) have arisen, indicating a very promising line of research.

Combination of LC–MS- and GC–MS-based Metabolomics to Study the Effect of Ozonated Autohemotherapy on Human Blood

Ozonated autohemotherapy (O3-AHT) is a medical approach during which blood obtained from the patient is ozonated and injected back into the body. Despite an increasing number of evidence that O3-AHT is safe, this type of therapy remains controversial. To extend knowledge about the changes in blood evoked by O3-AHT, LC-MS- and GC-MS-based metabolic fingerprinting was used to compare plasma samples obtained from blood before and after the treatment with potentially therapeutic concentrations of ozone. The procedure was performed in PVC bags utilized for blood storage to study also possible interactions between ozone and plastic. By use of GC-MS, an increase in lactic acid and pyruvic acid was observed, which indicated an increased rate of glycolysis. With LC-MS, changes in plasma antioxidants were observed. Moreover, concentrations of lipid oxidation products (LOP) and lysophospholipids were increased after ozone treatment. This is the first report of increased LOPs metabolites after ozonation of blood. Seven metabolites detected by LC-QTOF-MS only in ozonated samples could be considered as novel biomarkers of oxidative stress. Several plasticizers have been detected by both techniques in blood stored in PVC bags. PVC is known to be an ozone resistant material, but ozonation of blood in PVC bags stimulates leaching of plasticizers into the blood.

In-vial dual extraction liquid chromatography coupled to mass spectrometry applied to streptozotocin-treated diabetic rats. Tips and pitfalls of the method.

The aim of metabolomics studies is the comprehensive and quantitative analysis of all metabolites in a cell, tissue or organism. This approach requires sample preparation methods to be fast, reproducible and able to extract a wide range of analytes with different polarities, as well as analytical platforms able to detect the extracted metabolites. Recently, we have developed a one-step extraction method consisting of a lipophilic and hydrophilic layer within a single vial insert, in-vial dual extraction (IVDE). In order to check possible application of this method to real biological case, analysis of plasma samples obtained from three streptozotocin-induced diabetic and three control rats was performed. Analytical validity of the method was proved by the calculation (in quality control samples) of relative standard deviation (RSD) for detected metabolites. The percentage of metabolites with RSD<30% was 93% for Fatty acyls, 80% for Glycerolipids, 93% for Glycerophospholipids, 68% for Sterol lipids, and 91% for Sphingolipids. IVDE allowed for selection of more than 600 different features discriminating two studied groups. For around 40% of these masses putative identification was possible. Adequate, with several considerations described within this paper, application of IVDE method enables wide metabolite coverage from a single 20μL plasma aliquot. Within the features putatively identified, glycerolipids and glycerophospholipids arose as the most important groups of compounds discriminating diabetic rats from controls. All discriminating metabolites give an idea of the large metabolic differences that can be present in non-controlled type 1 diabetes.

Proteomics biomarkers for non-small cell lung cancer.

In the last decade, proteomic analysis has become an integral tool for investigation of tumor biology, complementing the genetic analysis. The idea of proteomics is to characterize proteins by evaluation of their expressions, functions, and interactions. Proteomics may also provide information about post-translational modifications of proteins and evaluate their value as specific disease biomarkers. The major purpose of clinical proteomics studies is to improve diagnostic procedures including the precise evaluation of biological features of tumor cells and to understand the molecular pathogenesis of cancers to invent novel therapeutic strategies and targets. This review briefly describes the latest reports in proteomic studies of NSCLC. It contains a summary of the methods used to detect proteomic markers in different types of biological material and their clinical application as diagnostic, prognostic, and predictive biomarkers compiled on the basis of the most recent literature and our own experience.

A Single In-Vial Dual Extraction Strategy for the Simultaneous Lipidomics and Proteomics Analysis of HDL and LDL Fractions

A single in-vial dual extraction (IVDE) procedure for the subsequent analysis of lipids and proteins in the high-density lipoprotein (HDL) and low-density lipoprotein (LDL) fractions derived from the same biological sample is presented. On the basis of methyl-tert-butyl ether (MTBE) extraction, IVDE leads to the formation of three phases: a protein pellet at the bottom, an aqueous phase with polar compounds, and an ether phase with lipophilic compounds. After sample extraction, performed within a high-performance liquid chromatography vial insert, the ether phase was directly injected for lipid fingerprinting, while the protein pellet, after evaporation of the remaining sample, was used for proteomics analysis. Human HDL and LDL isolates were used to test the suitability of the IVDE methodology for lipid and protein analysis from a single sample in terms of data quality and matching composition to that of HDL and LDL. Subsequently, HDL and LDL fractions isolated from ApoE-KO and wild-type mice were used to validate the capacity of IVDE for revealing changes in lipid and protein abundance. Results indicate that IVDE can be successfully used for the subsequent analysis of lipids and proteins with the advantages of time saving, simplicity, and reduced sample amount.

Vasopressin acts on platelets to generate procoagulant activity.

The aim of our study was to examine whether arginine vasopressin (AVP) is able to evoke in human platelets a procoagulant response due to activation of an Na+/H+ exchanger. It was found that treatment of platelets with AVP (20–100 nmol/l) results in generation of a weak calcium signal, activation of Na+/H+ exchanger, aggregation, and development of a procoagulant response. The AVP-evoked procoagulant response was dose and time dependent, weaker than that produced by collagen or monensin (mimics Na+/H+ exchanger), and less pronounced following the inhibition of Na+/H+ exchanger by 5-(N-ethyl-N-isopropyl) amiloride or genistein. Flow cytometry studies reveal that in-vitro platelet treatment with AVP results in an unimodal left shift in the forward and side scatter of the entire platelet population, indicating morphological changes on the plasma membrane. The shift was dose related, weaker than that evoked by collagen, similar to that produced by monensin and strongly reduced in the presence of 5-(N-ethyl-N-isopropyl) amiloride or genistein. Using flow cytometry, we demonstrated enhanced expression of phosphatidylserine on the AVP-treated platelets. AVP-evoked phosphatidylserine exposure was dose dependent, inhibited by 5-(N-ethyl-N-isopropyl) amiloride or genistein and weaker than that produced by collagen. AVP in a dose-dependent manner produced a rise in platelet volume. The swelling was inhibited by 5-(N-ethyl-N-isopropyl) amiloride, and its kinetics was similar to that observed in the presence of monensin. We conclude that prolonged treatment of platelets with AVP results in a procoagulant response, which may occur as a consequence of Na+ influx mediated by Na+/H+ exchanger.

Potential first trimester metabolomic biomarkers of abnormal birth weight in healthy pregnancies

Macrosomia and low birth weight (LBW) can be associated with pregnancy complications and may affect the long‐term health of the child. The aim of this study was to evaluate the metabolomic serum profiles of healthy pregnant women to identify early biomarkers of macrosomia and LBW and to understand mechanisms leading to abnormal fetal growth not related to mothers body mass index or presence of gestational diabetes.