Danuta Dudzik

Metabolic fingerprint of Gestational Diabetes Mellitus.

UNLABELLED Gestational Diabetes (GDM) is causing severe short- and long-term complications for mother, fetus or neonate. As yet, the metabolic alterations that are specific for the development of GDM have not been fully determined, which also precludes the early diagnosis and prognosis of this pathology. In this pilot study, we determine the metabolic fingerprint, using a multiplatform LC-QTOF/MS, GC-Q/MS and CE-TOF/MS system, of plasma and urine samples of 20 women with GDM and 20 with normal glucose tolerance in the second trimester of pregnancy. Plasma fingerprints allowed for the discrimination of GDM pregnant women from controls. In particular, lysoglycerophospholipids showed a close association with the glycemic state of the women. In addition, we identified some metabolites with a strong discriminative power, such as LPE(20:1), (20:2), (22:4); LPC(18:2), (20:4), (20:5); LPI(18:2), (20:4); LPS(20:0) and LPA(18:2), as well as taurine-bile acids and long-chain polyunsaturated fatty acid derivatives. Finally, we provide evidence for the implication of these compounds in metabolic routes, indicative of low-grade inflammation and altered redox-balance, that may be related with the specific pathophysiological context of the genesis of GDM. This highlights their potential use as prognostic markers for the identification of women at risk to develop severe glucose intolerance during pregnancy. BIOLOGICAL SIGNIFICANCE Gestational Diabetes Mellitus (GDM) is increasing worldwide and, although diabetes usually remits after pregnancy, women with GDM have a high risk of developing postpartum type 2-diabetes, particularly when accompanied by obesity. Therefore, understanding the pathophysiology of GDM, as well as the identification of potentially modifiable risk factors and early diagnostic markers for GDM are relevant issues. In the present study, we devised a multiplatform metabolic fingerprinting approach to obtain a comprehensive picture of the early metabolic alternations that occur in GDM, and may reflect on the specific pathophysiological context of the disease. Future studies at later stages of gestation will allow us to validate the discriminant power of the identified metabolites.

Quality assurance procedures for mass spectrometry untargeted metabolomics. a review

Graphical abstract Figure. No Caption available. HighlightsUnderstanding metabolomics challenges helps to control unwanted variance.Unwanted variation, bias and any source of possible errors should be minimized.Quality assurance plays the crucial role in quality of metabolomics studies.Standardized methods and SOPs for metabolomics are required. ABSTRACT Untargeted metabolomics, as a global approach, has already proven its great potential and capabilities for the investigation of health and disease, as well as the wide applicability for other research areas. Although great progress has been made on the feasibility of metabolomics experiments, there are still some challenges that should be faced and that includes all sources of fluctuations and bias affecting every step involved in multiplatform untargeted metabolomics studies. The identification and reduction of the main sources of unwanted variation regarding the pre‐analytical, analytical and post‐analytical phase of metabolomics experiments is essential to ensure high data quality. Nowadays, there is still a lack of information regarding harmonized guidelines for quality assurance as those available for targeted analysis. In this review, sources of variations to be considered and minimized along with methodologies and strategies for monitoring and improvement the quality of the results are discussed. The given information is based on evidences from different groups among our own experiences and recommendations for each stage of the metabolomics workflow. The comprehensive overview with tools presented here might serve other researchers interested in monitoring, controlling and improving the reliability of their findings by implementation of good experimental quality practices in the untargeted metabolomics study.

Optimization and validation of a chiral GC–MS method for the determination of free d-amino acids ratio in human urine: Application to a Gestational Diabetes Mellitus study

Gestational Diabetes Mellitus (GDM) is defined as glucose intolerance with onset or first recognition during pregnancy. It is affecting approximately up to 14% of all pregnancies with an increasing tendency. GDM has been related to relevant short-term and long-term health complications for both mother and offspring. Recent studies strongly emphasized the role of several essential amino acids in the pathogenesis of obesity and highlighted their strong correlation with insulin resistance, but there are no references related to modifications in D-AAs in biological fluids. As D-AA elimination proceeds mainly by renal excretion, urine was the selected sample to evaluate the alterations in free D-AAs ratio in a GDM study. Only 1 mL of first void urine or standard solution was required for purification, by using a Discovery DSC-SCX SPE cartridge (500 mg/3 mL) and derivatization into their N(O)-pentafluoropropionyl amino acid 2-propyl esters. Enantiomeric separation was carried out by GC-MS on a Chirasil-L-Val N-propionyl-L-valine-tert-butylamide polysiloxane fused-silica capillary column (25 m×0.25 mm I.D., 0.12 μm film thickness, Agilent Technologies, Waldbronn, Germany), under programmed temperature elution. Detection was performed with an ion trap mass analyzer, operating in the full scan mode in the m/z 50-350 range. 14 pairs of derivatives of D-and L-AAs were separated. The steps of sample preparation, derivatization and GC-MS conditions were optimized for both urine and standards. Several conditions affecting the SPE procedure, such as sorbent mass/volume ratio of the cartridge, sample dilution and pH, were optimized. Volume of reagents and solvents and reaction temperature and time were also tested for the derivatization. Regarding the GC-MS parameters, split ratio, temperature program and mass range were optimized. The final method was validated in terms of linearity, sensitivity, accuracy and precision for D-Ala, D-Pro, D-Ser, D-Met, D-Phe, D-Glu, D-Orn and D-Lys. Identification of AAs in urine samples was based on retention time and mass spectra. Urine from 20 women with GDM and 20 pregnant women with normal glucose tolerance (after 2-h 75-g oral glucose tolerance test), matched according to the week of gestation and age (22-28 week of gestation and age 24-37 years), were enrolled into the study. %D-Relative amounts were determined for Ala, Val, Thr, Ser, Leu, Asx (Asp+Asn), Glx (Glu+Gln), Met, Phe, Tyr, Orn and Lys. Statistically significant differences (p<0.05) were observed only for D-Phe and higher values were found in the GDM group. It is possible that D-Phe could be involved in metabolic/signaling pathways to compensate early stages of insulin resistance, although further work is necessary to confirm this hypothesis.

LC-MS-based metabolomics identification of novel biomarkers of chorioamnionitis and its associated perinatal neurological damage.

Chorioamnionitis is a complication of pregnancy associated with significant maternal and perinatal long-term adverse outcomes. We apply high-throughput amniotic fluid (AF) metabolomics analysis for better understanding the pathophysiological mechanism of chorioamnionitis and its associated perinatal neurological injury and to provide meaningful information about new potential biomarkers. AF samples (n = 40) were collected from women at risk of chorioamnionits. Detailed clinical information on each pregnancy was obtained from obstetrical and neonatal medical examination. Liquid chromatography (LC)/mass spectrometry (MS) followed by data alignment and filtration as well as univariate and multivariate statistical analysis was performed. Statistically significant differences were found in 60 masses in positive and 115 in negative ionization mode obtained with LC/quadrupole time-of-flight MS (LC-QTOF-MS) between women with and without chorioamnionitis. Identified compounds were mainly related to glycerophospholipids and sphingolipids metabolism. From them, LPE(16:0)/LPE(P-16:0) and especially lactosylceramides emerged as the best biomarker candidates. Sulfocholic acid, trioxocholenoic acids, and LPC(18:2) were particularly increased in women with chorioamnionitis whose newborns developed perinatal brain damage. Therefore, we propose LPE(16:0)/LPE(P-16:0) and lactosylceramides as biomarkers for chorioamnionitis as well as LPC(18:2), trioxocholenoic acid, and sulfocholic acid for its associated perinatal brain damage. Metabolomics fingerprinting of AF enables the prediction of pregnancy-related disorders and the development of new diagnostics strategies.

Differences and similarities between LC-MS derived serum fingerprints of patients with B-cell malignancies.

Multiple myeloma (MM) and chronic lymphocytic leukaemia (CLL) are closely related B‐cell non‐Hodgkins lymphomas. MM, a plasma cell malignancy, is the second most common haematopoietic cancer in Western countries, with the median survival time of 3–4 years. CLL, a lymphocyte B malignancy, is the most common leukaemia in Western countries. About 25–30% of all CLL patients do not survive the period of 5 years following diagnosis. Both malignancies are complicated, not fully understood and incurable with the current standard treatment. Biologically, MM and CLL may be preceded by associated precursor conditions, that is, monoclonal gammopathy of undetermined significance for MM and its cellular counterpart and monoclonal B‐cell lymphocytosis for CLL. Similarities and differences in the biology of these malignancies prompted us to evaluate their metabolomics in stages requiring chemotherapy. Fingerprinting of serum metabolites by the use of LC‐MS has never been applied in studies on MM and CLL patients. Obtained results revealed metabolites common for both malignancies (e.g. fatty acids, acylcarnitines, sphingolipids, phospholipids, phenylalanylphenylalanine and isoprene) as well as those which render them different (e.g. lysophosphatidylcholines, monoacylglycerols, aminocaproic acid, phenylacetylglutamine).

GC–MS based Gestational Diabetes Mellitus longitudinal study: Identification of 2-and 3-hydroxybutyrate as potential prognostic biomarkers

HIGHLIGHTSGC–MS offers high‐throughput molecular profiling to study metabolic signature of diabetes.Metabolomics may serve for early prediction of GDM and developing T2DM.2‐hydroxybutyrate and 3‐hydroxybutyrate may be potential prognostic biomarkers.Metabolomics is bringing knowledge gaps in current management of diabetes.Translation of metabolomics research for clinical practice is desired. ABSTRACT Gestational Diabetes Mellitus (GDM) causes severe short‐ and long‐term complications for the mother, fetus and neonate, including type 2‐diabetes (T2DM) later in life. In this pilot study, GC–Q/MS analysis was applied for plasma metabolomics fingerprinting of 24 healthy and 24 women with GDM at different stages of gestation (second and third trimester) and postpartum (one and three months). Multivariate (unsupervised and supervised) statistical analysis was performed to investigate variance in the data, identify outliers and for unbiased assessment of data quality. Plasma fingerprints allowed for the discrimination of GDM pregnant women from controls both in the 2nd and 3rd trimesters of gestation. However, metabolic profiles tended to be similar after delivery. Follow up of these women revealed that 4 of them developed T2DM within 2 years postpartum. Multivariate PLS‐DA models limited to women with GDM showed clear separation 3 months postpartum. In the 2nd trimester of gestation there was also a clear separation between GDM women that were normoglycemic after pregnancy and those with recognized postpartum T2DM. Metabolites that had the strongest discriminative power between these groups in the 2nd trimester of gestation were 2‐hydroxybutyrate, 3‐hydroxybutyrate, and stearic acid. We have described, that early GDM comprises metabotypes that are associated with the risk of future complications, including postpartum T2DM. In this pilot study, we provide evidence that 2‐hydroxybutyrate and 3‐hydroxybutyrate may be considered as future prognostic biomarkers to predict the onset of diabetic complications in women with gestational diabetes after delivery.

New Biochemical Insights into the Mechanisms of Pulmonary Arterial Hypertension in Humans.

Diagnosis of pulmonary arterial hypertension (PAH) is difficult due to the lack of specific clinical symptoms and biomarkers, especially at early stages. We compared plasma metabolic fingerprints of PAH patients (n = 20) with matched healthy volunteers (n = 20) using, for the first time, untargeted multiplatform metabolomics approach consisting of high-performance liquid and gas chromatography coupled with mass spectrometry. Multivariate statistical analyses were performed to select metabolites that contribute most to groups’ classification (21 from liquid in both ionization modes and 9 from gas chromatography-mass spectrometry). We found metabolites related to energy imbalance, such as glycolysis-derived metabolites, as well as metabolites involved in fatty acid, lipid and amino acid metabolism. We observed statistically significant changes in threitol and aminomalonic acid in PAH patients, which could provide new biochemical insights into the pathogenesis of the disease. The results were externally validated on independent case and control cohorts, confirming up to 16 metabolites as statistically significant in the validation study. Multiplatform metabolomics, followed by multivariate chemometric data analysis has a huge potential for explaining pathogenesis of PAH and for searching potential and new more specific and less invasive markers of the disease.

Differential amniotic fluid cytokine profile in women with chorioamnionitis with and without funisitis

Abstract Objectives: To evaluate whether the amniotic fluid (AF) cytokine profile in women with chorioamnionitis may differentiate between those with and without funisitis. Subjects and methods: Forty women at high risk of chorioamnionitis were studied. Gestational age at study was 26.94. Amniocentesis, universal and specific polymerase chain reaction, and microbiological cultures were performed. AF IL-1β, IL-2, IL-4, IL-6, IL 8, IL-10, IL-12, TNF-alpha, IFN-gamma, and MMP-8 were measured by multiplex assay. After delivery, the placenta and umbilical cord were studied histologically. Comparisons were made between three groups: controls, and chorioamnionitis with and without funisitis. Results: In 25 cases, the histological findings were normal (61.5%). The remaining 15 composed of 9 cases of chorioamnionitis alone (9/40; 23.1%) and 6 cases of chorioamnionitis plus funisitis (6/40; 15.4%). All AF cytokine levels were significantly higher in the cases with chorioamnionitis in comparison to controls, except for IFN-gamma. The comparisons between the three groups showed significant differences between chorioamnionitis alone and chorioamnionitis plus funisitis in IL-1β, IL-6, IL-10, IL-12, IL-8, and TNF-alpha, with the levels being higher when funisitis was present. Logistic regression found a powerful predictive model for funisitis including the following cytokinesIL-4, IL-10, IL-12, and IL-8. Conclusions: Measurements of AF interleukins 4, 10, 12, and 8 allow to identify cases with funisitisin women at high risk of chorioamnionitis.

A review of blood sample handling and pre-processing for metabolomics studies

Metabolomics has been found to be applicable to a wide range of clinical studies, bringing a new era for improving clinical diagnostics, early disease detection, therapy prediction and treatment efficiency monitoring. A major challenge in metabolomics, particularly untargeted studies, is the extremely diverse and complex nature of biological specimens. Despite great advances in the field there still exist fundamental needs for considering pre‐analytical variability that can introduce bias to the subsequent analytical process and decrease the reliability of the results and moreover confound final research outcomes. Many researchers are mainly focused on the instrumental aspects of the biomarker discovery process, and sample related variables sometimes seem to be overlooked. To bridge the gap, critical information and standardized protocols regarding experimental design and sample handling and pre‐processing are highly desired. Characterization of a range variation among sample collection methods is necessary to prevent results misinterpretation and to ensure that observed differences are not due to an experimental bias caused by inconsistencies in sample processing. Herein, a systematic discussion of pre‐analytical variables affecting metabolomics studies based on blood derived samples is performed. Furthermore, we provide a set of recommendations concerning experimental design, collection, pre‐processing procedures and storage conditions as a practical review that can guide and serve for the standardization of protocols and reduction of undesirable variation.

Metabolomic Fingerprinting in the Comprehensive Study of Liver Changes Associated with Onion Supplementation in Hypercholesterolemic Wistar Rats

The consumption of functional ingredients has been suggested to be a complementary tool for the prevention and management of liver disease. In this light, processed onion can be considered as a source of multiple bioactive compounds with hepatoprotective properties. The liver fingerprint of male Wistar rats (n = 24) fed with three experimental diets (control (C), high-cholesterol (HC), and high-cholesterol enriched with onion (HCO) diets) was obtained through a non-targeted, multiplatform metabolomics approach to produce broad metabolite coverage. LC-MS, CE-MS and GC-MS results were subjected to univariate and multivariate analyses, providing a list of significant metabolites. All data were merged in order to figure out the most relevant metabolites that were modified by the onion ingredient. Several relevant metabolic changes and related metabolic pathways were found to be impacted by both HC and HCO diet. The model highlighted several metabolites (such as hydroxybutyryl carnitine and palmitoyl carnitine) modified by the HCO diet. These findings could suggest potential impairments in the energy−lipid metabolism, perturbations in the tricarboxylic acid cycle (TCA) cycle and β-oxidation modulated by the onion supplementation in the core of hepatic dysfunction. Metabolomics shows to be a valuable tool to evaluate the effects of complementary dietetic approaches directed to hepatic damage amelioration or non-alcoholic fatty liver disease (NAFLD) prevention.