Stanislaw Deja

Serum and urine metabolomic fingerprinting in diagnostics of inflammatory bowel diseases.

AIM To evaluate the utility of serum and urine metabolomic analysis in diagnosing and monitoring of inflammatory bowel diseases (IBD). METHODS Serum and urine samples were collected from 24 patients with ulcerative colitis (UC), 19 patients with the Crohns disease (CD) and 17 healthy controls. The activity of UC was assessed with the Simple Clinical Colitis Activity Index, while the activity of CD was determined using the Harvey-Bradshaw Index. The analysis of serum and urine samples was performed using proton nuclear magnetic resonance (NMR) spectroscopy. All spectra were exported to Matlab for preprocessing which resulted in two data matrixes for serum and urine. Prior to the chemometric analysis, both data sets were unit variance scaled. The differences in metabolite fingerprints were assessed using partial least-squares-discriminant analysis (PLS-DA). Receiver operating characteristic curves and area under curves were used to evaluate the quality and prediction performance of the obtained PLS-DA models. Metabolites responsible for separation in models were tested using STATISTICA 10 with the Mann-Whitney-Wilcoxon test and the Students t test (α = 0.05). RESULTS The comparison between the group of patients with active IBD and the group with IBD in remission provided good PLS-DA models (P value 0.002 for serum and 0.003 for urine). The metabolites that allowed to distinguish these groups were: N-acetylated compounds and phenylalanine (up-regulated in serum), low-density lipoproteins and very low-density lipoproteins (decreased in serum) as well as glycine (increased in urine) and acetoacetate (decreased in urine). The significant differences in metabolomic profiles were also found between the group of patients with active IBD and healthy control subjects providing the PLS-DA models with a very good separation (P value < 0.001 for serum and 0.003 for urine). The metabolites that were found to be the strongest biomarkers included in this case: leucine, isoleucine, 3-hydroxybutyric acid, N-acetylated compounds, acetoacetate, glycine, phenylalanine and lactate (increased in serum), creatine, dimethyl sulfone, histidine, choline and its derivatives (decreased in serum), as well as citrate, hippurate, trigonelline, taurine, succinate and 2-hydroxyisobutyrate (decreased in urine). No clear separation in PLS-DA models was found between CD and UC patients based on the analysis of serum and urine samples, although one metabolite (formate) in univariate statistical analysis was significantly lower in serum of patients with active CD, and two metabolites (alanine and N-acetylated compounds) were significantly higher in serum of patients with CD when comparing jointly patients in the remission and active phase of the diseases. Contrary to the results obtained from the serum samples, the analysis of urine samples allowed to distinguish patients with IBD in remission from healthy control subjects. The metabolites of importance included in this case up-regulated acetoacetate and down-regulated citrate, hippurate, taurine, succinate, glycine, alanine and formate. CONCLUSION NMR-based metabolomic fingerprinting of serum and urine has the potential to be a useful tool in distinguishing patients with active IBD from those in remission.

Metabolomics provide new insights on lung cancer staging and discrimination from chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) and lung cancer are widespread lung diseases. Cigarette smoking is a high risk factor for both the diseases. COPD may increase the risk of developing lung cancer. Thus, it is crucial to be able to distinguish between these two pathological states, especially considering the early stages of lung cancer. Novel diagnostic and monitoring tools are required to properly determine lung cancer progression because this information directly impacts the type of the treatment prescribed. In this study, serum samples collected from 22 COPD and 77 lung cancer (TNM stages I, II, III, and IV) patients were analyzed. Then, a collection of NMR metabolic fingerprints was modeled using discriminant orthogonal partial least squares regression (OPLS-DA) and further interpreted by univariate statistics. The constructed discriminant models helped to successfully distinguish between the metabolic fingerprints of COPD and lung cancer patients (AUC training=0.972, AUC test=0.993), COPD and early lung cancer patients (AUC training=1.000, AUC test=1.000), and COPD and advanced lung cancer patients (AUC training=0.983, AUC test=1.000). Decreased acetate, citrate, and methanol levels together with the increased N-acetylated glycoproteins, leucine, lysine, mannose, choline, and lipid (CH3-(CH2)n-) levels were observed in all lung cancer patients compared with the COPD group. The evaluation of lung cancer progression was also successful using OPLS-DA (AUC training=0.811, AUC test=0.904). Based on the results, the following metabolite biomarkers may prove useful in distinguishing lung cancer states: isoleucine, acetoacetate, and creatine as well as the two NMR signals of N-acetylated glycoproteins and glycerol.

Follicular Adenomas Exhibit a Unique Metabolic Profile. 1H NMR Studies of Thyroid Lesions

Thyroid cancer is the most common endocrine malignancy. However, more than 90% of thyroid nodules are benign. It remains unclear whether thyroid carcinoma arises from preexisting benign nodules. Metabolomics can provide valuable and comprehensive information about low molecular weight compounds present in living systems and further our understanding of the biology regulating pathological processes. Herein, we applied 1H NMR-based metabolic profiling to identify the metabolites present in aqueous tissue extracts of healthy thyroid tissue (H), non-neoplastic nodules (NN), follicular adenomas (FA) and malignant thyroid cancer (TC) as an alternative way of investigating cancer lesions. Multivariate statistical methods provided clear discrimination not only between healthy thyroid tissue and pathological thyroid tissue but also between different types of thyroid lesions. Potential biomarkers common to all thyroid lesions were identified, namely, alanine, methionine, acetone, glutamate, glycine, lactate, tyrosine, phenylalanine and hypoxanthine. Metabolic changes in thyroid cancer were mainly related to osmotic regulators (taurine and scyllo- and myo-inositol), citrate, and amino acids supplying the TCA cycle. Thyroid follicular adenomas were found to display metabolic features of benign non-neoplastic nodules and simultaneously displayed a partial metabolic profile associated with malignancy. This finding allows the discrimination of follicular adenomas from benign non-neoplastic nodules and thyroid cancer with similar accuracy. Moreover, the presented data indicate that follicular adenoma could be an individual stage of thyroid cancer development.

Application of (1)H NMR-based serum metabolomic studies for monitoring female patients with rheumatoid arthritis.

Rheumatoid arthritis is a chronic autoimmune-based inflammatory disease that leads to progressive joint degeneration, disability, and an increased risk of cardiovascular complications, which is the main cause of mortality in this population of patients. Although several biomarkers are routinely used in the management of rheumatoid arthritis, there is a high demand for novel biomarkers to further improve the early diagnosis of rheumatoid arthritis, stratification of patients, and the prediction of a better response to a specific therapy. In this study, the metabolomics approach was used to provide relevant biomarkers to improve diagnostic accuracy, define prognosis and predict and monitor treatment efficacy. The results indicated that twelve metabolites were important for the discrimination of healthy control and rheumatoid arthritis. Notably, valine, isoleucine, lactate, alanine, creatinine, GPC  APC and histidine relative levels were lower in rheumatoid arthritis, whereas 3-hydroxyisobutyrate, acetate, NAC, acetoacetate and acetone relative levels were higher. Simultaneously, the analysis of the concentration of metabolites in rheumatoid arthritis and 3 months after induction treatment revealed that L1, 3-hydroxyisobutyrate, lysine, L5, acetoacetate, creatine, GPC+APC, histidine and phenylalanine were elevated in RA, whereas leucine, acetate, betaine and formate were lower. Additionally, metabolomics tools were employed to discriminate between patients with different IL-17A genotypes. Metabolomics may provide relevant biomarkers to improve diagnostic accuracy, define prognosis and predict and monitor treatment efficacy in rheumatoid arthritis.

Chemometrics as a tool of origin determination of Polish monofloral and multifloral honeys.

The aim of this study was to evaluate the application of chemometrics studies to determine the botanical origin of Polish monofloral honeys using NMR spectroscopy. Aqueous extracts of six kinds of honeys, namely, heather (Calluna vulgaris L.), buckwheat (Fagopyrum esculentum L), lime (Tilia L), rape (Brassica napus L. var. napus), acacia (Acacia Mill.), and multifloral ones, were analyzed. Multivariate chemometric data analysis was performed using principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). Chemometric analysis supported by pollen analysis revealed the incorrect classification of acacia honeys by the producers. Characteristic motives for each honey were identified, which allowed chemical profiles of tested honeys to be built. Thus, phenylacetic acid and dehydrovomifoliol (4-hydroxy-4-[3-oxo-1-butenyl]-3,5,5-trimethylcyclohex-2-en-1-one) were proposed to be markers of Polish heather honey. Formic acid and tyrosine were found to be the most characteristic compounds of buckwheat honey, whereas 4-(1-hydroxy-1-methylethyl)cyclohexane-1,3-dienecarboxylic acid was confirmed as a marker of lime honey.

1H NMR-based metabolomics studies of urine reveal differences between type 1 diabetic patients with high and low HbAc1 values.

The aim of this study was to investigate relation between level of HbAc1 and concentration of metabolites in urine of T1D patients. To test this hypothesis the (1)H NMR (proton nuclear magnetic resonance) target analysis of crucial urine metabolites combined with chemometric approach were applied. Urine samples were collected from 30 children and teenagers aged 4-19 with T1D and 12 healthy children, aged 9, as control group. Patients were divided into two groups according to their level of glycated hemoglobin (HbA1c): below (L-T1D) and above 6.5% (H-T1D). The multivariate data analysis (OPLS-DA) was used to explore data and generate the models for selected groups of patients. Two tailed unpaired t-test was used for statistical analysis of quantified metabolites. Comparing to L-T1D patients, H-T1D group exhibited increased levels of alanine, pyruvate and branched amino acid valine that might be related with endogenous glucose production pathway from proteins as well as in the case of T2D. Application of (1)H NMR spectroscopy together with target analysis and chemometric tools based on urine metabolite concentration enable to monitor T1D patients. This methodology can be used as supporting tool for marker HbA1c analysis providing comprehensive information about T1D progression and treatment efficiency.

Metabolomics of Human Amniotic Fluid and Maternal Plasma during Normal Pregnancy.

Metabolic profiles of amniotic fluid and maternal blood are sources of valuable information about fetus development and can be potentially useful in diagnosis of pregnancy disorders. In this study, we applied 1H NMR-based metabolic profiling to track metabolic changes occurring in amniotic fluid (AF) and plasma (PL) of healthy mothers over the course of pregnancy. AF and PL samples were collected in the 2nd (T2) and 3rd (T3) trimester, prolonged pregnancy (PP) until time of delivery (TD). A multivariate data analysis of both biofluids reviled a metabolic switch-like transition between 2nd and 3rd trimester, which was followed by metabolic stabilization throughout the rest of pregnancy probably reflecting the stabilization of fetal maturation and development. The differences were further tested using univariate statistics at α = 0.001. In plasma the progression from T2 to T3 was related to increasing levels of glycerol, choline and ketone bodies (3-hydroxybutyrate and acetoacetate) while pyruvate concentration was significantly decreased. In amniotic fluid, T2 to T3 transition was associated with decreasing levels of glucose, carnitine, amino acids (valine, leucine, isoleucine, alanine, methionine, tyrosine, and phenylalanine) and increasing levels of creatinine, succinate, pyruvate, choline, N,N-dimethylglycine and urocanate. Lactate to pyruvate ratio was decreased in AF and conversely increased in PL. The results of our study, show that metabolomics profiling can be used to better understand physiological changes of the complex interdependencies of the mother, the placenta and the fetus during pregnancy. In the future, these results might be a useful reference point for analysis of complicated pregnancies.

Serum and urine 1 H NMR-based metabolomics in the diagnosis of selected thyroid diseases

Early detection of nodular thyroid diseases including thyroid cancer is still primarily based on invasive procedures such as fine-needle aspiration biopsy. Therefore, there is a strong need for development of new diagnostic methods that could provide clinically useful information regarding thyroid nodular lesions in a non-invasive way. In this study we investigated 1H NMR based metabolic profiles of paired urine and blood serum samples, that were obtained from healthy individuals and patients with nodular thyroid diseases. Estimation of predictive potential of metabolites was evaluated using chemometric methods and revealed that both urine and serum carry information sufficient to distinguish between patients with nodular lesions and healthy individuals. Data fusion allowed to further improve prediction quality of the models. However, stratification of tumor types and their differentiation in relation to each other was not possible.

The NQO1 bioactivatable drug, β-lapachone, alters the redox state of NQO1+ pancreatic cancer cells, causing perturbation in central carbon metabolism

Many cancer treatments, such as those for managing recalcitrant tumors like pancreatic ductal adenocarcinoma, cause off-target toxicities in normal, healthy tissue, highlighting the need for more tumor-selective chemotherapies. β-Lapachone is bioactivated by NAD(P)H:quinone oxidoreductase 1 (NQO1). This enzyme exhibits elevated expression in most solid cancers and therefore is a potential cancer-specific target. β-Lapachones therapeutic efficacy partially stems from the drugs induction of a futile NQO1-mediated redox cycle that causes high levels of superoxide and then peroxide formation, which damages DNA and causes hyperactivation of poly(ADP-ribose) polymerase, resulting in extensive NAD+/ATP depletion. However, the effects of this drug on energy metabolism due to NAD+ depletion were never described. The futile redox cycle rapidly consumes O2, rendering standard assays of Krebs cycle turnover unusable. In this study, a multimodal analysis, including metabolic imaging using hyperpolarized pyruvate, points to reduced oxidative flux due to NAD+ depletion after β-lapachone treatment of NQO1+ human pancreatic cancer cells. NAD+-sensitive pathways, such as glycolysis, flux through lactate dehydrogenase, and the citric acid cycle (as inferred by flux through pyruvate dehydrogenase), were down-regulated by β-lapachone treatment. Changes in flux through these pathways should generate biomarkers useful for in vivo dose responses of β-lapachone treatment in humans, avoiding toxic side effects. Targeting the enzymes in these pathways for therapeutic treatment may have the potential to synergize with β-lapachone treatment, creating unique NQO1-selective combinatorial therapies for specific cancers. These findings warrant future studies of intermediary metabolism in patients treated with β-lapachone.

Do Differences in Chemical Composition of Stem and Cap of Amanita muscaria Fruiting Bodies Correlate with Topsoil Type

Fly agaric (Amanita muscaria) was investigated using a 1H NMR-based metabolomics approach. The caps and stems were studied separately, revealing different metabolic compositions. Additionally, multivariate data analyses of the fungal basidiomata and the type of soil were performed. Compared to the stems, A. muscaria caps exhibited higher concentrations of isoleucine, leucine, valine, alanine, aspartate, asparagine, threonine, lipids (mainly free fatty acids), choline, glycerophosphocholine (GPC), acetate, adenosine, uridine, 4-aminobutyrate, 6-hydroxynicotinate, quinolinate, UDP-carbohydrate and glycerol. Conversely, they exhibited lower concentrations of formate, fumarate, trehalose, α- and β-glucose. Six metabolites, malate, succinate, gluconate, N-acetylated compounds (NAC), tyrosine and phenylalanine, were detected in whole A. muscaria fruiting bodies but did not show significant differences in their levels between caps and stems (P value>0.05 and/or OPLS-DA loading correlation coefficient <0.4). This methodology allowed for the differentiation between the fruiting bodies of A. muscaria from mineral and mineral-organic topsoil. Moreover, the metabolomic approach and multivariate tools enabled to ascribe the basidiomata of fly agaric to the type of topsoil. Obtained results revealed that stems metabolome is more dependent on the topsoil type than caps. The correlation between metabolites and topsoil contents together with its properties exhibited mutual dependences.