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.

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.

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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.

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.

Burkitt lymphoma (BL) is a rapidly growing tumor, characterized by high anabolic requirements. The MYC oncogene plays a central role in the pathogenesis of this malignancy, controlling genes involved in apoptosis, proliferation, and cellular metabolism. Serine biosynthesis pathway (SBP) couples glycolysis to folate and methionine cycles, supporting biosynthesis of certain amino acids, nucleotides, glutathione, and a methyl group donor, S-adenosylmethionine (SAM). We report that BLs overexpress SBP enzymes, phosphoglycerate dehydrogenase (PHGDH) and phosphoserine aminotransferase 1 (PSAT1). Both genes are controlled by the MYC-dependent ATF4 transcription factor. Genetic ablation of PHGDH/PSAT1 or chemical PHGDH inhibition with NCT-503 decreased BL cell lines proliferation and clonogenicity. NCT-503 reduced glutathione level, increased reactive oxygen species abundance, and induced apoptosis. Consistent with the role of SAM as a methyl donor, NCT-503 decreased DNA and histone methylation, and led to the re-expression of ID4, KLF4, CDKN2B and TXNIP tumor suppressors. High H3K27me3 level is known to repress the MYC negative regulator miR-494. NCT-503 decreased H3K27me3 abundance, increased the miR-494 level, and reduced the expression of MYC and MYC-dependent histone methyltransferase, EZH2. Surprisingly, chemical/genetic disruption of SBP did not delay BL and breast cancer xenografts growth, suggesting the existence of mechanisms compensating the PHGDH/PSAT1 absence in vivo.

Chronic obstructive pulmonary disease, COPD, affects the condition of the entire human organism and causes multiple comorbidities. Pathological lung changes lead to quantitative changes in the composition of the metabolites in different body fluids. The obstructive sleep apnea syndrome, OSAS, occurs in conjunction with chronic obstructive pulmonary disease in about 10–20 % of individuals who have COPD. Both conditions share the same comorbidities and this makes differentiating them difficult. The aim of this study was to investigate whether it is possible to diagnose a patient with either COPD or the OSA syndrome using a set of selected metabolites and to determine whether the metabolites that are present in one type of biofluid (serum, exhaled breath condensate or urine) or whether a combination of metabolites that are present in two biofluids or whether a set of metabolites that are present in all three biofluids are necessary to correctly diagnose a patient. A quantitative analysis of the metabolites in all three biofluid samples was performed using 1H NMR spectroscopy. A multivariate bootstrap approach that combines partial least squares regression with the variable importance in projection score (VIP-score) and selectivity ratio (SR) was adopted in order to construct discriminant diagnostic models for the groups of individuals with COPD and OSAS. A comparison study of all of the discriminant models that were constructed and validated showed that the discriminant partial least squares model using only ten urine metabolites (selected with the SR approach) has a specificity of 100 % and a sensitivity of 86.67 %. This model (AUCtest = 0.95) presented the best prediction performance. The main conclusion of this study is that urine metabolites, among the others, present the highest probability for correctly identifying patents with COPD and the lowest probability for an incorrect identification of the OSA syndrome as developed COPD. Another important conclusion is that the changes in the metabolite levels of exhaled breath condensates do not appear to be specific enough to differentiate between patients with COPD and OSAS.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-015-0808-5) contains supplementary material, which is available to authorized users.

Candida species, although they are present as commensal organisms in the digestive tract of healthy individuals, can produce a broad spectrum of serious illnesses in compromised hosts. Fluconazole, a water-soluble triazole with bioavailability greater than 90 %, has been extensively used to treat a wide range of Candida infections. However, a growing resistance of microorganisms in the treatment leads to the discovery of new drugs or modifications of existing ones. The aim of the present study was to investigate whether coordination of Cu(II) ions to fluconazole affects its antifungal activity. The in vitro susceptibility tests and antifungal studies were performed with two Candida spp.: Candidaglabrata and Candida albicans. Overall, 34 strains of the former and 16 strains of the latter were treated with fluconazole, its Cu(II) complex and free Cu(II) ions. The obtained MIC values in 16 cases of the C. glabrata and in 5 cases of the C. albicans were lower for the complex in comparison to the drug. This implies that the complex is more effective against particular strains than the parent drug. The most significant improvement in the complex drug efficacy was observed for fluconazole-resistant species.Electronic supplementary materialThe online version of this article (doi:10.1007/s00044-014-1275-7) contains supplementary material, which is available to authorized users.