NMR-based Metabolomic Profiling Research Articles

Salecan ameliorates LPS-induced acute lung injury through regulating Keap1-Nrf2/HO-1 pathway in mice

Acute lung injury (ALI) is a severe clinical condition with high mortality, characterized by rapid onset and limited treatment options. The pathogenesis of ALI involves inflammation and oxidative stress. The polysaccharide salecan, a water-soluble β-(1,3)-D-glucan, has been found to possess numerous pharmaceutical effects, including anti-inflammatory properties, inhibition of oxidative stress, and anti-fatigue effects. This study aims to investigate the protective effect and underlying mechanism of salecan against LPS-induced ALI in mice. Using an in vivo LPS-induced ALI mouse model and an in vitro RAW264.7 cell system, we investigated the role of salecan in ALI with various experimental approaches, including histological staining, quantitative real-time PCR, flow cytometry, western blot analysis, and other relevant assays. Pre-treatment with salecan effectively attenuated LPS-induced ALI in vivo, reducing the severity of pulmonary edema, inflammation, and oxidative stress. NMR-based metabolomic profiling analysis revealed that salecan attenuated LPS-induced metabolic imbalances associated with ALI. Furthermore, salecan downregulated Keap1 and upregulated Nrf2 and HO-1 protein levels, indicating its modulation of the Keap1-Nrf2/HO-1 signaling pathway as a potential mechanism underlying its protective effects against ALI. In vitro studies on RAW264.7 cells revealed that salecan exhibited binding affinity towards macrophages, thereby alleviating LPS-induced apoptosis and inflammation, which underpin its therapeutic potential against ALI. Our study suggests that salecan can alleviate LPS-induced ALI by modulating oxidative stress, inflammatory response, and apoptosis through the activation of the Keap1-Nrf2/HO-1 pathway. These findings provide novel insights into the potential therapeutic use of salecan for the treatment of ALI.

NMR-Metabolomic Profiling and Genome Mining Drive the Discovery of Cyclic Decapeptides from a Marine Streptomyces.

The integration of NMR-metabolomic and genomic analyses can provide enhanced identification of structural properties as well as key biosynthetic information, thus achieving the targeted discovery of new natural products. For this purpose, NMR-based metabolomic profiling of the marine-derived Streptomyces sp. S063 (CGMCC 14582) was performed, by which N-methylated peptides possessing unusual negative 1H NMR chemical shift values were tracked. Meanwhile, genome mining of this strain revealed the presence of an unknown NRPS gene cluster (len) with piperazic-acid-encoding genes (lenE and lenF). Under the guidance of the combined information, two cyclic decapeptides, lenziamides D1 (1) and B1 (2), were isolated from Streptomyces sp. S063, which contains piperazic acids with negative 1H NMR values. The structures of 1 and 2 were determined by extensive spectroscopic analysis combined with Marfey's method and ECD calculations. Furthermore, we provided a detailed model of lenziamide (1 and 2) biosynthesis in Streptomyces sp. S063. In the cytotoxicity evaluation, 1 and 2 showed moderate growth inhibition against the human cancer cells HEL, H1975, H1299, and drug-resistant A549-taxol with IC50 values of 8-24 μM.

NMR-based metabolomic profiling identifies inflammation and muscle-related metabolites as predictors of incident type 2 diabetes mellitus beyond glucose: The Di@bet.es study

AimsThe aim of this study was to combine nuclear magnetic resonance-based metabolomics and machine learning to find a glucose-independent molecular signature associated with future type 2 diabetes mellitus development in a subgroup of individuals from the Di@bet.es study. MethodsThe study group included 145 individuals developing type 2 diabetes mellitus during the 8-year follow-up, 145 individuals matched by age, sex and BMI who did not develop diabetes during the follow-up but had equal glucose concentrations to those who did and 145 controls matched by age and sex. A metabolomic analysis of serum was performed to obtain the lipoprotein and glycoprotein profiles and 15 low molecular weight metabolites. Several machine learning-based models were trained. ResultsLogistic regression performed the best classification between individuals developing type 2 diabetes during the follow-up and glucose-matched individuals. The area under the curve was 0.628, and its 95% confidence interval was 0.510–0.746. Glycoprotein-related variables, creatinine, creatine, small HDL particles and the Johnson-Neyman intervals of the interaction of Glyc A and Glyc B were statistically significant. ConclusionsThe model highlighted a relevant contribution of inflammation (glycosylation pattern and HDL) and muscle (creatinine and creatine) in the development of type 2 diabetes as independent factors of hyperglycemia.

NMR-Metabolomics Reveals a Metabolic Shift after Surgical Resection of Non-Small Cell Lung Cancer

Lung cancer can be detected by measuring the patient's plasma metabolomic profile using nuclear magnetic resonance (NMR) spectroscopy. This NMR-based plasma metabolomic profile is patient-specific and represents a snapshot of the patient's metabolite concentrations. The onset of non-small cell lung cancer (NSCLC) causes a change in the metabolite profile. However, the level of metabolic changes after complete NSCLC removal is currently unknown. Fasted pre- and postoperative plasma samples of 74 patients diagnosed with resectable stage I-IIIA NSCLC were analyzed using 1H-NMR spectroscopy. NMR spectra (s = 222) representing two preoperative and one postoperative plasma metabolite profile at three months after surgical resection were obtained for all patients. In total, 228 predictors, i.e., 228 variables representing plasma metabolite concentrations, were extracted from each NMR spectrum. Two types of supervised multivariate discriminant analyses were used to train classifiers presenting a strong differentiation between the pre- and postoperative plasma metabolite profiles. The validation of these trained classification models was obtained by using an independent dataset. A trained multivariate discriminant classification model shows a strong differentiation between the pre- and postoperative NSCLC profiles with a specificity of 96% (95% CI [86-100]) and a sensitivity of 92% (95% CI [81-98]). Validation of this model results in an excellent predictive accuracy of 90% (95% CI [77-97]) and an AUC value of 0.97 (95% CI [0.93-1]). The validation of a second trained model using an additional preoperative control sample dataset confirms the separation of the pre- and postoperative profiles with a predictive accuracy of 93% (95% CI [82-99]) and an AUC value of 0.97 (95% CI [0.93-1]). Metabolite analysis reveals significantly increased lactate, cysteine, asparagine and decreased acetate levels in the postoperative plasma metabolite profile. The results of this paper demonstrate that surgical removal of NSCLC generates a detectable metabolic shift in blood plasma. The observed metabolic shift indicates that the NSCLC metabolite profile is determined by the tumor's presence rather than donor-specific features. Furthermore, the ability to detect the metabolic difference before and after surgical tumor resection strongly supports the prospect that NMR-generated metabolite profiles via blood samples advance towards early detection of NSCLC recurrence.

Untargeted plasma 1H NMR-based metabolomic profiling in different stages of chronic kidney disease

Chronic kidney disease (CKD) is a serious public health issue affecting thousands of people worldwide. CKD diagnosis is usually made by Estimated Glomerular Filtration Rate (eGFR) and albuminuria, which limit the knowledge of the mechanisms behind CKD progression. The aim of the present study was to identify changes in the metabolomic profile that occur as CKD advances. In this sense, 77 plasma samples from patients with CDK were evaluated by 1D and 2D Nuclear Magnetic Resonance Spectroscopy (NMR). The NMR data showed significant changes in the metabolomic profile of CKD patients and the control group. Principal component analysis (PCA) clustered CKD and control patients into three distinct groups, control, stage 1 (G1)–stage 4 (G4) and stage 5 (G5). Lactate, glucose, acetate and creatinine were responsible for discriminating the control group from all the others CKD stages. Valine, alanine, glucose, creatinine, glutamate and lactate were responsible for the clustering of G1–G4 stages. G5 was discriminated by calcium ethylenediamine tetraacetic acid, magnesium ethylenediamine tetraacetic acid, creatinine, betaine/choline/trimethylamine N-oxide (TMAO), lactate and acetate. CKD G5 plasma pool which was submitted in MetaboAnalyst 4.0 platform (MetPA) analysis and showed 13 metabolic pathways involved in CKD physiopathology. Metabolic changes associated with glycolysis and gluconeogenesis allowed discriminating between CKD and control patients. The determination of involved molecules in TMAO generation in G5 suggests an important role in this uremic toxin linked to CKD and cardiovascular diseases. The aforementioned results propose the feasibility of metabolic assessment of CKD by NMR during treatment and disease progression.

NMR-based metabolomics to determine the fluctuation of metabolites in hydroponic purslane crops at different harvesting times

Purslane (Portulaca oleracea L.) has a high content of nutrients and medicinal effects that depend on the genotype, harvesting time, and production system. The objective of the present research work was to elucidate the NMR-based metabolomics profiling of three native purslane cultivars from Mexico (Xochimilco, Mixquic, and Cuautla) grown under hydroponic conditions and harvested in three different times (32, 39, and 46 days after emergence). Thirty-nine metabolites identified in the 1H NMR spectra of aerial parts of purslane, 5 sugars, 15 amino acids, 8 organic acids, 3 caffeoylquinic acids, as well as 2 alcohols and 3 nucleosides, choline, O-phosphocholine and trigonelline were also detected. A total of 37 compounds were detected in native purslane from Xochimilco and Cuautla, whereas 39 compounds were detected in purslane from Mixquic. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) separated the cultivars into three clusters. Mixquic cultivar had the highest number of differential compounds (amino acids and carbohydrates), followed by Xochimilco and Cuautla cultivars, respectively. Changes in the metabolome were observed in latest times of harvest for all the cultivars studied. The differential compounds were glucose, fructose, galactose, pyruvate, choline, and 2-hydroxysobutyrate. The results obtained in this investigation may contribute to selecting the best cultivar of purslane and the best time in which the levels of nutrients are optimal.

NMR-Based Metabolomic Analysis for the Effects of Trimethylamine N-Oxide Treatment on C2C12 Myoblasts under Oxidative Stress.

The gut microbial metabolite trimethylamine N-oxide (TMAO) has received increased attention due to its close relationship with cardiovascular disease and type 2 diabetes. In previous studies, TMAO has shown both harmful and beneficial effects on various tissues, but the underlying molecular mechanisms remain to be clarified. Here, we explored the effects of TMAO treatment on H2O2-impaired C2C12 myoblasts, analyzed metabolic changes and identified significantly altered metabolic pathways through nuclear magnetic resonance-based (NMR-based) metabolomic profiling. The results exhibit that TMAO treatment partly alleviated the H2O2-induced oxidative stress damage of cells and protected C2C12 myoblasts by improving cell viability, increasing cellular total superoxide dismutase capacity, improving the protein expression of catalase, and reducing the level of malondialdehyde. We further showed that H2O2 treatment decreased levels of branched-chain amino acids (isoleucine, leucine and valine) and several amino acids including alanine, glycine, threonine, phenylalanine and histidine, and increased the level of phosphocholine related to cell membrane structure, while the TMAO treatment partially reversed the changing trends of these metabolite levels by improving the integrity of the cell membranes. This study indicates that the TMAO treatment may be a promising strategy to alleviate oxidative stress damage in skeletal muscle.

Development, Validation, and Use of 1H-NMR Spectroscopy for Evaluating the Quality of Acerola-Based Food Supplements and Quantifying Ascorbic Acid.

Acerola (Malpighia emarginata D.C.) is an exotic fruit with high agro-industrial potential due to its high content of ascorbic acid (AA), phenolic compounds, and carotenoid pigments. Acerola fruit is processed into concentrated juice or powder to be incorporated into food supplements. The ascorbic acid content of concentrated juice or powders must be controlled and well assessed. Therefore, the development of optimal methods and procedures for the rapid and accurate determination of the ascorbic acid content in juice concentrate and juice powder remains of considerable commercial interest. NMR spectroscopy is currently a powerful spectroscopic tool for the qualitative and quantitative analysis of molecules of all types and sizes. Firstly, this article presents the NMR-based metabolomic profiling of acerola juice and concentrate powder to describe and compare their composition. Thirty-six metabolites were identified. The AA over choline ratio and the NMR metabolomic profiles could be used for authentication in the future. Secondly, a rapid (8 min), reliable, and non-destructive method for the quantification of ascorbic acid by 1D 1H-NMR spectroscopy was developed and validated. The LOD and LOQ were 0.05 and 0.15 mg/mL, respectively. These two approaches could be combined to better characterize ingredients derived from acerola and incorporated into food supplements.

NMR-Based Metabolomic Profiling of Mungbean Infected with Mungbean Yellow Mosaic India Virus.

Mungbean is an important legume mainly cultivated in Southeast Asia known for cheap source of food protein. Yellow mosaic disease (YMD) of mungbean is one of the most damaging diseases caused by mungbean yellow mosaic virus (MYMV) and mungbean yellow mosaic India virus (MYMIV) in India. The genetic basis of YMD resistance of mungbean is not well studied yet. Our present studies aimed to explore the genetic basis of YMD resistance through molecular, biochemical and metabolomics approach. Molecular analysis of YMV-infected mungbean plant materials revealed the presence of MYMIV. Chlorophyll contents were estimated as mosaic symptoms that cause chlorosis and necrosis in infected leaves. Chlorophyll a, b and total chlorophyll content were significantly reduced by 27-55% in infected samples compared non-infected control samples. 1H NMR-based metabolomic profiling of virus-infected mungbean were carried out, and we found that vital changes occurred during the development of MYMIV infection in mungbean. A total of fifty metabolites were identified in mungbean leaf samples. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) separated the severely infected sample from the non-infected samples. Orthogonal partial least discrimination analysis (OPLS-DA) revealed significant differences in MYMIV-infected and non-infected control samples. The featured metabolites in MYMIV infected and control samples were amino acids, carbohydrates, and organic acids. Relative abundance of sucrose, γ-amino butyric acid (GABA), proline, alanine, phenylalanine, tryptophan, pyruvate, ascorbate, and citrates were found as differential metabolites. Our results suggest that metabolic changes in infected mungbean samples is related to the viral acquisition. The present study may help in better understanding the metabolic alterations during biotic stress in mungbean.

NMR-based metabolomic profiling can differentiate follicular lymphoma from benign lymph node tissues and may be predictive of outcome

Follicular lymphoma (FL) is a cancer of B-cells, representing the second most common type of non-Hodgkin lymphoma and typically diagnosed at advanced stage in older adults. In contrast to the wide range of available molecular genetic data, limited data relating the metabolomic features of follicular lymphoma are known. Metabolomics is a promising analytical approach employing metabolites (molecules < 1 kDa in size) as potential biomarkers in cancer research. In this pilot study, we performed proton nuclear magnetic resonance spectroscopy (1H-NMR) on 29 cases of FL and 11 control patient specimens. The resulting spectra were assessed by both unsupervised and supervised statistical methods. We report significantly discriminant metabolomic models of common metabolites distinguishing FL from control tissues. Within our FL case series, we also report discriminant metabolomic signatures predictive of progression-free survival.

Modifying the maternal microbiota alters the gut–brain metabolome and prevents emotional dysfunction in the adult offspring of obese dams

Maternal obesity disturbs brain-gut-microbiota interactions and induces negative affect in the offspring, but its impact on gut and brain metabolism in the offspring (F1) are unknown. Here, we tested whether perinatal intake of a multispecies probiotic could mitigate the abnormal emotional behavior in the juvenile and adult offspring of obese dams. Untargeted NMR-based metabolomic profiling and gene-expression analysis throughout the gut-brain axis were then used to investigate the biology underpinning behavioral changes in the dams and their offspring. Prolonged high-fat diet feeding reduced maternal gut short-chain fatty acid abundance, increased markers of peripheral inflammation, and decreased the abundance of neuroactive metabolites in maternal milk during nursing. Both juvenile (postnatal day [PND] 21) and adult (PND112) offspring of obese dams exhibited increased anxiety-like behavior, which were prevented by perinatal probiotic exposure. Maternal probiotic treatment increased gut butyrate and brain lactate in the juvenile and adult offspring and increased the expression of prefrontal cortex PFKFB3, a marker of glycolytic metabolism in astrocytes. PFKFB3 expression correlated with the increase in gut butyrate in the juvenile and adult offspring. Maternal obesity reduced synaptophysin expression in the adult offspring, while perinatal probiotic exposure increased expression of brain-derived neurotrophic factor. Finally, we showed that the resilience of juvenile and adult offspring to anxiety-like behavior was most prominently associated with increased brain lactate abundance, independent of maternal group. Taken together, we show that maternal probiotic supplementation exerts a long-lasting effect on offspring neuroplasticity and the offspring gut-liver-brain metabolome, increasing resilience to emotional dysfunction induced by maternal obesity.

Comprehensive multi-component analysis for authentication and differentiation of 6 Dendrobium species by 2D NMR-based metabolomic profiling

Discrimination of the multiple botanical origins of herbal medicines is important to appropriately control their qualities and comprehensively understand their therapeutic effects. In our study, the chemical profiling of 6 different botanical origins of Dendrobii herba was analyzed using 2D 1H–13C HSQC. High-throughput data processing and analysis methods were developed and applied to detect and quantitatively characterize the spectral features of the 6 different Dendrobium species. Principal components analysis (PCA) and hierarchical cluster analysis (HCA) analyses of these spectral features showed a clear distinction among different Dendrobium species. Characteristic metabolite markers of each of the 6 Dendrobium species were identified by integrating the pairwise partial least-squares discriminant analysis (PLS-DA) and univariate hypothesis test. Besides, 8 major common metabolites were identified and found to have comparable content in all 6 Dendrobium species. Our results allow species authentication and differentiation of Dendrobium. It may also represent a universal protocol for authentication and quality assessment of medicinal herbs.

Hexokinase 2 Is a Pivot for Lovastatin-induced Glycolysis-to-Autophagy Reprogramming in Triple-Negative Breast Cancer Cells.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer with limited therapeutic options available. We have recently demonstrated that lovastatin, a 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase inhibitor, suppresses TNBC cell proliferation and stemness properties in vitro and in vivo. However, the mechanisms through which lovastatin inhibits TNBC cells are not fully understood. Here, we used 1H NMR-based metabolomic profiling to investigate lovastatin-induced metabolic changes in TNBC cell line MDA-MB-231. Among the 46 metabolites identified, lactate demonstrated the highest variable importance in projection (VIP) score. Glycolysis stress test revealed that lovastatin significantly decreased the extracellular acidification rate (ECAR) in MDA-MB-231 cells. Furthermore, lovastatin treatment down-regulated the levels of glycolysis-related proteins including GLUT1, PFK1, and PKM2 in MDA-MB-231 but not non-TNBC MDA-MB-453 cells. In addition, lovastatin induced autophagy as evidenced by increased LC3 puncta formation and LC3-II/I ratio, increased AMPK phosphorylation, and decreased Akt phosphorylation. We also revealed the interaction between the glycolytic enzyme hexokinase 2 (HK2) and the mitochondrial membrane protein voltage-dependent anion channel 1 (VDAC1), an important regulator of autophagy. Further bioinformatics analysis revealed that VDAC1 was expressed at a higher level in breast cancer than normal tissues and higher level of VDAC1 predicted poorer survival outcomes in breast cancer patients. The present study suggests that lovastatin might exert anti-tumor activity by reprogramming glycolysis toward autophagy in TNBC cells through HK2-VDAC1 interaction.

NMR-based metabolomics approach to evaluate the toxicological risks of Tibetan medicine ‘Ershiwuwei Shanhu’ pill in rats

Ethnopharmacological relevance‘Ershiwuwei Shanhu’ Pill (ESP) is a classic Tibetan medicine to treat neurological disorders in nervous system, especially for neurological pains and epilepsy. It contains many Tibet-specific mineral medicines, among which Halloysite (Halloysitum rubrum, HR) was regarded as the main active one. As mineral medicines contain heavy metals with poor solubility, the doubts about its safety restricted its clinical application and further development. Aim of the studyA 7-day acute toxicity of ESP and its main active mineral medicine HR was systematically studied for investigating the safety of ESP and exploring the role of HR in ESP's potential toxicity. Materials and methodsIn this study, the acute oral toxicity assessment of formula-ESP and HR were performed on rats for 7 days at doses equivalent to 10 (1 g/kg) and 40 times (4 g/kg) the typical clinical dose (0.1 g/kg). 1H NMR based metabolomics profiling, aided with biochemical analysis and histopathology, was conducted to explore the global metabolic changes in the livers and kidneys of the administrated rats. ResultsHigh-dose HR caused oxidative stress, energy metabolism disorders, purine metabolism impairments and amino acid metabolism imbalance in rats, resulting in hepatotoxicity and nephrotoxicity, which in accordance with the increased biochemical index in blood (ALT, AST, BUN and CRE). ESP (low-dose) induced metabolites changes were far more less than HR in livers, showcasing the distinct advantage of formula in reducing toxicity. Furthermore, low-dose ESP disturbed renal metabolism in a way similar to high-dose HR, which implies that HR might be the major source of the potential nephrotoxicity of ESP. ConclusionHR exhibited potential hepatoxicity and nephrotoxicity, but the formula- ‘Ershiwuwei Shanhu’ Pill which contains HR is considered relatively safe.

NMR-Based Lipid Metabolite Profiles to Predict Outcomes in Patients Undergoing Interventional Therapy for a Hepatocellular Carcinoma (HCC): A Substudy of the SORAMIC Trial.

Simple SummaryA hepatocellular carcinoma (HCC) is the most common cause of death in patients suffering from chronic liver diseases. In order to improve the prediction of outcomes in HCC patients, there is a need for new biomarkers. This pilot study aimed at identifying serum metabolites for the prediction of outcomes of HCC patients using nuclear magnetic resonance (NMR) spectroscopy. This analysis revealed that high serum concentrations of myo-inositol or dimethylamine were associated with an improved overall survival. In contrast, high concentrations of total cholesterol, LDL-cholesterol and LDL particles (LDL-P) were associated with a decreased overall survival. The identification of novel biomarkers using this NMR-based technology holds promise for opening new directions in the conduction of interventional trials in HCCs.Background: This exploratory study aimed to evaluate lipidomic and metabolomic profiles in patients with early and advanced HCCs and to investigate whether certain metabolic parameters may predict the overall survival in these patients. Methods: A total of 60 patients from the prospective, randomized-controlled, multicenter phase II SORAMIC trial were included in this substudy; among them were 30 patients with an early HCC who underwent radiofrequency ablation combined with sorafenib or a placebo and 30 patients with an advanced HCC who were treated with a selective internal radiation therapy (SIRT) plus sorafenib vs. sorafenib alone. The blood serum of these patients was analyzed using a standardized nuclear magnetic resonance (NMR) platform. All tested metabolites were correlated with the overall survival. Results: The overall survival (OS) was significantly higher in patients with an early HCC (median OS: 34.0 months) compared with patients with an advanced HCC (median OS: 12.0 months) (p < 0.0001). Patients with high serum concentrations of myo-inositol (MI) had a higher overall survival compared with patients with low concentrations (21.6 vs. 13.8 months) with a Pearson correlation coefficient of 0.331 (p = 0.011). Patients with high serum concentrations of dimethylamine had a higher overall survival compared with patients with low concentrations (25.1 vs. 19.7 months) with a Pearson correlation coefficient of 0.279 (p = 0.034). High concentrations of total cholesterol, LDL-cholesterol and LDL particles (LDL-P) were associated with a decreased overall survival. Conclusions: NMR-based lipidomic and metabolomic profiling has the potential to identify individual metabolite biomarkers that predict the outcome of patients with an HCC exposed to non-invasive therapeutic management.

Metabolomic/lipidomic profiling of COVID-19 and individual response to tocilizumab.

The current pandemic emergence of novel coronavirus disease (COVID-19) poses a relevant threat to global health. SARS-CoV-2 infection is characterized by a wide range of clinical manifestations, ranging from absence of symptoms to severe forms that need intensive care treatment. Here, plasma-EDTA samples of 30 patients compared with age- and sex-matched controls were analyzed via untargeted nuclear magnetic resonance (NMR)-based metabolomics and lipidomics. With the same approach, the effect of tocilizumab administration was evaluated in a subset of patients. Despite the heterogeneity of the clinical symptoms, COVID-19 patients are characterized by common plasma metabolomic and lipidomic signatures (91.7% and 87.5% accuracy, respectively, when compared to controls). Tocilizumab treatment resulted in at least partial reversion of the metabolic alterations due to SARS-CoV-2 infection. In conclusion, NMR-based metabolomic and lipidomic profiling provides novel insights into the pathophysiological mechanism of human response to SARS-CoV-2 infection and to monitor treatment outcomes.

Discovery and validation of an NMR-based metabolomic profile in urine as TB biomarker

Despite efforts to improve tuberculosis (TB) detection, limitations in access, quality and timeliness of diagnostic services in low- and middle-income countries are challenging for current TB diagnostics. This study aimed to identify and characterise a metabolic profile of TB in urine by high-field nuclear magnetic resonance (NMR) spectrometry and assess whether the TB metabolic profile is also detected by a low-field benchtop NMR spectrometer. We included 189 patients with tuberculosis, 42 patients with pneumococcal pneumonia, 61 individuals infected with latent tuberculosis and 40 uninfected individuals. We acquired the urine spectra from high and low-field NMR. We characterised a TB metabolic fingerprint from the Principal Component Analysis. We developed a classification model from the Partial Least Squares-Discriminant Analysis and evaluated its performance. We identified a metabolic fingerprint of 31 chemical shift regions assigned to eight metabolites (aminoadipic acid, citrate, creatine, creatinine, glucose, mannitol, phenylalanine, and hippurate). The model developed using low-field NMR urine spectra correctly classified 87.32%, 85.21% and 100% of the TB patients compared to pneumococcal pneumonia patients, LTBI and uninfected individuals, respectively. The model validation correctly classified 84.10% of the TB patients. We have identified and characterised a metabolic profile of TB in urine from a high-field NMR spectrometer and have also detected it using a low-field benchtop NMR spectrometer. The models developed from the metabolic profile of TB identified by both NMR technologies were able to discriminate TB patients from the rest of the study groups and the results were not influenced by anti-TB treatment or TB location. This provides a new approach in the search for possible biomarkers for the diagnosis of TB.

Analysis of metabolomics profile in hypothyroid patients before and after thyroid hormone replacement.

The serum metabolic changes occurring during the transition from hypothyroidism to euthyroidism are not known. This study aimed to determine the metabolomic profile in hypothyroid patients before (HypoT0) and after (HypoT1) euthyroidism achieved through levothyroxine (L-T4) treatment. Eighteen patients with overt primary hypothyroidism were recruited for the study. All patients were treated with L-T4 to achieve euthyroidism. Thyrotropin (TSH), free thyroxine (FT4), free triiodothyronine (FT3) and metabolomics profiles were measured before and after 3months of treatment. The euthyroid control group consisted of 28 healthy volunteers. Metabolomics analysis was performed using Nuclear Magnetic Resonance (NMR) spectroscopy. 1H NMR-based metabolomics profiling of patients with newly diagnosed hypothyroidism (HypoT0) showed significantly higher levels of citrate, creatinine, glycerol, myo-inositol and serine, and lower levels of proline and taurine compared to controls. Interestingly, some metabolic changes were persistent three months after pharmacological treatments, despite normal serum TSH and thyroid hormone concentrations (HypoT1). When an Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) model was built to evaluate possible differences in the metabolic profile between HypoT0 and HypoT1, the data obtained were not significantly different. These results suggest that metabolic changes in the patients with hypothyroidism may persist after normalization of serum levels of FT3, FT4, and TSH, which currently represent the gold standard in laboratory testing for diagnosis and evaluation of thyroid pathology. So, the metabolomics approach may contribute to integrate classical hormone assays and to determine the euthyroid status achievement with greater efficacy.

Differentiating, evaluating, and classifying three quinoa ecotypes by washing, cooking and germination treatments, using 1H NMR-based metabolomic approach.

We processed three quinoa ecotypes as they are commonly consumed in a daily diet. For the treatments, quinoa seeds were washed, cooked, and/or germinated. Following treated, we used 1H NMR-based metabolomic profiling to explore differences between the ecotypes. Then, for a non-targeted and targeted food fingerprint analysis of samples, we performed multivariable data analyses, including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and hierarchical cluster analysis. From our study, we were able to discriminate each quinoa ecotype regardless of treatment based on its metabolomic profiling. Additionally, we were able to identify 30 metabolites that were useful to determine the effect of each treatment on nutritional composition. Germination increased the content of most metabolites irrespective of ecotype. In general, ecotype CQE_03 was different from ecotypes CQE_01 and CQE_02. Our phytochemical analysis revealed the effects of washing, cooking, and/or germination, particularly on saponins content.

NMR-based metabolomic profiling of the differential concentration of phytomedicinal compounds in pericarp, skin and seeds of Momordica charantia (bitter melon)

Momordica charantia is a medicinal plant which is widely used in different traditional medicinal systems to treat several diseases. We have identified the differential distribution of phytomedicinally important metabolites in the pericarp, skin and seeds of M. charantia fruit via NMR spectroscopy. Multivariate statistical analysis showed a clustering of the metabolic profiles of seeds and pericarp, and their clear separation from the metabolic profile of the skin. The total phenolic and flavonoid content of the fruit extracts were estimated via bioassays, the radical scavenging activity was estimated via in vitro DPPH and ABTS assays and an inhibitory activity test of α-glucosidase was also performed. The pericarp and seeds contained significant amounts of phenolic compounds and flavonoids, indicating that they are a good source for antioxidants. The skin contained a significantly higher amount of phytosterols such as Charantin and momordicine, which are known to correlate with antidiabetic activity.