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Prostate cancer continues to be a prominent health concern for men globally. Current screening techniques, primarily the prostate-specific antigen (PSA) test and digital rectal examination (DRE), possess inherent limitations, with prostate biopsy being the definitive diagnostic procedure. The invasive nature of the biopsy and other drawbacks of current screening tests create the need for non-invasive and more accurate diagnostic methods. This study utilized 1 H-NMR (Proton Nuclear Magnetic Resonance) based serum metabolomics to differentiate between prostate cancer (PCa) and benign prostatic hyperplasia (BPH). Serum samples from 40 PCa and 41 BPH patients were analysed using 1 H-NMR spectroscopy. PepsNMR was utilized for preprocessing the raw NMR data, and the binned spectra were examined for patterns distinguishing PCa and BPH. Principal component analysis (PCA) showed a moderate separation between PCa and BPH, highlighting the distinct metabolic profiles of both conditions. A logistic regression model was then developed, which demonstrated good performance in distinguishing between the two conditions. The results showed significant variance in multiple metabolites between PCa and BPH, such as isovaleric acid, ethylmalonic acid, formate, and glutamic acid. This research underlines the potential of 1 H-NMR-based serum metabolomics as a promising tool for improved prostate cancer screening, offering an alternative to the limitations of current screening methods.

Background: One-dimensional proton nuclear magnetic resonance (1D 1 H NMR) spectroscopy is a non-destructive, non-targeted analytical technique providing both qualitative and quantitative insights, particularly beneficial for mixture analysis. However, the qualitative analysis of 1D 1 H NMR spectra for mixture samples is laborious and time-consuming, involving extensive database searches and verification experiments like spiking. This process heavily relies on the analyst's expertise, leading to efficiency discrepancies. There is a pressing need for a reliable method to streamline operations and enhance the efficiency of qualitative analysis in complex mixtures., Results: We introduce a library-aided method for spectral profiling, named LAMAIS. This method achieves compound identification through similarity assessment between samples and template data, allowing rapid, automatic compound identification and full-spectrum peak assignment without the need for fitting. LAMAIS correctly identifies over 90 % of components in synthetic mixtures and more than 75 % in experimental mixtures, surpassing other representative methods with a higher F2 score. Our reference library, which currently includes 71 compounds, is tailored to capture the commonality of primary metabolites across diverse plant species. The analysis of real-world samples yielded encouraging results, underscoring LAMAIS's versatility as an auxiliary tool suitable for a variety of botanical sources. For analyst convenience, interactive graphics are utilized as the output format., Significance: LAMAIS excels, demonstrating competitiveness and reliability. The approach minimizes repetitive tasks and sample wastage, improving the efficiency of 1D 1 H NMR qualitative analysis. Constructing a reference library effectively preserves knowledge, mitigates reliance on human experience, and addresses gaps in the analysis of plant source samples., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Metabolomics has emerged as a powerful tool for identifying biomarkers of disease, and nuclear magnetic resonance (NMR) spectroscopy allows for the simultaneous detection of a wide range of metabolites. However, due to complex interactions within metabolic networks, metabolites often exhibit high correlation and collinearity. To address this challenge, self-organizing maps (SOMs) of Kohonen maps and counter propagation-artificial neural networks (CP-ANN) were employed in this study to model proton nuclear magnetic resonance spectroscopic ( 1 HNMR) data from control samples and breast cancer (BC) patients. Blood serum samples from a control group (n=24) and BC patients (n=18) were used to extract metabolites using methanol and chloroform solvents in optimum extraction conditions. The 1 HNMR data was preprocessed by performing phase, baseline, and shift corrections. Subsequently, the preprocessed data was modeled using Kohonen network as an unsupervised technique and CP-ANN as a supervised technique. In this regard, the model built with CP-ANN successfully distinguished between the two classes with an accuracy of 100 % for both group and sensitivity of 96 % and 100 % for control group and BC patients, respectively. Additionally, CP-ANN algorithm demonstrated predictive capabilities by accurately classifying test samples with 90 % sensitivity, 98 % specificity, and 96 % accuracy for control group and 100 % sensitivity, 90 % specificity, and 96 % accuracy for BC patients. Furthermore, analysis of the resulting topological map revealed 14 significant variables (biomarkers) such as sarcosine, lysine, trehalose, tryptophan, and betaine that effectively differentiated between healthy individuals and BC patients., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In this work, the HR MAS NMR (high-resolution magic-angle spinning nuclear magnetic resonance) spectroscopy technique was combined with standard histological examinations to investigate the metabolic features of high-grade serous ovarian cancer (HGSOC) with a special focus on the relation between a metabolic profile and a cancer cell fraction. The studied group consisted of 44 patients with HGSOC and 18 patients with benign ovarian tumors. Normal ovarian tissue was also excised from 13 control patients. The metabolic profiles of 138 tissue specimens were acquired on a Bruker Avance III 400 MHz spectrometer. The NMR spectra of the HGSOC samples could be discriminated from those acquired from the non-transformed tissue and were shown to depend on tumor purity. The most important features that differentiate the samples with a high fraction of cancer cells from the samples containing mainly fibrotic stroma are the increased intensities in the spectral regions corresponding to phosphocholine/glycerophosphocholine, phosphoethanolamine/serine, threonine, uridine nucleotides and/or uridine diphosphate (UDP) nucleotide sugars. Higher levels of glutamine, glutamate, acetate, lysine, alanine, leucine and isoleucine were detected in the desmoplastic stroma within the HGSOC lesions compared to the stroma of benign tumors. The HR MAS NMR analysis of the metabolic composition of the epithelial and stromal compartments within HGSOC contributes to a better understanding of the disease's biology.

Serum 1 H NMR metabolomics has been used as a diagnostic tool for screening type 2 diabetes (T2D) with chronic kidney disease (CKD) as comorbidity. This work aimed to evaluate 1 H NMR data to detect the initial kidney damage and CKD in T2D subjects, through multivariate statistical analysis. Clinical data and biochemical parameters were obtained for classifying five experimental groups using KDIGO guidelines: Control (healthy subjects), T2D, T2D-CKD-mild, T2D-CKD-moderate, and T2D-CKD-severe. Serum 1 H NMR spectra were recorded to follow two strategies: one based on metabolite-to-creatinine (Met/Cr) ratios as targeted metabolomics, and the second one based on untargeted metabolomics from the 1 H NMR profile. A prospective biomarkers panel of the early stage of T2D-CKD based in metabolite-to-creatinine ratio (ornithine/Cr, serine/Cr, mannose/Cr, acetate/Cr, acetoacetate/Cr, formate/Cr, and glutamate/Cr) was proposed. Later, a statistical model based on non-targeted metabolomics was used to predict initial CKD, and its metabolic pathway analysis allowed identifying the most affected pathways: phenylalanine, tyrosine, and tryptophan biosynthesis; valine, leucine, and isoleucine degradation; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; and histidine metabolism. Nonetheless, further studies with a larger cohort are advised to precise ranges in metabolite-to-creatinine ratios and evaluate the prediction pertinency to detect initial CKD in T2D patients in both statistical models proposed., Competing Interests: Declaration of Competing Interest Alma L. Saucedo reports financial support was provided by National Council for Science and Technology. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The antimicrobial effects of high hydrostatic pressure (HHP) treatments on chill-stored seafood are well-documented, while their impact on the metabolic profile of seafood, especially the metabolome of fish flesh, and remains underexplored. Addressing this gap, this study investigates the effects of HHP on the metabolome of chill-stored rose shrimp by conducting multivariate data analysis based on untargeted proton nuclear magnetic resonance observations. Vacuum-packed rose shrimp samples were subjected to HHP at 0, 400, 500, and 600 MPa for 10 min and then stored at 2-4°C. The microorganism analysis and metabolic analysis were carried out on days 1 and 14. HHP treatment effectively deactivated Lactobacillus spp., Escherichia coli, Pseudomonas spp., total Coliforms, and sulfite-reducing anaerobic bacteria. Consequently, HHP treatment significantly reduced the formation rate of decay-related metabolites, such as hypoxanthine, trimethylamine, and biogenic amines, which exhibited significant accumulation in untreated samples. Multivariate unsupervised analyses provided insights into the overall changes in the metabolite profile induced by HHP. Metabolic pathway analysis revealed several pathways underlying spoilage, including pyruvate metabolism, valine, leucine, and isoleucine biosynthesis, purine metabolism, methane metabolism, glycine, serine, and threonine metabolism, citrate cycle (TCA cycle), glycolysis/gluconeogenesis, alanine, aspartate, and glutamate metabolism, sulfur metabolism, pantothenate and CoA biosynthesis, glutathione metabolism, and glyoxylate and dicarboxylate metabolism. Importantly, these pathways underwent alterations due to the application of HHP, particularly at high-pressure levels. In summary, the results unveil the potential mechanisms of HHP effects on chill-stored rose shrimps., (© 2024 The Author(s). Journal of Food Science published by Wiley Periodicals LLC on behalf of Institute of Food Technologists.)

Urine is an equally attractive biofluid for metabolomics analysis, as it is a challenging matrix analytically. Accurate urine metabolite concentration estimates by Nuclear Magnetic Resonance (NMR) are hampered by pH and ionic strength differences between samples, resulting in large peak shift variability. Here we show that calculating the spectra of original samples from mixtures of samples using linear algebra reduces the shift problems and makes various error estimates possible. Since the use of two-dimensional (2D) NMR to confirm metabolite annotations is effectively impossible to employ on every sample of large sample sets, stabilization of metabolite peak positions increases the confidence in identifying metabolites, avoiding the pitfall of oranges-to-apples comparisons.

Objectives: This study aimed to reveal the anti-fibrotic effects of Botrychium ternatum (Thunb.) Sw. (BT) against idiopathic pulmonary fibrosis (IPF) and to preliminarily analyze its potential mechanism on bleomycin-induced IPF rats., Methods: The inhibition of fibrosis progression in vivo was assessed by histopathology combined with biochemical indicators. In addition, the metabolic regulatory mechanism was investigated using 1H-nuclear magnetic resonance-based metabolomics combined with multivariate statistical analysis., Key Findings: Firstly, biochemical analysis revealed that BT notably suppressed the expression of hydroxyproline and transforming growth factor-β1 in the pulmonary tissue. Secondly, Masson's trichrome staining and hematoxylin and eosin showed that BT substantially improved the structure of the damaged lung and significantly inhibited the proliferation of collagen fibers and the deposition of extracellular matrix. Finally, serum metabolomic analysis suggested that BT may exert anti-fibrotic effects by synergistically regulating tyrosine metabolism; phenylalanine, tyrosine and tryptophan biosynthesis; and synthesis and degradation of ketone bodies., Conclusions: Our study not only clarifies the potential anti-fibrotic mechanism of BT against IPF at the metabolic level but also provides a theoretical basis for developing BT as an effective anti-fibrotic agent., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Royal Pharmaceutical Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Introduction: Breeding for oil palm resistance against basal stem rot caused by Ganoderma boninense is challenging and time-consuming. Advanced oil palm gene pools are very limited, hence it is assumed that parental palms have experienced genetic drift and lost their resistance genes against Ganoderma. High-throughput selection criteria should be developed. Metabolomic analysis using 1 H nuclear magnetic resonance (NMR) spectroscopy is easy, and the resulting metabolite can be used as a diagnostic tool for detecting disease in various host-pathogen combinations., Objectives: The objective of this study was to identify metabolite variations in Dura (D) and Pisifera (P) parental palms with different resistance levels against Ganoderma and moderately resistant DxP using 1 H NMR analysis., Methods: Leaf tissues of seven different oil palm categories consisting of: resistant, moderate, and susceptible Dura (D); moderate and susceptible Pisifera (P); resistant Tenera/Pisifera (T/P) parental palms; and moderately resistant DxP variety progenies, were sampled and their metabolites were determined using NMR spectroscopy., Results: Twenty-nine types of metabolites were identified, and most of the metabolites fall in the monosaccharides, amino acids, and fatty acids compound classes. The PCA, PLS-DA, and heatmap multivariate analysis indicated two identified groups of resistance based on their metabolites. The first group consisted of resistant T/P, moderate P, resistant D, and moderately resistant DxP. In contrast, the second group consisted of susceptible P, moderate D, and susceptible D. Glycerol and ascorbic acid were detected as biomarker candidates by OPLS-DA to differentiate moderately resistant DxP from susceptible D and P. The pathway analysis suggested that glycine, serine, and threonine metabolism and taurine and hypotaurine metabolism were involved in the oil palm defense mechanism against Ganoderma., Conclusion: A metabolomic study with 1 H NMR was able to describe the metabolite composition that could differentiate the characteristics of oil palm resistance against basal stem rot (BSR) caused by G. boninense. These metabolites revealed in this study have enormous potential to become support tools for breeding new oil palm varieties with higher resistance against BSR., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Wilson disease (WD) is an autosomal recessive disorder characterized by abnormal copper metabolism. The accumulation of copper in the liver can progress to liver fibrosis and, ultimately, cirrhosis, which is a primary cause of death in WD patients. Metabonomic technology offers an effective approach to investigate the traditional Chinese medicine (TCM) syndrome types of WD-related liver fibrosis by monitoring the alterations in small molecule metabolites within the body. In this study, we employed 1 H-Nuclear Magnetic Resonance ( 1 H NMR) metabonomics to assess the metabolic profiles associated with five TCM syndrome types of WD-related liver fibrosis and analyzed the diagnostic and predictive capabilities of various metabolites. The study found a variety of metabolites, each with varying levels of diagnostic and predictive capabilities. Furthermore, the discerned differential metabolic pathways were primarily associated with various pathways involving carbohydrate metabolism, amino acid metabolism, and lipid metabolism. This study has identified various characteristic metabolic markers and pathways associated with different TCM syndromes of liver fibrosis in WD, providing a substantial foundation for investigating the mechanisms underlying these TCM syndromes., Competing Interests: Declaration of Competing Interest The authors declare that they have no Conflict of Interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The MAPK signaling pathway with BRAF mutations has been shown to drive the pathogenesis of 40-60% of melanomas. Inhibitors of this pathway's BRAF and MEK components are currently used to treat these malignancies. However, responses to these treatments are not always successful. Therefore, identifying noninvasive biomarkers to predict treatment responses is essential for personalized medicine in melanoma. Using noninvasive 1 H magnetic resonance spectroscopy ( 1 H MRS), we previously showed that BRAF inhibition reduces lactate and alanine tumor levels in the early stages of effective therapy and could be considered as metabolic imaging biomarkers for drug response. The present work demonstrates that these metabolic changes observed by 1 H MRS and those assessed by 31 P MRS are also found in preclinical human melanoma models treated with MEK inhibitors. Apart from 1 H and 31 P MRS, additional supporting in vitro biochemical analyses are described. Our results indicate significant early metabolic correlations with response levels to MEK inhibition in the melanoma models and are consistent with our previous study of BRAF inhibition. Given these results, our study supports the potential clinical utility of noninvasive MRS to objectively image metabolic biomarkers for the early prediction of melanoma's response to MEK inhibition.

Introduction: The prevalence of type 2 diabetes has surged to epidemic proportions and despite treatment administration/adherence, some individuals experience poorly controlled diabetes. While existing literature explores metabolic changes in type 2 diabetes, understanding metabolic derangement in poorly controlled cases remains limited., Objective: This investigation aimed to characterize the urine metabolome of poorly controlled type 2 diabetes in a South African cohort., Method: Using an untargeted proton nuclear magnetic resonance metabolomics approach, urine samples from 15 poorly controlled type 2 diabetes patients and 25 healthy controls were analyzed and statistically compared to identify differentiating metabolites., Results: The poorly controlled type 2 diabetes patients were characterized by elevated concentrations of various metabolites associated with changes to the macro-fuel pathways (including carbohydrate metabolism, ketogenesis, proteolysis, and the tricarboxylic acid cycle), autophagy and/or apoptosis, an uncontrolled diet, and kidney and liver damage., Conclusion: These results indicate that inhibited cellular glucose uptake in poorly controlled type 2 diabetes significantly affects energy-producing pathways, leading to apoptosis and/or autophagy, ultimately contributing to kidney and mild liver damage. The study also suggests poor dietary compliance as a cause of the patient's uncontrolled glycemic state. Collectively these findings offer a first-time comprehensive overview of urine metabolic changes in poorly controlled type 2 diabetes and its association with secondary diseases, offering potential insights for more targeted treatment strategies to prevent disease progression, treatment efficacy, and diet/treatment compliance., (© 2024. The Author(s).)

Metabolomics has become an important tool in elucidating the complex relationship between a plant genotype and phenotype. For over 20 years, nuclear magnetic resonance (NMR) spectroscopy has been known for its robustness, quantitative capabilities, simplicity, and cost-efficiency. 1 H NMR is the method of choice for analyzing a broad range of relatively abundant metabolites, which can be used for both capturing the plant chemical profile at one point in time and understanding the pathways that underpin plant defense. This systematic Review explores how 1 H NMR-based plant metabolomics has contributed to understanding the role of various compounds in plant responses to biotic stress, focusing on both primary and secondary metabolites. It clarifies the challenges and advantages of using 1 H NMR in plant metabolomics, interprets common trends observed, and suggests guidelines for method development and establishing standard procedures.

Objective: Malignant pleural effusion (MPE) is a common complication of lung cancer with poor prognosis. Benign pleural effusion (BPE), such as tuberculous and pneumonic pleural effusion, usually has a good prognosis. Differential diagnosis between MPE and BPE remains a clinical challenge., Methods: 52 MPE, 93 BPE, and their corresponding serum samples were analyzed by hydrogen nuclear magnetic resonance ( 1 HNMR) based metabolomics., Results: The 1 HNMR study showed that some amino acids and betaine in MPE are significantly altered in pleural effusion and serum compared to BPE patients. Levels of serum glucose and glutamine have strong positive correlation with those in pleural effusion (r>0.6) for MPE patients. The area under the receiver operating characteristic curve (AUROC) values of metabolites in pleural effusion or serum were less than 0.805 in differentiating MPE from BPE. Improved an AUROC value of 0.901 was observed using pleural effusion-serum ratios of glutamic acid in differentiating MPE from BPE, which was further validated by 15 double-blind samples., Conclusions: Compared with BPE patients, amino acids and betaine in MPE are significantly altered in pleural effusion and serum. Pleural effusion-serum ratio of glutamic acid may contribute to the rapid diagnosis of MPE from BPE by 1 HNMR analysis., (© 2024 by the Association of Clinical Scientists, Inc.)

The classical approach restricts the detection of metabolites in serum samples by using nuclear magnetic resonance (NMR) spectroscopy; however, the presence of copious proteins and lipoproteins emphasize and necessitate the development of a contemporary, high-throughput tactic. To eliminate the lipoproteins and proteins from sera to engender filtered sera (FS), the study was executed with 50 μl serum obtained from five healthy individuals with 5 years of age difference from 25 to 45 years old and the application of a unique mechanical filter with molecular weight cut-off value of 2KDa. The 10 μl FS from each individual was pooled to make 50 μl final volume filled in a co-axial tube for acquisition of a battery of 1D/2D investigations at 800 MHz NMR spectrometer and the assigned metabolites was confirmed through mass spectrometry as well as by comparing 1 H NMR spectra of individual metabolites. This innovative tactic is commissioning to reveal more than 100 metabolites. In contrast to the protein precipitation method, 24 new metabolites were recognized in the present study. The present innovative approach characterizes nucleosides, nitrogenous bases, and volatile metabolites that possibly produce a landmark for the delineation of a comprehensive metabolic profile applicable for detection of the molecular cause of pathogenicity, early-stage disease detection and prognosis, inborn error of metabolism, and forensic investigations exerting the least volume of FS and NMR spectroscopy. The assignment of 100 metabolites using 1 H NMR-based FS is described for the first time in the present report., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Xylella fastidiosa is a xylem-limited bacterium causing a range of economically important plant diseases in hundreds of crops. Over the last decade, a severe threat due to Olive Quick Decline Syndrome (OQDS), caused by Xylella fastidiosa subspecies pauca, affected the Salento olive groves (Apulia, South-East Italy). Very few phyto-therapeutics, including a Zn/Cu citric acid biocomplex foliar treatment, were evaluated to mitigate this disease. However, the traditional foliar applications result in the agro-actives reaching only partially their target. Therefore the development of novel endo-therapeutic systems was suggested. Metabolite fingerprinting is a powerful method for monitoring both, disease progression and treatment effects on the plant metabolism, allowing biomarkers detection. We performed, for the first time, short-term monitoring of metabolic pathways reprogramming for infected Ogliarola salentina and Cima di Melfi olive trees after precision intravascular biocomplex delivery using a novel injection system. Upon endo therapy, we observed specific variations in the leaf content of some metabolites. In particular, the 1 H NMR-based metabolomics approach showed, after the injection, a significant decrease of both the disease biomarker quinic acid and mannitol with simultaneous increase of polyphenols and oleuropein related compounds in the leaf's extracts. This combined metabolomics/endo-therapeutic methodology provided useful information in the comprehension of plant physiology for future applications in OQDS control., (© 2022. The Author(s).)

Nuclear Magnetic Resonance (NMR) based metabolomic analysis of whole saliva has provided potential diagnostic biomarkers for numerous human diseases contributing to a better understanding of their mechanisms. However, a comprehensive interpretation of the significance of metabolites in whole, parotid, and submandibular/sublingual saliva subtypes is still missing. Precision and reproducibility of sample preparation is an essential step. Here, we present a simple and efficient protocol for saliva 1 H-NMR metabolic profiling. This procedure has been specifically designed and optimized for the identification and quantification of low concentration metabolites (as low as 1.1 μM) and is suitable for all the saliva subtypes., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Hepatocellular carcinoma (HCC) is one of the most extensive and most deadly cancers in the world. Biomarkers for early diagnosis of HCC are still lacking, and noninvasive and effective biomarkers are urgently needed. Metabolomics is committed to studying the changes of metabolites under stimulation, and provides a new approach for discovery of potential biomarkers. In the current work, 1 H nuclear magnetic resonance (NMR) metabolomics approach was utilized to explore the potential biomarkers in HCC progression, and the biomarker panel was evaluated by receiver operating characteristic (ROC) curve analyses. Our results revealed that a biomarker panel consisting of hippurate, creatinine, putrescine, choline, and taurine might be involved in HCC progression. Functional pathway analysis showed that taurine and hypotaurine metabolism is markedly involved in the occurrence and development of HCC. Furthermore, our results indicated that the TPA activity and the level and expression of PKM2 were gradually increased in HCC progression. This research provides a scientific basis for screening potential biomarkers of HCC., (© 2021. Springer-Verlag GmbH Germany, part of Springer Nature.)

Microalgae biotechnology has made it possible to derive secondary bioactive metabolites from microalgae strains that have opened up their entire potential to uncover a wide range of novel metabolic capabilities and turn these into bio-products for the development of sustainable bio-refineries. Nuclear Magnetic Resonance Technology (NMR) has been one of the most successful and functional research technology over the past two decades to analyse the composition, structure and functionality of distinct metabolites in the different microalgae strains. This technology offers qualitative as well as quantitative knowledge about the endogenous metabolites and lipids of low molecular mass to offer a good picture of the physiological state of biological samples in metabolomics and lipidomics studies. Henceforth, this review is aimed at introducing the metabolomics and lipidomics studies into the field of NMR technology and also highlights the protocols for the isolation and metabolic measurements of metabolites from microalgae that should be redirected to resource recovery and value-added products with a systematic and holistic approach for scalability or sustainability., (Copyright © 2021 Elsevier Inc. All rights reserved.)

The aim of the study was to investigate differences in metabolic profiles between patients with major depressive disorder (MDD) with full remission (FR) and healthy controls (HCs). A total of 119 age-matched MDD patients with FR (n = 47) and HCs (n = 72) were enrolled and randomly split into training and testing sets. A 1 H-nuclear magnetic resonance (NMR) spectroscopy-based metabolomics approach was used to identify differences in expressions of plasma metabolite biomarkers. Eight metabolites, including histidine, succinic acid, proline, acetic acid, creatine, glutamine, glycine, and pyruvic acid, were significantly differentially-expressed in the MDD patients with FR in comparison with the HCs. Metabolic pathway analysis revealed that pyruvate metabolism via the tricarboxylic acid cycle linked to amino acid metabolism was significantly associated with the MDD patients with FR. An algorithm based on these metabolites employing a linear support vector machine differentiated the MDD patients with FR from the HCs with a predictive accuracy, sensitivity, and specificity of nearly 0.85. A metabolomics-based approach could effectively differentiate MDD patients with FR from HCs. Metabolomic signatures might exist long-term in MDD patients, with metabolic impacts on physical health even in patients with FR., (© 2021. The Author(s).)

Biologically important ions such as Ca, K, Mg, Fe, and Zn play major roles in numerous biological processes, and their homeostatic balance is necessary for the maintenance of cellular activities. Sudden and severe loss in homeostasis of just one biologically important ion can cause a cascade of negative effects. The ability to quickly, accurately, and reliably quantify biologically important ions in samples of human bio-fluids is something that has been sorely lacking within the field of metabolomics. 1 H-NMR spectra. The foundation of our investigation was the a-priori knowledge that free ethylenediaminetetraacetic acid (EDTA) produces two clear single peaks on 1 H-NMR spectra, and that EDTA chelated to different ions produces unique 1 H-NMR spectral patterns due to 3D conformational changes in the chemical structure of chelated-EDTA and varying degrees of electronegativity. The aim of this study was to develop and test a 1 H-NMR-based method, with application specifically to the field of metabolomics, to quantify biologically important ions within the physiological pH range of 6.50-7.50 using EDTA as a chelating agent. Our method produced linear, accurate, precise, and repeatable results for Ca, Mg, and Zn; however, K and Fe did not chelate with EDTA.

Our purpose is to detect the metabolic alterations in acute stroke-like lesions (SLLs) and further investigate the correlations between metabolic concentrations and focal cerebral blood flow in patients with mitochondrial myopathy, encephalopathy, lactic acidosis and stroke-like episodes (MELAS) using proton MR spectroscopy ( 1 H-MRS) and arterial spin labeling (ASL). A total of 23 patients with MELAS at acute stage of stroke-like episodes (SLEs) and 20 normal controls (NC) were recruited in this study, respectively. All subjects underwent conventional MRI and 1 H-MRS. In addition, ASL was performed in each patient. The measurements of creatine (Cr), choline (Cho), N-acetyl aspartate (NAA), lactate (Lac), glutamine/glutamate (Glx) levels and the ratios of Cho/Cr, NAA/Cr, Lac/Cr and Glx/Cr in acute SLLs for MELAS patients and left parietal and occipital lobes for NC were measured using LC-model software. Furthermore, in MELAS group, the associations between relative cerebral blood flow (rCBF) and metabolite concentrations in acute SLLs were also assessed. In MELAS group, acute SLLs were identified with metabolic abnormalities and increased rCBF. Specifically, compared with controls, MELAS patients exhibited significantly higher Lac, Cho levels and Lac/Cr, Cho/Cr ratios, and lower NAA, Glx levels and NAA/Cr and Glx/Cr ratios. Moreover, for MELAS patients, Lac concentration in acute SLLs was positively correlated with focal rCBF. This study exhibited the neural injury with increasing lactate and cerebral blood flow in the acute SLEs. It might shed light on the underlying biochemical mechanism of mitochondrial cytopathy and angiopathy in MELAS., (Copyright © 2021 Elsevier B.V. and Mitochondria Research Society. All rights reserved.)

We conducted a study to identify the fecal metabolite profile and its proximity to the ruminal metabolism of Nelore steers based on an untargeted metabolomic approach. Twenty-six Nelore were feedlot with same diet during 105 d. Feces and rumen fluid were collected before and at slaughter, respectively. The metabolomics analysis indicated 49 common polar metabolites in the rumen and feces. Acetate, propionate, and butyrate were the most abundant polar metabolites in both bio-samples. The rumen presented significantly higher concentrations of the polar compounds when compared to feces (P < 0.05); even though, fecal metabolites presented an accentuated representability of the ruminal fluid metabolites. All fatty acids present in the ruminal fluid were also observed in the feces, except for C20:2n6 and C20:4n6. The identified metabolites offer information on the main metabolic pathways (higher impact factor and P < 0.05), as synthesis and degradation of ketone bodies; the alanine, aspartate and glutamate metabolisms, the glycine, serine; and threonine metabolism and the pyruvate metabolism. The findings reported herein on the close relationship between the ruminal fluid and feces metabolic profiles may offer new metabolic information, in addition to facilitating the sampling for metabolism investigation in animal production and health routines.

Objectives: Milletia speciosa Champ (MS), a traditional Chinese medicine, has the abilities of antistress, antifatigue, anti-oxidation and so on. In our previous study, MS was found to antidepression while the underlying mechanism of which needs further elucidation., Methods: Here, a proton nuclear magnetic resonance (1H-NMR)-based metabonomics combined network pharmacology research approach was performed to investigate the antidepressive mechanism of MS act on mouse with chronic unpredictable mild stress-induced depression., Key Findings: Results showed that MS could alleviate the ethology of depression (including sucrose preference degree, crossing lattice numbers and stand-up times) and disordered biochemical parameters (5-hydroxytryptamine, norepinephrine and brain-derived neurotrophic factor). Metabonomics study and network pharmacology analysis showed that MS might improve depression through synergistically regulating five targets including Maoa, Maob, Ache, Ido1 and Comt, and three metabolic pathways such as tryptophan metabolism, synthesis of neurotransmitter and phospholipid metabolism., Conclusions: This study for the first time preliminary clarified the potential antidepressive mechanism of MS and provided theoretical basis for developing MS into novel effective antidepressant., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Royal Pharmaceutical Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Tuberculosis (TB) is a major cause of morbidity and mortality in children, and early diagnosis and treatment are crucial to reduce long-term morbidity and mortality. In this study, we explore whether urine nuclear magnetic resonance (NMR)-based metabolomics could be used to identify differences in the metabolic response of children with different diagnostic certainty of TB. We included 62 children with signs and symptoms of TB and 55 apparently healthy children. Six of the children with presumptive TB had bacteriologically confirmed TB, 52 children with unconfirmed TB, and 4 children with unlikely TB. Urine metabolic fingerprints were identified using high- and low-field proton NMR platforms and assessed with pattern recognition techniques such as principal components analysis and partial least squares discriminant analysis. We observed differences in the metabolic fingerprint of children with bacteriologically confirmed and unconfirmed TB compared to children with unlikely TB (p = 0.041 and p = 0.013, respectively). Moreover, children with unconfirmed TB with X-rays compatible with TB showed differences in the metabolic fingerprint compared to children with non-pathological X-rays (p = 0.009). Differences in the metabolic fingerprint in children with different diagnostic certainty of TB could contribute to a more accurate characterisation of TB in the paediatric population. The use of metabolomics could be useful to improve the prediction of TB progression and diagnosis in children.

The diagnosis of mechanical asphyxia remains one of the most difficult issues in forensic pathology. Asphyxia ultimately results in cardiac arrest (CA) and, as there are no specific markers, the differential diagnosis of primitive CA and CA secondary to asphyxiation relies on circumstantial details and on the pathologist experience, lacking objective evidence. Histological examination is currently considered the gold standard for CA post-mortem diagnosis. Here we present the comparative results of histopathology versus those previously obtained by 1 H nuclear magnetic resonance (NMR) metabolomics in a swine model, originally designed for clinical purposes, exposed to two different CA causes, namely ventricular fibrillation and asphyxia. While heart and brain microscopical analysis could identify the damage induced by CA without providing any additional information on the CA cause, metabolomics allowed the identification of clearly different profiles between the two groups and showed major differences between asphyxiated animals with good and poor outcomes. Minute-by-minute plasma sampling allowed to associate these modifications to the pre-arrest asphyxial phase showing a clear correlation to the cellular effect of mechanical asphyxia reproduced in the experiment. The results suggest that metabolomics provides additional evidence beyond that obtained by histology and immunohistochemistry in the differential diagnosis of CA.

Intrauterine growth restriction (IUGR) is a fetal adverse condition, ascribed by limited oxygen and nutrient supply from the mother to the fetus. Management of IUGR is an ongoing challenge because of its connection with increased fetal mortality, preterm delivery and postnatal pathologies. Untargeted nuclear magnetic resonance ( 1 H NMR) metabolomics was applied in 84 umbilical cord blood and maternal blood samples obtained from 48 IUGR and 36 appropriate for gestational age (AGA) deliveries. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) followed by pathway and enrichment analysis generated classification models and revealed significant metabolites that were associated with altered pathways. A clear association between maternal and cord blood altered metabolomic profile was evidenced in IUGR pregnancies. Increased levels of the amino acids alanine, leucine, valine, isoleucine and phenylalanine were prominent in IUGR pregnancies indicating a connection with impaired amino acid metabolism and transplacental flux. Tryptophan was individually connected with cord blood discrimination while 3-hydroxybutyrate assisted only maternal blood discrimination. Lower glycerol levels in IUGR samples ascribed to imbalance between gluconeogenesis and glycolysis pathways, suggesting poor glycolysis. The elevated levels of branched chain amino acids (leucine, isoleucine and valine) in intrauterine growth restricted pregnancies were linked with increased insulin resistance.

The utility of low sample volume in vitro diagnostic (IVDr) proton nuclear magnetic resonance ( 1 H NMR) spectroscopic experiments on blood plasma for information recovery from limited availability or high value samples was exemplified using plasma from patients with SARS-CoV-2 infection and normal controls. 1 H NMR spectra were obtained using solvent-suppressed 1D, spin-echo (CPMG), and 2-dimensional J-resolved (JRES) spectroscopy using both 3 mm outer diameter SampleJet NMR tubes (100 μL plasma) and 5 mm SampleJet NMR tubes (300 μL plasma) under in vitro diagnostic conditions. We noted near identical diagnostic models in both standard and low volume IVDr lipoprotein analysis (measuring 112 lipoprotein parameters) with a comparison of the two tubes yielding R 2 values ranging between 0.82 and 0.99 for the 40 paired lipoprotein parameters samples. Lipoprotein measurements for the 3 mm tubes were achieved without time penalty over the 5 mm tubes as defined by biomarker recovery for SARS-CoV-2. Overall, biomarker pattern recovery for the lipoproteins was extremely similar, but there were some small positive offsets in the linear equations for several variables due to small shimming artifacts, but there was minimal degradation of the biological information. For the standard untargeted 1D, CPMG, and JRES NMR experiments on the same samples, the reduced signal-to-noise was more constraining and required greater scanning times to achieve similar differential diagnostic performance (15 min per sample per experiment for 3 mm 1D and CPMG, compared to 4 min for the 5 mm tubes). We conclude that the 3 mm IVDr method is fit-for-purpose for quantitative lipoprotein measurements, allowing the preparation of smaller volumes for high value or limited volume samples that is common in clinical studies. If there are no analytical time constraints, the lower volume experiments are equally informative for untargeted profiling.

Introduction: Methods for the automated and accurate identification of metabolites in 1D 1 H-NMR samples are crucial, but this is still an unsolved problem. Most available tools are mainly focused on metabolite quantification, thus limiting the number of metabolites that can be identified. Also, most only use reference spectra obtained under the same specific conditions of the target sample, limiting the use of available knowledge., Objectives: The main goal of this work was to develop novel methods to perform metabolite annotation from 1D 1 H-NMR peaks with enhanced reliability, to aid the users in metabolite identification. An essential step was to construct a vast and up-do-date library of reference 1D 1 H-NMR peak lists collected under distinct experimental conditions., Methods: Three different algorithms were evaluated for their capacity to correctly annotate metabolites present in both synthetic and real samples and compared to publicly available tools. The best proposed method was evaluated in a plethora of scenarios, including missing references, missing peaks and peak shifts, to assess its annotation accuracy, precision and recall., Results: We gathered 1816 peak lists for 1387 different metabolites from several sources across different conditions for our reference library. A new method, NMRFinder, is proposed and allows matching 1D 1 H-NMR samples with all the reference peak lists in the library, regardless of acquisition conditions. Metabolites are scored according to the number of peaks matching the samples, how unique their peaks are in the library and how close the spectrum acquisition conditions are in relation to those of the samples. Results show a true positive rate of 0.984 when analysing computationally created samples, while 71.8% of the metabolites were annotated when analysing samples from previously identified public datasets., Conclusion: NMRFinder performs metabolite annotation reliably and outperforms previous methods, being of great value in helping the user to ultimately identify metabolites. It is implemented in the R package specmine.

Objective: Following surgery, focal seizures relapse in 20% to 50% of cases due to the difficulty of delimiting the epileptogenic zone (EZ) by current imaging or electrophysiological techniques. Here, we evaluate an unbiased metabolomics approach based on ex vivo and in vivo nuclear magnetic resonance spectroscopy (MRS) methods to discriminate the EZ in a mouse model of mesiotemporal lobe epilepsy (MTLE)., Methods: Four weeks after unilateral injection of kainic acid (KA) into the dorsal hippocampus of mice (KA-MTLE model), we analyzed hippocampal and cortical samples with high-resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS). Using advanced multivariate statistics, we identified the metabolites that best discriminate the injected dorsal hippocampus (EZ) and developed an in vivo MEGAPRESS MRS method to focus on the detection of these metabolites in the same mouse model., Results: Multivariate analysis of HRMAS data provided evidence that γ-aminobutyric acid (GABA) is largely increased in the EZ of KA-MTLE mice and is the metabolite that best discriminates the EZ when compared to sham and, more importantly, when compared to adjacent brain regions. These results were confirmed by capillary electrophoresis analysis and were not reversed by a chronic exposition to an antiepileptic drug (carbamazepine). Then, using in vivo noninvasive GABA-edited MRS, we confirmed that a high GABA increase is specific to the injected hippocampus of KA-MTLE mice., Significance: Our strategy using ex vivo MRS-based untargeted metabolomics to select the most discriminant metabolite(s), followed by in vivo MRS-based targeted metabolomics, is an unbiased approach to accurately define the EZ in a mouse model of focal epilepsy. Results suggest that GABA is a specific biomarker of the EZ in MTLE., (© 2020 International League Against Epilepsy.)

Celery (Apium graveolens L. var dulce) is a widely cultivated vegetable which is popularly consumed due to its nutrient content and contains bioactive metabolites with positive effects on human physiology. In this study, 1 H NMR spectroscopy coupled with multivariate statistical analyses was used to distinguish celery stem and leaf samples from different geographical origins. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were employed to investigate the differences between celery extracts from three geographical origins: Australia, Taiwan and China. Sugars, amino acids and organic acids were found to contribute significantly to the differentiation between origins, with mannitol identified as an important discriminating metabolite. It was demonstrated that NMR-based metabolomics is an effective approach for establishing reliable metabolomic fingerprints and profiles, enabling the identification of metabolite biomarkers for the possible discrimination of geographical origin., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

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 1 H 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&#95;03 was different from ecotypes CQE&#95;01 and CQE&#95;02. Our phytochemical analysis revealed the effects of washing, cooking, and/or germination, particularly on saponins content., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Cardiovascular diseases are the leading cause of death worldwide. Changes in lifestyle and/or pharmacological treatment are able to reduce the burden of coronary artery diseases (CAD) and early diagnosis is crucial for the timely and optimal management of the disease. Stress testing is a good method to measure the burden of CAD but it is time consuming and pharmacological testing may not fully mimic exercise test. The objectives of the present project were to characterize the metabolic profile of the population undergoing pharmacological and exercise stress testing to evaluate possible differences between them, and to assess the capacity of 1 H NMR spectroscopy to predict positive stress testing. Pattern recognition was applied to 1 H NMR spectra from serum of patients undergoing stress test and metabolites were quantified. The effects of the stress test, confounding variables and the ability to predict ischemia were evaluated using OPLS-DA. There was an increase in lactate and alanine concentrations in post-test samples in patients undergoing exercise test, but not in those submitted to pharmacological testing. However, when considering only pharmacological patients, those with a positive test result, showed increased serum lactate, that was masked by the much larger amount of lactate associated to exercise testing. In conclusion, we have established that pharmacological stress test does not reproduce the dynamic changes observed in exercise stress. Although there is promising evidence suggesting that 1 H NMR based metabolomics could predict stress test results, further studies with much larger populations will be required in order to obtain a definitive answer.

Introduction: The inherent sensitivity of metabolomics allows the detection of subtle alterations in biological pathways, making it a powerful tool to study biomarkers and the mechanisms that underlie cancer., Objectives: The purpose of this work was to characterize the urinary metabolic profile of prostate cancer (PCa) patients and cancer-free controls to obtain a holistic coverage of PCa metabolome., Methods: Two groups of samples, a training set (n = 41 PCa and n = 42 controls) and an external validation set (n = 18 PCa and n = 18 controls) were analyzed using a dual analytical platform, namely gas chromatography-mass spectrometry (GC-MS) and proton nuclear magnetic resonance spectroscopy ( 1 H NMR)., Results: The multivariate analysis models revealed a good discrimination between cases and controls with an AUC higher than 0.8, a sensitivity ranging from 67 to 89%, a specificity ranging from 74 to 89% and an accuracy from 73 to 86%, considering the training and external validation sets. A total of 28 metabolites (15 from GC-MS and 13 from 1 H NMR) accounted for the separation. These discriminant metabolites are involved in 14 biochemical pathways, indicating that PCa is highly linked to dysregulation of metabolic pathways associated with amino acids and energetic metabolism., Conclusion: These findings confirmed the complementary information provided by GC-MS and 1 H NMR, enabling a more comprehensive picture of the altered metabolites, underlying pathways and deepening the understanding of PCa development and progression.

Aim: The discrimination of renal cell carcinoma from renal angiomyolipoma (RAML) is crucial for the effective treatment of each. Materials & methods: Serum samples were analyzed by nuclear magnetic resonance spectroscopy-based metabolomics and a number of metabolites were further quantified by HPLC-UV. Results: Clear-cell renal carcinoma (ccRCC) was characterized by drastic disruptions in energy, amino acids, creatinine and uric acid metabolic pathways. A logistic model for the differential diagnosis of RAML from ccRCC was established using the combination of serum levels of uric acid, the ratio of uric acid to hypoxanthine and the ratio of hypoxanthine to creatinine as variables with area under the curve of the receiver operating characteristic curve value of 0.907. Conclusion: Alterations in serum purine metabolites may be used as potential metabolic markers for the differential diagnosis of ccRCC and RAML.

Soups show diverse health functions, which could be linked to their original nutrient profiles and metabolites derived from digestion. NMR spectroscopy is a robust and rapid method that unveils or identifies the chemical composition of food or food-derived metabolites. In the current study, the 1H-NMR spectroscopy approach was applied to identify the differences in metabolic profiling of two kinds of home-cooked freshwater fish soups (crucian carp and snakehead fish) before and after in vitro gastrointestinal digestion. The nutritional profiles of these soups were studied using the 1H-NMR method for the first time. Two metabolomics methods, PCA (Principal Component Analysis) and OPLS-DA (Orthogonal Partial Least Squares Discriminant Analysis), were used to analyze the data. On the whole, levels of amino acid metabolites such as valine (Val), tyrosine, choline, taurine (Tau) and glycine were higher in the crucian carp soup, whereas higher levels of fatty acids and unsaturated fatty acids were found in the snakehead soup. Furthermore, the high content of seven metabolites valine, leucine, EPA C20:5 (PUFA eicosapentaenoic acid), acetic acid, taurine, GPCho (phosphatidylcholine) and creatine showed an upward trend after simulated gastrointestinal digestion. The results demonstrate that the 1H-NMR metabolic profile of different fish soups can shed some light on our understanding of food functional properties and dietary therapy. Furthermore, changes of metabolites in digested fish soups could reveal information about chemical compounds which play important roles in the body.

Proton Nuclear Magnetic Resonance (NMR) spectroscopic analysis of urine generates rich but complex spectra. Retrieving metabolite information from such spectra is challenging due to signal overlapping, chemical shift changes, and large concentration variations of complex urine metabolome. This study demonstrates a new method, Signature Mapping (SigMa), for the rapid and efficient conversion of raw urine NMR spectra into an informative metabolite table. The principle behind SigMa relies on a division of the urine NMR spectra into Signature Signals (SS), Signals of Unknown spin Systems (SUS) and bins of complex unresolved regions (BINS). The method allows simultaneous detection of urinary metabolites in large NMR metabolomics studies using a SigMa chemical shift library and a new automatic peak picking algorithm. For quantification of SS and SUS SigMa uses multivariate curve resolution, while the unresolved inter-SS spectral regions are binned (BINS). SigMa is tested on three human urine 1 H-NMR datasets including spiking experiments, and has proven to be extraordinarily efficient, quantitatively reliable and robust., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

In vivo nuclear magnetic resonance (NMR) is rapidly evolving as a critical tool as it offers real-time metabolic information, which is crucial for delineating complex toxic response pathways in living systems. Organisms such as Daphnia magna (water fleas) and Hyalella azteca (freshwater shrimps) are commonly 13 C-enriched to increase the signal in NMR experiments. A key goal of in vivo NMR is to monitor how molecules (nutrients, contaminants, or drugs) are metabolized. Conventionally, these studies would normally involve using a 13 C-enriched probe molecule and feeding this to an organism at natural abundance, in turn allowing the fate of the probe molecule to be selectively analyzed. The drawback of such an approach is that there is a limited range of 13 C-enriched probe molecules, and if available, they are extremely cost prohibitive. Uniquely, when utilizing 13 C organisms, a reverse strategy of isotopic filtering becomes possible. The concept described here uses 1 H detection in combination with a 13 C filter on living organisms. The purpose is to suppress all 1 H signals from the organism (i.e., 1 H attached to 13 C), leaving only the probe molecule ( 1 H attached to 12 C). Because the probe molecule can be selectively observed using this approach, it then makes it possible to follow and discern processes such as bioconversion, bioaccumulation, and excretion in vivo. As the approach uses 1 H detection, it provides excellent detection limits in the nanogram range. In this article, the approach is introduced, optimized on standards, and then applied to follow nicotine biotransformation and lipid assimilation in vivo to demonstrate the concept.

Due to its capability for high-throughput screening 1 H nuclear magnetic resonance (NMR) spectroscopy is commonly used for metabolite research. The key problem in 1 H NMR spectroscopy of multicomponent mixtures is overlapping of component signals and that is increasing with the number of components, their complexity and structural similarity. It makes metabolic profiling, that is carried out through matching acquired spectra with metabolites from the library, a hard problem. Here, we propose a method for nonlinear blind separation of highly correlated components spectra from a single 1 H NMR mixture spectra. The method transforms a single nonlinear mixture into multiple high-dimensional reproducible kernel Hilbert Spaces (mRKHSs). Therein, highly correlated components are separated by sparseness constrained nonnegative matrix factorization in each induced RKHS. Afterwards, metabolites are identified through comparison of separated components with the library comprised of 160 pure components. Thereby, a significant number of them are expected to be related with diabetes type 2. Conceptually similar methodology for nonlinear blind separation of correlated components from two or more mixtures is presented in the Supplementary material. Single-mixture blind source separation is exemplified on: (i) annotation of five components spectra separated from one 1 H NMR model mixture spectra; (ii) annotation of fifty five metabolites separated from one 1 H NMR mixture spectra of urine of subjects with and without diabetes type 2. Arguably, it is for the first time a method for blind separation of a large number of components from a single nonlinear mixture has been proposed. Moreover, the proposed method pinpoints urinary creatine, glutamic acid and 5-hydroxyindoleacetic acid as the most prominent metabolites in samples from subjects with diabetes type 2, when compared to healthy controls., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Inflammation has been revealed to play a central role in the onset and progression of many illnesses. Nuclear magnetic resonance (NMR) based metabolomics method was adopted to evaluate the effects of Phoenix dactylifera seeds, in particular the Algerian date variety of Deglet on the metabolome of the LPS-IFN-γ-induced RAW 264.7 cells. Variations in the extracellular and intracellular profiles emphasized the differences in the presence of tyrosine, phenylalanine, alanine, proline, asparagine, isocitrate, inosine and lysine. Principal component analysis (PCA) revealed noticeable clustering patterns between the treated and induced RAW cells based on the metabolic profile of the extracellular metabolites. However, the effects of treatment on the intracellular metabolites appears to be less distinct as suggested by the PCA and heatmap analyses. A clear group segregation was observed for the intracellular metabolites from the treated and induced cells based on the orthogonal partial least squares-discriminant analysis (OPLS-DA) score plot. Likewise, 11 of the metabolites in the treated cells were significantly different from those in the induced groups, including amino acids and succinate. The enrichment analysis demonstrated that treatment with Deglet seed extracts interfered with the energy and of amino acids metabolism. Overall, the obtained data reinforced the possible application of Deglet seeds as a functional food with anti-inflammatory properties., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Background: The illegal undeclared addition of reconstituted milk powder to ultra-heat treated (UHT) milk to lower production costs is an example of economically motivated adulteration. This activity not only defrauds consumers but also places honest traders at a disadvantage, which could damage the reputation of milk producers and reduce the integrity of the markets. In this research, a non-targeted analytical strategy that combines proton ( 1 H) nuclear magnetic resonance (NMR) spectroscopy with a chemometrics data mining tool was developed for the authentication of bovine UHT milk., Results: Unsupervised principal component analysis was used to distinguish UHT and tap-water-reconstituted powdered milk. Partial least squares-discriminant analysis (PLS-DA) with R 2 (Y) and Q 2 equal to 0.859 and 0.748, respectively, was used to differentiate UHT and reconstituted milk samples. Three compounds were selected as biomarkers to distinguish UHT and reconstituted milk and identified according to the standard NMR-spectra database. Finally, a PLS-DA model was established, according to the characteristic spectral bands, to identify UHT milk and reconstituted milk., Conclusion: This procedure demonstrated the feasibility of using non-targeted NMR profiling combined with chemometric analysis to combat mislabeling and fraudulent practices in milk production. © 2019 Society of Chemical Industry., (© 2019 Society of Chemical Industry.)

We show an alternative way to visualize time course NMR data without the application of multivariate data analysis, based on the temporal change of the metabolome of hazelnuts after mold infestation. Fresh hazelnuts were inoculated with eight different natural mold species and the growth was studied over a period of 14 days. The data were plotted in a color-coded scheme showing metabolic changes as a function of chemical shift, which we named signal pattern plot. This plot graphically displays alteration (trend) of a respected signal over time and allows visual interpretation in a simple manner. Changes are compared with a reference sample stored under identical conditions as the infected nuts. The plot allows, at a glance, the recognition of individual landmarks specific to a sample group as well as common features of the spectra. Each sample reveals an individual signal pattern. The plot facilitates the recognition of signals that belong to biological relevant metabolites. Betaine and five signals were identified that specifically changed upon mold infestation. Graphical abstract.

Previous studies have compared fecal metabolites from healthy and colorectal cancer (CRC) patients to predict the pro-CRC signatures. However, the systemic mechanistic link between feces and colonic tissues of CRC patients is still limited. The current study was a paralleled investigation of colonic tumor tissues and their normal adjacent tissues alongside patient-matched feces by using 1 H nuclear magnetic resonance spectroscopy combined with pattern recognition to investigate how fecal metabolic phenotypes are linked to the changes in colorectal tumor profiles. A set of overlapping discriminatory metabolites across feces and tumor tissues of CRC were identified, including elevated levels of lactate, glutamate, alanine, succinate and reduced amounts of butyrate. These changes could indicate the networks for metabolic pathway perturbations in CRC potentially involved in the disruptions of glucose and glycolytic metabolism, TCA cycle, glutaminolysis, and short chain fatty acids metabolism. Furthermore, changes in fecal acetate were positively correlated with alterations of glucose and myo-inositol in colorectal tumor tissues, implying enhanced energy production for rapid cell proliferation. Compared to other fecal metabolites, acetate demonstrated the highest diagnostic performance for diagnosing CRC, with an AUC of 0.843 in the training set, and a good predictive ability in the validation set. Overall, these associations provide evidence of distinct metabolic signatures and metabolic pathway disturbances between the colonic tissues and feces within the same individual, and changes of fecal metabolic signature could reflect the CRC tissue microenvironment, highlighting the significance of the distinct fecal metabolic profiles as potential novel and noninvasive relevant indicators for CRC detection., (© 2019 UICC.)

The study of metabolomics in natural products using the diverse analytical instruments including GC-MS, LC-MS, and NMR is useful for the exploration of physiological and biological effects and the investigation of drug discovery and health functional foods. Cordyceps militaris has been very attractive to natural medicine as a traditional Chinese medicine, due to its various bioactive properties including anti-cancer and anti-oxidant effects. In this study, we analyzed the metabolite profile in 50% ethanol extracts of C. militaris fruit bodies from three development periods (growth period, matured period, and aging period) using 1 H-NMR, and identified 44 metabolites, which are classified as 16 amino acids, 10 organic acids, 5 carbohydrates, 3 nucleotide derivatives, and 10 other compounds. Among the three development periods of the C. militaris fruit body, the aging period showed significantly higher levels of metabolites including cordycepin, mannitol (cordycepic acid), and β-glucan. Interestingly, these bioactive metabolites are positively correlated with antitumor growth effect; the extract of the aging period showed significant inhibition of HepG2 hepatic cancer cell proliferation. These results showed that the aging period during the development of C. militaris fruit bodies was more highly enriched with bioactive metabolites that are associated with cancer cell growth inhibition.

Herein we report on the 1 H NMR-based metabolomics profiling of ten new races of Capsicum annuum cv. serrano, cultivated in Mexico. Forty eight metabolites (including sugars, amino acids, organic acids, polyphenolic acids and alcohols) were identified and quantified by 2D NMR and qNMR, respectively. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) separated the ten races into two clusters, from which citric acid, formic acid, fumaric acid, malic acid, glucose, fructose, sucrose and galactose were found as differential metabolites. This is the first study describing the chemical profiling of ten new races of Capsicum annuum cv. serrano and the spectrometric method used presently is characterized by great simplicity, robustness and reproducibility. Thus, this technique can be used for establishing reliable metabolomic fingerprints of different races of Capsicum annuum cv. serrano., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Although metalaxyl and metalaxyl-M are widely used fungicides, very little is known about their subacute and enantiospecific effects on the earthworm metabolome. In this study, Eisenia fetida were exposed to metalaxyl and metalaxyl-M at three concentrations (0.5, 5 and 50 mg/kg) for seven days. ¹H nuclear magnetic resonance (¹H-NMR)-based untargeted metabolomics showed that metalaxyl and metalaxyl-M exposure disturbed earthworms' metabolism at all three concentrations. Endogenous metabolites, such as succinate, arginine, aspartate, urea, asparagine, alanine, trimethylamine, taurine, cysteine, serine, threonine, histidine, lysine, glucose, choline, carnitine, citric acid, alpha-ketoisovaleric acid, fumaric acid and so on, were significantly changed. These results indicate that metalaxyl and metalaxyl-M produce different, enantiospecific disturbances in the earthworm metabolism, particularly in the tricarboxylic acid (TCA) and urea cycles. The application of untargeted metabolomics thus provides more information for evaluating the toxic risks of metalaxyl and metalaxyl-M.

Background: Tuberculosis meningitis (TBM) is the most severe form of tuberculosis, and currently lacks efficient diagnostic approaches. Metabolomics has the potential to differentiate patients with TBM from those with other forms of meningitis and meningitis-negative individuals. However, no systemic metabolomics research has compared the cerebrospinal fluid (CSF) of these patients., Methods: 1 H nuclear magnetic resonance (NMR) was used for CSF metabolic profiling. Principal component analysis and orthogonal signal correction-partial least squares-discriminant analysis (OPLS-DA) were used to screen for important variables. The Human Metabolome Database was used to identify metabolites, and MetaboAnalyst 4.0 was used for pathway analysis and over-representation analysis., Results: OPLS-DA modeling could distinguish TBM from other forms of meningitis, and several significantly changed metabolites were identified. Additionally, 23, 6, and 21 metabolites were able to differentiate TBM from viral meningitis, bacterial meningitis, and meningitis-negative groups, respectively. Pathway analysis indicated that these metabolites were mainly involved in carbohydrate and amino acid metabolism, and over-representation analysis indicated that some of these pathways were over-represented., Conclusions: The metabolites identified have the potential to serve as biomarkers for TBM diagnosis, and carbohydrate and amino acid metabolism are perturbed in the CSF of patents with TBM. Metabolomics is a valuable approach for screening TBM biomarkers. With further investigation, the metabolites identified in this study could aid in TBM diagnosis., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Cardiac hypertrophy (CH) is a major risk factor for many serious heart diseases. Sustained CH is catastrophic, resulting in cardiac dysfunction that eventually leads to heart failure (HF). Baoyuan decoction (BYD) is a famous traditional Chinese medicine (TCM) formula for supplementing and reinforcing Qi, clinically used for the treatment of cardiovascular diseases (CVDs). However, the therapeutic effects of BYD on CH remain unidentified. We herein investigated the effect of BYD on isoproterenol (ISO)-induced CH in rats and the underlying mechanisms by comprehensive pharmacodynamics and 1 H NMR-based dynamic metabolomics analysis of the plasma and urine samples. Results showed that BYD treatment markedly attenuated ISO-induced CH as evidenced by decreasing the left ventricular wall thickness, pathological cardiomyocyte hypertrophy, myocardial collagen fiber deposition and apoptosis, and plasma natriuretic peptide levels. Multivariate trajectory analysis revealed that the BYD treatment could restore the CH-disturbed plasma and urinary metabolite profiles towards the normal metabolic status featuring with a time-dependent tendency. Moreover, the key metabolic alterations in CH rats at different BYD-treated time stages involved energy metabolism, oxidative stress responses, amino acid metabolism, and gut microbiota metabolism. Of particularly, the significant roles of BYD for treating CH lie in the improvement of cardiac energy generation and antioxidant capacity. Our investigation provides a holistic view of BYD for therapeutic intervention of CH through monitoring of the dynamic metabolic changes and the results indicate that BYD may be applied as a potential agent for treating CH., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Nuclear magnetic resonance (NMR) spectroscopy is an established analytical platform for analyzing metabolic profiles of cells, tissues, and body fluids. There are several advantages in introducing an NMR-based study design into metabolomics studies, including a fast and comprehensive detection, characterization, and quantification of dozens of endogenous metabolites in a single NMR spectrum. Quantitative proton 1 H-NMR is the most useful NMR-based platform for metabolomics. The frozen tissues can be analyzed noninvasively using a high-resolution magic angle spinning (HR-MAS) 1 H-NMR spectroscopy; or several extraction techniques can be applied to detect additional metabolites using a conventional liquid-based NMR technique. In this chapter, we report on tissue collection, handling, extraction methods, and 1 H-NMR acquisition protocols developed in the past decades for a precise and quantitative NMR-metabolomics approach. The NMR acquisition protocols (both HR-MAS and conventional 1 H-NMR spectroscopy) and spectral analysis steps are also presented. Since NMR can be applied "in vivo" using horizontal bore MRI scanners, several in vivo sequences for localized 1 H-MRS (magnetic resonance spectroscopy) are presented which can be directly applied for noninvasive detection of brain metabolites.