Nuclear Magnetic Resonance-based Metabolomics Research Articles

COLMAR1d: A Web Server for Automated, Quantitative One-Dimensional Nuclear Magnetic Resonance-Based Metabolomics at Arbitrary Magnetic Fields.

The field of metabolomics, which is quintessential in today's omics research, involves the large-scale detection, identification, and quantification of small-molecule metabolites in a wide range of biological samples. Nuclear magnetic resonance spectroscopy (NMR) has emerged as a powerful tool for metabolomics due to its high resolution, reproducibility, and exceptional quantitative nature. One of the key bottlenecks of metabolomics studies, however, remains the accurate and automated analysis of the resulting NMR spectra with good accuracy and minimal human intervention. Here, we present the COLMAR1d platform, consisting of a public web server and an optimized database, for one-dimensional (1D) NMR-based metabolomics analysis to address these challenges. The COLMAR1d database comprises more than 480 metabolites from GISSMO enabling a database query of spectra measured at arbitrary magnetic field strengths, as is demonstrated for spectra acquired between 1H resonance frequencies of 80 MHz and 1.2 GHz of mouse serum, DMEM cell growth medium, and wine. COLMAR1d combines the GISSMO metabolomics database concept with the latest tools for automated processing, spectral deconvolution, database querying, and globally optimized mixture analysis for improved accuracy and efficiency. By leveraging advanced computational algorithms, COLMAR1d offers a user-friendly, automated platform for quantitative 1D NMR-based metabolomics analysis allowing a wide range of applications, including biomarker discovery, metabolic pathway elucidation, and integration with multiomics strategies.

Nuclear magnetic resonance-based metabolomics with machine learning for predicting progression from prediabetes to diabetes.

Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the progression from prediabetes to diabetes. This prospective study included 13,489 participants with prediabetes who had metabolomic data from the UK Biobank. Circulating metabolites were quantified via NMR spectroscopy. Cox proportional hazard (CPH) models were performed to estimate the associations between metabolites and diabetes risk. Supporting vector machine, random forest, and extreme gradient boosting were used to select the optimal metabolite panel for prediction. CPH and random survival forest (RSF) models were utilized to validate the predictive ability of the metabolites. During a median follow-up of 13.6 years, 2525 participants developed diabetes. After adjusting for covariates, 94 of 168 metabolites were associated with risk of progression to diabetes. A panel of nine metabolites, selected by all three machine-learning algorithms, was found to significantly improve diabetes risk prediction beyond conventional risk factors in the CPH model (area under the receiver-operating characteristic curve, 1 year: 0.823 for risk factors + metabolites vs 0.759 for risk factors, 5 years: 0.830 vs 0.798, 10 years: 0.801 vs 0.776, all p < 0.05). Similar results were observed from the RSF model. Categorization of participants according to the predicted value thresholds revealed distinct cumulative risk of diabetes. Our study lends support for use of the metabolite markers to help determine individuals with prediabetes who are at high risk of progressing to diabetes and inform targeted and efficient interventions. Shanghai Municipal Health Commission (2022XD017). Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212501). Shanghai Municipal Human Resources and Social Security Bureau (2020074). Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR4006). Science and Technology Commission of Shanghai Municipality (22015810500).

331 1H Nuclear magnetic resonance-based metabolomics of serum from growing beef steers following a combined viral bacterial respiratory disease challenge

331 1H Nuclear magnetic resonance-based metabolomics of serum from growing beef steers following a combined viral bacterial respiratory disease challenge

Effects of Trimethylamine N-Oxide in Improving Exercise Performance in Mice: A 1H-NMR-Based Metabolomic Analysis Approach.

To improve exercise performance, the supplement of nutrients has become a common practice before prolonged exercise. Trimethylamine N-oxide (TMAO) has been shown to ameliorate oxidative stress damage, which may be beneficial in improving exercise capacity. Here, we assessed the effects of TMAO on mice with exhaustive swimming, analyzed the metabolic changes, and identified significantly altered metabolic pathways of skeletal muscle using a nuclear magnetic resonance-based (NMR-based) metabolomics approach to uncover the effects of TMAO improving exercise performance of mice. We found that TMAO pre-administration markedly prolonged the exhaustive time in mice. Further investigation showed that TMAO pre-administration increased levels of 3-hydroxybutyrate, isocitrate, anserine, TMA, taurine, glycine, and glutathione and disturbed the three metabolic pathways related to oxidative stress and protein synthesis in skeletal muscle. Our results provide a metabolic mechanistic understanding of the effects of TMAO supplements on the exercise performance of skeletal muscle in mice. This work may be beneficial in exploring the potential of TMAO to be applied in nutritional supplementation to improve exercise performance. This work will lay a scientific foundation and be beneficial to exploring the potential of TMAO to apply in nutritional supplementation.

Exploring the potential of ultrasound combined with sucrose treatments for germination and growth of sunflower sprouts

Ultrasound is a promising physical method for enhancing the germination of leguminous plants, while sucrose supplementation can boost plant growth and metabolism. However, limited studies have explored the synergistic effects and metabolic mechanisms of ultrasound and sucrose on sunflower sprouts. This study comprehensively investigated the impacts of ultrasound, sucrose, and their combination (US-Suc) on the germination and seedling growth of sunflower seeds. Overall, the combine treatment significantly increased the germination rate, length and weight of sunflower sprouts, compared to either individual treatment. Scanning electron microscope images showed that the US-Suc combination induced a distinct microstructural alteration, enhancing the water absorption capacity of sunflower seeds. Additionally, sunflower seeds exhibited increased activity of endogenous enzymes with antioxidant properties following US-Suc treatment. Lastly, Nuclear magnetic resonance-based metabolomics analysis demonstrated that the US-Suc treatment had a distinct regulatory impact on the metabolic profile compared to either treatment alone. The metabolism of carbohydrates and phenolic compounds was significantly up-regulated upon US-Suc treatment, leading to improved plant growth and resistance to environmental stress. In conclusion, pre-treating with ultrasound in combination with external sucrose application showed great promise in seed germination, enhancing nutrient accumulation in sunflower sprouts by accelerating water uptake and boosting enzyme activity. Thus, US-Suc represents an effective approach to increase both yield and nutritional value of sunflower sprouts.

Alterations in Vagal Tone Are Associated with Changes in the Gut Microbiota of Adults with Anxiety and Depression Symptoms: Analysis of Fecal Metabolite Profiles.

Accumulating evidence suggests that interactions between the brain and gut microbiota significantly impact brain function and mental health. In the present study, we aimed to investigate whether young, healthy adults without psychiatric diagnoses exhibit differences in metabolic stool and microbiota profiles based on depression/anxiety scores and heart rate variability (HRV) parameters. Untargeted nuclear magnetic resonance-based metabolomics was used to identify fecal metabolic profiles. Results were subjected to multivariate analysis through principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), and the metabolites were identified through VIP score. Metabolites separating asymptomatic and symptomatic groups were acetate, valine, and glutamate, followed by sugar regions, glutamine, acetone, valerate, and acetoacetate. The main metabolites identified in high vagal tone (HVT) and low vagal tone (LVT) groups were acetate, valerate, and glutamate, followed by propionate and butyrate. In addition to the metabolites identified by the PLS-DA test, significant differences in aspartate, sarcosine, malate, and methionine were observed between the groups. Levels of acetoacetate were higher in both symptomatic and LVT groups. Valerate levels were significantly increased in the symptomatic group, while isovalerate, propionate, glutamate, and acetone levels were significantly increased in the LVT group. Furthermore, distinct abundance between groups was only confirmed for the Firmicutes phylum. Differences between participants with high and low vagal tone suggest that certain metabolites are involved in communication between the vagus nerve and the brain.

Changes in the urinary metabolome accompanied alterations in body mass and composition in women with overweight – impact of high versus low protein breakfast

IntroductionUnderstanding why subjects with overweight and with obesity vary in their response to dietary interventions is of major interest for developing personalized strategies for body mass regulation.ObjectivesThe aim of this study was to investigate the relationship between changes in the urine metabolome and body mass during a breakfast meal intervention. Furthermore, we aimed to elucidate if the baseline urine metabolome could predict the response to the two types of breakfast meals (high versus low protein) during the intervention.MethodsA total of 75 young, women with overweight were randomly allocated to one of two intervention groups: (1) High-protein (HP) or (2) low-protein (LP) breakfast as part of their habitual diet during a 12-week intervention. Beside the breakfast meal, participants were instructed to eat their habitual diet and maintain their habitual physical activity level. Nuclear magnetic resonance-based metabolomics was conducted on urine samples collected at baseline (wk 0), mid-intervention (wk 6), and at endpoint (wk 12). At baseline and endpoint, body mass was measured and DXA was used to measure lean body mass and fat mass.ResultsThe baseline urine metabolite profile showed a slightly higher correlation (R2 = 0.56) to body mass in comparison with lean body mass (R2 = 0.51) and fat mass (R2 = 0.53). Baseline 24-h urinary excretion of trigonelline (p = 0.04), N, N-dimethylglycine (p = 0.02), and trimethylamine (p = 0.03) were significantly higher in individuals who responded with a reduction in body mass to the HP breakfast.ConclusionsDifferences in the urine metabolome were seen for women that obtained a body weight loss in the response to the HP breakfast intervention and women who did not obtain a body weight loss, indicating that the urine metabolome contains information about the metabolic phenotype that influences the responsiveness to dietary interventions.

Fibroblast growth factor 21 enhances learning and memory performance in mice by regulating hippocampal L-lactate homeostasis

Fibroblast growth factor 21 (FGF21) is one endogenous metabolic molecule that functions as a regulator in glucose and lipid homeostasis. However, the effect of FGF21 on L-lactate homeostasis and its mechanism remains unclear until now. Forty-five Six-week-old male C57BL/6 mice were divided into three groups: control, L-lactate, and FGF21 (1.5 mg/kg) groups. At the end of the treatment, nuclear magnetic resonance-based metabolomics, and key proteins related to L-lactate homeostasis were determined respectively to evaluate the efficacy of FGF21 and its mechanisms. The results showed that, compared to the vehicle group, the L-lactate-treated mice displayed learning and memory performance impairments, as well as reduced hippocampal ATP and NADH levels, but increased oxidative stress, mitochondrial dysfunction, and apoptosis, which suggesting inhibited L-lactate-pyruvate conversion in the brain. Conversely, FGF21 treatment ameliorated the L-lactate accumulation state, accompanied by restoration of the learning and memory defects, indicating enhanced L-lactate uptake and utilization in hippocampal neurons. We demonstrated that maintaining constant L-lactate-pyruvate flux is essential for preserving neuronal bioenergetic and redox levels. FGF21 contributed to preparing the brain for situations of high availability of L-lactate, thus preventing neuronal vulnerability in metabolic reprogramming.

Impact of Distinct Antiandrogen Exposures on the Plasma Metabolome in Feminizing Gender-affirming Hormone Therapy.

The plasma metabolome is a functional readout of metabolic activity and is associated with phenotypes exhibiting sexual dimorphism, such as cardiovascular disease. Sex hormones are thought to play a key role in driving sexual dimorphism. Gender-affirming hormone therapy (GAHT) is a cornerstone of transgender care, but longitudinal changes in the plasma metabolome with feminizing GAHT have not been described. Blood samples were collected at baseline and after 3 and 6 months of GAHT from transgender women (n = 53). Participants were randomized to different anti-androgens, cyproterone acetate or spironolactone. Nuclear magnetic resonance-based metabolomics was used to measure 249 metabolic biomarkers in plasma. Additionally, we used metabolic biomarker data from an unrelated cohort of children and their parents (n = 3748) to identify sex- and age-related metabolite patterns. We identified 43 metabolic biomarkers altered after 6 months in both anti-androgen groups, most belonging to the very low- or low-density lipoprotein subclasses, with all but 1 showing a decrease. We observed a cyproterone acetate-specific decrease in glutamine, glycine, and alanine levels. Notably, of the metabolic biomarkers exhibiting the most abundant "sex- and age-related" pattern (higher in assigned female children and lower in assigned female adults, relative to assigned males), 80% were significantly lowered after GAHT, reflecting a shift toward the adult female profile. Our results suggest an anti-atherogenic signature in the plasma metabolome after the first 6 months of feminizing GAHT, with cyproterone acetate also reducing specific plasma amino acids. This study provides novel insight into the metabolic changes occurring across feminizing GAHT.

Metabolomic analysis reveals the biological characteristics of giant congenital melanocytic nevi

Giant congenital melanocytic nevi (GCMN) is a congenital cutaneous developmental deformity tumor that usually occurs at birth or in the first few weeks after birth, but its pathogenesis is still unclear. In this study, nuclear magnetic resonance-based metabolomics strategy was employed to evaluate the metabolic variations in serum and urine of the GCMN patients in order to understand its underlying biochemical mechanism and provide a potential intervention idea. Twenty-nine metabolites were observed to change significantly in serum and urine metabolomes, which are mainly involved in a variety of metabolic pathways including glyoxylate and dicarboxylate metabolism, TCA cycle and metabolisms of amino acids. The substantial cores of all the disturbed metabolic pathways are related to amino acid metabolism and carbohydrate metabolism and regulate the physiological state of the GCMN patients. Our results provide the physiological basis and physiological responses of GCMN and will be helpful for better understanding the molecular mechanisms of GCMN in future research.

Synergistic metabolism of salivary MUC5B in oral commensal bacteria during early biofilm formation.

Bacterial metabolism in oral biofilms is comprised of complex networks of nutritional chains and biochemical regulations. These processes involve both intraspecies and interspecies networks as well as interactions with components from host saliva, gingival crevicular fluid, and dietary intake. In a previous paper, a large salivary glycoprotein, mucin MUC5B, was suggested to promote a dental health-related phenotype in the oral type strain of Streptococcus gordonii DL1, by regulating bacterial adhesion and protein expression. In this study, nuclear magnetic resonance-based metabolomics was used to examine the effects on the metabolic output of monospecies compared to dual species early biofilms of two clinical strains of oral commensal bacteria, S. gordonii and Actinomyces naeslundii, in the presence of MUC5B. The presence of S. gordonii increased colonization of A. naeslundii on salivary MUC5B, and both commensals were able to utilize MUC5B as a sole nutrient source during early biofilm formation. The metabolomes suggested that the bacteria were able to release mucin carbohydrates from oligosaccharide side chains as well as amino acids from the protein core. Synergistic effects were also seen in the dual species biofilm metabolome compared to the monospecies, indicating that A. naeslundii and S. gordonii cooperated in the degradation of salivary MUC5B. A better understanding of bacterial interactions and salivary-mediated regulation of early dental biofilm activity is meaningful for understanding oral biofilm physiology and may contribute to the development of future prevention strategies for biofilm-induced oral disease.IMPORTANCEThe study of bacterial interactions and salivary-mediated regulation of early dental biofilm activity is of interest for understanding oral microbial adaptation to environmental cues and biofilm maturation. Findings in oral commensals can prove useful from the perspectives of both oral and systemic health of the host, as well as the understanding of general microbial biofilm physiology. The knowledge may provide a basis for the development of prognostic biomarkers, or development of new treatment strategies, related to oral health and disease and possibly also to other biofilm-induced conditions. The study is also an important step toward developing the methodology for similar studies in other species and/or growth conditions.

Abstract P128: Detection Of Congenital Intrahepatic Vascular Shunt In C57BL/6 Mice

Portosystemic shunt (PSS) is an abnormal vein that leaks blood from portal vein into systemic circulation, thus bypassing the liver. We previously reported its sporadic occurrence as a congenital defect in C57BL/6 mice as well as in humans. PSS results in vascular, hepatic and metabolic anomalies. Therefore, in experimental protocols using C57BL/6 mice, ones with PSS should be identified as outliers. Accordingly, we sought methods to discern 8-weeks-old male C57BL/6 mice (n=6) as non-PSS or PSS mice. Serum total bile acids (TBA) is a bona fide biomarker of PSS in mice (non-PSS: 6.1±1.0 vs PSS: 80.6±8.2 μM; p&lt;0.001), but utility of this biomarker is limited by its cost and invasiveness, particularly if large numbers of mice are to be screened. This led us to investigate whether use of urine might serve as a simple, inexpensive, non-invasive means of PSS diagnosis. Since urinary TBA were comparable between non-PSS (4.1±1.3 μM) and PSS mice (4.8±1.7 μM; p=0.45), we asked whether there are other urinary metabolites that could be exploited as biomarkers. Nuclear magnetic resonance-based metabolomics uncovered that Krebs cycle intermediates, i.e. , citrate (non-PSS: 1.0±0.1 v s PSS: 1.6±0.04; p&lt;0.001), α-ketoglutarate (non-PSS: 1.0±0.1 v s PSS: 3.6±0.2; p&lt;0.001), and fumarate (non-PSS: 1.0±0.2 v s PSS: 3.7±0.4; p&lt;0.001) were strikingly elevated (expressed in fold change) in the urine of PSS mice. We leveraged the pH-lowering properties of such metabolites as the basis for two urine-based screening tests. Firstly, using pH test strips with a 5.5-8.0 range, we detected the differences in their urinary pH (non-PSS: 6.3±0.1 v s PSS: 5.6±0.1; p&lt;0.001). Secondly, the difference in urinary pH was additionally quantified using the phenol red assay (non-PSS: 0.9±0.1 v s PSS: 0.2±0.03 at OD 560nm ; p&lt;0.001). Using a larger cohort of non-PSS (n=44) and PSS (n=27) mice, we determined that the urinary phenol red assay had an accuracy of 95.8%, sensitivity of 96.3%, and specificity of 95.5% in identifying PSS mice. Taken together, these results demonstrate the feasibility of using urine as a liquid biopsy to screen for murine PSS. Application of our urinary PSS screening protocols importantly enables stratification of PSS mice, which confound cardiovascular and metabolic studies.

Balanced replacement of fish meal with Hermetia illucens meal allows efficient hepatic nutrient metabolism and increases fillet lipid quality in gilthead sea bream (Sparus aurata)

In the present study, gilthead sea bream (Sparus aurata) was reared using sustainable feeds containing insect meal from Hermetia illucens larvae. Proteomics and proton nuclear magnetic resonance-based metabolomics analysis were used to assess the metabolic impact of the tested feeds in sea bream liver, whereas the composition of muscle fillets was characterized by means of metabolomics and gas chromatography of fatty acid methyl esters. Including 10% of insect meal while correspondingly reducing fish meal did not substantially alter the metabolism of dietary nutrients, leading to small but significant effects solely on lauric acid content of sea bream fillets. Furthermore, a few alterations in some markers of immune response, such as leukocyte elastase inhibitor-like, granzyme B (G, H)-like, and two associated ortholog groups, serpin B and chymase, were found. In the fish group fed with insect meal, liver morphology analysis showed no structural damage or inflammation and a lower amount of hepatic lipid deposition and accumulation.

Comparative Metabolomics Analysis of Weedy Rice (Oryza spp.) across Peninsular Malaysia

Weedy rice (Oryza spp.) is a notorious weed that invades paddy fields and hampers the rice’s production and yield quality; thus, it has become a major problem for rice farmers worldwide. Weedy rice comprises a diverse morphology and phenotypic variation; however, the metabolome and chemical phenotypes of weedy rice grains have not been explored. Therefore, this study is aimed to investigate the metabolite profiles and chemical diversity of Malaysian weedy rice. Thirty-one biotypes of weedy rice grains were collected from selected rice granaries in different states of Peninsular Malaysia, including Selangor, Perak, Penang, Kedah, Perlis, Kelantan, and Terengganu. In addition to the weedy rice samples, four cultivated rice varieties (MR219, MR220, MR220 CL2, and MARDI Siraj 297) were subjected to nuclear magnetic resonance-based metabolomics. The PLS-DA and OPLS-DA models revealed a clear separation between the weedy rice and cultivated rice, which was contributed by the higher level of γ-aminobutyric acid (GABA), α-glucose, fumaric acid, and phenylalanine in the weedy rice, whilst valine, leucine, isoleucine, fatty acids, 2,3-butanediol, threonine, alanine, butyric acid, choline, γ-oryzanol, fructose, β-glucose, sucrose, ferulic acid, and formic acid were found dominant in the cultivated rice. Interestingly, the models also showed a separation between the weedy rice samples collected from the west coast and east coast regions of Peninsular Malaysia. The metabolites responsible for the separation, i.e., threonine, alanine, butyric acid, fructose, β-glucose, and formic acid, were found higher in the west coast samples, and the east coast samples were discriminated by higher levels of valine, leucine, isoleucine, fatty acids, 2,3-butanediol, choline, GABA, γ-oryzanol, α-glucose, sucrose, fumaric acid, ferulic acid, and phenylalanine. This study is the first to provide insights into the metabolite profiles and chemical phenotypes of Malaysian weedy rice that could be influenced by genotype and environmental conditions. The information on the weedy rice metabolome and omics data is important for further research on weed management and crop improvement.

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.

Quantitative plasma profiling by 1H NMR-based metabolomics: impact of sample treatment.

Introduction: There is evidence that sample treatment of blood-based biosamples may affect integral signals in nuclear magnetic resonance-based metabolomics. The presence of macromolecules in plasma/serum samples makes investigating low-molecular-weight metabolites challenging. It is particularly relevant in the targeted approach, in which absolute concentrations of selected metabolites are often quantified based on the area of integral signals. Since there are a few treatments of plasma/serum samples for quantitative analysis without a universally accepted method, this topic remains of interest for future research. Methods: In this work, targeted metabolomic profiling of 43 metabolites was performed on pooled plasma to compare four methodologies consisting of Carr-Purcell-Meiboom-Gill (CPMG) editing, ultrafiltration, protein precipitation with methanol, and glycerophospholipid solid-phase extraction (g-SPE) for phospholipid removal; prior to NMR metabolomics analysis. The effect of the sample treatments on the metabolite concentrations was evaluated using a permutation test of multiclass and pairwise Fisher scores. Results: Results showed that methanol precipitation and ultrafiltration had a higher number of metabolites with coefficient of variation (CV) values above 20%. G-SPE and CPMG editing demonstrated better precision for most of the metabolites analyzed. However, differential quantification performance between procedures were metabolite-dependent. For example, pairwise comparisons showed that methanol precipitation and CPMG editing were suitable for quantifying citrate, while g-SPE showed better results for 2-hydroxybutyrate and tryptophan. Discussion: There are alterations in the absolute concentration of various metabolites that are dependent on the procedure. Considering these alterations is essential before proceeding with the quantification of treatment-sensitive metabolites in biological samples for improving biomarker discovery and biological interpretations. The study demonstrated that g-SPE and CPMG editing are effective methods for removing proteins and phospholipids from plasma samples for quantitative NMR analysis of metabolites. However, careful consideration should be given to the specific metabolites of interest and their susceptibility to the sample treatment procedures. These findings contribute to the development of optimized sample preparation protocols for metabolomics studies using NMR spectroscopy.

Serum and urine metabolomic biomarkers for predicting prognosis in patients with immunoglobulin A nephropathy.

Immunoglobulin A nephropathy (IgAN) is the most prevalent form of glomerulonephritis worldwide. Prediction of disease progression in IgAN can help to provide individualized treatment based on accurate risk stratification. We performed proton nuclear magnetic resonance-based metabolomics analyses of serum and urine samples from healthy controls, non-progressor (NP), and progressor (P) groups to identify metabolic profiles of IgAN disease progression. Metabolites that were significantly different between the NP and P groups were selected for pathway analysis. Subsequently, we analyzed multivariate area under the receiver operating characteristic (ROC) curves to evaluate the predictive power of metabolites associated with IgAN progression. We observed several distinct metabolic fingerprints of the P group involving the following metabolic pathways: glycolipid metabolism; valine, leucine, and isoleucine biosynthesis; aminoacyl-transfer RNA biosynthesis; glycine, serine, and threonine metabolism; and glyoxylate and dicarboxylate metabolism. In multivariate ROC analyses, the combinations of serum glycerol, threonine, and proteinuria (area under the curve [AUC], 0.923; 95% confidence interval [CI], 0.667-1.000) and of urinary leucine, valine, and proteinuria (AUC, 0.912; 95% CI, 0.667-1.000) showed the highest discriminatory ability to predict IgAN disease progression. This study identified serum and urine metabolites profiles that can aid in the identification of progressive IgAN and proposed perturbed metabolic pathways associated with the identified metabolites.

Nuclear magnetic resonance-based metabolomics in goat ovarian follicular fluid

During folliculogenesis, follicular fluid (FF) supplies many of the nutrients, metabolic compounds, and signaling molecules that are essential for the accumulation of metabolic substrate stores within the oocyte. The use of metabolomics can identify metabolites produced in FF that are related to oocyte quality. Thus, this work aimed to carry out a qualitative metabolomic study using Hydrogen Nuclear Magnetic Resonance Spectroscopy (1H NMR) to characterize FF components of goats after hormonal estrus synchronization (HES) and no estrus synchronization (NES). Fluids of small (< 3 mm) and large (≥ 3 mm) follicles were collected and evaluated. 1H NMR analyses permitted to characterize seventeen metabolites, comprising sugars, amino acids, short chain organic acids, fatty acids residues, besides acetylated glycoprotein, choline phosphate and choline glycerophosphate. Qualitative differences were observed between follicles at different stages of development (small vs large). Finally, further quantitative studies will be needed to clarify the FF synthesis in goats and the effects on the quality of oocytes.

Novel biomarkers and emerging tools to identify causal molecular pathways in hypertension and associated cardiovascular diseases.

Hypertension (HT) is a modifiable risk factor for life-threatening cardiovascular diseases (CVDs) including coronary artery disease, heart failure, or stroke. Despite significant progress in understanding the pathophysiological mechanisms of the disease, the molecular pathways targeted by HT treatment remain largely unchanged. This warrants the need for finding novel biomarkers, which are causally related to persistent high blood pressure (BP) and may be pharmacologically targeted. Analytical output derived from large-scale biobanks, containing high-throughput genetic and biochemical data, such as OLINK and SomaScan-based proteomics or Nuclear Magnetic Resonance-based metabolomics, as well as novel analytical tools including the Mendelian randomization (MR) approach, enabling genetic causal inference, may create new treatment opportunities for HT and related CVDs. MR analysis may constitute additional evidence for observational studies and facilitate selection of drug targets for clinical testing and has been already used to nominate potentially causal biomarkers for HT and CVDs such as circulating glycine, branched-chain amino acids, lipoprotein(a), insulin-like growth factor 1, or fibronectin 1. Using the MR framework, genetic proxies for targets of already known drugs, such as statins, PCSK9, and ACE inhibitors, may additionally be informative about potential side effects and eventually contribute to more personalized medicine. Finally, genetic causal inference may disentangle independent direct effects of correlated traits such as lipid classes or markers of inflammation on cardiovascular clinical outcomes such as atherosclerosis and HT. While several novel HT-targeting drugs are currently under clinical investigation (e.g. brain renin-angiotensin-aldosterone system inhibitors or endothelin-1 receptor antagonists), analysis of high-throughput proteomic and metabolomic data from well-powered studies may deliver novel druggable molecular targets for HT and associated CVDs.

Nuclear Magnetic Resonance-Based Metabolomics Approach Revealed the Intervention Effect of Using Complementary and Alternative Medicine (CAM) by CKD Patients.

Chronic kidney disease (CKD) is the end point of a number of systemic chronic diseases. The prevalence of CKD is increasing worldwide and recent epidemiological studies are showing the high prevalence of renal failure in CKD patients using complementary and alternative medicines (CAMs). Clinicians believe that biochemical profiles of CKD patients using CAM (referred here to as CAM-CKD) may be different compared to those on standard clinical treatment and should be managed differently. The present study aims to explore the potential of the NMR-based metabolomics approach to reveal the serum metabolic disparity between CKD and CAM-CKD patients with respect to normal control (NC) subjects and if the differential metabolic patterns can provide rationale for the efficacy and safety of standard and/or alternative therapies. Serum samples were obtained from 30 CKD patients, 43 CAM-CKD patients, and 47 NC subjects. The quantitative serum metabolic profiles were measured using 1D 1H CPMG NMR experiments performed at 800 MHz NMR spectrometer. The serum metabolic profiles were compared using various multivariate statistical analysis tools available on MetaboAnalyst (freely available web-based software) such as partial least-squares discriminant analysis (PLS-DA) and random forest (a machine learning) classification method. The discriminatory metabolites were identified based on variable importance in projection (VIP) statistics and further evaluated for statistical significance (i.e., p < 0.05) using either Student t-test or ANOVA statistics. PLS-DA models were capable of clustering CKD and CAM-CKD with considerably high values of Q 2 and R 2. Compared to CAM-CKD patients, the sera of CKD patients were characterized by (a) elevated levels of urea, creatinine, citrate, glucose, glycerol, and phenylalanine and phenylalanine-to-tyrosine ratio (PTR) and (b) decreased levels of various amino acids (such leucine, isoleucine, valine, and alanine), high-density lipoproteins, lactate, and acetate. These changes suggested that CKD patients manifest severe oxidative stress, hyperglycemia (with dampened glycolysis), increased protein energy wasting, and reduced lipid/membrane metabolism. Statistically significant and strong positive correlation of PTR with serum creatinine levels suggested the role of oxidative stress in the progression of kidney disease. Significant differences in metabolic patterns between CKD and CAM-CKD patients were observed. With respect to NC subjects, the serum metabolic changes were more aberrant in CKD patients compared to CAM-CKD patients. The aberrant metabolic changes in CKD patients with manifestations of higher oxidative stress compared to CAM-CKD patients could explain clinical discrepancies between CKD and CAM-CKD patients and further advocate the use of different treatment strategies for CKD and CAM-CKD patients.