Quantitative NMR-Based Biomedical Metabolomics: Current Status and Applications

Follicular fluid profiling unveils anti-Müllerian hormone alongside glycolytic and mitochondrial dysfunction as markers of polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is a prevalent endocrine disorder affecting women of reproductive age, yet the molecular mechanisms influencing its pathophysiology remain poorly defined. A comprehensive prospective case-control study was conducted to elucidate the follicular fluid (FF) hormone and metabolite profile in women with PCOS and its implications for oocyte maturation and fertilization. The study involved 40 age- and body mass index (BMI)-matched women undergoing in vitro fertilization (IVF), including 20 diagnosed with PCOS and 20 controls with infertility due to tubal or male factors. A distinctive hormone profile in the FF of women with PCOS was identified, characterized by significantly higher anti-Müllerian hormone (AMH) levels (24.90 ± 17.61 vs. 16.68 ± 17.67 pmol/L, p = 0.0039) and lower progesterone (8253 ± 4748 vs. 25362 ± 10862 ng/mL, p < 0.0001) and estradiol levels (388.23 ± 210.58 vs. 651.48 ± 390.79 ng/mL, p = 0.0208) compared to normoovulatory controls. Moreover, a metabolite fingerprint associated with glycolytic and mitochondrial dysfunction was observed, as evidenced by lower lactate (4575.44 ± 1507.76 vs. 5595.34 ± 1073.32 μmol/L, p = 0.0182) and formate (64.51 ± 16.06 vs. 75.81 ± 16.63 μmol/L, p = 0.0351) levels and higher citrate levels (136.93 ± 52.53 vs. 109.15 ± 24.17 μmol/L, p = 0.0409) in the FF of women with PCOS. These findings suggest that the molecular profile of the FF in women with PCOS might be related to granulosa cell glycolytic and mitochondrial dysfunction, which can have a negative impact on oocyte fertilization potential. The study provides an integrative analysis of the FF hormone and metabolite profile in women with PCOS, offering insights into the molecular mechanisms underlying the reproductive dysfunctions associated with this condition.

Proton-Nuclear Magnetic Resonance Metabolomics of Gingival Crevicular Fluid During Orthodontic Tooth Movement With Aligners

OBJECTIVES

To determine the correlation between orthodontic tooth movement and a pre-defined set of gingival crevicular fluid (GCF) metabolites through proton nuclear magnetic resonance (1H-NMR) spectroscopy.

MATERIALS AND METHODS

A clinical randomised prospective split-mouth study comparing the GCF metabolites around stationary and moving second maxillary molars. Twenty-four healthy subjects diagnosed with dental class II malocclusion undergoing orthodontic clear aligner treatment (CAT) were enrolled. GCF samples from the mesial and distal sulcus of second molars under stationary conditions or under 1 N of distalising force were harvested at baseline, 1 h, 7 days and 21 days after the application of CAT. 1H-NMR was utilised for GCF sample analysis. The 2-dimensional total correlation spectroscopy spectral signature of 35 known GCF metabolites was compared in moving and stationary teeth. Principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), variable importance in projection (VIP) score and area under the curve (AUC) were computed utilising MetaboAnalyst 5.0 software.

RESULTS

VIP-score values showed statistically significant differences between the metabolites involved in moving and stationary molars (p < 0.05). PCA and PLS-DA results showed potential differences between the metabolite clusters. The variation of the 1H-NMR signals of Glutamine, Uracil, N-Acetylneuraminate and alpha-ketoglutarate contributes primarily to the variance across metabolites in moving versus stationary teeth at 1 h, 7 days and 21 days.

CONCLUSION

High values of Glutamine and low values of Uracil, N-Acetylneurinamate and alpha-ketoglutarate could be utilised to predict the progress of orthodontic tooth movement over time. Knowledge of metabolites predictive of tooth movement could contribute to the design of tailored orthodontic treatment planning, reducing time, costs and side-effects.

Network Flow Methods for NMR-Based Compound Identification

In this work, we introduce a novel method for compound identification in mixtures based on nuclear magnetic resonance spectra. Contrary to many other methods, our approach can be used without peak-picking the mixture spectrum and simultaneously optimizes the fit of all individual compound spectra in a given library. At the core of the method, a minimum cost flow problem is solved on a network consisting of nodes that represent spectral peaks of the library compounds and the mixture. We show that our approach can outperform other popular algorithms by applying it to a standard compound identification task for 2D 1H,13C HSQC spectra of artificial mixtures and a natural sample using a library of 501 compounds. Moreover, our method retrieves individual compound concentrations with at least semiquantitative accuracy for artificial mixtures with up to 34 compounds. A software implementation of the minimum cost flow method is available on GitHub (https://github.com/GeoMetabolomics-ICBM/mcfNMR).

Metabolomics as a Tool for Unraveling the Impact of Enantioselectivity in Cellular Metabolism

Metabolomics is an emerging interdisciplinary field focused on the comprehensive analysis of all metabolites within biological samples, making it valuable for areas such as drug development, and environmental analysis. Many compounds, including pharmaceuticals and agrochemicals that have been extensively studied by metabolomics are chiral. The intrinsic chirality of biological targets can lead to a selective recognition of enantiomers resulting in distinct pharmacokinetic, pharmacodynamic and/or toxicological profiles (enantioselectivity). Given that metabolomics captures an instant snapshot of an organism's metabolic state, it serves as a powerful tool to investigate chiral compounds and understand enantioselective effects. Herein, a systematic compilation of scientific literature was performed and 48 enantioselectivity studies using metabolomics were selected. These studies revealed an increasing focus on chiral pesticides (77%), the use of animal models (59%), reliance on LC-MS techniques (52%), and predominantly untargeted approaches (83%). Enantioselective effects were described in most studies. This review describes significant advances in this emerging field and highlights the use of metabolomics to unravel the role of stereochemistry in cellular metabolism by the examination of enantiomer-specific metabolic effects. Furthermore, it elucidates enantioselectivity mechanism that can be further applied to other groups of chiral compounds.

Urinary Metabolic Profiling During Epileptogenesis in Rat Model of Lithium-Pilocarpine-Induced Temporal Lobe Epilepsy

Background/Objectives: Temporal lobe epilepsy (TLE) often develops following an initial brain injury, where specific triggers lead to epileptogenesis-a process transforming a healthy brain into one prone to spontaneous, recurrent seizures. Although electroencephalography (EEG) remains the primary diagnostic tool for epilepsy, it cannot predict the risk of epilepsy after brain injury. This limitation highlights the need for biomarkers, particularly those measurable in peripheral samples, to assess epilepsy risk. This study investigated urinary metabolites in a rat model of TLE to identify biomarkers that track epileptogenesis progression across the acute, latent, and chronic phases and elucidate the underlying mechanisms. Methods: Status epilepticus (SE) was induced in rats using repeated intraperitoneal injections of lithium chloride-pilocarpine hydrochloride. Urine samples were collected 48 h, 1 week, and 6 weeks after SE induction. Nuclear magnetic resonance spectrometry was used for metabolomic analysis, and statistical evaluations were performed using MetaboAnalyst 6.0. Differences between epileptic and control groups were represented using the orthogonal partial least squares discriminant analysis (OPLS-DA) model. Volcano plot analysis identified key metabolic changes, applying a fold-change threshold of 1.5 and a p-value < 0.05. Results: The acute phase exhibited elevated levels of acetic acid, dihydrothymine, thymol, and trimethylamine, whereas glycolysis and tricarboxylic acid cycle metabolites, including pyruvic and citric acids, were reduced. Both the acute and latent phases showed decreased theobromine, taurine, and allantoin levels, with elevated 1-methylhistidine in the latent phase. The chronic phase exhibited reductions in pimelic acid, tiglylglycine, D-lactose, and xanthurenic acid levels. Conclusions: These findings highlight stage-specific urinary metabolic changes in TLE, suggesting distinct metabolites as biomarkers for epileptogenesis and offering insights into the mechanisms underlying SE progression.

Harnessing NMR technology for enhancing field crop improvement: applications, challenges, and future perspectives

INTRODUCTION

Nuclear Magnetic Resonance (NMR) spectroscopy has emerged as a transformative technology in agricultural research, offering powerful analytical capabilities for field crop improvement. With global challenges such as food security and climate change intensifying, there is an urgent need for innovative methodologies to enhance our understanding of plant health, metabolic pathways, and crop-environment interactions. NMR's ability to provide nondestructive, real-time analysis of plant metabolites and soil chemistry positions it as a critical tool for addressing these pressing concerns.

OBJECTIVE

This review aims to elucidate the potential of NMR spectroscopy in advancing field crop improvement by highlighting its applications, challenges, and future perspectives in agricultural methodologies. The focus is on the evolution and application of NMR in agricultural research, particularly in metabolomics, phenotyping, and quality assessment.

METHOD

A comprehensive literature review was conducted to analyze recent advancements in NMR applications in agriculture. Particular emphasis was given to high-resolution magic angle spinning (HR-MAS) and time-domain NMR techniques, which have been instrumental in elucidating plant metabolites and soil chemistry. Studies showcasing the integration of NMR with complementary technologies for enhanced metabolic profiling and genetic marker identification were reviewed.

RESULTS

Findings indicate that NMR spectroscopy is an indispensable tool in agriculture due to its ability to identify biomarkers indicative of crop resilience, monitor soil composition, and contribute to food safety and quality assessments. The integration of NMR with other technologies has accelerated metabolic profiling, aiding in the breeding of high-yielding and stress-resistant crop varieties. However, challenges such as sensitivity limitations and the need for standardization remain.

CONCLUSION

NMR spectroscopy holds immense potential for revolutionizing agricultural research and crop improvement. Overcoming existing challenges, such as sensitivity and standardization, is crucial for its broader application in practical agricultural settings. Collaborative efforts among researchers, agronomists, and policymakers will be essential for leveraging NMR technology to address global food security challenges and promote sustainable agricultural practices.

The Metabolic Profile of Plasma During Epileptogenesis in a Rat Model of Lithium-Pilocarpine-Induced Temporal Lobe Epilepsy

Temporal lobe epilepsy (TLE) arises mostly because of an initial injury. Certain stimuli can make a normal brain prone to repeated, spontaneous seizures via a process called epileptogenesis. This study examined the plasma metabolomics profile in rats with the induced TLE to identify feasible biomarkers that can distinguish progression of epileptogenesis in three different time points and reveal the underlying mechanisms of epileptogenesis. Status epilepticus (SE) was induced by repetitive intraperitoneal injections of low-dose lithium chloride-pilocarpine hydrocholoride. Blood samples were collected 48 h, 1 week, and 6 weeks after SE, respectively. Plasma metabolites were analyzed by nuclear magnetic resonance (NMR) spectrometry. Statistical analysis was performed using MetaboAnalyst 6.0. An orthogonal partial least squares discriminant analysis (OPLS-DA) model was employed to represent variations between the TLE model groups and respective controls. Volcano plot analysis was used to identify key features, applying a fold-change criterion of 1.5 and a t-test threshold of 0.05. 48 h after SE, dimethyl sulfone (DMSO2) and creatinine levels were decreased, whereas glycine and creatine levels were increased. The only metabolite that changed 1 week after SE was pyruvic acid, which was increased compared to its control level. Lactic acid, pyruvic acid, and succinic acid levels were increased 6 weeks after SE. The identified metabolites were especially related to the tricarboxylic acid cycle and glycine, serine, and threonine metabolism. The results illustrate that distinct plasma metabolites can function as phase-specific biomarkers in TLE and reveal new insights into the mechanisms underlying SE.

Pseudo-3D HSQC0, a method to create a true 2D HSQC0-plane. Application to softwood extract analysis

Pseudo-3D HSQC0 provides an alternative and easy way to record and analyze quantitative HSQC0-data. In the original time-zero extrapolated 1H-13C HSQC (HSQC0), three separate 2D constant-time (CT) HSQC-experiments (HSQCi, i = 1-3) are acquired, where either 1,2 or 3 consecutive CT-HSQC-propagators are repeated in each pulse sequence, and the 2D integral data from the three 2D experiments is analyzed via linear regression. In the presented pseudo-3D HSQC0, HSQCi is one of the dimensions and all data is contained within one dataset, which is recorded in interleaved manner by acquiring the same t1-value for each HSQCi-point before t1-incrementation. The 3D-nature of the data allows the utilization of backward linear prediction to calculate an actual time-zero 2D HSQC0 spectrum, which can be analyzed using normal 2D integration procedures for quantitative results. In all, the pseudo-3D enables straightforward, intuitive and easy analysis of the quantitative 2D HSQC0 spectrum/plane. As the recorded pseudo 3D data contains the normal HSQCi planes, also the classic linear regression analysis can be applied.

Uncovering the Metabolic Footprint of New Psychoactive Substances by Metabolomics: A Systematic Review

New psychoactive substances (NPSs) emerged in the 2000s as legal alternatives to illicit drugs and quickly became a huge public health threat due to their easy accessibility online, limited information, and misleading labels. Synthetic cannabinoids and synthetic cathinones are the most reported groups of NPSs. Despite NPSs being widely studied, due to their structural diversity and the constant emergence of novel compounds with unknown properties, the development of new techniques is required to clarify their mode of action and evaluate their toxicological effects. Metabolomics has been a useful tool to evaluate the metabolic effects of several xenobiotics. Herein, a systematic review was performed, following PRISMA guidelines, regarding metabolomic studies on synthetic cathinones and synthetic cannabinoids to evaluate their effects in cellular metabolism. In the studies, in vivo models were the most employed (86%) and the analysis mostly followed untargeted approaches (75%) using LC-MS techniques (67%). Both groups of NPSs seem to primarily interfere with energy metabolism-related pathways. Even though this type of study is still limited, metabolomics holds great promise as a tool to clarify mechanisms of actions, identify biomarkers of exposure, and explain the toxicological effects of NPSs.

1H Nuclear Magnetic Resonance-Based Metabolomic Profiling and Comparison of Human Milk across Different Lactation Stages in Secretors and Nonsecretors: A Study of Chinese Lactating Women

BACKGROUND

Human milk oligosaccharides (HMOs) and other milk-derived metabolites are crucial for infant health, influencing gut microbiota and overall development.

OBJECTIVE

This study aimed to uncover insights into the variations of HMOs and non-HMO metabolites based on secretor (Se) status, lactation time, mode of delivery, and infant sex.

METHODS

An exploratory cross-sectional study was designed to compare the concentrations of HMOs and non-HMOs metabolites in milk samples from 129 lactating Chinese women within 1 y postpartum. Nuclear magnetic resonance analysis was employed for the identification and quantification of the metabolites. The metabolites measured were grouped into sugars, free amino acids, fatty acids, and metabolites related to energy metabolism. The influences of delivery mode and infant sex on milk metabolite composition were explored.

RESULTS

Uniform Manifold Approximation and Projection analysis of HMOs profiles revealed distinct clustering based on Se status, with significant differences in 2'-fucosyllactose (2'-FL) and 3-fucosyllactose (3-FL) concentrations observed between Se+ and Se- groups. A decreasing trend for 2'-FL and 6'-sialyllactose concentrations, along with an increase in 3-FL concentrations, was observed with increasing lactating period within 12 mo postpartum. Non-HMOs metabolite analysis indicated that Se status only affected glutamate concentrations. An increase in glutamine concentrations was observed 3-9 mo postpartum. A continuous increase in o-phosphocholine concentrations was noted in 12 mo postpartum, along with reductions in citrate and sn-glycero-phosphocholine concentrations. Delivery mode and infant sex did not affect both HMOs and non-HMOs concentrations.

CONCLUSIONS

Metabolomic analysis of human milk reveals significant variation of HMOs, but not in non-HMOs, based on Se status. Changes in certain HMOs and non-HMOs concentrations were also observed over the 1 y of lactation. Understanding how these metabolites change over time may influence recommendations for maternal diet, supplementation, and the timing of breastfeeding to ensure optimal nutrient delivery to the infant.

Predicting Disease-Metabolite Associations Based on the Metapath Aggregation of Tripartite Heterogeneous Networks

The exploration of the interactions between diseases and metabolites holds significant implications for the diagnosis and treatment of diseases. However, traditional experimental methods are time-consuming and costly, and current computational methods often overlook the influence of other biological entities on both. In light of these limitations, we proposed a novel deep learning model based on metapath aggregation of tripartite heterogeneous networks (MAHN) to explore disease-related metabolites. Specifically, we introduced microbes to construct a tripartite heterogeneous network and employed graph convolutional network and enhanced GraphSAGE to learn node features with metapath length 3. Additionally, we utilized node-level and semantic-level attention mechanisms, a more granular approach, to aggregate node features with metapath length 2. Finally, the reconstructed association probability is obtained by fusing features from different metapaths into the bilinear decoder. The experiments demonstrate that the proposed MAHN model achieved superior performance in five-fold cross-validation with Acc (91.85%), Pre (90.48%), Recall (93.53%), F1 (91.94%), AUC (97.39%), and AUPR (97.47%), outperforming four state-of-the-art algorithms. Case studies on two complex diseases, irritable bowel syndrome and obesity, further validate the predictive results, and the MAHN model is a trustworthy prediction tool for discovering potential metabolites. Moreover, deep learning models integrating multi-omics data represent the future mainstream direction for predicting disease-related biological entities.

Protective Contribution of Rosmarinic Acid in Rosemary Extract Against Copper-Induced Oxidative Stress

Rosemary extract (Rosmarinus officinalis) is a natural source of bioactive compounds with significant antioxidant properties. Among these, rosmarinic acid is celebrated for its potent antioxidant, anti-inflammatory, antimicrobial, and neuroprotective properties, making it a valuable component in both traditional medicine and modern therapeutic research. Neurodegenerative diseases like Alzheimer's and Parkinson's are closely linked to oxidative damage, and research indicates that rosmarinic acid may help protect neurons by mitigating this harmful process. Rosmarinic acid is able to bind cupric ions (Cu2+) and interfere with the production of reactive oxygen species (ROS) produced by copper through Fenton-like reactions. This study aims to further evaluate the contribution of rosmarinic acid within rosemary extract by comparing its activity to that of isolated rosmarinic acid. By using a detailed approach that includes chemical characterization, antioxidant capacity assessment, and neuroprotective activity testing, we have determined whether the combined components in rosemary extract enhance or differ from the effects of rosmarinic acid alone. This comparison is crucial for understanding whether the full extract offers added benefits beyond those of isolated rosmarinic acid in combating oxidative stress and Aβ-induced toxicity.

A Reproducibility Crisis for Clinical Metabolomics Studies

Cancer is a leading cause of world-wide death and a major subject of clinical studies focused on the identification of new diagnostic tools. An in-depth meta-analysis of 244 clinical metabolomics studies of human serum samples highlights a reproducibility crisis. A total of 2,206 unique metabolites were reported as statistically significant across the 244 studies, but 72% (1,582) of these metabolites were identified by only one study. Further analysis shows a random disparate disagreement in reported directions of metabolite concentration changes when detected by multiple studies. Statistical models revealed that 1,867 of the 2,206 metabolites (85%) are simply statistical noise. Only 3 to 12% of these metabolites reach the threshold of statistical significance for a specific cancer type. Our findings demonstrate the absence of a detectable metabolic response to cancer and provide evidence of a serious need by the metabolomics community to establish widely accepted best practices to improve future outcomes.

Physiological and metabolic responses of Sophora tonkinensis to cadmium stress

Sophora tonkinensis is a significant medicinal plant indigenous to China and Vietnam. In China, S. tonkinensis is mainly grown naturally on limestone mountains or is cultivated artificially in arable land. Heavy metal contamination in agricultural soil, particularly cadmium (Cd), poses serious threats to soil health, as well as the growth and productivity of S. tonkinensis. However, information regarding the physiological and metabolic mechanism of S. tonkinensis under Cd toxicity conditions remains limited. In this study, a hydroponic experiment was conducted to investigate the physiological and metabolic responses of S. tonkinensis to varying concentrations of Cd (0, 20, 40, 60, 80 μM), designated as T0, T1, T2, T3, and T4 respectively. The results indicated that the Cd stress significantly impaired the growth and physiological activity of S. tonkinensis. Specifically, reductions were observed in plant height (15.3% to 37.1%) along with shoot fresh weight (9.6% to 36.3%), shoot dry weight (8.2% to 34.1%), root fresh weight (6.7% to 38.2%) and root dry weight (5.1% to 51.3%). This impairment was attributed to a higher uptake and accumulation of Cd in the roots. The decrease in growth was closely linked to the increased production of reactive oxygen species (ROS), which led to cellular damage under Cd toxicity; however, increased antioxidant enzyme activities improved the stress tolerance of S. tonkinensis's stress to Cd toxicity. Non-targeted metabolomic analyses identified 380 differential metabolites (DMs) in the roots of S. tonkinensis subjected to varying level of Cd stress, including amino acids, organic acids, fatty acids, ketones, and others compounds. Further KEGG pathway enrichment analysis revealed that several pathways, such as ABC transporters, isoflavonoid biosynthesis, and pyrimidine metabolism were involved in the response to Cd. Notably, the isoflavonoid biosynthesis pathway was significantly enriched in both T0 vs. T2 and T0 vs. the higher level (80 μM) of Cd stress, highlighting its significance in the plant responses to Cd stress. In conclusion, the identification of key pathways and metabolites is crucial for understanding Cd stress tolerance in S. tonkinensis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-024-01522-w.

Quantifying the extended energy metabolome of industrially important microorganisms (Saccharomyces cerevisiae) using ultra-performance liquid chromatography with mass spectrometry

This study has developed a new targeted methodology for the separation, detection, and quantification of metabolites from the wider energy metabolome of industrially important microorganisms such as Saccharomyces cerevisiae in a single analytical sample. This has been achieved using UHPLC-MS technology in HILIC mode. Absolute concentrations of metabolites nicotinamide adenine dinucleotide (NAD), nicotinamide adenine dinucleotide reduced (NADH), nicotinamide adenine dinucleotide phosphate (NADP), nicotinamide adenine dinucleotide phosphate reduced (NADPH), flavin adenine dinucleotide (FAD), adenosine-monophosphate (AMP), adenosine-diphosphate (ADP), and adenosine-triphosphate (ATP) were determined in a single extraction and analytical methodology. This study demonstrated the development of a rapid, statistically robust, and reproducible methodology through regression calibrations of standard samples from 0.1 to 100 µMol providing a correlation value of r2 = >0.98 for all metabolites. Sample preparation, extraction and analytical methodologies used showed high accuracy, sensitivity, and recovery. With an LOD and LOQ for the targeted analysis of metabolites from the wider energy metabolism in a single sample and analytical run with the lowest LOD of 0.055 nMol (±0.002) and lowest LOQ of 0.167 nMol (±0.006). This method was then applied to Saccharomyces cerevisiae cell culture to evaluate the methodology in industrially used microbial cultures. Results obtained have been statistically determined to be robust and reproducible through recovery analysis using deuterated and isotopically labelled internal standards AMP-15N, ADP-15N and ATP-d14.

Circulating fatty acids, genetic susceptibility and hypertension: a prospective cohort study

Background

Combining genetic risk factors and plasma fatty acids (FAs) can be used as an effective method of precision medicine to prevent hypertension risk.

Methods

A total of 195,250 participants in the UK Biobank cohort were included in this study from 2006-2010. Polygenic risk scores (PRSs) were calculated for hypertension using single-nucleotide polymorphisms (SNPs). Concentrations of plasma FAs, including polyunsaturated fatty acids (PUFAs), monounsaturated fatty acids (MUFAs) and saturated fatty acids (SFAs), were tested by nuclear magnetic resonance. The Cox model was used to test for the main effects of PRS, different plasma FAs and their joint effects on hypertension. Relative excess risk due to interaction (RERI) and the attributable proportion due to interaction (AP) were used to test the additive interaction.

Results

Plasma PUFAs, n-3 PUFAs, MUFAs and SFAs were related to the risk of hypertension (PUFAs: HR, 0.878; 95% CI, 0.868-0.888; MUFAs: HR, 1.13; 95% CI, 1.123-1.150; SFAs: HR, 1.086; 95% CI, 1.074-1.098; n-3 PUFAs: HR, 0.984; 95% CI, 0.973-0.995). Moreover, an additive interaction was found between PRS and plasma FAs, which could contribute to an approximately 10-18% risk of hypertension, and the associations between high plasma MUFAs and a high PRS of hypertension were the strongest positive [RERI: 0.178 (95% CI: 0.062, 0.294), AP: 0.079 (95% CI: 0.027, 0.130)].

Conclusion

Increased plasma MUFAs or SFAs and decreased plasma PUFAs or n-3 PUFAs were associated with hypertension risk, especially among people at high genetic risk.

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.

Discrimination of serum samples of prostate cancer and benign prostatic hyperplasia with 1H-NMR metabolomics

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 1H-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 1H-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 1H-NMR-based serum metabolomics as a promising tool for improved prostate cancer screening, offering an alternative to the limitations of current screening methods.

A single dose of glycogen phosphorylase inhibitor improves cognitive functions of aged mice and affects the concentrations of metabolites in the brain

Inhibition of glycogen phosphorylase (Pyg) - a regulatory enzyme of glycogen phosphorolysis - influences memory formation in rodents. We have previously shown that 2-week intraperitoneal administration of a Pyg inhibitor BAY U6751 stimulated the "rejuvenation" of the hippocampal proteome and dendritic spines morphology and improved cognitive skills of old mice. Given the tedious nature of daily intraperitoneal drug administration, in this study we investigated whether a single dose of BAY U6751 could induce enduring behavioral effects. Obtained results support the efficacy of such treatment in significantly improving the cognitive performance of 20-22-month-old mice. Metabolomic analysis of alterations observed in the hippocampus, cerebellum, and cortex reveal that the inhibition of glycogen phosphorolysis impacts not only glucose metabolism but also various other metabolic processes.

Perturbations in mitochondrial metabolism associated with defective cardiolipin biosynthesis: An in-organello real-time NMR study

Mitochondria are central to cellular metabolism; hence, their dysfunction contributes to a wide array of human diseases. Cardiolipin, the signature phospholipid of the mitochondrion, affects proper cristae morphology, bioenergetic functions, and metabolic reactions carried out in mitochondrial membranes. To match tissue-specific metabolic demands, cardiolipin typically undergoes an acyl tail remodeling process with the final step carried out by the phospholipid-lysophospholipid transacylase tafazzin. Mutations in tafazzin are the primary cause of Barth syndrome. Here, we investigated how defects in cardiolipin biosynthesis and remodeling impacts metabolic flux through the TCA cycle and associated yeast pathways. Nuclear magnetic resonance was used to monitor in real-time the metabolic fate of 13C3-pyruvate in isolated mitochondria from three isogenic yeast strains. We compared mitochondria from a WT strain to mitochondria from a Δtaz1 strain that lacks tafazzin and contains lower amounts of unremodeled cardiolipin and mitochondria from a Δcrd1 strain that lacks cardiolipin synthase and cannot synthesize cardiolipin. We found that the 13C-label from the pyruvate substrate was distributed through twelve metabolites. Several of the metabolites were specific to yeast pathways including branched chain amino acids and fusel alcohol synthesis. While most metabolites showed similar kinetics among the different strains, mevalonate concentrations were significantly increased in Δtaz1 mitochondria. Additionally, the kinetic profiles of α-ketoglutarate, as well as NAD+ and NADH measured in separate experiments, displayed significantly lower concentrations for Δtaz1 and Δcrd1 mitochondria at most time points. Taken together, the results show how cardiolipin remodeling influences pyruvate metabolism, tricarboxylic acid cycle flux, and the levels of mitochondrial nucleotides.

Assessing the Metabolic Variations of Invasive and Noninvasive Unilateral Retinoblastoma Patients

Retinoblastoma (Rb) is a pediatric eye cancer which if diagnosed at later stages can lead to Rb invasion into the choroid, optic nerve, sclera, or beyond, with the potential of undergoing metastasis. Cancer cells, including Rb cells, reprogram their metabolic circuits for their own survival and progression, which provides a great opportunity to monitor the extent of Rb progression based on metabolic differences. Henceforth, the present study aims to map the metabolic variations in patients with invasive (primarily enucleated eyes with high-risk histopathological features) and noninvasive (eyes salvaged with treatment) unilateral retinoblastoma (Rb) using nuclear magnetic resonance (NMR) based serum metabolomics. Quantification of differential metabolites in the serum obtained from 9 patients with invasive and 4 with noninvasive unilateral Rb along with 6 controls (no retinal pathology) was carried out using 1H NMR spectroscopy. A total of 71 metabolites, such as organic acids, amino acids, carbohydrates, and others, were identified in the serum obtained from 9 patients with invasive and 4 with noninvasive unilateral Rb. Partial least-squares discriminant analysis (PLS-DA) models depicted distinct grouping of invasive and noninvasive Rb patients and controls. Differential metabolic fingerprints were observed for invasive and noninvasive Rb patients based on their biostatistical analyses with respect to controls. Remarkable perturbation was observed among various metabolites such as 4-aminobutyrate, 2-phosphoglycerate, O-phosphocholine, proline, Sn-glycero-3-phosphocholine (Sn-GPC), and O-phosphoethanolamine in noninvasive and invasive Rb patients with most of the effects being heightened in the latter group. Metabolic changes unique to invasive and noninvasive Rb patients were also observed. Multivariate receiver operating characteristics (ROC) analysis unveiled the highest accuracy and potency of ROC models 2 and 5 to distinguish the noninvasive and invasive Rb from controls, respectively. Metabolites identified in the serum of patients with invasive and noninvasive Rb may aid in advancing our knowledge about Rb tumor biology. Differential aberrant metabolic variations in patients with invasive Rb compared to those with noninvasive Rb may guide the decision of enucleation versus globe salvage.

Trace element detection in anhydrous minerals by micro-scale quantitative nuclear magnetic resonance spectroscopy

Nominally anhydrous minerals (NAMs) composing Earth's and planetary rocks incorporate microscopic amounts of volatiles. However, volatile distribution in NAMs and their effect on physical properties of rocks remain controversial. Thus, constraining trace volatile concentrations in NAMs is tantamount to our understanding of the evolution of rocky planets and planetesimals. Here, we present an approach of trace-element quantification using micro-scale Nuclear Magnetic Resonance (NMR) spectroscopy. This approach employs the principle of enhanced mass-sensitivity in NMR microcoils. We were able to demonstrate that this method is in excellent agreement with standard methods across their respective detection capabilities. We show that by simultaneous detection of internal reference nuclei, the quantification sensitivity can be substantially increased, leading to quantifiable trace volatile element amounts of about 50 ng/g measured in a micro-meter sized single anorthitic mineral grain, greatly enhancing detection capabilities of volatiles in geologically important systems.

Comprehensive characterization of differential glycation in hepatocellular carcinoma using tissue proteomics with stable isotopic labeling

Glycation is a non-enzymatic posttranslational modification coming from the reaction between reducing sugars and free amino groups in proteins, where early glycation products (fructosyl-lysine, FL) and advanced glycation end products (AGEs) are formed. The occurrence of glycation and accumulation of AGEs have been closely associated with hepatocellular carcinoma (HCC). Here, we reported the characterization of differential glycation in HCC using tissue proteomics with stable isotopic labeling; early glycation-modified peptides were enriched with boronate affinity chromatography (BAC), and AGEs-modified peptides were fractionated with basic reversed-phase separation. By this integrated approach, 3717 and 1137 early and advanced glycated peptides corresponding to 4007 sites on 1484 proteins were identified with a false discovery rate (FDR) of no more than 1%. One hundred fifty-five sites were modified with both early and advanced end glycation products. Five early and 7 advanced glycated peptides were quantified to be differentially expressed in HCC tissues relative to paired adjacent tissues. Most (8 out of 10) of the proteins corresponding to the differential glycated peptides have previously been reported with dysregulation in HCC. The results together may deepen our knowledge of glycation as well as provide insights for therapeutics.

Quantitative 31P NMR Spectroscopy Platform Method for the Assay of Oligonucleotides as Pure Drug Substances and in Drug Product Formulations Using the Internal Standard Method

One of the most widely used techniques for the quantification of small interfering ribonucleic acid (siRNA) is the ultraviolet (UV) spectroscopy method. However, due to uncertainties in the extinction coefficient affecting the accuracy of the method and a sample preparation including several dilution steps, the purpose of this study was to explore the possibility of determining the content of siRNA by a platform method using quantitative 31P nuclear magnetic resonance (31P-qNMR) and the internal standard method. In this paper, acquisition time, selection of a suitable internal certified reference material, signal selection used for quantification, relaxation delay, and precision are discussed. In addition, the robustness of the method and the ability to apply this platform method to both drug substance (DS) and drug product samples is also discussed. Quantifications of siRNA determined by the 31P-qNMR platform method were on average 98.5%w/w when adjusting for the sodium and water contents. The data confirmed the applicability of 31P-qNMR in siRNA content determinations. The quantifications were compared to quantifications determined by the traditional UV spectroscopy method by F- and t-tests. The statistical tests showed that the platform 31P-qNMR method provided more accurate results (mass balance close to 100% w/w) compared to the traditional UV spectroscopy method when analyzing DS samples.

Fermentation Analytical Technology (FAT): Monitoring industrial E. coli fermentations using absolute quantitative 1H NMR spectroscopy

BACKGROUND

To perform fast, reproducible, and absolute quantitative measurements in an automated manner has become of paramount importance when monitoring industrial processes, including fermentations. Due to its numerous advantages - including its inherent quantitative nature - Proton Nuclear Magnetic Resonance (1H NMR) spectroscopy provides an ideal tool for the time-resolved monitoring of fermentations. However, analytical conditions, including non-automated sample preparation and long relaxation times (T1) of some metabolites, can significantly lengthen the experimental time and make implementation in an industrial set up unfeasible.

RESULTS

We present a high throughput method based on Standard Operating Procedures (SOPs) and 1H NMR, which lays the foundation for what we call Fermentation Analytical Technology (FAT). Our method was developed for the accurate absolute quantification of metabolites produced during Escherichia coli industrial fermentations. The method includes: (1) a stopped flow system for non-invasive sample collection followed by sample quenching, (2) automatic robot-assisted sample preparation, (3) fast 1H NMR measurements, (4) metabolites quantification using multivariate curve resolution (MCR), and (5) metabolites absolute quantitation using a novel correction factor (k) to compensate for the short recycle delay (D1) employed in the 1H NMR measurements. The quantification performance was tested using two sample types: buffer solutions of chemical standards and real fermentation samples. Five metabolites - glucose, acetate, alanine, phenylalanine and betaine - were quantified. Absolute quantitation ranged between 0.64 and 3.40 mM in pure buffer, and 0.71-7.76 mM in real samples.

SIGNIFICANCE

The proposed method is generic and can be straight forward implemented to other types of fermentations, such as lactic acid, ethanol and acetic acid fermentations. It provides a high throughput automated solution for monitoring fermentation processes and for quality control through absolute quantification of key metabolites in fermentation broth. It can be easily implemented in an at-line industrial setting, facilitating the optimization of the manufacturing process towards higher yields and more efficient and sustainable use of resources.

Perturbations in mitochondrial metabolism associated with defective cardiolipin biosynthesis: An in-organello real-time NMR study

Mitochondria are central to cellular metabolism; hence, their dysfunction contributes to a wide array of human diseases including cancer, cardiopathy, neurodegeneration, and heritable pathologies such as Barth syndrome. Cardiolipin, the signature phospholipid of the mitochondrion promotes proper cristae morphology, bioenergetic functions, and directly affects metabolic reactions carried out in mitochondrial membranes. To match tissue-specific metabolic demands, cardiolipin typically undergoes an acyl tail remodeling process with the final step carried out by the phospholipid-lysophospholipid transacylase tafazzin. Mutations in the tafazzin gene are the primary cause of Barth syndrome. Here, we investigated how defects in cardiolipin biosynthesis and remodeling impact metabolic flux through the tricarboxylic acid cycle and associated pathways in yeast. Nuclear magnetic resonance was used to monitor in real-time the metabolic fate of 13C3-pyruvate in isolated mitochondria from three isogenic yeast strains. We compared mitochondria from a wild-type strain to mitochondria from a Δtaz1 strain that lacks tafazzin and contains lower amounts of unremodeled cardiolipin, and mitochondria from a Δcrd1 strain that lacks cardiolipin synthase and cannot synthesize cardiolipin. We found that the 13C-label from the pyruvate substrate was distributed through about twelve metabolites. Several of the identified metabolites were specific to yeast pathways, including branched chain amino acids and fusel alcohol synthesis. Most metabolites showed similar kinetics amongst the different strains but mevalonate and α-ketoglutarate, as well as the NAD+/NADH couple measured in separate nuclear magnetic resonance experiments, showed pronounced differences. Taken together, the results show that cardiolipin remodeling influences pyruvate metabolism, tricarboxylic acid cycle flux, and the levels of mitochondrial nucleotides.

Identification of Key Factors in Cartilage Tissue During the Progression of Osteoarthritis Using a Non-targeted Metabolomics Strategy

This research was to reveal the key factors in the progression of osteoarthritis (OA) using non-targeted metabolomics and to find targeted therapies for patients with OA. Twenty-two patients with knee OA scheduled for total knee arthroplasty were divided into two groups: Kellgren-Lawrence (KL) grade 3 (n = 16) and grade 4 (n = 6), according to plain X-rays of the knee. After the operation, the cartilages of femur samples were analyzed using non-targeted metabolomics. When compared with grade 3 patients, the levels of choline, 2-propylpiperidine, rhamnose, and monomethyl glutaric acid were higher; while 1-methylhistamine, sphingomyelin (SM) (d18:1/14:0), zeranol, 3- (4-hydroxyphenyl)-1-propanol, 5-aminopentanamide, dihydrouracil, 2-hydroxypyridine, and 3-amino-2-piperidone were lower in grade 4 patients. Furthermore, some metabolic pathways were found to be significantly different in two groups such as the pantothenate and coenzyme A (CoA) biosynthesis pathway, the glycerophospholipid metabolism pathway, histidine metabolism pathway, lysine degradation pathway, glycine, serine and threonine metabolism pathway, fructose and mannose metabolism pathway, the pyrimidine metabolism pathway, and beta-alanine metabolism pathway. This work used non-targeted metabolomics and screened out differential metabolites and metabolic pathways, providing a reliable theoretical basis for further study of specific markers and their specific pathways in the progression of OA.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-023-00123-z.

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Endogenous estrogen metabolites as oxidative stress mediators and endometrial cancer biomarkers

BACKGROUND

Endometrial cancer is the most common gynecologic malignancy found in developed countries. Because therapy can be curative at first, early detection and diagnosis are crucial for successful treatment. Early diagnosis allows patients to avoid radical therapies and offers conservative management options. There are currently no proven biomarkers that predict the risk of disease occurrence, enable early identification or support prognostic evaluation. Consequently, there is increasing interest in discovering sensitive and specific biomarkers for the detection of endometrial cancer using noninvasive approaches.

CONTENT

Hormonal imbalance caused by unopposed estrogen affects the expression of genes involved in cell proliferation and apoptosis, which can lead to uncontrolled cell growth and carcinogenesis. In addition, due to their ability to cause oxidative stress, estradiol metabolites have both carcinogenic and anticarcinogenic properties. Catechol estrogens are converted to reactive quinones, resulting in oxidative DNA damage that can initiate the carcinogenic process. The molecular anticancer mechanisms are still not fully understood, but it has been established that some estradiol metabolites generate reactive oxygen species and reactive nitrogen species, resulting in nitro-oxidative stress that causes cancer cell cycle arrest or cell death. Therefore, identifying biomarkers that reflect this hormonal imbalance and the presence of endometrial cancer in minimally invasive or noninvasive samples such as blood or urine could significantly improve early detection and treatment outcomes.

The hidden treasures in endophytic fungi: a comprehensive review on the diversity of fungal bioactive metabolites, usual analytical methodologies, and applications

This review provides a comprehensive overview of the key aspects of the natural metabolite production by endophytic fungi, which has attracted significant attention due to its diverse biological activities and wide range of applications. Synthesized by various fungal species, these metabolites encompass compounds with therapeutic, agricultural, and commercial significance. We delved into strategies and advancements aimed at optimizing fungal metabolite production. Fungal cultivation, especially by Aspergillus, Penicillium, and Fusarium, plays a pivotal role in metabolite biosynthesis, and researchers have explored both submerged and solid-state cultivation processes to harness the full potential of fungal species. Nutrient optimization, pH, and temperature control are critical factors in ensuring high yields of the targeted bioactive metabolites especially for scaling up processes. Analytical methods that includes High-Performance Liquid Chromatography (HPLC), Liquid Chromatography-Mass Spectrometry (LC-MS), Gas Chromatography-Mass Spectrometry (GC-MS), Nuclear Magnetic Resonance (NMR), and Mass Spectrometry (MS), are indispensable for the identification and quantification of the compounds. Moreover, genetic engineering and metabolic pathway manipulation have emerged as powerful tools to enhance metabolite production and develop novel fungal strains with increased yields. Regulation and control mechanisms at the genetic, epigenetic, and metabolic levels are explored to fine-tune the biosynthesis of fungal metabolites. Ongoing research aims to overcome the complexity of the steps involved to ensure the efficient production and utilization of fungal metabolites.

Best Practices in NMR Metabolomics: Current State

A literature survey was conducted to identify current practices used by NMR metabolomics investigators when conducting and reporting their metabolomics studies. A total of 463 papers from 2020 and 80 papers from 2010 were selected from PubMed and were manually analyzed by a team of investigators to assess the extent and completeness of the experimental procedures and protocols reported. A significant number of the papers did not report on essential experimental details, incompletely stated which statistical methods were used, improperly applied supervised multivariate statistical analyses, or lacked validation of statistical models. A large diversity of protocols and software were identified, which suggests a lack of consensus and a relatively limited use of commonly agreed upon standards for conducting and reporting NMR metabolomics studies. The overall intent of the survey is to inform and encourage the NMR metabolomics community to develop and adopt best-practices for the field.

Modified PSYCHE NMR - The possibility for the direct semi-quantitation of components in mixtures

A series of commonly occurring biologically relevant compounds were analysed using internally referenced PSYCHE NMR and the accuracy/precision compared with that achieved using conventional qNMR. The effect of chemical shift, coupling constants, swept frequency pulse angle, excitation sculpting and the impact of signal overlap in mixtures was evaluated in terms of statistically significant variation between the two techniques. It is shown that suitably optimised PSYCHE NMR represents a potentially reliable method for the semi-quantitation of mixtures of compounds, whose spectroscopic signals overlap in conventional qNMR analysis and hence cannot be accurately quantified with that technique. This is of particular relevance for complex mixtures of natural products. Of particular note is the effect of the excitation sculpting block introduced to supress baseline artefacts.

Extended automated quantification algorithm (AQuA) for targeted 1H NMR metabolomics of highly complex samples: application to plant root exudates

INTRODUCTION

The Automated Quantification Algorithm (AQuA) is a rapid and efficient method for targeted NMR-based metabolomics, currently optimised for blood plasma. AQuA quantifies metabolites from 1D-1H NMR spectra based on the height of only one signal per metabolite, which minimises the computational time and workload of the method without compromising the quantification accuracy.

OBJECTIVES

To develop a fast and computationally efficient extension of AQuA for quantification of selected metabolites in highly complex samples, with minimal prior sample preparation. In particular, the method should be capable of handling interferences caused by broad background signals.

METHODS

An automatic baseline correction function was combined with AQuA into an automated workflow, the extended AQuA, for quantification of metabolites in plant root exudate NMR spectra that contained broad background signals and baseline distortions. The approach was evaluated using simulations as well as a spike-in experiment in which known metabolite amounts were added to a complex sample matrix.

RESULTS

The extended AQuA enables accurate quantification of metabolites in 1D-1H NMR spectra with varying complexity. The method is very fast (< 1 s per spectrum) and can be fully automated.

CONCLUSIONS

The extended AQuA is an automated quantification method intended for 1D-1H NMR spectra containing broad background signals and baseline distortions. Although the method was developed for plant root exudates, it should be readily applicable to any NMR spectra displaying similar issues as it is purely computational and applied to NMR spectra post-acquisition.

Serum Metabolomics of Retinoblastoma: Assessing the Differential Serum Metabolic Signatures of Unilateral and Bilateral Patients

Retinoblastoma (Rb) is the most common pediatric eye cancer. To identify the biomarkers for early diagnosis and monitoring the progression of Rb in patients, mapping of the alterations in their metabolic profiles is essential. The present study aims at exploring the metabolic disparity in serum from Rb patients and controls using NMR-based metabolomics. A total of 72 metabolites, including carbohydrates, amino acids, and organic acids, were quantified in serum samples from 24 Rb patients and 26 controls. Distinct clusters of Rb patients and controls were obtained using the partial least-squares discriminant analysis (PLS-DA) model. Further, univariate and multivariate analyses of unilateral and bilateral Rb patients with respect to their age-matched controls depicted their distinct metabolic fingerprints. Metabolites including 2-phosphoglycerate, 4-aminobutyrate, proline, O-phosphocholine, O-phosphoethanolamine, and Sn-glycero-3-phosphocholine (Sn-GPC) showed significant perturbation in both unilateral and bilateral Rb patients. However, metabolic differences among the bilateral Rb cases were more pronounced than those in unilateral Rb cases with respect to controls. In addition to major discriminatory metabolites for Rb, unilateral and bilateral Rb cases showed specific metabolic changes, which might be the result of their differential genetic/somatic mutational backgrounds. This further suggests that the aberrant metabolic perturbation in bilateral patients signifies the severity of the disease in Rb patients. The present study demonstrated that identified serum metabolites have potential to serve as a noninvasive method for detection of Rb, discriminate bilateral from unilateral Rb patients, and aid in better understanding of the RB tumor biology.

Assessment of the Purity of IMM-H014 and Its Related Substances for the Treatment of Metabolic-Associated Fatty Liver Disease Using Quantitative Nuclear Magnetic Resonance Spectroscopy

An accurate, rapid, and selective quantitative nuclear magnetic resonance method was developed and validated to assess the purity of IMM-H014, a novel drug for the treatment of metabolic-associated fatty liver disease (MAFLD), and four related substances (impurities I, II, III, and IV). In this study, we obtained spectra of IMM--H014 and related substances in deuterated chloroform using dimethyl terephthalate (DMT) as the internal standard reference. Quantification was performed using the 1H resonance signals at δ 8.13 ppm for DMT and δ 6.5-7.5 ppm for IMM-H014 and its related substances. Several key experimental parameters were investigated and optimized, such as pulse angle and relaxation delay. Methodology validation was conducted based on the International Council for Harmonization guidelines and verified with satisfactory specificity, precision, linearity, accuracy, robustness, and stability. In addition, the calibration results of the samples were consistent with those obtained from the mass balance method. Thus, this research provides a reliable and practical protocol for purity analysis of IMM-H014 and its critical impurities and contributes to subsequent clinical quality control research.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

The Kynurenine/Tryptophan Ratio as a Promising Metabolomic Biomarker for Diagnosing the Spectrum of Tuberculosis Infection and Disease

The metabolic system and immunology used to be seen as distinct fields of study. Recent developments in our understanding of how the immune system operates in health and disease have connected these fields to complex systems. An effective technique for identifying probable abnormalities of metabolic homeostasis brought on by disease is metabolomics, which is defined as the thorough study of small molecule metabolic intermediates within a biological system that collectively make up the metabolome. A prognostic metabolic biomarker with adequate prognostic accuracy for tuberculosis progression has recently been created. The rate-limiting host enzyme for the conversion of tryptophan to kynurenine, indoleamine 2,3-dioxygenase (IDO), is greatly elevated in the lungs of tuberculosis disease patients. Targeted study on tryptophan in tuberculosis disease indicates that such decreases may also resembled this upregulation. Although tuberculosis diagnosis has improved with the use of interferon release assay and tuberculosis nucleic acid amplification, tuberculosis control is made difficult by the lack of a biomarker to diagnose active tuberculosis disease. We hope that the reader of this work can develop an understanding of the advantages of metabolomics testing, particularly as a sort of testing that can be used for both diagnosing and monitoring a patient's response to treatment for tuberculosis.

Simple and User-Friendly Methodology for Crystal Water Determination by Quantitative Proton NMR Spectroscopy in Deuterium Oxide

In drug research and development, knowledge of the precise structure of an active ingredient is crucial. However, it is equally important to know the water content of the drug molecule, particularly the number of crystal waters present in its structure. Such knowledge ensures the avoidance of drug dosage and formulation errors since the number of water molecules affects the physicochemical and pharmaceutical properties of the molecule. Several methods have been used for crystal water measurements of organic compounds, of which thermogravimetry and crystallography may be the most common ones. To the best of our knowledge, solution-state NMR spectroscopy has not been used for crystal water determination in deuterium oxide. Quantitative NMR (qNMR) method will be presented in the paper with a comparison of single-crystal X-ray diffraction and thermogravimetric analysis results. The qNMR method for water content measurement is straightforward, reproducible, and accurate, including measurement of 1H NMR spectrum before and after the addition of the analyte compound, and the result can be calculated after integration of the reference compound, analyte, and HDO signals using the given equation. In practical terms, there is no need for weighing the samples under study, which makes it simple and is a clear advantage to the current determination methods. In addition, the crystal structures of two model bisphosphonates used herein are reported: that of monopotassium etidronate dihydrate and monosodium zoledronate trihydrate.

Fingerprinting and profiling in metabolomics of biosamples

This review focuses on metabolomics from an NMR point of view. It attempts to cover the broad scope of metabolomics and describes the NMR experiments that are most suitable for each sample type. It is addressed not only to NMR specialists, but to all researchers who wish to approach metabolomics with a clear idea of what they wish to achieve but not necessarily with a deep knowledge of NMR. For this reason, some technical parts may seem a bit naïve to the experts. The review starts by describing standard metabolomics procedures, which imply the use of a dedicated 600 MHz instrument and of four properly standardized 1D experiments. Standardization is a must if one wants to directly compare NMR results obtained in different labs. A brief mention is also made of standardized pre-analytical procedures, which are even more essential. Attention is paid to the distinction between fingerprinting and profiling, and the advantages and disadvantages of fingerprinting are clarified. This aspect is often not fully appreciated. Then profiling, and the associated problems of signal assignment and quantitation, are discussed. We also describe less conventional approaches, such as the use of different magnetic fields, the use of signal enhancement techniques to increase sensitivity, and the potential of field-shuttling NMR. A few examples of biomedical applications are also given, again with the focus on NMR techniques that are most suitable to achieve each particular goal, including a description of the most common heteronuclear experiments. Finally, the growing applications of metabolomics to foodstuffs are described.

Quantitative 1H NMR with global spectral deconvolution approach for quality assessment of natural and cultured Cordyceps sinensis

Cordyceps sinensis is a precious medicinal food which has been successfully cultivated indoors. It remains to be investigated for a simultaneous comparison on aqueous components of natural and cultivated samples. Herein, an approach of quantitative nuclear magnetic resonance (qNMR) analysis combined with global spectral deconvolution (GSD) was established for simultaneous quantification of 26 aqueous components in C. sinensis. Processed by GSD, the distorted baselines of 1H NMR spectra were greatly improved, and overlapped signals were also well separated so as to achieve accurate identification and quantitation of components in C. sinensis. Method validation by UHPLC-QTOF-MS and TOF-SIMS analysis revealed that qNMR combined with GSD is a reliable approach for simultaneous quantification of multiple components including characteristic markers of glutamine, GABA and trehalose in authentic and fake C. sinensis. The well-established qNMR approach can be used for quality assessment of natural and cultivated C. sinensis as well as differentiation from fake ones.

Enabling honey quality and authenticity with NMR and LC-IRMS based platform

Honey has been known for economically motivated adulteration around the world, because of its high demand and short supply. As consequence increasing honey production using the deliberate addition of sugar syrups while claiming a fictitious origin and diversifying it to increase its value. Generally, honey testing is supervised by a set of guidelines and quality parameters to ensure its quality and authenticity. As per the many regulatory bodies, current honey scams have been challenging to identify with conventional methods, so quality control labs require sophisticated technology. With these paradigm shifts, the aim of the present review is focused on the authenticity of honey through two important cutting-edge methods viz LC-IRMS and NMR. The LC-IRMS aids in the detection of added C₃ and C₄ sugars. Whereas NMR has provided a potent solution by allowing the classification of botanical varieties and geographical origin along with the quantification of a set of quality parameters in a single experiment.

Urine Metabolites and Bioactive Compounds from Functional Food: Applications of Liquid Chromatography Mass Spectrometry

Bioactive compounds in functional foods, medicinal plants and others are considered attractive value-added molecules based on their wide range of bioactivity. It is clear that an important role is occupied by polyphenol, phenolic compounds and others. Urine is an effective biofluid to evaluate and monitor alterations in homeostasis and other processes related to metabolism. The current review provides a detailed description of the formation of urine in human body, various aspects relevant to sampling and analysis of urinary metabolites before presenting recent developments leveraging on metabolite profiling of urine. For the profiling of small molecules in urine, advancement of liquid chromatography mass tandem spectrometry (LC/MS/MS), establishment of standardized chemical fragmentation libraries, computational resources, data-analysis approaches with pattern recognition tools have made it an attractive option. The profiling of urinary metabolites gives an overview of the biomarkers associated with the diet and evaluates its biological effects. Metabolic pathways such as glycolysis, tricarboxylic acid cycle, amino acid metabolism, energy metabolism, purine metabolism and others can be evaluated. Finally, a combination of metabolite profiling with chemical standardization and bioassay in functional food and medicinal plants will likely lead to the identification of new biomarkers and novel biochemical insights.

NMR metabolite quantification of a synthetic urine sample: an inter-laboratory comparison of processing workflows

INTRODUCTION

Absolute quantification of individual metabolites in complex biological samples is crucial in targeted metabolomic profiling.

OBJECTIVES

An inter-laboratory test was performed to evaluate the impact of the NMR software, peak-area determination method (integration vs. deconvolution) and operator on quantification trueness and precision.

METHODS

A synthetic urine containing 32 compounds was prepared. One site prepared the urine and calibration samples, and performed NMR acquisition. NMR spectra were acquired with two pulse sequences including water suppression used in routine analyses. The pre-processed spectra were sent to the other sites where each operator quantified the metabolites using internal referencing or external calibration, and his/her favourite in-house, open-access or commercial NMR tool.

RESULTS

For 1D NMR measurements with solvent presaturation during the recovery delay (zgpr), 20 metabolites were successfully quantified by all processing strategies. Some metabolites could not be quantified by some methods. For internal referencing with TSP, only one half of the metabolites were quantified with a trueness below 5%. With peak integration and external calibration, about 90% of the metabolites were quantified with a trueness below 5%. The NMRProcFlow integration module allowed the quantification of several additional metabolites. The number of quantified metabolites and quantification trueness improved for some metabolites with deconvolution tools. Trueness and precision were not significantly different between zgpr- and NOESYpr-based spectra for about 70% of the variables.

CONCLUSION

External calibration performed better than TSP internal referencing. Inter-laboratory tests are useful when choosing to better rationalize the choice of quantification tools for NMR-based metabolomic profiling and confirm the value of spectra deconvolution tools.

The metabolic contribution of SKN-1/Nrf2 to the lifespan of Caenorhabditis elegans

BACKGROUND AND AIMS

SKN-1, a C. elegans transcription factor analogous to the mammalian NF-E2-related factor (Nrf2), has been known to promote oxidative stress resistance aiding nematodes' longevity. Although SKN-1's functions suggest its implication in lifespan modulation through cellular metabolism, the actual mechanism of how metabolic rearrangements contribute to SKN-1's lifespan modulation has yet to be well characterized. Therefore, we performed the metabolomic profiling of the short-lived skn-1-knockdown C. elegans.

METHODS

We analyzed the metabolic profile of the skn-1-knockdown worms with nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-tandem mass spectrometry (LC-MS/MS) and obtained distinctive metabolomic profiles compared to WT worms. We further extended our study with gene expression analysis to examine the expression level of genes encoding all metabolic enzymes.

RESULTS

A significant increase in the phosphocholine and AMP/ATP ratio, potential biomarkers of aging, was observed, accompanied by a decrease in the transsulfuration metabolites, NADPH/NADP⁺ ratio, and total glutathione (GSHt), which are known to be involved in oxidative stress defense. skn-1-RNAi worms also exhibited an impairment in the phase II detoxification system, confirmed by the lower conversion rate of paracetamol to paracetamol-glutathione. By further examining the transcriptomic profile, we found a decrease in the expression of cbl-1, gpx, T25B9.9, ugt, and gst, which are involved in GSHt and NADPH synthesis as well as in the phase II detoxification system.

CONCLUSION

Our multi-omics results consistently revealed that the cytoprotective mechanisms, including cellular redox reactions and xenobiotic detoxification system, contribute to the roles of SKN-1/Nrf2 in the lifespan of worms.

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.

An Optimized Two-Dimensional Quantitative Nuclear Magnetic Resonance Strategy for the Rapid Quantitation of Diester-Type C19-Diterpenoid Alkaloids from Aconitum carmichaelii

With the development of nuclear magnetic resonance (NMR) spectrometers and probes, two-dimensional quantitative nuclear magnetic resonance (2D qNMR) technology with a high signal resolution and great application potential has become increasingly accessible for the quantitation of complex mixtures. However, the requirement that the relaxation recovery time be equal to at least five times T₁ (longitudinal relaxation time) makes it difficult for 2D qNMR to simultaneously achieve high quantitative accuracy and high data acquisition efficiency. By comprehensively using relaxation optimization and nonuniform sampling, we successfully established an optimized 2D qNMR strategy for HSQC experiments at the half-hour level and then accurately quantified the diester-type C₁₉-diterpenoid alkaloids in Aconitum carmichaelii. The optimized strategy had the advantages of high efficiency, high accuracy, good reproducibility, and low cost and thus could serve as a reference to optimize 2D qNMR experiments for quantitative analysis of natural products, metabolites, and other complex mixtures.

A different approach to the quantification of human seminal plasma metabolites using high-resolution NMR spectroscopy

In this study, a reliable method was established for the absolute quantification of metabolite concentrations in human seminal plasma using ERETIC2, a quantification tool developed by Bruker based on the PULCON principle. The performance of the ERETIC2 was examined using an AVANCE III HD NMR spectrometer (600 MHz) equipped with a triple inverse 1.7 mm TXI probe in terms of some experimental parameters that may affect the accuracy and precision of the quantitative results. Then, the accuracy, precision, and repeatibility of ERETIC2 were determined using L-asparagine solutions at different concentrations. And it was evaluated by comparing it with the classical internal standard (IS) quantification method. The relative standard deviation (RSD) values for ERETIC2 were calculated in the range of 0.55-1.90% and the minimum recovery value was 99.9%, while the RSD values for the IS method were calculated in the range of 0.88-5.83% and recovery value was minimum 91.0%. Besides, the RSD values of the inter-day precisions for the ERETIC2 and IS methods were obtained to be in the range of 1.25 - 3.03% and 0.97 - 3.46%, respectively. Finally, the concentration values of seminal plasma metabolites were determined using different pulse programs with both methods for samples obtained from normozoospermic control and azoospermic patient groups. The results proved that this quantification method developed using NMR spectroscopy is easy to use in complex sample systems such as biological fluids and is a good alternative to the classical internal standard method in terms of accuracy and sensitivity. In addition, the improvement of the spectral resolution and sensitivity with the microcoil probe technology and the possibility of analyzing with minimum sample quantities has contributed positively to the results of this method.

Blood-based liquid biopsy: insights into early detection, prediction, and treatment monitoring of bladder cancer

Bladder cancer (BC) is a clinical challenge worldwide with late clinical presentation, poor prognosis, and low survival rates. Traditional cystoscopy and tissue biopsy are routine methods for the diagnosis, prognosis, and monitoring of BC. However, due to the heterogeneity and limitations of tumors, such as aggressiveness, high cost, and limited applicability of longitudinal surveillance, the identification of tumor markers has attracted significant attention in BC. Over the past decade, liquid biopsies (e.g., blood) have proven to be highly efficient methods for the discovery of BC biomarkers. This noninvasive sampling method is used to analyze unique tumor components released into the peripheral circulation and allows serial sampling and longitudinal monitoring of tumor progression. Several liquid biopsy biomarkers are being extensively studied and have shown promising results in clinical applications of BC, including early detection, detection of microscopic residual disease, prediction of recurrence, and response to therapy. Therefore, in this review, we aim to provide an update on various novel blood-based liquid biopsy markers and review the advantages and current limitations of liquid biopsy in BC therapy. The role of blood-based circulating tumor cells, circulating tumor DNA, cell-free RNA, exosomes, metabolomics, and proteomics in diagnosis, prognosis, and treatment monitoring, and their applicability to the personalized management of BC, are highlighted.