NMR-based metabolomics: from sample preparation to applications in nutrition research

Associations of diet quality and food consumption with serum biomarkers for lipid and amino acid metabolism in Finnish children: the PANIC study

PURPOSE

To investigate the associations of overall diet quality and dietary factors with serum biomarkers for lipid and amino acid metabolism in a general population of children.

METHODS

We studied 194 girls and 209 boys aged 6-8 years participating in the Physical Activity and Nutrition in Children study. Food consumption was assessed by 4-day food records and diet quality was quantified by the Finnish Children Healthy Eating Index (FCHEI). Fasting serum fatty acids, amino acids, apolipoproteins, as well as lipoprotein particle sizes were analyzed with high-throughput nuclear magnetic resonance spectroscopy. Data were analyzed using linear regression adjusted for age, sex, and body fat percentage.

RESULTS

FCHEI was directly associated with the ratio of polyunsaturated (PUFA) to saturated fatty acids (SFA) (PUFA/SFA), the ratio of PUFA to monounsaturated fatty acids (MUFA) (PUFA/MUFA), the ratio of PUFA to total fatty acids (FA) (PUFA%), the ratio of omega-3-fatty acids to total FA (omega-3 FA%), and inversely associated with the ratio of MUFA to total FA (MUFA%), alanine, glycine, histidine and very-low density lipoprotein (VLDL) particle size. Consumption of vegetable oils and vegetable-oil-based margarine (≥ 60% fat) was directly associated with PUFA/SFA, PUFA/MUFA, PUFA%, the ratio of omega-6 FA to total FA (omega-6 FA%), and inversely associated with SFA, MUFA, SFA to total FA (SFA%), MUFA%, alanine and VLDL particle size. Consumption of high-fiber grain products directly associated with PUFA/SFA, PUFA/MUFA, omega-3 FA%, omega-6 FA%, PUFA% and inversely associated with SFA and SFA%. Fish consumption directly related to omega-3 FA and omega-3 FA%. Consumption of sugary products was directly associated with histidine and VLDL particle size. Vegetable, fruit, and berry consumption had direct associations with VLDL particle size and the ratio of apolipoprotein B to apolipoprotein A1. Consumption of low fat (< 1%) milk was directly associated with phenylalanine. A higher consumption of high-fat (≥ 1%) milk was associated with lower serum MUFA/SFA and higher SFA%. Sausage consumption was directly related to SFA% and histidine. Red meat consumption was inversely associated with glycine.

CONCLUSIONS

Better diet quality, higher in intake of dietary sources of unsaturated fat and fiber, and lower in sugary product intake were associated with more favorable levels of serum biomarkers for lipid and amino acid metabolism independent of adiposity.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT01803776, registered March 3, 2013.

Metabolomics: An Emerging "Omics" Platform for Systems Biology and Its Implications for Huntington Disease Research

Huntington's disease (HD) is a progressive, fatal neurodegenerative disease characterized by motor, cognitive, and psychiatric symptoms. The precise mechanisms of HD progression are poorly understood; however, it is known that there is an expansion of the trinucleotide cytosine-adenine-guanine (CAG) repeat in the Huntingtin gene. Important new strategies are of paramount importance to identify early biomarkers with predictive value for intervening in disease progression at a stage when cellular dysfunction has not progressed irreversibly. Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under certain conditions and is becoming an essential tool for the systemic characterization of metabolites to provide a snapshot of the functional and pathophysiological states of an organism and support disease diagnosis and biomarker discovery. This review briefly highlights the historical progress of metabolomic methodologies, followed by a more detailed review of the use of metabolomics in HD research to enable a greater understanding of the pathogenesis, its early prediction, and finally the main technical platforms in the field of metabolomics.

Non-targeted LC-MS/MS metabolomic profiling of human plasma uncovers a novel Mediterranean diet biomarker panel

INTRODUCTION

Consumption of a Mediterranean diet (MD) has established health benefits, and the identification of novel biomarkers could enable objective monitoring of dietary pattern adherence.

OBJECTIVES

The present investigation performed untargeted metabolomics on blood plasma from a controlled study of MD adherence, to identify novel blood-based metabolite biomarkers associated with the MD pattern, and to build a logistic regression model that could be used to characterise MD adherence.

METHODS

A hundred and thirty-five plasma samples from n = 58 patients collected at different time points were available. Using a 14-point scale MD Score (MDS) subjects were divided into 'high' or 'low' MDS adherence groups and liquid chromatography-mass spectrometry (LC-MS/MS) was applied for analysis.

RESULTS

The strongest association with MDS was pectenotoxin 2 seco acid (r = 0.53; ROC = 0.78), a non-toxic marine xenobiotic metabolite. Several lipids were useful biomarkers including eicosapentaenoic acid, the structurally related lysophospholipid (20:5(5Z,8Z,11Z,14Z,17Z)/0:0), a phosphatidylcholine (P-18:1(9Z)/16:0) and also xi-8-hydroxyhexadecanedioic acid. Two metabolites negatively correlated with MDS, these were the monoacylglycerides (0:0/16:1(9Z)/0:0) and (0:0/20:3(5Z,8Z,11Z)/0:0). By stepwise elimination we selected a panel of 3 highly discriminatory metabolites and developed a linear regression model which identified 'high MDS' individuals with high sensitivity and specificity [AUC (95% CI) 0.83 (0.76-0.97)].

CONCLUSION

Our study highlights the utility of metabolomics as an approach for developing novel panels of dietary biomarkers. Quantitative profiling of these metabolites is required to validate their utility for evaluating dietary adherence.

Exploring Human Metabolome after Wine Intake-A Review

Wine has a rich history dating back to 2200 BC, originally recognized for its medicinal properties. Today, with the aid of advanced technologies like metabolomics and sophisticated analytical techniques, we have gained remarkable insights into the molecular-level changes induced by wine consumption in the human organism. This review embarks on a comprehensive exploration of the alterations in human metabolome associated with wine consumption. A great number of 51 studies from the last 25 years were reviewed; these studies systematically investigated shifts in metabolic profiles within blood, urine, and feces samples, encompassing both short-term and long-term studies of the consumption of wine and wine derivatives. Significant metabolic alterations were observed in a wide variety of metabolites belonging to different compound classes, such as phenolic compounds, lipids, organic acids, and amino acids, among others. Within these classes, both endogenous metabolites as well as diet-related metabolites that exhibited up-regulation or down-regulation following wine consumption were included. The up-regulation of short-chain fatty acids and the down-regulation of sphingomyelins after wine intake, as well as the up-regulation of gut microbial fermentation metabolites like vanillic and syringic acid are some of the most important findings reported in the reviewed literature. Our results confirm the intact passage of certain wine compounds, such as tartaric acid and other wine acids, to the human organism. In an era where the health effects of wine consumption are of growing interest, this review offers a holistic perspective on the metabolic underpinnings of this centuries-old tradition.

Applications of Solution NMR Spectroscopy in Quality Assessment and Authentication of Bovine Milk

Nuclear magnetic resonance (NMR) spectroscopy is emerging as a promising technique for the analysis of bovine milk, primarily due to its non-destructive nature, minimal sample preparation requirements, and comprehensive approach to untargeted milk analysis. These inherent strengths of NMR make it a formidable complementary tool to mass spectrometry-based techniques in milk metabolomic studies. This review aims to provide a comprehensive overview of the applications of NMR techniques in the quality assessment and authentication of bovine milk. It will focus on the experimental setup and data processing techniques that contribute to achieving accurate and highly reproducible results. The review will also highlight key studies that have utilized commonly used NMR methodologies in milk analysis, covering a wide range of application fields. These applications include determining milk animal species and feeding regimes, as well as assessing milk nutritional quality and authenticity. By providing an overview of the diverse applications of NMR in milk analysis, this review aims to demonstrate the versatility and significance of NMR spectroscopy as an invaluable tool for milk and dairy metabolomics research and hence, for assessing the quality and authenticity of bovine milk.

Blood Metabolite Profiling of Antarctic Expedition Members: An 1H NMR Spectroscopy-Based Study

Serum samples from eight participants during the XV winter-over at Concordia base (Antarctic expedition) collected at defined time points, including predeparture, constituted the key substrates for a specific metabolomics study. To ascertain acute changes and chronic adaptation to hypoxia, the metabolic profiles of the serum samples were analyzed using NMR spectroscopy, with principal components analysis (PCA) followed by partial least squares and orthogonal partial least squares discriminant analyses (PLS-DA and OPLS-DA) used as supervised classification methods. Multivariate data analyses clearly highlighted an adaptation period characterized by an increase in the levels of circulating glutamine and lipids, mobilized to supply the body energy needs. At the same time, a reduction in the circulating levels of glutamate and N-acetyl glycoproteins, stress condition indicators, and proinflammatory markers were also found in the NMR data investigation. Subsequent pathway analysis showed possible perturbations in metabolic processes, potentially related to the physiological adaptation, predominantly found by comparing the baseline (at sea level, before mission onset), the base arrival, and the mission ending collected values.

Filling gaps in animal welfare assessment through metabolomics

Sustainability has become a central issue in Italian livestock systems driving food business operators to adopt high standards of production concerning animal husbandry conditions. Meat sector is largely involved in this ecological transition with the introduction of new label claims concerning the defense of animal welfare (AW). These new guarantees referred to AW provision require new tools for the purpose of authenticity and traceability to assure meat supply chain integrity. Over the years, European Union (EU) Regulations, national, and international initiatives proposed provisions and guidelines for assuring AW introducing requirements to be complied with and providing tools based on scoring systems for a proper animal status assessment. However, the comprehensive and objective assessment of the AW status remains challenging. In this regard, phenotypic insights at molecular level may be investigated by metabolomics, one of the most recent high-throughput omics techniques. Recent advances in analytical and bioinformatic technologies have led to the identification of relevant biomarkers involved in complex clinical phenotypes of diverse biological systems suggesting that metabolomics is a key tool for biomarker discovery. In the present review, the Five Domains model has been employed as a vademecum describing AW. Starting from the individual Domains-nutrition (I), environment (II), health (III), behavior (IV), and mental state (V)-applications and advances of metabolomics related to AW setting aimed at investigating phenotypic outcomes on molecular scale and elucidating the biological routes most perturbed from external solicitations, are reviewed. Strengths and weaknesses of the current state-of-art are highlighted, and new frontiers to be explored for AW assessment throughout the metabolomics approach are argued. Moreover, a detailed description of metabolomics workflow is provided to understand dos and don'ts at experimental level to pursue effective results. Combining the demand for new assessment tools and meat market trends, a new cross-strategy is proposed as the promising combo for the future of AW assessment.

Identification of a Biomarker Panel for Diagnosis of Early Childhood Caries Using Salivary Metabolic Profile

Several studies have demonstrated that nuclear magnetic resonance (NMR) metabolic profiles can differentiate patients with caries from healthy individuals; however, these studies only identified individual metabolites. The present study aimed to identify a salivary metabolite biomarker panel for the diagnosis of early childhood caries (ECC). Saliva samples from children with and without caries were analyzed using NMR spectroscopy. Multivariate and univariate analyses were performed to identify the discriminating metabolites. Selected metabolites were further evaluated and used to detect ECC. The saliva samples of children with ECC were characterized based on the increased levels of formate, glycerophosphocholine, and lactate and reduced levels of alanine, glycine, isoleucine, lysine, proline, and tyrosine. The levels of these metabolites were significantly different from those in the control in the ECC subgroup according to caries severity and correlated with the number of decayed and filled teeth or surfaces. Subsequently, an optimal salivary metabolite biomarker panel comprising formate, lactate, proline, and glycine was developed. This panel exhibited a better diagnostic performance for ECC than a single metabolite. These results demonstrate that salivary metabolic signatures can reflect oral conditions associated with dental caries, thereby emphasizing the importance of distinct salivary metabolic profiles as potential biomarkers of ECC.

NMR-Based Metabolomics: A New Paradigm to Unravel Defense-Related Metabolites in Insect-Resistant Cotton Variety through Different Multivariate Data Analysis Approaches

Cotton (Gossypium hirsutum) is an economically important crop and is widely cultivated around the globe. However, the major problem of cotton is its high vulnerability to biotic and abiotic stresses. It has been around three decades since the cotton plant was genetically engineered with genes encoding insecticidal proteins (mainly Cry proteins) with an aim to protect it against insect attack. Several studies have been reported on the impact of these genes on cotton production and fiber quality. However, the metabolites responsible for conferring resistance in genetically modified cotton need to be explored. The current work aims to unveil the key metabolites responsible for insect resistance in Bt cotton and also compare the conventional multivariate analysis methods with deep learning approaches to perform clustering analysis. We aim to unveil the marker compounds which are responsible for inducing insect resistance in cotton plants. For this purpose, we employed 1H-NMR spectroscopy to perform metabolite profiling of Bt and non-Bt cotton varieties, and a total of 42 different metabolites were identified in cotton plants. In cluster analysis, deep learning approaches (linear discriminant analysis (LDA) and neural networks) showed better separation among cotton varieties compared to conventional methods (principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLSDA)). The key metabolites responsible for inter-class separation were terpinolene, α-ketoglutaric acid, aspartic acid, stigmasterol, fructose, maltose, arabinose, xylulose, cinnamic acid, malic acid, valine, nonanoic acid, citrulline, and shikimic acid. The metabolites which regulated differently with the level of significance p < 0.001 amongst different cotton varieties belonged to the tricarboxylic acid cycle (TCA), Shikimic acid, and phenylpropanoid pathways. Our analyses underscore a biosignature of metabolites that might involve in inducing insect resistance in Bt cotton. Moreover, novel evidence from our study could be used in the metabolic engineering of these biological pathways to improve the resilience of Bt cotton against insect/pest attacks. Lastly, our findings are also in complete support of employing deep machine learning algorithms as a useful tool in metabolomics studies.

How to employ metabolomic analysis to research on functions of prebiotics and probiotics in poultry gut health?

Gut health can be considered one of the major, manageable constituents of the animal immunity and performance. The fast spread of intestinal diseases, and increase of antimicrobial resistance have been observed, therefore the intestinal health has become not only economically relevant, but also highly important subject addressing the interest of public health. It is expected, that the strategies to control infections should be based on development of natural immunity in animals and producing resilient flocks using natural solutions, whilst eliminating antibiotics and veterinary medicinal products from action. Probiotics and prebiotics have been favored, because they have potential to directly or indirectly optimize intestinal health by manipulating the metabolism of the intestinal tract, including the microbiota. Studying the metabolome of probiotics and gut environment, both in vivo, or using the in vitro models, is required to attain the scientific understanding about the functions of bioactive compounds in development of gut health and life lasting immunity. There is a practical need to identify new metabolites being the key bioactive agents regulating biochemical pathways of systems associated with gut (gut-associated axes). Technological advancement in metabolomics studies, and increasing access to the powerful analytical platforms have paved a way to implement metabolomics in exploration of the effects of prebiotics and probiotics on the intestinal health of poultry. In this article, the basic principles of metabolomics in research involving probiotics and probiotics are introduced, together with the overview of existing strategies and suggestions of their use to study metabolome in poultry.

Sugarcane Metabolome Compositional Stability in Pretreatment Processes for NMR Measurements

Sugarcane is essential for global sugar production and its compressed juice is a key raw material for industrial products. Sugarcane juice includes various metabolites with abundances and compositional balances influencing product qualities and functionalities. Therefore, understanding the characteristic features of the sugarcane metabolome is important. However, sugarcane compositional variability and stability, even in pretreatment processes for nuclear magnetic resonance (NMR)-based metabolomic studies, remains elusive. The objective of this study is to evaluate sugarcane juice metabolomic variability affected by centrifugation, filtration, and thermal pretreatments, as well as the time-course changes for determining optimal conditions for NMR-based metabolomic approach. The pretreatment processes left the metabolomic compositions unchanged, indicating that these pretreatments are compatible with one another and the studied metabolomes are comparable. The thermal processing provided stability to the metabolome for more than 32 h at room temperature. Based on the determined analytical conditions, we conducted an NMR-based metabolomic study to discriminate the differences in the harvest period and allowed for successfully identifying the characteristic metabolome. Our findings denote that NMR-based sugarcane metabolomics enable us to provide an opportunity to collect a massive amount of data upon collaboration between multiple researchers, resulting in the rapid construction of useful databases for both research purposes and industrial use.

1H NMR-Based Metabolomics Reveals the Intrinsic Interaction of Age, Plasma Signature Metabolites, and Nutrient Intake in the Longevity Population in Guangxi, China

Health and longevity populations have distinct metabolic and nutrient intake profiles. However, the relationship between biomarkers of longevity-related metabolites and dietary nutrient intake profiles, as well as metabolic markers associated with longevity features, have not been fully elucidated. Therefore, ¹H nuclear magnetic resonance (¹H NMR)-based plasma metabolomics profiling was conducted in the present study to identify potential metabolites which can be used as specific markers for the evaluation of healthy aging. Plasma samples were obtained from centenarians and nonagenarians from the longevous region, and elderly participants aged 60–89 from the longevous region, as well as a low centenarian ratio region. The results showed that participants from longevous regions exhibited higher plasma levels of citrate, tyrosine, choline, carnitine, and valine, as well as lower contents of VLDL, lactate, alanine, N-acetyl glycoprotein (NAG), trimethylamine oxide (TMAO), α-glucose, β-glucose, and unsaturated lipids. The differential plasma metabolites were associated with an alteration in glycolysis/gluconeogenesis; aminoacyl-tRNA biosynthesis; alanine, aspartate, and glutamate metabolism; and phenylalanine, tyrosine, and tryptophan biosynthesis in participants from longevous regions. The signature metabolites were associated with higher dietary fiber intake, as well as lower energy and fat intake. The results of the present study demonstrate key longevity signature metabolites in plasma, and the dietary patterns identified provide a basis for further health and longevity research.

Comparison of Microflow and Analytical Flow Liquid Chromatography Coupled to Mass Spectrometry Global Metabolomics Methods Using a Urea Cycle Disorder Mouse Model

Microscale-based separations are increasingly being applied in the field of metabolomics for the analysis of small-molecule metabolites. These methods have the potential to provide improved sensitivity, less solvent waste, and reduced sample-size requirements. Ion-pair free microflow-based global metabolomics methods, which we recently reported, were further compared to analytical flow ion-pairing reagent containing methods using a sample set from a urea cycle disorder (UCD) mouse model. Mouse urine and brain homogenate samples representing healthy, diseased, and disease-treated animals were analyzed by both methods. Data processing was performed using univariate and multivariate techniques followed by analyte trend analysis. The microflow methods performed comparably to the analytical flow ion-pairing methods with the ability to separate the three sample groups when analyzed by partial least-squares analysis. The number of detected metabolic features present after each data processing step was similar between the microflow-based methods and the ion-pairing methods in the negative ionization mode. The observed analyte trend and coverage of known UCD biomarkers were the same for both evaluated approaches. The 12.5-fold reduction in sample injection volume required for the microflow-based separations highlights the potential of this method to support studies with sample-size limitations.

1H NMR Combined with Multivariate Statistics for Discrimination of Female and Male Flower Buds of Populus tomentosa

¹H Nuclear Magnetic Resonance (¹H NMR) combined with multivariate statistics was adopted to discriminate female and male flower buds of Populus tomentosa in the study. Samples of 11 female and 16 male flower buds of P. tomentosa were collected in Beijing, China. ¹H NMR spectra were acquired on a 400 MHz spectrometer. In total, 30 chemical compounds were identified with standards and literature according to chemical shifts, peak areas, and multiplicity. Principal component analysis (PCA), hierarchical clustering analysis (HCA), and supervised orthogonal partial least squares-discriminant analysis (OPLS-DA) were applied to discriminate female and male flower buds. An apparent grouping trend (R²X, 0.809; Q², 0.903) between female and male groups was exhibited with PCA and HCA. The two groups were also well discriminated with OPLS-DA (R²X, 0.808; R²Y, 0.976; Q², 0.960). Combined with variable importance in projection (VIP) > 1.0 and p < 0.05 of OPLS-DA, it was found that the content of daucosterol, β-sitosterol, ursolic acid, and betulonic acid in male group was higher than that in female, which should be the key differences of chemical constituents in female and male flower buds of P. tomentosa. The study demonstrated that ¹H NMR combined with multivariate statistics could be used to discriminate female and male plants and clarify differences, which provided a novel method to identify the gender of dioecious plants.

NMR-based Metabolomic Techniques Identify the Anticancer Effects of Three Polyphyllins in HepG2 Cells

Background:

Rhizoma Paridis (RP) is a traditional Chinese herb used for the treatment of tumors, detoxification and hemostasia. Studies show the main components of RP are Polyphyllin I (PPI), polyphyllin VI (PPVI), and polyphyllin VII (PPVII). However, the pharmaco-mechanisms of these compounds are not clear.

Objective:

By used 1 H nuclear magnetic resonance (1 H-NMR) based metabolomics approach to identify the Anticancer effects of PPI, PPVI and PPVII in HepG2 cells. Methods 1 H nuclear magnetic resonance (1 H-NMR) based metabolomics approach was applied to investigate the toxicological effect of PPI, PPVI, PPVII on HepG2 cells. Multivariate statistical analysis was employed to examine the metabolic changes and abnormal metabolic pathways, including Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and orthogonal PLS-DA (OPLS-DA).

Results:

The results showed that the effects of metabolic phenotypes were affected separately by PPI, PPVI, and PPVII. The metabolic phenotypes were also changed over time. The characteristic metabolites were varied by affecting different polyphylins, which were identified by the reconstructed OPLSDA loading plots. According to the characteristic metabolites, the mainly disturbed metabolic pathways were found, such as alanine, aspartate and glutamate metabolism, pyruvate metabolism, glycine, serine, and threonine metabolism.

Conclusion:

The current work could allow us to understand the therapeutic effect of RP in metabolism. It also indicated that RP would be a promising candidate for liver cancer treatment.

1H-NMR-Based Metabolomics: An Integrated Approach for the Detection of the Adulteration in Chicken, Chevon, Beef and Donkey Meat

Meat is a rich source of energy that provides high-value animal protein, fats, vitamins, minerals and trace amounts of carbohydrates. Globally, different types of meats are consumed to fulfill nutritional requirements. However, the increasing burden on the livestock industry has triggered the mixing of high-price meat species with low-quality/-price meat. This work aimed to differentiate different meat samples on the basis of metabolites. The metabolic difference between various meat samples was investigated through Nuclear Magnetic Resonance spectroscopy coupled with multivariate data analysis approaches like principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (OPLS-DA). In total, 37 metabolites were identified in the gluteal muscle tissues of cow, goat, donkey and chicken using ¹H-NMR spectroscopy. PCA was found unable to completely differentiate between meat types, whereas OPLS-DA showed an apparent separation and successfully differentiated samples from all four types of meat. Lactate, creatine, choline, acetate, leucine, isoleucine, valine, formate, carnitine, glutamate, 3-hydroxybutyrate and α-mannose were found as the major discriminating metabolites between white (chicken) and red meat (chevon, beef and donkey). However, inosine, lactate, uracil, carnosine, format, pyruvate, carnitine, creatine and acetate were found responsible for differentiating chevon, beef and donkey meat. The relative quantification of differentiating metabolites was performed using one-way ANOVA and Tukey test. Our results showed that NMR-based metabolomics is a powerful tool for the identification of novel signatures (potential biomarkers) to characterize meats from different sources and could potentially be used for quality control purposes in order to differentiate different meat types.

Recent advances in NMR-based metabolomics of alcoholic beverages

Alcoholic beverages have a complex chemistry that can be influenced by their alcoholic content, origin, fermentation process, additives, and contaminants. The complex composition of these beverages leave them susceptible to fraud, potentially compromising their authenticity, quality, and market value, thus increasing risks to consumers' health. In recent years, intensive studies have been carried out on alcoholic beverages using different analytical techniques to evaluate the authenticity, variety, age, and fermentation processes that were used. Among these techniques, NMR-based metabolomics holds promise in profiling the chemistry of alcoholic beverages, especially in Asia where metabolomics studies on alcoholic beverages remain limited.

Screening of metabolites in the treatment of liver cancer xenografts HepG2/ADR by psoralen-loaded lipid nanoparticles

OBJECTIVE

Our study aimed to find potential biomarkers for drug resistance in liver cancer cells using metabolomics and further to evaluate the potential of psoralen-loaded polymer lipid nanoparticles (PSO-PLNs) to reverse the resistance of cells to doxorubicin.

METHODS

We used LC-MS-based non-targeted metabolomics, also known as global metabolite profiling, to screen in serum and urine of mice engrafted with a liver cancer cell line sensitive (HepG2/S) or resistant to doxorubicin (HepG2/ADR) for differentially regulated metabolites. We subsequently quantified the abundance of these metabolites in serum and the urine of mice. The mice were engrafted with HepG2 cells resistant against doxorubicin and were treated with I) doxorubicin, II) a combination of doxorubicin and psoralen and III) a combination of doxorubicin and psoralen packed in polymer lipid nanoparticles.

RESULTS

Metabolites found to be differentially present in urine of mice engrafted with resistant HepG2 cells were: hippuric acid, hyaluronic acid, pantothenic acid, and betaine; retinoic acid and α-linolenic acid were found to be reduced in serum samples of mice with HepG2 cells resistant to doxorubicin. The targeted analysis showed that the degree of regression of metabolic markers in groups differed: treatment group 2 had stronger degree of regression than treatment group 1 and the negative control group had the smallest, which indicates that the PSO-PLNs have superior properties compared with other treatments.

CONCLUSION

Psoralen reverses drug resistance of liver cancer cells and its efficacy can be increased by encapsulation in polymer lipid nanoparticles.

A systematic approach to development of analytical scale and microflow-based liquid chromatography coupled to mass spectrometry metabolomics methods to support drug discovery and development

As the reliance on metabolic biomarkers within drug discovery and development increases, there is also an increased demand for global metabolomics methods to provide broad metabolome coverage and sensitivity towards differences in metabolite expression and reproducibility. A systematic approach is necessary for the development, and evaluation, of metabolomics methods using either conventional techniques or when establishing new methods that allow for additional gains in sensitivity and a reduction in requirements for amounts of a biological sample, such as those seen with methods based on microseparations. We developed a novel standard mixture and used a systematic approach for the development and optimization of optimal, ion-pair free, liquid chromatography-mass spectrometry (LC-MS) global profiling methods. These methods were scaled-down to microflow-based LC separations and compared with analytical flow ion-pairing reagent containing methods. Average peak volume improvements of 7- and 22-fold were observed in the positive and negative ionization mode microflow methods as compared to the ion-pairing reagent analytical flow methods, respectively. The linear range of the newly developed microflow methods showed up to a 10-fold increase in the lower limit of detection in the negative ionization mode. The developed microflow LC-MS methods were further evaluated using wild-type mouse plasma where up to a 9-fold increase in peak volume was observed.

NMR-Based Metabolomics in Cancer Research

Nuclear magnetic resonance (NMR) spectroscopy offers reproducible quantitative analysis and structural identification of metabolites in various complex biological samples, such as biofluids (plasma, serum, and urine), cells, tissue extracts, and even intact organs. Therefore, NMR-based metabolomics, a mainstream metabolomic platform, has been extensively applied in many research fields, including pharmacology, toxicology, pathophysiology, nutritional intervention, disease diagnosis/prognosis, and microbiology. In particular, NMR-based metabolomics has been successfully used for cancer research to investigate cancer metabolism and identify biomarker and therapeutic targets. This chapter highlights the innovations and challenges of NMR-based metabolomics platform and its applications in cancer research.

Narrative review of metabolomics in cardiovascular disease

Cardiovascular diseases are accompanied by disorders in the cardiac metabolism. Furthermore, comorbidities often associated with cardiovascular disease can alter systemic and myocardial metabolism contributing to worsening of cardiac performance and health status. Biomarkers such as natriuretic peptides or troponins already support diagnosis, prognosis and treatment of patients with cardiovascular diseases and are represented in international guidelines. However, as cardiovascular diseases affect various pathophysiological pathways, a single biomarker approach cannot be regarded as ideal to reveal optimal clinical application. Emerging metabolomics technology allows the measurement of hundreds of metabolites in biological fluids or biopsies and thus to characterize each patient by its own metabolic fingerprint, improving our understanding of complex diseases, significantly altering the management of cardiovascular diseases and possibly personalizing medicine. This review outlines current knowledge, perspectives as well as limitations of metabolomics for diagnosis, prognosis and treatment of cardiovascular diseases such as heart failure, atherosclerosis, ischemic and non-ischemic cardiomyopathy. Furthermore, an ongoing research project tackling current inconsistencies as well as clinical applications of metabolomics will be discussed. Taken together, the application of metabolomics will enable us to gain more insights into pathophysiological interactions of metabolites and disease states as well as improving therapies of patients with cardiovascular diseases in the future.

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection

BACKGROUND

Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets.

OBJECTIVE

The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics.

METHODS

Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics.

RESULTS

In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed.

CONCLUSION

In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

Fractionation platform for target identification using off-line directed two-dimensional chromatography, mass spectrometry and nuclear magnetic resonance

The unambiguous identification of unknown compounds is of utmost importance in the field of metabolomics. However, current identification workflows often suffer from error-sensitive methodologies, which may lead to incorrect structure annotations of small molecules. Therefore, we have developed a comprehensive identification workflow including two highly complementary techniques, i.e. liquid chromatography (LC) combined with mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR), and used it to identify five taste-related retention time and m/z features in soy sauce. An off-line directed two-dimensional separation was performed in order to purify the features prior to the identification. Fractions collected during the first dimension separation (reversed phase low pH) were evaluated for the presence of remaining impurities next to the features of interest. Based on the separation between the feature and impurities, the most orthogonal second dimension chromatography (hydrophilic interaction chromatography or reversed phase high pH) was selected for further purification. Unknown compounds down to tens of micromolar concentrations were tentatively annotated by MS and structurally confirmed by MS and NMR. The mass (0.4-4.2 μg) and purity of the isolated compounds were sufficient for the acquisition of one and two-dimensional NMR spectra. The use of a directed two-dimensional chromatography allowed for a fractionation that was tailored to each feature and remaining impurities. This makes the fractionation more widely applicable to different sample matrices than one-dimensional or fixed two-dimensional chromatography. Five proline-based 2,5-diketopiperazines were successfully identified in soy sauce. These cyclic dipeptides might contribute to taste by giving a bitter flavour or indirectly enhancing umami flavour.

Signatures and Clinical Significance of Amino Acid Flux in Sarcopenia: A Systematic Review and Meta-Analysis

BACKGROUND

Dysregulation of amino acids is closely linked to the initiation and progression of sarcopenia. We summarized recent advancements in the studies of amino acid profiles in sarcopenia and systematically presented the clinical significance of amino acid flux in sarcopenia.

METHODS

We systematically searched in MEDLINE, EMBASE, and Cochrane library from inception to June 1, 2021 to capture all studies examining metabolomics of sarcopenia. We used the following keywords: sarcopenia, metabonomics, metabolomics, amino acid profile, and mass spectrometry. Original articles comparing amino acid patterns between persons with and without sarcopenia were included. Two independent investigators independently completed title and abstract screening, data extraction, and quality evaluation. We used a random effects model to examine the association between amino acids levels and sarcopenia. Sensitivity analyses restricted the analyses to studies in which muscle mass was measured by bioelectrical impedance analysis. Study quality was evaluated according to the Agency for Healthcare Research and Quality (AHRQ) checklist.

RESULTS

The systematic research yielded six eligible articles, comprising 1,120 participants. Five studies used muscle mass in combination with physical performance and/or muscle strength as the criteria to diagnose sarcopenia, while one study used muscle mass as a diagnostic criterion alone. We found that the concentrations of branched-chain amino acids leucine (standardized mean difference [SMD] -1.249; 95% confidence interval [CI]: -2.275, -0.223, P = 0.02, I² = 97.7%), isoleucine (SMD -1.077; 95% CI: -2.106, -0.049, P = 0.04, I² = 97.8%), and aromatic amino acid tryptophan (SMD -0.923; 95% CI: -1.580, -0.265, P = 0.01, I² = 89.9%) were significantly reduced in individuals with sarcopenia. Study results were robust in sensitivity analysis.

CONCLUSIONS

The homeostasis of amino acids is critical to maintaining muscle health. The profiles of amino acids might be useful biomarkers for the characterization of sarcopenia. Future studies are warranted to study the clinical significance of amino acids in the diagnosis and treatment of sarcopenia.

The application of 1H NMR to explore the taste difference caused by taste-active metabolites of different Chinese sauce-stewed beef

In this study, we developed a method for the quantification of taste-active metabolites of Chinese commercial sauce-stewed beef by using 1H NMR spectroscopy coupled with multivariate data analysis. This method was applied to analyze the taste difference which caused by taste-active metabolites of different Chinese sauce-stewed beef. Beef samples demonstrated to consist of 25 metabolites, including amino acids, sugars, organic acids, nucleic aides, and their derivatives. PC1 and PC2 explained a total of 85.1 and 13.1% of variables, respectively. Metabolites such as isoleucine, histidine, glutamate, pyroglutamate, sucrose, lactate, creatine, carnitine, and creatinine were kept at a higher levels compared with other metabolites in the four products. Sensory evaluation was also done to help analyze the taste difference. This microcosmic approach of using high-resolution NMR spectrometry to analyze beef products has rarely been reported. This work established a feasible method to distinguish the taste difference of different Chinese sauce-stewed beef.

Heavy metals interfere with plasma metabolites, including lipids and amino acids, in patients with breast cancer

The aim of the present study was to examine the association between plasma heavy metals and the metabolome in patients with breast cancer (BC), and the association with cancer development. Nuclear magnetic resonance was used to determine the metabolites involved and an inductively coupled plasma mass spectrometry system was used to quantify the heavy metals in the plasma samples. It was indicated that cadmium was significantly higher in the plasma of patients with BC compared with that in the control population (~15-fold increase). Chromium, arsenic and lead were also elevated in the plasma of patients with BC by ~3.24, 2.14 and 1.52 fold, respectively. A number of small molecules, including amino acids and salts, were altered in the plasma of patients with BC compared with the control population. Another notable finding in this investigation was that plasma lipid levels were elevated in patients with BC compared with those in the control population. The findings of the present study suggest that exposure to heavy metals, including cadmium, arsenic, chromium and lead, may influence blood lipid levels and other small molecule metabolites, which in turn may be involved in BC development. Further studies surrounding urinary heavy metals and the metabolome are required to further determine the impact of metals on metabolism and on BC development.

An NMR based panorama of the heterogeneous biology of acute respiratory distress syndrome (ARDS) from the standpoint of metabolic biomarkers

Acute respiratory distress syndrome (ARDS), manifested by intricate etiology and pathophysiology, demands careful clinical surveillance due to its high mortality and imminent life support measures. NMR based metabolomics provides an approach for ARDS which culminates from a wide spectrum of illness thereby confounding early manifestation and prognosis predictors. ¹ H NMR with its manifold applications in critical disease settings can unravel the biomarker of ARDS thus holding potent implications by providing surrogate endpoints of clinical utility. NMR metabolomics which is the current apogee platform of omics trilogy is contributing towards the possible panacea of ARDS by subsequent validation of biomarker credential on larger datasets. In the present review, the physiological derangements that jeopardize the whole metabolic functioning in ARDS are exploited and the biomarkers involved in progression are addressed and substantiated. The following sections of the review also outline the clinical spectrum of ARDS from the standpoint of NMR based metabolomics which is an emerging element of systems biology. ARDS is the main premise of intensivists textbook, which has been thoroughly reviewed along with its incidence, progressive stages of severity, new proposed diagnostic definition, and the preventive measures and the current pitfalls of clinical management. The advent of new therapies, the need for biomarkers, the methodology and the contemporary promising approaches needed to improve survival and address heterogeneity have also been evaluated. The review has been stepwise illustrated with potent biometrics employed to selectively pool out differential metabolites as diagnostic markers and outcome predictors. The following sections have been drafted with an objective to better understand ARDS mechanisms with predictive and precise biomarkers detected so far on the basis of underlying physiological parameters having close proximity to diseased phenotype. The aim of this review is to stimulate interest in conducting more studies to help resolve the complex heterogeneity of ARDS with biomarkers of clinical utility and relevance.

Application of 1H-NMR Metabolomics for the Discovery of Blood Plasma Biomarkers of a Mediterranean Diet

The Mediterranean diet (MD) is a dietary pattern well-known for its benefits in disease prevention. Monitoring adherence to the MD could be improved by discovery of novel dietary biomarkers. The MEDiterranean Diet in Northern Ireland (MEDDINI) intervention study monitored the adherence of participants to the MD for up to 12 months. This investigation aimed to profile plasma metabolites, correlating each against the MD score of participants (n = 58). Based on an established 14-point scale MD score, subjects were classified into two groups (“low” and “high”). ¹H-Nuclear Magnetic Resonance (¹H-NMR) metabolomic analysis found that citric acid was the most significant metabolite (p = 5.99 × 10⁻⁴*; q = 0.03), differing between ‘low’ and ‘high’. Furthermore, five additional metabolites significantly differed (p < 0.05; q < 0.35) between the two groups. Discriminatory metabolites included: citric acid, pyruvic acid, betaine, mannose, acetic acid and myo-inositol. Additionally, the top five most influential metabolites in multivariate models were also citric acid, pyruvic acid, betaine, mannose and myo-inositol. Metabolites significantly correlated with the consumption of certain food types. For example, citric acid positively correlated fruit, fruit juice and vegetable constituents of the diet, and negatively correlated with sweet foods alone or when combined with carbonated drinks. Citric acid was the best performing biomarker and this was enhanced by paired ratio with pyruvic acid. The present study demonstrates the utility of metabolomic profiling for effectively assessing adherence to MD and the discovery of novel dietary biomarkers.

Integration of dry-column flash chromatography with NMR and FTIR metabolomics to reveal cytotoxic metabolites from Amphoricarpos autariatus

Metabolomics generate a profile of small molecules from plant extracts, which could be directly responsible for bioactivity effects. Using dry-column flash chromatography enabled a rapid and inexpensive method for the very efficient separation of plant extract with a high resolution. This separation method coupled to NMR and FTIR-based metabolomics is applied to identify bioactive natural products. OPLS multivariate analysis method, was used for correlation the chemical composition of the plant extracts, Amphoricarpos autariatus, with the results of cytotoxic activity against Human cervical adenocarcinoma cell line (HeLa) and epithelial lung cancer cell line (A549). In this way, the highest contribution to the cytotoxic activity was recorded for the guaianolide sesquiterpene lactones named amphoricarpolides. The compounds indicated as bioactive after metabolomics analysis were tested, and their cytotoxic activity were confirmed.

Analysis of metabolome changes in the HepG2 cells of apatinib treatment by using the NMR-based metabolomics

Neovascularization is required for the growth of tumors, vascular endothelial growth factor (VEGF) and related signal pathways are important in tumor angiogenesis. Apatinib is a highly selective and potent antiangiogenesis drug targeting the receptor of VEGFR2, blocking downstream signal transduction and inhibiting angiogenesis of tumor tissue. Apatinib has a wide range of antitumor activities in vitro and in vivo, but its effect on metabolic changes has not deeply research at present. Nowadays, our research first systematically studied the metabolic changes affected by apatinib in the HepG2 cells at the half-maximal inhibitory concentration value. We used the metabolomics by using ¹ H nuclear magnetic resonance (¹ H-NMR) to analyze the HepG2 cell culture media. Multivariable Statistics was applied to analyze the ¹ H-NMR spectra of the cell media, including principal component analysis, partial least squares discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA). Compared with the uncultured and cultured media (negative/positive control), the metabolic phenotypes were changed in the apatinib treatment with a continuous effect over time. The metabolic pathway analysis is shown that the mainly disturbed metabolic pathways pyruvate metabolism, alanine, aspartate, and glutamate metabolism and amino acid metabolism associated with them in the apatinib treatment. The differential metabolites which were identified from the reconstructed OPLS-DA loading plots also reflected in these disturbed metabolic pathways. Our works could allow us to well understand the therapeutic effect of apatinib, especially in metabolism.

Advances in metabolomics of thyroid cancer diagnosis and metabolic regulation

Thyroid cancers (TCs) are the most frequent endocrine malignancy with an unpredictable fast-growing incidence, especially in females all over the world. Fine-needle aspiration biopsy (FNAB) analysis is an accurate diagnostic method for detecting thyroid nodules and classification of TC. Though simplicity, safety, and accuracy of FNAB, 15-30% of cases are indeterminate, and it is not possible to determine the exact cytology of the specimen. This demands the need for innovative methods capable to find crucial biomarkers with adequate sensitivity for diagnosis and prediction in TC researches. Cancer-based metabolomics is a vast emerging field focused on the detection of a large set of metabolites extracted from biofluids or tissues. Using analytical chemistry procedures allows for the potential recognition of cancer-based metabolites for the purposes of advancing the era of personalized medicine. Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) coupled with separation techniques e.g., gas chromatography (GC) and liquid chromatography (LC) are the main approaches for metabolic studies in cancers. The immense metabolite profiling has provided a chance to discover novel biomarkers for early detection of thyroid cancer and reduce unnecessary aggressive surgery. In this review, we recapitulate the recent advances and developed methods of diverse metabolomics tools and metabolic phenotypes of thyroid cancer, following a brief discussion of recent challenges in the thyroid cancer diagnosis.

NMR Spectroscopy for Metabolomics Research

Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metabolomic studies. It will also explore some of the unique strengths of NMR-based metabolomics, particularly with regard to isotope selection/detection, mixture deconvolution via 2D spectroscopy, automation, and the ability to noninvasively analyze native tissue specimens. Finally, this review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications.

Recent Advances in the Application of Metabolomics for Nutrition and Health

Metabolomics is the study of small molecules called metabolites in biological samples. Application of metabolomics to nutrition research has expanded in recent years, with emerging literature supporting multiple applications. Key examples include applications of metabolomics in the identification and development of objective biomarkers of dietary intake, in developing personalized nutrition strategies, and in large-scale epidemiology studies to understand the link between diet and health. In this review, we provide an overview of the current applications and identify key challenges that need to be addressed for the further development of the field. Successful development of metabolomics for nutrition research has the potential to improve dietary assessment, help deliver personalized nutrition, and enhance our understanding of the link between diet and health.

Metabolic Signature of a Functional High-Catechin Tea after Acute and Sustained Consumption in Healthy Volunteers through 1H NMR Based Metabolomics Analysis of Urine

Functional tea beverages have emerged as a novel approach to achieving health benefits associated with tea. The use of metabolomics may improve the evaluation of their consumption and their effects. The current study aimed at exploring the urinary signature of exposure to a functional high-catechin tea (HCT) using untargeted NMR-based metabolomics. Ten volunteers participated in a crossover intervention study. Individuals consumed an HCT or a control beverage over a period of 28 days. Multilevel partial least-squares discriminant analysis (ML-PLS-DA) was used for paired comparisons. A further crossover model was performed to assess the significant changes. The consumption of the HCT resulted in the excretion of theanine, epicatechin, pyrogallol sulfate, and higher levels of 3-methyl-2-oxovalerate and succinate, as well as unknown compounds. In conclusion, the present work established novel urinary signatures of a functional drink. Such signatures may be potential biomarkers and/or reflect certain benefits of functional tea beverages.

NMR-Based Μetabolomics of the Lipid Fraction of Organic and Conventional Bovine Milk

Origin and quality identification in dairy products is an important issue and also an extremely challenging and complex experimental procedure. The objective of the present work was to compare the metabolite profile of the lipid fraction of organic and conventional bovine milk using NMR metabolomics analysis. ¹H-NMR and 1D TOCSY NMR methods of analysis were performed on extracted lipid fraction of lyophilized milk. For this purpose, 14 organic and 16 conventional retail milk samples were collected monthly, and 64 bulk-tank (58 conventional and 6 organics) milk samples were collected over a 14-month longitudinal study in Cyprus. Data were treated with multivariate methods (PCA, PLS-DA). Minor components were identified and quantified, and modification of the currently used equations is proposed. A significantly increased % content of conjugated (9-cis, 11-trans)18:2 linoleic acid (CLA), α-linolenic acid, linoleic acid, allylic protons and total unsaturated fatty acids (UFA) and decreased % content for caproleic acid were observed in the organic samples compared to the conventional ones. The present work confirms that lipid profile is affected by contrasting management system (organic vs. conventional), and supports the potential of NMR-based metabolomics for the rapid analysis and authentication of the milk from its lipid profile.

The magic angle view to food: magic-angle spinning (MAS) NMR spectroscopy in food science

Nuclear Magnetic Resonance (NMR) spectroscopy has been used in food science and nutritional studies for decades and is one of the major analytical platforms in metabolomics. Many foods are solid or at least semi-solid, which denotes that the molecular motions are restricted as opposed to in pure liquids. While the majority of NMR spectroscopy is performed on liquid samples and a solid material gives rise to constraints in terms of many chemical analyses, the magic angle thrillingly enables the application of NMR spectroscopy also on semi-solid and solid materials. This paper attempts to review how magic-angle spinning (MAS) NMR is used from 'farm-to-fork' in food science.

Metabolomic Insights into Human Arboviral Infections: Dengue, Chikungunya, and Zika Viruses

The global burden of arboviral diseases and the limited success in controlling them calls for innovative methods to understand arbovirus infections. Metabolomics has been applied to detect alterations in host physiology during infection. This approach relies on mass spectrometry or nuclear magnetic resonance spectroscopy to evaluate how perturbations in biological systems alter metabolic pathways, allowing for differentiation of closely related conditions. Because viruses heavily depend on host resources and pathways, they present unique challenges for characterizing metabolic changes. Here, we review the literature on metabolomics of arboviruses and focus on the interpretation of identified molecular features. Metabolomics has revealed biomarkers that differentiate disease states and outcomes, and has shown similarities in metabolic alterations caused by different viruses (e.g., lipid metabolism). Researchers investigating such metabolomic alterations aim to better understand host⁻virus dynamics, identify diagnostically useful molecular features, discern perturbed pathways for therapeutics, and guide further biochemical research. This review focuses on lessons derived from metabolomics studies on samples from arbovirus-infected humans.

Identification of antiplasmodial triterpenes from Keetia species using NMR-based metabolic profiling

INTRODUCTION

The increase in multidrug resistance and lack of efficacy in malaria therapy has propelled the urgent discovery of new antiplasmodial drugs, reviving the screening of secondary metabolites from traditional medicine. In plant metabolomics, NMR-based strategies are considered a golden method providing both a holistic view of the chemical profiles and a correlation between the metabolome and bioactivity, becoming a corner stone of drug development from natural products.

OBJECTIVE

Create a multivariate model to identify antiplasmodial metabolites from ¹H NMR data of two African medicinal plants, Keetia leucantha and K. venosa.

METHODS

The extracts of twigs and leaves of Keetia species were measured by ¹H NMR and the spectra were submitted to orthogonal partial least squares (OPLS) for antiplasmodial correlation.

RESULTS

Unsupervised ¹H NMR analysis showed that the effect of tissues was higher than species and that triterpenoids signals were more associated to Keetia twigs than leaves. OPLS-DA based on Keetia species correlated triterpene signals to K. leucantha, exhibiting a higher concentration of triterpenoids and phenylpropanoid-conjugated triterpenes than K. venosa. In vitro antiplasmodial correlation by OPLS, validated for all Keetia samples, revealed that phenylpropanoid-conjugated triterpenes were highly correlated to the bioactivity, while the acyclic squalene was found as the major metabolite in low bioactivity samples.

CONCLUSION

NMR-based metabolomics combined with supervised multivariate data analysis is a powerful strategy for the identification of bioactive metabolites in plant extracts. Moreover, combination of statistical total correlation spectroscopy with 2D NMR allowed a detailed analysis of different triterpenes, overcoming the challenge posed by their structure similarity and coalescence in the aliphatic region.

Metabolomics and Biomarker Discovery

Recently, metabolomics-the study of metabolite profiles within biological samples-has found a wide range of applications. This chapter describes the different techniques available for metabolomic analysis, the various samples that can be utilised for analysis and applications of both global and targeted metabolomic analysis to biomarker discovery in medicine.

Nuclear Magnetic Resonance (NMR) Spectroscopy in Food Science: A Comprehensive Review

Nuclear magnetic resonance (NMR) spectroscopy is a robust method, which can rapidly analyze mixtures at the molecular level without requiring separation and/or purification steps, making it ideal for applications in food science. Despite its increasing popularity among food scientists, NMR is still an underutilized methodology in this area, mainly due to its high cost, relatively low sensitivity, and the lack of NMR expertise by many food scientists. The aim of this review is to help bridge the knowledge gap that may exist when attempting to apply NMR methodologies to the field of food science. We begin by covering the basic principles required to apply NMR to the study of foods and nutrients. A description of the discipline of chemometrics is provided, as the combination of NMR with multivariate statistical analysis is a powerful approach for addressing modern challenges in food science. Furthermore, a comprehensive overview of recent and key applications in the areas of compositional analysis, food authentication, quality control, and human nutrition is provided. In addition to standard NMR techniques, more sophisticated NMR applications are also presented, although limitations, gaps, and potentials are discussed. We hope this review will help scientists gain some of the knowledge required to apply the powerful methodology of NMR to the rich and diverse field of food science.

NMR-Based Metabolomic Approach To Elucidate the Differential Cellular Responses during Mitigation of Arsenic(III, V) in a Green Microalga

Nuclear magnetic resonance (NMR)-based metabolomic approach is a high-throughput fingerprinting technique that allows a rapid snapshot of metabolites without any prior knowledge of the organism. To demonstrate the applicability of NMR-based metabolomics in the field of microalgal-based bioremediation, novel freshwater microalga Scenedesmus sp. IITRIND2 that showed hypertolerance to As(III, V) was chosen for evaluating the metabolic perturbations during arsenic stress in both its oxidation states As(III) and As(V). Using NMR spectroscopy, we were able to identify and quantify an array of ∼45 metabolites, including amino acids, sugars, organic acids, phosphagens, osmolytes, nucleotides, etc. The NMR metabolomic experiments were complemented with various biophysical techniques to establish that the microalga tolerated the arsenic stress using a complex interplay of metabolites. The two different arsenic states distinctly influenced the microalgal cellular mechanisms due to their altered physicochemical properties. Eighteen differentially identified metabolites related to bioremediation of arsenic were then correlated to the major metabolic pathways to delineate the variable stress responses of microalga in the presence of As(III, V).

NMR detection in biofluid extracts at sub-μM concentrations via para-H2 induced hyperpolarization

NMR spectroscopy is one of the most powerful techniques to simultaneously obtain qualitative and quantitative information in chemical analysis. Despite its versatility, the applications of NMR in the study of biofluids are often limited by the insensitivity of the technique, further aggravated by the poor signal dispersion in the (1)H spectra. Recent advances in para-H2 induced hyperpolarization have proven to address both these limitations for specific classes of compounds. Herein, this approach is for the first time applied for quantitative determination in biofluid extracts. We demonstrate that a combination of solid phase extraction, para-hydrogen induced hyperpolarization and selective NMR detection quickly reveals a doping substance, nikethamide, at sub-μM concentrations in urine. We suggest that this method can be further optimized for the detection of different analytes in various biofluids, anticipating a wider application of hyperpolarized NMR in metabolomics and pharmacokinetics studies in the near future.

Transformed Root Culture: From Genetic Transformation to NMR-Based Metabolomics

Hairy root (HR) culture is considered as "green factory" for mass production of bioactive molecules with pharmaceutical relevance. As such, HR culture has an immense potential as a valuable platform to elucidate biosynthetic pathways and physiological processes, generate recombinant therapeutic proteins, assist molecular breeding, and enhance phytoremediation efforts. However, some plant species appear recalcitrant to the classical Agrobacterium rhizogenes transformation techniques. Sonication-assisted Agrobacterium-mediated transformation (SAArT) is a highly effective method to deliver bacteria to target plant tissues that includes exposure of the explants to short periods of ultrasound in the presence of the bacteria.Nuclear magnetic resonance (NMR)-based metabolomics is one of the most powerful and suitable platforms for identifying and obtaining structural information on a wide range of compounds with a high analytical precision. In terms of plant science, NMR metabolomics is used to determine the phytochemical variations of medicinal plants or commercial cultivars in certain environments and conditions, including biotic stress and plant biotic interaction, structural determination of natural products, quality control of herbal drugs or dietary supplements, and comparison of metabolite differences between plants and their respective in vitro cultures.In this chapter, we attempt to summarize our knowledge and expertise in induction of hairy roots from rare and recalcitrant plant species by SAArT technique and further methodology for extraction of secondary metabolites of moderate to high polarity and their identification by using NMR-based metabolomics.

1H-NMR metabolomics analysis of nutritional components from two kinds of freshwater fish brain extracts

There are a number of bioactive compounds in freshwater fish brains, and their functional roles have not been clearly elucidated. NMR-based metabolic profiling could enable rapid characterization of the nutritional composition a fish's brain. Here, two kinds of freshwater fish brains were investigated, crucian carp and yellow catfish. A ¹H-NMR based metabolomic approach was used to illustrate the nutritional components of these two kinds of brain. At first, the microwave method was utilized to cease the activity of the enzymes in the brain, and the chemicals were extracted for NMR analysis. These two kinds of brain had significant differences in metabolic patterns, and the chemical compositions of the yellow catfish brain were similar to those of rodent and human brains. Furthermore, most of the different metabolites were significantly higher in the yellow catfish, except for acetamide. This study could provide comprehensive information regarding the utilization of fish heads during processing of fish and dietary nutrition guidance.

There are a number of bioactive compounds in freshwater fish brains, and their functional roles have not been clearly elucidated. NMR-based metabolic profiling could provide a rapid characterization of a fish brain's nutritional composition.

NMR-based untargeted metabolomic study of hydrogen peroxide-induced development and diapause termination in brine shrimp

Artemia diapause has been extensively studied in embryonic biology for a long time. It has been demonstrated that hydrogen peroxide (H₂O₂) can increase the hatching rate in the development and diapause termination of Artemia cysts. This study used an untargeted ¹H NMR-based metabolomic approach to explore the physiological regulation of H₂O₂ in initiating the development and terminating the diapause of Artemia cysts. This experiment was divided into two parts. In the first part, we analyzed three groups with or without H₂O₂ as control-0h, control-5h and H₂O₂ (180μM)-5h; in the second part, after 7-d incubation, the non-hatching cysts were treated with different H₂O₂ concentrations as low as 180μM and as high 1800μM. The results showed that arginine and proline metabolism were up-regulated after 5h, and H₂O₂ up-regulated valine, leucine and isoleucine biosynthesis in the development of cysts. In the second part, low H₂O₂ (180μM) showed alanine, aspartate and glutamate metabolism, but high H₂O₂ (1800μM) also up-regulated arginine and proline metabolism, as in the control group without H₂O₂ stimulus. These results suggest that enough H₂O₂ can catalyze cell transcription and translation in Artemia cysts, and it improves the cell growth rate, thus allowing embryo cells to grow again.