Application of NMR metabolomics to search for human disease biomarkers

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.

Evaluating protocols for reproducible targeted metabolomics by NMR

Metabolomics aims to study the downstream effects of variables like diet, environment, or disease on a given biological system. However, inconsistencies in sample preparation, data acquisition/processing protocols lead to reproducibility and accuracy concerns. A systematic study was conducted to assess how sample preparation methods and data analysis platforms affect metabolite susceptibility. A targeted panel of 25 metabolites was evaluated in 69 clinical metabolomics samples prepared following three different protocols: intact, ultrafiltration, and protein precipitation. The resulting metabolic profiles were characterized by 1D 1H nuclear magnetic resonance (NMR) spectroscopy and analyzed with Chenomx v8.3 and SMolESY software packages. Greater than 90% of the metabolites were extracted more efficiently using protein precipitation than filtration, which aligns with previously reported results. Additionally, analysis of data processing software suggests that metabolite concentrations were overestimated by Chenomx batch-fitting, which only appears reliable for determining relative fold changes rather than absolute quantification. However, an assisted-fit method provided sufficient guidance to achieve accurate results while avoiding a time-consuming fully manual-fitting approach. By combining our results with previous studies, we can now provide a list of 5 common metabolites [2-hydroxybutyrate (2-HB), choline, dimethylamine (DMA), glutamate, lactate] with a high degree of variability in reported fold changes and standard deviations that need careful consideration before being annotated as potential biomarkers. Our results show that sample preparation and data processing package critically impact clinical metabolomics study success. There is a clear need for an increased degree of standardization and harmonization of methods across the metabolomics community to ensure reliable outcomes.

Artificial Intelligence in Metabolomics: A Current Review

Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health.

A Linear Predictor Based on FTIR Spectral Biomarkers Improves Disease Diagnosis Classification: An Application to Multiple Sclerosis

Multiple sclerosis (MS) is a neurodegenerative disease of the central nervous system that can lead to long-term disability. The diagnosis of MS is not simple and requires many instrumental and clinical tests. Sampling easily collected biofluids using spectroscopic approaches is becoming of increasing interest in the medical field to integrate and improve diagnostic procedures. Here we present a statistical approach where we combine a number of spectral biomarkers derived from the ATR-FTIR spectra of blood plasma samples of healthy control subjects and MS patients, to obtain a linear predictor useful for discriminating between the two groups of individuals. This predictor provides a simple tool in which the contribution of different molecular components is summarized and, as a result, the sensitivity (80%) and specificity (93%) of the identification are significantly improved compared to those obtained with typical classification algorithms. The strategy proposed can be very helpful when applied to the diagnosis of diseases whose presence is reflected in a minimal way in the analyzed biofluids (blood and its derivatives), as it is for MS as well as for other neurological disorders.

Potential of in vitro nuclear magnetic resonance of biofluids and tissues in clinical research

Body fluids, cells, and tissues contain a wide variety of metabolites that consist of a mixture of various low-molecular-weight compounds, including amino acids, peptides, lipids, nucleic acids, and organic acids, which makes comprehensive analysis more difficult. Quantitative nuclear magnetic resonance (NMR) spectroscopy is a well-established analytical technique for analyzing the metabolic profiles of body fluids, cells, and tissues. It enables fast and comprehensive detection, characterization, a high level of experimental reproducibility, minimal sample preparation, and quantification of various endogenous metabolites. In recent times, NMR-based metabolomics has been appreciably utilized in diverse branches of medicine, including microbiology, toxicology, pathophysiology, pharmacology, nutritional intervention, and disease diagnosis/prognosis. In this review, the utility of NMR-based metabolomics in clinical studies is discussed. The significance of in vitro NMR-based metabolomics as an effective tool for detecting metabolites and their variations in different diseases are discussed, together with the possibility of identifying specific biomarkers that can contribute to early detection and diagnosis of disease.

ATR-FTIR spectroscopy of plasma supported by multivariate analysis discriminates multiple sclerosis disease

Multiple sclerosis (MS) is one of the most common neurodegenerative diseases showing various symptoms both of physical and cognitive type. In this work, we used attenuated total reflection Fourier transformed infrared (ATR-FTIR) spectroscopy to analyze plasma samples for discriminating MS patients from healthy control individuals, and identifying potential spectral biomarkers helping the diagnosis through a quick non-invasive blood test. The cohort of the study consists of 85 subjects, including 45 MS patients and 40 healthy controls. The differences in the spectral features both in the fingerprint region (1800-900 cm^-1) and in the high region (3050-2800 cm^-1) of the infrared spectra were highlighted also with the support of different chemometric methods, to capture the most significant wavenumbers for the differentiation. The results show an increase in the lipid/protein ratio in MS patients, indicating changes in the level (metabolism) of these molecular components in the plasma. Moreover, the multivariate tools provided a promising rate of success in the diagnosis, with 78% sensitivity and 83% specificity obtained through the random forest model in the fingerprint region. The MS diagnostic tools based on biomarkers identification on blood (and blood component, like plasma or serum) are very challenging and the specificity and sensitivity values obtained in this work are very encouraging. Overall, the results obtained suggest that ATR-FTIR spectroscopy on plasma samples, requiring minimal or no manipulation, coupled with statistical multivariate approaches, is a promising analytical tool to support MS diagnosis through the identification of spectral biomarkers.

Targeting mitochondrial impairment for the treatment of cardiovascular diseases: From hypertension to ischemia-reperfusion injury, searching for new pharmacological targets

Mitochondria and mitochondrial proteins represent a group of promising pharmacological target candidates in the search of new molecular targets and drugs to counteract the onset of hypertension and more in general cardiovascular diseases (CVDs). Indeed, several mitochondrial pathways result impaired in CVDs, showing ATP depletion and ROS production as common traits of cardiac tissue degeneration. Thus, targeting mitochondrial dysfunction in cardiomyocytes can represent a successful strategy to prevent heart failure. In this context, the identification of new pharmacological targets among mitochondrial proteins paves the way for the design of new selective drugs. Thanks to the advances in omics approaches, to a greater availability of mitochondrial crystallized protein structures and to the development of new computational approaches for protein 3D-modelling and drug design, it is now possible to investigate in detail impaired mitochondrial pathways in CVDs. Furthermore, it is possible to design new powerful drugs able to hit the selected pharmacological targets in a highly selective way to rescue mitochondrial dysfunction and prevent cardiac tissue degeneration. The role of mitochondrial dysfunction in the onset of CVDs appears increasingly evident, as reflected by the impairment of proteins involved in lipid peroxidation, mitochondrial dynamics, respiratory chain complexes, and membrane polarization maintenance in CVD patients. Conversely, little is known about proteins responsible for the cross-talk between mitochondria and cytoplasm in cardiomyocytes. Mitochondrial transporters of the SLC25A family, in particular, are responsible for the translocation of nucleotides (e.g., ATP), amino acids (e.g., aspartate, glutamate, ornithine), organic acids (e.g. malate and 2-oxoglutarate), and other cofactors (e.g., inorganic phosphate, NAD⁺, FAD, carnitine, CoA derivatives) between the mitochondrial and cytosolic compartments. Thus, mitochondrial transporters play a key role in the mitochondria-cytosol cross-talk by leading metabolic pathways such as the malate/aspartate shuttle, the carnitine shuttle, the ATP export from mitochondria, and the regulation of permeability transition pore opening. Since all these pathways are crucial for maintaining healthy cardiomyocytes, mitochondrial carriers emerge as an interesting class of new possible pharmacological targets for CVD treatments.

Leveraging the HMBC to Facilitate Metabolite Identification

The accuracy and ease of metabolite assignments from a complex mixture are expected to be facilitated by employing a multispectral approach. The two-dimensional (2D) 1H-13C heteronuclear single quantum coherence (HSQC) and 2D 1H-1H-total correlation spectroscopy (TOCSY) are the experiments commonly used for metabolite assignments. The 2D 1H-13C HSQC-TOCSY and 2D 1H-13C heteronuclear multiple-bond correlation (HMBC) are routinely used by natural products chemists but have seen minimal usage in metabolomics despite the unique information, the nearly complete 1H-1H and 1H-13C and spin systems provided by these experiments that may improve the accuracy and reliability of metabolite assignments. The use of a 13C-labeled feedstock such as glucose is a routine practice in metabolomics to improve sensitivity and to emphasize the detection of specific metabolites but causes severe artifacts and an increase in spectral complexity in the HMBC experiment. To address this issue, the standard HMBC pulse sequence was modified to include carbon decoupling. Nonuniform sampling was also employed for rapid data collection. A dataset of reference 2D 1H-13C HMBC spectra was collected for 94 common metabolites. 13C-13C spin connectivity was then obtained by generating a covariance pseudo-spectrum from the carbon-decoupled HMBC and the 1H-13C HSQC-TOCSY spectra. The resulting 13C-13C pseudo-spectrum provides a connectivity map of the entire carbon backbone that uniquely describes each metabolite and would enable automated metabolite identification.

Urine 1H-NMR Metabolomics to Discriminate Neurocysticercosis Patients from Healthy Controls: An Exploratory Study

The diagnosis of neurocysticercosis (NCC) is principally based on neuroimaging (magnetic resonance imaging or computed tomography), instrumentation that is scarcely available in the rural regions where Taenia solium transmission, primarily occurs due to poor sanitation conditions. Immunological assays for antigen or antibody detection complement the neuroimaging approach. However, no field-applicable assays to diagnose viable NCC or to guide the referral of cases for neuroimaging or for appropriate management are available. We performed an exploratory study on urine and serum samples using 1H-nuclear magnetic resonance (NMR)-based metabolomics to discriminate NCC patients (n = 14) from healthy control subjects (n = 22). Metabolic profiles demonstrated a discrimination between the urines of NCC patients and noninfected control subjects with a moderate predictive accuracy (R2 = 0.999, Q2 = 0.434). NMR metabolomics analysis has been proven useful in depicting biomarkers linked to other infectious diseases, various types of cancer, and other disorders. Our results, albeit preliminary, open a door to the development of better methods for detecting NCC through the identification of biomarkers participating in disturbed metabolic pathways.

Circulating Metabolites as Biomarkers of Disease in Patients with Mesial Temporal Lobe Epilepsy

A major challenge in the clinical management of patients with mesial temporal lobe epilepsy (MTLE) is identifying those who do not respond to antiseizure medication (ASM), allowing for the timely pursuit of alternative treatments such as epilepsy surgery. Here, we investigated changes in plasma metabolites as biomarkers of disease in patients with MTLE. Furthermore, we used the metabolomics data to gain insights into the mechanisms underlying MTLE and response to ASM. We performed an untargeted metabolomic method using magnetic resonance spectroscopy and multi- and univariate statistical analyses to compare data obtained from plasma samples of 28 patients with MTLE compared to 28 controls. The patients were further divided according to response to ASM for a supplementary and preliminary comparison: 20 patients were refractory to treatment, and eight were responsive to ASM. We only included patients using carbamazepine in combination with clobazam. We analyzed the group of patients and controls and found that the profiles of glucose (p = 0.01), saturated lipids (p = 0.0002), isoleucine (p = 0.0001), β-hydroxybutyrate (p = 0.0003), and proline (p = 0.02) were different in patients compared to controls (p < 0.05). In addition, we found some suggestive metabolites (without enough predictability) by multivariate analysis (VIP scores > 2), such as lipoproteins, lactate, glucose, unsaturated lipids, isoleucine, and proline, that might be relevant to the process of pharmacoresistance in the comparison between patients with refractory and responsive MTLE. The identified metabolites for the comparison between MTLE patients and controls were linked to different biological pathways related to cell-energy metabolism and pathways related to inflammatory processes and the modulation of neurotransmitter release and activity in MTLE. In conclusion, in addition to insights into the mechanisms underlying MTLE, our results suggest that plasma metabolites may be used as disease biomarkers. These findings warrant further studies exploring the clinical use of metabolites to assist in decision-making when treating patients with MTLE.

Diagnostic Value of 1H NMR-Based Metabolomics in Acute Lymphoblastic Leukemia, Acute Myeloid Leukemia, and Breast Cancer

Cancer refers to a massive number of diseases distinguished by the development of abnormal cells that divide uncontrollably and have the capability of infiltration and destroying the normal body tissue. It is critical to detect biomarkers that are early detectable and noninvasive to save millions of lives. The aim of the present work is to use NMR as a noninvasive diagnostic tool for cancer diseases. This study included 30 plasma and 21 urine samples of patients diagnosed with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), 25 plasma and 17 urine samples of patients diagnosed with breast cancer (BC), and 9 plasma and urine samples obtained from healthy individuals as controls. They were prepared for NMR measurements; then, the metabolites were identified and the data were analyzed using multivariate statistical procedures. The OPLS-DA score plots clearly discriminated ALL, AML, and BC from healthy controls. Plots of the PLS-DA loadings and S-line plots showed that all metabolites in plasma were greater in BC than in the healthy controls, whereas lactate, O-acetylcarnitine, pyruvate, trimethylamine-N-oxide (TMAO), and glucose were higher in healthy controls than in ALL and AML. On the other hand, urine samples showed lower amounts of lactate, melatonin, pyruvate, and succinate in all of the studied types of cancer when compared to those of healthy controls. 1H NMR can be a successful and noninvasive tool for the diagnosis of different types of cancer.

The Potential of Metabolomics in Biomedical Applications

The metabolome offers a dynamic, comprehensive, and precise picture of the phenotype. Current high-throughput technologies have allowed the discovery of relevant metabolites that characterize a wide variety of human phenotypes with respect to health, disease, drug monitoring, and even aging. Metabolomics, parallel to genomics, has led to the discovery of biomarkers and has aided in the understanding of a diversity of molecular mechanisms, highlighting its application in precision medicine. This review focuses on the metabolomics that can be applied to improve human health, as well as its trends and impacts in metabolic and neurodegenerative diseases, cancer, longevity, the exposome, liquid biopsy development, and pharmacometabolomics. The identification of distinct metabolomic profiles will help in the discovery and improvement of clinical strategies to treat human disease. In the years to come, metabolomics will become a tool routinely applied to diagnose and monitor health and disease, aging, or drug development. Biomedical applications of metabolomics can already be foreseen to monitor the progression of metabolic diseases, such as obesity and diabetes, using branched-chain amino acids, acylcarnitines, certain phospholipids, and genomics; these can assess disease severity and predict a potential treatment. Future endeavors should focus on determining the applicability and clinical utility of metabolomic-derived markers and their appropriate implementation in large-scale clinical settings.

1H-Nuclear Magnetic Resonance Analysis of Urine as Diagnostic Tool for Organic Acidemias and Aminoacidopathies

The utility of low-resolution 1H-NMR analysis for the identification of biomarkers provided evidence for rapid biochemical diagnoses of organic acidemia and aminoacidopathy. 1H-NMR, with a sensitivity expected for a field strength of 400 MHz at 64 scans was used to establish the metabolomic urine sample profiles of an infant population diagnosed with small molecule Inborn Errors of Metabolism (smIEM) compared to unaffected individuals. A qualitative differentiation of the 1H-NMR spectral profiles of urine samples obtained from individuals affected by different organic acidemias and aminoacidopathies was achieved in combination with GC-MS. The smIEM disorders investigated in this study included phenylalanine metabolism; isovaleric, propionic, 3-methylglutaconicm and glutaric type I acidemia; and deficiencies in medium chain acyl-coenzyme and holocarboxylase synthase. The observed metabolites were comparable and similar to those reported in the literature, as well as to those detected with higher-resolution NMR. In this study, diagnostic marker metabolites were identified for the smIEM disorders. In some cases, changes in metabolite profiles differentiated post-treatments and follow-ups while allowing for the establishment of different clinical states of a biochemical disorder. In addition, for the first time, a 1H-NMR-based biomarker profile was established for holocarboxylase synthase deficiency spectrum.

Multi-omic strategies applied to the study of pharmacoresistance in mesial temporal lobe epilepsy

Mesial temporal lobe epilepsy (MTLE) is the most common type of focal epilepsy in adults, and hippocampal sclerosis (HS) is a frequent histopathological feature in patients with MTLE. Pharmacoresistance is present in at least one-third of patients with MTLE with HS (MTLE+HS). Several hypotheses have been proposed to explain the mechanisms of pharmacoresistance in epilepsy, including the effect of genetic and molecular factors. In recent years, the increased knowledge generated by high-throughput omic technologies has significantly improved the power of molecular genetic studies to discover new mechanisms leading to disease and response to treatment. In this review, we present and discuss the contribution of different omic modalities to understand the basic mechanisms determining pharmacoresistance in patients with MTLE+HS. We provide an overview and a critical discussion of the findings, limitations, new approaches, and future directions of these studies to improve the understanding of pharmacoresistance in MTLE+HS. However, it is important to point out that, as with other complex traits, pharmacoresistance to anti-seizure medications is likely a multifactorial condition in which gene-gene and gene-environment interactions play an important role. Thus, studies using multidimensional approaches are more likely to unravel these intricate biological processes.

Sweat metabolome and proteome: Recent trends in analytical advances and potential biological functions

Metabolome and proteome profiling of biofluids, e.g., urine, plasma, has generated vast and ever-increasing amounts of knowledge over the last few decades. Paradoxically, omics analyses of sweat, one of the most readily available human biofluids, have lagged behind. This review capitalizes on the current knowledge and state of the art analytical advances of sweat metabolomics and proteomics. Moreover, current applications of sweat omics such as the discovery of disease biomarkers and monitoring athletic performance are also presented in this review. Another area of emerging knowledge that has been highlighted herein lies in the role of skin host-microbiome interactions in shaping the sweat metabolite-protein profiles. Discussion of future research directions describes the need to have a better grasp of sweat chemicals and to better understand how they function as aided by advances in omics tools. Overall, the role of sweat as an information-rich biofluid that could complement the exploration of the skin metabolome/proteome is emphasized.

Analysis of Salivary Metabolites by Nuclear Magnetic Resonance Before and After Oral Mucosa Cleaning of Infants in the Pre-dental Period

The aim of the present study was to verify if a protocol for cleaning the oral cavity of infants in the pre-dental period can reduce extrinsic salivary metabolites observed through Nuclear Magnetic Resonance (NMR). A cross-sectional clinical study with a convenience sample was conducted, and infants were recruited at the UFRJ Pediatric Dentistry Clinic. Participants who had used antibiotics and/or antifungals up to 3 months before and whose legal guardians did not consent or sign the Informed Consent Form were excluded. An anamnesis was performed with the guardians and the participants' intraoral clinical examination. Initial collection of unstimulated total saliva was performed using an automatic pipette with sterile plastic tips in the buccal floor region, at least 1 h after the last feeding. Subsequently, the infants' oral mucosa was cleaned with gauze moistened with filtered water, and after 5 min, a new collection was performed, using the same methodology. The obtained samples were immediately transferred on ice to the laboratory, centrifuged (10,000 g), and stored at −80°C. The NMR analyses were performed using a 500-MHz spectrometer Bruker, Germany); evaluations were done via the 1H and 1H-1H TOCSY spectra for metabolite signaling. Eleven pre-dental infants were evaluated, with a mean age of 3.8 months, including six girls (55%). Of these, nine participants (82%) were exclusively breastfed. The higher presence of components such as lactose, glucose, sugars, acetate, alanine, and lactate were observed in the samples before oral mucosa cleaning. Regarding the type of diet, more lactose was observed in the saliva of patients who were exclusively breastfed than those that received mixed feeding. We conclude that the oral mucosa cleaning of infants in the pre-dental period tends to reduce the concentration of extrinsic components from the diet, such as lactose, in the salivary metabolomic profile analyzed by NMR.

Applying metabolomics to veterinary pharmacology and therapeutics

Metabolomics is the large-scale study of low-molecular-weight substances in a biological system in a given physiological state at a given time point. Metabolomics can be applied to identify predictors of inter-individual variability in drug response, provide clinicians with data useful for decision-making processes in drug selection, and inform about the pharmacokinetics and pharmacodynamics of a drug. It is, therefore, an exceptional approach for gaining new understanding effects in the field of comparative veterinary pharmacology. However, the incorporation of metabolomics into veterinary pharmacology and toxicology is not yet widespread, and this is probably, at least in part, a result of its highly multidisciplinary nature. This article reviews the potential applications of metabolomics in veterinary pharmacology and therapeutics. It integrates key concepts for designing metabolomics studies and analyzing and interpreting metabolomics data, providing solid foundations for applying metabolomics to the study of drugs in all veterinary species.

Ketone body 3-hydroxybutyrate as a biomarker of aggression

Human aggression is a complex behaviour, the biological underpinnings of which remain poorly known. To gain insights into aggression biology, we studied relationships with aggression of 11 low-molecular-weight metabolites (amino acids, ketone bodies), processed using ¹H nuclear magnetic resonance spectroscopy. We used a discovery sample of young adults and an independent adult replication sample. We studied 725 young adults from a population-based Finnish twin cohort born 1983-1987, with aggression levels rated in adolescence (ages 12, 14, 17) by multiple raters and blood plasma samples at age 22. Linear regression models specified metabolites as the response variable and aggression ratings as predictor variables, and included several potential confounders. All metabolites showed low correlations with aggression, with only one-3-hydroxybutyrate, a ketone body produced during fasting-showing significant (negative) associations with aggression. Effect sizes for different raters were generally similar in magnitude, while teacher-rated (age 12) and self-rated (age 14) aggression were both significant predictors of 3-hydroxybutyrate in multi-rater models. In an independent replication sample of 960 adults from the Netherlands Twin Register, higher aggression (self-rated) was also related to lower levels of 3-hydroxybutyrate. These exploratory epidemiologic results warrant further studies on the role of ketone metabolism in aggression.

Indication of high lipid content in epithelial-mesenchymal transitions of breast tissues

The epithelial-mesenchymal transition (EMT) is a crucial process in cancer progression and metastasis. Study of metabolic changes during the EMT process is important in seeking to understand the biochemical changes associated with cancer progression, not least in scoping for therapeutic strategies aimed at targeting EMT. Due to the potential for high sensitivity and specificity, Raman spectroscopy was used here to study the metabolic changes associated with EMT in human breast cancer tissue. For Raman spectroscopy measurements, tissue from 23 patients were collected, comprising non-lesional, EMT and non-EMT formalin-fixed and paraffin embedded breast cancer samples. Analysis was made in the fingerprint Raman spectra region (600-1800 cm-1) best associated with cancer progression biochemical changes in lipid, protein and nucleic acids. The ANOVA test followed by the Tukey's multiple comparisons test were conducted to see if there existed differences between non-lesional, EMT and non-EMT breast tissue for Raman spectroscopy measurements. Results revealed that significant differences were evident in terms of intensity between the non-lesional and EMT samples, as well as the EMT and non-EMT samples. Multivariate analysis involving independent component analysis, Principal component analysis and non-negative least square were used to analyse the Raman spectra data. The results show significant differences between EMT and non-EMT cancers in lipid, protein, and nucleic acids. This study demonstrated the capability of Raman spectroscopy supported by multivariate analysis in analysing metabolic changes in EMT breast cancer tissue.

The main effects of elevated CO2 and soil-water deficiency on 1H NMR-based metabolic fingerprints of Coffea arabica beans by factorial and mixture design

The metabolic response of Coffea arabica trees in the face of the rising atmospheric concentration of carbon dioxide (CO₂) combined with the reduction in soil-water availability is complex due to the various (bio)chemical feedbacks. Modern analytical tools and the experimental advance of agronomic science tend to advance in the understanding of the metabolic complexity of plants. In this work, Coffea arabica trees were grown in a Free-Air Carbon Dioxide Enrichment dispositive under factorial design (2²) conditions considering two CO₂ levels and two soil-water availabilities. The ¹H NMR mixture design-fingerprinting effects of CO₂ and soil-water levels on beans were strategically investigated using the principal component analysis (PCA), analysis of variance (ANOVA) - simultaneous component analysis (ASCA) and partial least squares-discriminant analysis (PLS-DA). From the ASCA, the CO₂ factor had a significant effect on changing the ¹H NMR profile of fingerprints. The soil-water factor and interaction (CO₂ × soil-water) were not significant. ¹H NMR fingerprints with PCA, ASCA and PLS-DA analysis determined spectral profiles for fatty acids, caffeine, trigonelline and glucose increases in beans from current CO₂, while quinic acid/chlorogenic acids, malic acid and kahweol/cafestol increased in coffee beans from elevated CO₂. PLS-DA results revealed a good classification performance between the significant effect of the atmospheric CO₂ levels on the fingerprints, regardless of the soil-water availabilities. Finally, the PLS-DA model showed good prediction ability, successfully classifying validation data-set of coffee beans collected over the vertical profile of the plants and included several fingerprints of different extracting solvents. The results of this investigation suggest that the association of experimental design, mixture design, PCA, ASCA and PLS-DA can provide accurate information on a series of metabolic changes provoked by climate changes in products of commercial importance, in addition to minimizing the extra work necessary in classic analytical approaches, encouraging the development of similar strategies.

Application of Metabolomics in Diagnosis and Treatment of Chronic Liver Diseases

Chronic liver disease represents stepwise destruction of the liver parenchyma after chronic liver injury, which is often difficult to be diagnosed accurately. Thus, the development of specific biomarkers of chronic liver disease is important. Metabolomics is a powerful tool for biomarker exploration, which enables the exploration of disease pathogenesis or drug action mechanisms at the global metabolic level. The metabolomics workflow generally includes collection, preparation, and analysis of samples, and data processing and bioinformatics. A metabolomics study can simultaneously detect the dysfunctions in the glucose, lipid, amino-acid, and nucleotide metabolisms. Hence, it facilitates the obtaining of a better understanding of the pathogenesis of chronic liver disease and its diagnosis. Many effective drugs could reverse the change of comprehensive biochemical phenotypes induced by chronic liver disease. They can even potentially restore the normal metabolic signatures of patients. Increasingly more researchers have begun to apply metabolomics technologies to diagnose chronic liver disease and investigate the mechanism of action of effective drugs or the variations in drug responses. We are convinced that deepening the understanding of the metabolic alterations could extend their use as powerful biomarkers, promoting the more effective clinical diagnosis and treatment of chronic liver disease in the future.

Vibrational Spectroscopy for Identification of Metabolites in Biologic Samples

Vibrational spectroscopy (mid-infrared (IR) and Raman) and its fingerprinting capabilities offer rapid, high-throughput, and non-destructive analysis of a wide range of sample types producing a characteristic chemical "fingerprint" with a unique signature profile. Nuclear magnetic resonance (NMR) spectroscopy and an array of mass spectrometry (MS) techniques provide selectivity and specificity for screening metabolites, but demand costly instrumentation, complex sample pretreatment, are labor-intensive, require well-trained technicians to operate the instrumentation, and are less amenable for implementation in clinics. The potential for vibration spectroscopy techniques to be brought to the bedside gives hope for huge cost savings and potential revolutionary advances in diagnostics in the clinic. We discuss the utilization of current vibrational spectroscopy methodologies on biologic samples as an avenue towards rapid cost saving diagnostics.

NMR as a "Gold Standard" Method in Drug Design and Discovery

Studying disease models at the molecular level is vital for drug development in order to improve treatment and prevent a wide range of human pathologies. Microbial infections are still a major challenge because pathogens rapidly and continually evolve developing drug resistance. Cancer cells also change genetically, and current therapeutic techniques may be (or may become) ineffective in many cases. The pathology of many neurological diseases remains an enigma, and the exact etiology and underlying mechanisms are still largely unknown. Viral infections spread and develop much more quickly than does the corresponding research needed to prevent and combat these infections; the present and most relevant outbreak of SARS-CoV-2, which originated in Wuhan, China, illustrates the critical and immediate need to improve drug design and development techniques. Modern day drug discovery is a time-consuming, expensive process. Each new drug takes in excess of 10 years to develop and costs on average more than a billion US dollars. This demonstrates the need of a complete redesign or novel strategies. Nuclear Magnetic Resonance (NMR) has played a critical role in drug discovery ever since its introduction several decades ago. In just three decades, NMR has become a "gold standard" platform technology in medical and pharmacology studies. In this review, we present the major applications of NMR spectroscopy in medical drug discovery and development. The basic concepts, theories, and applications of the most commonly used NMR techniques are presented. We also summarize the advantages and limitations of the primary NMR methods in drug development.

Circulating Metabolites as Potential Biomarkers for Neurological Disorders-Metabolites in Neurological Disorders

There are, still, limitations to predicting the occurrence and prognosis of neurological disorders. Biomarkers are molecules that can change in different conditions, a feature that makes them potential tools to improve the diagnosis of disease, establish a prognosis, and monitor treatments. Metabolites can be used as biomarkers, and are small molecules derived from the metabolic process found in different biological media, such as tissue samples, cells, or biofluids. They can be identified using various strategies, targeted or untargeted experiments, and by different techniques, such as high-performance liquid chromatography, mass spectrometry, or nuclear magnetic resonance. In this review, we aim to discuss the current knowledge about metabolites as biomarkers for neurological disorders. We will present recent developments that show the need and the feasibility of identifying such biomarkers in different neurological disorders, as well as discuss relevant research findings in the field of metabolomics that are helping to unravel the mechanisms underlying neurological disorders. Although several relevant results have been reported in metabolomic studies in patients with neurological diseases, there is still a long way to go for the clinical use of metabolites as potential biomarkers in these disorders, and more research in the field is needed.

Phosphorus NMR and Its Application to Metabolomics

Stable isotopes are routinely employed by NMR metabolomics to highlight specific metabolic processes and to monitor pathway flux. 13C-carbon and 15N-nitrogen labeled nutrients are convenient sources of isotope tracers and are commonly added as supplements to a variety of biological systems ranging from cell cultures to animal models. Unlike 13C and 15N, 31P-phosphorus is a naturally abundant and NMR active isotope that does not require an external supplemental source. To date, 31P NMR has seen limited usage in metabolomics because of a lack of reference spectra, difficulties in sample preparation, and an absence of two-dimensional (2D) NMR experiments, but 31P NMR has the potential of expanding the coverage of the metabolome by detecting phosphorus-containing metabolites. Phosphorylated metabolites regulate key cellular processes, serve as a surrogate for intracellular pH conditions, and provide a measure of a cell's metabolic energy and redox state, among other processes. Thus, incorporating 31P NMR into a metabolomics investigation will enable the detection of these key cellular processes. To facilitate the application of 31P NMR in metabolomics, we present a unified protocol that allows for the simultaneous and efficient detection of 1H-, 13C-, 15N-, and 31P-labeled metabolites. The protocol includes the application of a 2D 1H-31P HSQC-TOCSY experiment to detect 31P-labeled metabolites from heterogeneous biological mixtures, methods for sample preparation to detect 1H-, 13C-, 15N-, and 31P-labeled metabolites from a single NMR sample, and a data set of one-dimensional (1D) 31P NMR and 2D 1H-31P HSQC-TOCSY spectra of 38 common phosphorus-containing metabolites to assist in metabolite assignments.

Inorganic Pyrophosphatase-Nanodiamond Conjugates Hydrolyze Pyrophosphate in Human Synovial Fluid

The present work is focused on testing enzyme-based agents for the partial dissolution of calcium pyrophosphate (CaPPi) deposits in the cartilages and synovial fluid of patients with pyrophosphate arthropathy (CPPD disease). Previously, we suggested that inorganic pyrophosphatases (PPases) immobilized on nanodiamonds of detonation synthesis (NDs) could be appropriate for this purpose. We synthesized and characterized conjugates of NDs and PPases from Escherichia coli and Mycobacterium tuberculosis. The conjugates showed high enzymatic activity and resistance to inhibition by calcium and fluoride. Here, we tested the effectiveness of pyrophosphate (PPi) hydrolysis by the conjugates in an in vitro model system simulating the ionic composition of the synovial fluid and in the samples of synovial fluid of patients with CPPD via NMR spectroscopy. The conjugates of both PPases efficiently hydrolyzed triclinic crystalline calcium pyrophosphate (t-CPPD) in the model system. We evaluated the number of phosphorus-containing compounds in the synovial fluid, showed the possibility of PPi detection in it, and estimated the hydrolytic activity of the PPase conjugates. The soluble and immobilized PPases were able to hydrolyze a significant amount of PPi (1 mM) in the synovial fluid in short periods of time (24 h). The maximum activity was demonstrated for Mt-PPase immobilized on ND-NH-(CH2)6-NH2 (2.24 U mg-1).

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.

NMR-based metabolomics and fluxomics: developments and future prospects

Recent NMR developments are acting as game changers for metabolomics and fluxomics – a critical and perspective review.

Library-assisted nonlinear blind separation and annotation of pure components from a single 1H nuclear magnetic resonance mixture spectra

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

NMR-Based Metabolomics for the Assessment of Inhaled Pharmacotherapy in Chronic Obstructive Pulmonary Disease Patients

The aim of this proof-of-concept, pilot study was the evaluation of the effects of steroid administration and suspension of an inhaled corticosteroid (ICS)-long-acting β₂-agonist (LABA) extrafine fixed dose combination (FDC) on metabolomic fingerprints in subjects with chronic obstructive pulmonary disease (COPD). We hypothesized that a comprehensive metabolomics approach discriminates across inhaled pharmacotherapies and that their effects on metabolomic signatures depend on the biological fluids analyzed. We performed metabolomics via nuclear magnetic resonance (NMR) spectroscopy in exhaled breath condensate (EBC), sputum supernatants, serum, and urine. Fourteen patients suffering from COPD who were on regular inhaled fluticasone propionate/salmeterol therapy (visit 1) were consecutively treated with 2-week beclomethasone dipropionate/formoterol (visit 2), 4-week formoterol alone (visit 3), and 4-week beclomethasone/formoterol (visit 4). The comprehensive NMR-based metabolomics approach showed differences across all pharmacotherapies and that different biofluids provided orthogonal information. Serum formate was lower at visits 1 versus 3 (P = 0.03), EBC formate was higher at visit 1 versus 4 (P = 0.03), and urinary 1-methyl-nicotinamide was lower at 3 versus 4 visit (P = 0.002). NMR-based metabolomics of different biofluids distinguishes across inhaled pharmacotherapies, provides complementary information on the effects of an extrafine ICS/LABA FDC on metabolic fingerprints in COPD patients, and might be useful for elucidating the ICS mechanism of action.

Interplay between Alzheimer's disease and global glucose metabolism revealed by the metabolic profile alterations of pancreatic tissue and serum in APP/PS1 transgenic mice

Increasing evidence suggests that there is a correlation between type 2 diabetes mellitus (T2D) and Alzheimer's disease (AD). Increased Aβ polypeptide production in AD patients would promote metabolic abnormalities, insulin signaling dysfunction and perturbations in glucose utilization, thus leading to the onset of T2D. However, the metabolic mechanisms underlying the interplay between AD and its diabetes-promoting effects are not fully elucidated. Particularly, systematic metabolomics analysis has not been performed for the pancreas tissues of AD subjects, which play key roles in the glucose metabolism of living systems. In the current study, we characterized the dynamic metabolic profile alterations of the serum and the pancreas of APP/PS1 double-transgenic mice (an AD mouse model) using the untargeted metabolomics approaches. Serum and pancreatic tissues of APP/PS1 transgenic mice and wild-type mice were extracted and subjected to NMR analysis to evaluate the functional state of pancreas in the progress of AD. Multivariate analysis of principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were conducted to define the global and the local (pancreas) metabolic features associated with the possible initiation of T2D in the progress of AD. Our results showed the onset of AD-induced global glucose metabolism disorders in AD mice. Hyperglycemia and its accompanying metabolic disorders including energy metabolism down-regulation and oxidative stress were observed in the serum of AD mice. Meanwhile, global disturbance of branched-chain amino acid (BCAA) metabolism was detected, and the change of BCAA (leucine) was positively correlated to the alteration of glucose. Moreover, increased level of glucose and enhanced energy metabolism were observed in the pancreas of AD mice. The results suggest that the diabetes-promoting effects accompanying the progress of AD are achieved by down-regulating the global utilization of glucose and interfering with the metabolic function of pancreas. Since T2D is a risk factor for the pathogenesis of AD, our findings suggest that targeting the glucose metabolism dysfunctions might serve as a supplementary therapeutic strategy for Alzheimer's disease.

A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases

Modern high-throughput experiments provide us with numerous potential associations between genes and diseases. Experimental validation of all the discovered associations, let alone all the possible interactions between them, is time-consuming and expensive. To facilitate the discovery of causative genes, various approaches for prioritization of genes according to their relevance for a given disease have been developed. In this article, we explain the gene prioritization problem and provide an overview of computational tools for gene prioritization. Among about a hundred of published gene prioritization tools, we select and briefly describe 14 most up-to-date and user-friendly. Also, we discuss the advantages and disadvantages of existing tools, challenges of their validation, and the directions for future research.

Evaluation of In Silico Multifeature Libraries for Providing Evidence for the Presence of Small Molecules in Synthetic Blinded Samples

The current gold standard for unambiguous molecular identification in metabolomics analysis is comparing two or more orthogonal properties from the analysis of authentic reference materials (standards) to experimental data acquired in the same laboratory with the same analytical methods. This represents a significant limitation for comprehensive chemical identification of small molecules in complex samples. The process is time consuming and costly, and the majority of molecules are not yet represented by standards. Thus, there is a need to assemble evidence for the presence of small molecules in complex samples through the use of libraries containing calculated chemical properties. To address this need, we developed a Multi-Attribute Matching Engine (MAME) and a library derived in part from our in silico chemical library engine (ISiCLE). Here, we describe an initial evaluation of these methods in a blinded analysis of synthetic chemical mixtures as part of the U.S. Environmental Protection Agency's (EPA) Non-Targeted Analysis Collaborative Trial (ENTACT, Phase 1). For molecules in all mixtures, the initial blinded false negative rate (FNR), false discovery rate (FDR), and accuracy were 57%, 77%, and 91%, respectively. For high evidence scores, the FDR was 35%. After unblinding of the sample compositions, we optimized the scoring parameters to better exploit the available evidence and increased the accuracy for molecules suspected as present. The final FNR, FDR, and accuracy were 67%, 53%, and 96%, respectively. For high evidence scores, the FDR was 10%. This study demonstrates that multiattribute matching methods in conjunction with in silico libraries may one day enable reduced reliance on experimentally derived libraries for building evidence for the presence of molecules in complex samples.

Evaluation of sample preparation protocols for quantitative NMR-based metabolomics

INTRODUCTION

Quantification of metabolites in biological fluids and tissues by NMR spectroscopy is challenged by the presence of abundant macromolecules and lipoproteins in samples, which give broad signals in the NMR spectra. To improve the quality of NMR spectra the different protocols for protein and lipid removal from the sample are used.

OBJECTIVES

This work is aimed at the evaluation of the effectiveness of various methods of purification of blood serum from proteins and lipids for ¹H NMR metabolomic profiling.

METHODS

The advantages and limitations of different methods of the sample preparation for NMR-based quantitative metabolomics have been compared, including ultrafiltration, methanol and ethanol extractions with and without additional lipid removal, and methanol-chloroform extraction.

RESULTS

The concentrations of 30 abundant metabolites extracted from human blood serum have been measured. It is found that ultrafiltration provides the best lipid removal, but causes significant and inhomogeneous metabolite losses. Ethanol and methanol extractions demonstrate similar performance with the minimal metabolite losses, and are ideal for fluids and tissues with low lipid content. The additional purification of alcohol extracts from lipids allows for the significant improving of NMR spectra, but causes additional metabolite losses.

CONCLUSIONS

The methanol-chloroform extraction seems to be an optimal method for tissues with the high lipid content, providing a satisfactory lipid removal and low metabolite losses. The ultrafiltration leads to large losses of metabolites (up to 60%) and for this reason is not suitable for quantitative analysis.

Recent Advances and Challenges in Steroid Metabolomics for Biomarker Discovery

BACKGROUND

Steroid hormones belong to a group of low-molecular weight compounds which are responsible for maintenance of various body functions, thus, their accurate assessment is crucial for evaluation of biosynthetic defects. The development of reliable methods allowing disease diagnosis is essential to improve early detection of various disorders connected with altered steroidogenesis. Currently, the field of metabolomics offers several improvements in terms of sensitivity and specificity of the diagnostic methods when opposed to classical diagnostic approaches. The combination of hyphenated techniques and pattern recognition methods allows to carry out a comprehensive assessment of the slightest alterations in steroid metabolic pathways and can be applied as a tool for biomarker discovery.

METHODS

We have performed an extensive literature search applying various bibliographic databases for peer-reviewed articles concentrating on the applications of hyphenated techniques and pattern recognition methods incorporated into the steroid metabolomic approach for biomarker discovery.

RESULTS

The review discusses strengths, challenges and recent developments in steroidbased metabolomics. We present methods of sample collection and preparation, methods of separation and detection of steroid hormones in biological material, data analysis, and interpretation as well as examples of applications of steroid metabolomics for biomarker discovery (cancer, mental and central nervous system disorders, endocrine diseases, monitoring of drug therapy and doping control).

CONCLUSION

Information presented in this review will be valuable to anyone interested in the application of metabolomics for biomarker discovery with a special emphasis on disorders of steroid hormone synthesis and metabolism.

An Improved Method for Measuring Absolute Metabolite Concentrations in Small Biofluid or Tissue Samples

The measurement of absolute metabolite concentrations in small samples remains a significant analytical challenge. This is particularly the case when the sample volume is only a few microliters or less and cannot be determined accurately via direct measurement. We previously developed volume determination with two standards (VDTS) as a method to address this challenge for biofluids. As a proof-of-principle, we applied VDTS to NMR spectra of polar metabolites in the hemolymph (blood) of the tiny yet powerful genetic model Drosophila melanogaster. This showed that VDTS calculation of absolute metabolite concentrations in fed versus starved Drosophila larvae is more accurate than methods utilizing normalization to total spectral signal. Here, we introduce paired VDTS (pVDTS), an improved VDTS method for biofluids and solid tissues that implements the statistical power of paired control and experimental replicates. pVDTS utilizes new equations that also include a correction for dilution errors introduced by the variable surface wetness of solid samples. We then show that metabolite concentrations in Drosophila larvae are more precisely determined and logically consistent using pVDTS than using the original VDTS method. The refined pVDTS workflow described in this study is applicable to a wide range of different tissues and biofluids.

NMR Metabolomics Protocols for Drug Discovery

Drug discovery is an extremely difficult and challenging endeavor with a very high failure rate. The task of identifying a drug that is safe, selective, and effective is a daunting proposition because disease biology is complex and highly variable across patients. Metabolomics enables the discovery of disease biomarkers, which provides insights into the molecular and metabolic basis of disease and may be used to assess treatment prognosis and outcome. In this regard, metabolomics has evolved to become an important component of the drug discovery process to resolve efficacy and toxicity issues and as a tool for precision medicine. A detailed description of an experimental protocol is presented that outlines the application of NMR metabolomics to the drug discovery pipeline. This includes (1) target identification by understanding the metabolic dysregulation in diseases, (2) predicting the mechanism of action of newly discovered or existing drug therapies, (3) and using metabolomics to screen a chemical lead to assess biological activity. Unlike other OMICS approaches, the metabolome is "fragile" and may be negatively impacted by improper sample collection, storage, and extraction procedures. Similarly, biologically irrelevant conclusions may result from incorrect data collection, preprocessing or processing procedures, or the erroneous use of univariate and multivariate statistical methods. These critical concerns are also addressed in the protocol.

Metabolomics Analyses from Tissues in Parkinson's Disease

Metabolomics has been successfully applied to study neurological and neurodegenerative disorders including Parkinson's disease for (1) the identification of potential biomarkers of onset and disease progression; (2) the identification of novel mechanisms of disease progression; and (3) the assessment of treatment prognosis and outcome. Reproducible and efficient extraction of metabolites is imperative to the success of any metabolomics investigation. Unlike other omics techniques, the composition of the metabolome can be negatively impacted by the preparation, processing, and handling of these samples. The proper choice of data collection, preprocessing, and processing protocols is similarly important to the design of an effective metabolomics experiment. Likewise, the correct application of univariate and multivariate statistical methods is essential for providing biologically relevant insights. In this chapter, we have outlined a detailed metabolomics workflow that addresses all of these issues. A step-by-step protocol from the preparation of neuronal cells and metabolomic tissue samples to their metabolic analyses using nuclear magnetic resonance, mass spectrometry, and chemometrics is presented.

Impact of Sample Storage on the NMR Fecal Water Metabolome

The study of the fecal metabolome is an important area of research to better understand the human gut microbiome and its impact on human health and diseases. However, there is a lack of work in examining the impact of storage and processing conditions on the metabolite levels of fecal water. Furthermore, there is no universal protocol used for the storage of fecal samples and preparation of fecal water. The objective of the current study was to examine the impact of different storage conditions on fecal samples prior to metabolite extraction. Fecal samples obtained from nine healthy individuals were processed under different conditions: (1) fresh samples prepared immediately after collection, (2) fecal samples stored at 4 °C for 24 h prior to processing, and (3) fecal samples stored at -80 °C for 24 h prior to processing. All samples were analyzed using NMR spectroscopy, multivariate statistical analysis, and repeated measures ANOVA. Samples which were frozen at -80 °C prior to extraction of the metabolites exhibited an increase in the number of metabolites including branched-chain amino acids, aromatic amino acids, and tricarboxylic acid cycle intermediates. Storage of fecal samples at 4 °C ensured higher fidelity to freshly processed samples leading to the recommendation that fecal samples should not be frozen prior to extraction of fecal water. Furthermore, the work highlights the need to standardize sample storage of fecal samples to allow for the accurate study of the fecal metabolome.

Insights into gemcitabine resistance and the potential for therapeutic monitoring

INTRODUCTION

Gemcitabine is an important component of pancreatic cancer clinical management. Unfortunately, acquired gemcitabine resistance is widespread and there are limitations to predicting and monitoring therapeutic outcomes.

OBJECTIVE

To investigate the potential of metabolomics to differentiate pancreatic cancer cells that develops resistance or respond to gemcitabine treatment.

RESULTS

We applied 1D ¹H and 2D ¹H-¹³C HSQC NMR methods to profile the metabolic signature of pancreatic cancer cells. ¹³C₆-glucose labeling identified 30 key metabolites uniquely altered between wild-type and gemcitabine-resistant cells upon gemcitabine treatment. Gemcitabine resistance was observed to reprogram glucose metabolism and to enhance the pyrimidine synthesis pathway. Myo-inositol, taurine, glycerophosphocholine and creatinine phosphate exhibited a "binary switch" in response to gemcitabine treatment and acquired resistance.

CONCLUSION

Metabolic differences between naïve and resistant pancreatic cancer cells and, accordingly, their unique responses to gemcitabine treatment were revealed, which may be useful in the clinical setting for monitoring a patient's therapeutic response.

Metabolomic profile of systemic sclerosis patients

Systemic sclerosis (SSc) is an autoimmune disease of unknown aetiology characterized by vascular lesions, immunological alterations and diffuse fibrosis of the skin and internal organs. Since recent evidence suggests that there is a link between metabolomics and immune mediated disease, serum metabolic profile of SSc patients and healthy controls was investigated by ¹H-NMR and GC-MS techniques. The results indicated a lower level of aspartate, alanine, choline, glutamate, and glutarate in SSc patients compared with healthy controls. Moreover, comparing patients affected by limited SSc (lcSSc) and diffuse SSc (dcSSc), 6 discriminant metabolites were identified. The multivariate analysis performed using all the metabolites significantly different revealed glycolysis, gluconeogenesis, energetic pathways, glutamate metabolism, degradation of ketone bodies and pyruvate metabolism as the most important networks. Aspartate, alanine and citrate yielded a high area under receiver-operating characteristic (ROC) curves (AUC of 0.81; CI 0.726–0.93) for discriminating SSc patients from controls, whereas ROC curve generated with acetate, fructose, glutamate, glutamine, glycerol and glutarate (AUC of 0.84; CI 0.7–0.98) discriminated between lcSSc and dcSSc. These results indicated that serum NMR-based metabolomics profiling method is sensitive and specific enough to distinguish SSc from healthy controls and provided a feasible diagnostic tool for the diagnosis and classification of the disease.

NMR-based metabolomics of biofluids in cancer

This review describes the current status of NMR-based metabolomics of biofluids with respect to cancer risk assessment, detection, disease characterization, prognosis, and treatment monitoring. While the metabolism of cancer cells is altered compared with that of non-proliferating cells, the metabolome of blood and urine reflects the entire organism. We conclude that many studies show impressive associations between biofluid metabolomics and cancer progression, but translation to clinical practice is currently hindered by lack of validation, difficulties in biological interpretation, and non-standardized analytical procedures.

Metabolic fingerprinting of joint tissue of collagen-induced arthritis (CIA) rat: In vitro, high resolution NMR (nuclear magnetic resonance) spectroscopy based analysis

Rheumatoid arthritis (RA) is a systemic autoimmune disease whose major characteristics persistent joint inflammation that results in joint destruction and failure of the function. Collagen-induced arthritis (CIA) rat is an autoimmune disease model and in many ways shares features with RA. The CIA is associated with systemic manifestations, including alterations in the metabolism. Nuclear magnetic resonance (NMR) spectroscopy-based metabolomics has been successfully applied to the perchloric acid extract of the joint tissue of CIA rat and control rat for the analysis of aqueous metabolites. GPC (Glycerophosphocholine), carnitine, acetate, and creatinine were important discriminators of CIA rats as compared to control rats. Level of lactate (significance; p = 0.004), alanine (p = 0.025), BCA (Branched-chain amino acids) (p = 0.006) and creatinine (p = 0.023) was significantly higher in CIA rats as compared to control rats. Choline (p = 0.038) and GPC (p = 0.009) were significantly reduced in CIA rats as compared to control rats. Choline to GPC correlation was good and negative (Pearson correlation = -0.63) for CIA rats as well as for control rats (Pearson correlation = -0.79). All these analyses collectively considered as metabolic fingerprinting of the joint tissue of CIA rat as compared to control rat. The metabolic fingerprinting of joint tissue of CIA rats was different as compared to control rats. The metabolic fingerprinting reflects inflammatory disease activity in CIA rats with synovitis, demonstrating that underlying inflammatory process drives significant changes in metabolism that can be measured in the joint tissue. Therefore, the outcome of this study may be helpful for understanding the mechanism of metabolic processes in RA. This may be also helpful for the development of advanced diagnostic methods and therapy for RA.

Alpha-oxoglutarate inhibits the proliferation of immortalized normal bladder epithelial cells via an epigenetic switch involving ARID1A

Interstitial cystitis (IC) is a chronic urinary tract disease that is characterized by unpleasant sensations, such as persistent pelvic pain, in the absence of infection or other identifiable causes. We previously performed comprehensive metabolomics profiling of urine samples from IC patients using nuclear magnetic resonance and gas-chromatography/mass spectrometry and found that urinary α-oxoglutarate (α-OG), was significantly elevated. α-OG, a tricarboxylic acid (TCA) cycle intermediate, reportedly functions to suppress the proliferation of immortalized normal human bladder epithelial cells. Here, we identified AT-rich interactive domain 1 A (ARID1A), a key chromatin remodeler, as being hypomethylated and upregulated by α-OG treatment. This was done through EPIC DNA methylation profiling and subsequent biochemical approaches, including quantitative RT-PCR and western blot analyses. Furthermore, we found that α-OG almost completely suppresses ten-eleven translocation (TET) activity, but does not affect DNA methyltransferase (DNMT) activity. Altogether, our studies reveal the potential role of α-OG in epigenetic remodeling through its effects on ARID1A and TET expression in the bladder. This may provide a new possible therapeutic strategy in treating IC.

Direct Imaging of Superwetting Behavior on Solid-Liquid-Vapor Triphase Interfaces

A solid-liquid-vapor interface dominated by a three-phase contact line usually serves as an active area for interfacial reactions and provides a vital clue to surface behavior. Recently, direct imaging of the triphase interface of superwetting interfaces on the microscale/nanoscale has attracted broad scientific attention for both theoretical research and practical applications, and has gradually become an efficient and intuitive approach to explore the wetting behaviors of various multiphase interfaces. Here, recent progress on characterizing the solid-liquid-vapor triphase interface on the microscale/nanoscale with diverse types of imaging apparatus is summarized. Moreover, the accurate, visible, and quantitative information that can be obtained shows the real interfacial morphology of the wetting behaviors of multiphase interfaces. On the basis of fundamental research, technical innovations in imaging and complicated multiphase interfaces of the superwetting surface are also briefly presented.

NMR Metabolomics for Stem Cell type discrimination

Cell metabolism is a key determinant factor for the pluripotency and fate commitment of Stem Cells (SCs) during development, ageing, pathological onset and progression. We derived and cultured selected subpopulations of rodent fetal, postnatal, adult Neural SCs (NSCs) and postnatal glial progenitors, Olfactory Ensheathing Cells (OECs), respectively from the subventricular zone (SVZ) and the olfactory bulb (OB). Cell lysates were analyzed by proton Nuclear Magnetic Resonance (¹H-NMR) spectroscopy leading to metabolites identification and quantitation. Subsequent multivariate analysis of NMR data by Principal Component Analysis (PCA), and Partial Least Square Discriminant Analysis (PLS-DA) allowed data reduction and cluster analysis. This strategy ensures the definition of specific features in the metabolic content of phenotypically similar SCs sharing a common developmental origin. The metabolic fingerprints for selective metabolites or for the whole spectra demonstrated enhanced peculiarities among cell types. The key result of our work is a neat divergence between OECs and the remaining NSC cells. We also show that statistically significant differences for selective metabolites characterizes NSCs of different ages. Finally, the retrived metabolome in cell cultures correlates to the physiological SC features, thus allowing an integrated bioengineering approach for biologic fingerprints able to dissect the (neural) SC molecular specificities.