Application of NMR metabolomics to search for human disease biomarkers in blood

Elucidating Causal Relationships Among Gut Microbiota, Human Blood Metabolites, and Knee Osteoarthritis: Evidence from a Two-Stage Mendelian Randomization Analysis

Background: Although previous observational studies suggest a potential association between gut microbiota (GM) and knee osteoarthritis (KOA), the causal relationships remain unclear, particularly concerning the role of blood metabolites (BMs) as potential mediators. Elucidating these interactions is crucial for understanding the mechanisms underlying KOA progression and may inform the development of novel therapeutic strategies. Objective: This study aimed to determine the causal relationship between GM and KOA and to quantify the potential mediating role of BMs. Methods: Instrumental variables (IVs) for GM and BMs were retrieved from the MiBioGen consortium and metabolomics genome-wide association studies (GWAS) databases. KOA-associated single-nucleotide polymorphisms were sourced from the FinnGen consortium. Inverse-variance weighted approach was utilized as the main analytical method for Mendelian randomization (MR) analysis, complemented by MR-Egger, simple mode, weighted mode, and weighted median methods. The causal relationships between GM, BMs, and KOA were sequentially analyzed by multivariate MR. False discovery rate correction was applied to account for multiple comparisons in the MR results. Sensitivity analyses and reverse MR analysis were also conducted to verify the reliability of the findings. Finally, a two-step approach was employed to determine the proportion of BMs mediating the effects of GM on KOA. Results: MR analysis identified seven gut microbial species that are causally associated with KOA. Additionally, MR analysis of 1091 BMs and 309 metabolite ratios revealed 13 metabolites that influence the risk of KOA. Through two-step analysis, three BMs were identified as mediators of the effects of two GMs on KOA. Among them, 6-hydroxyindole sulfate exhibited the highest mediation percentage (10.26%), followed by N-formylanthranilic acid (6.55%). Sensitivity and reverse causality analyses further supported the robustness of these findings. Conclusion: This research identified specific GMs and BMs that have a causal association with KOA. These findings provide critical insights into how GM may influence KOA risk by modulating specific metabolites, which could be valuable for the targeted treatment and prevention of KOA.

Variations in Quinolinic Acid Levels in Tuberculosis Patients with Diabetes Comorbidity: A Pilot Prospective Cohort Study

Objective

We aimed to investigate dysregulated metabolic pathways and identify diagnostic and therapeutic targets in patients with tuberculosis-diabetes (TB-DM).

Methods

In our prospective cohort study, plasma samples were collected from healthy individuals, diabetic (DM) patients, untreated TB-only (TB-0)/TB-DM patients (TB-DM-0), and cured TB (TB-6)/TB-DM patients (TB-DM-6) to measure the levels of amino acids, fatty acids, and other metabolites in plasma using high-throughput targeted quantification methods.

Results

Significantly different biological processes and biomarkers were identified in DM, TB-DM-0, and TB-DM-6 patients. Moreover, quinolinic acid (QA) showed excellent predictive accuracy for distinguishing between DM patients and TB-DM-0 patients, with an AUC of 1 (95% CI 1-1). When differentiating between TB-DM-0 patients and TB-DM-6 patients, the AUC was 0.9297 (95% CI 0.8460-1). Compared to those in DM patients, the QA levels were significantly elevated in TB-DM-0 patients and decreased significantly after antituberculosis treatment. We simultaneously compared healthy controls and untreated tuberculosis patients and detected an increase in the level of QA in the plasma of tuberculosis patients, which decreased following treatment.

Conclusion

These findings improve the current understanding of tuberculosis treatment in patients with diabetes. QA may serve as an ideal diagnostic biomarker for TB-DM patients and contribute to the development of more effective treatments.

Evolution of a Combined UV/Vis and NMR Setup with Fixed Pathlengths for Mass-limited Samples

The investigation of reaction mechanisms is a complex task that usually requires the use of several techniques. To obtain as much information as possible on the reaction and any intermediates - possibly invisible to one technique - the combination of techniques is a solution. In this work we present a new setup for combined UV/Vis and NMR spectroscopy and compare it to an established alternative. The presented approach allows a versatile usage of different commercially-available components like mirrors and fiber bundles as well as different fixed pathlengths according to double transmission or single transmission measurements. While a previous approach is based on a dip-probe setup for conventional NMR probes, the new one is based on a micro-Helmholtz coil array (LiquidVoxel™). This makes the use of rectangular cuvettes possible, which ensure well-defined pathlengths allowing for quantification of species. Additionally, very low quantities of compound can be analyzed due to the microfabrication and small cuvette size used. As proof-of-principle this new setup for combined UV/Vis and NMR spectroscopy is used to examine a well-studied photochromic system of the dithienylethene compound class. A thorough comparison of the pros and cons of the two setups for combined UV/Vis and NMR measurements is performed.

Decoding active compounds and molecular targets of herbal medicine by high-throughput metabolomics technology: A systematic review

Clinical experiences of herbal medicine (HM) have been used to treat a variety of human intractable diseases. As the treatment of diseases using HM is characterized by multi-components and multi-targets, it is difficult to determine the bio-active components, explore the molecular targets and reveal the mechanisms of action. Metabolomics is frequently used to characterize the effect of external disturbances on organisms because of its unique advantages on detecting changes in endogenous small-molecule metabolites. Its systematicity and integrity are consistent with the effective characteristics of HM. After HM intervention, metabolomics can accurately capture and describe the behavior of endogenous metabolites under the disturbance of functional compounds, which will be used to decode the bioactive ingredients of HM and expound the molecular targets. Metabolomics can provide an approach for explaining HM, addressing unclear clinical efficacy and undefined mechanisms of action. In this review, the metabolomics strategy and its applications in HM are systematically introduced, which offers valuable insights for metabolomics methods to characterizing the pharmacological effects and molecular targets of HM.

High-throughput detection allied with machine learning for precise monitoring of significant serum metabolic changes in Helicobacter pylori infection

High-throughput detection of large-scale samples is the foundation for rapidly accessing massive metabolic data in precision medicine. Machine learning is a powerful tool for uncovering valuable information hidden within massive data. In this work, we achieved the extraction of a single fingerprinting of 1 μL serum within 5 s through a high-throughput detection platform based on functionalized nanoparticles. We quickly obtained over a thousand serum metabolic fingerprintings (SMFs) including those of individuals with Helicobacter pylori (HP) infection. Combining four classical machine learning models and enrichment analysis, we attempted to extract and confirm useful information behind these SMFs. Based on all fingerprint signals, all four models achieved area under the curve (AUC) values of 0.983-1. In particular, orthogonal partial least squares discriminant analysis (OPLS-DA) model obtained value of 1 in both the discovery and validation sets. Fortunately, we identified six significant metabolic features, all of which can greatly contribute to the monitoring of HP infection, with AUC values ranging from 0.906 to 0.985. The combination of these six significant metabolic features can enable the precise monitoring of HP infection in serum, with over 95 % of accuracy, specificity and sensitivity. The OPLS-DA model displayed optimal performance and the corresponding scatter plot visualized the clear distinction between HP and HC. Interestingly, they exhibit a consistent reduction trend compared to healthy controls, prompting us to explore the possible metabolic pathways and potential mechanism. This work demonstrates the potential alliance between high-throughput detection and machine learning, advancing their application in precision medicine.

Serum metabolic signatures for Alzheimer's Disease reveal alterations in amino acid composition: a validation study

INTRODUCTION

Alzheimer's Disease (AD) is complex and novel approaches are urgently needed to aid in diagnosis. Blood is frequently used as a source for biomarkers; however, its complexity prevents proper detection. The analytical power of metabolomics, coupled with statistical tools, can assist in reducing this complexity.

OBJECTIVES

Thus, we sought to validate a previously proposed panel of metabolic blood-based biomarkers for AD and expand our understanding of the pathological mechanisms involved in AD that are reflected in the blood.

METHODS

In the validation cohort serum and plasma were collected from 25 AD patients and 25 healthy controls. Serum was analysed for metabolites using nuclear magnetic resonance (NMR) spectroscopy, while plasma was tested for markers of neuronal damage and AD hallmark proteins using single molecule array (SIMOA).

RESULTS

The diagnostic performance of the metabolite biomarker panel was confirmed using sparse-partial least squares discriminant analysis (sPLS-DA) with an area under the curve (AUC) of 0.73 (95% confidence interval: 0.59-0.87). Pyruvic acid and valine were consistently reduced in the discovery and validation cohorts. Pathway analysis of significantly altered metabolites in the validation set revealed that they are involved in branched-chain amino acids (BCAAs) and energy metabolism (glycolysis and gluconeogenesis). Additionally, strong positive correlations were observed for valine and isoleucine between cerebrospinal fluid p-tau and t-tau.

CONCLUSIONS

Our proposed panel of metabolites was successfully validated using a combined approach of NMR and sPLS-DA. It was discovered that cognitive-impairment-related metabolites belong to BCAAs and are involved in energy metabolism.

Improving quality of analysis by suppression of unwanted signals through band-selective excitation in NMR spectroscopy for metabolomics studies

INTRODUCTION

Nuclear Magnetic Resonance (NMR) spectroscopy stands as a preeminent analytical tool in the field of metabolomics. Nevertheless, when it comes to identifying metabolites present in scant amounts within various types of complex mixtures such as plants, honey, milk, and biological fluids and tissues, NMR-based metabolomics presents a formidable challenge. This predicament arises primarily from the fact that the signals emanating from metabolites existing in low concentrations tend to be overshadowed by the signals of highly concentrated metabolites within NMR spectra.

OBJECTIVES

The aim of this study is to tackle the issue of intense sugar signals overshadowing the desired metabolite signals, an optimal pulse sequence with band-selective excitation has been proposed for the suppression of sugar's moiety signals (SSMS). This sequence serves the crucial purpose of suppressing unwanted signals, with a particular emphasis on mitigating the interference caused by sugar moieties' signals.

METHODS

We have implemented this comprehensive approach to various NMR techniques, including 1D 1H presaturation (presat), 2D J-resolved (RES), 2D 1H-1H Total Correlation Spectroscopy (TOCSY), and 2D 1H-13C Heteronuclear Single Quantum Coherence (HSQC) for the samples of dates-flesh, honey, a standard stock solution of glucose, and nine amino acids, and commercial fetal bovine serum (FBS).

RESULTS

The outcomes of this approach were significant. The suppression of the high-intensity sugar signals has considerably enhanced the visibility and sensitivity of the signals emanating from the desired metabolites.

CONCLUSION

This, in turn, enables the identification of a greater number of metabolites. Additionally, it streamlines the experimental process, reducing the time required for the comparative quantification of metabolites in statistical studies in the field of metabolomics.

Pre-diagnostic blood biomarkers for adult glioma

Glioma is one of the most common malignant primary brain tumours in adults, of which, glioblastoma is the most prevalent and malignant entity. Glioma is often diagnosed at a later stage of disease progression, which means it is associated with significant mortality and morbidity. Therefore, there is a need for earlier diagnosis of these tumours, which would require sensitive and specific biomarkers. These biomarkers could better predict glioma onset to improve diagnosis and therapeutic options for patients. While liquid biopsies could provide a cheap and non-invasive test to improve the earlier detection of glioma, there is little known on pre-diagnostic biomarkers which predate disease detection. In this review, we examine the evidence in the literature for pre-diagnostic biomarkers in glioma, including metabolomics and proteomics. We also consider the limitations of these approaches and future research directions of pre-diagnostic biomarkers for glioma.

Untargeted metabolomics of pulmonary tuberculosis patient serum reveals potential prognostic markers of both latent infection and outcome

Currently, there are no particularly effective biomarkers to distinguish between latent tuberculosis infection (LTBI) and active pulmonary tuberculosis (PTB) and evaluate the outcome of TB treatment. In this study, we have characterized the changes in the serum metabolic profiles caused by Mycobacterium tuberculosis (Mtb) infection and standard anti-TB treatment with isoniazid-rifampin-pyrazinamide-ethambutol (HRZE) using GC-MS and LC-MS/MS. Seven metabolites, including 3-oxopalmitic acid, akeboside ste, sulfolithocholic acid, 2-decylfuran (4,8,8-trimethyldecahydro-1,4-methanoazulen-9-yl)methanol, d-(+)-camphor, and 2-methylaminoadenosine, were identified to have significantly higher levels in LTBI and untreated PTB patients (T0) than those in uninfected healthy controls (Un). Among them, akeboside Ste and sulfolithocholic acid were significantly decreased in PTB patients with 2-month HRZE (T2) and cured PTB patients with 2-month HRZE followed by 4-month isoniazid-rifampin (HR) (T6). Receiver operator characteristic curve analysis revealed that the combined diagnostic model showed excellent performance for distinguishing LT from T0 and Un. By analyzing the biochemical and disease-related pathways, we observed that the differential metabolites in the serum of LTBI or TB patients, compared to healthy controls, were mainly involved in glutathione metabolism, ascorbate and aldarate metabolism, and porphyrin and chlorophyll metabolism. The metabolites with significant differences between the T0 group and the T6 group were mainly enriched in niacin and nicotinamide metabolism. Our study provided more detailed experimental data for developing laboratory standards for evaluating LTBI and cured PTB.

Plasm Metabolomics Study in Pulmonary Metastatic Carcinoma

Background

The lung is one of the most common metastatic sites of malignant tumors. Early detection of pulmonary metastatic carcinoma can effectively reduce relative cancer mortality. Human metabolomics is a qualitative and quantitative study of low-molecular metabolites in the body. By studying the plasm metabolomics of patients with pulmonary metastatic carcinoma or other lung diseases, we can find the difference in plasm levels of low-molecular metabolites among them. These metabolites have the potential to become biomarkers of lung metastases.

Methods

Patients with pulmonary nodules admitted to our department from February 1, 2019, to May 31, 2019, were collected. According to the postoperative pathological results, they were divided into three groups: pulmonary metastatic carcinoma (PMC), benign pulmonary nodules (BPN), and primary lung cancer (PLC). Moreover, healthy people who underwent physical examination were enrolled as the healthy population group (HPG) during the same period. On the one hand, to study lung metastases screening in healthy people, PMC was compared with HPG. The multivariate statistical analysis method was used to find the significant low-molecular metabolites between the two groups, and their discriminating ability was verified by the ROC curve. On the other hand, from the perspective of differential diagnosis of lung metastases, three groups with different pulmonary lesions (PMC, BPN, and PLC) were compared as a whole, and then the other two groups were compared with PMC, respectively. The main low-molecular metabolites were selected, and their discriminating ability was verified.

Results

In terms of lung metastases screening for healthy people, four significant low-molecular metabolites were found by comparison of PMC and HPG. They were O-arachidonoyl ethanolamine, adrenoyl ethanolamide, tricin 7-diglucuronoside, and p-coumaroyl vitisin A. In terms of the differential diagnosis of pulmonary nodules, the significant low-molecular metabolites selected by the comparison of the three groups as a whole were anabasine, octanoylcarnitine, 2-methoxyestrone, retinol, decanoylcarnitine, calcitroic acid, glycogen, and austalide L. For the comparison of PMC and BPN, L-tyrosine, indoleacrylic acid, and lysoPC (16 : 0) were selected, while L-octanoylcarnitine, retinol, and decanoylcarnitine were selected for the comparison of PMC and PLC. Their AUCs of ROC are all greater than 0.80. It indicates that these substances have a strong ability to differentiate between pulmonary metastatic carcinoma and other pulmonary nodule lesions.

Conclusion

Through the research of plasm metabolomics, it is possible to effectively detect the changes in some low-molecular metabolites among primary lung cancer, pulmonary metastatic carcinoma, and benign pulmonary nodule patients and healthy people. These significant metabolites have the potential to be biomarkers for screening and differential diagnosis of lung metastases.

NMR Metabolomics in Serum Fingerprinting of Schizophrenia Patients in a Serbian Cohort

Schizophrenia is a widespread mental disorder that leads to significant functional impairments and premature death. The state of the art indicates gaps in the understanding and diagnosis of this disease, but also the need for personalized and precise approaches to patients through customized medical treatment and reliable monitoring of treatment response. In order to fulfill existing gaps, the establishment of a universal set of disorder biomarkers is a necessary step. Metabolomic investigations of serum samples of Serbian patients with schizophrenia (51) and healthy controls (39), based on NMR analyses associated with chemometrics, led to the identification of 26 metabolites/biomarkers for this disorder. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models with prediction accuracies of 0.9718 and higher were accomplished during chemometric analysis. The established biomarker set includes aspartate/aspartic acid, lysine, 2-hydroxybutyric acid, and acylglycerols, which are identified for the first time in schizophrenia serum samples by NMR experiments. The other 22 identified metabolites in the Serbian samples are in accordance with the previously established NMR-based serum biomarker sets of Brazilian and/or Chinese patient samples. Thirteen metabolites (lactate/lactic acid, threonine, leucine, isoleucine, valine, glutamine, asparagine, alanine, gamma-aminobutyric acid, choline, glucose, glycine and tyrosine) that are common for three different ethnic and geographic origins (Serbia, Brazil and China) could be a good start point for the setup of a universal NMR serum biomarker set for schizophrenia.

Elevated Circulatory Proline to Glutamine Ratio (PQR) in Endometriosis and Its Potential as a Diagnostic Biomarker

Endometriosis (EM) is a hormone-dependent gynecological disease associated with chronic pelvic pain and altered immuno-inflammatory processes. It shares some cancer-like characteristics such as increased proline biosynthesis and activated glutaminolysis. Both proline and glutamine are interconvertible metabolically, and studies have shown their roles in cancer cell metabolic reprogramming, redox homeostasis, occurrence/development of endometrial carcinoma, and its further progression toward the malignant state. So based on this, we hypothesized that the circulatory proline to glutamine ratio (PQR) would be altered in EM and may serve as an indicative biomarker to improve the clinical diagnosis of EM. In present study, the circulatory-PQR levels were estimated for 39 EM patients and 48 age matched healthy female subjects using 800 MHz NMR spectroscopy. Among 39 EM patients, 15 were in the clinical stages I to II and referred to here as moderate EM (MEM) patients and 24 were in the clinical stages III to IV and referred here as severe EM (SEM) patients. The circulatory-PQR levels were significantly increased in EM patients (0.99 ± 0.13 μM in MEM; 1.39 ± 0.22 μM in SEM) compared to normal control (NC) subjects (0.52 ± 0.05 μM in NC). Further, the circulatory PQR levels exhibit the highest diagnostic potential with area under receiver operating characteristic (AUROC) curve values equal to 0.87 ± 0.04 [95%CI = 0.79-0.96] for MEM and 0.89 ± 0.04 [95% CI = 0.82-0.96] for SEM. These results suggested that circulatory-PQR has significant potential to serve as a noninvasive biomarker for diagnostic/prognostic screening of EM and further underscored the importance of these two nonessential amino acids (proline and glutamine) in cancer metabolism.

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.

NMR-based Metabolomics and Fatty Acid Profiles to Unravel Biomarkers in Preclinical Animal Models of Compulsive Behavior

Compulsivity is a key manifestation of inhibitory control deficit and a cardinal symptom of psychopathological conditions such as obsessive-compulsive and attention-deficit hyperactivity disorders, in which metabolic alterations have raised attention as putative biomarkers for early identification. The present study assessed the metabolic profile in a preclinical model of a compulsive phenotype of rats. We used the schedule-induced polydipsia (SIP) method to classify male Wistar rats into high drinkers (HDs) or low drinkers (LDs) according to their compulsive drinking rate developed by exposure to a fixed-time 60 s (FT-60) schedule of reinforcement with water available ad libitum during 20 sessions. Before and after SIP, blood samples were collected for subsequent serum analysis by nuclear magnetic resonance spectroscopy coupled to multivariate analysis. Although no differences existed in the pre-SIP set, the compulsive drinking behavior induced remarkable metabolic alterations: HD rats selected by SIP exhibited a hyperlipidemic, hypoglycemic, and hyperglutaminergic profile compared with their low-compulsive counterparts. Interestingly, these alterations were not attributable to the mere exposure to reward pellets because a control experiment did not show differences between HDs and LDs after 20 sessions of pellet consumption without intermittent reinforcement. Our results shed light toward the implication of dietary and metabolic factors underpinning the vulnerability to compulsive behaviors.

Metabolomics comparison of serum and urine in dairy cattle using proton nuclear magnetic resonance spectroscopy

OBJECTIVE

The aim of the study was to conduct metabolic profiling of dairy cattle serum and urine using proton nuclear magnetic resonance (1H-NMR) spectroscopy and to compare the results obtained with those of other dairy cattle herds worldwide so as to provide a basic dataset to facilitate research on metabolites in serum and urine.

METHODS

Six dairy cattle were used in this study; all animals were fed the same diet, which was composed of total mixed ration; the fed amounts were based on voluntary intake. Blood from the jugular neck vein of each steer was collected at the same time using a separate serum tube. Urine samples were collected by hand sweeping the perineum. The metabolites were determined by 1H-NMR spectroscopy, and the obtained data were statistically analyzed by performing principal component analysis, partial least squares-discriminant analysis, variable importance in projection scores, and metabolic pathway data using Metaboanalyst 4.0.

RESULTS

The total number of metabolites in the serum and urine was measured to be 115 and 193, respectively, of which 47 and 81, respectively were quantified. Lactate (classified as an organic acid) and urea (classified as an aliphatic acylic compound) exhibited the highest concentrations in serum and urine, respectively. Some metabolites that have been associated with diseases such as ketosis, bovine respiratory disease, and metritis, and metabolites associated with heat stress were also found in the serum and urine samples.

CONCLUSION

The metabolites measured in the serum and urine could potentially be used to detect diseases and heat stress in dairy cattle. The results could also be useful for metabolomic research on the serum and urine of ruminants in Korea.

Serum lysophospholipidome of dietary origin as a suitable susceptibility/risk biomarker of human hypercholesterolemia: A cross-sectional study

BACKGROUND & AIMS

Whether bioactive lysophospholipids (lyso-PLs) and trimethylamine-N-oxide (TMAO) serve as non-invasive biomarkers in early human hypercholesterolemia (HC) is unknown. This study aimed to assess whether serum lyso-PLs and plasma TMAO may be suitable susceptibility/risk biomarkers of HC in humans. Secondarily, we aimed to evaluate the relationships between targeted metabolites, diet composition and circulating liver transaminases, and verify these results in hamsters.

METHODS

A targeted metabolomics and lipidomics approach determined plasma TMAO and serum lysophosphatidylcholines (lyso-PCs) and lysophosphatidylethanolamines (lyso-PEs) in low (L-LDL-c) and moderate to high (MH-LDL-c) LDL-cholesterol subjects. Additionally, the relationships between targeted metabolites, liver transaminases and diet, particularly fatty acid intake, were tested. In parallel, plasma and liver lyso-PL profiles were studied in 16 hamsters fed a moderate high-fat (HFD) or low-fat (LFD) diet for 30 days.

RESULTS

Predictive models identified lyso-PC15:0 and lyso-PE18:2 as the most discriminant lyso-PLs among groups. In MH-LDL-c (n = 48), LDL-cholesterol and saturated FAs were positively associated with lyso-PC15:0, whereas in L-LDL-c (n = 70), LDL-cholesterol and polyunsaturated fatty acids (PUFAs) were negatively and positively related to lyso-PE18:2, respectively. Interestingly, in MH-LDL-c, the lower lyso-PE 18:2 concentrations were indicative of higher LDL-cholesterol levels. Intrahepatic accumulation of lyso-PLs-containing essential n-6 PUFAs, including lyso-PE18:2, were higher in HFD-fed hamsters than LFD-fed hamsters.

CONCLUSIONS

Overall, results revealed a possible hepatic adaptive mechanism to counteract diet-induced steatosis in animal and hypercholesterolemia progression in humans. In particular, low serum lyso-PE18:2 suggests a suitable susceptibility/risk biomarker of HC in humans.

Human disease biomarker panels through systems biology

As more uses for biomarkers are sought after for an increasing number of disease targets, single-target biomarkers are slowly giving way for biomarker panels. These panels incorporate various sources of biomolecular and clinical data to guarantee a higher robustness and power of separation for a clinical test. Multifactorial diseases such as psychiatric disorders show great potential for clinical use, assisting medical professionals during the analysis of risk and predisposition, disease diagnosis and prognosis, and treatment applicability and efficacy. More specific tests are also being developed to assist in ruling out, distinguishing between, and confirming suspicions of multifactorial diseases, as well as to predict which therapy option may be the best option for a given patient's biochemical profile. As more complex datasets are entering the field, involving multi-omic approaches, systems biology has stepped in to facilitate the discovery and validation steps during biomarker panel generation. Filtering biomolecules and clinical data, pre-validating and cross-validating potential biomarkers, generating final biomarker panels, and testing the robustness and applicability of those panels are all beginning to rely on machine learning and systems biology and research in this area will only benefit from advances in these approaches.

Nuclear Magnetic Resonance Spectroscopy for Quantitative Analysis: A Review for Its Application in the Chemical, Pharmaceutical and Medicinal Domains

Nuclear magnetic resonance (NMR) is a rapid and accurate analytical tool for qualification and quantification. The capacity of NMR of being quantitative can also justify the calibration of other analytical methods. In pharmaceutical domain, quantitative NMR (qNMR) can be applied in the identification and quantification of drug simultaneously. The early drug development stage requires a minimum sample for analysis. Thus, priority should be given to utilize this technique to attain results with least investment, rapid analysis time and minimum sample consumption. This technique is a significant phenomenon to identify impurities, drug substance, residual solvents of in-process control (IPC) samples and characterizing the formulations. From an analyst's perspective, qNMR proved to be a routine practice in pharmaceutical industry to qualify any drug product. The absolute and relative methods offer great help in quantifying the component of interest in the process control samples and finished products. This review highlights the evolution of NMR application in the pharmaceutical industry, where determining the purity of drug substance, drug product and establishing the identity of impurities and its level are the challenging aspects. NMR in medicinal field emerging as a numero uno for Covid-19 severity detection and its dire consequences, accelerated vaccine development and the mapping of SAR-COV-2 RNA and proteins via chemical shift assignments.

Development of an NMR-Based Platform for the Direct Structural Annotation of Complex Natural Products Mixtures

The development of new "omics" platforms is having a significant impact on the landscape of natural products discovery. However, despite the advantages that such platforms bring to the field, there remains no straightforward method for characterizing the chemical landscape of natural products libraries using two-dimensional nuclear magnetic resonance (2D-NMR) experiments. NMR analysis provides a powerful complement to mass spectrometric approaches, given the universal coverage of NMR experiments. However, the high degree of signal overlap, particularly in one-dimensional NMR spectra, has limited applications of this approach. To address this issue, we have developed a new data analysis platform for complex mixture analysis, termed MADByTE (Metabolomics and Dereplication by Two-Dimensional Experiments). This platform employs a combination of TOCSY and HSQC spectra to identify spin system features within complex mixtures and then matches spin system features between samples to create a chemical similarity network for a given sample set. In this report we describe the design and construction of the MADByTE platform and demonstrate the application of chemical similarity networks for both the dereplication of known compound scaffolds and the prioritization of bioactive metabolites from a bacterial prefractionated extract library.

NMR-Based Metabolomics Approach to Explore Brain Metabolic Changes Induced by Prenatal Exposure to Autism-Inducing Chemicals

NMR offers the unique potential to holistically screen hundreds of metabolites and has already proved to be a powerful technique able to provide a global picture of a wide range of metabolic processes underlying complex and multifactorial diseases, such as neurodegenerative and neurodevelopmental diseases. The aim of this study was to apply an NMR-based metabolomics approach to explore brain metabolic changes in both male and female rats induced by prenatal exposure to two chemicals associated with autism disorders-the organophosphorus pesticide chlorpyrifos (CPF) and the antiepileptic drug valproic acid (VPA)-at different postnatal ages. Depending on the age and on the brain region (hippocampus and cerebellum), several metabolites were shown to be significantly affected by exposure to both compounds. The evaluation of the spectral profiles revealed that the nervous-system-specific metabolite N-acetylaspartate (NAA), amino acid neurotransmitters (e.g., glutamate, glutamine, GABA, glycine), pyroglutamic acid, unsaturated fatty acids, and choline-based compounds are discriminant biomarkers. Additionally, metabolic changes varied as a function of age, but importantly not of sex.

Ion Mobility–Mass Spectrometry for Bioanalysis

This paper aims to cover the main strategies based on ion mobility spectrometry (IMS) for the analysis of biological samples. The determination of endogenous and exogenous compounds in such samples is important for the understanding of the health status of individuals. For this reason, the development of new approaches that can be complementary to the ones already established (mainly based on liquid chromatography coupled to mass spectrometry) is welcomed. In this regard, ion mobility spectrometry has appeared in the analytical scenario as a powerful technique for the separation and characterization of compounds based on their mobility. IMS has been used in several areas taking advantage of its orthogonality with other analytical separation techniques, such as liquid chromatography, gas chromatography, capillary electrophoresis, or supercritical fluid chromatography. Bioanalysis is not one of the areas where IMS has been more extensively applied. However, over the last years, the interest in using this approach for the analysis of biological samples has clearly increased. This paper introduces the reader to the principles controlling the separation in IMS and reviews recent applications using this technique in the field of bioanalysis.

Simple and Rapid (Extraction) Protocol for NMR Metabolomics-Direct Measurement of Hydrophilic and Hydrophobic Metabolites Using Slice Selection

Investigating the metabolic profiles of solid sample materials with solution nuclear magnetic resonance (NMR) spectroscopy requires the extraction of these metabolites. This is commonly done by using two immiscible solvents such as water and chloroform for extraction. Subsequent solvent removal makes these extraction procedures very time-consuming. To shorten the preparation time of the NMR sample, the following protocol is proposed: the metabolites from a solid or liquid sample are extracted directly in the NMR tube, the NMR tube is centrifuged, and the metabolite profiles in the aqueous and organic phases are determined by using slice-selective proton NMR experiments. This protocol was tested with 11 black teas and 11 green teas, which can be easily distinguished by their metabolic profiles in the aqueous phase. As a test case for liquid samples, 29 milk samples were investigated. The geographical origin of the diaries where the milk was processed could not be determined unequivocally from the metabolic profiles of the hydrophilic metabolites; however, this was easily seen in the lipid profiles. As shown for the different test samples, the extraction protocol in combination with slice-selection NMR experiments is suitable for metabolic investigations. Because samples are rapidly processed, this approach can be used to explore different extraction strategies for metabolite isolation.

Prognostic value of endogenous and exogenous metabolites in liver transplantation

Liver transplantation has been widely accepted as an effective intervention for end-stage liver diseases and early hepatocellular carcinomas. However, a variety of postoperative complications and adverse reactions have baffled medical staff and patients. Currently, transplantation monitoring relies primarily on nonspecific biochemical tests, whereas diagnosis of multiple complications depends on invasive pathological examination. Therefore, a noninvasive monitoring method with high selectivity and specificity is desperately needed. This review summarized the potential of endogenous small-molecule metabolites as biomarkers for assessing graft function, ischemia-reperfusion injury and liver rejection. Exogenous metabolites, mainly those immunosuppressive agents with high intra- and inter-individual variability, were also discussed for transplantation monitoring.

Metabolomics Comparison of Hanwoo (Bos taurus coreanae) Biofluids Using Proton Nuclear Magnetic Resonance Spectroscopy

The aim of this study was to identify the metabolomic profiles of rumen fluid, serum, and urine from Hanwoo (Bos taurus coreanae), using proton nuclear magnetic resonance (1H-NMR) spectroscopy. In all, 189, 110, and 188 metabolites were identified in rumen fluid, serum, and urine, and 107, 49, and 99 were quantified, respectively. Organic acids, carbohydrates, and aliphatic acyclic compound metabolites were present at the highest concentrations in rumen fluid, serum, and urine, respectively. In addition, acetate, glucose, and urea were the most highly concentrated individual metabolites in rumen fluid, serum, and urine, respectively. In all, 77 metabolites were commonly identified, and 19 were quantified across three biofluids. Metabolic pathway analysis showed that the common quantified metabolites could provide relevant information about three main metabolic pathways, phenylalanine, tyrosine, and tryptophan biosynthesis; caffeine metabolism; and histidine metabolism. These results can be useful as reference values for future metabolomic research on Hanwoo biofluids in Korea.

Metabolomic tools used in marine natural product drug discovery

Introduction: The marine environment is a very promising resource for natural product research, with many of these reaching the market as new drugs, especially in the field of cancer therapy as well as the drug discovery pipeline for new antimicrobials. Exploitation for bioactive marine compounds with unique structures and novel bioactivity such as the isoquinoline alkaloid; trabectedin, the polyether macrolide; halichondrin B, and the peptide; dolastatin 10, requires the use of analytical techniques, which can generate unbiased, quantitative, and qualitative data to benefit the biodiscovery process. Metabolomics has shown to bridge this understanding and facilitate the development of new potential drugs from marine sources and particularly their microbial symbionts.Areas covered: In this review, articles on applied secondary metabolomics ranging from 1990-2018 as well as to the last quarter of 2019 were probed to investigate the impact of metabolomics on drug discovery for new antibiotics and cancer treatment.Expert opinion: The current literature review highlighted the effectiveness of metabolomics in the study of targeting biologically active secondary metabolites from marine sources for optimized discovery of potential new natural products to be made accessible to a R&D pipeline.

A Unified Conceptual Framework for Metabolic Phenotyping in Diagnosis and Prognosis

Understanding metabotype (multicomponent metabolic characteristics) variation can help to generate new diagnostic and prognostic biomarkers, as well as models, with potential to impact on patient management. We present a suite of conceptual approaches for the generation, analysis, and understanding of metabotypes from body fluids and tissues. We describe and exemplify four fundamental approaches to the generation and utilization of metabotype data via multiparametric measurement of (i) metabolite levels, (ii) metabolic trajectories, (iii) metabolic entropies, and (iv) metabolic networks and correlations in space and time. This conceptual framework can underpin metabotyping in the scenario of personalized medicine, with the aim of improving clinical outcomes for patients, but the framework will have value and utility in areas of metabolic profiling well beyond this exemplar.