The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research. Methods Mol Biol. 2015;1277:161-193. [PMID: 25677154 DOI: 10.1007/978-1-4939-2377-9_13]

Longitudinal non-targeted metabolomic profiling of urine samples for monitoring of kidney transplantation patients

The assessment of kidney function within the first year following transplantation is crucial for predicting long-term graft survival. This study aimed to develop a robust and accurate model using metabolite profiles to predict early long-term outcomes in patient groups at the highest risk of early graft loss. A group of 61 kidney transplant recipients underwent thorough monitoring during a one-year follow-up period, which included a one-week hospital stay and follow-up assessments at three and six months. Based on their 12-month follow-up serum creatinine levels: Group 2 had levels exceeding 1.5 mg/dl, while Group 1 had levels below 1.5 mg/dl. Metabolites were detected by mass spectrometer and first pre-processed. Univariate and multivariate statistical analyses were employed to identify significant differences between the two groups. Nineteen metabolites were found to differ significantly in the 1st week, and seventeen metabolites in the 3rd month (adjusted p-value < 0.05, quality control (QC) < 30, a fold change (FC) > 1.1 or a FC < 0.91, Variable Influence on Projection (VIP) > 1). However, no significant differences were observed in the 6th month. These distinctive metabolites mainly belonged to lipid, fatty acid, and amino acid categories. Ten models were constructed using a backward conditional approach, with the best performance seen in model 5 for Group 2 at the 1st-week mark (AUC 0.900) and model 3 at the 3rd-month mark (AUC 0.924). In conclusion, the models developed in the early stages may offer potential benefits in the management of kidney transplant patients.

Unveiling the impact of harvest time on Dioscorea opposita Thunb. cv. Tiegun maturity by NMR-based metabolomics and LC-MS/MS analysis

BACKGROUND

Dioscorea opposita Thunb. cv. Tiegun maturity (DM) is an important factor influencing its quality. However, there are few studies on the impact of harvest time on its maturation. In the present study, a NMR-based metabolomics approach was applied to investigate the dynamic metabolic changes of D. opposita Thunb. cv. Tiegun at six different harvest stages: stage 1 (S1), stage 2 (S2), Stage 3 (S3), stage 4 (S4), stage 5 (S5) and stage 6 (S6).

RESULTS

Principal component analysis showed distinct segregation of samples obtained from S1, S2 and S3 compared to those derived from S4, S5 and S6. Interestingly, these samples from the two periods were obtained before and after frost, indicating that frost descent might be important for DM. Eight differential metabolites responsible for good separation of different groups were identified by the principal component analysis loading plot and partial least squares-discriminant analysis. In addition, quantitative analysis of these metabolites using liquid chromatography-tandem mass spectrometry determined the effects of harvest time on these metabolite contents, two of which, sucrose and allantoin, were considered as potential biomarkers to determine DM.

CONCLUSION

The present study demonstrated that NMR-based metabolomics approach could serve as a powerful tool to identify differential metabolites during harvesting processes, also offering a fresh insight into understanding the DM and the potential mechanism of quality formation. © 2024 Society of Chemical Industry.

A multilevel LC-HRMS and NMR correlation workflow towards foodomics advancement: Application in table olives

Foodomics employs advanced analytical techniques to provide answers regarding food composition, authenticity control, marker identification and issues related to food quality and safety. Nuclear magnetic resonance (NMR) spectroscopy and chromatography hyphenated to mass spectrometry (MS) are the main analytical platforms used in this field. Nevertheless, they are rarely employed in an integrated manner, and even then, the contribution of each technique remains vague. Table olives (Olea europaea L.) are a food commodity of high economic and nutritional value with an increasing production tendency over the last two decades, which, however, suffers from extensive fraud incidents and quality determination uncertainties. Thus, the current attempt aims towards two axes with the first being the multilevel integration of LC-HRMS and NMR data of the same samples and table olives being the selected matrix. In more detail, UPLC-HRMS/MS-based analysis was compared at different stages within an untargeted metabolomics workflow with an NMR-based study and the complementarity of the two platforms was evaluated. Furthermore, statistical heterospectroscopy (SHY), rarely employed in foodomics, combining the spectroscopic with spectrometric datasets and aiming to increase the confidence level of annotated biomarkers was applied. Amongst these lines, the second parallel axis of this study was the detailed characterization of table olives' metabolome in search for quality markers considering the impact of geographical (from Northern to Southern Greece) and botanical origin (Kalamon, Konservolia, Chalkidikis cultivars), as well as processing parameters (Spanish, Greek). To that end, using deep dereplication tools including statistical methods, with SHY employed for the first time in table olives, different biomarkers, belonging to the classes of phenyl alcohols, phenylpropanoids, flavonoids, secoiridoids and triterpenoids were identified as responsible for the observed classifications. The current binary pipeline, focusing on biomarkers' identification confidence, could be suggested as a meaningful workflow not only in olive-based products, but also in food quality control and foodomics in general.

UPLC-Triple-TOF-MS-based serum metabonomic revealed the alleviating effect of QingYan Formula on perimenopausal syndrome rats

The study aimed to investigate the relieving effect of QingYan Formula (QYF) in treating perimenopausal syndrome. A combination of metabonomic analysis and in vitro pharmacodynamic experiments was employed to achieve this objective.Over a period of 12 weeks, ovariectomized (OVX) rats were orally administered QYF's 70 % ethanol extract or estradiol valerate (EV). The results demonstrate that QYF restored the estrous cycle of ovariectomized rats and exhibited significant estrogenic activity, as indicated by reversal of uterine and vagina atrophy, improvement of serum estradiol level and decrease of serum luteinizing hormone(LH) level. Additionally, QYF administration effectively reduced high bone turnover and repaired trabecular microstructure damage. Metabonomic analysis of the OVX rats treated with QYF revealed the identification of 55 different metabolites in the serum, out of which 35 may be potential biomarkers. QYF could regulate the disturbed metabolic pathways including the Biosynthesis of unsaturated fatty acids, arachidonic acid metabolism, bile secretion, and steroid hormone biosynthesis. PI3KCA, SRC, and MAPK3 are potential therapeutic targets for QYF therapeutic effects. These findings support the efficacy of QYF in alleviating perimenopausal syndrome and regulating lipid metabolic disorders in OVX rats.

Plasma infrared fingerprinting with machine learning enables single-measurement multi-phenotype health screening

Infrared spectroscopy is a powerful technique for probing the molecular profiles of complex biofluids, offering a promising avenue for high-throughput in vitro diagnostics. While several studies showcased its potential in detecting health conditions, a large-scale analysis of a naturally heterogeneous potential patient population has not been attempted. Using a population-based cohort, here we analyze 5,184 blood plasma samples from 3,169 individuals using Fourier transform infrared (FTIR) spectroscopy. Applying a multi-task classification to distinguish between dyslipidemia, hypertension, prediabetes, type 2 diabetes, and healthy states, we find that the approach can accurately single out healthy individuals and characterize chronic multimorbid states. We further identify the capacity to forecast the development of metabolic syndrome years in advance of onset. Dataset-independent testing confirms the robustness of infrared signatures against variations in sample handling, storage time, and measurement regimes. This study provides the framework that establishes infrared molecular fingerprinting as an efficient modality for populational health diagnostics.

A practical introduction to holo-omics

Holo-omics refers to the joint study of non-targeted molecular data layers from host-microbiota systems or holobionts, which is increasingly employed to disentangle the complex interactions between the elements that compose them. We navigate through the generation, analysis, and integration of omics data, focusing on the commonalities and main differences to generate and analyze the various types of omics, with a special focus on optimizing data generation and integration. We advocate for careful generation and distillation of data, followed by independent exploration and analyses of the single omic layers to obtain a better understanding of the study system, before the integration of multiple omic layers in a final model is attempted. We highlight critical decision points to achieve this aim and flag the main challenges to address complex biological questions regarding the integrative study of host-microbiota relationships.

Untargeted faecal metabolomics for the discovery of biomarkers and treatment targets for inflammatory bowel diseases

The gut microbiome has been recognised as a key component in the pathogenesis of inflammatory bowel diseases (IBD), and the wide range of metabolites produced by gut bacteria are an important mechanism by which the human microbiome interacts with host immunity or host metabolism. High-throughput metabolomic profiling and novel computational approaches now allow for comprehensive assessment of thousands of metabolites in diverse biomaterials, including faecal samples. Several groups of metabolites, including short-chain fatty acids, tryptophan metabolites and bile acids, have been associated with IBD. In this Recent Advances article, we describe the contribution of metabolomics research to the field of IBD, with a focus on faecal metabolomics. We discuss the latest findings on the significance of these metabolites for IBD prognosis and therapeutic interventions and offer insights into the future directions of metabolomics research.

A principal component analysis of metabolome and cognitive decline among Japanese older adults: cross-sectional analysis using Tohoku Medical Megabank Cohort Study

BackgroundDementia is the leading cause of disability and imposes a significant burden on society. Previous studies have suggested an association between metabolites and cognitive decline. Although the metabolite composition differs between Western and Asian populations, studies targeting Asian populations remain scarce.MethodsThis cross-sectional study used data from a cohort survey of community-dwelling older adults aged ≥ 60 years living in Miyagi, Japan, conducted by Tohoku Medical Megabank Organization between 2013 and 2016. Forty-three metabolite variables quantified using nuclear magnetic resonance spectroscopy were used as explanatory variables. Dependent variable was the presence of cognitive decline (≤ 23 points), assessed by the Mini-Mental State Examination. Principal component (PC) analysis was performed to reduce the dimensionality of metabolite variables, followed by logistic regression analysis to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for cognitive decline.ResultsA total of 2,940 participants were included (men: 49.0%, mean age: 67.6 years). Among them, 1.9% showed cognitive decline. The first 12 PC components (PC1-PC12) accounted for 71.7% of the total variance. Multivariate analysis showed that PC1, which mainly represented essential amino acids, was associated with lower odds of cognitive decline (OR = 0.89; 95% CI, 0.80-0.98). PC2, which mainly included ketone bodies, was associated with cognitive decline (OR = 1.29; 95% CI, 1.11-1.51). PC3, which included amino acids, was associated with lower odds of cognitive decline (OR = 0.81; 95% CI, 0.66-0.99).ConclusionAmino acids are protectively associated with cognitive decline, whereas ketone metabolites are associated with higher odds of cognitive decline.

Using Synthetic Biology to Understand the Function of Plant Specialized Metabolites

Plant specialized metabolites (PSMs) are variably distributed across taxa, tissues, and ecological contexts; this variability has inspired many theories about PSM function, which, to date, remain poorly tested because predictions have outpaced the available data. Advances in mass spectrometry-based metabolomics have enabled unbiased PSM profiling, and molecular biology techniques have produced PSM-free plants; the combination of these methods has accelerated our understanding of the complex ecological roles that PSMs play in plants. Synthetic biology techniques and workflows are producing high-value, structurally complex PSMs in quantities and purities sufficient for both medicinal and functional studies. These workflows enable the reengineering of PSM transport, externalization, structural diversity, and production in novel taxa, facilitating rigorous tests of long-standing theoretical predictions about why plants produce so many different PSMs in particular tissues and ecological contexts. Plants use their chemical prowess to solve ecological challenges, and synthetic biology workflows are accelerating our understanding of these evolved functions.

Phytochemical analysis of water and ethanol liquid extracts prepared using freshly harvested leaves of Mitragyna speciosa (Korth.)

Mitragyna speciosa, also known as kratom, has been reported to have a broad range of pharmacological properties. Freshly harvested leaves and their water extracts are consumed in Southeast Asia while preparations made from dried leaf material are consumed in Western countries. Our study evaluated the phytochemical composition of freshly harvested kratom leaves using LCMS/MS analysis of water and ethanol liquid extracts. Mitragynine and its congeners, including 7-hydroxymitragynine, speciocilliatine, speciogynine, paynantheine, as well as bioactive phenolics including chlorogenic acid, o-coumaric acid, quercitrin, and rutin were identified. However, 7-hydroxymitragynine was detected solely in the water-liquid extract. Currently, unknown compounds were also present in the chromatograms and mass spectra. The study results support that 7-hydroxymitragynine is a post-harvest oxidative derivative or metabolite of mitragynine. Further rigorous and comprehensive evaluations of the phytochemical composition of freshly harvested kratom leaves utilising advanced spectrometric methods are needed to establish the full spectrum of phytochemicals within the plant.

Tear metabolomics for the diagnosis of primary open-angle glaucoma

Primary Open-Angle Glaucoma (POAG) is the most prevalent glaucoma type, and the leading cause of irreversible visual impairment and blindness worldwide. Identification of early POAG biomarkers is of enormous value, as there is not an effective treatment for the glaucomatous optic nerve degeneration (OND). In this pilot study, a metabolomic analysis, by using proton (1H) nuclear magnetic resonance (NMR) spectroscopy was conducted in tears, in order to determine the changes of specific metabolites in the initial glaucoma eyes and to discover potential diagnostic biomarkers. A classification model, based on the metabolomic fingerprint in tears was generated as a non-invasive tool to support the preclinical and clinical POAG diagnosis. 1H NMR spectra were acquired from 30 tear samples corresponding to the POAG group (n = 11) and the control group (n = 19). Data were analysed by multivariate statistics (partial least squares-discriminant analysis: PLS-DA) to determine a model capable of differentiating between groups. The whole data set was split into calibration (65%)/validation (35%), to test the performance and the ability for glaucoma discrimination. The calculated PLS-DA model showed an area under the curve (AUC) of 1, as well as a sensitivity of 100% and a specificity of 83.3% to distinguish POAG group versus control group tear data. This model included 11 metabolites, potential biomarkers of the disease. When comparing the study groups, a decrease in the tear concentration of phenylalanine, phenylacetate, leucine, n-acetylated compounds, formic acid, and uridine, was found in the POAG group. Moreover, an increase in the tear concentration of taurine, glycine, urea, glucose, and unsaturated fatty acids was observed in the POAG group. These results highlight the potential of tear metabolomics by 1H NMR spectroscopy as a non-invasive approach to support early POAG diagnosis and in order to prevent visual loss.

Advanced Mass Spectrometry-Based Biomarker Identification for Metabolomics of Diabetes Mellitus and Its Complications

Over the years, there has been notable progress in understanding the pathogenesis and treatment modalities of diabetes and its complications, including the application of metabolomics in the study of diabetes, capturing attention from researchers worldwide. Advanced mass spectrometry, including gas chromatography-tandem mass spectrometry (GC-MS/MS), liquid chromatography-tandem mass spectrometry (LC-MS/MS), and ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-ESI-Q-TOF-MS), etc., has significantly broadened the spectrum of detectable metabolites, even at lower concentrations. Advanced mass spectrometry has emerged as a powerful tool in diabetes research, particularly in the context of metabolomics. By leveraging the precision and sensitivity of advanced mass spectrometry techniques, researchers have unlocked a wealth of information within the metabolome. This technology has enabled the identification and quantification of potential biomarkers associated with diabetes and its complications, providing new ideas and methods for clinical diagnostics and metabolic studies. Moreover, it offers a less invasive, or even non-invasive, means of tracking disease progression, evaluating treatment efficacy, and understanding the underlying metabolic alterations in diabetes. This paper summarizes advanced mass spectrometry for the application of metabolomics in diabetes mellitus, gestational diabetes mellitus, diabetic peripheral neuropathy, diabetic retinopathy, diabetic nephropathy, diabetic encephalopathy, diabetic cardiomyopathy, and diabetic foot ulcers and organizes some of the potential biomarkers of the different complications with the aim of providing ideas and methods for subsequent in-depth metabolic research and searching for new ways of treating the disease.

Advancements in metabolomics research in benign gallbladder diseases: A review

The burgeoning field of metabolomics has piqued the interest of researchers in the context of benign gallbladder diseases, which include conditions such as gallbladder polyps, gallstones, and cholecystitis, which are common digestive system disorders. As metabolomics continues to advance, researchers have increasingly focused their attention on its applicability in the study of benign gallbladder diseases to provide new perspectives for diagnostic, therapeutic, and prognostic evaluation. This comprehensive review primarily describes the techniques of liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and nuclear magnetic resonance and their respective applications in the study of benign gallbladder disease. Metabolomics has made remarkable progress in various aspects of these diseases, ranging from early diagnosis, etiological research, assessment of disease progression and prognosis, and optimization of therapeutic strategies. However, challenges remain in the field of metabolomics in the study of benign gallbladder diseases. These include issues related to data processing and analysis, biomarker discovery and validation, interdisciplinary research integration, and the advancement of personalized medicine. This article attempts to summarize research findings to date, highlight future research directions, and provide a reference point for metabolomics research in benign gallbladder disease.

Comparison of Various Extraction Approaches for Optimized Preparation of Intracellular Metabolites from Human Mesenchymal Stem Cells and Fibroblasts for NMR-Based Study

Metabolomics has proven to be a sensitive tool for monitoring biochemical processes in cell culture. It enables multi-analysis, clarifying the correlation between numerous metabolic pathways. Together with other analysis, it thus provides a global view of a cell's physiological state. A comprehensive analysis of molecular changes is also required in the case of mesenchymal stem cells (MSCs), which currently represent an essential portion of cells used in regenerative medicine. Reproducibility and correct measurement are closely connected to careful metabolite extraction, and sample preparation is always a critical point. Our study aimed to compare the efficiencies of four harvesting and six extraction methods. Several organic reagents (methanol, ethanol, acetonitrile, methanol-chloroform, MTBE) and harvesting approaches (trypsinization vs. scraping) were tested. We used untargeted nuclear magnetic resonance spectroscopy (NMR) to determine the most efficient method for the extraction of metabolites from human adherent cells, specifically human dermal fibroblasts adult (HDFa) and dental pulp stem cells (DPSCs). A comprehensive dataset of 29 identified and quantified metabolites were determined to possess statistically significant differences in the abundances of several metabolites when the cells were detached mechanically to organic solvent compared to when applying enzymes mainly in the classes of amino acids and peptides for both types of cells. Direct scraping to organic solvent is a method that yields higher abundances of determined metabolites. Extraction with the use of different polar reagents, 50% and 80% methanol, or acetonitrile, mostly showed the same quality. For both HDFa and DPSC cells, the MTBE method, methanol-chloroform, and 80% ethanol extractions showed higher extraction efficiency for the most identified and quantified metabolites Thus, preparation procedures provided a cell sample processing protocol that focuses on maximizing extraction yield. Our approach may be useful for large-scale comparative metabolomic studies of human mesenchymal stem cell samples.

Metabolomic and proteomic applications to exercise biomedicine

Objectives

'OMICs encapsulates study of scaled data acquisition, at the levels of DNA, RNA, protein, and metabolite species. The broad objectives of OMICs in biomedical exercise research are multifarious, but commonly relate to biomarker development and understanding features of exercise adaptation in health, ageing and metabolic diseases.

Methods

This field is one of exponential technical (i.e., depth of feature coverage) and scientific (i.e., in health, metabolic conditions and ageing, multi-OMICs) progress adopting targeted and untargeted approaches.

Results

Key findings in exercise biomedicine have led to the identification of OMIC features linking to heritability or adaptive responses to exercise e.g., the forging of GWAS/proteome/metabolome links to cardiovascular fitness and metabolic health adaptations. The recent addition of stable isotope tracing to proteomics ('dynamic proteomics') and metabolomics ('fluxomics') represents the next phase of state-of-the-art in 'OMICS.

Conclusions

These methods overcome limitations associated with point-in-time 'OMICs and can be achieved using substrate-specific tracers or deuterium oxide (D2O), depending on the question; these methods could help identify how individual protein turnover and metabolite flux may explain exercise responses. We contend application of these methods will shed new light in translational exercise biomedicine.

A metabolomic perspective on the mechanisms by which environmental pollutants and lifestyle lead to male infertility

The incidence of male infertility (MI) is rising annually. According to epidemiological studies, environmental pollution (e.g., organic, inorganic, and air pollutants), occupational exposure (e.g., high temperature, organic solvents, and pesticides), and poor lifestyle (e.g., diet, sleep, smoking, alcohol consumption, and exercise) are important non-genetic causative factors of MI. Due to multiple and complex causative factors, the dose-effect relationship, and the uncertainty of pathogenicity, the pathogenesis of MI is far from fully clarified. Recent data show that the pathogenesis of MI can be monitored by the metabolites in serum, seminal plasma, urine, testicular tissue, sperm, and other biological samples. It is considered that these metabolites are closely related to MI phenotypes and can directly reflect the individual pathological and physiological conditions. Therefore, qualitative and quantitative analysis of the metabolome, the related metabolic pathways, and the identification of biomarkers will help to explore the MI-related metabolic problems and provide valuable insights into its pathogenic mechanisms. Here, we summarized new findings in MI metabolomics biomarkers research and their abnormal metabolic pathways triggered by the presented non-genetic risk factors, providing a metabolic landscape of semen and seminal plasma in general MI patients. Then, we compared the similarities and differences in semen and seminal plasma biomarkers between MI patients exposed to environmental and poor lifestyle factors and MI patients in general, and summarized some common biomarkers. We provide a better understanding of the biological underpinnings of MI pathogenesis, which might offer novel diagnostic, prognostic, and precise treatment approaches to MI.

Food authentication, current issues, analytical techniques, and future challenges: A comprehensive review

Food authentication and contamination are significant concerns, especially for consumers with unique nutritional, cultural, lifestyle, and religious needs. Food authenticity involves identifying food contamination for many purposes, such as adherence to religious beliefs, safeguarding health, and consuming sanitary and organic food products. This review article examines the issues related to food authentication and food fraud in recent periods. Furthermore, the development and innovations in analytical techniques employed to authenticate various food products are comprehensively focused. Food products derived from animals are susceptible to deceptive practices, which can undermine customer confidence and pose potential health hazards due to the transmission of diseases from animals to humans. Therefore, it is necessary to employ suitable and robust analytical techniques for complex and high-risk animal-derived goods, in which molecular biomarker-based (genomics, proteomics, and metabolomics) techniques are covered. Various analytical methods have been employed to ascertain the geographical provenance of food items that exhibit rapid response times, low cost, nondestructiveness, and condensability.

Anticancer properties and metabolomic profiling of Shorea roxburghii extracts toward gastrointestinal cancer cell lines

BACKGROUND

Gastrointestinal cancer (GIC) ranks as the highest cause of cancer-related deaths globally. GIC patients are often diagnosed at advanced stages, limiting effective treatment options. Chemotherapy, the common GIC recommendation, has significant disadvantages such as toxicity and adverse effects. Natural products contain substances with diverse pharmacological characteristics that promise for use in cancer therapeutics. In this study, the flower of renowned Asian medicinal plant, Shorea roxburghii was collected and extracted to investigate its phytochemical contents, antioxidant, and anticancer properties on GIC cells.

METHODS

The phytochemical contents of Shorea roxburghii extract were assessed using suitable methods. Phenolic content was determined through the Folin-Ciocalteu method, while flavonoids were quantified using the aluminum chloride (AlCl3) method. Antioxidant activity was evaluated using the FRAP and DPPH assays. Cytotoxicity was assessed in GIC cell lines via the MTT assay. Additionally, intracellular ROS levels and apoptosis were examined through flow cytometry techniques. The correlation between GIC cell viability and phytochemicals, 1H-NMR analysis was conducted.

RESULTS

Among the four different solvent extracts, ethyl acetate extract had the highest phenolic and flavonoid contents. Water extract exhibited the strongest reducing power and DPPH scavenging activity following by ethyl acetate. Interestingly, ethyl acetate extract demonstrated the highest inhibitory activity against three GIC cell lines (KKU-213B, HepG2, AGS) with IC50 values of 91.60 µg/ml, 39.38 µg/ml, and 35.59 µg/ml, while showing less toxicity to normal fibroblast cells. Ethyl acetate extract induced reactive oxygen species and apoptosis in GIC cell lines by downregulating anti-apoptotic protein Bcl-2. Metabolic profiling-based screening revealed a positive association between reduced GIC cell viability and phytochemicals like cinnamic acid and its derivatives, ferulic acid and coumaric acid.

CONCLUSIONS

This study highlights the potential of natural compounds in Shorea roxburghii in the development of more effective and safer anticancer agents as options for GIC as well as shedding light on new avenues for cancer treatment.

Comparison of extraction methods in vitro Plasmodium falciparum: A 1H NMR and LC-MS joined approach

Malaria is a parasitic disease that remains a global concern and the subject of many studies. Metabolomics has emerged as an approach to better comprehend complex pathogens and discover possible drug targets, thus giving new insights that can aid in the development of antimalarial therapies. However, there is no standardized method to extract metabolites from in vitro Plasmodium falciparum intraerythrocytic parasites, the stage that causes malaria. Additionally, most methods are developed with either LC-MS or NMR analysis in mind, and have rarely been evaluated with both tools. In this work, three extraction methods frequently found in the literature were reproduced and samples were analyzed through both LC-MS and 1H NMR, and evaluated in order to reveal which is the most repeatable and consistent through an array of different tools, including chemometrics, peak detection and annotation. The most reliable method in this study proved to be a double extraction with methanol and methanol/water (80:20, v/v). Metabolomic studies in the field should move towards standardization of methodologies and the use of both LC-MS and 1H NMR in order to make data more comparable between studies and facilitate the achievement of biologically interpretable information.

Metabolomics for the diagnosis of bladder cancer: A systematic review

Objective

Metabolomics has been extensively utilized in bladder cancer (BCa) research, employing mass spectrometry and nuclear magnetic resonance spectroscopy to compare various variables (tissues, serum, blood, and urine). This study aimed to identify potential biomarkers for early BCa diagnosis.

Methods

A search strategy was designed to identify clinical trials, descriptive and analytical observational studies from databases such as Medline, Embase, Cochrane Central Register of Controlled Trials, and Latin American and Caribbean Literature in Health Sciences. Inclusion criteria comprised studies involving BCa tissue, serum, blood, or urine profiling using widely adopted metabolomics techniques like mass spectrometry and nuclear magnetic resonance. Primary outcomes included description of metabolites and metabolomics profiling in BCa patients and the association of metabolites and metabolomics profiling with BCa diagnosis compared to control patients. The risk of bias was assessed using the Quality Assessment of Studies of Diagnostic Accuracy.

Results

The search strategy yielded 2832 studies, of which 30 case-control studies were included. Urine was predominantly used as the primary sample for metabolite identification. Risk of bias was often unclear inpatient selection, blinding of the index test, and reference standard assessment, but no applicability concerns were observed. Metabolites and metabolomics profiles associated with BCa diagnosis were identified in glucose, amino acids, nucleotides, lipids, and aldehydes metabolism.

Conclusion

The identified metabolites in urine included citric acid, valine, tryptophan, taurine, aspartic acid, uridine, ribose, phosphocholine, and carnitine. Tissue samples exhibited elevated levels of lactic acid, amino acids, and lipids. Consistent findings across tissue, urine, and serum samples revealed downregulation of citric acid and upregulation of lactic acid, valine, tryptophan, taurine, glutamine, aspartic acid, uridine, ribose, and phosphocholine.

Liquid biopsy techniques and lung cancer: diagnosis, monitoring and evaluation

Lung cancer stands as the most prevalent form of cancer globally, posing a significant threat to human well-being. Due to the lack of effective and accurate early diagnostic methods, many patients are diagnosed with advanced lung cancer. Although surgical resection is still a potential means of eradicating lung cancer, patients with advanced lung cancer usually miss the best chance for surgical treatment, and even after surgical resection patients may still experience tumor recurrence. Additionally, chemotherapy, the mainstay of treatment for patients with advanced lung cancer, has the potential to be chemo-resistant, resulting in poor clinical outcomes. The emergence of liquid biopsies has garnered considerable attention owing to their noninvasive nature and the ability for continuous sampling. Technological advancements have propelled circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), extracellular vesicles (EVs), tumor metabolites, tumor-educated platelets (TEPs), and tumor-associated antigens (TAA) to the forefront as key liquid biopsy biomarkers, demonstrating intriguing and encouraging results for early diagnosis and prognostic evaluation of lung cancer. This review provides an overview of molecular biomarkers and assays utilized in liquid biopsies for lung cancer, encompassing CTCs, ctDNA, non-coding RNA (ncRNA), EVs, tumor metabolites, TAAs and TEPs. Furthermore, we expound on the practical applications of liquid biopsies, including early diagnosis, treatment response monitoring, prognostic evaluation, and recurrence monitoring in the context of lung cancer.

Rapid characterization of non-volatile phenolic compounds reveals the reliable chemical markers for authentication of traditional Chinese medicine Xiang-ru among confusing Elsholtzia species

The aerial parts of Mosla chinensis Maxim. and Mosla chinensis cv. 'Jiangxiangru' (MCJ) are widely utilized in traditional Chinese medicine (TCM), known collectively as Xiang-ru. However, due to clinical effectiveness concerns and frequent misidentification, the original plants have increasingly been substituted by various species within the genera Elsholtzia and Mosla. The challenge in distinguishing between these genera arises from their similar morphological and metabolic profiles. To address this issue, our study introduced a rapid method for metabolic characterization, employing high-resolution mass spectrometry-based metabolomics. Through detailed biosynthetic and chemometric analyses, we pinpointed five phenolic compounds-salviaflaside, cynaroside, scutellarein-7-O-D-glucoside, rutin, and vicenin-2-among 203 identified compounds, as reliable chemical markers for distinguishing Xiang-ru from closely related Elsholtzia species. This methodology holds promise for broad application in the analysis of plant aerial parts, especially in verifying the authenticity of aromatic traditional medicinal plants. Our findings underscore the importance of non-volatile compounds as dependable chemical markers in the authentication process of aromatic traditional medicinal plants.

Blood metabolomic and transcriptomic signatures stratify patient subgroups in multiple sclerosis according to disease severity

There are no blood-based biomarkers distinguishing patients with relapsing-remitting (RRMS) from secondary progressive multiple sclerosis (SPMS) although evidence supports metabolomic changes according to MS disease severity. Here machine learning analysis of serum metabolomic data stratified patients with RRMS from SPMS with high accuracy and a putative score was developed that stratified MS patient subsets. The top differentially expressed metabolites between SPMS versus patients with RRMS included lipids and fatty acids, metabolites enriched in pathways related to cellular respiration, notably, elevated lactate and glutamine (gluconeogenesis-related) and acetoacetate and bOHbutyrate (ketone bodies), and reduced alanine and pyruvate (glycolysis-related). Serum metabolomic changes were recapitulated in the whole blood transcriptome, whereby differentially expressed genes were also enriched in cellular respiration pathways in patients with SPMS. The final gene-metabolite interaction network demonstrated a potential metabolic shift from glycolysis toward increased gluconeogenesis and ketogenesis in SPMS, indicating metabolic stress which may trigger stress response pathways and subsequent neurodegeneration.

Multiomics insights into the female reproductive aging

The human female reproductive lifespan significantly diminishes with age, leading to decreased fertility, reduced fertility quality and endocrine function disorders. While many aspects of aging in general have been extensively documented, the precise mechanisms governing programmed aging in the female reproductive system remain elusive. Recent advancements in omics technologies and computational capabilities have facilitated the emergence of multiomics deep phenotyping. Through the application and refinement of various high-throughput omics methods, a substantial volume of omics data has been generated, deepening our comprehension of the pathogenesis and molecular underpinnings of reproductive aging. This review highlights current and emerging multiomics approaches for investigating female reproductive aging, encompassing genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics. We elucidate their influence on fundamental cell biology and translational research in the context of reproductive aging, address the limitations and current challenges associated with multiomics studies, and offer a glimpse into future prospects.

Chemo-Selective Single-Cell Metabolomics Reveals the Spatiotemporal Behavior of Exogenous Pollutants During Xenopus Laevis Embryogenesis

In-depth profiling of embryogenesis-associated endogenous and exogenous metabolic changes can reveal potential bio-effects resulting from human-made chemicals and underlying mechanisms. Due to the lack of potent tools for monitoring spatiotemporal distribution and bio-transformation behavior of dynamic metabolites at single-cell resolution, however, how and to what extent environmental chemicals may influence or interfere embryogenesis largely remain unclear. Herein, a zero-sample-loss micro-biopsy-based mass spectrometric platform is presented for quantitative, chemo-selective, high-coverage, and minimal-destructive profiling of development-associated cis-diol metabolites, which are critical for signal transduction and epigenome regulation, at both cellular level and tissue level of Xenopus laevis. Using this platform, three extraordinary findings that are otherwise hard to achieve are revealed: 1) there are characteristically different cis-diol metabolic signatures among oocytes, anterior and posterior part of tailbud-stage embryos; 2) halogenated cis-diols heavily accumulate at the posterior part of tailbud-stage embryos of Xenopus laevis; 3) dimethachlon, a kind of exogenous fungicide that is widely used as pesticide, may be bio-transformed and accumulated in vertebrate animals in environment. Thus, this study opens a new avenue to simultaneously monitoring intercellular and intraembryonic heterogeneity of endogenous and exogenous metabolites, providing new insights into metabolic remolding during embryogenesis and putting a warning on potential environmental risk.

Microbiome metabolite quantification methods enabling insights into human health and disease

Many of the health-associated impacts of the microbiome are mediated by its chemical activity, producing and modifying small molecules (metabolites). Thus, microbiome metabolite quantification has a central role in efforts to elucidate and measure microbiome function. In this review, we cover general considerations when designing experiments to quantify microbiome metabolites, including sample preparation, data acquisition and data processing, since these are critical to downstream data quality. We then discuss data analysis and experimental steps to demonstrate that a given metabolite feature is of microbial origin. We further discuss techniques used to quantify common microbial metabolites, including short-chain fatty acids (SCFA), secondary bile acids (BAs), tryptophan derivatives, N-acyl amides and trimethylamine N-oxide (TMAO). Lastly, we conclude with challenges and future directions for the field.

Biochemical Profiling of Urine Metabolome in Premature Infants Based on LC-MS Considering Maternal Influence

In this study, Liquid Chromatography-Mass Spectrometry (LC-MS)-based metabolomics profiling was conducted to elucidate the urinary profiles of premature infants during early and late postnatal stages. As a result, we discovered significant excretion of maternal drugs in early-stage infants and identified crucial metabolites like hormones and amino acids. These findings shed light on the maternal impact on neonatal metabolism and underscore the beneficial effects of breastfeeding on the metabolism of essential amino acids in infants. This research not only enhances our understanding of maternal-infant nutritional interactions and their long-term implications for preterm infants but also offers critical insights into the biochemical characteristics and physiological mechanisms of preterm infants, laying a groundwork for future clinical studies focused on neonatal development and health.

Classifying patients with psoriatic arthritis according to their disease activity status using serum metabolites and machine learning

INTRODUCTION

Psoriatic arthritis (PsA) is a heterogeneous inflammatory arthritis, affecting approximately a quarter of patients with psoriasis. Accurate assessment of disease activity is difficult. There are currently no clinically validated biomarkers to stratify PsA patients based on their disease activity, which is important for improving clinical management.

OBJECTIVES

To identify metabolites capable of classifying patients with PsA according to their disease activity.

METHODS

An in-house solid-phase microextraction (SPME)-liquid chromatography-high resolution mass spectrometry (LC-HRMS) method for lipid analysis was used to analyze serum samples obtained from patients classified as having low (n = 134), moderate (n = 134) or high (n = 104) disease activity, based on psoriatic arthritis disease activity scores (PASDAS). Metabolite data were analyzed using eight machine learning methods to predict disease activity levels. Top performing methods were selected based on area under the curve (AUC) and significance.

RESULTS

The best model for predicting high disease activity from low disease activity achieved AUC 0.818. The best model for predicting high disease activity from moderate disease activity achieved AUC 0.74. The best model for classifying low disease activity from moderate and high disease activity achieved AUC 0.765. Compounds confirmed by MS/MS validation included metabolites from diverse compound classes such as sphingolipids, phosphatidylcholines and carboxylic acids.

CONCLUSION

Several lipids and other metabolites when combined in classifying models predict high disease activity from both low and moderate disease activity. Lipids of key interest included lysophosphatidylcholine and sphingomyelin. Quantitative MS assays based on selected reaction monitoring, are required to quantify the candidate biomarkers identified.

3D superstructure based metabolite profiling for glaucoma diagnosis

Metabolome analysis has gained widespread application in disease diagnosis owing to its ability to provide comprehensive information, including disease phenotypes. In this study, we utilized 3D superstructures fabricated through evaporation-induced microprinting to analyze the metabolome for glaucoma diagnosis. 3D superstructures offer the following advantages: high hotspot density per unit volume of the structure extending from two to three dimensions, excellent signal repeatability due to the reproducibility and defect tolerance of 3D printing, and high thermal stability due to the PVP-enclosed capsule form. Leveraging the superior optical properties of the 3D superstructure, we aimed to classify patients with glaucoma. The signal obtained from the 3D superstructure was employed in a Deep Neural Network (DNN) classification model to accurately classify glaucoma patients. The sensitivity and specificity of the model were determined as 92.05% and 93.51%, respectively. Additionally, the fabrication of 3D superstructures can be performed at any stage, significantly reducing measurement time. Furthermore, their thermal stability allows for the analysis of smaller samples. One notable advantage of 3D superstructures is their versatility in accommodating different target materials. Consequently, they can be utilized for a wide range of metabolic analyses and disease diagnoses.

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.

Stability Studies of Kynurenine Pathway Metabolites in Blood Components Define Optimal Blood Processing Conditions

The kynurenine pathway (KP) is the main pathway of tryptophan (TRP) metabolism that generates energy for multiple cellular processes. The activity of this pathway has been shown to be dysregulated in multiple human diseases. The resultant modulation of metabolites has been suggested to comprise biomarkers to track disease progression or could identify new therapeutic targets. While metabolite changes can be measured readily in blood, there is limited knowledge on the effect of blood matrices and sample processing time may have on the stability of KP metabolites. Understanding the stability of KP metabolites in blood is integral to obtaining accurate KP data to correlate with clinical pathology. Hence, the aim of this study was to assess the concentration of KP metabolites in matched whole blood, plasma and serum. The impact of pre-analytical sample processing time in the various blood matrices was also analysed. Serum and plasma had the higher concentration of KP metabolites compared to whole blood. Furthermore, concentrations of KP metabolites declined when the collected blood was processed after 24 hours storage at 4°C. Our study shows that that type of blood matrix and the time to processing have an impact on the stability of the KP metabolites. Serum or plasma are the preferred choice of matrix and the isolation of these matrices from whole blood is best performed immediately after collection for optimal analytical KP data.

Metabolomics, Lipidomics, and Antipsychotics: A Systematic Review

Antipsychotics are an important pharmacotherapy option for the treatment of many mental illnesses. Unfortunately, selecting antipsychotics is often a trial-and-error process due to a lack of understanding as to which medications an individual patient will find most effective and best tolerated. Metabolomics, or the study of small molecules in a biosample, is an increasingly used omics platform that has the potential to identify biomarkers for medication efficacy and toxicity. This systematic review was conducted to identify metabolites and metabolomic pathways associated with antipsychotic use in humans. Ultimately, 42 studies were identified for inclusion in this review, with all but three studies being performed in blood sources such as plasma or serum. A total of 14 metabolite classes and 12 lipid classes were assessed across studies. Although the studies were highly heterogeneous in approach and mixed in their findings, increases in phosphatidylcholines, decreases in carboxylic acids, and decreases in acylcarnitines were most consistently noted as perturbed in patients exposed to antipsychotics. Furthermore, for the targeted metabolomic and lipidomic studies, seven metabolites and three lipid species had findings that were replicated. The most consistent finding for targeted studies was an identification of a decrease in aspartate with antipsychotic treatment. Studies varied in depth of detail provided for their study participants and in study design. For example, in some cases, there was a lack of detail on specific antipsychotics used or concomitant medications, and the depth of detail on sample handling and analysis varied widely. The conclusions here demonstrate that there is a large foundation of metabolomic work with antipsychotics that requires more complete reporting so that an objective synthesis such as a meta-analysis can take place. This will then allow for validation and clinical application of the most robust findings to move the field forward. Future studies should be carefully controlled to take advantage of the sensitivity of metabolomics while limiting potential confounders that may result from participant heterogeneity and varied analysis approaches.

Landscape of Innovative Methods for Early Diagnosis of Gastric Cancer: A Systematic Review

From a global perspective, gastric cancer (GC) persists as a significant healthcare issue. In the Western world, the majority of cases are discovered at late stages, when the treatment is generally unsuccessful. There are no organized screening programs outside of Asia (Japan and Republic of Korea). Traditional diagnosis techniques (such as upper endoscopy), conventional tumor markers (CEA, CA19-9, and CA72-4), radiographic imaging, and CT scanning all have drawbacks. The gold standard for the earliest detection of cancer and related premalignant lesions is still endoscopy with a proper biopsy follow-up. Since there are currently no clinically approved biomarkers for the early diagnosis of GC, the identification of non-invasive biomarkers is expected to help improve the prognosis and survival rate of these patients. The search for new screening biomarkers is currently underway. These include genetic biomarkers, such as circulating tumor cells, microRNAs, and exosomes, as well as metabolic biomarkers obtained from biofluids. Meanwhile, cutting-edge high-resolution endoscopic technologies are demonstrating promising outcomes in the visual diagnosis of mucosal lesions with the aid of linked color imaging and machine learning models. Following the PRISMA guidelines, this study examined the articles in databases such as PubMed, resulting in 167 included articles. This review discusses the currently available and emerging methods for diagnosing GC early on, as well as new developments in the endoscopic detection of early lesions of the stomach.

Metabolomics-a powerful tool in livestock research

Advancements in the Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS) along with recent developments in omics sciences have resulted in a better understanding of molecular mechanisms and pathways associated with the physio-pathological state of the animal. Metabolomics is a post-genomics tool that deals with small molecular metabolites in a given set of time which provides clear information about the status of an organism. Recently many researchers mainly focus their research on metabolomics studies due to its valuable information in the various fields of livestock management and precision dairying. The main aim of the present review is to provide an insight into the current research output from different sources and application of metabolomics in various areas of livestock including nutri-metabolomics, disease diagnosis advancements, reproductive disorders, pharmaco-metabolomics, genomics studies, and dairy production studies. The present review would be helpful in understanding the metabolomics methodologies and use of livestock metabolomics in various areas in a brief way.

OrbiFragsNets. A tool for automatic annotation of orbitrap MS2 spectra using networks grade as selection criteria

We introduce OrbiFragsNets, a tool for automatic annotation of MS2 spectra generated by Orbitrap instruments, as well as the concepts of chemical consistency and fragments networks. OrbiFragsNets takes advantage of the specific confidence interval for each peak in every MS2 spectrum, which is an unclear idea across the high-resolution mass spectrometry literature. The spectrum annotations are expressed as fragments networks, a set of networks with the possible combinations of annotations for the fragments. The model behind OrbiFragsNets is briefly described here and explained in detail in the constantly updated manual available in the GitHub repository. This new approach in MS2 spectrum de novo automatic annotation proved to perform as good as well established tools such as RMassBank and SIRIUS.•A new approach on automatic annotation of Orbitrap MS2 spectra is introduced.•Possible spectrum annotation are described as independent consistent networks, with annotations for each fragment as nodes, and annotations for the mass difference between fragments as edges.•Annotation process is described as the selection of the most connected fragments network.

Endocrine-disrupting compounds and metabolomic reprogramming in breast cancer

Endocrine-disrupting chemicals pose a growing threat to human health through their increasing presence in the environment and their potential interactions with the mammalian endocrine systems. Due to their structural similarity to hormones like estrogen, these chemicals can interfere with endocrine signaling, leading to many deleterious effects. Exposure to estrogenic endocrine-disrupting compounds (EDC) is a suggested risk factor for the development of breast cancer, one of the most frequently diagnosed cancers in women. However, the mechanisms through which EDCs contribute to breast cancer development remain elusive. To rapidly proliferate, cancer cells undertake distinct metabolic programs to utilize existing nutrients in the tumor microenvironment and synthesize macromolecules de novo. EDCs are known to dysregulate cell signaling pathways related to cellular metabolism, which may be an important mechanism through which they exert their cancer-promoting effects. These altered pathways can be studied via metabolomic analysis, a new advancement in -omics technologies that can interrogate molecular pathways that favor cancer development and progression. This review will summarize recent discoveries regarding EDCs and the metabolic reprogramming that they may induce to facilitate the development of breast cancer.

Dietary Supplementation With NAD+-Boosting Compounds in Humans: Current Knowledge and Future Directions

Advancing age and many disease states are associated with declines in nicotinamide adenine dinucleotide (NAD+) levels. Preclinical studies suggest that boosting NAD+ abundance with precursor compounds, such as nicotinamide riboside or nicotinamide mononucleotide, has profound effects on physiological function in models of aging and disease. Translation of these compounds for oral supplementation in humans has been increasingly studied within the last 10 years; however, the clinical evidence that raising NAD+ concentrations can improve physiological function is unclear. The goal of this review was to synthesize the published literature on the effects of chronic oral supplementation with NAD+ precursors on healthy aging and age-related chronic diseases. We identified nicotinamide riboside, nicotinamide riboside co-administered with pterostilbene, and nicotinamide mononucleotide as the most common candidates in investigations of NAD+-boosting compounds for improving physiological function in humans. Studies have been performed in generally healthy midlife and older adults, adults with cardiometabolic disease risk factors such as overweight and obesity, and numerous patient populations. Supplementation with these compounds is safe, tolerable, and can increase the abundance of NAD+ and related metabolites in multiple tissues. Dosing regimens and study durations vary greatly across interventions, and small sample sizes limit data interpretation of physiological outcomes. Limitations are identified and future research directions are suggested to further our understanding of the potential efficacy of NAD+-boosting compounds for improving physiological function and extending human health span.

Predictive value of metabolic profiling in cardiovascular risk scores: analysis of 75 000 adults in UK Biobank

BACKGROUND

Metabolic profiling (the extensive measurement of circulating metabolites across multiple biological pathways) is increasingly employed in clinical care. However, there is little evidence on the benefit of metabolic profiling as compared with established atherosclerotic cardiovascular disease (CVD) risk scores.

METHODS

UK Biobank is a prospective study of 0.5 million participants, aged 40-69 at recruitment. Analyses were restricted to 74 780 participants with metabolic profiling (measured using nuclear magnetic resonance) and without CVD at baseline. Cox regression was used to compare model performance before and after addition of metabolites to QRISK3 (an established CVD risk score used in primary care in England); analyses derived three models, with metabolites selected by association significance or by employing two different machine learning approaches.

RESULTS

We identified 5097 incident CVD events within the 10-year follow-up. Harrell's C-index of QRISK3 was 0.750 (95% CI 0.739 to 0.763) for women and 0.706 (95% CI 0.696 to 0.716) for men. Adding selected metabolites did not significantly improve measures of discrimination in women (Harrell's C-index of three models are 0.759 (0.747 to 0.772), 0.759 (0.746 to 0.770) and 0.759 (0.748 to 0.771), respectively) or men (0.710 (0.701 to 0.720), 0.710 (0.700 to 0.719) and 0.710 (0.701 to 0.719), respectively), and neither did it improve reclassification or calibration.

CONCLUSION

This large-scale study applied both conventional and machine learning approaches to assess the potential benefit of metabolic profiling to well-established CVD risk scores. However, there was no evidence that metabolic profiling improved CVD risk prediction in this population.

Present and future of pure shift NMR in metabolomics

NMR is one of the most powerful techniques for the analysis of biological samples in the field of metabolomics. However, the high complexity of fluids, tissues, or other biological materials taken from living organisms is still a challenge for state-of-the-art pulse sequences, thereby limiting the detection, the identification, and the quantification of metabolites. In this context, the resolution enhancement provided by broadband homonuclear decoupling methods, which allows for simplifying 1 H multiplet patterns into singlets, has placed this so-called pure shift technique as a promising approach to perform metabolic profiling with unparalleled level of detail. In recent years, the many advances achieved in the design of pure shift experiments has paved the way to the analysis of a wide range of biological samples with ultra-high resolution. This review leads the reader from the early days of the main pure shift methods that have been successfully developed over the last decades to address complex samples, to the most recent and promising applications of pure shift NMR to the field of NMR-based metabolomics.

Pharmacokinetics in Pharmacometabolomics: Towards Personalized Medication

Indiscriminate drug administration may lead to drug therapy results with varying effects on patients, and the proposal of personalized medication can help patients to receive effective drug therapy. Conventional ways of personalized medication, such as pharmacogenomics and therapeutic drug monitoring (TDM), can only be implemented from a single perspective. The development of pharmacometabolomics provides a research method for the realization of precise drug administration, which integrates the environmental and genetic factors, and applies metabolomics technology to study how to predict different drug therapeutic responses of organisms based on baseline metabolic levels. The published research on pharmacometabolomics has achieved satisfactory results in predicting the pharmacokinetics, pharmacodynamics, and the discovery of biomarkers of drugs. Among them, the pharmacokinetics related to pharmacometabolomics are used to explore individual variability in drug metabolism from the level of metabolism of the drugs in vivo and the level of endogenous metabolite changes. By searching for relevant literature with the keyword "pharmacometabolomics" on the two major literature retrieval websites, PubMed and Web of Science, from 2006 to 2023, we reviewed articles in the field of pharmacometabolomics that incorporated pharmacokinetics into their research. This review explains the therapeutic effects of drugs on the body from the perspective of endogenous metabolites and pharmacokinetic principles, and reports the latest advances in pharmacometabolomics related to pharmacokinetics to provide research ideas and methods for advancing the implementation of personalized medication.

Metabolomics analysis of human cumulus cells obtained from cumulus-oocyte complexes with different developmental potential

STUDY QUESTION

Is the abundance of certain biochemical compounds in human cumulus cells (CCs) related to oocyte quality?

SUMMARY ANSWER

Malonate, 5-oxyproline, and erythronate were positively associated with pregnancy potential.

WHAT IS KNOWN ALREADY

CCs are removed and discarded prior to ICSI, thereby constituting an interesting biological material on which to perform molecular analysis aimed to predict oocyte developmental competence. Mitochondrial DNA content and transcriptional analyses in CC have been shown to provide a poor predictive value of oocyte competence, but the untargeted analysis of biochemical compounds (metabolomics) has been unexplored.

STUDY DESIGN, SIZE, DURATION

CCs were obtained from three groups of cumulus-oocyte complexes (COCs) of known developmental potential: oocytes not developing to blastocyst following ICSI (Bl-); oocytes developing to blastocyst but failing to establish pregnancy following embryo transfer (P-); and oocytes developing to blastocyst able to establish a pregnancy (P+). Metabolomics analyses were performed on 12 samples per group, each sample comprising the CC recovered from a single COC.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Human CC samples were obtained from IVF treatments. Only unfrozen oocytes and embryos not submitted to preimplantation genetic testing were included in the analysis. Metabolomics analysis was performed by ultra-high performance liquid chromatography-tandem mass spectroscopy.

MAIN RESULTS AND THE ROLE OF CHANCE

The analysis identified 98 compounds, five of which were differentially abundant (P < 0.05) between groups: asparagine, proline, and malonate were less abundant in P- compared to Bl-, malonate and 5-oxoproline were less abundant in P- group compared to P+, and erythronate was less abundant in Bl- group compared to P+. No significant association between the abundance of the compounds identified and donor age or BMI was noted.

LIMITATIONS, REASONS FOR CAUTION

Data dispersion and the lack of coherence between developmental groups preclude the direct use of metabolic markers in clinical practice, where the uterine environment plays a major role in pregnancy outcome. The abundance of other compounds not detected by the analysis may be associated with oocyte competence. As donors were lean (only two with BMI > 30 kg/m2) and young (<34 years old), a possible effect of obesity or advanced age on the CC metabolome could not be determined.

WIDER IMPLICATIONS OF THE FINDINGS

The abundance of malonate, 5-oxyproline, and erythronate in CC was significantly higher in COCs ultimately establishing pregnancy, providing clues on the pathways required for oocyte competence. The untargeted analysis uncovered the presence of compounds that were not expected in CC, such as β-citrylglutamate and the neurotransmitter N-acetyl-aspartyl-glutamate, which may play roles in chromatin remodeling and signaling, respectively.

STUDY FUNDING/COMPETING INTEREST(S)

Research was supported by the Industrial Doctorate Project IND2017/BIO-7748 funded by Madrid Region Government. The authors declare no competing interest.

TRIAL REGISTRATION NUMBER

N/A.

Urinary biomarkers for hepatocellular carcinoma: current knowledge for clinicians

Hepatocellular carcinoma (HCC) is the most predominant primary liver cancer, causing many illnesses and deaths worldwide. The insidious clinical presentation, difficulty in early diagnosis, and the highly malignant nature make the prognosis of HCC extremely poor. The complex and heterogeneous pathogenesis of HCC poses significant challenges to developing therapies. Urine-based biomarkers for HCC, including diagnostic, prognostic, and monitoring markers, may be valuable supplements to current tools such as serum α-fetoprotein (AFP) and seem promising for progress in precision medicine. Herein, we reviewed the major urinary biomarkers for HCC and assessed their potential for clinical application. Molecular types, testing platforms, and methods for building multimolecule models in the included studies have shown great diversity, thus providing abundant novel tools for future clinical transformation and applications.

Neopetroside-B alleviates doxorubicin-induced cardiotoxicity via mitochondrial protection

Doxorubicin, a member of the anthracycline family, is a widely prescribed anticancer chemotherapy drug. Unfortunately, cumulative doses of doxorubicin can cause mitochondrial dysfunction, leading to acute or chronic cardiotoxicity. This study demonstrated that Neopetroside-B (NPS-B) protects cardiomyocytes in the presence of doxorubicin. NPS-B improved mitochondrial function in cardiomyocytes by increasing ATP production and oxygen consumption rates. On the other hand, NPS-B negatively influenced cancer cell lines by increasing reactive oxygen species. We analyzed NPS-B-influenced metabolites (VIP > 1.0; AUC>0.7; p < 0.05) and proteins (FC > 2.0) and constructed metabolite-protein enrichment, which showed that NPS-B affected uracil metabolism and NAD-binding proteins (e.g., aldehyde dehydrogenase and glutathione reductase) in cardiomyocytes. However, for the cancer cells, NPS-B decreased the NAD+/NADH balance, impairing cell viability. In a xenograft mouse model treated with doxorubicin, NPS-B reduced cardiac fibrosis and improved cardiac function. NPS-B may be a beneficial intervention to reducing doxorubicin-induced cardiotoxicity with anticancer effects.

An epidemiological introduction to human metabolomic investigations

Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.

Mapping the Acetylamino and Carboxyl Groups on Glycans by Engineered α-Hemolysin Nanopores

Glycan is a crucial class of biological macromolecules with important biological functions. Functional groups determine the chemical properties of glycans, which further affect their biological activities. However, the structural complexity of glycans has set a technical hurdle for their direct identification. Nanopores have emerged as highly sensitive biosensors that are capable of detecting and characterizing various analytes. Here, we identified the functional groups on glycans with a designed α-hemolysin nanopore containing arginine mutations (M113R), which is specifically sensitive to glycans with acetamido and carboxyl groups. Molecular dynamics simulations indicated that the acetamido and carboxyl groups of the glycans produce unique electrical signatures by forming polar and electrostatic interactions with the M113R nanopores. Using these electrical features as the fingerprints, we mapped the length of the glycans containing acetamido and carboxyl groups at the monosaccharide, disaccharide, and trisaccharide levels. This proof-of-concept study provides a promising foundation for developing single-molecule glycan fingerprinting libraries and demonstrates the capability of biological nanopores in glycan sequencing.

A Solid-Phase Microextraction-Liquid Chromatography-Mass Spectrometry Method for Analyzing Serum Lipids in Psoriatic Disease

Approximately 25% of psoriasis patients have an inflammatory arthritis termed psoriatic arthritis (PsA). There is strong interest in identifying and validating biomarkers that can accurately and reliably predict conversion from psoriasis to PsA using novel technologies such as metabolomics. Lipids, in particular, are of key interest in psoriatic disease. We sought to develop a liquid chromatography-mass spectrometry (LC-MS) method to be used in conjunction with solid-phase microextraction (SPME) for analyzing fatty acids and similar molecules. A total of 25 chromatographic methods based on published lipid studies were tested on two LC columns. As a proof of concept, serum samples from psoriatic disease patients (n = 27 psoriasis and n = 26 PsA) were processed using SPME and run on the selected LC-MS method. The method that was best for analyzing fatty acids and fatty acid-like molecules was optimized and applied to serum samples. A total of 18 tentatively annotated features classified as fatty acids and other lipid compounds were statistically significant between psoriasis and PsA groups using both multivariate and univariate approaches. The SPME-LC-MS method developed and optimized was capable of detecting fatty acids and similar lipids that may aid in differentiating psoriasis and PsA patients.

Research of Pesticide Metabolites in Human Brain Tumor Tissues by Chemometrics-Based Gas Chromatography-Mass Spectrometry Analysis for a Hypothetical Correlation between Pesticide Exposure and Risk Factor of Central Nervous System Tumors

Pesticides are widely used, resulting in continuing human exposure with potential health impacts. Some exposures related to agricultural works have been associated with neurological disorders. Since the 2000s, the hypothesis of the role of pesticides in the occurrence of central nervous system (CNS) tumors has been better documented in the literature. However, the etiology of childhood brain cancers still remains largely unknown. The major objective of this work was to assess the potential role of pesticide exposure as a risk factor for CNS tumors based on questionnaires and statistical analysis of information collected from patients hospitalized in the Neurosurgery Department of the Habib Bourguiba Hospital Medium in Sfax, Tunisia, during the period from January 1, 2022, to May 31, 2023. It also aimed to develop a simple and rapid analytical method by the gas chromatography-mass spectrometry technique for the research traces of pesticide metabolites in some collected human brain tumor tissues in order to more emphasize our hypothesis for such a correlation between pesticide exposure and brain tumor development. Patients with a history of high-risk exposure were selected to conduct further analysis. Chemometric methods were adapted to discern intrinsic variation between pathological and control groups and ascertain effective separation with the identification of differentially expressed metabolites accountable for such variations. Three samples revealed traces of pesticide metabolites that were mostly detected at an early age. The histopathological diagnosis was medulloblastoma for a 10-year-old child and high-grade gliomas for 27- and 35-year-old adults. The bivariate analyses (odds ratio >1 and P value <5%) confirmed the great probability of developing cancer by an exposure case. The Cox proportional hazards model revealed the risk of carcinogenicity beyond the age of 50 as a long-term effect of pesticide toxicity. Our study supports the correlation between pesticide exposure and the risk of development of human brain tumors, suggesting that preconception pesticide exposure, and possibly exposure during pregnancy, is associated with an increased childhood brain tumor risk. This hypothesis was enhanced in identifying traces of metabolites from the carbamate insecticide class known for their neurotoxicity and others from pyridazinone, organochlorines (OCs), triazole fungicide, and N-nitroso compounds known for their carcinogenicity. The 2D-OXYBLOT analysis confirmed the neurotoxicity effect of insecticides to induce oxidative damage in CNS cells. Aldicarb was implicated in brain carcinogenicity confirmed by the identification of oxime metabolites in a stress degradation study. Revealing "aziridine" metabolites from the OC class may better emphasize the theory of detecting traces of pesticide metabolites at an early age. Overall, our findings lead to the recommendation of limiting the residential use of pesticides and the support of public health policies serving this objective that we need to be vigilant in the postmarketing surveillance of human health impacts.

The Impact of Mechanical Cues on the Metabolomic and Transcriptomic Profiles of Human Dermal Fibroblasts Cultured in Ultrashort Self-Assembling Peptide 3D Scaffolds

Cells' interactions with their microenvironment influence their morphological features and regulate crucial cellular functions including proliferation, differentiation, metabolism, and gene expression. Most biological data available are based on in vitro two-dimensional (2D) cellular models, which fail to recapitulate the three-dimensional (3D) in vivo systems. This can be attributed to the lack of cell-matrix interaction and the limitless access to nutrients and oxygen, in contrast to in vivo systems. Despite the emergence of a plethora of 3D matrices to address this challenge, there are few reports offering a proper characterization of these matrices or studying how the cell-matrix interaction influences cellular metabolism in correlation with gene expression. In this study, two tetrameric ultrashort self-assembling peptide sequences, FFIK and FIIK, were used to create in vitro 3D models using well-described human dermal fibroblast cells. The peptide sequences are derived from naturally occurring amino acids that are capable of self-assembling into stable hydrogels without UV or chemical cross-linking. Our results showed that 2D cultured fibroblasts exhibited distinct metabolic and transcriptomic profiles compared to 3D cultured cells. The observed changes in the metabolomic and transcriptomic profiles were closely interconnected and influenced several important metabolic pathways including the TCA cycle, glycolysis, MAPK signaling cascades, and hemostasis. Data provided here may lead to clearer insights into the influence of the surrounding microenvironment on human dermal fibroblast metabolic patterns and molecular mechanisms, underscoring the importance of utilizing efficient 3D in vitro models to study such complex mechanisms.

The Expectation and Reality of the HepG2 Core Metabolic Profile

To represent the composition of small molecules circulating in HepG2 cells and the formation of the "core" of characteristic metabolites that often attract researchers' attention, we conducted a meta-analysis of 56 datasets obtained through metabolomic profiling via mass spectrometry and NMR. We highlighted the 288 most commonly studied compounds of diverse chemical nature and analyzed metabolic processes involving these small molecules. Building a complete map of the metabolome of a cell, which encompasses the diversity of possible impacts on it, is a severe challenge for the scientific community, which is faced not only with natural limitations of experimental technologies, but also with the absence of transparent and widely accepted standards for processing and presenting the obtained metabolomic data. Formulating our research design, we aimed to reveal metabolites crucial to the Hepg2 cell line, regardless of all chemical and/or physical impact factors. Unfortunately, the existing paradigm of data policy leads to a streetlight effect. When analyzing and reporting only target metabolites of interest, the community ignores the changes in the metabolomic landscape that hide many molecular secrets.

Serum NMR-Based Metabolomics Profiling Identifies Lipoprotein Subfraction Variables and Amino Acid Reshuffling in Myeloma Development and Progression

Multiple myeloma (MM) is an incurable hematological cancer. It is preceded by monoclonal gammopathy of uncertain significance (MGUS)-an asymptomatic phase. It has been demonstrated that early detection increases the 5-year survival rate. However, blood-based biomarkers that enable early disease detection are lacking. Metabolomic and lipoprotein subfraction variable profiling is gaining traction to expand our understanding of disease states and, more specifically, for identifying diagnostic markers in patients with hematological cancers. This study aims to enhance our understanding of multiple myeloma (MM) and identify candidate metabolites, allowing for a more effective preventative treatment. Serum was collected from 25 healthy controls, 20 patients with MGUS, and 30 patients with MM. 1H-NMR (Nuclear Magnetic Resonance) spectroscopy was utilized to evaluate serum samples. The metabolite concentrations were examined using multivariate, univariate, and pathway analysis. Metabolic profiles of the MGUS patients revealed lower levels of alanine, lysine, leucine but higher levels of formic acid when compared to controls. However, metabolic profiling of MM patients, compared to controls, exhibited decreased levels of total Apolipoprotein-A1, HDL-4 Apolipoprotein-A1, HDL-4 Apolipoprotein-A2, HDL Free Cholesterol, HDL-3 Cholesterol and HDL-4 Cholesterol. Lastly, metabolic comparison between MGUS to MM patients primarily indicated alterations in lipoproteins levels: Total Cholesterol, HDL Cholesterol, HDL Free Cholesterol, Total Apolipoprotein-A1, HDL Apolipoprotein-A1, HDL-4 Apolipoprotein-A1 and HDL-4 Phospholipids. This study provides novel insights into the serum metabolic and lipoprotein subfraction changes in patients as they progress from a healthy state to MGUS to MM, which may allow for earlier clinical detection and treatment.