High-Coverage Quantitative Metabolomics of Human Urine: Effects of Freeze-Thaw Cycles on the Urine Metabolome and Biomarker Discovery

Serum metabolomics reveals the effectiveness of human placental mesenchymal stem cell therapy for Crohn's disease

Mesenchymal stem cell (MSC) therapy offers a promising cure for Crohn's disease (CD), however, its therapeutic effects vary significantly due to individual differences. Therefore, identifying easily detectable biomarkers is essential to assess the efficacy of MSC therapy. In this study, SAMP1/Yit mice were used as a model of CD, which develop spontaneous chronic ileitis, closely resembling the characteristics present in CD patients. Serum metabolic alterations during treatment were analyzed, through the application of differential 12C-/13C-dansylation labeling liquid chromatography-mass spectrometry. Based on the significant differences and time-varying trends of serum amine/phenol-containing metabolites abundance between the control group, the model group, and the treatment group, four serum biomarkers were ultimately screened for evaluating the efficacy of MSC treatment for CD, namely 4-hydroxyphenylpyruvate, 4-hydroxyphenylacetaldehyde, caffeate, and N-acetyltryptamine, whose abundances both increased in the serum of CD model mice and decreased after MSC treatment. These metabolic alterations were associated with tyrosine metabolism, which was validated by the dysregulation of related enzymes. The discovery of biomarkers may help to improve the targeting and effectiveness of treatment and provide innovative prospects for the clinical application of MSC for CD.

Development and Application of a Selective Analytical Method for Indole Metabolites in Urine: Dietary Exposure Biomarkers for Broccoli Consumption

The identification of dietary exposure biomarkers is crucial for advancing our understanding of the health benefits of specific foods. Broccoli, a vegetable with well-known anticancer properties, contains active ingredients, such as isothiocyanates with indole side chains. Hence, indole metabolites related to broccoli consumption have the potential to serve as biomarkers of dietary exposure. In this work, we developed a new analytical method for indole metabolites in urine using a poly(deep eutectic solvents)-molecularly imprinted polymer/vinyl-functionalized graphene oxide (PDESs-MIP/VGO) in miniaturized centrifugal pipet-tip solid-phase extraction (CPT-SPE) coupled with liquid chromatography. This method integrates the strengths of PDESs-MIP/VGO, including rich adsorption interactions, high adsorption capacity, and excellent selectivity, with the simplicity and cost-effectiveness of CPT-SPE. The proposed method demonstrated low limits of quantification (1.2-2.5 ng mL-1), high accuracy (91.7-104.8%), and good precision (relative standard deviation ≤4.4%). By applying this method to analyze indole metabolites in urine, our results suggested that indole-3-carbinol and indole-3-acetonitrile have the potential to emerge as reliable dietary exposure biomarkers for broccoli intake. Furthermore, highly selective analytical methods based on molecular imprinting technology are advantageous for precise screening and analysis of dietary exposure biomarkers associated with food consumption.

Challenges and recent advances in quantitative mass spectrometry-based metabolomics

The field of metabolomics has gained tremendous interest in recent years. Whether the goal is to discover biomarkers related to certain pathologies or to better understand the impact of a drug or contaminant, numerous studies have demonstrated how crucial it is to understand variations in metabolism. Detailed knowledge of metabolic variabilities can lead to more effective treatments, as well as faster or less invasive diagnostics. Exploratory approaches are often employed in metabolomics, using relative quantitation to look at perturbations between groups of samples. Most metabolomics studies have been based on metabolite profiling using relative quantitation, with very few studies using an approach for absolute quantitation. Using accurate quantitation facilitates the comparison between different studies, as well as enabling longitudinal studies. In this review, we discuss the most widely used techniques for quantitative metabolomics using mass spectrometry (MS). Various aspects will be addressed, such as the use of external and/or internal standards, derivatization techniques, in vivo isotopic labelling, or quantitative MS imaging. The principles, as well as the associated limitations and challenges, will be described for each approach.

Dietary acid load in health and disease

Maintaining an appropriate acid-base equilibrium is crucial for human health. A primary influencer of this equilibrium is diet, as foods are metabolized into non-volatile acids or bases. Dietary acid load (DAL) is a measure of the acid load derived from diet, taking into account both the potential renal acid load (PRAL) from food components like protein, potassium, phosphorus, calcium, and magnesium, and the organic acids from foods, which are metabolized to bicarbonate and thus have an alkalinizing effect. Current Western diets are characterized by a high DAL, due to large amounts of animal protein and processed foods. A chronic low-grade metabolic acidosis can occur following a Western diet and is associated with increased morbidity and mortality. Nutritional advice focusing on DAL, rather than macronutrients, is gaining rapid attention as it provides a more holistic approach to managing health. However, current evidence for the role of DAL is mainly associative, and underlying mechanisms are poorly understood. This review focusses on the role of DAL in multiple conditions such as obesity, cardiovascular health, impaired kidney function, and cancer.

Stability of volatile organic compound metabolites in urine at various storage temperatures and freeze-thaw cycles for 8 months

The urinary concentrations of mercapturic acid metabolites of volatile organic compounds (VOCs) have been used as biomarkers of human exposure to this class of chemicals. However, long-term stability of these VOC metabolites (VOCMs) in urine at various storage conditions such as temperature, duration, and freeze-thaw cycles is not known. In this study, spot urine samples collected from three volunteers, stored at 22 °C (room temperature: RT), 4 °C (refrigerator) and -20 °C (freezer) for up to 240 days were analyzed at weekly to monthly interval for a total of 19 time points. Samples stored at 4 °C and -20 °C underwent 18 freeze-thaw cycles at RT for 30 min at each of the time points. Among 38 VOCMs analyzed, up to 18 metabolites were detected at concentrations above their respective detection limits on Day 0 (baseline concentration), and the concentrations of several VOCMs declined with the storage duration. Eight to ten VOCMs were lost completely within 240 days of storage at RT, compared to between two and five at 4 °C and between one and seven at -20 °C. The loss rate varied depending on the sample, storage temperature, VOCM, and number of freeze-thaw cycles. Storage of urine at RT led to a rapid loss of VOCMs in comparison to that stored at 4 °C or -20 °C. Among VOCMs measured, CEMA, SBMA, GAMA, DHBMA, AMCC, TCVMA, and HPMMA were lost more rapidly than the other metabolites. CMEMA, a major VOCM found in all three urines at baseline, exhibited a rapid loss in those of two volunteers but not of the other volunteer, suggesting sample to sample variation in lose rates. Freeze-thaw cycles considerably affected VOCM concentrations in urines stored at 4 °C or -20 °C. It is recommended that urine samples are analyzed for VOCMs within a couple of months of collection and stored at temperatures below -20 °C, with minimal or no freeze-thaw cycles. This study highlights the need for appropriate storage conditions to maintain the integrity of samples for biomonitoring studies.

Neuro-Urology and Biobanking: An Integrated Approach for Advancing Research and Improving Patient Care

Understanding the molecular mechanisms underlying neuro-urological disorders is crucial for the development of targeted therapeutic interventions. Through the establishment of comprehensive biobanks, researchers can collect and store various biological specimens, including urine, blood, tissue, and DNA samples, to study these mechanisms. In the context of neuro-urology, biobanking facilitates the identification of genetic variations, epigenetic modifications, and gene expression patterns associated with neurogenic lower urinary tract dysfunction. These conditions often present as symptoms of neurological diseases such as Alzheimer's disease, multiple sclerosis, Parkinson's disease, spinal cord injury, and many others. Biobanking of tissue specimens from such patients is essential to understand why these diseases cause the respective symptoms and what can be done to alleviate them. The utilization of high-throughput technologies, such as next-generation sequencing and gene expression profiling, enables researchers to explore the molecular landscape of these conditions in an unprecedented manner. The development of specific and reliable biomarkers resulting from these efforts may help in early detection, accurate diagnosis, and effective monitoring of neuro-urological conditions, leading to improved patient care and management. Furthermore, these biomarkers could potentially facilitate the monitoring of novel therapies currently under investigation in neuro-urological clinical trials. This comprehensive review explores the synergistic integration of neuro-urology and biobanking, with particular emphasis on the translation of biobanking approaches in molecular research in neuro-urology. We discuss the advantages of biobanking in neuro-urological studies, the types of specimens collected and their applications in translational research. Furthermore, we highlight the importance of standardization and quality assurance when collecting samples and discuss challenges that may compromise sample quality and impose limitations on their subsequent utilization. Finally, we give recommendations for sampling in multicenter studies, examine sustainability issues associated with biobanking, and provide future directions for this dynamic field.

Machine learning liquid chromatography retention time prediction model augments the dansylation strategy for metabolite analysis of urine samples

Herein, a standalone software equipped with a graphic user interface (GUI) is developed to predict liquid chromatography mass spectrometry (LC-MS) retention times (RTs) of dansylated metabolites. Dansylation metabolomics strategy developed by Li et al. narrows down a vast chemical space of metabolites into the metabolites containing amines and phenolic hydroxyls. Combined with differential isotope labeling, e.g., 12C-reagent labeled individual samples spiked with a 13C-reagent labeled reference or pooled sample, LC-MS analysis of the dansylated samples enables accurate relative quantification of all labeled metabolites. Herein, the LC-RTs for dansylated metabolites are predicted using an artificial neural network (ANN) machine-learning model. For the ANN modeling, 315 dansylated urine metabolites obtained from the DnsID database are used. The ANN LC-RT prediction model was reliable, with a mean absolute deviation of 0.74 min for the 30 min LC run. In the RT model, a deviation of more than 2 min was observed in only 3.2% of the total 315 metabolites, while a deviation of 1.5 min or more was observed in 11% of the metabolites. Furthermore, it was found that the LC-RT prediction was also reliable even for metabolites containing both amine and phenolic functional groups that can undergo dansylation on either one of the two functional groups, resulting in the generation of two isomeric forms. This RT-prediction model is embedded into a user-friendly GUI and can be used for identifying nontargeted dansylated metabolites with unknown RTs, along with accurate mass measurements. Furthermore, it is demonstrated that the developed software can help identify metabolites from a urine sample of an anonymous healthy pregnant woman.