Targeted metabolomics

A Comprehensive Overview of Postbiotics with a Special Focus on Discovery Techniques and Clinical Applications

The increasing interest in postbiotics, a term gaining recognition alongside probiotics and prebiotics, aligns with a growing number of clinical trials demonstrating positive outcomes for specific conditions. Postbiotics present several advantages, including safety, extended shelf life, ease of administration, absence of risk, and patentability, making them more appealing than probiotics alone. This review covers various aspects, starting with an introduction, terminology, classification of postbiotics, and brief mechanisms of action. It emphasizes microbial metabolomics as the initial step in discovering novel postbiotics. Commonly employed techniques such as NMR, GC-MS, and LC-MS are briefly outlined, along with their application principles and limitations in microbial metabolomics. The review also examines existing research where these techniques were used to identify, isolate, and characterize postbiotics derived from different microbial sources. The discovery section concludes by highlighting challenges and future directions to enhance postbiotic discovery. In the second half of the review, we delve deeper into numerous published postbiotic clinical trials to date. We provide brief overviews of system-specific trial applications, their objectives, the postbiotics tested, and their outcomes. The review concludes by highlighting ongoing applications of postbiotics in extended clinical trials, offering a comprehensive overview of the current landscape in this evolving field.

Integrated gene-metabolite association network analysis reveals key metabolic pathways in gastric adenocarcinoma

Gastric adenocarcinoma is one of the most death cause cancers worldwide. Metabolomics is an effective approach for investigating the occurrence and progression of cancer and detecting prognostic biomarkers by studying the profiles of small bioactive molecules. To fully decipher the functional roles of the disrupted metabolites that modulate the cellular mechanism of gastric cancer, integrated gene-metabolite association network methods are critical to map the associations between metabolites and genes. In this study, we constructed a knowledge-based gene-metabolite association network of gastric cancer using the dysregulated metabolites and genes between gastric cancer patients and control group. The topological pathway analysis and gene-protein-metabolite-disease association analysis revealed four key gene-metabolite pathways which include eleven metabolites associated with modulated genes. The integrated gene-metabolite association network enables mechanistic investigation and provides a comprehensive overview regarding the investigation of molecular mechanisms of gastric cancer, which facilitates the in-depth understanding of metabolic biomarker roles in gastric cancer.

Rapid LA-REIMS-based metabolic fingerprinting of serum discriminates aflatoxin-exposed from non-exposed pregnant women: a prospective cohort from the Butajira Nutrition, Mental Health, and Pregnancy (BUNMAP) Study in rural Ethiopia

To date, the changes in maternal metabolic response associated with prenatal aflatoxin exposure remain largely unknown. This study investigated the effects of prenatal aflatoxin exposure on the maternal serum metabolome in rural Ethiopia. A total of 309 pregnant women were enrolled prospectively, and their serum aflatoxin concentrations were measured using targeted liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Serum metabolic fingerprints were obtained using laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS), followed by combination of univariate and multivariate statistical modelling to evaluate changes in circulating metabolic features between aflatoxin-exposed and unexposed mothers and to select discriminatory metabolic features. The analysis revealed that 81.8% of women were exposed to aflatoxins, with a median concentration of 12.9 pg/mg albumin. The orthogonal partial least square discriminant analysis (OPLS-DA) regression model demonstrated significant disparities in the serum metabolome when comparing Ethiopian pregnant women with low vs high aflatoxin exposure. Thirty-two differentially expressed metabolic features were identified, affecting aminoacyl-tRNA biosynthesis pathway. Several discriminatory metabolites have been identified, including glutamine, tryptophan, tyrosine, carnosine, and 1-methylnicotinamide. In conclusion, our findings indicate that aflatoxin exposure during pregnancy have shown disparities in the maternal serum metabolome, primarily affecting protein synthesis. Further research is needed to identify specific metabolite biomarkers and elucidate the underlying mechanisms.

Spatio-temporal plant hormonomics: from tissue to subcellular resolution

Due to technological advances in mass spectrometry, significant progress has been achieved recently in plant hormone research. Nowadays, plant hormonomics is well established as a fully integrated scientific field focused on the analysis of phytohormones, mainly on their isolation, identification, and spatiotemporal quantification in plants. This review represents a comprehensive meta-study of the advances in the phytohormone analysis by mass spectrometry over the past decade. To address current trends and future perspectives, Web of Science data were systematically collected and key features such as mass spectrometry-based analyses were evaluated using multivariate data analysis methods. Our findings showed that plant hormonomics is currently divided into targeted and untargeted approaches. Both aim to miniaturize the sample, allowing high-resolution quantification to be covered in plant organs as well as subcellular compartments. Therefore, we can study plant hormone biosynthesis, metabolism, and signalling at a spatio-temporal resolution. Moreover, this trend has recently been accelerated by technological advances such as fluorescence-activated cell sorting or mass spectrometry imaging.

Comparative metabolites profiling of different solvent extracts of Asparagus species cladodes using liquid chromatography-mass spectrometry-based metabolomics and molecular networking

INTRODUCTION

Asparagus species are naturally distributed worldwide and are known for their pharmacological properties that offer cures for various ailments. However, the metabolic choreography of these Asparagus species is not well characterized, and the compounds contributing to their bioactivities remain unknown.

OBJECTIVE

This study aimed to profile and compare the metabolomes of three Asparagus species cladodes using different solvent extractions.

METHODS

An ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry-based metabolomics and molecular networking approach was used to study the effects of different solvents (ethyl acetate, methanol, and chloroform) with varying polarity on metabolites extraction and identification of bioactive compounds from three Asparagus species cladodes (Asparagus falcatus, Asparagus plumosus, and Asparagus densiflorus 'Meyersii').

RESULTS

Multivariate statistical analyses (mainly principal component analysis) revealed a significant separation between the three solvents and the three species, indicating notable metabolic differences. A total of 118 metabolites were identified in the three species extracted with the different solvents, with methanolic and chloroform extracts containing more metabolites compared with ethyl acetate extracts. These metabolites were identified as belonging to the flavonoids, cinnamic acids, organooxygen compounds, steroids, fatty acids, benzenes, and glycerophospholipids compound classes. Furthermore, these compounds classes were differentially distributed among the three species, indicating chemical/chemotaxis differences between the compared species. Chloroform and methanol are recommended as the optimal solvents to obtain a high content of phytochemical compounds from Asparagus species cladodes.

Advances in metabolomics profiling of pediatric kidney diseases: A review

Pediatric renal diseases encompass a diverse array of pathological conditions, often engendering enduring ramifications. Metabolomics, an emergent branch of omics sciences, endeavors to holistically delineate alterations in metabolite compositions through the amalgamation of sophisticated analytical chemistry techniques and robust statistical methodologies. Recent advancements in metabolomics research within the realm of pediatric nephrology have been substantial, offering promising avenues for the identification of robust biomarkers, the elaboration of novel therapeutic targets, and the intricate elucidation of molecular mechanisms. The present discourse aims to critically review the progress in metabolomics profiling pertinent to pediatric renal disorders over the previous 12 years.

GC-MS validation and analysis of targeted plasma metabolites related to carbonyl stress in type 2 diabetes mellitus patients with and without acute coronary syndrome

Methylglyoxal (MG) is responsible for advanced glycation end-product formation, the mechanisms leading to diabetes pathogenesis and complications like acute coronary syndrome (ACS). Sugar metabolites, amino acids and fatty acids are possible substrates for MG. The study aimed to measure plasma MG substrate levels using a validated gas chromatography-mass spectrometry (GC-MS) method and explore their association with ACS risk in type 2 diabetes mellitus (T2DM). The study included 150 T2DM patients with ACS as cases and 150 T2DM without ACS as controls for the analysis of glucose, fructose, ribulose, sorbitol, glycerol, pyruvate, lactate, glycine, serine, threonine, C16:0, C16:1, C18:0, C18:1, C18:2, C18:3, C20:0 and C22:6 by GC-MS. Validated GC-MS methods were accurate, precise and sensitive. Cases significantly differed in plasma MG and metabolite levels except for lactate, C16:0, C18:0, C18:2, and C18:3 levels compared with controls. On multivariable logistic regression, plasma C20:0, C18:1, glycine and glycerol levels had increased odds of ACS risk. On multivariate receiver operating characteristic analysis, a model containing plasma C20:0, C16:1, C18:1, C18:2, serine, glycerol, lactate and threonine levels had the highest area under the curve value (0.932) for ACS diagnosis. In conclusion, plasma C20:0, C16:1, C18:1, glycine, glycerol and sorbitol levels were associated with ACS risk in T2DM.

Metabolomics and cellular altered pathways in cancer biology: A review

Cancer is a deadly disease that affects a cell's metabolism and surrounding tissues. Understanding the fundamental mechanisms of metabolic alterations in cancer cells would assist in developing cancer treatment targets and approaches. From this perspective, metabolomics is a great analytical tool to clarify the mechanisms of cancer therapy as well as a useful tool to investigate cancer from a distinct viewpoint. It is a powerful emerging technology that detects up to thousands of molecules in tissues and biofluids. Like other "-omics" technologies, metabolomics involves the comprehensive investigation of micromolecule metabolites and can reveal important details about the cancer state that is otherwise not apparent. Recent developments in metabolomics technologies have made it possible to investigate cancer metabolism in greater depth and comprehend how cancer cells utilize metabolic pathways to make the amino acids, nucleotides, and lipids required for tumorigenesis. These new technologies have made it possible to learn more about cancer metabolism. Here, we review the cellular and systemic effects of cancer and cancer treatments on metabolism. The current study provides an overview of metabolomics, emphasizing the current technologies and their use in clinical and translational research settings.

Spatial multi-omics: deciphering technological landscape of integration of multi-omics and its applications

The emergence of spatial multi-omics has helped address the limitations of single-cell sequencing, which often leads to the loss of spatial context among cell populations. Integrated analysis of the genome, transcriptome, proteome, metabolome, and epigenome has enhanced our understanding of cell biology and the molecular basis of human diseases. Moreover, this approach offers profound insights into the interactions between intracellular and intercellular molecular mechanisms involved in the development, physiology, and pathogenesis of human diseases. In this comprehensive review, we examine current advancements in multi-omics technologies, focusing on their evolution and refinement over the past decade, including improvements in throughput and resolution, modality integration, and accuracy. We also discuss the pivotal contributions of spatial multi-omics in revealing spatial heterogeneity, constructing detailed spatial atlases, deciphering spatial crosstalk in tumor immunology, and advancing translational research and cancer therapy through precise spatial mapping.

Metabolomics combined with intestinal microbiota reveals the mechanism of compound Qilian tablets against diabetic retinopathy

Background

Diabetic retinopathy (DR) is one of the common chronic complications of diabetes mellitus, which has developed into the leading cause of irreversible visual impairment in adults worldwide. Compound Qilian tablets (CQLT) is a traditional Chinese medicine (TCM) developed for treating DR, but its mechanism is still unclear. This study explored the mechanism of action of CQLT in treating DR through metabolomics and intestinal microbiota.

Methods

Histopathologic examination of the pancreas and retina of Zucker diabetic fatty (ZDF) rats and immunohistochemistry were used to determine the expression levels of retinal nerve damage indicators ionized calcium binding adaptor molecule-1 (Iba-1) and glial fibrillary acidic protein (GFAP). Rat fecal samples were tested by LC-MS metabolomics to search for potential biomarkers and metabolic pathways for CQLT treatment of DR. Characteristic nucleic acid sequences of rat intestinal microbiota from each group were revealed using 16S rDNA technology to explore key microbes and related pathways for CQLT treatment of DR. At the same time, we investigated the effect of CQLT on the gluconeogenic pathway.

Results

After CQLT intervention, islet cell status was improved, Iba-1 and GFAP expression were significantly decreased, and abnormal retinal microvascular proliferation and exudation were ameliorated. Metabolomics results showed that CQLT reversed 20 differential metabolites that were abnormally altered in DR rats. Intestinal microbiota analysis showed that treatment with CQLT improved the abundance and diversity of intestinal flora. Functional annotation of metabolites and intestinal flora revealed that glycolysis/gluconeogenesis, alanine, aspartate and glutamate metabolism, starch and sucrose metabolism were the main pathways for CQLT in treating DR. According to the results of correlation analysis, there were significant correlations between Iba-1, GFAP, and intestinal microbiota and metabolites affected by CQLT. In addition, we found that CQLT effectively inhibited the gluconeogenesis process in diabetic mice.

Conclusion

In conclusion, CQLT could potentially reshape intestinal microbiota composition and regulate metabolite profiles to protect retinal morphology and function, thereby ameliorating the progression of DR.

Metabolomics studies in common multifactorial eye disorders: a review of biomarker discovery for age-related macular degeneration, glaucoma, diabetic retinopathy and myopia

Introduction

Multifactorial Eye disorders are a significant public health concern and have a huge impact on quality of life. The pathophysiological mechanisms underlying these eye disorders were not completely understood since functional and low-throughput biological tests were used. By identifying biomarkers linked to eye disorders, metabolomics enables early identification, tracking of the course of the disease, and personalized treatment.

Methods

The electronic databases of PubMed, Scopus, PsycINFO, and Web of Science were searched for research related to Age-Related macular degeneration (AMD), glaucoma, myopia, and diabetic retinopathy (DR). The search was conducted in August 2023. The number of cases and controls, the study's design, the analytical methods used, and the results of the metabolomics analysis were all extracted. Using the QUADOMICS tool, the quality of the studies included was evaluated, and metabolic pathways were examined for distinct metabolic profiles. We used MetaboAnalyst 5.0 to undertake pathway analysis of differential metabolites.

Results

Metabolomics studies included in this review consisted of 36 human studies (5 Age-related macular degeneration, 10 Glaucoma, 13 Diabetic retinopathy, and 8 Myopia). The most networked metabolites in AMD include glycine and adenosine monophosphate, while methionine, lysine, alanine, glyoxylic acid, and cysteine were identified in glaucoma. Furthermore, in myopia, glycerol, glutamic acid, pyruvic acid, glycine, cysteine, and oxoglutaric acid constituted significant metabolites, while glycerol, glutamic acid, lysine, citric acid, alanine, and serotonin are highly networked metabolites in cases of diabetic retinopathy. The common top metabolic pathways significantly enriched and associated with AMD, glaucoma, DR, and myopia were arginine and proline metabolism, methionine metabolism, glycine and serine metabolism, urea cycle metabolism, and purine metabolism.

Conclusion

This review recapitulates potential metabolic biomarkers, networks and pathways in AMD, glaucoma, DR, and myopia, providing new clues to elucidate disease mechanisms and therapeutic targets. The emergence of advanced metabolomics techniques has significantly enhanced the capability of metabolic profiling and provides novel perspectives on the metabolism and underlying pathogenesis of these multifactorial eye conditions. The advancement of metabolomics is anticipated to foster a deeper comprehension of disease etiology, facilitate the identification of novel therapeutic targets, and usher in an era of personalized medicine in eye research.

Clinical applications of volatilomic assays

The study of metabolomics is revealing immense potential for diagnosis, therapy monitoring, and understanding of pathogenesis processes. Volatilomics is a subcategory of metabolomics interested in the detection of molecules that are small enough to be released in the gas phase. Volatile compounds produced by cellular processes are released into the blood and lymph, and can reach the external environment through different pathways, such as the blood-air interface in the lung that are detected in breath, or the blood-water interface in the kidney that leads to volatile compounds detected in urine. Besides breath and urine, additional sources of volatile compounds such as saliva, blood, feces, and skin are available. Volatilomics traces its roots back over fifty years to the pioneering investigations in the 1970s. Despite extensive research, the field remains in its infancy, hindered by a lack of standardization despite ample experimental evidence. The proliferation of analytical instrumentations, sample preparations and methods of volatilome sampling still make it difficult to compare results from different studies and to establish a common standard approach to volatilomics. This review aims to provide an overview of volatilomics' diagnostic potential, focusing on two key technical aspects: sampling and analysis. Sampling poses a challenge due to the susceptibility of human samples to contamination and confounding factors from various sources like the environment and lifestyle. The discussion then delves into targeted and untargeted approaches in volatilomics. Some case studies are presented to exemplify the results obtained so far. Finally, the review concludes with a discussion on the necessary steps to fully integrate volatilomics into clinical practice.

Unraveling Protein-Metabolite Interactions in Precision Nutrition: A Case Study of Blueberry-Derived Metabolites Using Advanced Computational Methods

Metabolomics, the study of small-molecule metabolites within biological systems, has become a potent instrument for understanding cellular processes. Despite its profound insights into health, disease, and drug development, identifying the protein partners for metabolites, especially dietary phytochemicals, remains challenging. In the present study, we introduced an innovative in silico, structure-based target prediction approach to efficiently predict protein targets for metabolites. We analyzed 27 blood serum metabolites from nutrition intervention studies' blueberry-rich diets, known for their health benefits, yet with elusive mechanisms of action. Our findings reveal that blueberry-derived metabolites predominantly interact with Carbonic Anhydrase (CA) family proteins, which are crucial in acid-base regulation, respiration, fluid balance, bone metabolism, neurotransmission, and specific aspects of cellular metabolism. Molecular docking showed that these metabolites bind to a common pocket on CA proteins, with binding energies ranging from -5.0 kcal/mol to -9.0 kcal/mol. Further molecular dynamics (MD) simulations confirmed the stable binding of metabolites near the Zn binding site, consistent with known compound interactions. These results highlight the potential health benefits of blueberry metabolites through interaction with CA proteins.

Discrimination and prediction of Qingzhuan tea storage year using quantitative chemical profile combined with multivariate analysis: Advantages of MRMHR based targeted quantification metabolomics

The duration of storage significantly influences the quality and market value of Qingzhuan tea (QZT). Herein, a high-resolution multiple reaction monitoring (MRMHR) quantitative method for markers of QZT storage year was developed. Quantitative data alongside multivariate analysis were employed to discriminate and predict the storage year of QZT. Furthermore, the content of the main biochemical ingredients, catechins and alkaloids, and free amino acids (FAA) were assessed for this purpose. The results show that targeted marker-based models exhibited superior discrimination and prediction performance among four datasets. The R2Xcum, R2Ycum and Q2cum of orthogonal projection to latent structure-discriminant analysis discrimination model were close to 1. The correlation coefficient (R2) and the root mean square error of prediction of the QZT storage year prediction model were 0.9906 and 0.63, respectively. This study provides valuable insights into tea storage quality and highlights the potential application of targeted markers in food quality evaluation.

Bacterial metabolomics: current applications for human welfare and future aspects

An imbalanced microbiome is linked to several diseases, such as cancer, inflammatory bowel disease, obesity, and even neurological disorders. Bacteria and their by-products are used for various industrial and clinical purposes. The metabolites under discussion were chosen based on their biological impacts on host and gut microbiota interactions as established by metabolome research. The separation of bacterial metabolites by using statistics and machine learning analysis creates new opportunities for applications of bacteria and their metabolites in the environmental and medical sciences. Thus, the metabolite production strategies, methodologies, and importance of bacterial metabolites for human well-being are discussed in this review.

Exploration of the Amino Acid Metabolic Profiling and Pathway in Clonorchis sinensis-Infected Rats Revealed by the Targeted Metabolomic Analysis

Background: Clonorchiasis remains a serious public health problem. However, the molecular mechanism underlying clonorchiasis remains largely unknown. Amino acid (AA) metabolism plays key roles in protein synthesis and energy sources, and improves immunity in pathological conditions. Therefore, this study aimed to explore the AA profiles of spleen in clonorchiasis and speculate the interaction between the host and parasite. Methods: Here targeted ultrahigh performance liquid chromatography multiple reaction monitoring mass spectrometry was applied to discover the AA profiles in spleen of rats infected with Clonorchis sinensis. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis (KEGG) was performed to characterize the dysregulated metabolic pathways. Results: Pathway analysis revealed that phenylalanine, tyrosine, and tryptophan biosynthesis and β-alanine metabolism were significantly altered in clonorchiasis. There were no significant correlations between 14 significant differential AAs and interleukin (IL)-1β. Although arginine, asparagine, histidine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, and valine were positively correlated with IL-6, IL-10, tumor necrosis factor (TNF)-α as well as aspartate aminotransferase and alanine aminotransferase; β-alanine and 4-hydroxyproline were negatively correlated with IL-6, IL-10, and TNF-α. Conclusion: This study reveals the dysregulation of AA metabolism in clonorchiasis and provides a useful insight of metabolic mechanisms at the molecular level.

Deciphering metabolic crosstalk in context: lessons from inflammatory diseases

Metabolism plays a crucial role in regulating the function of immune cells in both health and disease, with altered metabolism contributing to the pathogenesis of cancer and many inflammatory diseases. The local microenvironment has a profound impact on the metabolism of immune cells. Therefore, immunological and metabolic heterogeneity as well as the spatial organization of cells in tissues should be taken into account when studying immunometabolism. Here, we highlight challenges of investigating metabolic communication. Additionally, we review the capabilities and limitations of current technologies for studying metabolism in inflamed microenvironments, including single-cell omics techniques, flow cytometry-based methods (Met-Flow, single-cell energetic metabolism by profiling translation inhibition (SCENITH)), cytometry by time of flight (CyTOF), cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq), and mass spectrometry imaging. Considering the importance of metabolism in regulating immune cells in diseased states, we also discuss the applications of metabolomics in clinical research, as well as some hurdles to overcome to implement these techniques in standard clinical practice. Finally, we provide a flowchart to assist scientists in designing effective strategies to unravel immunometabolism in disease-relevant contexts.

Metabolomics for dental caries diagnosis: Past, present, and future

Dental caries, a prevalent global infectious condition affecting over 95% of adults, remains elusive in its precise etiology. Addressing the complex dynamics of caries demands a thorough exploration of taxonomic, potential, active, and encoded functions within the oral ecosystem. Metabolomic profiling emerges as a crucial tool, offering immediate insights into microecosystem physiology and linking directly to the phenotype. Identified metabolites, indicative of caries status, play a pivotal role in unraveling the metabolic processes underlying the disease. Despite challenges in metabolite variability, the use of metabolomics, particularly via mass spectrometry and nuclear magnetic resonance spectroscopy, holds promise in caries research. This review comprehensively examines metabolomics in caries prevention, diagnosis, and treatment, highlighting distinct metabolite expression patterns and their associations with disease-related bacterial communities. Pioneering in approach, it integrates singular and combinatory metabolomics methodologies, diverse biofluids, and study designs, critically evaluating prior limitations while offering expert insights for future investigations. By synthesizing existing knowledge, this review significantly advances our comprehension of caries, providing a foundation for improved prevention and treatment strategies.

The Omics Revolution in Understanding Chicken Reproduction: A Comprehensive Review

Omics approaches have significantly contributed to our understanding of several aspects of chicken reproduction. This review paper gives an overview of the use of omics technologies such as genomics, transcriptomics, proteomics, and metabolomics to elucidate the mechanisms of chicken reproduction. Genomics has transformed the study of chicken reproduction by allowing the examination of the full genetic makeup of chickens, resulting in the discovery of genes associated with reproductive features and disorders. Transcriptomics has provided insights into the gene expression patterns and regulatory mechanisms involved in reproductive processes, allowing for a better knowledge of developmental stages and hormone regulation. Furthermore, proteomics has made it easier to identify and quantify the proteins involved in reproductive physiology to better understand the molecular mechanisms driving fertility, embryonic development, and egg quality. Metabolomics has emerged as a useful technique for understanding the metabolic pathways and biomarkers linked to reproductive performance, providing vital insights for enhancing breeding tactics and reproductive health. The integration of omics data has resulted in the identification of critical molecular pathways and biomarkers linked with chicken reproductive features, providing the opportunity for targeted genetic selection and improved reproductive management approaches. Furthermore, omics technologies have helped to create biomarkers for fertility and embryonic viability, providing the poultry sector with tools for effective breeding and reproductive health management. Finally, omics technologies have greatly improved our understanding of chicken reproduction by revealing the molecular complexities that underpin reproductive processes.

Exploring the broad-spectrum pharmacological activity of two less studied Australian native fruits: chemical characterisation using LCMS-driven metabolomics

Australian fruits such as native currant (Acrotriche depressa) and lemon aspen (Acronychia acidula) are under-examined in terms of their therapeutic potential. In this study, the in vitro antiproliferative activity of native currant and lemon aspen extracts (water and ethanol) against MCF7 breast adenocarcinoma cells was determined using the Alamar blue assay. The most potent extracts (native currant water, NC-W; native currant ethanol, NC-Et; lemon aspen ethanol, LA-Et) were further evaluated using flow cytometry to detect the potential induction of apoptosis in MCF7 cells whereas 2',7'-dichlorodihydrofluorescein diacetate (H2DCFDA) assay was implemented to understand the impact of the extracts on the intracellular reactive oxygen species (ROS) levels in MCF7 cells. Furthermore, the antioxidant activity of the extracts was assessed using ABTS [2,2'-azinobis-(3-ethylbenzothiazoline-6-sulfonate)], and CUPRAC (cupric reducing antioxidant capacity) assays. The antimicrobial susceptibility testing of NC-W, NC-Et, and LA-Et was carried out against Gram-positive (Staphylococcus aureus), Gram-negative (Escherichia coli), and yeast (Candida albicans) strains using a resazurin-based assay. Additionally, potential metabolites in the NC-W and NC-Et extracts were analysed with liquid chromatography-mass spectrometry (LC-MS) driven metabolomics and chemometrics to spot differential and major metabolites. A dose-dependent antiproliferative activity was conferred by the NC extracts against MCF7 cells. Of the two LA extracts, only LA-Et showed a dose-dependent antiproliferative activity at higher concentrations. Both NC extracts and LA-Et induced apoptosis in MCF7 cells. None of the extracts increased the production of ROS significantly in MCF7 cells compared to the untreated control. A dose-dependent antioxidant activity was observed in both antioxidant assays. Both NC and LA extracts showed a similar minimum inhibitory concentration (MIC) value against S. aureus. Only LA-Et showed activity against E. coli, while NC-W and NC-Et were less active. All extracts showed MIC values of >1500 μg mL-1 against C. albicans. The metabolomics analysis revealed an abundance of flavonoids, fatty acyl derivatives, carbohydrates, carboxylic acids and their derivatives, and alkaloid compounds as potential bioactive metabolites in the NC extracts. In conclusion, both NC and LA showed antiproliferative (against MCF7 breast adenocarcinoma cells through the induction of apoptosis), strong antioxidant and minimal antimicrobial properties.

Fecal microbiota transplantation: current challenges and future landscapes

SUMMARYGiven the importance of gut microbial homeostasis in maintaining health, there has been considerable interest in developing innovative therapeutic strategies for restoring gut microbiota. One such approach, fecal microbiota transplantation (FMT), is the main "whole gut microbiome replacement" strategy and has been integrated into clinical practice guidelines for treating recurrent Clostridioides difficile infection (rCDI). Furthermore, the potential application of FMT in other indications such as inflammatory bowel disease (IBD), metabolic syndrome, and solid tumor malignancies is an area of intense interest and active research. However, the complex and variable nature of FMT makes it challenging to address its precise functionality and to assess clinical efficacy and safety in different disease contexts. In this review, we outline clinical applications, efficacy, durability, and safety of FMT and provide a comprehensive assessment of its procedural and administration aspects. The clinical applications of FMT in children and cancer immunotherapy are also described. We focus on data from human studies in IBD in contrast with rCDI to delineate the putative mechanisms of this treatment in IBD as a model, including colonization resistance and functional restoration through bacterial engraftment, modulating effects of virome/phageome, gut metabolome and host interactions, and immunoregulatory actions of FMT. Furthermore, we comprehensively review omics technologies, metagenomic approaches, and bioinformatics pipelines to characterize complex microbial communities and discuss their limitations. FMT regulatory challenges, ethical considerations, and pharmacomicrobiomics are also highlighted to shed light on future development of tailored microbiome-based therapeutics.

Associations between cord blood acetaminophen biomarkers and childhood asthma with and without allergic comorbidities

BACKGROUND

Previous studies have linked prenatal acetaminophen use to increased asthma risk in children. However, none have explored this association while differentiating between asthma cases with and without other allergic conditions or by employing objective biomarkers to assess acetaminophen exposure.

OBJECTIVE

To evaluate whether the detection of acetaminophen biomarkers in cord blood is associated with the subgroups of asthma both with and without allergic comorbidities in children.

METHODS

Acetaminophen biomarkers, including unchanged acetaminophen and acetaminophen glucuronide, were measured in neonatal cord blood samples from the Boston Birth Cohort. Asthma subgroups were defined on the basis of physician diagnoses of asthma and other allergic conditions (atopic dermatitis and allergic rhinitis). Multinomial regressions were used to evaluate the associations between acetaminophen biomarkers and asthma subgroups, adjusting for multiple confounders, including potential indications for maternal acetaminophen use such as maternal fever.

RESULTS

The study included 142 children with asthma and at least 1 other allergic condition, 55 children with asthma but no other allergic condition, and 613 children free of asthma. Detection of acetaminophen in cord blood, reflecting maternal exposure to acetaminophen shortly before delivery, was associated with 3.73 times the odds of developing asthma without allergic comorbidities (95% CI: 1.79-7.80, P = .0004). In contrast, the detection of acetaminophen in cord blood was not associated with an elevated risk of asthma with allergic comorbidities. Analysis of acetaminophen glucuronide yielded consistent results.

CONCLUSION

In a prospective birth cohort, cord blood acetaminophen biomarkers were associated with an increased risk of childhood asthma without allergic comorbidities, but were not associated with childhood asthma with allergic comorbidities.

Integration of targeted metabolome and transcript profiling of Pseudomonas syringae-triggered changes in defence-related phytochemicals in oat plants

MAIN CONCLUSION

A gene-to-metabolite approach afforded new insights regarding defence mechanisms in oat plants that can be incorporated into plant breeding programmes for the selection of markers and genes related to disease resistance. Monitoring metabolite levels and changes therein can complement and corroborate transcriptome (mRNA) data on plant-pathogen interactions, thus revealing mechanisms involved in pathogen attack and host defence. A multi-omics approach thus adds new layers of information such as identifying metabolites with antimicrobial properties, elucidating metabolomic profiles of infected and non-infected plants, and reveals pathogenic requirements for infection and colonisation. In this study, two oat cultivars (Dunnart and SWK001) were inoculated with Pseudomonas syringae pathovars, pathogenic and non-pathogenic on oat. Following inoculation, metabolites were extracted with methanol from leaf tissues at 2, 4 and 6 days post-infection and analysed by multiple reaction monitoring (MRM) on a triple quadrupole mass spectrometer system. Relatedly, mRNA was isolated at the same time points, and the cDNA analysed by quantitative PCR (RT-qPCR) for expression levels of selected gene transcripts associated with avenanthramide (Avn) biosynthesis. The targeted amino acids, hydroxycinnamic acids and Avns were successfully quantified. Distinct cultivar-specific differences in the metabolite responses were observed in response to pathogenic and non-pathogenic strains. Trends in aromatic amino acids and hydroxycinnamic acids seem to indicate stronger activation and flux through these pathways in Dunnart as compared to SWK001. A positive correlation between hydroxycinnamoyl-CoA:hydroxyanthranilate N-hydroxycinnamoyl transferase (HHT) gene expression and the abundance of Avn A in both cultivars was documented. However, transcript profiling of selected genes involved in Avn synthesis did not reveal a clear pattern to distinguish between the tolerant and susceptible cultivars.

Transforming Big Data into AI-ready data for nutrition and obesity research

OBJECTIVE

Big Data are increasingly used in obesity and nutrition research to gain new insights and derive personalized guidance; however, this data in raw form are often not usable. Substantial preprocessing, which requires machine learning (ML), human judgment, and specialized software, is required to transform Big Data into artificial intelligence (AI)- and ML-ready data. These preprocessing steps are the most complex part of the entire modeling pipeline. Understanding the complexity of these steps by the end user is critical for reducing misunderstanding, faulty interpretation, and erroneous downstream conclusions.

METHODS

We reviewed three popular obesity/nutrition Big Data sources: microbiome, metabolomics, and accelerometry. The preprocessing pipelines, specialized software, challenges, and how decisions impact final AI- and ML-ready products were detailed.

RESULTS

Opportunities for advances to improve quality control, speed of preprocessing, and intelligent end user consumption were presented.

CONCLUSIONS

Big Data have the exciting potential for identifying new modifiable factors that impact obesity research. However, to ensure accurate interpretation of conclusions arising from Big Data, the choices involved in preparing AI- and ML-ready data need to be transparent to investigators and clinicians relying on the conclusions.

Mitochondrial Biomarkers in the Omics Era: A Clinical-Pathophysiological Perspective

Mitochondrial diseases (MDs) affect 4300 individuals, with different ages of presentation and manifestation in any organ. How defects in mitochondria can cause such a diverse range of human diseases remains poorly understood. In recent years, several published research articles regarding the metabolic and protein profiles of these neurogenetic disorders have helped shed light on the pathogenetic mechanisms. By investigating different pathways in MDs, often with the aim of identifying disease biomarkers, it is possible to identify molecular processes underlying the disease. In this perspective, omics technologies such as proteomics and metabolomics considered in this review, can support unresolved mitochondrial questions, helping to improve outcomes for patients.

Cross-Validation of Metabolic Phenotypes in SARS-CoV-2 Infected Subpopulations Using Targeted Liquid Chromatography-Mass Spectrometry (LC-MS)

To ensure biological validity in metabolic phenotyping, findings must be replicated in independent sample sets. Targeted workflows have long been heralded as ideal platforms for such validation due to their robust quantitative capability. We evaluated the capability of liquid chromatography-mass spectrometry (LC-MS) assays targeting organic acids and bile acids to validate metabolic phenotypes of SARS-CoV-2 infection. Two independent sample sets were collected: (1) Australia: plasma, SARS-CoV-2 positive (n = 20), noninfected healthy controls (n = 22) and COVID-19 disease-like symptoms but negative for SARS-CoV-2 infection (n = 22). (2) Spain: serum, SARS-CoV-2 positive (n = 33) and noninfected healthy controls (n = 39). Multivariate modeling using orthogonal projections to latent structures discriminant analyses (OPLS-DA) classified healthy controls from SARS-CoV-2 positive (Australia; R2 = 0.17, ROC-AUC = 1; Spain R2 = 0.20, ROC-AUC = 1). Univariate analyses revealed 23 significantly different (p < 0.05) metabolites between healthy controls and SARS-CoV-2 positive individuals across both cohorts. Significant metabolites revealed consistent perturbations in cellular energy metabolism (pyruvic acid, and 2-oxoglutaric acid), oxidative stress (lactic acid, 2-hydroxybutyric acid), hypoxia (2-hydroxyglutaric acid, 5-aminolevulinic acid), liver activity (primary bile acids), and host-gut microbial cometabolism (hippuric acid, phenylpropionic acid, indole-3-propionic acid). These data support targeted LC-MS metabolic phenotyping workflows for biological validation in independent sample sets.

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.

The hidden treasures in endophytic fungi: a comprehensive review on the diversity of fungal bioactive metabolites, usual analytical methodologies, and applications

This review provides a comprehensive overview of the key aspects of the natural metabolite production by endophytic fungi, which has attracted significant attention due to its diverse biological activities and wide range of applications. Synthesized by various fungal species, these metabolites encompass compounds with therapeutic, agricultural, and commercial significance. We delved into strategies and advancements aimed at optimizing fungal metabolite production. Fungal cultivation, especially by Aspergillus, Penicillium, and Fusarium, plays a pivotal role in metabolite biosynthesis, and researchers have explored both submerged and solid-state cultivation processes to harness the full potential of fungal species. Nutrient optimization, pH, and temperature control are critical factors in ensuring high yields of the targeted bioactive metabolites especially for scaling up processes. Analytical methods that includes High-Performance Liquid Chromatography (HPLC), Liquid Chromatography-Mass Spectrometry (LC-MS), Gas Chromatography-Mass Spectrometry (GC-MS), Nuclear Magnetic Resonance (NMR), and Mass Spectrometry (MS), are indispensable for the identification and quantification of the compounds. Moreover, genetic engineering and metabolic pathway manipulation have emerged as powerful tools to enhance metabolite production and develop novel fungal strains with increased yields. Regulation and control mechanisms at the genetic, epigenetic, and metabolic levels are explored to fine-tune the biosynthesis of fungal metabolites. Ongoing research aims to overcome the complexity of the steps involved to ensure the efficient production and utilization of fungal metabolites.

Evaluation of novel candidate filtration markers from a global metabolomic discovery for glomerular filtration rate estimation

Creatinine and cystatin-C are recommended for estimating glomerular filtration rate (eGFR) but accuracy is suboptimal. Here, using untargeted metabolomics data, we sought to identify candidate filtration markers for a new targeted assay using a novel approach based on their maximal joint association with measured GFR (mGFR) and with flexibility to consider their biological properties. We analyzed metabolites measured in seven diverse studies encompasing 2,851 participants on the Metabolon H4 platform that had Pearson correlations with log mGFR and used a stepwise approach to develop models to < -0.5 estimate mGFR with and without inclusion of creatinine that enabled selection of candidate markers. In total, 456 identified metabolites were present in all studies, and 36 had correlations with mGFR < -0.5. A total of 2,225 models were developed that included these metabolites; all with lower root mean square errors and smaller coefficients for demographic variables compared to estimates using untargeted creatinine. Seventeen metabolites were chosen, including 12 new candidate filtration markers. The selected metabolites had strong associations with mGFR and little dependence on demographic factors. Candidate metabolites were identified with maximal joint association with mGFR and minimal dependence on demographic variables across many varied clinical settings. These metabolites are excreted in urine and represent diverse metabolic pathways and tubular handling. Thus, our data can be used to select metabolites for a multi-analyte eGFR determination assay using mass spectrometry that potentially offers better accuracy and is less prone to non-GFR determinants than the current eGFR biomarkers.

Gut microbiota and metabolic biomarkers in metabolic dysfunction-associated steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD), a replacement of the nomenclature employed for NAFLD, is the most prevalent chronic liver disease worldwide. Despite its high global prevalence, NAFLD is often under-recognized due to the absence of reliable noninvasive biomarkers for diagnosis and staging. Growing evidence suggests that the gut microbiome plays a significant role in the occurrence and progression of NAFLD by causing immune dysregulation and metabolic alterations due to gut dysbiosis. The rapid advancement of sequencing tools and metabolomics has enabled the identification of alterations in microbiome signatures and gut microbiota-derived metabolite profiles in numerous clinical studies related to NAFLD. Overall, these studies have shown a decrease in α-diversity and changes in gut microbiota abundance, characterized by increased levels of Escherichia and Prevotella, and decreased levels of Akkermansia muciniphila and Faecalibacterium in patients with NAFLD. Furthermore, bile acids, short-chain fatty acids, trimethylamine N-oxide, and tryptophan metabolites are believed to be closely associated with the onset and progression of NAFLD. In this review, we provide novel insights into the vital role of gut microbiome in the pathogenesis of NAFLD. Specifically, we summarize the major classes of gut microbiota and metabolic biomarkers in NAFLD, thereby highlighting the links between specific bacterial species and certain gut microbiota-derived metabolites in patients with NAFLD.

Metabolomic associations of impaired awareness of hypoglycaemia in type 1 diabetes

This study investigates impaired awareness of hypoglycaemia (IAH), a complication of insulin therapy affecting 20-40% of individuals with type 1 diabetes. The exact pathophysiology is unclear, therefore we sought to identify metabolic signatures in IAH to elucidate potential pathophysiological pathways. Plasma samples from 578 individuals of the Dutch type 1 diabetes biomarker cohort, 67 with IAH and 108 without IAH (NAH) were analysed using the targeted metabolomics Biocrates AbsoluteIDQ p180 assay. Eleven metabolites were significantly associated with IAH. Genome-wide association studies of these 11 metabolites identified significant single nucleotide polymorphisms (SNPs) in C22:1-OH and phosphatidylcholine diacyl C36:6. After adjusting for the SNPs, 11 sphingomyelins and phosphatidylcholines were significantly higher in the IAH group in comparison to NAH. These metabolites are important components of the cell membrane and have been implicated to play a role in cell signalling in diabetes. These findings demonstrate the potential role of phosphatidylcholine and sphingomyelins in IAH.

Integrated microbiome and metabolomics analysis reveal the relationship between plant-specialized metabolites and microbial community in Phellodendron amurense

Phellodendron amurense is the essential source of bisbenzylisoquinoline alkaloids (BIAs), making it a highly valued raw material in traditional Chinese medicine. The plant's root secondary metabolism is intricately linked to the microbial communities that surround it. However, the root-associated microbiomes of P. amurense, as well as the potential correlation between its bioactive compounds and these microbiomes, remain poorly understood. Here, the metabolic profiles of root, rhizosphere, and bulk soils of P. amurense revealed the dramatic differences in the relative content of plant-specialized metabolites. A total of 31, 21, and 0 specialized metabolites in P. amurense were identified in the root, rhizosphere soil, and bulk soil, respectively, with higher content of the seven major BIAs observed in the rhizosphere compared with that in the bulk soils. The composition of the bulk and rhizosphere microbiomes was noticeably distinct from that of the endospheric microbiome. The phylum Cyanobacteria accounted for over 60% of the root endosphere communities, and the α-diversity in root was the lowest. Targeted seven BIAs, namely, berberine, palmatine, magnocurarine, phellodendrine, jatrorrhizine, tetrahydropalmatine, and magnoflorine, were significantly positively correlated with Nectriaceae and Sphingobacteriaceae. This study has illuminated the intricate interaction networks between P. amurense root-associated microorganisms and their key chemical compounds, providing the theoretical foundation for discovering biological fertilizers and laying the groundwork for cultivating high-quality medicinal plants.

Metabolic diversity of human macrophages: potential influence on Staphylococcus aureus intracellular survival

Staphylococcus aureus is a leading cause of medical device-associated biofilm infections. This is influenced by the ability of S. aureus biofilm to evade the host immune response, which is partially driven by the anti-inflammatory cytokine interleukin-10 (IL-10). Here, we show that treatment of human monocyte-derived macrophages (HMDMs) with IL-10 enhanced biofilm formation, suggesting that macrophage anti-inflammatory programming likely plays an important role during the transition from planktonic to biofilm growth. To identify S. aureus genes that were important for intracellular survival in HMDMs and how this was affected by IL-10, transposon sequencing was performed. The size of the S. aureus essential genome was similar between unstimulated HMDMs and the outgrowth control (18.5% vs 18.4%, respectively, with 54.4% overlap) but increased to 22.5% in IL-10-treated macrophages, suggesting that macrophage polarization status exerts differential pressure on S. aureus. Essential genes for S. aureus survival within IL-10-polarized HMDMs were dominated by negative regulatory pathways, including nitrogen and RNA metabolism, whereas S. aureus essential genes within untreated HMDMs were enriched in biosynthetic pathways such as purine and pyrimidine biosynthesis. To explore how IL-10 altered the macrophage intracellular metabolome, targeted metabolomics was performed on HMDMs from six individual donors. IL-10 treatment led to conserved alterations in distinct metabolites that were increased (dihydroxyacetone phosphate, glyceraldehyde-3-phosphate, and acetyl-CoA) or reduced (fructose-6-phosphate, aspartic acid, and ornithine) across donors, whereas other metabolites were variable. Collectively, these findings highlight an important aspect of population-level heterogeneity in human macrophage responsiveness that should be considered when translating results to a patient population.IMPORTANCEOne mechanism that Staphylococcus aureus biofilm elicits in the host to facilitate infection persistence is the production of the anti-inflammatory cytokine interleukin-10 (IL-10). Here, we show that exposure of human monocyte-derived macrophages (HMDMs) to IL-10 promotes S. aureus biofilm formation and programs intracellular bacteria to favor catabolic pathways. Examination of intracellular metabolites in HMDMs revealed heterogeneity between donors that may explain the observed variability in essential genes for S. aureus survival based on nutrient availability for bacteria within the intracellular compartment. Collectively, these studies provide novel insights into how IL-10 polarization affects S. aureus intracellular survival in HMDMs and the importance of considering macrophage heterogeneity between human donors as a variable when examining effector mechanisms.

Metabolomics and lipidomics signature in celiac disease: a narrative review

Celiac disease (CD) is a chronic immune-mediated inflammatory disease of the small intestine caused by aberrant immune responses to consumed gluten proteins. CD is diagnosed by a combination of the patients reported symptoms, serologic and endoscopic biopsy evaluation of the small intestine; and adherence to a strict gluten-free diet (GFD) is considered the only available therapeutic approach for this disorder. Novel approaches need to be considered for finding new biomarkers to help this disorder diagnosis and finding a new alternative therapeutic method for this group of patients. Metabolomics and lipidomics are powerful tools to provide highly accurate and sensitive biomarkers. Previous studies indicated a metabolic fingerprint for CD deriving from alterations in gut microflora or intestinal permeability, malabsorption, and energy metabolism. Moreover, since CD is characterized by increased intestinal permeability and due to the importance of membrane lipid components in controlling barrier integrity, conducting lipidomics studies in this disorder is of great importance. In the current study, we tried to provide a critical overview of metabolomic and lipidomic changes in CD.

Medicinal Plants against Viral Infections: A Review of Metabolomics Evidence for the Antiviral Properties and Potentials in Plant Sources

Most plants have developed unique mechanisms to cope with harsh environmental conditions to compensate for their lack of mobility. A key part of their coping mechanisms is the synthesis of secondary metabolites. In addition to their role in plants' defense against pathogens, they also possess therapeutic properties against diseases, and their use by humans predates written history. Viruses are a unique class of submicroscopic agents, incapable of independent existence outside a living host. Pathogenic viruses continue to pose a significant threat to global health, leading to innumerable fatalities on a yearly basis. The use of medicinal plants as a natural source of antiviral agents has been widely reported in literature in the past decades. Metabolomics is a powerful research tool for the identification of plant metabolites with antiviral potentials. It can be used to isolate compounds with antiviral capacities in plants and study the biosynthetic pathways involved in viral disease progression. This review discusses the use of medicinal plants as antiviral agents, with a special focus on the metabolomics evidence supporting their efficacy. Suggestions are made for the optimization of various metabolomics methods of characterizing the bioactive compounds in plants and subsequently understanding the mechanisms of their operation.

Metabolomics to Study Human Aging: A Review

In the last years, with the increase in the average life expectancy, the world's population is progressively aging, which entails social, health and economic problems. In this sense, the need to better understand the physiology of the aging process becomes an urgent need. Since the study of aging in humans is challenging, cellular and animal models are widely used as alternatives. Omics, namely metabolomics, have emerged in the study of aging, with the aim of biomarker discovering, which may help to uncomplicate this complex process. This paper aims to summarize different models used for aging studies with their advantages and limitations. Also, this review gathers the published articles referring to biomarkers of aging already discovered using metabolomics approaches, comparing the results obtained in the different studies. Finally, the most frequently used senescence biomarkers are described, along with their importance in understanding aging.

Metabolomics in Animal Models of Bronchial Asthma and Its Translational Importance for Clinics

Bronchial asthma is an extremely heterogenous chronic respiratory disorder with several distinct endotypes and phenotypes. These subtypes differ not only in the pathophysiological changes and/or clinical features but also in their response to the treatment. Therefore, precise diagnostics represent a fundamental condition for effective therapy. In the diagnostic process, metabolomic approaches have been increasingly used, providing detailed information on the metabolic alterations associated with human asthma. Further information is brought by metabolomic analysis of samples obtained from animal models. This article summarizes the current knowledge on metabolomic changes in human and animal studies of asthma and reveals that alterations in lipid metabolism, amino acid metabolism, purine metabolism, glycolysis and the tricarboxylic acid cycle found in the animal studies resemble, to a large extent, the changes found in human patients with asthma. The findings indicate that, despite the limitations of animal modeling in asthma, pre-clinical testing and metabolomic analysis of animal samples may, together with metabolomic analysis of human samples, contribute to a novel way of personalized treatment of asthma patients.

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

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

Exploring the interplay between metabolomics and genetics in Parkinson's disease: Insights from ongoing research and future avenues

Parkinson's disease (PD) is a widespread neurodegenerative disorder, whose complex aetiology remains under construction. While rare variants have been associated with the monogenic PD form, most PD cases are influenced by multiple genetic and environmental aspects. Nonetheless, the pathophysiological pathways and molecular networks involved in monogenic/idiopathic PD overlap, and genetic variants are decisive in elucidating the convergent underlying mechanisms of PD. In this scenario, metabolomics has furnished a dynamic and systematic picture of the synergy between the genetic background and environmental influences that impact PD, making it a valuable tool for investigating PD-related metabolic dysfunctions. In this review, we performed a brief overview of metabolomics current research in PD, focusing on significant metabolic alterations observed in idiopathic PD from different biofluids and strata and exploring how they relate to genetic factors associated with monogenic PD. Dysregulated amino acid metabolism, lipid metabolism, and oxidative stress are the critical metabolic pathways implicated in both genetic and idiopathic PD. By merging metabolomics and genetics data, it is possible to distinguish metabolic signatures of specific genetic backgrounds and to pinpoint subgroups of PD patients who could derive personalized therapeutic benefits. This approach holds great promise for advancing PD research and developing innovative, cost-effective treatments.

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.

Identification and validation of diagnostic biomarkers for intrahepatic cholestasis of pregnancy based on untargeted and targeted metabolomics analyses of urine metabolite profiles

BACKGROUND

Intrahepatic cholestasis of pregnancy (ICP) is a prevalent pregnancy-specific complication that presents with maternal itching and elevated serum bile acid levels. ICP is associated with unfavorable pregnancy outcomes, severely decreasing the pregnant woman's quality of life. Timely identification of ICP is crucial for effective management and improved outcomes.

METHODS

We collected urine samples from 8 patients with ICP and 8 healthy individuals. We used Liquid Chromatography-Mass Spectrometry (LC-MS) to detect metabolite expression levels, then conducted a series of bioinformatic analyses to explore the potential biological meanings of differentially expressed metabolites, and preliminarily discovered several candidate biomarkers. To validate these candidate biomarkers, we performed Gas Chromatography-Mass Spectrometry (GC-MS) detection and analyzed their diagnostic values using receiver operating characteristic (ROC) curve.

RESULTS

Untargeted metabolomics data showed that 6129 positive peaks and 6218 negative peaks were extracted from each specimen. OPLS-DA analysis and the heat map for cluster analysis showed satisfactory capability in discriminating ICP specimens from controls. Subsequent analysis extracted 64 significantly differentially expressed metabolites, which could be potential biomarkers for diagnosis of ICP. Based on the KEGG enrichment analyses, six candidate biomarkers were preliminarily identified. Two most promising biomarkers (3-hydroxypropionic acid and uracil) were validated by targeted metabolomics analyses with the area under the curve (AUC) of 0.920 and 0.850 respectively.

CONCLUSION

Based on preliminary screening from untargeted metabolomics and subsequent validation through targeted metabolomics, 3-hydroxypropionic acid and uracil were identified as promising diagnostic biomarkers for ICP.

Physicochemical properties, phenolic content and in vitro digestion profile of Chinese black rice (Oryza sativa L.)

Yangxian black rice, as one of the ancient Chinese black rice varieties, is widely planted in the Yangxian area of China. This study investigated the physicochemical properties, phenolic content and in vitro digestion profile of Chinese black rice under gradient milling treatment. The chemical composition, color, pasting and thermal properties of black rice with different milling degrees were comprehensively compared. In vitro digestion analysis indicated that cooked rice flour had higher rapidly digestible starch (RDS) and lower resistant starch (RS) contents compared with the uncooked one. Besides, all cooked black rice samples exhibited high predicted glycemic index (pGI) value and whole black rice showed a lower pGI than refined rice. The microstructure and the abundance of phenolic compounds in the solid matrix during different treatments or digestion stages were observed by CLSM. Furthermore, a total of 102 phenolic constituents were absolutely quantified by targeted metabolomics techniques. Methanol extraction and moderate cooking treatment contributed to the release of phenolic compounds from the solid matrix of whole black rice. Besides, compared to the gastric digestion stage, the transition in the intestinal environment caused a decrease in the majority of the analyzed polyphenols. Identifying the phenolic constituents was favorable for a better elucidation of the chemical basis of the function and nutritional value of Chinese black rice.

Survival analysis of patient groups defined by unsupervised machine learning clustering methods based on patient metabolomic data

Purpose

Meta-analyses failed to accurately identify patients with non-metastatic breast cancer who are likely to benefit from chemotherapy, and metabolomics could provide new answers. In our previous published work, patients were clustered using five different unsupervised machine learning (ML) methods resulting in the identification of three clusters with distinct clinical and simulated survival data. The objective of this study was to evaluate the survival outcomes, with extended follow-up, using the same 5 different methods of unsupervised machine learning.

Experimental design

Forty-nine patients, diagnosed between 2013 and 2016, with non-metastatic BC were included retrospectively. Median follow-up was extended to 85.8 months. 449 metabolites were extracted from tumor resection samples by combined Liquid chromatography-mass spectrometry (LC-MS). Survival analyses were reported grouping together Cluster 1 and 2 versus cluster 3. Bootstrap optimization was applied.

Results

PCA k-means, K-sparse and Spectral clustering were the most effective methods to predict 2-year progression-free survival with bootstrap optimization (PFSb); as bootstrap example, with PCA k-means method, PFSb were 94% for cluster 1&2 versus 82% for cluster 3 (p = 0.01). PCA k-means method performed best, with higher reproducibility (mean HR=2 (95%CI [1.4-2.7]); probability of p ≤ 0.05 85%). Cancer-specific survival (CSS) and overall survival (OS) analyses highlighted a discrepancy between the 5 ML unsupervised methods.

Conclusion

Our study is a proof-of-principle that it is possible to use unsupervised ML methods on metabolomic data to predict PFS survival outcomes, with the best performance for PCA k-means. A larger population study is needed to draw conclusions from CSS and OS analyses.

Metabolomics in severe traumatic brain injury: a scoping review

BACKGROUND

Diagnosis and prognostication of severe traumatic brain injury (sTBI) continue to be problematic despite years of research efforts. There are currently no clinically reliable biomarkers, though advances in protein biomarkers are being made. Utilizing Omics technology, particularly metabolomics, may provide new diagnostic biomarkers for sTBI. Several published studies have attempted to determine the specific metabolites and metabolic pathways involved; these studies will be reviewed.

AIMS

This scoping review aims to summarize the current literature concerning metabolomics in sTBI, review the comprehensive data, and identify commonalities, if any, to define metabolites with potential clinical use. In addition, we will examine related metabolic pathways through pathway analysis.

METHODS

Scoping review methodology was used to examine the current literature published in Embase, Scopus, PubMed, and Medline. An initial 1090 publications were identified and vetted with specific inclusion criteria. Of these, 20 publications were selected for further examination and summary. Metabolic data was classified using the Human Metabolome Database (HMDB) and arranged to determine the 'recurrent' metabolites and classes found in sTBI. To help understand potential mechanisms of injury, pathway analysis was performed using these metabolites and the Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Database.

RESULTS

Several metabolites related to sTBI and their effects on biological pathways were identified in this review. Across the literature, proline, citrulline, lactate, alanine, valine, leucine, and serine all decreased in adults post sTBI, whereas both octanoic and decanoic acid increased. Hydroxy acids and organooxygen compounds generally increased following sTBI, while most carboxylic acids decreased. Pathway analysis showed significantly affected glycine and serine metabolism, glycolysis, branched-chain amino acid (BCAA) metabolism, and other amino acid metabolisms. Interestingly, no tricarboxylic acid cycle metabolites were affected.

CONCLUSION

Aside from a select few metabolites, classification of a metabolic profile proved difficult due to significant ambiguity between study design, sample size, type of sample, metabolomic detection techniques, and other confounding variables found in sTBI literature. Given the trends found in some studies, further metabolomics investigation of sTBI may be useful to identify clinically relevant metabolites.

Dynamics of Organic Acids during the Droplet-Vitrification Cryopreservation Procedure Can Be a Signature of Oxidative Stress in Pogostemon yatabeanus

Cryopreservation in liquid nitrogen (LN, -196 °C) is a unique option for the long-term conservation of threatened plant species with non-orthodox or limitedly available seeds. In previous studies, a systematic approach was used to develop a droplet-vitrification (DV) cryopreservation protocol for Postemon yatabeanus shoot tips that includes preculture with 10% sucrose, osmoprotection with C4-35%, cryoprotection with A3-80% vitrification solution, and a three-step regrowth starting with the ammonium-free medium. The tricarboxylic acid (TCA) cycle is a crucial component of plant cell metabolism as it is involved in redox state regulation and energy provision. We hypothesized that organic acids (OAs) associated with the TCA and its side reactions indirectly indicate metabolism intensity and oxidative stress development in shoot tips under the cryopreservation procedure. In this study, the contents of 14 OAs were analyzed using gas chromatography-tandem mass spectrometry (GC-MS/MS) in P. yatabeanus shoot tips in a series of treatments including individual steps of the DV procedure, additional stress imposed by non-optimum protocol conditions (no preculture, no osmoprotection, various vitrification solution composition, using vials instead of aluminum foils, etc.) and regrowth on different media with or without ammonium or growth regulators. The possible relation of OA content with the total cryoprotectant (CPA) concentration and shoot tips regeneration percentage was also explored. Regeneration of cryopreserved shoot tips reduced in descending order as follows: standard protocol condition (91%) > non-optimum vitrification solution (ca. 68%) > non-optimum preculture (60-62%) > regrowth medium (40-64%) > no osmoprotection, cryopreservation in vials (28-30%). Five OAs (glycolic, malic, citric, malonic, and lactic) were the most abundant in the fresh (control) shoot tips. The dynamic pattern of OAs during the DV procedure highly correlated (r = 0.951) with the total CPA concentration employed: it gradually increased through the preculture, osmoprotection, and cryoprotection, peaked at cooling/rewarming (6.38-fold above control level), and returned to the fresh control level after 5 days of regrowth (0.89-fold). The contents of four OAs (2-hydroxybutyric, 3-hydroxypropionic, lactic, and glycolic) showed the most significant (10-209-fold) increase at the cooling/rewarming step. Lactic and glycolic acids were the major OAs at cooling/rewarming, accounting for 81% of the total OAs content. The OAs were categorized into three groups based on their dynamics during the cryopreservation protocol, and these groups were differently affected by protocol step modifications. However, there was no straightforward relationship between the dynamics of OAs and shoot tip regeneration. The results suggest that active modulation of OAs metabolism may help shoot tips to cope with osmotic stress and the chemical cytotoxicity\ of CPAs. Further intensive studies are needed to investigate the effect of cryopreservation on cell primarily metabolism and identify oxidative stress-related biomarkers in plant materials.

Eco-Metabolomics Applied to the Chemical Ecology of Poison Frogs (Dendrobatoidea)

Amphibians are one of the most remarkable sources of unique natural products. Biogenic amines, peptides, bufodienolides, alkaloids, and volatile organic compounds have been characterized in different species. The superfamily Dendrobatoidea represents one of the most enigmatic cases of study in chemical ecology because their skin secretome is composed by a complex mixture (i.e. cocktail) of highly lethal and noxious unique alkaloid structures. While chemical defences from dendrobatoids (families Dendrobatidae and Aromobatidae) have been investigated employing ecological, behavioral, phylogenetic and evolutionary perspectives, studies about the analytical techniques needed to perform the chemical characterization have been neglected for many years. Therefore, our aim is to summarize the current methods applied for the characterization of chemical profiles in dendrobatoids and to illustrate innovative Eco-metabolomics strategies that could be translated to this study model. This approach could be extended to natural products other than alkaloids and implemented for the chemical analysis of different species of dendrobatoids employing both low- and high-resolution mass spectrometers. Here, we overview important biological features to be considered, procedures that could be applied to perform the chemical characterization, steps and tools to perform an Eco-metabolomic analysis, and a final discussion about future perspectives.

Plasma untargeted metabolomics with proteinase K discloses phospholipid signature associated with pulmonary arterial hypertension

Pulmonary arterial hypertension is a rare but life-threatening and clinically heterogeneous disease. The diagnostic schedule of this disorder is complex, and no specific indicator of the arterial etiology has been explored. In this study, untargeted plasma metabolomics was applied to evaluate the metabolic fingerprints of pulmonary arterial hypertension patients. Plasma samples were prepared using a new approach, which applies proteinase K during the sample preparation procedure to increase the metabolite coverage. The metabolic fingerprints were determined via LC-MS and subsequently analyzed with the use of both uni- and multivariate statistics. A total of 21 metabolites were discovered to be significantly altered in pulmonary arterial hypertensive patients. The metabolites were mainly related to the phospholipid metabolic pathways. In this study, decreases were found in the phosphatidylcholines (PCs) [PC(32:0), PC(40:7), PC(42:7)], phosphatidylethanolamine PE(18:0/18:2), lysophosphatidylethanolamines (LPEs) [LPE(22:6), LPE(18:2), LPE(18:0), LPE(20:4), LPE(20:1), LPE(20:0)], lysophosphatidylcholine LPC(20:4) and lysophosphatidylserine LPS(19:0), as well as increase of sphingomyelin SM(36:2), in the plasma samples of pulmonary arterial hypertensive patients in comparison to the control group. Besides their function as components of the biological membranes, these metabolites are also involved in the intracellular signaling pathways that are related to cell proliferation and apoptosis. The results obtained during this study confirm the potential of (untargeted) metabolomics to identify the molecular characteristics of the pathophysiology of pulmonary arterial hypertension. The clinical relevance of this study constitutes the selection of a metabolic panel that can potentially detect and properly diagnose the disease.

Biomarkers in sepsis-looking for the Holy Grail or chasing a mirage!

Sepsis is defined as a life-threatening organ dysfunction caused by the dysregulated host response to infection. It is a complex syndrome and is characterized by physiologic, pathologic and biochemical abnormalities in response to an infection. Diagnosis of sepsis is based on history, physical examination and other investigations (including biomarkers) which may help to increase the certainty of diagnosis. Biomarkers have been evaluated in the past for many diseases and have been evaluated for sepsis as well. Biomarkers may find a possible role in diagnosis, prognostication, therapeutic monitoring and anti-microbial stewardship in sepsis. Since the pathophysiology of sepsis is quite complex and is incompletely understood, a single biomarker that may be robust enough to provide all information has not been found as of yet. However, many biomarkers have been studied and some of them have applications at the bedside and guide clinical decision-making. We evaluated the PubMed database to search for sepsis biomarkers for diagnosis, prognosis and possible role in antibiotic escalation and de-escalation. Clinical trials, meta-analyses, systematic reviews and randomized controlled trials were included. Commonly studied biomarkers such as procalcitonin, Soluble urokinase-type plasminogen activator (Supar), presepsin, soluble triggering receptor expressed on myeloid cells 1, interleukin 6, C-reactive protein, etc., have been described for their possible applications as biomarkers in septic patients. The sepsis biomarkers are still an area of active research with newer evidence adding to the knowledge base continuously. For patients presenting with sepsis, early diagnosis and prompt resuscitation and early administration of anti-microbials (preferably within 1 h) and source control are desired goals. Biomarkers may help us in the diagnosis, prognosis and therapeutic monitoring of septic patients. The marker redefining our view on sepsis is yet a mirage that clinicians and researchers continue to chase.

Identification of Plasma Biomarkers in Drug-Naïve Schizophrenia Using Targeted Metabolomics

OBJECTIVE

Schizophrenia (SCZ) is a severe psychiatric disorder with unknown etiology and lacking specific biomarkers. Herein, we aimed to explore plasma biomarkers relevant to SCZ using targeted metabolomics.

METHODS

Sixty drug-naïve SCZ patients and 36 healthy controls were recruited. Psychotic symptoms were assessed using the Positive and Negative Syndrome Scale. We analyzed the levels of 271 metabolites in plasma samples from all subjects using targeted metabolomics, and identified metabolites that differed significantly between the two groups. Then we evaluated the diagnostic power of the metabolites based on receiver operating characteristic curves, and explored metabolites associated with the psychotic symptoms in SCZ patients.

RESULTS

Twenty-six metabolites showed significant differences between SCZ patients and healthy controls. Among them, 12 metabolites were phosphatidylcholines and cortisol, ceramide (d18:1/22:0), acetylcarnitine, and γ-aminobutyric acid, which could significantly distinguish SCZ from healthy controls with the area under the curve (AUC) above 0.7. Further, a panel consisting of the above 4 metabolites had an excellent performance with an AUC of 0.867. In SCZ patients, phosphatidylcholines were positively related with positive symptoms, and cholic acid was positively associated with negative symptoms.

CONCLUSION

Our study provides insights into the metabolite alterations associated with SCZ and potential biomarkers for its diagnosis and symptom severity assessment.

Association between acetaminophen metabolites and CYP2E1 DNA methylation level in neonate cord blood in the Boston Birth Cohort

BACKGROUND

Acetaminophen is a commonly used medication by pregnant women and is known to cross the placenta. However, little is known about the biological mechanisms that regulate acetaminophen in the developing offspring. Cytochrome 2E1 (CYP2E1) is the primary enzyme responsible for the conversion of acetaminophen to its toxic metabolite. Ex vivo studies have shown that the CYP2E1 gene expression in human fetal liver and placenta is largely controlled by DNA methylation (DNAm) at CpG sites located in the gene body of CYP2E1 at the 5' end. To date, no population studies have examined the association between acetaminophen metabolite and fetal DNAm of CYP2E1 at birth.

METHODS

We utilized data from the Boston Birth Cohort (BBC) which represents an urban, low-income, racially and ethnically diverse population in Boston, Massachusetts. Acetaminophen metabolites were measured in the cord plasma of newborns enrolled in BBC between 2003 and 2013 using liquid chromatography-tandem mass spectrometry. DNAm at 28 CpG sites of CYP2E1 was measured by Illumina Infinium MethylationEPIC BeadChip. We used linear regression to identify differentially methylated CpG sites and the "DiffVar" method to identify differences in methylation variation associated with the detection of acetaminophen, adjusting for cell heterogeneity and batch effects. The false discovery rate (FDR) was calculated to account for multiple comparisons.

RESULTS

Among the 570 newborns included in this study, 96 (17%) had detectable acetaminophen in cord plasma. We identified 7 differentially methylated CpGs (FDR < 0.05) associated with the detection of acetaminophen and additional 4 CpGs showing a difference in the variation of methylation (FDR < 0.05). These CpGs were all located in the gene body of CYP2E1 at the 5' end and had a 3-6% lower average methylation level among participants with detectable acetaminophen compared to participants without. The CpG sites we identified overlap with previously identified DNase hypersensitivity and open chromatin regions in the ENCODE project, suggesting potential regulatory functions.

CONCLUSIONS

In a US birth cohort, we found detection of cord biomarkers of acetaminophen was associated with DNAm level of CYP2E1 in cord blood. Our findings suggest that DNA methylation of CYP2E1 may be an important regulator of acetaminophen levels in newborns.