A review of metabolomics approaches and their application in identifying causal pathways of childhood asthma

Metabolic changes during cold ischemic preservation and reperfusion in porcine lung transplants

Lung transplantation is a cornerstone in treating patients with end-stage lung disease, yet ischemia-reperfusion injury poses significant complications in posttransplant recovery. This study aimed to understand the effects of donor type, cold ischemic time (CIT), and reperfusion on metabolic changes in lung grafts. Porcine donor lungs underwent different CITs on ice: minimal time (control), 6 hours (CIT-6H), and 30 hours (CIT-30H). Additionally, lungs recovered from animals after brain death (BD) underwent 24-hour CIT (BD-CIT-24H). Both CIT-30H and BD-CIT-24H lungs underwent ex vivo lung perfusion for 12 hours, followed by left lung transplantation and reperfusion for 2 hours. Lung tissue samples were subjected to metabolomic analysis. Cold preservation induced time-dependent changes of certain metabolites. In the BD-CIT-24H group, while most trends in metabolite levels were similar to those in the CIT-30H group, some were markedly different. In CIT-30H lungs, reperfusion induced significant changes in the carbohydrate and amino acid pathways, along with consumption of energy substrates and reduction in antioxidants. BD donor lungs exhibited significantly reduction in lysophospholipids after reperfusion. Understanding these metabolic changes in the lung grafts shed lights on the mechanism of ischemia-reperfusion injury, offering valuable insights for future development of targeted strategies to improve donor lung preservation and clinical outcome.

Metabolomics in Childhood Asthma - a Promising Tool to Meet Various Clinical Needs

PURPOSE OF REVIEW

The aim of our review is to summarize the available literature where metabolomics was used in studies on childhood asthma, and to find metabolites that are diagnostic biomarker candidates in childhood asthma. Moreover, the review also describes studies related to metabo-endotypes and heterogeneity of childhood asthma, severity of the disease, and response to drug treatment.

RECENT FINDINGS

Metabolomics has opened up new perspectives in childhood asthma investigation. Based on the available literature, we found nine metabolites that demonstrated the highest diagnostic potential for differentiation between children with asthma and healthy controls: adenine, adenosine, benzoic acid, hypoxanthine, p-cresol, taurocholate, threonine, tyrosine, and 1-methyl nicotinamide. Many of the identified metabolites are closely associated with inflammatory processes responsible for asthma. Metabolomic analysis also contributed to characterizing new asthma endotypes highlighting the heterogeneity of pediatric asthma. Metabolomics can bring about valuable insights, which, when integrated with other omic disciplines, can facilitate the diagnosis and management of childhood asthma and the search for new biomarkers of the disease. Improvements in the detection of asthma in preschool children, including asthma endotypes, will ease application of proper treatment and enable elimination of unnecessary test treatment of corticosteroids in young patients.

Metabolomic profiles in allergic rhinitis: A systematic review and meta-analysis

BACKGROUND

Allergic rhinitis (AR) is a prevalent chronic inflammatory condition that significantly affects patient quality of life and poses a substantial public health burden. Recent advancements in metabolomics have facilitated a deeper understanding of the metabolic pathways involved in AR, offering potential for new biomarkers and therapeutic targets.

OBJECTIVE

To conduct a systematic review and meta-analysis of clinical studies summarizing the metabolomic profiles of AR to gain deeper insights into the metabolic changes and pathologic processes underlying AR.

METHODS

A comprehensive literature search was conducted across PubMed, Embase, Scopus, and Web of Science databases up to October 2024. A qualitative review of the screened studies was performed, followed by meta-analyses of metabolites reported in at least 2 studies. High-impact targets, pathways, and their associations were identified using bioinformatic analyses.

RESULTS

A total of 21 studies, encompassing 84 metabolites associated with AR, met the inclusion criteria. There were 7 metabolites that consistently exhibited up-regulation in AR across multiple studies and were included in the meta-analysis. Pathway enrichment analyses revealed significant involvement of pathways such as "valine, leucine, and isoleucine biosynthesis" and "linoleic acid metabolism" in AR pathogenesis. The metabolite-pathway-gene network analysis highlighted key functional connections between metabolites, pathways, and immune response genes.

CONCLUSION

This comprehensive analysis indicates that differential metabolites may play pivotal roles in AR pathogenesis, offering potential biomarkers and therapeutic targets. Further studies are necessary to validate these findings and elucidate the complex metabolic pathways involved in AR.

Metabolomics Applications for Diagnosing Peri-Implantitis: A Systematic Review of In Vivo Studies

Background/Objectives: Peri-implantitis is a prevalent inflammatory condition affecting dental implants, leading to increased treatment costs, patient dissatisfaction, and potential implant failure. Novel biomarker-based approaches may contribute to early detection, thereby decreasing the burden of the disease. The aim of this review was to assess in vivo studies using metabolomics to identify the metabolic profiles and potential biomarkers of peri-implantitis. Methods: The protocol for this study was registered with PROSPERO (CRD42025634865). Five databases and grey literature sources (PubMed, Scopus, Web of Science, ProQuest, and Google Scholar) were searched using keywords related to metabolomics and peri-implantitis. Studies were selected by independent, inter-calibrated researchers. Data were extracted using predefined, custom forms. The risk of bias was assessed using the ROBINS-I tool. Results: An electronic literature search retrieved 543 articles, of which five were selected. All studies were published within the last five years of the search. All but one study used untargeted metabolomics, and all studies identified metabolites associated with peri-implantitis or distinct metabolomic profiles of peri-implantitis. SCFAs and lysine metabolites were recurring in the results, confirming the findings of previous metabolomic studies on periodontal disease. Conclusions: Metabolomics has not been widely used to study peri-implantitis. Evidence from existing studies confirms the findings of metabolomics studies on periodontitis. Several metabolites related to PI are associated with immune response, tissue degradation, and cellular energy pathways. Integrating -omics technologies into peri-implantitis diagnosis may facilitate biomarker discovery and improve early detection strategies.

Untargeted Metabolomic Analysis of Exhaled Breath Condensate Identifies Disease-Specific Signatures in Adults With Asthma

PURPOSE

An objective test for the auxiliary diagnosis of asthma is still lacking. The aim of this study was to discriminate asthma signatures via an untargeted metabolomic analysis of exhaled breath condensate.

MATERIALS AND METHODS

This study enrolled 19 patients diagnosed with asthma and 23 healthy volunteers. Samples of exhaled breath condensate (EBC) were collected from both groups. Untargeted metabolomic analyses of EBC were used to identify disease-specific signatures for asthma.

RESULT

There were 30 identifiable differentially expressed metabolites and 7 disordered metabolic pathways between the EBCs of asthmatic patients and healthy control subjects. The main differential pathways included biosynthesis of unsaturated fatty acids, HIF-1 signalling pathway, Glutathione metabolism, Ascorbate and aldarate metabolism, and fatty acid biosynthesis. The integrated machine learning method was used to construct an asthma EBC metabolomic signature model from four differential metabolites; 3,4'-dimethoxy-2'-hydroxychalcone, C17-sphinganine, (z)-6-octadecenoic acid, and 2-butylaniline. The model showed a high level of discrimination efficiency (area under curve (AUC) = 0.98), with robust validation through logistic regression (LR), random forest (RF), and support vector machine (SVM) (LR AUC = 0.98, RF AUC = 0.94, SVM AUC = 1.00). The discriminative ability of the EBC metabolomic signature model in both the training set (AUC = 1.0) and testing data (AUC = 0.817) was superior to that of FeNO (AUC = 0.515 and 0.567, respectively) and FEV1/FVC % predicted (AUC = 0.767 and 0.765, respectively). Among the four biomarkers, (z)-6-octadecenoic acid was significantly correlated with serum IgE.

CONCLUSION

The EBC metabolomic signature model demonstrated good feasibility for assisting in the diagnosis of asthma in adults.

Metabolomic studies of respiratory infections in early life: A narrative review

Respiratory infections are a leading cause of morbidity and mortality during the early life period, and experiencing recurrent infections may increase the risk of developing chronic respiratory diseases, such as asthma. Over the last several decades, metabolomics methods have been applied to inform upon the underlying biochemistry of pediatric respiratory infection response, to discriminate between respiratory infection types, and to identify biomarkers of severity and susceptibility. While these studies have demonstrated the power of applying metabolomics to the study of pediatric respiratory infection and contributed to an understanding of respiratory infections during the unique period of immune development, key differences in study design, infection type(s) of interest, biosamples, metabolomics measurement methods, and lack of external validation have limited the translation of these findings into the clinic. The purpose of this review is to summarize overlaps across existing studies of commonly reported metabolomics findings and emphasize areas of opportunity for future study. We highlight several metabolomics pathways-such as the citric acid cycle and sphingolipid metabolism-that have been reported consistently in respiratory infection response. We then discuss putatively identified metabolomic markers to discriminate between respiratory infection types and possible markers of infection severity and proneness. Finally, we close with a summary and perspective of future directions of the field.

Diet-Induced Proteomic and Metabolomic Signatures in Chronic Kidney Disease: A Precision Nutrition Approach

Background: Diet is a key modifiable factor that can either support renal health or accelerate the onset and progression of chronic kidney disease (CKD). Recent advances in multiomics, particularly proteomics and metabolomics, significantly enhanced our understanding of the molecular mechanisms linking diet to CKD risk. Proteomics offers a comprehensive analysis of protein expression, structure, and interactions, revealing how dietary components regulate cellular processes and signaling pathways. Meanwhile, metabolomics provides a detailed profile of low-molecular-weight compounds, including endogenous metabolites and diet-derived molecules, offering insights into the metabolic states that influence kidney function. Methods: We have conducted a narrative review of key papers from databases such as PubMed, Scopus, and Web of Science to explore the potential of proteomic and metabolomic analysis in identifying molecular signatures associated with diet in human and animal biological samples, such as blood plasma, urine, and in kidney tissues. These signatures help elucidate how specific foods, food groups, and overall dietary patterns may either contribute to or mitigate CKD risk. Results: Recent studies the impact of high-fat diets on protein expression involved in energy metabolism, inflammation, and fibrosis, identifying early biomarkers of kidney injury. Metabolic, including disruptions in in fatty acid metabolism, glucose regulation, and amino acid pathways, have been recognized as key indicators of CKD risk. Additionally, several studies explore specific metabolites found in biological fluids and renal tissue in response to protein-rich foods, assessing their potential roles in a progressive loss of kidney function. Emerging evidence also suggests that dietary interventions targeting the gut microbiota may help alleviate inflammation, oxidative stress, and toxin accumulation in chronic kidney disease. Notably, recent findings highlight metabolomic signatures linked to beneficial shifts in gut microbial metabolism, particularly in the context of prebiotic supplementation. Conclusions: By integrating proteomics and metabolomics, future research can refine precision nutrition strategies, helping mitigate CKD progression. Expanding large-scale studies and clinical trials will be essential in translating these molecular insights into actionable dietary guidelines.

Sputum Metabolomic Signature and Dynamic Change of Cough Variant Asthma

Cough variant asthma (CVA), a common reason for chronic cough, is a globally prevalent and burdensome condition. The heterogeneity of CVA and a lack of knowledge concerning the exact molecular pathogenesis has hampered its clinical management. This study presents the first sputum metabolome of patients with CVA, revealing the dynamic change during treatment and exploring biomarkers related to the occurrence and treatment response of CVA. We found that arginine biosynthesis, purine metabolism, and pyrimidine metabolism pathways were enriched in CVA compared with healthy controls. Part of the metabolic disturbances could be reversed by antiasthmatic medication. The levels of dipeptides/tripeptides (alanyl tyrosine, Gly-Tyr-Ala, Ala-Leu, and Thr-Leu) were significantly associated with sputum neutrophil or eosinophil percentages in patients with CVA. Differential metabolites before treatment between effective and ineffective treatment groups were enriched in purine metabolism, thiamine metabolism, and arginine metabolism. 2-Isopropylmalate was downregulated in CVA and increased after treatment, and the effective treatment group had a lower 2-isopropylmalate level before treatment. Random forest and logistic regression models identified glutathione, thiamine phosphate, alanyl tyrosine, and 2'-deoxyadenosine as markers for distinguishing CVA from healthy controls (all areas under the curve >0.8). Thiamine phosphate might also be promising for predicting therapy responsiveness (area under the curve, 0.684). These findings imply that disturbed mitochondrial energy metabolism and imbalanced oxidation-reduction homeostasis probably underlay the metabolic pathogenesis of CVA.

Modular comparison of untargeted metabolomics processing steps

BACKGROUND

Untargeted metabolomics requires robust and reliable strategies for data processing to extract relevant information form the underlying raw data. Multiple platforms for data processing are available, but the choice of software tool can have an impact on the analysis. This study provides a comprehensive evaluation of four workflows based on commonly used metabolomics software tools: XCMS, Compound Discoverer, MS-DIAL, and MZmine. These tools were applied to a dataset derived from bovine saliva samples spiked with small polar molecules analyzed by anion exchange chromatography coupled to high resolution mass spectrometry.

RESULTS

The analysis revealed significant differences in the number and overlap of detected features, with only approximately 8 % of the features included in all four peak tables. Among the overlapping features, MS-DIAL demonstrated the greatest similarity to manual integration, while XCMS and MZmine also performed well. In contrast, Compound Discoverer had issues to reliably integrate high baseline peaks. This study also explores various post-processing strategies, including missing value imputation, transformation, scaling, and filtering. The assessment of missing values indicated that they primarily originated from low abundance, making imputation with small values the most effective approach. No clear evidence suggested that transformation is necessary for downstream statistical analyses. Auto scaling emerged as the most suitable strategy for data scaling. Low thresholds for blank filtering were found to be the most effective in enhancing data quality. The optimization of filtering thresholds required a careful balance to remove unnecessary information while retaining vital data.

SIGNIFICANCE AND NOVELTY

This work provides an overview of commonly applied strategies in untargeted metabolomics analysis, emphasizing the importance of careful workflow selection and optimization. It serves as a resource for refining data processing strategies to achieve accurate and reliable results, while also offering fresh insights into the challenges encountered throughout the untargeted metabolomics processing pipeline.

Metabolomic Analysis of Nicotine-Induced Metabolic Disruptions and Their Amelioration by Resveratrol

This study investigates the metabolic disruptions caused by nicotine (NIC) exposure, with a particular focus on amino acid and lipid metabolism, and evaluates resveratrol (RSV) as a potential protective agent. Mice were divided into four groups: control (CON), NIC-exposed, NIC + RSV-treated, and RSV-only. NIC exposure resulted in significant weight loss, elevated glucose levels, altered lipid profiles, and organ damage, particularly in the liver and kidneys. Increased inflammation was evidenced by elevated levels of IL-6 and CRP. In contrast, RSV treatment mitigated these effects by improving lipid profiles, glycemic indices, and reducing inflammatory markers. Histopathological analysis confirmed reduced tissue damage in the NIC + RSV group compared to the NIC-alone group. Metabolomics analysis using LC-MS/MS revealed significant dysregulation in lipid, amino acid, and nucleotide metabolism in NIC-exposed mice. Fold-change analysis identified altered metabolites, including sphingomyelin 36:1;02 (p < 0.001), valine (p < 0.001), triacylglycerol 4:0-18:1 (p < 0.001), and ceramide 32:1;02 (p < 0.001). Amino acids such as arginine, phenylalanine, glutamic acid, tyrosine, and lysine, as well as NIC metabolites like nornicotine and cotinine, were identified, underscoring molecular fragmentation analysis findings. RSV treatment partially restored metabolic balance, highlighting its role as a metabolic modulator. This study underscores the therapeutic potential of RSV in alleviating NIC-induced metabolic dysfunctions by restoring lipid homeostasis and reducing inflammation. Additionally, it emphasizes the importance of RSV in addressing NIC-related metabolic impairments and the need for noninvasive biomarkers for early disease detection.

Artificial Intelligence in Metabolomics: A Current Review

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

Metabolomics and microbiome co-analysis reveals altered innate immune responses in Charybdis japonica following Aeromonas hydrophila infection

A comprehensive bioinformatics analysis was conducted to elucidate the innate immune response of Charybdis japonica following exposure to Aeromonas hydrophila. This study integrated metabolomics, 16S rRNA sequencing, and enzymatic activity data to dissect the immune mechanisms activated in response to infection. Infection with A. hydrophila resulted in an increased abundance of beneficial intestinal genera such as Photobacterium spp., Rhodobacter spp., Polaribacter spp., Psychrilyobacter spp., and Mesoflavibacter spp. These probiotics appear to suppress A. hydrophila colonization by competitively dominating the intestinal microbiota. Key metabolic pathways affected included fatty acid biosynthesis, galactose metabolism, and nitrogen metabolism, highlighting their role in the crab's intestinal response. Enzymatic analysis revealed a decrease in activities of hexokinase, phosphofructokinase, and pyruvate kinase, which are essential for energy homeostasis and ATP production necessary for stress responses. Additionally, reductions were observed in the activities of acetyl-CoA carboxylase and fatty acid synthase. Gene expression analysis showed downregulation in Peroxiredoxin 1 (PRDX1), Peroxiredoxin 2 (PRDX2), glutathione-S-transferase (GST), catalase (CAT), and glutathione (GSH), with concurrent increases in malondialdehyde (MDA) levels, indicating severe oxidative stress. This study provides insights into the molecular strategies employed by marine crabs to counteract bacterial invasions in their natural habitat.

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Deleterious Effects of Heat Stress on the Tomato, Its Innate Responses, and Potential Preventive Strategies in the Realm of Emerging Technologies

The tomato is a fruit vegetable rich in nutritional and medicinal value grown in greenhouses and fields worldwide. It is severely sensitive to heat stress, which frequently occurs with rising global warming. Predictions indicate a 0.2 °C increase in average surface temperatures per decade for the next three decades, which underlines the threat of austere heat stress in the future. Previous studies have reported that heat stress adversely affects tomato growth, limits nutrient availability, hammers photosynthesis, disrupts reproduction, denatures proteins, upsets signaling pathways, and damages cell membranes. The overproduction of reactive oxygen species in response to heat stress is toxic to tomato plants. The negative consequences of heat stress on the tomato have been the focus of much investigation, resulting in the emergence of several therapeutic interventions. However, a considerable distance remains to be covered to develop tomato varieties that are tolerant to current heat stress and durable in the perspective of increasing global warming. This current review provides a critical analysis of the heat stress consequences on the tomato in the context of global warming, its innate response to heat stress, and the elucidation of domains characterized by a scarcity of knowledge, along with potential avenues for enhancing sustainable tolerance against heat stress through the involvement of diverse advanced technologies. The particular mechanism underlying thermotolerance remains indeterminate and requires further elucidatory investigation. The precise roles and interplay of signaling pathways in response to heat stress remain unresolved. The etiology of tomato plants' physiological and molecular responses against heat stress remains unexplained. Utilizing modern functional genomics techniques, including transcriptomics, proteomics, and metabolomics, can assist in identifying potential candidate proteins, metabolites, genes, gene networks, and signaling pathways contributing to tomato stress tolerance. Improving tomato tolerance against heat stress urges a comprehensive and combined strategy including modern techniques, the latest apparatuses, speedy breeding, physiology, and molecular markers to regulate their physiological, molecular, and biochemical reactions.

Racial Differences in Vaginal Fluid Metabolites and Association with Systemic Inflammation Markers among Ovarian Cancer Patients: A Pilot Study

The vaginal microbiome differs by race and contributes to inflammation by directly producing or consuming metabolites or by indirectly inducing host immune response, but its potential contributions to ovarian cancer (OC) disparities remain unclear. In this exploratory cross-sectional study, we examine whether vaginal fluid metabolites differ by race among patients with OC, if they are associated with systemic inflammation, and if such associations differ by race. Study participants were recruited from the Ovarian Cancer Epidemiology, Healthcare Access, and Disparities Study between March 2021 and September 2022. Our study included 36 study participants with ovarian cancer who provided biospecimens; 20 randomly selected White patients and all 16 eligible Black patients, aged 50-70 years. Acylcarnitines (n = 45 species), sphingomyelins (n = 34), and ceramides (n = 21) were assayed on cervicovaginal fluid, while four cytokines (IL-1β, IL-10, TNF-α, and IL-6) were assayed on saliva. Seven metabolites showed >2-fold differences, two showed significant differences using the Wilcoxon rank-sum test (p < 0.05; False Discovery Rate > 0.05), and 30 metabolites had coefficients > ±0.1 in a Penalized Discriminant Analysis that achieved two distinct clusters by race. Arachidonoylcarnitine, the carnitine adduct of arachidonic acid, appeared to be consistently different by race. Thirty-eight vaginal fluid metabolites were significantly correlated with systemic inflammation biomarkers, irrespective of race. These findings suggest that vaginal fluid metabolites may differ by race, are linked with systemic inflammation, and hint at a potential role for mitochondrial dysfunction and sphingolipid metabolism in OC disparities. Larger studies are needed to verify these findings and further establish specific biological mechanisms that may link the vaginal microbiome with OC racial disparities.

The value of prospective metabolomic susceptibility endotypes: broad applicability for infectious diseases

BACKGROUND

As new infectious diseases (ID) emerge and others continue to mutate, there remains an imminent threat, especially for vulnerable individuals. Yet no generalizable framework exists to identify the at-risk group prior to infection. Metabolomics has the advantage of capturing the existing physiologic state, unobserved via current clinical measures. Furthermore, metabolomics profiling during acute disease can be influenced by confounding factors such as indications, medical treatments, and lifestyles.

METHODS

We employed metabolomic profiling to cluster infection-free individuals and assessed their relationship with COVID severity and influenza incidence/recurrence.

FINDINGS

We identified a metabolomic susceptibility endotype that was strongly associated with both severe COVID (ORICUadmission = 6.7, p-value = 1.2 × 10-08, ORmortality = 4.7, p-value = 1.6 × 10-04) and influenza (ORincidence = 2.9; p-values = 2.2 × 10-4, βrecurrence = 1.03; p-value = 5.1 × 10-3). We observed similar severity associations when recapitulating this susceptibility endotype using metabolomics from individuals during and after acute COVID infection. We demonstrate the value of using metabolomic endotyping to identify a metabolically susceptible group for two-and potentially more-IDs that are driven by increases in specific amino acids, including microbial-related metabolites such as tryptophan, bile acids, histidine, polyamine, phenylalanine, and tyrosine metabolism, as well as carbohydrates involved in glycolysis.

INTERPRETATIONS

These metabolites may be identified prior to infection to enable protective measures for these individuals.

FUNDING

The Longitudinal EMR and Omics COVID-19 Cohort (LEOCC) and metabolomic profiling were supported by the National Heart, Lung, and Blood Institute and the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health.

Association of Metabolites, Nutrients, and Toxins in Maternal and Cord Serum with Asthma, IgE, SPT, FeNO, and Lung Function in Offspring

The role of metabolites, nutrients, and toxins (MNTs) in sera at the end of pregnancy and of their association with offspring respiratory and allergic disorders is underexplored. Untargeted approaches detecting a variety of compounds, known and unknown, are limited. In this cohort study, we first aimed at discovering associations of MNTs in grandmaternal (F0) serum with asthma, immunoglobulin E, skin prick tests, exhaled nitric oxide, and lung function parameters in their parental (F1) offspring. Second, for replication, we tested the identified associations of MNTs with disorders in their grandchildren (F2-offspring) based on F2 cord serum. The statistical analyses were sex-stratified. Using liquid chromatography/high-resolution mass spectrometry in F0, we detected signals for 2286 negative-ion lipids, 59 positive-ion lipids, and 6331 polar MNTs. Nine MNTs (one unknown MNT) discovered in F0-F1 and replicated in F2 showed higher risks of respiratory/allergic outcomes. Twelve MNTs (four unknowns) constituted a potential protection in F1 and F2. We recognized MNTs not yet considered candidates for respiratory/allergic outcomes: a phthalate plasticizer, an antihistamine, a bile acid metabolite, tryptophan metabolites, a hemiterpenoid glycoside, triacylglycerols, hypoxanthine, and polyphenol syringic acid. The findings suggest that MNTs are aspirants for clinical trials to prevent adverse respiratory/allergic outcomes.

Monitoring long-term chemical exposome by characterizing the hair metabolome using a high-resolution mass spectrometry-based suspect screening approach

Hair has recently emerged as a biospecimen for characterizing the long-term chemical exposome in biomonitoring investigations spanning several months, as chemical compounds circulating in the bloodstream accumulate in hair. Although there has been interest in using human hair as a biospecimen for exposome studies, it has yet to be widely adopted compared to blood and urine. Here, we applied a high-resolution mass spectrometry (HRMS)-based suspect screening strategy to characterize the long-term chemical exposome in human hair. Hair samples were collected from 70 subjects and cut into 3 cm segments, which were then mixed to prepare pooled samples. The pooled hair samples underwent a sample preparation procedure, and the hair extracts were further analyzed using an HRMS-based suspect screening approach. An in-house chemical suspect list containing 1227 chemical entries from National Report on Human Exposure to Environmental Chemicals (Report) published by the U.S. CDC and the Exposome-Explorer 3.0 database developed by the WHO was subsequently used to screen and filter the suspect features against the HRMS dataset. Overall, we matched 587 suspect features in the HRMS dataset to 246 unique chemical formulas in the suspect list, and the structures of 167 chemicals were further identified through a fragmentation analysis. Among these, chemicals such as mono-2-ethylhexyl phthalate, methyl paraben, and 1-naphthol, which have been detected in the urine or blood for exposure assessment, were also identified in human hair. This suggests that hair reflects the accumulation of environmental compounds to which an individual is exposed. Exposure to exogenous chemicals may exert adverse effects on cognitive function, and we discovered 15 chemicals in human hair that may contribute to the pathogenesis of Alzheimer's disease. This finding suggests that human hair may be a promising biospecimen for monitoring long-term exposure to multiple environmental chemicals and perturbations in endogenous chemicals in biomonitoring investigations.

Concordance-Based Batch Effect Correction for Large-Scale Metabolomics

For a large-scale metabolomics study, sample collection, preparation, and analysis may last several days, months, or even (intermittently) over years. This may lead to apparent batch effects in the acquired metabolomics data due to variability in instrument status, environmental conditions, or experimental operators. Batch effects may confound the true biological relationships among metabolites and thus obscure real metabolic changes. At present, most of the commonly used batch effect correction (BEC) methods are based on quality control (QC) samples, which require sufficient and stable QC samples. However, the quality of the QC samples may deteriorate if the experiment lasts for a long time. Alternatively, isotope-labeled internal standards have been used, but they generally do not provide good coverage of the metabolome. On the other hand, BEC can also be conducted through a data-driven method, in which no QC sample is needed. Here, we propose a novel data-driven BEC method, namely, CordBat, to achieve concordance between each batch of samples. In the proposed CordBat method, a reference batch is first selected from all batches of data, and the remaining batches are referred to as "other batches." The reference batch serves as the baseline for the batch adjustment by providing a coordinate of correlation between metabolites. Next, a Gaussian graphical model is built on the combined dataset of reference and other batches, and finally, BEC is achieved by optimizing the correction coefficients in the other batches so that the correlation between metabolites of each batch and their combinations are in concordance with that of the reference batch. Three real-world metabolomics datasets are used to evaluate the performance of CordBat by comparing it with five commonly used BEC methods. The present experimental results showed the effectiveness of CordBat in batch effect removal and the concordance of correlation between metabolites after BEC. CordBat was found to be comparable to the QC-based methods and achieved better performance in the preservation of biological effects. The proposed CordBat method may serve as an alternative BEC method for large-scale metabolomics that lack proper QC samples.

Effect of DHA-Enriched Phospholipids from Fish Roe on Rat Fecal Metabolites: Untargeted Metabolomic Analysis

Lipid metabolism disorder has become an important hidden danger threatening human health, and various supplements to treat lipid metabolism disorder have been studied. Our previous studies have shown that DHA-enriched phospholipids from large yellow croaker (Larimichthys Crocea) roe (LYCRPLs) have lipid-regulating effects. To better explain the effect of LYCRPLs on lipid regulation in rats, the fecal metabolites of rats were analyzed from the level of metabolomics in this study, and GC/MS metabolomics measurements were performed to figure out the effect of LYCRPLs on fecal metabolites in rats. Compared with the control (K) group, 101 metabolites were identified in the model (M) group. There were 54, 47, and 57 metabolites in the low-dose (GA), medium-dose (GB), and high-dose (GC) groups that were significantly different from that of group M, respectively. Eighteen potential biomarkers closely related to lipid metabolism were screened after intervention with different doses of LYCRPLs on rats, which were classified into several metabolic pathways in rats, including pyrimidine metabolism, the citric acid cycle (TCA cycle), the metabolism of L-cysteine, carnitine synthesis, pantothenate and CoA biosynthesis, glycolysis, and bile secretion. L-cysteine was speculated to be a useful biomarker of LYCRPLs acting on rat fecal metabolites. Our findings indicated that LYCRPLs may regulate lipid metabolism disorders in SD rats by activating these metabolic pathways.

Multi-Omics Analysis of Lung Tissue Demonstrates Changes to Lipid Metabolism during Allergic Sensitization in Mice

Allergy and asthma pathogenesis are associated with the dysregulation of metabolic pathways. To understand the effects of allergen sensitization on metabolic pathways, we conducted a multi-omics study using BALB/cJ mice sensitized to house dust mite (HDM) extract or saline. Lung tissue was used to perform untargeted metabolomics and transcriptomics while both lung tissue and plasma were used for targeted lipidomics. Following statistical comparisons, an integrated pathway analysis was conducted. Histopathological changes demonstrated an allergic response in HDM-sensitized mice. Untargeted metabolomics showed 391 lung tissue compounds were significantly different between HDM and control mice (adjusted p < 0.05); with most compounds mapping to glycerophospholipid and sphingolipid pathways. Several lung oxylipins, including 14-HDHA, 8-HETE, 15-HETE, 6-keto-PGF1α, and PGE2 were significantly elevated in HDM-sensitized mice (p < 0.05). Global gene expression analysis showed upregulated calcium channel, G protein–signaling, and mTORC1 signaling pathways. Genes related to oxylipin metabolism such as Cox, Cyp450s, and cPla2 trended upwards. Joint analysis of metabolomics and transcriptomics supported a role for glycerophospholipid and sphingolipid metabolism following HDM sensitization. Collectively, our multi-omics results linked decreased glycerophospholipid and sphingolipid compounds and increased oxylipins with allergic sensitization; concurrent upregulation of associated gene pathways supports a role for bioactive lipids in the pathogenesis of allergy and asthma.

Efficacy evaluation, active ingredients, and multitarget exploration of herbal medicine

Evidence shows that herbal medicine (HM) could be beneficial for the treatment of various diseases. However, complexities present in HM due to the unclear bioactive compounds, mechanisms of action, undetermined targets for therapy, and nonspecific features for metabolism, are currently an obstacle for the progression of novel drug discovery. Metabolomics could be a potential tool to overcome these issues and for the understanding of HM from a small-molecule metabolism level. The chinmedomics-based metabolomics method assesses the overall metabolism of organisms with a holistic view and shows great potential for understanding metabolic pathways, evaluating curative effects, clarifying mechanisms, discovering active ingredients, and precision medicine. This review focuses on the efficacy evaluation, active ingredient discovery, and target exploration of HM based on metabolomics and chinmedomics.

Application of Metabolomics in Obesity-Related Childhood Asthma Subtyping: A Narrative Scoping Review

Obesity-related asthma is a heterogeneous childhood asthma phenotype with rising prevalence. Observational studies identify early-life obesity or weight gain as risk factors for childhood asthma development. The reverse association is also described, children with asthma have a higher risk of being obese. Obese children with asthma have poor symptom control and an increased number of asthma attacks compared to non-obese children with asthma. Clinical trials have also identified that a proportion of obese children with asthma do not respond as well to usual treatment (e.g., inhaled corticosteroids). The heterogeneity of obesity-related asthma phenotypes may be attributable to different underlying pathogenetic mechanisms. Although few childhood obesity-related asthma endotypes have been described, our knowledge in this field is incomplete. An evolving analytical profiling technique, metabolomics, has the potential to link individuals' genetic backgrounds and environmental exposures (e.g., diet) to disease endotypes. This will ultimately help define clinically relevant obesity-related childhood asthma subtypes that respond better to targeted treatment. However, there are challenges related to this approach. The current narrative scoping review summarizes the evidence for metabolomics contributing to asthma subtyping in obese children, highlights the challenges associated with the implementation of this approach, and identifies gaps in research.

Applications of machine learning in metabolomics: Disease modeling and classification

Metabolomics research has recently gained popularity because it enables the study of biological traits at the biochemical level and, as a result, can directly reveal what occurs in a cell or a tissue based on health or disease status, complementing other omics such as genomics and transcriptomics. Like other high-throughput biological experiments, metabolomics produces vast volumes of complex data. The application of machine learning (ML) to analyze data, recognize patterns, and build models is expanding across multiple fields. In the same way, ML methods are utilized for the classification, regression, or clustering of highly complex metabolomic data. This review discusses how disease modeling and diagnosis can be enhanced via deep and comprehensive metabolomic profiling using ML. We discuss the general layout of a metabolic workflow and the fundamental ML techniques used to analyze metabolomic data, including support vector machines (SVM), decision trees, random forests (RF), neural networks (NN), and deep learning (DL). Finally, we present the advantages and disadvantages of various ML methods and provide suggestions for different metabolic data analysis scenarios.

Biochemical profiling of metabolomics in heavy metal-intoxicated impaired metabolism and its amelioration using plant-based bioactive compound

Exposure to Pb is widely spreading and has far-reaching negative effects on living systems. This study aimed to investigate the toxic effects of Pb, through biochemical profiling and the ameliorative effects of quercetin against Pb-toxicity. Twenty-five male Wistar albino mice were divided into the following five groups. The CON-group received normal saline; the Pb-group received PbAc; the Pb + Q-CRN group received lead acetate followed by quercetin; the Q-CRN group received quercetin; and the CRN group received corn oil. After 4 weeks, the mice were euthanized. It was speculated that Pb significantly increased the levels of serine, threonine, and asparagine and decreased the levels of valine, lysine, and glutamic acid in the plasma of Pb-group, thus impairing amino acid metabolism. However, in the Pb + Q-CRN group, the level of these six amino acids was restored significantly due to the ameliorative effect of quercetin. The presence of lipid metabolites (L-carnitine, sphinganine, phytosphingosine, and lysophosphatidylcholine) in mice serum was confirmed by ESI/MS. The GPx, SOD, GSH, and CAT levels were significantly decreased, and the MDA level was significantly increased, thus confirming the oxidative stress and lipid peroxidation in the Pb group. The antioxidant effect of quercetin was elucidated in the Pb + Q-CRN group. Expression of CPT-I, CPT-II, LCAT, CROT, CACT, and MTR genes was significantly upregulated in the liver of Pb goup mice. Hence, the findings of this study proved that Pb exposure induced oxidative stress, upregulated gene expression, and impaired the lipid and amino acid metabolism in mice.

Alterations in the molecular composition of COVID-19 patient urine, detected using Raman spectroscopic/computational analysis

We developed and tested a method to detect COVID-19 disease, using urine specimens. The technology is based on Raman spectroscopy and computational analysis. It does not detect SARS-CoV-2 virus or viral components, but rather a urine ‘molecular fingerprint’, representing systemic metabolic, inflammatory, and immunologic reactions to infection. We analyzed voided urine specimens from 46 symptomatic COVID-19 patients with positive real time-polymerase chain reaction (RT-PCR) tests for infection or household contact with test-positive patients. We compared their urine Raman spectra with urine Raman spectra from healthy individuals (n = 185), peritoneal dialysis patients (n = 20), and patients with active bladder cancer (n = 17), collected between 2016–2018 (i.e., pre-COVID-19). We also compared all urine Raman spectra with urine specimens collected from healthy, fully vaccinated volunteers (n = 19) from July to September 2021. Disease severity (primarily respiratory) ranged among mild (n = 25), moderate (n = 14), and severe (n = 7). Seventy percent of patients sought evaluation within 14 days of onset. One severely affected patient was hospitalized, the remainder being managed with home/ambulatory care. Twenty patients had clinical pathology profiling. Seven of 20 patients had mildly elevated serum creatinine values (>0.9 mg/dl; range 0.9–1.34 mg/dl) and 6/7 of these patients also had estimated glomerular filtration rates (eGFR) <90 mL/min/1.73m² (range 59–84 mL/min/1.73m²). We could not determine if any of these patients had antecedent clinical pathology abnormalities. Our technology (Raman Chemometric Urinalysis—Rametrix^®) had an overall prediction accuracy of 97.6% for detecting complex, multimolecular fingerprints in urine associated with COVID-19 disease. The sensitivity of this model for detecting COVID-19 was 90.9%. The specificity was 98.8%, the positive predictive value was 93.0%, and the negative predictive value was 98.4%. In assessing severity, the method showed to be accurate in identifying symptoms as mild, moderate, or severe (random chance = 33%) based on the urine multimolecular fingerprint. Finally, a fingerprint of ‘Long COVID-19’ symptoms (defined as lasting longer than 30 days) was located in urine. Our methods were able to locate the presence of this fingerprint with 70.0% sensitivity and 98.7% specificity in leave-one-out cross-validation analysis. Further validation testing will include sampling more patients, examining correlations of disease severity and/or duration, and employing metabolomic analysis (Gas Chromatography–Mass Spectrometry [GC-MS], High Performance Liquid Chromatography [HPLC]) to identify individual components contributing to COVID-19 molecular fingerprints.

Exogenous metabolite feeding on altering antibiotic susceptibility in Gram-negative bacteria through metabolic modulation: a review

BACKGROUND

The rise of antimicrobial resistance at an alarming rate is outpacing the development of new antibiotics. The worrisome trends of multidrug-resistant Gram-negative bacteria have enormously diminished existing antibiotic activity. Antibiotic treatments may inhibit bacterial growth or lead to induce bacterial cell death through disruption of bacterial metabolism directly or indirectly. In light of this, it is imperative to have a thorough understanding of the relationship of bacterial metabolism with antimicrobial activity and leverage the underlying principle towards development of novel and effective antimicrobial therapies.

OBJECTIVE

Herein, we explore studies on metabolic analyses of Gram-negative pathogens upon antibiotic treatment. Metabolomic studies revealed that antibiotic therapy caused changes of metabolites abundance and perturbed the bacterial metabolism. Following this line of thought, addition of exogenous metabolite has been employed in in vitro, in vivo and in silico studies to activate the bacterial metabolism and thus potentiate the antibiotic activity.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Exogenous metabolites were discovered to cause metabolic modulation through activation of central carbon metabolism and cellular respiration, stimulation of proton motive force, increase of membrane potential, improvement of host immune protection, alteration of gut microbiome, and eventually facilitating antibiotic killing. The use of metabolites as antimicrobial adjuvants may be a promising approach in the fight against multidrug-resistant pathogens.

Sputum Metabolomic Profiling Reveals Metabolic Pathways and Signatures Associated With Inflammatory Phenotypes in Patients With Asthma

PURPOSE

The molecular links between metabolism and inflammation that drive different inflammatory phenotypes in asthma are poorly understood. We aimed to identify the metabolic signatures and underlying molecular pathways of different inflammatory asthma phenotypes.

METHODS

In the discovery set (n = 119), untargeted ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) was applied to characterize the induced sputum metabolic profiles of asthmatic patients with different inflammatory phenotypes using orthogonal partial least-squares discriminant analysis (OPLS-DA), and pathway topology enrichment analysis. In the validation set (n = 114), differential metabolites were selected to perform targeted quantification. Correlations between targeted metabolites and clinical indices in asthmatic patients were analyzed. Logistic and negative binomial regression models were established to assess the association between metabolites and severe asthma exacerbations.

RESULTS

Seventy-seven differential metabolites were identified in the discovery set. Pathway topology analysis uncovered that histidine metabolism, glycerophospholipid metabolism, nicotinate and nicotinamide metabolism, linoleic acid metabolism as well as phenylalanine, tyrosine and tryptophan biosynthesis were involved in the pathogenesis of different asthma phenotypes. In the validation set, 24 targeted quantification metabolites were significantly expressed between asthma inflammatory phenotypes. Finally, adenosine 5'-monophosphate (adjusted relative risk [adj RR] = 1.000; 95% confidence interval [CI] = 1.000-1.000; P = 0.050), allantoin (adj RR = 1.000; 95% CI = 1.000-1.000; P = 0.043) and nicotinamide (adj RR = 1.001; 95% CI = 1.000-1.002; P = 0.021) were demonstrated to predict severe asthma exacerbation rates.

CONCLUSIONS

Different inflammatory asthma phenotypes have specific metabolic profiles in induced sputum. The potential metabolic signatures may identify therapeutic targets in different inflammatory asthma phenotypes.

An evaluation of the National Institutes of Health grants portfolio: identifying opportunities and challenges for multi-omics research that leverage metabolomics data

BACKGROUND

Through the systematic large-scale profiling of metabolites, metabolomics provides a tool for biomarker discovery and improving disease monitoring, diagnosis, prognosis, and treatment response, as well as for delineating disease mechanisms and etiology. As a downstream product of the genome and epigenome, transcriptome, and proteome activity, the metabolome can be considered as being the most proximal correlate to the phenotype. Integration of metabolomics data with other -omics data in multi-omics analyses has the potential to advance understanding of human disease development and treatment.

AIM OF REVIEW

To understand the current funding and potential research opportunities for when metabolomics is used in human multi-omics studies, we cross-sectionally evaluated National Institutes of Health (NIH)-funded grants to examine the use of metabolomics data when collected with at least one other -omics data type. First, we aimed to determine what types of multi-omics studies included metabolomics data collection. Then, we looked at those multi-omics studies to examine how often grants employed an integrative analysis approach using metabolomics data.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We observed that the majority of NIH-funded multi-omics studies that include metabolomics data performed integration, but to a limited extent, with integration primarily incorporating only one other -omics data type. Some opportunities to improve data integration may include increasing confidence in metabolite identification, as well as addressing variability between -omics approach requirements and -omics data incompatibility.

Guide to Metabolomics Analysis: A Bioinformatics Workflow

Metabolomics is an emerging field that quantifies numerous metabolites systematically. The key purpose of metabolomics is to identify the metabolites corresponding to each biological phenotype, and then provide an analysis of the mechanisms involved. Although metabolomics is important to understand the involved biological phenomena, the approach's ability to obtain an exhaustive description of the processes is limited. Thus, an analysis-integrated metabolomics, transcriptomics, proteomics, and other omics approach is recommended. Such integration of different omics data requires specialized statistical and bioinformatics software. This review focuses on the steps involved in metabolomics research and summarizes several main tools for metabolomics analyses. We also outline the most abnormal metabolic pathways in several cancers and diseases, and discuss the importance of multi-omics integration algorithms. Overall, our goal is to summarize the current metabolomics analysis workflow and its main analysis software to provide useful insights for researchers to establish a preferable pipeline of metabolomics or multi-omics analysis.

Multi-Omics Profiling Approach to Asthma: An Evolving Paradigm

Asthma is a complex multifactorial and heterogeneous respiratory disease. Although genetics is a strong risk factor of asthma, external and internal exposures and their interactions with genetic factors also play important roles in the pathophysiology of asthma. Over the past decades, the application of high-throughput omics approaches has emerged and been applied to the field of asthma research for screening biomarkers such as genes, transcript, proteins, and metabolites in an unbiased fashion. Leveraging large-scale studies representative of diverse population-based omics data and integrating with clinical data has led to better profiling of asthma risk. Yet, to date, no omic-driven endotypes have been translated into clinical practice and management of asthma. In this article, we provide an overview of the current status of omics studies of asthma, namely, genomics, transcriptomics, epigenomics, proteomics, exposomics, and metabolomics. The current development of the multi-omics integrations of asthma is also briefly discussed. Biomarker discovery following multi-omics profiling could be challenging but useful for better disease phenotyping and endotyping that can translate into advances in asthma management and clinical care, ultimately leading to successful precision medicine approaches.

How paediatric drug development and use could benefit from OMICs: a c4c expert group white paper

The safety and efficacy of pharmacotherapy in children, particularly preterms, neonates, and infants, is limited by a paucity of good quality data from prospective clinical drug trials. A specific challenge is the establishment of valid biomarkers. OMICs technologies may support these efforts, by complementary information about targeted and non-targeted molecules through systematic characterization and quantitation of biological samples. OMICs technologies comprise at least genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics in addition to the patient's phenotype. OMICs technologies are in part hypothesis-generating allowing an in depth understanding of disease pathophysiology and pharmacological mechanisms. Application of OMICs technologies in paediatrics faces major challenges before routine adoption. First, developmental processes need to be considered, including a sub-division into specific age groups as developmental changes clearly impact OMICs data. Second, compared to the adult population, the number of patients is limited as well as type and amount of necessary biomaterial, especially in neonates and preterms. Thus, advanced trial designs and biostatistical methods, non-invasive biomarkers, innovative biobanking concepts including data and samples from healthy children, as well as analytical approaches (e.g. liquid biopsies) should be addressed to overcome these obstacles. The ultimate goal is to link OMICs technologies with innovative analysis tools, like artificial intelligence at an early stage. The use of OMICs data based on a feasible approach will contribute to identify complex phenotypes and subpopulations of patients to improve development of medicines for children with potential economic advantages.

Airway Microbiome and Serum Metabolomics Analysis Identify Differential Candidate Biomarkers in Allergic Rhinitis

Allergic rhinitis (AR) is a common heterogeneous chronic disease with a high prevalence and a complex pathogenesis influenced by numerous factors, involving a combination of genetic and environmental factors. To gain insight into the pathogenesis of AR and to identity diagnostic biomarkers, we combined systems biology approach to analyze microbiome and serum composition. We collected inferior turbinate swabs and serum samples to study the microbiome and serum metabolome of 28 patients with allergic rhinitis and 15 healthy individuals. We sequenced the V3 and V4 regions of the 16S rDNA gene from the upper respiratory samples. Metabolomics was used to examine serum samples. Finally, we combined differential microbiota and differential metabolites to find potential biomarkers. We found no significant differences in diversity between the disease and control groups, but changes in the structure of the microbiota. Compared to the HC group, the AR group showed a significantly higher abundance of 1 phylum (Actinobacteria) and 7 genera (Klebsiella, Prevotella and Staphylococcus, etc.) and a significantly lower abundance of 1 genus (Pelomonas). Serum metabolomics revealed 26 different metabolites (Prostaglandin D2, 20-Hydroxy-leukotriene B4 and Linoleic acid, etc.) and 16 disrupted metabolic pathways (Linoleic acid metabolism, Arachidonic acid metabolism and Tryptophan metabolism, etc.). The combined respiratory microbiome and serum metabolomics datasets showed a degree of correlation reflecting the influence of the microbiome on metabolic activity. Our results show that microbiome and metabolomics analyses provide important candidate biomarkers, and in particular, differential genera in the microbiome have also been validated by random forest prediction models. Differential microbes and differential metabolites have the potential to be used as biomarkers for the diagnosis of allergic rhinitis.

Application of the Metabolomics Approach in Food Authentication

The authentication of food products is essential for food quality and safety. Authenticity assessments are important to ensure that the ingredients or contents of food products are legitimate and safe to consume. The metabolomics approach is an essential technique that can be utilized for authentication purposes. This study aimed to summarize food authentication through the metabolomics approach, to study the existing analytical methods, instruments, and statistical methods applied in food authentication, and to review some selected food commodities authenticated using metabolomics-based methods. Various databases, including Google Scholar, PubMed, Scopus, etc., were used to obtain previous research works relevant to the objectives. The review highlights the role of the metabolomics approach in food authenticity. The approach is technically implemented to ensure consumer protection through the strict inspection and enforcement of food labeling. Studies have shown that the study of metabolomics can ultimately detect adulterant(s) or ingredients that are added deliberately, thus compromising the authenticity or quality of food products. Overall, this review will provide information on the usefulness of metabolomics and the techniques associated with it in successful food authentication processes, which is currently a gap in research that can be further explored and improved.

Urinary metabotype of severe asthma evidences decreased carnitine metabolism independent of oral corticosteroid treatment in the U-BIOPRED study

INTRODUCTION

Asthma is a heterogeneous disease with poorly defined phenotypes. Severe asthmatics often receive multiple treatments including oral corticosteroids (OCS). Treatment may modify the observed metabotype, rendering it challenging to investigate underlying disease mechanisms. Here, we aimed to identify dysregulated metabolic processes in relation to asthma severity and medication.

METHODS

Baseline urine was collected prospectively from healthy participants (n=100), mild-to-moderate asthmatics (n=87) and severe asthmatics (n=418) in the cross-sectional U-BIOPRED cohort; 12-18-month longitudinal samples were collected from severe asthmatics (n=305). Metabolomics data were acquired using high-resolution mass spectrometry and analysed using univariate and multivariate methods.

RESULTS

Ninety metabolites were identified, with 40 significantly altered (p<0.05, FDR<0.05) in severe asthma and 23 by OCS use. Multivariate modelling showed that observed metabotypes in healthy participants and mild-to-moderate asthmatics differed significantly from severe asthmatics (p=2.6×10^-20), OCS-treated asthmatics differed significantly from non-treated (p=9.5×10^-4), and longitudinal metabotypes demonstrated temporal stability. Carnitine levels evidenced the strongest OCS-independent decrease in severe asthma. Reduced carnitine levels were associated with mitochondrial dysfunction via decreases in pathway enrichment scores of fatty acid metabolism and reduced expression of the carnitine transporter SLC22A5 in sputum and bronchial brushings.

CONCLUSIONS

This is the first large-scale study to delineate disease- and OCS-associated metabolic differences in asthma. The widespread associations with different therapies upon the observed metabotypes demonstrate the necessity to evaluate potential modulating effects on a treatment- and metabolite-specific basis. Altered carnitine metabolism is a potentially actionable therapeutic target that is independent of OCS treatment, highlighting the role of mitochondrial dysfunction in severe asthma.

Metabolomics Reveals Process of Allergic Rhinitis Patients with Single- and Double-Species Mite Subcutaneous Immunotherapy

Allergen immunotherapy (AIT) is the only treatment that can change the course of allergic diseases. However, there has not been any research on metabolic reactions in relation to AIT with single or mixed allergens. In this study, patients with allergic rhinitis caused by Dermatophagoides pteronyssinus (Der p) and Dermatophagoides farinae (Der f) were treated with single-mite (Der p) and double-mite (Der p:Der f = 1:1) subcutaneous immunotherapy (SCIT), respectively. To compare the efficacy and the dynamic changes of inflammation-related single- and double-species mite subcutaneous immunotherapy (SM-SCIT and DM-SCIT), we performed visual analogue scale (VAS) score, rhinoconjunctivitis quality of life questionnaire (RQLQ) score and serum metabolomics in allergic rhinitis patients during SCIT. VAS and RQLQ score showed no significant difference in efficacy between the two treatments. A total of 57 metabolites were identified, among which downstream metabolites (5(S)-HETE (Hydroxyeicosatetraenoic acid), 8(S)-HETE, 11(S)-HETE, 15(S)-HETE and 11-hydro TXB2) in the ω-6-related arachidonic acid and linoleic acid pathway showed significant differences after approximately one year of treatment in SM-SCIT or DM-SCIT, and the changes of the above serum metabolic components were correlated with the magnitude of RQLQ improvement, respectively. Notably, 11(S)-HETE decreased more with SM-SCIT, and thus it could be used as a potential biomarker to distinguish the two treatment schemes. Both SM-SCIT and DM-SCIT have therapeutic effects on patients with allergic rhinitis, but there is no significant difference in efficacy between them. The reduction of inflammation-related metabolites proved the therapeutic effect, and potential biomarkers (arachidonic acid and its downstream metabolites) may distinguish the options of SCIT.

Metabolomic Profiling of Human Urine Samples Using LC-TIMS-QTOF Mass Spectrometry

The identification of metabolites in biological samples is challenging due to their chemical and structural diversity. Ion mobility spectrometry (IMS) separates ionized molecules based on their mobility in a carrier buffer gas giving information about the ionic shape by measuring the rotationally averaged collision cross-section (CCS) value. This orthogonal descriptor, in combination with the m/z, isotopic pattern distribution, and MS/MS spectrum, has the potential to improve the identification of molecular molecules in complex mixtures. Urine metabolomics can reveal metabolic differences, which arise as a result of a specific disease or in response to therapeutic intervention. It is, however, complicated by the presence of metabolic breakdown products derived from a wide range of lifestyle and diet-related byproducts, many of which are poorly characterized. In this study, we explore the use of trapped ion mobility spectrometry (TIMS) via LC parallel accumulation with serial fragmentation (PASEF) for urine metabolomics. A total of 362 urine metabolites were characterized from 80 urine samples collected from healthy volunteers using untargeted metabolomics employing HILIC and RP chromatography. Additionally, three analytes (Trp, Phe, and Tyr) were selected for targeted quantification. Both the untargeted and targeted data was highly reproducible and reported CCS measurements for identified metabolites were robust in the presence of the urine matrix. A comparison of CCS values among different laboratories was also conducted, showing less than 1.3% ΔCCS values across different platforms. This is the first report of a human urine metabolite database compiled with CCS values experimentally acquired using an LC-PASEF TIMS-qTOF platform.

In Vitro Metabolism of Donepezil in Liver Microsomes Using Non-Targeted Metabolomics

Donepezil is a reversible acetylcholinesterase inhibitor that is currently the most commonly prescribed drug for the treatment of Alzheimer’s disease. In general, donepezil is known as a safe and well-tolerated drug, and it was not associated with liver abnormalities in several clinical trials. However, rare cases of drug-related liver toxicity have been reported since it has become commercially available. Few studies have investigated the metabolic profile of donepezil, and the mechanism of liver damage caused by donepezil has not been elucidated. In this study, the in vitro metabolism of donepezil was investigated using liquid chromatography–tandem mass spectrometry based on a non-targeted metabolomics approach. To identify metabolites, the data were subjected to multivariate data analysis and molecular networking. A total of 21 donepezil metabolites (17 in human liver microsomes, 21 in mice liver microsomes, and 17 in rat liver microsomes) were detected including 14 newly identified metabolites. One potential reactive metabolite was identified in rat liver microsomal incubation samples. Metabolites were formed through four major metabolic pathways: (1) O-demethylation, (2) hydroxylation, (3) N-oxidation, and (4) N-debenzylation. This study indicates that a non-targeted metabolomics approach combined with molecular networking is a reliable tool to identify and detect unknown drug metabolites.

Metabolomic mechanisms of short chain chlorinated paraffins toxicity in rats

Short chain chlorinated paraffins (SCCPs) have received increased interest worldwide since they were added to the list of controlled POPs in Annex A of the Stockholm Convention in 2017. Although many toxicological studies have already shown that SCCPs are hepatotoxic, nephrotoxic, and thyrotoxic to rodents, there have been few studies to date that have characterized changes in the metabolic pathways targeted by SCCPs. In this study, a UPLC-Q-TOF-MS based plasma metabolomics approach was used to investigate the toxicity of SCCPs in rats. Liver and kidney injury occurred rapidly after high-dose SCCP exposure, and the most relevant pathways affected were energy metabolism, amino acid metabolism, glycerophospholipid metabolism, nucleotide metabolism, and vitamin B metabolism. Exposure to SCCPs inhibited the tricarboxylic acid cycle and accelerated degradation. Fluctuating levels of phospholipids and nucleotides may have contributed to the neurotoxicity of SCCPs. In addition, the down regulation of folic acid induced by SCCPs may have led to malformations during the early development of laboratory animals. These results suggested that high exposure levels of SCCPs may have serious health risks and more research is needed to assess the health status of relevant occupational groups.

Application of Metabolomics in Pediatric Asthma: Prediction, Diagnosis and Personalized Treatment

Asthma in children remains a significant public health challenge affecting 5–20% of children in Europe and is associated with increased morbidity and societal healthcare costs. The high variation in asthma incidence among countries may be attributed to differences in genetic susceptibility and environmental factors. This respiratory disorder is described as a heterogeneous syndrome of multiple clinical manifestations (phenotypes) with varying degrees of severity and airway hyper-responsiveness, which is based on patient symptoms, lung function and response to pharmacotherapy. However, an accurate diagnosis is often difficult due to diversities in clinical presentation. Therefore, identifying early diagnostic biomarkers and improving the monitoring of airway dysfunction and inflammatory through non-invasive methods are key goals in successful pediatric asthma management. Given that asthma is caused by the interaction between genes and environmental factors, an emerging approach, metabolomics—the systematic analysis of small molecules—can provide more insight into asthma pathophysiological mechanisms, enable the identification of early biomarkers and targeted personalized therapies, thus reducing disease burden and societal cost. The purpose of this review is to present evidence on the utility of metabolomics in pediatric asthma through the analysis of intermediate metabolites of biochemical pathways that involve carbohydrates, amino acids, lipids, organic acids and nucleotides and discuss their potential application in clinical practice. Also, current challenges on the integration of metabolomics in pediatric asthma management and needed next steps are critically discussed.

Advances and Perspectives in Prostate Cancer Biomarker Discovery in the Last 5 Years through Tissue and Urine Metabolomics

Prostate cancer (PCa) is the second most diagnosed cancer in men worldwide. For its screening, serum prostate specific antigen (PSA) test has been largely performed over the past decade, despite its lack of accuracy and inability to distinguish indolent from aggressive disease. Metabolomics has been widely applied in cancer biomarker discovery due to the well-known metabolic reprogramming characteristic of cancer cells. Most of the metabolomic studies have reported alterations in urine of PCa patients due its noninvasive collection, but the analysis of prostate tissue metabolome is an ideal approach to disclose specific modifications in PCa development. This review aims to summarize and discuss the most recent findings from tissue and urine metabolomic studies applied to PCa biomarker discovery. Eighteen metabolites were found consistently altered in PCa tissue among different studies, including alanine, arginine, uracil, glutamate, fumarate, and citrate. Urine metabolomic studies also showed consistency in the dysregulation of 15 metabolites and, interestingly, alterations in the levels of valine, taurine, leucine and citrate were found in common between urine and tissue studies. These findings unveil that the impact of PCa development in human metabolome may offer a promising strategy to find novel biomarkers for PCa diagnosis.

Dengue and metabolomics in humans

BACKGROUND

Dengue virus causes dengue fever (DF)disease, transmitted by the mosquito Aedes aegypti. The symptoms could be severe and disable the affected individuals for weeks. The severe form, dengue hemorrhagic fever (DHF), can lead to death if not adequately attended to. Due to global warming, the vector mosquito will advance over new areas and expose more people to this disease over the next decades. Despite the severity, there are no treatments nor efficient vaccines available. Metabolomic studies have shown a new perspective to understand this disease better at a new molecular level.

AIM OF REVIEW

Many published works rely on samples obtained from animal studies. This review will mainly focus on human samples and cell culture experiments to view how the dengue virus affects the metabolomic profile.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The review compiles the sample sources, metabolomic techniques used, the detected compounds, and how they behave in different DF stages. This disease causes a significant change in many metabolites, but some results are still conflicting between studies. The results gathered here show that metabolomic approaches prove to be an excellent and viable way to expand knowledge about DF.

The potential pathways underlying the association of propyl-paraben exposure with aeroallergen sensitization and EASI score using metabolomics analysis

Propyl-paraben exposure is associated with aeroallergen sensitization, but its association with atopic dermatitis (AD) is inconclusive. No studies have been conducted on the metabolomic pathways underlying these associations. We investigated the associations between propyl-paraben exposure and aeroallergen sensitization, AD, and Eczema Area and Severity Index (EASI) score and identified the underlying pathways using untargeted metabolomics analysis. We enrolled 455 children in a general population study. Skin prick tests were performed with the assessment of EASI score. Urinary propyl-, butyl-, ethyl-, and methyl-paraben levels were measured. Untargeted metabolomics analysis was performed on the first and fifth urine propyl-paraben quintile groups. The highest urine propyl-paraben quintile group was associated with aeroallergen sensitization, but not with AD. Glycine, threonine, serine, ornithine, isoleucine, arabinofuranose, d-lyxofuranose, citrate, and picolinic acid levels were higher, whereas palmitic acid and 2-palmitoylglycerol levels were lower in the highest quintile propyl-paraben group, than in the lowest quintile group. The propyl-paraben-induced metabolic perturbations were associated with serine and glycine metabolisms, branched-chain amino acid metabolism, and ammonia recycling. Propyl-paraben exposure was associated with aeroallergen sensitization and EASI score, partially via metabolomic changes related with oxidative stress, mTOR, peroxisome proliferator-activated receptors pathway, aryl hydrocarbon receptor signaling pathways, and tricarboxylic acid cycle.

Combining metabolomics with bioanalysis methods to investigate the potential toxicity of dihexyl phthalate

Dihexyl phthalate (DHP) is one of the most commonly used phthalate esters in various plastic and consumer products. Human are inevitably exposed to DHPs. Although several animal and human experiments have revealed that DHP can cause multiple toxicities, few studies have previously assessed the effects of DHP exposure by liquid chromatography mass spectrometry (LC-MS) analysis combine with molecular biology methods on human cells. Therefore, the purpose of our study was to investigate the effect of DHP on human cell metabolism by systems biology methods. In this study, U₂ OS cancer cells were treated with 10 μM DHP for metabolomics analysis and apoptosis analysis at indicate time. Metabolomic study of the metabolic changes caused by DHP in U₂ OS cells was performed for the first time using integrative liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS). To investigate the possible reason of fatty acids level altered by DHP, we measured some key fatty acid synthesis and oxidation-related enzyme expression levels by quantitative real-time PCR (Q-PCR). Apoptotic cells were analyzed by flow cytometry and apoptosis-related gene expressions were measured by Q-PCR. 2',7'-Dichlorofluorescein diacetate (DCFH-DA) staining was used to evaluate ROS content. Partial least squares-discriminate analysis (PLS-DA) clearly showed that significant differences in metabolic profiles were observed in U₂ OS cells exposed to DHP compared with controls. A total of 58 putative metabolites in electrospray ionization source (ESI) + mode and 32 putative metabolites in ESI-mode were detected, the majority of the differential metabolites being lipids and lipid-like molecules. Among them, the altered fatty acids level corresponded to expression levels of genes encoding enzymes related to fatty acids synthesis and oxidation. Moreover, DHP induced reactive oxygen species (ROS) accumulation, promoted cell apoptosis and inflammation, and resulted in a significant increase in apoptosis and inflammation-related gene expression levels compared with controls. In summary, our results suggested that metabolomics combined with molecular bioanalysis methods could be an efficient tool to assess toxic effects, which contribute to explore the possible cytotoxicity mechanisms of DHP, and provide a basis for further research.

Umbilical cord blood metabolome differs in relation to delivery mode, birth order and sex, maternal diet and possibly future allergy development in rural children

Allergy is one of the most common diseases among young children yet all factors that affect development of allergy remain unclear. In a small cohort of 65 children living in the same rural area of south-west Sweden, we have previously found that maternal factors, including prenatal diet, affect childhood allergy risk, suggesting that in utero conditions may be important for allergy development. Here, we studied if metabolites in the umbilical cord blood of newborns may be related to development of childhood allergy, accounting for key perinatal factors such as mode of delivery, birth order and sex. Available umbilical cord blood plasma samples from 44 of the participants were analysed using gas chromatography-mass spectrometry metabolomics; allergy was diagnosed by specialised paediatricians at ages 18 months, 3 years and 8 years and included eczema, asthma, food allergy and allergic rhinoconjunctivitis. Nineteen cord blood metabolites were related to future allergy diagnosis though there was no clear pattern of up- or downregulation of metabolic pathways. In contrast, perinatal factors birth order, sex and mode of delivery affected several energy and biosynthetic pathways, including glutamate and aspartic acid—histidine metabolism (p = 0.004) and the tricarboxylic acid cycle (p = 0.006) for birth order; branched chain amino acid metabolism (p = 0.0009) and vitamin B₆ metabolism (p = 0.01) for sex; and glyoxylate and dicarboxylic acid metabolism (p = 0.005) for mode of delivery. Maternal diet was also related to some of the metabolites associated with allergy. In conclusion, the cord blood metabolome includes individual metabolites that reflect lifestyle, microbial and other factors that may be associated with future allergy diagnosis, and also reflects temporally close events/factors. Larger studies are required to confirm these associations, and perinatal factors such as birth order or siblings must be considered in future cord-blood metabolome studies.

The Role of Serum Metabolomics in Distinguishing Chronic Rhinosinusitis With Nasal Polyp Phenotypes

Background: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a heterogeneous disease characterized by different clinical features and treatment responsiveness. This study aimed to compare the serum metabolomics profiles between eosinophilic CRSwNP (eCRSwNP) and non-eosinophilic CRSwNP (neCRSwNP) and healthy controls (HC) and explore objective biomarkers for distinguishing eCRSwNP before surgery. Methods: Serum samples were collected from 33 neCRSwNP patients, 37 eCRSwNP patients, and 29 HC. Serum metabolomics profiles were investigated by ultra-high-performance liquid chromatography-mass spectrometry. Results: The analysis results revealed that neCRSwNP, eCRSwNP, and HC exhibited distinctive metabolite signatures. In addition, eCRSwNP could be distinguished from neCRSwNP referring to their serum metabolic profiles, and the top ten different metabolites were citrulline, choline, linoleic acid, adenosine, glycocholic acid, L-serine, triethanolamine, 4-guanidinobutyric acid, methylmalonic acid, and L-methionine, which were related to several most important pathways including arginine and proline metabolism; glycine, serine, and threonine metabolism; linoleic acid metabolism; and purine metabolism. Among these distinctive metabolites, citrulline, linoleic acid, adenosine, and 4-guanidinobutyric acid showed good predictabilities, and the serum levels of citrulline, linoleic acid, and adenosine were significantly correlated with tissue eosinophil (T-EOS) percentage and T-EOS count. Conclusion: eCRSwNP patients exhibited discriminative serum metabolic signatures in comparison with neCRSwNP patients and HC. These results suggested that metabolomics profiles contributed to understanding the pathophysiological mechanisms of CRSwNP and distinguishing its phenotypes.

Unconjugated bilirubin is associated with protection from early-life wheeze and childhood asthma

BACKGROUND

Wheeze and allergic sensitization are the strongest early-life predictors of childhood asthma development; the molecular origins of these early-life phenotypes are poorly understood.

OBJECTIVES

We sought to identify metabolites associated with early-life wheeze, allergic sensitization, and childhood asthma.

METHODS

We conducted a nested case-control study using Environmental influences on Child Health Outcomes Program cohorts for discovery and independent replication. Wheeze and allergic sensitization were defined by number of wheeze episodes and positive specific IgE at age 1 year, respectively. Asthma was defined as physician diagnosis of asthma at age 5 or 6 years. We used untargeted metabolomics, controlling for observed and latent confounding factors, to assess associations between the plasma metabolome and early-life wheeze, allergy, and childhood asthma.

RESULTS

Eighteen plasma metabolites were associated with first-year wheeze in the discovery cohort (n = 338). Z,Z unconjugated bilirubin (UCB) and its related metabolites exhibited a dose-response relationship with wheeze frequency; UCB levels were 13% (β = 0.87; 95% CI, 0.74-1.02) and 22% (β = 0.78; 95% CI, 0.68-0.91) lower in children with 1 to 3 and 4+ wheeze episodes compared with those who never wheezed, respectively. UCB levels were also associated with childhood asthma (β = 0.82; 95% CI, 0.68-0.98). Similar trends were observed in 2 independent cohorts. UCB was significantly negatively correlated with eicosanoid- and oxidative stress-related metabolites. There were no significant associations between metabolites and allergic sensitization.

CONCLUSIONS

We identified a novel inverse, dose-dependent association between UCB and recurrent wheeze and childhood asthma. Inflammatory lipid mediators and oxidative stress byproducts inversely correlated with UCB, suggesting that UCB modulates pathways critical to the development of early-life recurrent wheeze and childhood asthma.