Metabolomics for Investigating Physiological and Pathophysiological Processes

A global perspective on the status of clinical metabolomics in laboratory medicine - a survey by the IFCC metabolomics working group

OBJECTIVES

Metabolomics aims for comprehensive characterization and measurement of small molecule metabolites (<1700 Da) in complex biological matrices. This study sought to assess the current understanding and usage of metabolomics in laboratory medicine globally and evaluate the perception of its promise and future implementation.

METHODS

A survey was conducted by the IFCC metabolomics working group that queried 400 professionals from 79 countries. Participants provided insights into their experience levels, knowledge, and usage of metabolomics approaches, along with detailing the applications and methodologies employed.

RESULTS

Findings revealed a varying level of experience among respondents, with varying degrees of familiarity and utilization of metabolomics techniques. Targeted approaches dominated the field, particularly liquid chromatography coupled to a triple quadrupole mass spectrometer, with untargeted methods also receiving significant usage. Applications spanned clinical research, epidemiological studies, clinical diagnostics, patient monitoring, and prognostics across various medical domains, including metabolic diseases, endocrinology, oncology, cardiometabolic risk, neurodegeneration and clinical toxicology.

CONCLUSIONS

Despite optimism for the future of clinical metabolomics, challenges such as technical complexity, standardization issues, and financial constraints remain significant hurdles. The study underscores the promising yet intricate landscape of metabolomics in clinical practice, emphasizing the need for continued efforts to overcome barriers and realize its full potential in patient care and precision medicine.

Bioinformatics-driven untargeted metabolomic profiling for clinical screening of methamphetamine abuse

PURPOSE

Amphetamine-type stimulants are very common, and their usage is becoming a very big social problem all over the world. Thousands of addicts encounter several health problems including mental, metabolic, behavioral and neurological disorders. In addition to these, there are several reports about the elevated risk of tendency on committing criminal cases by addicted persons. Hence, methamphetamine addiction is not only an individual health problem but also a social problem. In our study, we aimed to investigate the pathogenesis of chronic usage of methamphetamine via untargeted metabolomics approach.

METHODS

38 plasma samples were carefully collected and extracted for untargeted metabolomics assay. A liquid-liquid extraction was performed to get as much metabolite as possible from the samples. After the extraction procedure, samples were transferred into vials and they were evaluated via time of flight mass spectrometry instrument.

RESULTS

Significantly, altered metabolites were identified by the fold analysis and Welch's test between the groups. 42 different compounds were annotated regarding to data-dependent acquisition method. Pathway analysis were also performed to understand the hazardous effect of methamphetamine on human body.

CONCLUSION

It has been reported that drug exposure may affect several metabolic pathways for amino acids, fats, energy metabolism and vitamins. An alternative bioinformatic model was also developed and validated in order to predict the chronic methamphetamine drug users in any criminal cases. This generated model passes the ROC curve analysis and permutation test and classify the controls and drug users correctly by evaluating the metabolic alterations between the groups.

Metabolomics and proteomics reveal the inhibitory effect of Lactobacillus crispatus on cervical cancer

Cervical cancer remains a significant global health issue due to its high morbidity and mortality rates. Recently, Lactobacillus crispatus has been recognized for its crucial role in maintaining cervical health. While some studies have explored the use of L. crispatus to mitigate cervical cancer, the underlying mechanisms remain largely unknown. In this study, we employed non-targeted proteomics and metabolomics to investigate how L. crispatus affects the growth of cervical cancer cells (SiHa) and normal cervical cells (Ect1/E6E7). Our findings indicated that the inhibitory effect of L. crispatus on SiHa cells was associated with various biological processes, notably the ferroptosis pathway. Specifically, L. crispatus was found to regulate the expression of proteins such as HMOX1, SLC39A14, VDAC2, ACSL4, and LPCAT3 by SiHa cells, which are closely related to ferroptosis. Additionally, it activated the tricarboxylic acid (TCA) cycle in SiHa cells, leading to increased levels of reactive oxygen species (ROS) and lipid peroxides (LPO). These results revealed the therapeutic potential of L. crispatus in targeting the ferroptosis pathway for cervical cancer treatment, opening new avenues for research and therapy in cervical cancer.

Serum taurine affects lung cancer progression by regulating tumor immune escape mediated by the immune microenvironment

INTRODUCTION

Taurine is a naturally occurring sulfonic acid involved in various physiological and pathological processes, such as the regulation of calcium signaling, immune function, inflammatory response, and cellular aging. It has the potential to predict tumor malignant transformation and formation. Our previous work discovered the elevated taurine in lung cancer patients. However, the precise impact and mechanism of elevated serum taurine levels on lung cancer progression and the suitability of taurine or taurine-containing drinks for lung cancer patients remain unclear.

OBJECTIVES

Our study aimed to systematically investigate the role of taurine in lung cancer, with the ultimate goal of contributing novel strategies for lung cancer treatment.

METHODS

Lung cancer C57 and nude mice models, RNA sequencing, and stable transfection were applied to explored the effects and mechanisms of taurine on lung cancer. Tissues of 129 non-small cell lung cancer (NSCLC) patients derived from 2014 to 2017 for immunohistochemistry were collected in Taihe Hospital.

RESULTS

Low doses of taurine, as well as taurine-infused beverages at equivalent doses, significantly enhanced lung tumor growth. Equally intriguing is that the promoting effect of taurine on lung cancer progression wanes as the dosage increases. The Nuclear factor erythroid 2-like 1 (Nfe2l1 or Nrf1)-reactive oxygen species (ROS)-PD-1 axis may be a potential mechanism for dual role of taurine in lung cancer progression. However, taurine's impacts on lung cancer progression and the anti-tumor function of Nfe2l1 were mainly determined by the immune competence. Taurine inhitited lung tumor growth probably by inhibiting NF-κB-mediated inflammatory responses in nude mice rather than by affecting Nfe2l1 function. As patients age increased, Nfe2l1 gene and protein gradually returned to the levels observed in healthy individuals, but lost its anti-lung cancer effects.

CONCLUSIONS

Taurine emerges as a potential biomarker for lung cancer progression, predicting poor prognosis and unsuitability for specific patients. Lung cancer patients, especially young patients, should be conscious of potential effects of taurine-containing drinks. Conversely, taurine or its drinks may be more suitable for older or immune-deficient patients.

Effect of the immune cells and plasma metabolites on rheumatoid arthritis: a mediated mendelian randomization study

Background

Increasing evidence indicates a close relationship between alterations in human immune cells and plasma metabolites with Rheumatoid Arthritis (RA). However, limited studies have left the causal relationships behind these links unclear.

Methods

A bidirectional Mendelian Randomization (MR) study was conducted, combined with mediation analysis, using data from genome-wide association study database covering 731 immune cell phenotypes and 1,400 plasma metabolite traits to explore their causal relationships with RA and potential mediating effects. The primary method used for MR analysis was inverse-variance weighted and False Discovery Rate (FDR) correction was applied to verify the robustness of our results.

Results

HLA DR on CD33- HLA DR+ (myeloid cell group) (OR, 1.422; 95% CI, 1.194-1.694; P < 0.001; PFDR = 0.012) increased the risk of developing RA. CD19 on IgD+ CD38- naive (B cell group) (OR, 0.969; 95% CI, 0.954-0.985; P < 0.001; PFDR = 0.021) reduced the risk of developing RA. RA was a risk factor for HLA DR on CD14- CD16+ monocytes (monocyte group) (OR, 1.242; 95% CI, 1.102-1.401; P < 0.001; PFDR  = 0.047). RA was a protective factor for memory B cell %lymphocyte (B cell group) (OR, 0.861; 95% CI, 0.795-0.933; P < 0.001; PFDR = 0.050), CD4+ CD8dim T cell %lymphocyte (TBNK group) (OR, 0.802; 95% CI, 0.711-0.904; P < 0.001; PFDR  = 0.043), CD4+ CD8dim T cell %leukocyte (TBNK group) (OR, 0.814; 95% CI, 0.726-0.913; P < 0.001; PFDR  = 0.046), CD24 on IgD+ CD24+ B cells (B cell group) (OR, 0.857; 95% CI, 0.793-0.927; P < 0.001; PFDR  = 0.038), and CD24 on unswitched memory B cells (B cell group) (OR, 0.867; 95% CI, 0.797-0.942; P < 0.001; PFDR  = 0.050). Increasing levels of docosatrienoate (22:3n3) (OR, 0.886; 95% CI, 0.838-0.936; P < 0.001; PFDR = 0.023) significantly reduced the risk of developing RA. The mediating effect of plasma metabolites in this context was not established.

Conclusion

This study provides genetic evidence for the intricate relationships between immune cells, plasma metabolites, and RA, highlighting the potential mechanisms involved. This will contribute to future directions in precision medicine and research.

NMR-based metabolomics analysis reveals the effect of environmental contamination exposure on fishermen living around the Mundaú Lagoon in Maceió (Alagoas, Brazil)

The Mundaú lagoon in Maceió (Alagoas, Brazil) is a crucial resource for the local population, particularly fishing communities. Recent studies have revealed potential toxic metal contamination in the lagoon, particularly with mercury (Hg) levels exceeding the maximum regulated values. This inorganic contaminant may be impacting the health of fishermen and the local population. In this context, metabolomics, a study of small-molecule metabolites, can offer insights into the physiological impact of environmental contamination on humans. Thus, volunteers from the control and exposed groups were selected, considering the main exposure criteria primarily defined by their proximity and interaction with the lagoon. Blood and urine samples were collected from the volunteers and subjected to analysis using NMR spectroscopy. The data underwent Principal Component Analysis (PCA) and Orthogonal Partial Least-Squares Discriminant Analysis (OPLS-DA) based on metabolic patterns to establish group discrimination or identification. Metabolic pathways were assessed through enrichment analysis. The study revealed several metabolic disturbances in the exposed group's urine and plasma samples compared to control group. Noteworthy findings included arginine and proline metabolism disruptions, indicative of ammonia recycling and urea cycle impairment. These changes suggest compromised ammonia detoxification in the exposed group. Disturbances in the tricarboxylic acid (TCA) cycle and the transfer of acetyl groups into mitochondria suggested systemic metabolic stress in energy metabolism. Furthermore, elevated carnitine and ketone levels may indicate compensatory responses to low TCA cycle activity. Alterations in glutamate and glutathione metabolism and imbalances in glutathione levels indicate oxidative stress and impaired detoxification. This study highlights significant metabolic changes in fishermen exposed to contaminated environments, which can affect various metabolic pathways, including energy metabolism and antioxidant processes, potentially making individuals more vulnerable to the adverse effects of environmental contaminants. Finally, this work highlights insights into the relationship between environmental contamination and metabolic pathways, particularly in regions with limited studies.

Design, Synthesis, Antitumor Activity and NMR-Based Metabolomics of Novel Amino Substituted Tetracyclic Imidazo[4,5-b]Pyridine Derivatives

Newly prepared tetracyclic imidazo[4,5-b]pyridine derivatives were synthesized to study their antiproliferative activity against human cancer cells. Additionally, the structure-activity was studied to confirm the impact of the N atom position in pyridine nuclei as well as the chosen amino side chains on antiproliferative activity. Targeted amino substituted regioisomers were prepared by using uncatalyzed amination from corresponding chloro substituted precursors. The most active compounds 6 a, 8 and 10 showed improved activity in comparison to standard drug etoposide with IC50 values in a nanomolar range of concentration (0.2-0.9 μM). NMR-based metabolomics is a powerful instrument to elucidate activity mechanism of new chemotherapeutics. Multivariate and univariate statistical analysis of metabolic profiles of non-small cell lung cancer cells before and after exposure to 6 a revealed significant changes in metabolism of essential amino acids, glycerophospholipids and oxidative defense. Insight into the changes of metabolic pathways that are heavily involved in cell proliferation and survival provide valuable guidelines for more detailed analysis of activity metabolism and possible targets of this class of bioactive compounds.

Knowledge translation and knowledge mobilization from the FoodBAll project

This report describes the knowledge mobilization and translation outcomes of the Canadian-funded portion of a large, international project called the Food Biomarker Alliance (FoodBAll), which ran from 2015 to 2019. This remarkably successful project led to a large number of important findings, outputs, and impacts. In particular, FoodBAll unequivocally demonstrated that metabolomics could be used to not only discover biomarkers of food intake (BFIs), but also to measure diet in a more objective manner. FoodBAll also created standards for assessing and validating BFIs, papers and databases describing BFIs, and kits for measuring BFIs and it laid the groundwork for many global studies exploring food composition and precision nutrition.

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

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

Understanding the impact of maternal and infant nutrition on infant/child health: multiethnic considerations, knowledge translation, and future directions for equitable health research

A mother's intrauterine environment influences her health and that of her offspring, at birth and in the future. Herein, we present an overview of our Canadian Institutes of Health Research (CIHR)-funded grant "Understanding the impact of maternal and infant nutrition on infant/child health"-set within The NutriGen Birth Cohort Alliance. NutriGen is a consortium of four Canadian prospective birth cohorts representing >5000 mother-child pairs of diverse ethnic groups including South Asians, White Europeans, and Indigenous peoples. We summarize our objectives and main findings on outcomes of maternal diet, gestational diabetes, birth weight, cardiometabolic health, the microbiome, and epigenetic modifications. We append this work with 10 key messages when conducting multiethnic research and review our knowledge translation products. We describe the clinical impact of our research on maternal and child health and conclude with future directions on biomarker discovery, expansion to other ethnic groups, and interventions for high-risk populations.

The prognosis and metabolite changes of NSCLC patients receiving first-line immunotherapy combined chemotherapy in different M1c categories according to 9th edition of TNM classification

BACKGROUND

The 9th edition of the TNM Classification for lung cancer delineates M1c into two subcategories: M1c1 (Multiple extrathoracic lesions within a single organ system) and M1c2 (Multiple extrathoracic lesions involving multiple organ systems). Existing research indicates that patients with lung cancer in stage M1c1 exhibit superior overall survival compared to those in stage M1c2. The primary frontline therapy for patients with advanced non-small cell lung cancer (NSCLC), lacking driver gene mutations, involves the use of immune checkpoint inhibitors (ICIs) combined with chemotherapy. Nevertheless, a dearth of evidence exists regarding potential survival disparities between NSCLC patients with M1c1 and M1c2 undergoing first-line immune-chemotherapy, and reliable biomarkers for predicting treatment outcomes are elusive. Serum metabolic profiles may elucidate distinct prognostic mechanisms, necessitating the identification of divergent metabolites in M1c1 and M1c2 undergoing combination therapy. This study seeks to scrutinize survival discrepancies between various metastatic patterns (M1c1 and M1c2) and pinpoint metabolites associated with treatment outcomes in NSCLC patients undergoing first-line ICIs combined with chemotherapy.

METHOD

In this study, 33 NSCLC patients lacking driver gene mutations diagnosed with M1c1, and 22 similarly diagnosed with M1c2 according to the 9th edition of TNM Classification, were enrolled. These patients received first-line PD-1 inhibitor plus chemotherapy. The relationship between metastatic patterns and progression-free survival (PFS) in patients undergoing combination therapy was analyzed using univariate and multivariate Cox regression models. Serum samples were obtained from all patients before treatment initiation for untargeted metabolomics analysis, aiming to identify differential metabolites.

RESULTS

In the univariate analysis of PFS, NSCLC patients in M1c1 receiving first-line PD-1 inhibitor plus chemotherapy exhibited an extended PFS (HR = 0.49, 95% CI, 0.27-0.88, p = 0.017). In multivariate PFS analyses, these M1c1 patients receiving first-line PD-1 inhibitor plus chemotherapy also demonstrated prolonged PFS (HR = 0.45, 95% CI, 0.22-0.92, p = 0.028). The serum metabolic profiles of M1c1 and M1c2 undergoing first-line PD-1 inhibitors plus chemotherapy displayed notable distinctions. In comparison to M1c1 patients, M1c2 patients exhibited alterations in various pathways pretreatment, including platelet activation, linoleic acid metabolism, and the VEGF signaling pathway. Diminished levels of lipid-associated metabolites (diacylglycerol, sphingomyelin) were correlated with adverse outcomes.

CONCLUSION

NSCLC patients in M1c1, devoid of driver gene mutations, receiving first-line PD-1 inhibitors combined with chemotherapy, experienced superior outcomes compared to M1c2 patients. Moreover, metabolomic profiles strongly correlated with the prognosis of these patients, and M1c2 patients with unfavorable outcomes manifested distinct changes in metabolic pathways before treatment. These changes predominantly involved alterations in lipid metabolism, such as decreased diacylglycerol and sphingomyelin, which may impact tumor migration and invasion.

Serum untargeted metabolomics analysis of the preventive mechanism of TAETEA Prebiotea on non-alcoholic fatty liver in rats

Pu-erh tea belongs to the six tea categories of black tea, according to the processing technology and quality characteristics, is divided into two types of raw tea and ripe tea. Raw tea is made from fresh leaves of tea as raw materials, through the process of greening, kneading, sun drying, steam molding and other processes made of tightly pressed tea. Ripe tea is made from Yunnan large-leafed sun green tea, using a specific process, post-fermentation (rapid post-fermentation or slow post-fermentation) processing of loose tea and tightly pressed tea. TAETEA Prebiotea is Puerh Ripe Tea, TAETEA Prebiotea has the effect of increasing insulin level and improving hyperglycemia in mice, and it also has the effect of regulating blood lipids, which can reduce the level of serum total cholesterol (TC) and triglycerides (TG), increase the level of high-density lipoprotein cholesterol (HDL-C), and improve the metabolism of lipids. Therefore, further experiments were conducted by us, and TAETEA Prebiotea was formulated into a suitable dose for the intervention of non alcoholic fatty liver disease (NAFLD) model rats, and at the end of the experiments, the samples of each group of experiments were analyzed and detected by the method of UHPLC-Q-Exactive LC-MS liquid-mass spectrometry methodology, and the relevant metabolites as well as metabolic pathways were analyzed by the method of Non targeted metabolomics analysis. As a result, 71 differential metabolites could be screened, of which 35 differential metabolites were up-regulated after intervention and 36 differential metabolites were down-regulated after intervention. Based on the KEGG pathway enrichment and Pathway Impact bubble diagram analysis, glycine, serine, threonine metabolism, arginine and proline metabolism, protein digestion and absorption, and central carbon metabolism in cancer may be the main metabolic pathways in which TAETEA Prebiotea exerted preventive effects on NAFLD rats, C00148 (Proline), C00300 (Creatine) and C00719 (Betaine) are the differential metabolites that play important regulatory roles.

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.

A new chemical derivatization reagent sulfonyl piperazinyl for the quantification of fatty acids using LC-MS/MS

In our previous study, a chemical derivatization reagent named 5-(dimethylamino) naphthalene-1-sulfonyl piperazine (Dns-PP) was developed to enhance the chromatographic retention and the mass spectrometric response of free fatty acids (FFAs) in reversed-phase liquid chromatography coupled with electrospray ionization-mass spectrometry (RPLC-ESI-MS). However, Dns-PP exhibited strong preferences for long-chain FFAs, with limited improvement for short- or medium-chain FFAs. In this study, a new series of labeling reagents targeting FFAs were designed, synthesized, and evaluated. Among these reagents, Tmt-PP (N2, N2, N4, N4-tetramethyl-6-(4-(piperazin-1-ylsulfonyl) phenyl)-1,3,5-triazine-2,4-diamine) exhibited the best MS response and was selected for further evaluations. We compared Tmt-PP with Dns-PP and four commonly used carboxyl labeling reagents from existing studies, demonstrating the advantages of Tmt-PP. Further comparisons between Tmt-PP and Dns-PP in measuring FFAs from biological samples revealed that Tmt-PP labeling enhanced the MS response for about 80 % (30/38) of the measured FFAs, particularly for short- and medium-chain FFAs. Moreover, Tmt-PP labeling significantly improved the chromatographic retention of short-chain FFAs. To ensure accurate quantification, we developed a stable isotope-labeled Tmt-PP (i.e., d12-Tmt-PP) to react with chemical standards and serve as one-to-one internal standards (IS). The method was validated for accuracy, precision, sensitivity, linearity, stability, extraction efficiency, as well as matrix effect. Overall, this study introduced a new chemical derivatization reagent Tmt-PP (d12-Tmt-PP), providing a sensitive and accurate option for quantifying FFAs in biological samples.

Combining metabolomics with network pharmacology to reveal the therapeutic mechanism of Dingchuan Decoction in rats with OVA-induced allergic asthma

Dingchuan Decoction (DCD) is a traditional Chinese medicine prescription commonly used in the treatment of asthma, but the mechanism of DCD in treating asthma has not yet been determined. In this study, we employed a combination of metabolomics and network pharmacology to investigate the mechanism of DCD in treating asthma. An allergic asthma rat model was induced by ovalbumin (OVA). Metabolomics based on 1H NMR and UHPLC-MS was used to identify differential metabolites and obtain the major metabolic pathways and potential targets. Network pharmacology was utilized to explore potential targets of DCD for asthma treatment. Finally, the results of metabolomics and network pharmacology were integrated to obtain the key targets and metabolic pathways of DCD for the therapy of asthma, and molecular docking was utilized to validate the key targets. A total of 76 important metabolites and 231 potential targets were identified through metabolomics. Using network pharmacology, 184 potential therapeutic targets were obtained. These 184 targets were overlaid with the 231 potential targets obtained through metabolomics and were analyzed in conjunction with metabolic pathways. Ultimately, the key targets were identified as aldehyde dehydrogenase 2 (ALDH2) and amine oxidase copper-containing 3 (AOC3), and the relevant metabolic pathways affected were glycolysis and gluconeogenesis as well as arginine and proline metabolism. Molecular docking showed that the key targets had high affinity with the relevant active ingredients in DCD, which further demonstrated that DCD may exert therapeutic effects by acting on the key targets. The present study demonstrated that DCD can alleviate OVA-induced allergic asthma and that DCD may have a therapeutic effect by regulating intestinal flora and polyamine metabolism through its effects on ALDH2 and AOC3.

Mass spectrometric approaches in discovering lipid biomarkers for COVID-19 by lipidomics: Future challenges and perspectives

Coronavirus disease 2019 (COVID-19) has emerged as a global health threat and has rapidly spread worldwide. Significant changes in the lipid profile before and after COVID-19 confirmed the significance of lipid metabolism in regulating the response to viral infection. Therefore, understanding the role of lipid metabolism may facilitate the development of new therapeutics for COVID-19. Owing to their high sensitivity and accuracy, mass spectrometry (MS)-based methods are widely used for rapidly identifying and quantifying of thousands of lipid species present in a small amount of sample. To enhance the capabilities of MS for the qualitative and quantitative analysis of lipids, different platforms have been combined to cover a wide range of lipidomes with high sensitivity, specificity, and accuracy. Currently, MS-based technologies are being established as efficient methods for discovering potential diagnostic biomarkers for COVID-19 and related diseases. As the lipidome of the host cell is drastically affected by the viral replication process, investigating lipid profile alterations in patients with COVID-19 and targeting lipid metabolism pathways are considered to be crucial steps in host-directed drug targeting to develop better therapeutic strategies. This review summarizes various MS-based strategies that have been developed for lipidomic analyzes and biomarker discoveries to combat COVID-19 by integrating various other potential approaches using different human samples. Furthermore, this review discusses the challenges in using MS technologies and future perspectives in terms of drug discovery and diagnosis of COVID-19.

Integration of Gut Microbiota with Transcriptomic and Metabolomic Profiling Reveals Growth Differences in Male Giant River Prawns (Macrobrachium rosenbergii)

The giant freshwater prawn (GFP; Macrobrachium rosenbergii), a tropical species cultured worldwide, has high market demand and economic value. Male GFP growth varies considerably; however, the mechanisms underlying these growth differences remain unclear. In this study, we collected gut and hemolymphatic samples of large (ML), medium (MM), and small (MS) male GFPs and used the 16S rRNA sequencing and liquid chromatography-mass spectrometry-based metabolomic methods to explore gut microbiota and metabolites associated with GFP growth. The dominant bacteria were Firmicutes and Proteobacteria; higher growth rates correlated with a higher Firmicutes/Bacteroides ratio. Serum metabolite levels significantly differed between the ML and MS groups. We also combined transcriptomics with integrative multiomic techniques to further elucidate systematic molecular mechanisms in the GFPs. The results revealed that Faecalibacterium and Roseburia may improve gut health in GFP through butyrate release, affecting physiological homeostasis and leading to metabolic variations related to GFP growth differences. Notably, our results provide novel, fundamental insights into the molecular networks connecting various genes, metabolites, microbes, and phenotypes in GFPs, facilitating the elucidation of differential growth mechanisms in GFPs.

Untargeted Metabolomics Revealed that Quercetin Inhibited Ferroptosis by Improving Metabolic Disorder in the Hippocampus of Perimenopausal Depression Model Rats

Perimenopausal depression is often accompanied by metabolic disorders, which have long-term harmful effects on women's physical and mental health. Quercetin, a kind of phytoestrogen, has anti-inflammatory, antioxidant, and nerve-protective effects, and can regulate various metabolic disorders. This study aims to investigate the effect of quercetin on hippocampal metabolic disorder in perimenopausal depression rat models based on untargeted metabolomics technology. The rat model of perimenopausal depression was established by ovariectomy combined with chronic unpredictable mild stress (OVX-CUMS). Rats with no difference in sucrose preference were randomly divided into four groups (n = 12): sham group, OVX-CUMS group (model group), model plus quercetin group, and model plus 17β-estradiol group. At the end of the experiment, hippocampal tissues were collected for untargeted metabolomics analysis, morphological analysis, and detection of related indicators. Metabolomics identified 23 differential metabolites in the model group, and the pathway analysis discovered hippocampus metabolic abnormalities including the metabolism of arachidonic acid metabolism, glycerophospholipid metabolism, and ubiquinone biosynthesis, accompanied by an increase in oxidative stress, inflammation, and lipid peroxidation indicators. At the same time, the morphological characteristics of ferroptosis occurred in the hippocampus in the model group. These abnormal changes were reversed by treatment with quercetin or 17β-estradiol. Quercetin can improve perimenopausal depression by regulating hippocampal metabolic disorders and reducing hippocampal ferroptosis in rats. These findings provide a new strategy for the use of quercetin in the prevention and treatment of perimenopausal depression.

The MACROD2 rs6110695 A>G Polymorphism and the Metabolites Indoleacrylic Acid and Butyrylcarnitine Potentially Have Clinical Relevance to WBC Count Prediction

Our previous study suggested that the Mono-ADP ribosylhydrolase 2 (MACROD2) rs6110695 A>G polymorphism is significantly associated with white blood cell (WBC) count in the Korean population. The present study aimed to evaluate the clinical relevance of the MACROD2 rs6110695 A>G polymorphism for predicting WBC count by utilizing plasma metabolites and a single-nucleotide polymorphism (SNP). Two groups were characterized by MACROD2 rs6110695 A>G SNP genotypes among 139 healthy subjects based on the genetic information provided in our previous work: rs6110695 AA genotype group (n = 129) and rs6110695 AG genotype group (n = 10). Plasma global metabolic profiling was performed using ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). To estimate the predictive abilities of WBC count models using the rs6110695 genotype and/or significant differential metabolites, multiple linear regression analysis and receiver operating characteristic (ROC) curve analysis were conducted. The AG genotype had greater WBC-to-apolipoprotein (apo) A-I ratios; counts of WBCs, lymphocytes, monocytes, and granulocytes; monocyte-to-lymphocyte ratio (MLR); and monocyte-to-platelet ratio (MPR) than the AA genotype. In terms of metabolic profile, indoleacetic acid, and butyrylcarnitine levels were considerably distinct between the two groups, and these metabolites were considered to be meaningful prognostic variables for the rs6110695 genotype. Finally, ROC curve analysis demonstrated that the model containing the rs6110695 genotype and the two main metabolites was reliable. The present study revealed that individuals carrying the rs6110695 AG genotype with high plasma indoleacrylic acid and butyrylcarnitine levels might have elevated WBC counts. The rs6110695 genotype and the concentrations of indoleacrylic acid and butyrylcarnitine could contribute to reducing the risk of chronic diseases in the future.

The diagnostic potential of urine in paediatric patients undergoing initial treatment for tuberculous meningitis

Tuberculous meningitis (TBM)-the extrapulmonary form of tuberculosis, is the most severe complication associated with tuberculosis, particularly in infants and children. The gold standard for the diagnosis of TBM requires cerebrospinal fluid (CSF) through lumbar puncture-an invasive sample collection method, and currently available CSF assays are often not sufficient for a definitive TBM diagnosis. Urine is metabolite-rich and relatively unexplored in terms of its potential to diagnose neuroinfectious diseases. We used an untargeted proton magnetic resonance (1H-NMR) metabolomics approach to compare the urine from 32 patients with TBM (stratified into stages 1, 2 and 3) against that from 39 controls in a South African paediatric cohort. Significant spectral bins had to satisfy three of our four strict cut-off quantitative statistical criteria. Five significant biological metabolites were identified-1-methylnicotinamide, 3-hydroxyisovaleric acid, 5-aminolevulinic acid, N-acetylglutamine and methanol-which had no correlation with medication metabolites. ROC analysis revealed that methanol lacked diagnostic sensitivity, but the other four metabolites showed good diagnostic potential. Furthermore, we compared mild (stage 1) TBM and severe (stages 2 and 3) TBM, and our multivariate metabolic model could successfully classify severe but not mild TBM. Our results show that urine can potentially be used to diagnose severe TBM.

Tumor Metabolism: A New Field for the Treatment of Glioma

The clinical treatment of glioma remains relatively immature. Commonly used clinical treatments for gliomas are surgery combined with chemotherapy and radiotherapy, but there is a problem of drug resistance. In addition, immunotherapy and targeted therapies also suffer from the problem of immune evasion. The advent of metabolic therapy holds immense potential for advancing more efficacious and tolerable therapies against this aggressive disease. Metabolic therapy alters the metabolic processes of tumor cells at the molecular level to inhibit tumor growth and spread, and lead to better outcomes for patients with glioma that are insensitive to conventional treatments. Moreover, compared with conventional therapy, it has less impact on normal cells, less toxicity and side effects, and higher safety. The objective of this review is to examine the changes in metabolic characteristics throughout the development of glioma, enumerate the current methodologies employed for studying tumor metabolism, and highlight the metabolic reprogramming pathways of glioma along with their potential molecular mechanisms. Importantly, it seeks to elucidate potential metabolic targets for glioblastoma (GBM) therapy and summarize effective combination treatment strategies based on various studies.

Neuroinflammation and Neurometabolomic Profiling in Fentanyl Overdose Mouse Model Treated with Novel β-Lactam, MC-100093, and Ceftriaxone

Opioid-related deaths are attributed to overdoses, and fentanyl overdose has been on the rise in many parts of the world, including the USA. Glutamate transporter 1 (GLT-1) has been identified as a therapeutic target in several preclinical models of substance use disorders, and β-lactams effectively enhance its expression and function. In the current study, we characterized the metabolomic profile of the nucleus accumbens (NAc) in fentanyl-overdose mouse models, and we evaluated the protective effects of the functional enhancement of GLT-1 using β-lactams, ceftriaxone, and MC-100093. BALB/c mice were divided into four groups: control, fentanyl, fentanyl/ceftriaxone, and fentanyl/MC-100093. While the control group was intraperitoneally (i.p.) injected with normal saline simultaneously with other groups, all fentanyl groups were i.p. injected with 1 mg/kg of fentanyl as an overdose after habituation with four repetitive non-consecutive moderate doses (0.05 mg/kg) of fentanyl for a period of seven days. MC-100093 (50 mg/kg) and ceftriaxone (200 mg/kg) were i.p. injected from days 5 to 9. Gas chromatography-mass spectrometry (GC-MS) was used for metabolomics, and Western blotting was performed to determine the expression of target proteins. Y-maze spontaneous alternation performance and the open field activity monitoring system were used to measure behavioral manifestations. Fentanyl overdose altered the abundance of about 30 metabolites, reduced the expression of GLT-1, and induced the expression of inflammatory mediators IL-6 and TLR-4 in the NAc. MC-100093 and ceftriaxone attenuated the effects of fentanyl-induced downregulation of GLT-1 and upregulation of IL-6; however, only ceftriaxone attenuated fentanyl-induced upregulation of TRL4 expression. Both of the β-lactams attenuated the effects of fentanyl overdose on locomotor activities but did not induce significant changes in the overall metabolomic profile. Our findings revealed that the exposure to a high dose of fentanyl causes alterations in key metabolic pathways in the NAc. Pretreatment with ceftriaxone and MC-100093 normalized fentanyl-induced downregulation of GLT-1 expression with subsequent attenuation of neuroinflammation as well as the hyperactivity, indicating that β-lactams may be promising drugs for treating fentanyl use disorder.

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.

Fufang Muji Granules Ameliorate Liver Fibrosis by Reducing Oxidative Stress and Inflammation, Inhibiting Apoptosis, and Modulating Overall Metabolism

Fufang Muji granules (FMGs) are a prominent modern prescription Chinese patent formulation derived from the Muji decoction. Utilized in clinical practice for nearly four decades, FMGs have demonstrated efficacy in treating liver diseases. However, the precise mechanism of action remains unclear. This study investigates the hepatoprotective effects of FMGs against liver fibrosis in rats based on untargeted metabolomics and elucidates their underlying mechanisms. A comprehensive model of liver fibrosis was established with 30% CCl4 (2 mL/kg) injected intraperitoneally, and a fat and sugar diet combined with high temperatures and humidity. Rats were orally administered FMGs (3.12 g/kg/d) once daily for six weeks. FMG administration resulted in improved liver fibrosis and attenuated hepatic oxidative stress and apoptosis. Furthermore, FMGs inhibited hepatic stellate cell activation and modulated transforming growth factor β1/Smad signaling. Additionally, FMG treatment influenced the expression levels of interleukin-6, interleukin-1β, and tumour necrosis factor alpha in the injured liver. Metabolic pathways involving taurine and hypotaurine metabolism, as well as primary bile acid biosynthesis, were identified as mechanisms of action for FMGs. Immunohistochemistry, quantitative reverse transcription polymerase chain reaction (RT-qPCR), and quantitative analysis also revealed that FMGs regulated taurine and hypotaurine metabolism and bile acid metabolism. These findings provide a valuable understanding of the role of FMGs in liver fibrosis management.

Study Profile of the Tsuruoka Metabolomics Cohort Study (TMCS)

The Tsuruoka Metabolomics Cohort Study (TMCS) is an ongoing population-based cohort study being conducted in the rural area of Yamagata Prefecture, Japan. This study aimed to enhance the precision prevention of multi-factorial, complex diseases, including non-communicable and aging-associated diseases, by improving risk stratification and prediction measures. At baseline, 11,002 participants aged 35-74 years were recruited in Tsuruoka City, Yamagata Prefecture, Japan, between 2012 and 2015, with an ongoing follow-up survey. Participants underwent various measurements, examinations, tests, and questionnaires on their health, lifestyle, and social factors. This study uses an integrative approach with deep molecular profiling to identify potential biomarkers linked to phenotypes that underpin disease pathophysiology and provide better mechanistic insights into social health determinants. The TMCS incorporates multi-omics data, including genetic and metabolomic analyses of 10,933 participants, and comprehensive data collection ranging from physical, psychological, behavioral, and social to biological data. The metabolome is used as a phenotypic probe because it is sensitive to changes in physiological and external conditions. The TMCS focuses on collecting outcomes for cardiovascular disease, cancer incidence and mortality, disability and functional decline due to aging and disease sequelae, and the variation in health status within the body represented by omics analysis that lies between exposure and disease. It contains several sub-studies on aging, heated tobacco products, and women's health. This study is notable for its robust design, high participation rate (89%), and long-term repeated surveys. Moreover, it contributes to precision prevention in Japan and East Asia as a well-established multi-omics platform.

Multiomics: Functional Molecular Biomarkers of Micronutrients for Public Health Application

Adequate micronutrient intake and status are global public health goals. Vitamin and mineral deficiencies are widespread and known to impair health and survival across the life stages. However, knowledge of molecular effects, metabolic pathways, biological responses to variation in micronutrient nutriture, and abilities to assess populations for micronutrient deficiencies and their pathology remain lacking. Rapidly evolving methodological capabilities in genomics, epigenomics, proteomics, and metabolomics offer unparalleled opportunities for the nutrition research community to link micronutrient exposure to cellular health; discover new, arguably essential micronutrients of microbial origin; and integrate methods of molecular biology, epidemiology, and intervention trials to develop novel approaches to assess and prevent micronutrient deficiencies in populations. In this review article, we offer new terminology to specify nutritional application of multiomic approaches and encourage collaboration across the basic to public health sciences to advance micronutrient deficiency prevention.

Dysregulations of amino acid metabolism and lipid metabolism in urine of children and adolescents with major depressive disorder: a case-control study

RATIONALE

The mechanisms underlying major depressive disorder (MDD) in children and adolescents are unclear. Metabolomics has been utilized to capture metabolic signatures of various psychiatric disorders; however, urinary metabolic profile of MDD in children and adolescents has not been studied.

OBJECTIVES

We analyzed urinary metabolites in children and adolescents with MDD to identify potential biomarkers and metabolic signatures.

METHODS

Here, liquid chromatography-mass spectrometry was used to profile metabolites in urine samples from 192 subjects, comprising 80 individuals with antidepressant-naïve MDD (AN-MDD), 37 with antidepressant-treated MDD (AT-MDD) and 75 healthy controls (HC). We performed orthogonal partial least squares discriminant analysis to identify differential metabolites and employed logistic regression and receiver operating characteristic analysis to establish a diagnostic panel.

RESULTS

In total, 143 and 71 differential metabolites were identified in AN-MDD and AT-MDD, respectively. These were primarily linked to lipid metabolism, molecular transport, and small molecule biochemistry. AN-MDD additionally exhibited dysregulated amino acid metabolism. Compared to HC, a diagnostic panel of seven metabolites displayed area under the receiver operating characteristic curves of 0.792 for AN-MDD, 0.828 for AT-MDD, and 0.799 for all MDD. Furthermore, the urinary metabolic profiles of children and adolescents with MDD significantly differed from those of adult MDD.

CONCLUSIONS

Our research suggests dysregulated amino acid metabolism and lipid metabolism in the urine of children and adolescents with MDD, similar to results in plasma metabolomics studies. This contributes to the comprehension of mechanisms underlying children and adolescents with MDD.

Metabolomic and lipidomic profiling of the spinal cord in type 2 diabetes mellitus rats with painful neuropathy

In this paper we investigated lipid and metabolite changes in diabetic neuropathy, using untargeted lipidomics and metabolomics analyses of the spinal cords from streptozotocin-treated diabetic rats.170 metabolites and 45 lipids were dysregulated in the painful diabetic neuropathy (PDN) phase. Pathway enrichment analysis revealed perturbations in starch and sucrose, tryptophan, pyrimidine, cysteine and methionine, thiamine, tyrosine, and nucleotides. The disturbance of tyrosine, tryptophan, methionine, triacylglycerol, and phosphatidylethanolamine metabolism indicated that pathological mechanisms in the PDN involved energy metabolism, oxidative stress, and neural reparative regeneration. These revelations offered potential biomarkers for PDN and enriched the comprehension of the complex molecular mechanisms characterizing PDN, establishing a solid foundation for subsequent inquiries into neural convalescence and recovery after PDN.

Nuclear Receptors and the Hidden Language of the Metabolome

Nuclear hormone receptors (NHRs) are a family of ligand-regulated transcription factors that control key aspects of development and physiology. The regulation of NHRs by ligands derived from metabolism or diet makes them excellent pharmacological targets, and the mechanistic understanding of how NHRs interact with their ligands to regulate downstream gene networks, along with the identification of ligands for orphan NHRs, could enable innovative approaches for cellular engineering, disease modeling and regenerative medicine. We review recent discoveries in the identification of physiologic ligands for NHRs. We propose new models of ligand-receptor co-evolution, the emergence of hormonal function and models of regulation of NHR specificity and activity via one-ligand and two-ligand models as well as feedback loops. Lastly, we discuss limitations on the processes for the identification of physiologic NHR ligands and emerging new methodologies that could be used to identify the natural ligands for the remaining 17 orphan NHRs in the human genome.

Falcon Probe: A High-Pressure and Robust Sampling Interface for Coupling Lossless Liquid Chromatography Injection with In Situ Nanoliter-Scale Sample Pretreatment

With the increasing demand for trace sample analysis, injecting trace samples into liquid chromatography-mass spectrometry (LC-MS) systems with minimal loss has become a major challenge. Herein, we describe an in situ LC-MS analytical probe, the Falcon probe, which integrates multiple functions of high-pressure sample injection without sample loss, high-efficiency LC separation, and electrospray. The main body of the Falcon probe is made of stainless steel and fabricated by the computer numerical control (CNC) technique, which has ultrahigh mechanical strength. By coupling a nanoliter-scale droplet reactor made of polyether ether ketone (PEEK) material, the Falcon probe-based LC-MS system was capable of operating at mobile-phase pressures up to 800 bar, which is comparable to those of conventional ultraperformance liquid chromatography (UPLC) systems. Using the probe pressing microamount in situ (PPMI) injection approach, the Falcon probe-based LC-MS system showed high separation efficiency and good repeatability with relative standard deviations (RSDs) of retention time and peak area of 1.8% and 9.9%, respectively, in peptide mixture analysis (n = 6). We applied this system to the analysis of a trace amount of 200 pg of HeLa protein digest and successfully identified an average of 766 protein groups (n = 5). By combining in situ sample pretreatment at the nanoliter range, we further applied the present system in single-cell proteomic analysis, and 241 protein groups were identified in single 293 cells, which preliminarily demonstrated its potential in the analysis of trace amounts of samples with complex compositions.

Adults prenatally exposed to the Dutch Famine exhibit a metabolic signature associated with a broad spectrum of common diseases

BACKGROUND

Exposure to famine in the prenatal period is associated with an increased risk of metabolic disease, including obesity and type 2 diabetes. We employed nuclear magnetic resonance (NMR) metabolomic profiling to identify the metabolic changes that are associated with survival of prenatal famine exposure during the Dutch Famine at the end of World War II and subsequently assess their link to disease.

METHODS

NMR metabolomics data were generated from serum in 480 individuals prenatally exposed to famine (mean 58.8 years, 0.5 SD) and 464 controls (mean 57.9 years, 5.4 SD). We tested associations of prenatal famine exposure with levels of 168 individual metabolic biomarkers and compared the metabolic biomarker signature of famine exposure with those of 154 common diseases.

RESULTS

Prenatal famine exposure was associated with higher concentrations of branched-chain amino acids ((iso)-leucine), aromatic amino acid (tyrosine), and glucose in later life (0.2-0.3 SD, p < 3 × 10-3). The metabolic biomarker signature of prenatal famine exposure was positively correlated to that of incident type 2 diabetes from the UK Biobank (r = 0.77, p = 3 × 10-27), also when re-estimating the signature of prenatal famine exposure among individuals without diabetes (r = 0.67, p = 1 × 10-18). Remarkably, this association extended to 115 common diseases for which signatures were available (0.3 ≤ r ≤ 0.9, p < 3.2 × 10-4). Correlations among metabolic signatures of famine exposure and disease outcomes were attenuated when the famine signature was adjusted for body mass index.

CONCLUSIONS

Prenatal famine exposure is associated with a metabolic biomarker signature that strongly resembles signatures of a diverse set of diseases, an observation that can in part be attributed to a shared involvement of obesity.

Metabolomic Disparities in Intraocular Fluid Across Varied Stages of Cataract Progression: Implications for the Analysis of Cataract Development

Introduction: The lens's metabolic demands are met through a continuous circulation of aqueous humor, encompassing a spectrum of components such as organic and inorganic ions, carbohydrates, glutathione, urea, amino acids, proteins, oxygen, carbon dioxide, and water. Metabolomics is a pivotal tool, offering an initial insight into the complexities of integrated metabolism. In this investigative study, we systematically scrutinize the composition of intraocular fluid in individuals afflicted with cataracts. Methods: The investigation involved a comprehensive analysis of aqueous humor samples from a cohort comprising 192 patients. These individuals were stratified by utilizing the SPONCS classification system, delineating distinct groups characterized by the hardness of cataracts. The analytical approach employed targeted quantitative metabolite analysis using HILIC-based liquid chromatography coupled with high-resolution mass spectrometric detection. The metabolomics data analysis was performed with MetaboAnalyst 5.0. Results: The results of the enrichment analysis have facilitated the inference that the discerned disparities among groups arise from disruptions in taurine and hypotaurine metabolism, variations in tryptophan metabolism, and modifications in mitochondrial beta-oxidation of short-chain saturated fatty acids and pyrimidine metabolism. Conclusion: A decline in taurine concentration precipitates diminished glutathione activity, prompting an elevated requirement for NAD+ and instigating tryptophan metabolism along the kynurenine pathway. Activation of this pathway is additionally prompted by interferon-gamma and UV radiation, leading to the induction of IDO. Concurrently, heightened mitochondrial beta-oxidation signifies a distinctive scenario in translocating fatty acids into the mitochondria, enhancing energy production.

Serum Metabolomics Uncovers the Mechanisms of Inulin in Preventing Non-Alcoholic Fatty Liver Disease

Inulin may be a promising therapeutic molecule for treating non-alcoholic fatty liver disease (NAFLD). However, the underlying mechanisms of its therapeutic activity remain unclear. To address this issue, a high-fat-diet-induced NAFLD mouse model was developed and treated with inulin. The NAFLD phenotype was evaluated via histopathological analysis and biochemical parameters, including serum levels of alanine aminotransferase, aspartate aminotransferase, liver triglycerides, etc. A serum metabolomics study was conducted using ultra-performance liquid chromatography coupled with tandem mass spectrometry. The results revealed that inulin mitigated NAFLD symptoms such as histopathological changes and liver cholesterol levels. Through the serum metabolomics study, 347 differential metabolites were identified between the model and control groups, and 139 differential metabolites were identified between the inulin and model groups. Additionally, 48 differential metabolites (such as phosphatidylserine, dihomo-γ-linolenic acid, L-carnitine, and 13-HODE) were identified as candidate targets of inulin and subjected to pathway enrichment analysis. The results revealed that these 48 differential metabolites were enriched in several metabolic pathways such as fatty acid biosynthesis and cardiolipin biosynthesis. Taken together, our results suggest that inulin might attenuate NAFLD partially by modulating 48 differential metabolites and their correlated metabolic pathways, constituting information that might help us find novel therapies for NAFLD.

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation

We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

Metabolomics of Benzene Exposure and Development of Biomarkers for Exposure Hazard Assessment

Benzene, a common industrial solvent, poses significant health risks including poisoning and hematopoietic diseases. However, its precise toxicity mechanisms remain unclear. To assess the health impact of prolonged benzene exposure through metabolomic analyses of exposed workers and benzene-poisoned mice, aiming to identify biomarkers and minimize occupational hazards. This study compared 18 benzene-exposed workers with 18 non-exposed workers, matching for age, lifestyle, and BMI. The metabolites in the workers' samples were analyzed using ultra-high-performance liquid chromatography and mass spectrometry. A larger study included 118 exposed and 158 non-exposed workers, incorporating surveys and routine blood and urine tests with differential metabolites targeted via an enzyme-linked immunosorbent assay. The animal studies consisted of two 15- and 60-day benzene staining and control experiments on 28 C57BL/6J mice, followed by sample collection and organ analysis. The data analysis employed eXtensible Computational Mass Spectrometry (XCMS), Python, MetaboAnalyst 6.0, and SPSS24.0. The exposed workers exhibited altered metabolites indicating external benzene exposure, lower glucose levels, and changes in white blood cell counts and urinary ketone bodies. The plasma metabolomics revealed disturbances in energy and lipid metabolism. The benzene-exposed mice displayed reduced weight gain, behavioral changes, and organ damage. Oxidative stress and abnormal purine and lipid metabolism were observed in both the long-term benzene-exposed workers and benzene-exposed mice. Metabolic markers for the early detection of benzene exposure hazards were identified, underscoring the need to mitigate occupational risks.

Enhancing aggression in Henan gamecocks via augmentation of serotonergic-dopaminergic signaling and attenuation of neuroimmune response

Animal aggression is one of the most conserved behaviors. Excessive and inappropriate aggression was a serious social concern across species. After long-term selection under strict stress conditions, Henan gamecock serves as a good model for studying aggressive behavior. In this research, we constructed a Henan game chicken backcross population containing 25% Rhode Island Red (RIR), and conducted brain transcriptomics and serum metabolomics analyses on Henan gamecock (HGR) through its comparison with its female encounters (HGH) and the male backcross birds (BGR). The study revealed that seven differential metabolites in serum and 172 differentially expressed genes in the brain were commonly shared in both HGR vs. HGH and HGR vs. BGR comparisons. They exhibited the same patterns of modulation in Henan gamecocks, following either HGH < HGR > BGR or HGH > HGR < BGR style. Therein, some neurological genes involving in serotonergic and dopaminergic signaling were upregulated, while the levels of many genes related with neuro-immune function were decreased in Henan gamecock. In addition, many unknown genes specifically or highly expressed in the brain of the Henan gamecock were identified. These genes are potentially key candidates for enhancing the bird's aggression. Multi-omics joint analysis revealed that tyrosine metabolism and neuroactive ligand-receptor interaction were commonly affected. Overall, our results propose that the aggressiveness of Henan gamecocks can be heightened by the activation of the serotonergic-dopaminergic metabolic process in the brain, which concurrently impairs the neuroimmune system. Further research is needed to identify the function of these unknown genes on the bird's aggressive behavior.

Metabolomic landscape of overall and common cancers in the UK Biobank: A prospective cohort study

Information about the NMR metabolomics landscape of overall, and common cancers is still limited. Based on a cohort of 83,290 participants from the UK Biobank, we used multivariate Cox regression to assess the associations between each of the 168 metabolites with the risks of overall cancer and 20 specific types of cancer. Then, we applied LASSO to identify important metabolites for overall cancer risk and obtained their associations using multivariate cox regression. We further conducted mediation analysis to evaluate the mediated role of metabolites in the effects of traditional factors on overall cancer risk. Finally, we included the 13 identified metabolites as predictors in prediction models, and compared the accuracies of our traditional models. We found that there were commonalities among the metabolic profiles of overall and specific types of cancer: the top 20 frequently identified metabolites for 20 specific types of cancer were all associated with overall cancer; most of the specific types of cancer had common identified metabolites. Meanwhile, the associations between the same metabolite with different types of cancer can vary based on the site of origin. We identified 13 metabolic biomarkers associated with overall cancer, and found that they mediated the effects of traditional factors. The accuracies of prediction models improved when we added 13 identified metabolites in models. This study is helpful to understand the metabolic mechanisms of overall and a wide range of cancers, and our results also indicate that NMR metabolites are potential biomarkers in cancer diagnosis and prevention.

Comparative analysis of circulating metabolomic profiles identifies shared metabolic alterations across distinct multistressor military training exercises

Military training provides insight into metabolic responses under unique physiological demands that can be comprehensively characterized by global metabolomic profiling to identify potential strategies for improving performance. This study identified shared changes in metabolomic profiles across three distinct military training exercises, varying in magnitude and type of stress. Blood samples collected before and after three real or simulated military training exercises were analyzed using the same untargeted metabolomic profiling platform. Exercises included a 2-wk survival training course (ST, n = 36), a 4-day cross-country ski march arctic training (AT, n = 24), and a 28-day controlled diet- and exercise-induced energy deficit (CED, n = 26). Log2-fold changes of greater than ±1 in 191, 121, and 64 metabolites were identified in the ST, AT, and CED datasets, respectively. Most metabolite changes were within the lipid (57-63%) and amino acid metabolism (18-19%) pathways and changes in 87 were shared across studies. The largest and most consistent increases in shared metabolites were found in the acylcarnitine, fatty acid, ketone, and glutathione metabolism pathways, whereas the largest decreases were in the diacylglycerol and urea cycle metabolism pathways. Multiple shared metabolites were consistently correlated with biomarkers of inflammation, tissue damage, and anabolic hormones across studies. These three studies of real and simulated military training revealed overlapping alterations in metabolomic profiles despite differences in environment and the stressors involved. Consistent changes in metabolites related to lipid metabolism, ketogenesis, and oxidative stress suggest a potential common metabolomic signature associated with inflammation, tissue damage, and suppression of anabolic signaling that may characterize the unique physiological demands of military training.NEW & NOTEWORTHY The extent to which metabolomic responses are shared across diverse military training environments is unknown. Global metabolomic profiling across three distinct military training exercises identified shared metabolic responses with the largest changes observed for metabolites related to fatty acids, acylcarnitines, ketone metabolism, and oxidative stress. These changes also correlated with alterations in markers of tissue damage, inflammation, and anabolic signaling and comprise a potential common metabolomic signature underlying the unique physiological demands of military training.

Machine Learning-Driven SERS Nanoendoscopy and Optophysiology

A frontier of analytical sciences is centered on the continuous measurement of molecules in or near cells, tissues, or organs, within the biological context in situ, where the molecular-level information is indicative of health status, therapeutic efficacy, and fundamental biochemical function of the host. Following the completion of the Human Genome Project, current research aims to link genes to functions of an organism and investigate how the environment modulates functional properties of organisms. New analytical methods have been developed to detect chemical changes with high spatial and temporal resolution, including minimally invasive surface-enhanced Raman scattering (SERS) nanofibers using the principles of endoscopy (SERS nanoendoscopy) or optical physiology (SERS optophysiology). Given the large spectral data sets generated from these experiments, SERS nanoendoscopy and optophysiology benefit from advances in data science and machine learning to extract chemical information from complex vibrational spectra measured by SERS. This review highlights new opportunities for intracellular, extracellular, and in vivo chemical measurements arising from the combination of SERS nanosensing and machine learning.

Causality of genetically determined metabolites on susceptibility to prevalent urological cancers: a two-sample Mendelian randomization study and meta-analysis

Background

Prevalent urological cancers, including kidney, prostate, bladder, and testicular cancers, contribute significantly to global cancer incidence and mortality. Metabolomics, focusing on small-molecule intermediates, has emerged as a tool to understand cancer etiology. Given the knowledge gap in this field, we employ a two-sample Mendelian randomization (MR) analysis to investigate the causal relationships between genetically determined metabolites (GDMs) and the susceptibility to four common urological cancers.

Methods

The study employs genome-wide association studies (GWAS) data from European populations, featuring the most extensive case count available for both blood metabolites and four prevalent urological cancers. Preliminary and secondary MR analyses were separately conducted, employing inverse variance weighted (IVW) as the primary method. Multiple statistical analyses, including the MR-Steiger test, Cochran's Q test, leave-one-out analysis, MR-Egger intercept analysis, and MR-PRESSO analysis, were executed to ensure robustness. Additionally, a meta-analysis was carried out to consolidate findings. The weighted median (WM) method was utilized for a relatively lenient correction (PWM < 0.05).

Results

After rigorous genetic variation filtering, 645 out of 1,400 metabolites were included in both preliminary and secondary MR analyses. Preliminary MR analysis identified 96 potential causal associations between 94 distinct metabolites and four urological cancers. Secondary analysis based on Finnish outcome data revealed 93 potential causal associations. Cross-database meta-analysis identified 68 blood metabolites associated with four urological cancers. Notably, 31 metabolites remained significant after using WM for correction, with additional 37 suggestive causal relationships. Reverse MR analysis revealed a significant causal association between genetically predicted prostate cancer and elevated 4-hydroxychlorothalonil levels (IVW, combined OR: 1.039, 95% CI 1.014-1.064, p = 0.002; WM, combined OR: 1.052, 95% CI 1.010-1.095, p = 0.014).

Conclusion

This comprehensive MR study provides insights into the causal relationships between blood metabolites and urological cancers, revealing potential biomarkers and therapeutic targets, thereby addressing gaps in understanding and laying the foundation for targeted interventions in urological cancer research and treatment.

Altered gut microbiota and metabolite profiles provide clues in understanding resistant hypertension

BACKGROUND

Resistant hypertension is a severe phenotype in hypertension that may be driven by interactions between genetic and environmental factors. Specific changes in gut microbiota and metabolites have been shown to influence cardiovascular disease progression. However, microbial and metabolomic changes associated with resistant hypertension remain elusive.

METHODS

In this study, the gut microbiome of 30 participants with resistant hypertension, 30 with controlled hypertension, and 30 nonhypertension was characterized using 16S rRNA amplicon sequencing. In addition, the serum metabolome of the same population was assessed by untargeted metabolomics.

RESULTS

The alpha diversity of microbiome in the resistant hypertension decreased, and changes were also observed in the composition of the gut microbiota. The resistant hypertension group was characterized by elevated levels of Actinobacteitia and Proteobacteria. Twenty-three genera were found to have significantly different abundances between resistant hypertension and controlled hypertension, as well as 55 genera with significantly different abundances between resistant hypertension and nonhypertension. Compared with the controlled hypertension group, the genera Rothia and Sharpea in resistant hypertension were more abundant. Compared with the nonhypertension group, the genera Escherichia-Shigella , Lactobacillus , Enterococcus were more abundant. Untargeted metabolomics provided distinctly different serum metabolic profiles for the three groups and identified a range of differential metabolites. These metabolites were mainly associated with the pathway of glycerophospholipid metabolism. Furthermore, correlation analysis provided evidence of new interactions between gut microbiota and metabolites in the resistant hypertension.

CONCLUSION

In conclusion, our study provides a comprehensive understanding of the resistant hypertension gut microbiota and metabolites, suggesting that treatment resistance in resistant hypertension patients may be related to the gut microbiota and serum metabolites.

Investigating the mechanism of cornel iridoid glycosides on type 2 diabetes mellitus using serum and urine metabolites in rats

ETHNOPHARMACOLOGICAL RELEVANCE

Cornel iridoid glycosides (CIG) are extracted from Corni fructus, a herbal medicine used in traditional Chinese medicine to treat diabetes. However, the antidiabetic effects of CIG and the underlying metabolic mechanisms require further exploration.

AIM OF THE STUDY

This study aimed to assess the antidiabetic effects and metabolic mechanism of CIG by performing metabolomic analyses of serum and urine samples of rats.

MATERIALS AND METHODS

A rat model of type 2 diabetes mellitus (T2DM) was established by administering a low dose of streptozotocin (30 mg/kg) intraperitoneally after 4 weeks of feeding a high-fat diet. The model was evaluated based on several parameters, including fasting blood glucose (FBG), random blood glucose (RBG), urine volume, liver index, body weight, histopathological sections, and serum biochemical parameters. Subsequently, serum and urine metabolomics were analyzed using ultra-high-pressure liquid chromatography coupled with linear ion trap-Orbitrap tandem mass spectrometry (UHPLC-LTQ-Orbitrap-MS). Data were analyzed using unsupervised principal component analysis (PCA) and supervised orthogonal partial least squares discriminant analysis (OPLS-DA). Differential metabolites were examined by the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways to explore the underlying mechanisms.

RESULTS

After 4 weeks of treatment with different doses of CIG, varying degrees of antidiabetic effects were observed, along with reduced liver and pancreatic injury, and improved oxidative stress levels. Compared with the T2DM group, 19 and 23 differential metabolites were detected in the serum and urine of the CIG treatment group, respectively. The key metabolites involved in pathway regulation include taurine, chenodeoxycholic acid, glycocholic acid, and L-tyrosine in the serum and glycine, hippuric acid, phenylacetylglycine, citric acid, and D-glucuronic acid in the urine, which are related to lipid, amino acid, energy, and carbohydrate metabolism.

CONCLUSIONS

This study confirmed the antidiabetic effects of CIG and revealed that CIG effectively controlled metabolic disorders in T2DM rats. This seems to be meaningful for the clinical application of CIG, and can benefit further studies on CIG mechanism.

Deciphering the tumor immune microenvironment from a multidimensional omics perspective: insight into next-generation CAR-T cell immunotherapy and beyond

Tumor immune microenvironment (TIME) consists of intra-tumor immunological components and plays a significant role in tumor initiation, progression, metastasis, and response to therapy. Chimeric antigen receptor (CAR)-T cell immunotherapy has revolutionized the cancer treatment paradigm. Although CAR-T cell immunotherapy has emerged as a successful treatment for hematologic malignancies, it remains a conundrum for solid tumors. The heterogeneity of TIME is responsible for poor outcomes in CAR-T cell immunotherapy against solid tumors. The advancement of highly sophisticated technology enhances our exploration in TIME from a multi-omics perspective. In the era of machine learning, multi-omics studies could reveal the characteristics of TIME and its immune resistance mechanism. Therefore, the clinical efficacy of CAR-T cell immunotherapy in solid tumors could be further improved with strategies that target unfavorable conditions in TIME. Herein, this review seeks to investigate the factors influencing TIME formation and propose strategies for improving the effectiveness of CAR-T cell immunotherapy through a multi-omics perspective, with the ultimate goal of developing personalized therapeutic approaches.

Chemical Composition of Commercial Cannabis

Cannabis is widely used for medicinal and recreational purposes. As a result, there is increased interest in its chemical components and their physiological effects. However, current information on cannabis chemistry is often outdated or scattered across many books and journals. To address this issue, we used modern metabolomics techniques and modern bioinformatics techniques to compile a comprehensive list of >6000 chemical constituents in commercial cannabis. The metabolomics methods included a combination of high- and low-resolution liquid chromatography-mass spectrometry (MS), gas chromatography-MS, and inductively coupled plasma-MS. The bioinformatics methods included computer-aided text mining and computational genome-scale metabolic inference. This information, along with detailed compound descriptions, physicochemical data, known physiological effects, protein targets, and referential compound spectra, has been made available through a publicly accessible database called the Cannabis Compound Database (https://cannabisdatabase.ca). Such a centralized, open-access resource should prove to be quite useful for the cannabis community.

Application of molecular networking to improve the compound annotation in liquid chromatography-mass spectrometry-based metabolomics analysis: A case study of Bupleuri radix

INTRODUCTION

Compound annotation is always a challenging step in metabolomics studies. The molecular networking strategy has been developed recently to organize the relationship between compounds as a network based on their tandem mass (MS2) spectra similarity, which can be used to improve compound annotation in metabolomics analysis.

OBJECTIVE

This study used Bupleuri Radix from different geographic areas to evaluate the performance of molecular networking strategy for compound annotation in liquid chromatography-mass spectrometry (LC-MS)-based metabolomics.

METHODOLOGY

The Bupleuri Radix extract was analyzed by LC-quadrupole time-of-flight MS under MSe acquisition mode. After raw data preprocessing, the resulting dataset was used for statistical analysis, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The chemical makers related to the sample growth place were selected using variable importance in projection (VIP) > 2, fold change (FC) > 2, and p < 0.05. The molecular networking analysis was applied to conduct the compound annotation.

RESULTS

The score plots of PCA showed that the samples were classified into two clusters depending on their growth place. Then, the PLS-DA model was constructed to explore the chemical changes of the samples further. Sixteen compounds were selected as chemical makers and tentatively annotated by the feature-based molecular networking (FBMN) analysis.

CONCLUSION

The results showed that the molecular networking method fully exploits the MS information and is a promising tool for facilitating compound annotation in metabolomics studies. However, the software used for feature extraction influenced the results of library searching and molecular network construction, which need to be taken into account in future studies.

Optimal transport for automatic alignment of untargeted metabolomic data

Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-MS poses a major challenge for biomarker discovery, annotation, and experimental comparison, necessitating the merging of multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability to data variations and hyperparameter dependence. Here, we introduce GromovMatcher, a flexible and user-friendly algorithm that automatically combines LC-MS datasets using optimal transport. By capitalizing on feature intensity correlation structures, GromovMatcher delivers superior alignment accuracy and robustness compared to existing approaches. This algorithm scales to thousands of features requiring minimal hyperparameter tuning. Manually curated datasets for validating alignment algorithms are limited in the field of untargeted metabolomics, and hence we develop a dataset split procedure to generate pairs of validation datasets to test the alignments produced by GromovMatcher and other methods. Applying our method to experimental patient studies of liver and pancreatic cancer, we discover shared metabolic features related to patient alcohol intake, demonstrating how GromovMatcher facilitates the search for biomarkers associated with lifestyle risk factors linked to several cancer types.

Network Analysis Reveals Protein Modules Associated with Childhood Respiratory Diseases

Background

The first year of life is a period of rapid immune development that can impact health trajectories and the risk of developing respiratory-related diseases, such as asthma, recurrent infections, and eczema. However, the biology underlying subsequent disease development remains unknown.

Methods

Using weighted gene correlation network analysis (WGCNA), we derived modules of highly correlated immune-related proteins in plasma samples from children at age 1 year (N=294) from the Vitamin D Antenatal Asthma Reduction Trial (VDAART). We applied regression analyses to assess relationships between protein modules and development of childhood respiratory diseases up to age 6 years. We then characterized genomic, environmental, and metabolomic factors associated with modules.

Results

WGCNA identified four protein modules at age 1 year associated with incidence of childhood asthma and/or recurrent wheeze (Padj range: 0.02-0.03), respiratory infections (Padj range: 6.3×10-9-2.9×10-6), and eczema (Padj=0.01) by age 6 years; three modules were associated with at least one environmental exposure (Padj range: 2.8×10-10-0.03) and disrupted metabolomic pathway(s) (Padj range: 2.8×10-6-0.04). No genome-wide SNPs were identified as significant genetic risk factors for any protein module. Relationships between protein modules with clinical, environmental, and 'omic factors were temporally sensitive and could not be recapitulated in protein profiles at age 6 years.

Conclusion

These findings suggested protein profiles as early as age 1 year predicted development of respiratory-related diseases through age 6 and were associated with changes in pathways related to amino acid and energy metabolism. These may inform new strategies to identify vulnerable individuals based on immune protein profiling.

A GC×GC-MS method based on solid-state modulator for non-targeted metabolomics: Comparison with traditional GC-MS method

The formidable challenge posed by the presence of extremely high amounts of compounds and large differences in concentrations in plasma significantly complicates non-targeted metabolomics analyses. In this study, a comprehensive two-dimensional gas chromatography-quadrupole mass spectrometry (GC×GC-qMS) method with a solid-state modulator (SSM) for non-targeted metabolomics in beagle plasma was first established based on a GC-MS method, and the qualitative and quantitative performance of the two platforms were compared. Identification of detected compounds was accomplished utilizing NIST database match scores, retention indices (RIs) and standards. Semi-quantification involved the calculation of peak area ratios to internal standards. Metabolite identification sheets were generated for plasma samples on both analytical platforms, featuring 22 representative metabolites chosen for validating qualitative accuracy, and for conducting comparisons of linearity, accuracy, precision, and sensitivity. The outcomes revealed a threefold increase in the number of identifiable metabolites on the GC×GC-MS platform, with lower limits of quantitation (LLOQs) reduced to 0.5-0.05 times those achieved on the GC-MS platform. Accuracy in quantification for both GC×GC-MS and GC-MS fell within the range of 85-115%, and the vast majority of intra- and inter-day precisions were within the range of 20%. These findings underscore that relative to the conventional GC-MS method, the GC×GC-MS method developed in this study, combined with SSM, exhibits enhanced qualitative capabilities, heightened sensitivity, and comparable accuracy and precision, rendering it more suitable for non-targeted metabolomics analyses.

Advances in Integrated Multi-omics Analysis for Drug-Target Identification

As an essential component of modern drug discovery, the role of drug-target identification is growing increasingly prominent. Additionally, single-omics technologies have been widely utilized in the process of discovering drug targets. However, it is difficult for any single-omics level to clearly expound the causal connection between drugs and how they give rise to the emergence of complex phenotypes. With the progress of large-scale sequencing and the development of high-throughput technologies, the tendency in drug-target identification has shifted towards integrated multi-omics techniques, gradually replacing traditional single-omics techniques. Herein, this review centers on the recent advancements in the domain of integrated multi-omics techniques for target identification, highlights the common multi-omics analysis strategies, briefly summarizes the selection of multi-omics analysis tools, and explores the challenges of existing multi-omics analyses, as well as the applications of multi-omics technology in drug-target identification.

Untargeted metabolomics reveal signatures of a healthy lifestyle

This cross-sectional study investigated differences in the plasma metabolome in two groups of adults that were of similar age but varied markedly in body composition and dietary and physical activity patterns. Study participants included 52 adults in the lifestyle group (LIFE) (28 males, 24 females) and 52 in the control group (CON) (27 males, 25 females). The results using an extensive untargeted ultra high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) metabolomics analysis with 10,535 metabolite peaks identified 486 important metabolites (variable influence on projections scores of VIP ≥ 1) and 16 significantly enriched metabolic pathways that differentiated LIFE and CON groups. A novel metabolite signature of positive lifestyle habits emerged from this analysis highlighted by lower plasma levels of numerous bile acids, an amino acid profile characterized by higher histidine and lower glutamic acid, glutamine, β-alanine, phenylalanine, tyrosine, and proline, an elevated vitamin D status, higher levels of beneficial fatty acids and gut microbiome catabolism metabolites from plant substrates, and reduced levels of N-glycan degradation metabolites and environmental contaminants. This study established that the plasma metabolome is strongly associated with body composition and lifestyle habits. The robust lifestyle metabolite signature identified in this study is consistent with an improved life expectancy and a reduced risk for chronic disease.