Understanding Metabolomics in Biomedical Research

Circulating medium- and long-chain acylcarnitines are associated with plasma P-tau181 in cognitively normal older adults

Alzheimer's disease (AD) pathogenesis involves dysregulation in diverse biochemical processes. Nevertheless, plasma tau phosphorylated at threonine 181 (P-tau181), a recognised AD biomarker, has been described to reflect early-stage cortical amyloid-β (Aβ) deposition in cognitively normal (CN) adults. Therefore, identifying changes in plasma metabolites associated with plasma P-tau181 at the pre-clinical stage may provide insights into underlying biochemical mechanisms to better understand initial AD pathogenesis. In the current study, plasma P-tau181, quantified via single molecule array (Simoa) technology, and plasma metabolites, quantified via targeted-mass spectrometry, were investigated for associations in CN older adults and upon stratification by positron emission tomography (PET)-Aβ load. In addition, the P-tau181-linked metabolites were evaluated for cognitive performance and neuroimaging markers of AD and the potential to distinguish between CN Aβ- and CN Aβ+ individuals. Significant positive associations of medium- and long-chain acylcarnitines (ACs) were observed with P-tau181 in the entire cohort, CN Aβ- and CN Aβ+, suggesting a link between initial Aβ pathology and fatty acid oxidation-mediated energy metabolism pathways. However, in CN Aβ-, additional linear associations of P-tau181 were observed with muscle metabolism and nitric oxide homeostasis-associated metabolites. Upon investigating the P-tau181-linked metabolites for cognitive performance, significant inverse correlations of the verbal and visual episodic memory and the global composite score were noted in CN Aβ+ with medium- and long-chain ACs, suggesting prognostic value of ACs accompanying weaker cognitive performance. While investigating neuroimaging markers, ACs had positive associations with PET-Aβ load and inverse associations with hippocampal volume in CN Aβ+, indicating connections of ACs with initial AD pathogenesis. Furthermore, based on receiver operating characteristics analysis, the associated ACs potentially classified PET-Aβ status in older adults. Therefore, plasma P-tau181-linked circulating ACs may serve as potential prognostic markers for initial AD pathogenesis in CN older adults. However, further cross-sectional and longitudinal research in highly characterised AD cohorts is needed to validate current findings.

Glabridin exhibits potent inhibitory effects against Toxoplasma gondii in vitro and in vivo

BACKGROUND

Toxoplasma gondii is an obligate protozoan parasite capable of infecting a wide range of warm-blooded animals and humans. Current treatment options, primarily pyrimethamine and sulfadiazine, have limitations, such as high recurrence rates, long treatment durations, and limited effectiveness against T. gondii. There is an unmet need for novel, safe, low-toxicity, and highly effective treatments. This study aimed to evaluate the anti-T. gondii effects of glabridin, a natural compound derived from the roots of a widely used medicinal plant.

METHODS

The cytotoxicity of glabridin in Vero cells was assessed using a CCK-8 cell viability assay. Quantitative polymerase chain reaction (qPCR) targeting the Tg-529 gene was developed to quantify T. gondii and assess the inhibitory effects of glabridin on parasite proliferation. Ultrastructural changes in T. gondii after treatment were examined using electron microscopy. The levels of reactive oxygen species (ROS) and mitochondrial membrane potential (ΔΨm) were examined to assess the effects of glabridin on ROS levels and ΔΨm in T. gondii tachyzoites. Additionally, metabolomics and transcriptomics analyses were conducted to investigate the mechanisms underlying glabridin's anti-T. gondii effects.

RESULTS

Glabridin exhibited low toxicity to host cells and effectively inhibited T. gondii invasion and proliferation in vitro in a time-dependent manner. Glabridin-treated tachyzoites exhibited significant structural alterations, along with increased ROS production and a reduction in ΔΨm. Metabolomic analysis indicated that glabridin significantly affected amino acid metabolism pathways in T. gondii. In vivo, glabridin treatment significantly improved survival rates in T. gondii-infected BALB/c mice at a dosage of 100 mg/kg.

CONCLUSIONS

This study demonstrates that glabridin has potent anti-T. gondii effects in vitro and in vivo, likely through disruption of amino acid metabolism in the parasite. These findings highlight glabridin's potential as a promising therapeutic agent for toxoplasmosis.

Current status of serum metabolites biomarkers for polyps and colorectal cancer: a systematic review

Background

Early detection of colorectal cancer (CRC) is crucial to enhance the disease treatment and prognosis of patients. Colonoscopy remains the gold standard for CRC detection; however, it requires trained personnel with expensive tools. Currently, serum metabolites have been discovered to be used to discriminate patients with polyps and CRC. This study aimed to identify the most commonly detected predictive serum metabolites for polyps and CRC.

Methods

A systematic search of the Web of Science, PubMed, and Cochrane Library databases was conducted using PRISMA guidelines. Ten studies investigating serum metabolite biomarkers of CRC and polyps using different analytical platforms and study populations were included. QUADOMICS tool was used to analyse the quality of the included studies. All reported metabolites were then enriched into the pathways using MetaboAnalyst 5.0.

Results

We found that several potential signature metabolites overlapped between studies, including tyrosine, lysine, cystine, arabinose, and lactate for CRC and lactate and glutamate for polyps. The most affected pathways related to CRC were the urea cycle, glutathione metabolism, purine metabolism, glutamate metabolism, and ammonia recycling. In contrast, those affected in the polyps were the urea cycle, glutamate metabolism, glutathione metabolism, arginine and proline metabolism, and carnitine synthesis.

Conclusions

This review has found commonly detected serum metabolites for polyps and CRC with huge potential to be used in clinical settings. However, the differences between altered pathways in polyps and CRC, other external factors, and their effects on the regulation level, sensitivity, and specificity of each identified metabolite remained unclear, which could benefit from a further extensive cohort study and well-defined analysis equipment.

A Comparison of Digestive Strategies for Teratoscincus roborowskii With Different Diet Compositions: Digestive Enzyme Activities, Gut Microbiota, and Metabolites

Animal gut microbiota play important roles in host immunity, nutrient metabolism, and energy acquisition. The gut microbiota and its metabolic products interact with the host in many different ways, influencing gut homoeostasis and health. Teratoscincus roborowskii is an endemic species which displays special frugivorous behavior, and it has been observed consuming grapes. To explore the effects of grape intake on the gut microbiota and metabolites of T. roborowskii, 16S rRNA sequencing and liquid chromatography mass spectrometry metabolomics were applied to investigate the gut microbiota and metabolite profiles of T. roborowskii fed with mealworms (LC group) and a mixture of mealworms and grapes (FG group). Our results demonstrated that a notable shift in microbiota composition occurred, particularly in terms of an increase in the probiotic Lactococcus in the FG group. The metabolite analysis revealed a significant enrichment of the pathways related to glucose metabolism in the FG group. In addition, the digestive enzyme activity analysis showed that the α-amylase and cellulase activities in the FG group were significantly higher than those of the LC group, which was consistent with the food type. A strong correlation between diet, gut microbiota, and fecal metabolites was observed, which may imply that different diets promote the establishment of host intestinal adaptation strategies. Our study provides a theoretical basis for host health and the scientific captive breeding of the desert lizards T. roborowskii.

Genome-wide association study of maternal plasma metabolites during pregnancy

Metabolites are key indicators of health and therapeutic targets, but their genetic underpinnings during pregnancy-a critical period for human reproduction-are largely unexplored. Using genetic data from non-invasive prenatal testing, we performed a genome-wide association study on 84 metabolites, including 37 amino acids, 24 elements, 13 hormones, and 10 vitamins, involving 34,394 pregnant Chinese women, with sample sizes ranging from 6,394 to 13,392 for specific metabolites. We identified 53 metabolite-gene associations, 23 of which are novel. Significant differences in genetic effects between pregnant and non-pregnant women were observed for 16.7%-100% of these associations, indicating gene-environment interactions. Additionally, 50.94% of genetic associations exhibited pleiotropy among metabolites and between six metabolites and eight pregnancy phenotypes. Mendelian randomization revealed potential causal relationships between seven maternal metabolites and 15 human traits and diseases. These findings provide new insights into the genetic basis of maternal plasma metabolites during pregnancy.

Exploring microbiota-gut-brain axis biomarkers linked to autism spectrum disorder in prenatally chlorpyrifos-exposed Fmr1 knock-out and wild-type male rats

Fmr1 (fragile X messenger ribonucleoprotein 1)-knockout (KO) rats, modeling the human Fragile X Syndrome (FXS), are of particular interest for exploring the ASD-like phenotype in preclinical studies. Gestational exposure to chlorpyrifos (CPF) has been associated with ASD diagnosis in humans and ASD-like behaviors in rodents and linked to the microbiota-gut-brain axis. In this study, we have used both Fmr1-KO and wild-type male rats (F2 generation) at postnatal days (PND) 7 and 40 obtained after F1 pregnant females were randomly exposed to 1 mg/kg/mL/day of CPF or vehicle. A nuclear magnetic resonance (NMR) metabolomics approach together with gene expression profiles of these F2 generation rats were employed to analyze different brain regions (such as prefrontal cortex, hippocampus, and cerebellum), whole large intestine (at PND7) and gut content (PND40). The statistical comparison of each matrix spectral profile unveiled tissue-specific metabolic fingerprints. Significant variations in some biomarker levels were detected among brain tissues of different genotypes, including taurine, myo-inositol, and 3-hydroxybutyric acid, and exposure to CPF induced distinct metabolic alterations, particularly in serine and myo-inositol. Additionally, this study provides a set of metabolites associated with gastrointestinal dysfunction in ASD, encompassing several amino acids, choline-derived compounds, bile acids, and sterol molecules. In terms of gene expression, genotype and gestational exposure to CPF had only minimal effects on decarboxylase 2 (gad2) and cholinergic receptor muscarinic 2 (chrm2) genes.

Overcoming donor variability and risks associated with fecal microbiota transplants through bacteriophage-mediated treatments

BACKGROUND

Fecal microbiota transplantation (FMT) and fecal virome transplantation (FVT, sterile filtrated donor feces) have been effective in treating recurrent Clostridioides difficile infections, possibly through bacteriophage-mediated modulation of the gut microbiome. However, challenges like donor variability, costly screening, coupled with concerns over pathogen transfer (incl. eukaryotic viruses) with FMT or FVT hinder their wider clinical application in treating less acute diseases.

METHODS

To overcome these challenges, we developed methods to broaden FVT's clinical application while maintaining efficacy and increasing safety. Specifically, we employed the following approaches: (1) chemostat-fermentation to reproduce the bacteriophage FVT donor component and remove eukaryotic viruses (FVT-ChP), (2) solvent-detergent treatment to inactivate enveloped viruses (FVT-SDT), and (3) pyronin-Y treatment to inhibit RNA virus replication (FVT-PyT). We assessed the efficacy of these processed FVTs in a C. difficile infection mouse model and compared them with untreated FVT (FVT-UnT), FMT, and saline.

RESULTS

FVT-SDT, FVT-UnT, and FVT-ChP reduced the incidence of mice reaching the humane endpoint (0/8, 2/7, and 3/8, respectively) compared to FMT, FVT-PyT, and saline (5/8, 7/8, and 5/7, respectively) and significantly reduced the load of colonizing C. difficile cells and associated toxin A/B levels. There was a potential elimination of C. difficile colonization, with seven out of eight mice treated with FVT-SDT testing negative with qPCR. In contrast, all other treatments exhibited the continued presence of C. difficile. Moreover, the results were supported by changes in the gut microbiome profiles, cecal cytokine levels, and histopathological findings. Assessment of viral engraftment following FMT/FVT treatment and host-phage correlations analysis suggested that transfer of phages likely were an important contributing factor associated with treatment efficacy.

CONCLUSIONS

This proof-of-concept study shows that specific modifications of FVT hold promise in addressing challenges related to donor variability and infection risks. Two strategies lead to treatments significantly limiting C. difficile colonization in mice, with solvent/detergent treatment and chemostat propagation of donor phages emerging as promising approaches. Video Abstract.

Optimization and validation of metabolomics methods for feline urine and serum towards application in veterinary medicine

BACKGROUND

Metabolomics is an emerging and powerful technology that offers a comprehensive view of an organism's physiological status. Although widely applied in human medicine, it is only recently making its introduction in veterinary medicine. As a result, validated metabolomics protocols in feline medicine are lacking at the moment. Since biological interpretation of metabolomics data can be misled by the extraction method used, species and matrix-specific optimized and validated metabolomic protocols are sorely needed.

RESULTS

Systematic optimization was performed using fractional factorial experiments for both serum (n = 57) and urine (n = 24), evaluating dilution for both matrices, and aliquot and solvent volume, protein precipitation time and temperature for serum. For the targeted (n = 76) and untargeted (n = 1949) validation of serum respectively, excellent instrumental, intra-assay and inter-day precision were observed (CV ≤ 15% or 30%, respectively). Linearity deemed sufficient both targeted and untargeted (R2 ≥ 0.99 or 0.90, respectively). An appropriate targeted recovery between 70 and 130% was achieved. For the targeted (n = 69) and untargeted (n = 2348) validation of the urinary protocol, excellent instrumental and intra-assay precision were obtained (CV ≤ 15% or 30%, respectively). Subsequently, the discriminative ability of our metabolomics methods was confirmed for feline chronic kidney disease (CKD) by univariate statistics (n = 41 significant metabolites for serum, and n = 55 for urine, p-value<0.05) and validated OPLS-DA models (R2(Y) > 0.95, Q2(Y) > 0.65, p-value<0.001 for both matrices).

SIGNIFICANCE

This study is the first to present an optimized and validated wholistic metabolomics methods for feline serum and urine using ultra-high performance liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry. This robust methodology opens avenues for biomarker panel selection and a deeper understanding of feline CKD pathophysiology and other feline applications.

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

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

A ternary correlation multi-symptom network strategy based on in vivo chemical profile identification and metabolomics to explore the molecular basis of Ephedra herb against viral pneumonia

Ephedra herb (EH), an important medicine prescribed in herbal formulas by Traditional Chinese Medicine practitioners, has been widely used in the treatment of viral pneumonia in China. However, the molecular basis of EH in viral pneumonia remains unclear. In this study, a ternary correlation multi-symptom network strategy was established based on in vivo chemical profile identification and metabolomics to explore the molecular basis of EH against viral pneumonia. Results showed that 143 compounds of EH and 70 prototype components were identified in vivo. EH could reduce alveolar-capillary barrier disruption in rats with viral pneumonia and significantly downregulate the expression of inflammatory factors and bronchoalveolar lavage fluid. Plasma metabolomics revealed that EH may be involved in the regulation of arachidonic acid, tryptophan, tyrosine, nicotinate, and nicotinamide metabolism. The multi-symptom network showed that 12 compounds have an integral function in the treatment of viral pneumonia by intervening in many pathways related to viruses, immunity and inflammation, and lung injury. Further verification demonstrated that sinapic acid and frambinone can regulate the expression of related genes. It has been shown to be a promising representative of the pharmacological constituents of ephedra.

Heavy metal exposure and metabolomics analysis: an emerging frontier in environmental health

Exposure to heavy metals in various populations can lead to extensive damage to different organs, as these metals infiltrate and bioaccumulate in the human body, causing metabolic disruptions in various organs. To comprehensively understand the metal homeostasis, inter-organ "traffic," and extensive metabolic alterations resulting from heavy metal exposure, employing complementary analytical methods is crucial. Metabolomics is pivotal in unraveling the intricacies of disease vulnerability by furnishing thorough understandings of metabolic changes linked to different metabolic diseases. This field offers exciting prospects for enhancing the disease prevention, early detection, and tailoring treatment approaches to individual needs. This article consolidates the existing knowledge on disease-linked metabolic pathways affected by the exposure of diverse heavy metals providing concise overview of the underlying impact mechanisms. The main aim is to investigate the connection between the altered metabolic pathways and long-term complex health conditions induced by heavy metals such as diabetes mellitus, cardiovascular diseases, renal disorders, inflammation, neurodegenerative diseases, reproductive risks, and organ damage. Further exploration of common pathways may unveil the shared targets for treating associated pathological conditions. In this article, the role of metabolomics in disease susceptibility is emphasized that metabolomics is expected to be routinely utilized for the diagnosis and monitoring of diseases and practical value of biomarkers derived from metabolomics, as well as determining their appropriate integration into extensive clinical settings.

Metabolomics and Risk of Dementia: A Systematic Review of Prospective Studies

BACKGROUND

The projected increase in the prevalence of dementia has sparked interest in understanding the pathophysiology and underlying causal factors in its development and progression. Identifying novel biomarkers in the preclinical or prodromal phase of dementia may be important for predicting early disease risk. Applying metabolomic techniques to prediagnostic samples in prospective studies provides the opportunity to identify potential disease biomarkers.

OBJECTIVE

The objective of this systematic review was to summarize the evidence on the associations between metabolite markers and risk of dementia and related dementia subtypes in human studies with a prospective design.

DESIGN

We searched PubMed, PsycINFO, and Web of Science databases from inception through December 8, 2023. Thirteen studies (mean/median follow-up years: 2.1-21.0 y) were included in the review.

RESULTS

Several metabolites detected in biological samples, including amino acids, fatty acids, acylcarnitines, lipid and lipoprotein variations, hormones, and other related metabolites, were associated with risk of developing dementia. Our systematic review summarized the adjusted associations between metabolites and dementia risk; however, our findings should be interpreted with caution because of the heterogeneity across the included studies and potential sources of bias. Further studies are warranted with well-designed prospective cohort studies that have defined study populations, longer follow-up durations, the inclusion of additional diverse biological samples, standardization of techniques in metabolomics and ascertainment methods for diagnosing dementia, and inclusion of other related dementia subtypes.

CONCLUSIONS

This study contributes to the limited systematic reviews on metabolomics and dementia by summarizing the prospective associations between metabolites in prediagnostic biological samples with dementia risk. Our review discovered additional metabolite markers associated with the onset of developing dementia and may help aid in the understanding of dementia etiology. The protocol is registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (https://www.crd.york.ac.uk/prospero/; registration ID: CRD42022357521).

Metabolomic Signatures of Exposure to Nitrate and Trihalomethanes in Drinking Water and Colorectal Cancer Risk in a Spanish Multicentric Study (MCC-Spain)

We investigated the metabolomic profile associated with exposure to trihalomethanes (THMs) and nitrate in drinking water and with colorectal cancer risk in 296 cases and 295 controls from the Multi Case-Control Spain project. Untargeted metabolomic analysis was conducted in blood samples using ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. A variety of univariate and multivariate association analyses were conducted after data quality control, normalization, and imputation. Linear regression and partial least-squares analyses were conducted for chloroform, brominated THMs, total THMs, and nitrate among controls and for case-control status, together with a N-integration model discriminating colorectal cancer cases from controls through interrogation of correlations between the exposure variables and the metabolomic features. Results revealed a total of 568 metabolomic features associated with at least one water contaminant or colorectal cancer. Annotated metabolites and pathway analysis suggest a number of pathways as potentially involved in the link between exposure to these water contaminants and colorectal cancer, including nicotinamide, cytochrome P-450, and tyrosine metabolism. These findings provide insights into the underlying biological mechanisms and potential biomarkers associated with water contaminant exposure and colorectal cancer risk. Further research in this area is needed to better understand the causal relationship and the public health implications.

Integrating metabolomics and network pharmacology to assess the effects of quercetin on lung inflammatory injury induced by human respiratory syncytial virus

Quercetin (QR) has significant anti-respiratory syncytial virus (RSV) effects. However, its therapeutic mechanism has not been thoroughly explored. In this study, a lung inflammatory injury model caused by RSV was established in mice. Untargeted lung tissue metabolomics was used to identify differential metabolites and metabolic pathways. Network pharmacology was used to predict potential therapeutic targets of QR and analyze biological functions and pathways modulated by QR. By overlapping the results of the metabolomics and the network pharmacology analyses, the common targets of QR that were likely to be involved in the amelioration of RSV-induced lung inflammatory injury by QR were identified. Metabolomics analysis identified 52 differential metabolites and 244 corresponding targets, while network pharmacology analysis identified 126 potential targets of QR. By intersecting these 244 targets with the 126 targets, hypoxanthine-guanine phosphoribosyltransferase (HPRT1), thymidine phosphorylase (TYMP), lactoperoxidase (LPO), myeloperoxidase (MPO), and cytochrome P450 19A1 (CYP19A1) were identified as the common targets. The key targets, HPRT1, TYMP, LPO, and MPO, were components of purine metabolic pathways. The present study demonstrated that QR effectively ameliorated RSV-induced lung inflammatory injury in the established mouse model. Combining metabolomics and network pharmacology showed that the anti-RSV effect of QR was closely associated with purine metabolism pathways.

Metabolic signature of HepaRG cells exposed to ethanol and tumor necrosis factor alpha to study alcoholic steatohepatitis by LC-MS-based untargeted metabolomics

Despite the high prevalence of alcoholic liver disease, its identification and characterization remain poor, especially in early stages such as alcoholic fatty liver disease and alcoholic steatohepatitis. This latter implies diagnostic difficulties, few therapeutic options and unclear mechanisms of action. To elucidate the metabolic alterations and pinpoint affected biochemical pathways, alcoholic steatohepatitis was simulated in vitro by exposing HepaRG cells to ethanol (IC₁₀, 368 mM) and tumor necrosis factor alpha (TNF-α, 50 ng/mL) for 24 h. This combined exposure was compared to solely ethanol-exposed as well as -nonexposed cells. Four different metabolomics platforms were used combining liquid chromatography, high-resolution mass spectrometry and drift tube ion mobility to elucidate both intracellular and extracellular metabolic alterations. Some of the key findings include the influence of TNF-α in the upregulation of hepatic triglycerides and the downregulation of hepatic phosphatidylethanolamines and phosphatidylcholines. S-Adenosylmethionine showed to play a central role in the progression of alcoholic steatohepatitis. In addition, fatty acyl esters of hydroxy fatty acid (FAHFA)-containing triglycerides were detected for the first time in human hepatocytes and their alterations showed a potentially important role during the progression of alcoholic steatohepatitis. Ethoxylated phosphorylcholine was identified as a potential new biomarker of ethanol exposure.

UPLC-Q-Exactive-based rats serum metabolomics for characterization of traditional Chinese medicine Natures and Flavors

ETHNOPHARMACOLOGY RELEVANT: "Four Natures and five "Flavors" comes from the high generalization of medicine pharmacological rules from clinical practice by ancients. "Flavor" and "Natures"are both descriptions of the effect properties of traditional Chinese medicine. At present, researchers have realized that the "Flavors" (Pungent, Sour, Sweet, Bitter and Salty) are related to the different pharmacological effects of treatment. The "Natures" (Warm, Hot, Cold and Cool) are closely related to energy and substance metabolism and contribute to the effect of the "Flavors". Since "Four Natures and five Flavors" are the rules derived from clinical practice, how to describe and characterize "Natures" and "Flavors" scientifically is still a problem that needs to be solved.

AIM OF THE STUDY

the aim is to objectively further understand the scientific connotations of properties ("Flavors"and "Natures") from the perspective of metabolomics and characterize them by metabolites.

MATERIALS AND METHOD

"Pungent-Neutral", "Sweet-Neutral and "Bitter-Neutral" TCMs were selected to characterize the "Flavors" (Pungent, Sweet and Bitter). "Pungent-Warm" and "Bitter-Cold" were selected to characterize the "Natures" (Warm and Cold). The rat serum metabolomics was performed on UHPLC-Q-Exactive. Metabolites were identified through the metabolites databases and related literature.

RESULTS

The "Flavors" and "Natures" metabolites were identified, respectively, including four "Pungent", four "Sweet" and thirteen "Bitter" characterized metabolites and thirteen "Cold" and sixteen "Warm"related metabolites.

CONCLUSIONS

The "Natures" characterized metabolites show the "Natures" are closely related to lipid and energy metabolism. The "Warm" may promote lipid metabolism to produce ATP to generate energy through bile acid metabolism and purine metabolism. The "Cold" may inhibit lipid metabolism to generate ATP to decrease energy through the way of tryptophan metabolism and purine metabolism. The "Flavors" characteristic metabolites can provide a theoretical basis for the rules of the "Flavors". These metabolites can also be used to characterize TCM's "Natures" and "Flavors" in the development of traditional Chinese medicine resources and quality control.

Associations of PFAS-related plasma metabolites with cholesterol and triglyceride concentrations

The wide-spread environmental pollutants per- and polyfluoroalkyl substances (PFAS) have repeatedly been associated with elevated serum cholesterol in humans. However, underlying mechanisms are still unclear. Furthermore, we have previously observed inverse associations with plasma triglycerides. To better understand PFAS-induced effects on lipid pathways we investigated associations of PFAS-related metabolite features with plasma cholesterol and triglyceride concentrations. We used 290 PFAS-related metabolite features that we previously discovered from untargeted liquid chromatography-mass spectometry metabolomics in a case-control study within the Swedish Västerbotten Intervention Programme cohort. Herein, we studied associations of these PFAS-related metabolite features with plasma cholesterol and triglyceride concentrations in plasma samples from 187 healthy control subjects collected on two occasions between 1991 and 2013. The PFAS-related features did not associate with cholesterol, but 50 features were associated with triglycerides. Principal component analysis on these features indicated that one metabolite pattern, dominated by glycerophospholipids, correlated with longer chain PFAS and associated inversely with triglycerides (both cross-sectionally and prospectively), after adjustment for confounders. The observed time-trend of the metabolite pattern resembled that of the longer chain PFAS, with higher levels during the years 2004-2010. Mechanisms linking PFAS exposures to triglycerides may thus occur via longer chain PFAS affecting glycerophospholipid metabolism. If the results reflect a cause-effect association, as implied by the time-trend and prospective analyses, this may affect the general adult population.

Comparison of Plasma Metabolites From Patients With Non-Small Cell Lung Cancer by Erlotinib Treatment and Skin Rash

Erlotinib is a necessary anticancer treatment for non-small cell lung cancer (NSCLC) patients yet it causes severe side effects such as skin rash. In this study, researchers compared the untargeted compound profiles before and after erlotinib administration to observe changes in blood metabolites in NSCLC patients. The levels of 1005 substances changed after taking erlotinib. The levels of 306 and 699 metabolites were found to have increased and decreased, respectively. We found 5539 substances with peak area differences based on the presence of skin rash. Carbohydrate, amino acid, and vitamin metabolic pathways were altered in response to the onset of erlotinib-induced skin rash. Finally, this study proposed using plasma metabolites to identify biomarker(s) induced by erlotinib, as well as target molecule(s), for the treatment of dermatological toxic effects.

Artificial Neural Networks Coupled with MALDI-TOF MS Serum Fingerprinting To Classify and Diagnose Pathological Pain Subtypes in Preclinical Models

Pathological pain subtypes can be classified as either neuropathic pain, caused by a somatosensory nervous system lesion or disease, or nociplastic pain, which develops without evidence of somatosensory system damage. Since there is no gold standard for the diagnosis of pathological pain subtypes, the proper classification of individual patients is currently an unmet challenge for clinicians. While the determination of specific biomarkers for each condition by current biochemical techniques is a complex task, the use of multimolecular techniques, such as matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), combined with artificial intelligence allows specific fingerprints for pathological pain-subtypes to be obtained, which may be useful for diagnosis. We analyzed whether the information provided by the mass spectra of serum samples of four experimental models of neuropathic and nociplastic pain combined with their functional pain outcomes could enable pathological pain subtype classification by artificial neural networks. As a result, a simple and innovative clinical decision support method has been developed that combines MALDI-TOF MS serum spectra and pain evaluation with its subsequent data analysis by artificial neural networks and allows the identification and classification of pathological pain subtypes in experimental models with a high level of specificity.

Differential Plasma Metabolites between High- and Low-Grade Meningioma Cases

Meningiomas (MGMs) are currently classified into grades I, II, and III. High-grade tumors are correlated with decreased survival rates and increased recurrence rates. The current grading classification is based on histological criteria and determined only after surgical tumor sampling. This study aimed to identify plasma metabolic alterations in meningiomas of different grades, which would aid surgeons in predefining the ideal surgical strategy. Plasma samples were collected from 51 patients with meningioma and classified into low-grade (LG) (grade I; n = 43), and high-grade (HG) samples (grade II, n = 5; grade III, n = 3). An untargeted metabolomic approach was used to analyze plasma metabolites. Statistical analyses were performed to select differential biomarkers among HG and LG groups. Metabolites were identified using tandem mass spectrometry along with database verification. Five and four differential biomarkers were identified for HG and LG meningiomas, respectively. To evaluate the potential of HG MGM metabolites to differentiate between HG and LG tumors, a receiving operating characteristic curve was constructed, which revealed an area under the curve of 95.7%. This indicates that the five HG MGM metabolites represent metabolic alterations that can differentiate between LG and HG meningiomas. These metabolites may indicate tumor grade even before the appearance of histological features.

Metabolome-wide association study of the relationship between chlorpyrifos exposure and first trimester serum metabolite levels in pregnant Thai farmworkers

INTRODUCTION

Organophosphate (OP) insecticides, including chlorpyrifos, have been linked with numerous harmful health effects on maternal and child health. Limited data are available on the biological mechanisms and endogenous pathways underlying the toxicity of chlorpyrifos exposures on pregnancy and birth outcomes. In this study, we measured a urinary chlorpyrifos metabolite and used high-resolution metabolomics (HRM) to identify biological perturbations associated with chlorpyrifos exposure among pregnant women in Thailand, who are disparately exposed to high levels of OP insecticides.

METHODS

This study included 50 participants from the Study of Asian Women and their Offspring's Development and Environmental Exposures (SAWASDEE). We used liquid chromatography-high resolution mass spectrometry to conduct metabolic profiling on first trimester serum samples collected from participants to evaluate metabolic perturbations in relation to chlorpyrifos exposures. We measured 3,5,6-trichloro-2-pyridinol (TCPy), a specific metabolite of chlorpyrifos and chlorpyrifos-methyl, in first trimester urine samples to assess the levels of exposures. Following an untargeted metabolome-wide association study workflow, we used generalized linear models, pathway enrichment analyses, and chemical annotation to identify significant metabolites and pathways associated with urinary TCPy levels.

RESULTS

In the 50 SAWASDEE participants, the median urinary TCPy level was 4.36 μg TCPy/g creatinine. In total, 691 unique metabolic features were found significantly associated with TCPy levels (p < 0.05) after controlling for confounding factors. Pathway analysis of metabolic features associated with TCPy indicated perturbations in 24 metabolic pathways, most closely linked to the production of reactive oxygen species and cellular damage. These pathways include tryptophan metabolism, fatty acid oxidation and peroxisome metabolism, cytochromes P450 metabolism, glutathione metabolism, and vitamin B3 metabolism. We confirmed the chemical identities of 25 metabolites associated with TCPy levels, including glutathione, cystine, arachidic acid, itaconate, and nicotinamide adenine dinucleotide.

DISCUSSION

The metabolic perturbations associated with TCPy levels were related to oxidative stress, cellular damage and repair, and systemic inflammation, which could ultimately contribute to health outcomes, including neurodevelopmental deficits in the child. These findings support the future development of sensitive biomarkers to investigate the metabolic underpinnings related to pesticide exposure during pregnancy and to understand its link to adverse outcomes in children.

Current Knowledge in Skin Metabolomics: Updates from Literature Review

Metabolomic profiling is an emerging field consisting of the measurement of metabolites in a biological system. Since metabolites can vary in relation to different stimuli, specific metabolic patterns can be closely related to a pathological process. In the dermatological setting, skin metabolomics can provide useful biomarkers for the diagnosis, prognosis, and therapy of cutaneous disorders. The main goal of the present review is to present a comprehensive overview of the published studies in skin metabolomics. A search for journal articles focused on skin metabolomics was conducted on the MEDLINE, EMBASE, Cochrane, and Scopus electronic databases. Only research articles with electronically available English full text were taken into consideration. Studies specifically focused on cutaneous microbiomes were also excluded from the present search. A total of 97 papers matched all the research criteria and were therefore considered for the present work. Most of the publications were focused on inflammatory dermatoses and immune-mediated cutaneous disorders. Skin oncology also turned out to be a relevant field in metabolomic research. Only a few papers were focused on infectious diseases and rarer genetic disorders. All the major metabolomic alterations published so far in the dermatological setting are described extensively in this review.

Current State and Challenges of the Global Outcomes of Dental Caries Research in the Meta-Omics Era

Despite significant healthcare advances in the 21st century, the exact etiology of dental caries remains unsolved. The past two decades have witnessed a tremendous growth in our understanding of dental caries amid the advent of revolutionary omics technologies. Accordingly, a consensus has been reached that dental caries is a community-scale metabolic disorder, and its etiology is beyond a single causative organism. This conclusion was based on a variety of microbiome studies following the flow of information along the central dogma of biology from genomic data to the end products of metabolism. These studies were facilitated by the unprecedented growth of the next- generation sequencing tools and omics techniques, such as metagenomics and metatranscriptomics, to estimate the community composition of oral microbiome and its functional potential. Furthermore, the rapidly evolving proteomics and metabolomics platforms, including nuclear magnetic resonance spectroscopy and/or mass spectrometry coupled with chromatography, have enabled precise quantification of the translational outcomes. Although the majority supports 'conserved functional changes' as indicators of dysbiosis, it remains unclear how caries dynamics impact the microbiota functions and vice versa, over the course of disease onset and progression. What compounds the situation is the host-microbiota crosstalk. Genome-wide association studies have been undertaken to elucidate the interaction of host genetic variation with the microbiome. However, these studies are challenged by the complex interaction of host genetics and environmental factors. All these complementary approaches need to be orchestrated to capture the key players in this multifactorial disease. Herein, we critically review the milestones in caries research focusing on the state-of-art singular and integrative omics studies, supplemented with a bibliographic network analysis to address the oral microbiome, the host factors, and their interactions. Additionally, we highlight gaps in the dental literature and shed light on critical future research questions and study designs that could unravel the complexities of dental caries, the most globally widespread disease.

Metabolic Signature of Ethanol-Induced Hepatotoxicity in HepaRG Cells by Liquid Chromatography-Mass Spectrometry-Based Untargeted Metabolomics

Alcoholic liver disease is highly prevalent but poorly identified and characterized, leading to knowledge gaps, which impairs early diagnosis. Excessive alcohol consumption is known to alter lipid metabolism, followed by progressive intracellular lipid accumulation, resulting in alcoholic fatty liver disease. In this study, HepaRG cells were exposed to ethanol at IC₁₀ and 1/10 IC₁₀ for 24 and 48 h. Metabolic alterations were investigated intra- and extracellularly with liquid chromatography-high-resolution mass spectrometry. Ion mobility was added as an extra separation dimension for untargeted lipidomics to improve annotation confidence. Distinctive patterns between exposed and control cells were consistently observed, with intracellular upregulation of di- and triglycerides, downregulation of phosphatidylcholines and phosphatidylethanolamines, sphingomyelins, and S-adenosylmethionine, among others. Several intracellular metabolic patterns could be related to changes in the extracellular environment, such as increased intracellular hydrolysis of sphingomyelins, leading to increased phosphorylcholine secretion. Carnitines showed alterations depending on the size of their carbon chain, which highlights the interplay between β-oxidation in mitochondria and peroxisomes. Potential new biomarkers of ethanol-induced hepatotoxicity have been observed, such as ceramides with a sphingadienine backbone, octanoylcarnitine, creatine, acetylcholine, and ethoxylated phosphorylcholine. The combination of the metabolic fingerprint and footprint enabled a comprehensive investigation of the pathophysiology behind ethanol-induced hepatotoxicity.

Defining Blood Plasma and Serum Metabolome by GC-MS

Metabolomics uses advanced analytical chemistry methods to analyze metabolites in biological samples. The most intensively studied samples are blood and its liquid components: plasma and serum. Armed with advanced equipment and progressive software solutions, the scientific community has shown that small molecules’ roles in living systems are not limited to traditional “building blocks” or “just fuel” for cellular energy. As a result, the conclusions based on studying the metabolome are finding practical reflection in molecular medicine and a better understanding of fundamental biochemical processes in living systems. This review is not a detailed protocol of metabolomic analysis. However, it should support the reader with information about the achievements in the whole process of metabolic exploration of human plasma and serum using mass spectrometry combined with gas chromatography.

Nutritional Metabolomics and the Classification of Dietary Biomarker Candidates: A Critical Review

Recent advances in metabolomics allow for more objective assessment of contemporary food exposures, which have been proposed as an alternative or complement to self-reporting of food intake. However, the quality of evidence supporting the utility of dietary biomarkers as valid measures of habitual intake of foods or complex dietary patterns in diverse populations has not been systematically evaluated. We reviewed nutritional metabolomics studies reporting metabolites associated with specific foods or food groups; evaluated the interstudy repeatability of dietary biomarker candidates; and reported study design, metabolomic approach, analytical technique(s), and type of biofluid analyzed. A comprehensive literature search of 5 databases (PubMed, EMBASE, Web of Science, BIOSIS, and CINAHL) was conducted from inception through December 2020. This review included 244 studies, 169 (69%) of which were interventional studies (9 of these were replicated in free-living participants) and 151 (62%) of which measured the metabolomic profile of serum and/or plasma. Food-based metabolites identified in ≥1 study and/or biofluid were associated with 11 food-specific categories or dietary patterns: 1) fruits; 2) vegetables; 3) high-fiber foods (grain-rich); 4) meats; 5) seafood; 6) pulses, legumes, and nuts; 7) alcohol; 8) caffeinated beverages, teas, and cocoas; 9) dairy and soya; 10) sweet and sugary foods; and 11) complex dietary patterns and other foods. We conclude that 69 metabolites represent good candidate biomarkers of food intake. Quantitative measurement of these metabolites will advance our understanding of the relation between diet and chronic disease risk and support evidence-based dietary guidelines for global health.

Defining NASH from a Multi-Omics Systems Biology Perspective

Non-alcoholic steatohepatitis (NASH) is a chronic liver disease affecting up to 6.5% of the general population. There is no simple definition of NASH, and the molecular mechanism underlying disease pathogenesis remains elusive. Studies applying single omics technologies have enabled a better understanding of the molecular profiles associated with steatosis and hepatic inflammation—the commonly accepted histologic features for diagnosing NASH, as well as the discovery of novel candidate biomarkers. Multi-omics analysis holds great potential to uncover new insights into disease mechanism through integrating multiple layers of molecular information. Despite the technical and computational challenges associated with such efforts, a few pioneering studies have successfully applied multi-omics technologies to investigate NASH. Here, we review the most recent technological developments in mass spectrometry (MS)-based proteomics, metabolomics, and lipidomics. We summarize multi-omics studies and emerging omics biomarkers in NASH and highlight the biological insights gained through these integrated analyses.

MSCAT: A Machine Learning Assisted Catalog of Metabolomics Software Tools

The bottleneck for taking full advantage of metabolomics data is often the availability, awareness, and usability of analysis tools. Software tools specifically designed for metabolomics data are being developed at an increasing rate, with hundreds of available tools already in the literature. Many of these tools are open-source and freely available but are very diverse with respect to language, data formats, and stages in the metabolomics pipeline. To help mitigate the challenges of meeting the increasing demand for guidance in choosing analytical tools and coordinating the adoption of best practices for reproducibility, we have designed and built the MSCAT (Metabolomics Software CATalog) database of metabolomics software tools that can be sustainably and continuously updated. This database provides a survey of the landscape of available tools and can assist researchers in their selection of data analysis workflows for metabolomics studies according to their specific needs. We used machine learning (ML) methodology for the purpose of semi-automating the identification of metabolomics software tool names within abstracts. MSCAT searches the literature to find new software tools by implementing a Named Entity Recognition (NER) model based on a neural network model at the sentence level composed of a character-level convolutional neural network (CNN) combined with a bidirectional long-short-term memory (LSTM) layer and a conditional random fields (CRF) layer. The list of potential new tools (and their associated publication) is then forwarded to the database maintainer for the curation of the database entry corresponding to the tool. The end-user interface allows for filtering of tools by multiple characteristics as well as plotting of the aggregate tool data to monitor the metabolomics software landscape.

Metabolomics in Parkinson's disease

Parkinson's disease (PD) is a multifactorial neurodegenerative disorder in which environmental (lifestyle, dietary, infectious disease) factors as well as genetic make-up play a role. Metabolomics, an evolving research field combining biomarker discovery and pathogenetics, is particularly useful in studying complex pathophysiology in general and Parkinson's disease (PD) specifically. PD, the second most frequent neurodegenerative disorder, is characterized by the loss of dopaminergic neurons in the substantia nigra and the presence of intraneural inclusions of α-synuclein aggregates. Although considered a predominantly movement disorder, PD is also associated with number of non-motor features. Metabolomics has provided useful information regarding this neurodegenerative process with the aim of identifying a disease-specific fingerprint. Unfortunately, many disease variables such as clinical presentation, motor system involvement, disease stage and duration substantially affect biomarker relevance. As such, metabolomics provides a unique approach to studying this multifactorial neurodegenerative disorder.

Urine metabolomic biomarkers for prediction of isolated fetal congenital heart defect

OBJECTIVE

To identify maternal second and third trimester urine metabolomic biomarkers for the detection of fetal congenital heart defects (CHDs).

STUDY DESIGN

This was a prospective study. Metabolomic analysis of randomly collected maternal urine was performed, comparing pregnancies with isolated, non-syndromic CHDs versus unaffected controls. Mass spectrometry (liquid chromatography and direct injection and tandem mass spectrometry, LC-MS-MS) as well as nuclear magnetic resonance spectrometry, ¹H NMR, were used to perform the analyses between 14 0/7 and 37 0/7 weeks gestation. A total of 36 CHD cases and 41 controls were compared. Predictive algorithms using urine markers alone or combined with, clinical and ultrasound (US) (four-chamber view) predictors were developed and compared.

RESULTS

A total of 222 metabolites were identified, of which 16 were overlapping between the two platforms. Twenty-three metabolite concentrations were found in significantly altered in CHD gestations on univariate analysis. The concentration of methionine was most significantly altered. A predictive algorithm combining metabolites (histamine, choline, glucose, formate, methionine, and carnitine) plus US four-chamber view achieved an AUC = 0.894; 95% CI, 0814-0.973 with a sensitivity of 83.8% and specificity of 87.8%. Enrichment pathway analysis identified several lipid related pathways that are dysregulated in CHD, including phospholipid biosynthesis, phosphatidylcholine biosynthesis, phosphatidylethanolamine biosynthesis, and fatty acid metabolism. This could be consistent with the increased risk of CHD in diabetic pregnancies.

CONCLUSIONS

We report a novel, noninvasive approach, based on the analysis of maternal urine for isolated CHD detection. Further, the dysregulation of lipid- and folate metabolism appears to support prior data on the mechanism of CHD.

Broad Metabolome Alterations Associated with the Intake of Oral Contraceptives Are Mediated by Cortisol in Premenopausal Women

The use of oral contraceptives (OCs) has been associated with elevated blood cortisol concentrations. However, metabolic downstream effects of OC intake are not well described. Here, we aimed to determine if the blood metabolome is associated with the use of OCs and to estimate if these associations might be statistically mediated by serum cortisol concentrations. Plasma metabolites measured with the Biocrates AbsoluteIDQ p180 Kit and serum cortisol concentrations measured by an immunoassay were determined in 391 premenopausal women (116 OC users) participating in two independent cohorts of the Study of Health in Pomerania (SHIP). After correction for multiple testing, 27 metabolites were significantly associated with OC intake in SHIP-TREND (discovery cohort), of which 25 replicated in SHIP-2. Inter alia, associated metabolites included 12 out of 38 phosphatidylcholines with diacyl residue, 7 out of 14 lysophosphatidylcholines and 5 out of 21 amino acids. The associations with phosphatidylcholines were statistically mediated by cortisol, whereas lysophosphatidylcholines showed no mediation effect. The results represent a step toward a better understanding of the metabolic consequences of OC intake. Connecting cortisol with metabolic consequences of OC intake could help to understand the mechanisms underlying adverse effects. The blood metabolome may serve as a biomarker for identifying users at high risk for developing such adverse effects.

The Efficacy of Pilates on Urinary Incontinence in Korean Women: A Metabolomics Approach

Pilates has been known as exercise intervention that improves the function of pelvic floor muscle (PFM) associated with impacting urinary incontinence (UI). This study investigated the effect of Pilates on UI in Korean women by determining the change in functional movement of PFM (FMP) and metabolic profiles. UI group with Pilates (UIP, n = 13) participated in 8-weeks Oov Pilates program, and 8 subjects were assigned to Control and UI group with no Pilates (UINP), respectively. Before and after 8 weeks, plasma samples were collected from all participants, and ultrasonography was used to measure the functional change of PFM for calculating FMP ratio. Plasma samples were analyzed by mass spectrometry to identify the change of metabolic features. After 8-weeks intervention, FMP ratio was remarkably decreased in UIP (48.1% ↓, p < 0.001), but not in Control and UINP (p > 0.05). In metabolic features, L-Glutamine (m/z: 147.07 [M + H]⁺), L-Cystathionine (m/z: 240.09 [M + NH₄]⁺), L-Arginine (m/z: 197.1 [M + Na]⁺), and L-1-Pyrroline-3-hydroxy-5-carboxylate (m/z: 147.07 [M + NH₄]⁺) were significantly elevated solely in UIP (p < 0.001). Our study elucidated that Pilates can ameliorate the FMP and enhance the specific metabolic characteristics, which was potentially associated with invigorated PFM contractility to effectively control the bladder base and continence.

Methionine transsulfuration pathway is upregulated in long-lived humans

Available evidences point to methionine metabolism as a key target to study the molecular adaptive mechanisms underlying differences in longevity. The plasma methionine metabolic profile was determined using a LC-MS/MS platform to systematically define specific phenotypic patterns associated with genotypes of human extreme longevity (centenarians). Our findings demonstrate the presence of a specific plasma profile associated with human longevity characterized by an enhanced transsulfuration pathway and tricarboxylic acid (TCA) cycle intermediates, as well as a reduced content of specific amino acids. Furthermore, our work reveals that centenarians maintain a strongly correlated methionine metabolism, suggesting an improved network integrity, homeostasis and more tightly regulated metabolism. We have discovered a particular methionine signature related to the condition of extreme longevity, allowing the identification of potential mechanisms and biomarkers of healthy aging.

Investigating metabolism by mass spectrometry: From steady state to dynamic view

Metabolism is the set of life-sustaining reactions in organisms. These biochemical reactions are organized in metabolic pathways, in which one metabolite is converted through a series of steps catalyzed by enzymes in another chemical compound. Metabolic reactions are categorized as catabolic, the breaking down of metabolites to produce energy, and/or anabolic, the synthesis of compounds that consume energy. The balance between catabolism of the preferential fuel substrate and anabolism defines the overall metabolism of a cell or tissue. Metabolomics is a powerful tool to gain new insights contributing to the identification of complex molecular mechanisms in the field of biomedical research, both basic and translational. The enormous potential of this kind of analyses consists of two key aspects: (i) the possibility of performing so-called targeted and untargeted experiments through which it is feasible to verify or formulate a hypothesis, respectively, and (ii) the opportunity to run either steady-state analyses to have snapshots of the metabolome at a given time under different experimental conditions or dynamic analyses through the use of labeled tracers. In this review, we will highlight the most important practical (e.g., different sample extraction approaches) and conceptual steps to consider for metabolomic analysis, describing also the main application contexts in which it is used. In addition, we will provide some insights into the most innovative approaches and progress in the field of data analysis and processing, highlighting how this part is essential for the proper extrapolation and interpretation of data.

Plasma metabolites associated with exposure to perfluoroalkyl substances and risk of type 2 diabetes - A nested case-control study

Perfluoroalkyl substances (PFAS) are widespread persistent environmental pollutants. There is evidence that PFAS induce metabolic perturbations in humans, but underlying mechanisms are still unknown. In this exploratory study, we investigated PFAS-related plasma metabolites for their associations with type 2 diabetes (T2D) to gain potential mechanistic insight in these perturbations. We used untargeted LC-MS metabolomics to find metabolites related to PFAS exposures in a case-control study on T2D (n = 187 matched pairs) nested within the Västerbotten Intervention Programme cohort. Following principal component analysis (PCA), six PFAS measured in plasma appeared in two groups: 1) perfluorononanoic acid, perfluorodecanoic acid and perfluoroundecanoic acid and 2) perfluorohexane sulfonic acid, perfluorooctane sulfonic acid and perfluorooctanoic acid. Using a random forest algorithm, we discovered metabolite features associated with individual PFAS and PFAS exposure groups which were subsequently investigated for associations with risk of T2D. PFAS levels correlated with 171 metabolite features (0.16 ≤ |r| ≤ 0.37, false discovery rate (FDR) adjusted p < 0.05). Out of these, 35 associated with T2D (p < 0.05), with 7 remaining after multiple testing adjustment (FDR < 0.05). PCA of the 35 PFAS- and T2D-related metabolite features revealed two patterns, dominated by glycerophospholipids and diacylglycerols, with opposite T2D associations. The glycerophospholipids correlated positively with PFAS and associated inversely with risk for T2D (Odds Ratio (OR) per 1 standard deviation (1-SD) increase in metabolite PCA pattern score = 0.2; 95% Confidence Interval (CI) = 0.1-0.4). The diacylglycerols also correlated positively with PFAS, but they associated with increased risk for T2D (OR per 1-SD = 1.9; 95% CI = 1.3-2.7). These results suggest that PFAS associate with two groups of lipid species with opposite relations to T2D risk.

Effect of Pioglitazone on Perihematomal Edema in Intracerebral Hemorrhage Mouse Model by Regulating NLRP3 Expression and Energy Metabolism

Objective

Cerebral edema is the predominant mechanism of secondary inflammation after intracerebral hemorrhage (ICH). Pioglitazone, peroxisome proliferator-activated receptor gamma agonist has been shown to play a role in regulation of central nervous system inflammation. Here, we examined the pharmacological effects of pioglitazone in an ICH mouse model and investigated its regulation on NLRP3 inflammasome and glucose metabolism.

Methods

The ICH model was established in C57 BL/6 mice by the stereotactical inoculation of blood (30 µL) into the right frontal lobe. The treatment group was administered i.p. pioglitazone (20 mg/kg) for 1, 3, and 6 days. The control group was administered i.p. phosphate-buffered saline for 1, 3, and 6 days. We investigated brain water contents, NLRP3 expression, and changes in the metabolites in the ICH model using liquid chromatography-tandem mass spectrometry.

Results

On day 3, brain edema in the mice treated with pioglitazone was decreased more than that in the control group. Expression levels of NLRP3 in the ICH model treated with pioglitazone were decreased more than those of the control mice on days 3 and 7. The pioglitazone group showed higher levels of glycolytic metabolites than those in the ICH mice. Lactate production was increased in the ICH mice treated with pioglitazone.

Conclusion

Our results demonstrated less brain swelling following ICH in mice treated with pioglitazone. Pioglitazone decreased NLRP3-related brain edema and increased anaerobic glycolysis, resulting in the production of lactate in the ICH mice model. NLRP3 might be a therapeutic target for ICH recovery.

Metabolomic characterization of semen from asthenozoospermic patients using ultra-high-performance liquid chromatography-tandem quadrupole time-of-flight mass spectrometry

Asthenozoospermia (AS) is a common factor of male infertility, and its pathogenesis remains unclear. The purpose of this study was to investigate the differential seminal plasma metabolic pattern in asthenozoospermic men and to identify potential biomarkers in relation to spermatogenic dysfunction using sensitive ultra-high-performance liquid chromatography-tandem quadruple time-of-flight MS (UHPLC-Q-TOF/MS). The samples of seminal plasma from patients with AS (n = 20) and healthy controls (n = 20) were checked and differentiated by UHPLC-Q-TOF/MS. Compared with the control group, the AS group showed a total of nine significantly different metabolites, including increases in creatinine, uric acid, N6 -methyladenosine (m6 A), uridine, and taurine and decreases in carnitine, nicotinamide, N-acetylputrescine and l-palmitoylcarnitine. By analyzing the correlation among these metabolites and clinical computer-assisted semen analysis reports, we found that m6 A is significantly correlated with not only the four decreased metabolites but also with sperm count, motility, and curvilinear velocity. Furthermore, nicotinamide was shown to correlate with other identified metabolites, indicating its important role in the metabolic pathway of AS. Current results implied that sensitive untargeted seminal plasma metabolomics could identify distinct metabolic patterns of AS and would help clinicians by offering novel cues for discovering the pathogenesis of male infertility.

Metabolomics in the study of spontaneous animal diseases

Using analytical chemistry techniques such as nuclear magnetic resonance (NMR) spectroscopy and liquid or gas chromatography-mass spectrometry (LC/GC-MS), metabolomics allows detection of most endogenous and exogenous metabolites in a biological sample. Metabolomics has a wide range of applications, and has been employed in nutrition science, toxicology, environmental studies, and systems biology. Metabolomics is particularly useful in biomedical science, and has been used for diagnostic laboratory testing, identifying targets for drug development, and monitoring drug metabolism, mode of action, and toxicity. Despite its immense potential, metabolomics remains underutilized in the study of spontaneous animal diseases. Our aim was to comprehensively review the existing literature on the use of metabolomics in spontaneous veterinary diseases. Three databases were used to find journal articles that applied metabolomics in veterinary medicine. A screening process was then conducted to eliminate references that did not meet the eligibility criteria; only primary research studies investigating spontaneous animal disease were included; 38 studies met the inclusion criteria. The main techniques used were NMR and MS. All studies detected metabolite alterations in diseased animals compared with non-diseased animals. Metabolomics was mainly used to study diseases of the digestive, reproductive, and musculoskeletal systems. Inflammatory conditions made up the largest proportion of studies when articles were categorized by disease process. Following a comprehensive analysis of the literature on metabolomics in spontaneous veterinary diseases, we concluded that metabolomics, although in its early stages in veterinary research, is a promising tool regarding diagnosis, biomarker discovery, and in uncovering new insights into disease pathophysiology.

Metabolomics and Machine Learning Approaches Combined in Pursuit for More Accurate Paracoccidioidomycosis Diagnoses

Brazil and many other Latin American countries are areas of endemicity for different neglected diseases, and the fungal infection paracoccidioidomycosis (PCM) is one of them. Among the clinical manifestations, pneumopathy associated with skin and mucosal lesions is the most frequent. PCM definitive diagnosis depends on yeast microscopic visualization and immunological tests, but both present ambiguous results and difficulty in differentiating PCM from other fungal infections. This research has employed metabolomics analysis through high-resolution mass spectrometry to identify PCM biomarkers in serum samples in order to improve diagnosis for this debilitating disease. To upgrade the biomarker selection, machine learning approaches, using Random Forest classifiers, were combined with metabolomics data analysis. The proposed combination of these two analytical methods resulted in the identification of a set of 19 PCM biomarkers that show accuracy of 97.1%, specificity of 100%, and sensitivity of 94.1%. The obtained results are promising and present great potential to improve PCM definitive diagnosis and adequate pharmacological treatment, reducing the incidence of PCM sequelae and resulting in a better quality of life.IMPORTANCE Paracoccidioidomycosis (PCM) is a fungal infection typically found in Latin American countries, especially in Brazil. The identification of this disease is based on techniques that may fail sometimes. Intending to improve PCM detection in patient samples, this study used the combination of two of the newest technologies, artificial intelligence and metabolomics. This combination allowed PCM detection, independently of disease form, through identification of a set of molecules present in patients' blood. The great difference in this research was the ability to detect disease with better confidence than the routine methods employed today. Another important point is that among the molecules, it was possible to identify some indicators of contamination and other infection that might worsen patients' condition. Thus, the present work shows a great potential to improve PCM diagnosis and even disease management, considering the possibility to identify concomitant harmful factors.

Comparison of Metabolomic Profiles of Organs in Mice of Different Strains Based on SPME-LC-HRMS

Given that the extent to which genetics alters the metabolomic profile of tissues is still poorly understood, the current study aimed to characterize and investigate the metabolite profiles of brain, liver, kidney and skeletal muscle of two common mouse inbred strains (BALB/c, C57BL/6) and one outbred stock (CD1) for strain-specific differences. Male mice (n = 15) at the age of 12 weeks were used: BALB/c (n = 5), C57BL/6 (n = 5) and CD1 (n = 5). Solid phase microextraction (SPME) was applied for the extraction of analytes from the tissues. SPME fibers (approximately 0.2 mm in diameter) coated with a biocompatible sorbent (4 mm length of hydrophilic-lipophilic balanced particles) were inserted into each organ immediately after euthanasia. Samples were analyzed using liquid chromatography coupled to a Q-Exactive Focus Orbitrap mass spectrometer. Distinct interstrain differences in the metabolomic patterns of brain and liver tissue were revealed. The metabolome of kidney and muscle tissue in BALB/c mice differed greatly from C57BL/6 and CD1 strains. The main compounds differentiating all the targeted organs were alpha-amino acids, purine nucleotides and fatty acid esters. The results of the study indicate that the baseline metabolome of organs, as well as different metabolic pathways, vary widely among general-purpose models of laboratory mice commonly used in biomedical research.

Precision Medicine in Childhood Asthma: Omic Studies of Treatment Response

Asthma is a heterogeneous and multifactorial respiratory disease with an important impact on childhood. Difficult-to-treat asthma is not uncommon among children, and it causes a high burden to the patient, caregivers, and society. This review aims to summarize the recent findings on pediatric asthma treatment response revealed by different omic approaches conducted in 2018–2019. A total of 13 studies were performed during this period to assess the role of genomics, epigenomics, transcriptomics, metabolomics, and the microbiome in the response to short-acting beta agonists, inhaled corticosteroids, and leukotriene receptor antagonists. These studies have identified novel associations of genetic markers, epigenetic modifications, metabolites, bacteria, and molecular mechanisms involved in asthma treatment response. This knowledge will allow us establishing molecular biomarkers that could be integrated with clinical information to improve the management of children with asthma.

Metabolomic profiles of induced pluripotent stem cells derived from patients with rheumatoid arthritis and osteoarthritis

Background

Metabolomics is the systemic study of the unique fingerprints of metabolites involved in cellular processes and biochemical reactions. The metabolomic approach is useful in diagnosing and predicting the development of rheumatoid arthritis (RA) and osteoarthritis (OA) and is emerging as a useful tool for identifying disease biomarkers. The aim of this study was to compare the metabolic blueprint of fibroblast-like synoviocyte (FLS) cells and induced pluripotent stem cells (iPSCs) derived from RA and OA patients.

Methods

Somatic cells of RA patients (n = 3) and OA patients (n = 3) were isolated, transduced with a lentiviral plasmid, and reprogrammed into iPSCs displaying pluripotency. Metabolic profiling of RA and OA patient–derived FLS cells and iPSCs was performed using liquid chromatography/mass spectrometry and statistical analysis. After normalization by the sum of the peak intensities through LC/MS, 37 metabolites were detected across RA and OA patients.

Results

The metabolites of RA and OA were distinguishable according to the PLS-DA analysis. LysoPC (20:4), 4-methoxychalcone, phosphorylcholine, and nicotinamide (NAM) were significantly higher in RA iPSCs than in OA iPSCs (p < 0.05). The NMNAT-3 enzyme, which catalyzes an important step in the biosynthesis of NAD⁺ from adenosine triphosphate, was also upregulated in RA iPSCs. Interestingly, the proliferation of RA iPSCs was significantly greater than OA iPSC proliferation (p < 0.05). NAM played a critical role in the proliferation of RA iPSCs but not in OA iPSCs. When iPSCs were treated with 100 nM of the NAM inhibitor tannic acid (TA), the proliferation of RA iPSCs was significantly reduced (p < 0.001).

Conclusions

The metabolites of RA and OA FLS cells and RA and OA iPSCs were all clearly distinguishable from each other. NAM played a critical role in the proliferation of RA iPSCs but not in OA iPSCs. TA effectively inhibited the expression of NAM in RA iPSCs and is a possible effective treatment for RA patients.

Inborn Errors of Metabolism in the Era of Untargeted Metabolomics and Lipidomics

Inborn errors of metabolism (IEMs) are a group of inherited diseases with variable incidences. IEMs are caused by disrupting enzyme activities in specific metabolic pathways by genetic mutations, either directly or indirectly by cofactor deficiencies, causing altered levels of compounds associated with these pathways. While IEMs may present with multiple overlapping symptoms and metabolites, early and accurate diagnosis of IEMs is critical for the long-term health of affected subjects. The prevalence of IEMs differs between countries, likely because different IEM classifications and IEM screening methods are used. Currently, newborn screening programs exclusively use targeted metabolic assays that focus on limited panels of compounds for selected IEM diseases. Such targeted approaches face the problem of false negative and false positive diagnoses that could be overcome if metabolic screening adopted analyses of a broader range of analytes. Hence, we here review the prospects of using untargeted metabolomics for IEM screening. Untargeted metabolomics and lipidomics do not rely on predefined target lists and can detect as many metabolites as possible in a sample, allowing to screen for many metabolic pathways simultaneously. Examples are given for nontargeted analyses of IEMs, and prospects and limitations of different metabolomics methods are discussed. We conclude that dedicated studies are needed to compare accuracy and robustness of targeted and untargeted methods with respect to widening the scope of IEM diagnostics.

Tissue-Specific Metabolic Profiles After Prolonged Cardiac Arrest Reveal Brain Metabolome Dysfunction Predominantly After Resuscitation

Background

Cardiac arrest (CA) has been a leading cause of death for many decades. Despite years of research, we still do not understand how each organ responds to the reintroduction of blood flow after prolonged CA. Following changes in metabolites of individual organs after CA and resuscitation gives context to the efficiency and limitations of current resuscitation protocols.

Methods and Results

Adult male Sprague–Dawley rats were arbitrarily assigned into 3 groups: control, 20 minutes of CA, or 20 minutes of CA followed by 30 minutes of cardiopulmonary bypass resuscitation. The rats were euthanized by decapitation to harvest brain, heart, kidney, and liver tissues. The obtained tissue samples were analyzed by ultra‐high‐performance liquid chromatography–high‐accuracy mass spectrometry for comprehensive metabolomics evaluation. After resuscitation, the brain showed decreased glycolysis metabolites and fatty acids and increased amino acids compared with control. Similarly, the heart displayed alterations mostly in amino acids. The kidney showed decreased amino acid and fatty acid pools with severely increased tricarboxylic acid cycle metabolites following resuscitation, while the liver showed minimal alterations with slight changes in the lipid pool. Each tissue has a distinct pattern of metabolite changes after ischemia/reperfusion. Furthermore, resuscitation worsens the metabolic dysregulation in the brain and kidney, while it normalizes metabolism in the heart.

Conclusions

Developing metabolic profiles using a global metabolome analysis identifies the variable nature of metabolites in individual organs after CA and reperfusion, establishing a stark contrast between the normalized heart and liver and the exacerbated brain and kidney, only after the reestablishment of blood circulation.

Global serum metabolomics profiling of colorectal cancer

Accurate diagnosis of colorectal cancer (CRC) relies on the use of invasive tools such as colonoscopy and sigmoidoscopy. Non-invasive tools are less sensitive in detecting the disease, particularly in the early stage. A number of researchers have used metabolomics analyses on serum/plasma samples of patients with CRC compared with normal healthy individuals in an effort to identify biomarkers for CRC. The aim of the present review is to compare reported serum metabolomics profiles of CRC and to identify common metabolites affected among these studies. A literature search was performed to include any experimental studies on global metabolomics profile of CRC using serum/plasma samples published up to March 2018. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool was used to assess the quality of the studies reviewed. In total, nine studies were included. The studies used various analytical platforms and were performed on different populations. A pathway enrichment analysis was performed using the data from all the studies under review. The most affected pathways identified were protein biosynthesis, urea cycle, ammonia recycling, alanine metabolism, glutathione metabolism and citric acid cycle. The metabolomics analysis revealed levels of metabolites of glycolysis, tricarboxylic acid cycle, anaerobic respiration, protein, lipid and glutathione metabolism were significantly different between cancer and control samples. Although the majority of differentiating metabolites identified were different in the different studies, there were several metabolites that were common. These metabolites include pyruvic acid, glucose, lactic acid, malic acid, fumaric acid, 3-hydroxybutyric acid, tryptophan, phenylalanine, tyrosine, creatinine and ornithine. The consistent dysregulation of these metabolites among the different studies suggest the possibility of common diagnostic biomarkers for CRC.

Metabolomics profiles of patients with Wilson disease reveal a distinct metabolic signature

INTRODUCTION

Wilson disease (WD) is characterized by excessive intracellular copper accumulation in liver and brain due to defective copper biliary excretion. With highly varied phenotypes and a lack of biomarkers for the different clinical manifestations, diagnosis and treatment can be difficult.

OBJECTIVE

The aim of the present study was to analyze serum metabolomics profiles of patients with Wilson disease compared to healthy subjects, with the goal of identifying differentially abundant metabolites as potential biomarkers for this condition.

METHODS

Hydrophilic interaction liquid chromatography-quadrupole time of flight mass spectrometry was used to evaluate the untargeted serum metabolome of 61 patients with WD (26 hepatic and 25 neurologic subtypes, 10 preclinical) compared to 15 healthy subjects. We conducted analysis of covariance with potential confounders (body mass index, age, sex) as covariates and partial least-squares analysis.

RESULTS

After adjusting for clinical covariates and multiple testing, we identified 99 significantly different metabolites (FDR < 0.05) between WD and healthy subjects. Subtype comparisons also revealed significantly different metabolites compared to healthy subjects: WD hepatic subtype (67), WD neurologic subtype (57), WD hepatic-neurologic combined (77), and preclinical (36). Pathway analysis revealed these metabolites are involved in amino acid metabolism, the tricarboxylic acid cycle, choline metabolism, and oxidative stress.

CONCLUSIONS

Patients with WD are characterized by a distinct metabolomics profile providing new insights into WD pathogenesis and identifying new potential diagnostic biomarkers.

Plasma sphingomyelins increase in pre-diabetic Korean men with abdominal obesity

Abdominal or visceral obesity is a well-known risk factor for metabolic diseases. However, whether abdominal obesity significantly affects plasma lipid profile during the development of type 2 diabetes has not been fully elucidated. We investigated the differences in plasma lipid concentrations in 63 participants categorized into six groups (middle-aged Korean men); Normal, Pre-diabetes (pre-DM), and Diabetes mellitus (DM) with or without abdominal obesity (AO or lean). The lipidomic profiles were determined by using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Sphingomyelin (SM) levels in plasma were significantly higher in the pre-DM with AO than in pre-DM with lean (p = 0.021). SM concentrations correlated positively with waist-to-hip ratio (WHR) (r = 0.256, p = 0.044), cholesteryl ester (CE) (r = 0.483, p < 0.0001), ceramide (r = 0.489, p < 0.0001) and plasmanyl phosphatidylcholine (PC) (r = 0.446, p < 0.0001). The present study found that pre-diabetic patients with AO were characterized by increased plasma concentrations of SM. Plasma SM levels in individuals with AO may be an early prognostic biomarker to better predict the progression toward type 2 diabetes and metabolic syndrome.

Metabolomics study of the therapeutic mechanism of a Chinese herbal formula on collagen-induced arthritis mice

Wenjinghuoluo (WJHL) prescription, the typical rheumatoid arthritis (RA) treatment compound in traditional Chinese medicine, shows favorable efficacy. The precise mechanism of WJHL on RA therapy is yet to be elucidated. This study aimed to determine the metabolic biomarkers in the early onset of RA and evaluate the regulation effect of WJHL on metabolite levels. Multivariate statistical analysis identified 93 biomarkers by precise MS/MS. These biomarkers played an important role in the regulation of key metabolic pathways associated with collagen-induced arthritis (CIA). A total of 68 biomarkers were related to the treatment of CIA by WJHL therapy. In addition, pathway analysis results showed six and three significant related pathways according to corresponding differential metabolites before and after WJHL therapy. Finally, disease and function prediction of ingenuity pathway analysis indicated that lipid metabolism, small molecule biochemistry, and carbohydrate metabolism were associated functions of WJHL therapy on CIA. Furthermore, top analysis-ready molecules of up-regulated thiamine and down-regulated arachidonic acid maybe the most related metabolites of WJHL therapy on CIA. The present work indicates that a metabolomics platform provides a new insight into understanding the mechanisms of action of natural medicines, such as WJHL.

Wenjinghuoluo (WJHL) prescription, the typical rheumatoid arthritis (RA) treatment compound in traditional Chinese medicine, shows favorable efficacy.

Metabolomic Profiling of Human Spermatozoa in Idiopathic Asthenozoospermia Patients Using Gas Chromatography-Mass Spectrometry

The purpose of this study was to describe the first metabolic profile of human sperm cells through the application of an untargeted platform based on gas chromatography-mass spectrometry (GC-MS). Sperm cell samples from patients diagnosed with idiopathic asthenozoospermia (n = 30) and healthy subjects (n = 30) were analyzed using a nontargeted metabolomics method based on GC-MS spectroscopy. The mass spectrometric data were collected using multivariate and univariate analyses to identify metabolites related to idiopathic asthenozoospermia. By using metabolomic strategies, we identified 33 metabolites, 27 of which were decreased in the idiopathic asthenozoospermia group compared with the normozoospermic group and six were increased in idiopathic asthenozoospermia. With respect to human sperm cells, some of these metabolites are reported here for the first time. Pathways for nucleoside, amino acid and energy metabolism, and the Krebs cycle were disturbed and were associated with idiopathic asthenozoospermia. The metabolic profiling provides an important first step in studying the pathophysiological mechanisms involved in IAS, and the identified metabolites may become potential biomarkers for its diagnosis and treatment.