UHPLC-Q-Orbitrap-HRMS-based global metabolomics reveal metabolome modifications in plasma of young women after cranberry juice consumption

Identification of Blood Biomarkers in Ischemic Stroke by Integrated Analysis of Metabolomics and Proteomics

We aimed to uncover the pathological mechanism of ischemic stroke (IS) using a combined analysis of untargeted metabolomics and proteomics. The serum samples from a discovery set of 44 IS patients and 44 matched controls were analyzed using a specific detection method. The same method was then used to validate metabolites and proteins in the two validation sets: one with 30 IS patients and 30 matched controls, and the other with 50 IS patients and 50 matched controls. A total of 105 and 221 differentially expressed metabolites or proteins were identified, and the association between the two omics was determined in the discovery set. Enrichment analysis of the top 25 metabolites and 25 proteins in the two-way orthogonal partial least-squares with discriminant analysis, which was employed to identify highly correlated biomarkers, highlighted 15 pathways relevant to the pathological process. One metabolite and seven proteins exhibited differences between groups in the validation set. The binary logistic regression model, which included metabolite 2-hydroxyhippuric acid and proteins APOM_O95445, MASP2_O00187, and PRTN3_D6CHE9, achieved an area under the curve of 0.985 (95% CI: 0.966-1) in the discovery set. This study elucidated alterations and potential coregulatory influences of metabolites and proteins in the blood of IS patients.

Studying temperature's impact on Brassica napus resistance to identify key regulatory mechanisms using comparative metabolomics

To investigate the effects of temperature on Brassica napus (canola) resistance to Leptosphaeria maculans (LM), the causal agent of blackleg disease, metabolic profiles of LM infected resistant (R) and susceptible (S) canola cultivars at 21 °C and 28 °C were analyzed. Metabolites were detected in cotyledons of R and S plants at 48- and 120-h post-inoculation with LM using UPLC-QTOF/MS. The mock-inoculated plants were used as controls. Some of the resistance-related specific pathways, including lipid metabolism, amino acid metabolism, carbohydrate metabolism, and aminoacyl-tRNA biosynthesis, were down-regulated in S plants but up-regulated in R plants at 21 °C. However, some of these pathways were down-regulated in R plants at 28 °C. Amino acid metabolism, lipid metabolism, alkaloid biosynthesis, phenylpropanoid biosynthesis, and flavonoid biosynthesis were the pathways linked to combined heat and pathogen stresses. By using network analysis and enrichment analysis, these pathways were identified as important. The pathways of carotenoid biosynthesis, pyrimidine metabolism, and lysine biosynthesis were identified as unique mechanisms related to heat stress and may be associated with the breakdown of resistance against the pathogen. The increased susceptibility of R plants at 28 °C resulted in the down-regulation of signal transduction pathway components and compromised signaling, particularly during the later stages of infection. Deactivating LM-specific signaling networks in R plants under heat stress may result in compatible responses and deduction in signaling metabolites, highlighting global warming challenges in crop disease control.

Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics

Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.

Biomarkers of Berry Intake: Systematic Review Update

Berries are rich in (poly)phenols, and these compounds may be beneficial to human health. Estimating berry consumption through self-reported questionnaires has been challenging due to compliance issues and a lack of precision. Estimation via food-derived biomarkers in biofluids was proposed as a complementary alternative. We aimed to review and update the existing evidence on biomarkers of intake for six different types of berries. A systematic literature search was performed to update a previous systematic review on PubMed, Web of Science, and Scopus from January 2020 until December 2022. Out of 42 papers, only 18 studies were eligible. A multimetabolite panel is suggested for blueberry and cranberry intake. Proposed biomarkers for blueberries include hippuric acid and malvidin glycosides. For cranberries, suggested biomarkers are glycosides of peonidin and cyanidin together with sulfate and glucuronide conjugates of phenyl-γ-valerolactone derivatives. No new metabolite candidates have been found for raspberries, strawberries, blackcurrants, and blackberries. Further studies are encouraged to validate these multimetabolite panels for improving the estimation of berry consumption.

Statistical methods and resources for biomarker discovery using metabolomics

Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.

Untargeted Metabolomics Analysis Using UHPLC-Q-TOF/MS Reveals Metabolic Changes Associated with Hypertension in Children

The mechanism of hypertension in children remains elusive. The objective of this study was to analyze plasma metabolomics characteristics to explore the potential mechanism of hypertension in children. Serum samples from 29 control children, 38 children with normal body mass index and simple hypertension (NBp), 8 children overweight with simple hypertension (OBp), 37 children with normal body mass index and H-type hypertension (NH) and 19 children overweight with H-type hypertension (OH) were analyzed by non-targeted metabolomics. A total of 1235 differential metabolites were identified between children with hypertension and normal controls, of which 193 metabolites including various lipids were significantly expressed. Compared with the control group, 3-dehydroepiandrosterone sulfate, oleic acid and linoleic acid were up-regulated, and gamma-muricholic acid was down-regulated in the NBp group; 3-dehydroepiandrosterone sulfate, 4-acetamidobutanoate and 1-hexadecanoyl-2-octadecadienoyl-sn-glyero-3-phosphocholine were up-regulated in the OBp group, whereas adenosine and 1-myristoyl-sn-glyero-3-phosphocholine were down-regulated; in the NH group, 1-palmitoyl-2-linoleoyl-sn-glycero-3-phosphocholine, phenol and 3-methoxytyramine were up-regulated, while pentadecanoic acid was down-regulated; in the OH group, NG,NG-dimethyl-L-arginine, 1-palmitoyl-sn-glycero-3-phosphocholine and monoethyl phthalate were up-regulated, while phloretin and glycine were down-regulated. The results showed that the children with hypertension had obvious disorders of lipid metabolism (especially in the overweight hypertension group), which led to the occurrence of hypertension. Additionally, the concentration of NO production-related NG, NG-dimethyl-L-arginine, was significantly increased, which may play an important role in H-type hypertension in children.

Predictive models built upon annotated and validated intake biomarkers in urine using paired or unpaired analysis helped to classify cranberry juice consumers in a randomized, double-blinded, placebo-controlled, and crossover study

Intake biomarkers of cranberry juice in women can assess consumption in clinical trials. Discriminant biomarkers in urine may explain urinary tract infection (UTI) preventive activities. We hypothesized that validated and annotated discriminant metabolites in human urine could be used as intake biomarkers in building predictive multivariate models to classify cranberry consumers. Urine samples were collected from 16 healthy women aged 18 to 29 years at baseline and after 3- and 21-day consumption of cranberry or placebo juice in a double-blind, crossover study. Urine metabolomes were analyzed using ultra high-performance liquid chromatography coupled with Orbitrap mass spectrometry. Paired and unpaired multivariate analyses were used to annotate or identify discriminant metabolic features after cranberry consumption. Twenty-six discriminant metabolic features (paired analysis) and 27 (unpaired analysis) after cranberry consumption in an open-label intervention were rediscovered in the blinded study. These metabolites included exogenous (quinic acid) and endogenous ones (hippuric acid). The paired analysis showed better model fitting with partial least-square discriminant analysis models built on all metabolites than the unpaired analysis. Predictive models built on shared metabolites by the unpaired analysis were able to classify cranberry juice consumers with 84.4% to 100% correction rates, overall better than the paired analysis (50%-100%). The double-blind study validated discriminant metabolites from a previous open-label study. These urinary metabolites may be associated with the ability of cranberries to prevent UTIs and serve as potential cranberry intake biomarkers. It reveals the importance of selecting the right predictive models to classify cranberry consumers with higher than 95% correction rates.

Integrative analysis of multi-omics data to detect the underlying molecular mechanisms for obesity in vivo in humans

BACKGROUND

Obesity is a complex, multifactorial condition in which genetic play an important role. Most of the systematic studies currently focuses on individual omics aspect and provide insightful yet limited knowledge about the comprehensive and complex crosstalk between various omics levels.

SUBJECTS AND METHODS

Therefore, we performed a most comprehensive trans-omics study with various omics data from 104 subjects, to identify interactions/networks and particularly causal regulatory relationships within and especially those between omic molecules with the purpose to discover molecular genetic mechanisms underlying obesity etiology in vivo in humans.

RESULTS

By applying differentially analysis, we identified 8 differentially expressed hub genes (DEHGs), 14 differentially methylated regions (DMRs) and 12 differentially accumulated metabolites (DAMs) for obesity individually. By integrating those multi-omics biomarkers using Mendelian Randomization (MR) and network MR analyses, we identified 18 causal pathways with mediation effect. For the 20 biomarkers involved in those 18 pairs, 17 biomarkers were implicated in the pathophysiology of obesity or related diseases.

CONCLUSIONS

The integration of trans-omics and MR analyses may provide us a holistic understanding of the underlying functional mechanisms, molecular regulatory information flow and the interactive molecular systems among different omic molecules for obesity risk and other complex diseases/traits.

Comparison of chemometric strategies for potential exposure marker discovery and false-positive reduction in untargeted metabolomics: application to the serum analysis by LC-HRMS after intake of Vaccinium fruit supplements

Untargeted liquid chromatographic-high-resolution mass spectrometric (LC-HRMS) metabolomics for potential exposure marker (PEM) discovery in nutrikinetic studies generates complex outputs. The correct selection of statistically significant PEMs is a crucial analytical step for understanding nutrition-health interactions. Hence, in this paper, different chemometric selection workflows for PEM discovery, using multivariate or univariate parametric or non-parametric data analyses, were comparatively tested and evaluated. The PEM selection protocols were applied to a small-sample-size untargeted LC-HRMS study of a longitudinal set of serum samples from 20 volunteers after a single intake of (poly)phenolic-rich Vaccinium myrtillus and Vaccinium corymbosum supplements. The non-parametric Games-Howell test identified a restricted group of significant features, thus minimizing the risk of false-positive retention. Among the forty-seven PEMs exhibiting a statistically significant postprandial kinetics, twelve were successfully annotated as purine pathway metabolites, benzoic and benzodiol metabolites, indole alkaloids, and organic and fatty acids, and five (i.e. octahydro-methyl-β-carboline-dicarboxylic acid, tetrahydro-methyl-β-carboline-dicarboxylic acid, citric acid, caprylic acid, and azelaic acid) were associated to Vaccinium berry consumption for the first time. The analysis of the area under the curve of the longitudinal dataset highlighted thirteen statistically significant PEMs discriminating the two interventions, including four intra-intervention relevant metabolites (i.e. abscisic acid glucuronide, catechol sulphate, methyl-catechol sulphate, and α-hydroxy-hippuric acid). Principal component analysis and sample classification through linear discriminant analysis performed on PEM maximum intensity confirmed the discriminating role of these PEMs.

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.

Potential Antihypertensive Mechanisms of the Egg White-Derived Peptide QIGLF in Spontaneously Hypertensive Rats Revealed Using Untargeted Serum Metabolomics

The angiotensin-converting enzyme (ACE) inhibitory peptide QIGLF derived from egg white was shown to have significant in vivo antihypertensive effects in our previous study, but the intervention mechanisms at the metabolic level are still unclear. The UPLC-QTOF/MS-based untargeted metabolomics approach was used to clarify the potential antihypertensive mechanisms of QIGLF in the serum of spontaneously hypertensive rats (SHRs). Multivariate statistical analysis showed a clear difference in the metabolite profiles between the QIGLF and model groups. The results suggested that eight potential biomarkers were identified, that is, adrenic acid, ursodeoxycholic acid, glycocholic acid, taurocholic acid, tryptophan, acetylindoxyl, tyrosine, and 2-phenylethanol, which were mainly involved in aromatic amino acid biosynthesis and metabolism, biosynthesis of bile acid, and biosynthesis of unsaturated fatty acids. QIGLF might exert antihypertensive effects by improving endothelial dysfunction. This study provides a theoretical basis for future research and application of ACE inhibitory peptides in the prevention and improvement of hypertension.

Nontargeted metabolomics-based mapping urinary metabolic fingerprints after exposure to acrylamide

Acrylamide classified as a probable carcinogen to humans is a high production volume chemical in industrial applications released to aquatic and environmental ecosystems, and also widely found in the thermal processing of starch-rich foods. To gain insight into the urinary metabolomics that may induce physiological responses stimulated by acrylamide, rats were orally administered with a single dose of 13C3-acrylamide (10 mg/kg bw) in the treatment group and urine samples were continuously collected every 2 h during the first 18 h and every 3 h during the period from 18 h to 36 h. A reliable nontargeted screening method for the analysis of urinary metabolomics in rats was developed using ultra-high performance liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry. All metabolites in urine of rats receiving isotope-labeled acrylamide were screened by validated orthogonal partial least squares-discriminant analyses compared to the animals in the control group, while exposure biomarkers were further confirmed according to the characteristic fragmentation rules and time-dependent profiles. Here we identified 2 new specific exposure biomarkers, named N-acetyl-S-(2-carbamoyl-2-hydroxyethyl)-L-cysteine-sulfoxide and N-acetyl-S-(2-carboxyl)-L-cysteine, compared to 4 currently acknowledged mercapturic acid adducts of acrylamide. In addition, our findings on analysis of acrylamide metabolic pathway and identification of exposure biomarkers confirmed that acrylamide could significantly affect energy metabolism and amino acid metabolism by the Kyoto Encyclopedia of Genes and Genomes pathway analysis for key metabolites. Homocysteine thiolactone and hypoxanthine may be potential biomarkers for the cardiotoxicity, while methionine sulfoxide, hippuric acid and melatonin may be specifically related to the neurotoxicity. Thus, the current study provided new evidence on the identification of emerging exposure biomarkers and specific signature metabolites related to the toxicity of acrylamide, and shed light on how acrylamide affected energy and amino acid metabolism by further mapping urinary metabolic fingerprints.

Potential Alternative to Nitrite in Roasted Lamb for Sensory Attributes: Atmospheric Nonthermal Plasma Treatment

Reducing or replacing sodium nitrite without compromising the sensory attributes of meat products has always been a focus of the meat industry. In this study, five treatments, CT (without nitrite and plasma treatment), NT (with nitrite treatment), PT15, PT30, and PT45 (without nitrite and with plasma treatment for 15, 30, and 45 min, respectively), were designed to investigate the effect of atmospheric nonthermal plasma treatment replacing nitrite on the sensory attributes of roasted lamb. Results showed that PT45 decreased the residual nitrite of roasted lamb by 30% compared with NT, and nitrite was not detected in the PT15 and PT30 samples. The inhibition effect of plasma treatment on the lipid oxidation reached values from 86.69% to 89.89% compared with NT. Compared with CT, the redness of plasma-treated samples was increased by 9.30% to 31.40%, and the redness of NT samples was increased by 30.87%. In addition, the volatile compounds (OAVs > 1) of the PT30 sample were higher than those of the NT sample. The overall sensory score of the PT30 sample was higher than that of the CT sample and was similar to that of the NT samples. In conclusion, the sensory attributes of roasted lamb were enhanced by plasma treatment, and the 30 min plasma treatment is recommended.

Metabolomic Profile of Personalized Donor Human Milk

Human milk could be considered an active and complex mixture of beneficial bacteria and bioactive compounds. Since pasteurization drastically reduces the microbial content, we recently demonstrated that pasteurized donor human milk (DHM) could be inoculated with different percentages (10% and 30%) of mother's own milk (MOM) to restore the unique live microbiota, resulting in personalized milk (RM10 and RM30, respectively). Pasteurization affects not only the survival of the microbiota but also the concentration of proteins and metabolites, in this study, we performed a comparative metabolomic analysis of the RM10, RM30, MOM and DHM samples to evaluate the impact of microbial restoration on metabolite profiles, where metabolite profiles clustered into four well-defined groups. Comparative analyses of DHM and MOM metabolomes determined that over one thousand features were significantly different. In addition, significant changes in the metabolite concentrations were observed in MOM and RM30 samples after four hours of incubation, while the concentration of metabolites in DHM remained constant, indicating that these changes are related to the microbial expansion. In summary, our analyses indicate that the metabolite profiles of DHM are significantly different from that of MOM, and the profile of MOM may be partially restored in DHM through microbial expansion.

American cranberries and health benefits - an evolving story of 25 years

Cranberries contain various types of bioactive components. Scientists have been studying cranberries' beneficial effects on urinary tract health since the 20th century. In the 21st century, the protection provided by cranberry phytochemicals against cancer and vascular diseases has drawn more attention from researchers. Anthocyanins, procyanidins, and flavonols in cranberries were all documented to have potential effects on cancer prevention. The cardiometabolic effects of cranberries have been investigated in several clinical trials. It was found that cranberries positively affect atherosclerotic cholesterol profiles and that they reduced several cardiometabolic risk factors. Nowadays, growing evidence suggests other important roles of cranberries in maintaining digestive health. Cranberry juice or cranberries have been shown to inhibit the colonization of H. pylori in stomach, and protect against intestinal inflammation. For future research, clinical trials with improved study design are urgently needed to demonstrate cranberries' benefits on urinary tract health and cardiometabolic diseases. Hypothesis-driven studies using animals or cell culture are needed to elucidate the mechanisms of cranberries' effects on digestive health. © 2018 Society of Chemical Industry.

Global Metabolomics in Allogeneic Hematopoietic Cell Transplantation Recipients Discordant for Chronic Graft-versus-Host Disease

Chronic graft-versus-host disease (cGVHD) remains a significant late effect issue for allogeneic hematopoietic cell transplantation (allo-HCT) survivors, contributing to morbidity and mortality. The etiology of cGVHD is not well elucidated. Owing to a lack of early diagnostic tests and pathophysiology ambiguity, targeted treatments remain limited. Biomarkers for prediction, control response, or prognostication have not yet been identified. Metabolomics, the quantification of metabolites, is a potential biomarker of cGVHD but has not been evaluated in this population. In this study, we examined global metabolites of stored plasma to identify differentially expressed metabolites of individuals discordant for cGVHD following allo-HCT. A descriptive, comparative, cross-sectional study design was used to examine differentially expressed metabolites of plasma samples obtained from 40 adult allo-HCT recipients (20 with cGVHD and 20 without cGVHD) from 2 parent studies. Metabolomics profiling was conducted at the University of Florida's Southeast Center for Integrative Metabolomics. Full experimental methods followed a previously published method. All statistical analyses were performed by a PhD-prepared, trained bioinformatics statistician. There were 10 differentially expressed metabolites between participants with cGVHD and those without cGVHD. Differential metabolites included those related to energy metabolism (n = 3), amino acid metabolism (n = 3), lipid metabolism (n = 2), caffeine metabolism (n = 1), and neurotransmission (n = 1). Serotonin had the greatest fold change (21.01). This study suggests that cGVHD may be associated with expanded cellular energy and potentially mitochondrial dysfunction. The differential metabolic profile between patients with and without cGVHD indicates metabolic perturbations that merit further exploration as potential biomarkers of cGVHD. These findings support the need for further examination using a larger, prospective study design to identify metabolomic risk factors that may signal the need for earlier preventive measures and earlier treatment to reduce cGVHD.

Food intake biomarkers for berries and grapes

Grapes and berries are two types of widely consumed fruits characterized by a high content in different phytochemicals. However, their accurate dietary assessment is particularly arduous, because of the already wide recognized bias associated with self-reporting methods, combined with the large range of species and cultivars and the fact that these fruits are popularly consumed not only in fresh and frozen forms but also as processed and derived products, including dried and canned fruits, beverages, jams, and jellies. Reporting precise type and/or quantity of grape and berries in FFQ or diaries can obviously be affected by errors. Recently, biomarkers of food intake (BFIs) rose as a promising tool to provide accurate information indicating consumption of certain food items. Protocols for performing systematic reviews in this field, as well as for assessing the validity of candidate BFIs have been developed within the Food Biomarker Alliance (FoodBAll) Project. This paper aims to evaluate the putative BIFs for blueberries, strawberries, raspberries, blackberries, cranberries, blackcurrant, and grapes. Candidate BFIs for grapes were resveratrol metabolites and tartaric acid. The metabolites considered as putative BFI for berries consumption were mostly anthocyanins derivatives together with several metabolites of ellagitannins and some aroma compounds. However, identification of BFIs for single berry types encountered more difficulties. In the absence of highly specific metabolites reported to date, we suggested some multi-metabolite panels that may be further investigated as putative biomarkers for some berry fruits.

Profiling of Endogenous and Gut Microbial Metabolites to Indicate Metabotype-Specific Dietary Responses: A Systematic Review

Upon dietary exposure, the endogenous metabolism responds to the diet-derived nutrients and bioactive compounds, such as phytochemicals. However, the responses vary remarkably due to the interplay with other dietary components, lifestyle exposures, and intrinsic factors, which lead to differences in endogenous regulatory metabolism. These physiological processes are evidenced as a signature profile composed of various metabolites constituting metabolic phenotypes, or metabotypes. The metabolic profiling of biological samples following dietary intake hence would provide information about diet-that is, as the intake biomarkers and the ongoing physiological reactions triggered by this intake-thereby enable evaluation of the metabolic basis required to distinguish the different metabotypes. The capacity of nontargeted metabolomics to also encompass the unprecedented metabolite species has enabled the profiling of multiple metabolites and the corresponding metabotypes with a single analysis, decoding the complex interplay between diet, other relevant factors, and health. In this systematic review, we screened 345 articles published in English in January 2007-July 2018, which applied the metabolomics approach to profile the changes of endogenous metabolites in the blood related to dietary interventions, either derived by metabolism of gut microbiota or the human host. We excluded all the compounds that were directly derived from diet, and also the dietary interventions focusing on supplementation with individual compounds. After the removal of less relevant studies and assessment of eligibility, 49 articles were included in this review. First, we mention the contribution of individual factors, either modifiable or nonmodifiable factors, in shaping metabolic profile. Then, how different aspects of the diet would affect the metabolic profiles are disentangled. Next, the classes of endogenous metabolites altered following included dietary interventions are listed. We also discuss the current challenges in the field, along with future research opportunities.

Modifications of the urinary metabolome in young women after cranberry juice consumption were revealed using the UHPLC-Q-orbitrap-HRMS-based metabolomics approach

The objectives of this research were to investigate urinary metabolome modifications and discover potential intake biomarkers in young women after cranberry juice consumption. Fifteen female college students were given either cranberry juice or apple juice for three days using a cross-over design. Urine samples were collected before and after juice consumption. The metabolome in the urine was analyzed using UHPLC-Q-orbitrap-HRMS-based metabolomics followed by orthogonal partial least squares-discriminant analyses (OPLS-DA). An S-plot was used to identify discriminant metabolites. Validated OPLS-DA analyses showed that cranberry juice consumption significantly altered the urinary metabolome. Compared to the baseline urine or urine after apple juice consumption, cranberry juice consumption increased urinary excretion of both exogenous and endogenous metabolites. The tentatively identified exogenous metabolites included quinic acid, coumaric acid, 4-hydroxy-5-(hydroxyphenyl)-valeric acid-O-sulphate, 5-(dihydroxyphenyl)-γ-valerolactone sulfate, diphenol glucuronide, 3,4-dihydroxyphenyl propionic acid, 3-(hydroxyphenyl) propionic acid, 4-O-methylgallic acid, trihydroxybenzoic acid and 1,3,5-trimethoxybenzene. Modifications of endogenous metabolites after cranberry juice consumption included the increases in homocitric acid, hippuric acid, 3-hydroxy-3-carboxymethyl-adipic acid, (2)3-isopropylmalate, pimelic acid and N-acetyl-l-glutamate 5-semialdehyde. These metabolites may serve as urinary biomarkers of cranberry juice consumption and contribute to the bioactivities of cranberries against urinary tract infection.

Cell metabolomics to study the cytotoxicity of carbon black nanoparticles on A549 cells using UHPLC-Q/TOF-MS and multivariate data analysis

Carbon black nanoparticles (CBNPs) are core component of fine particulate matter (PM2.5) in the atmosphere. It was reported that the particle in the atmosphere with smaller size and the larger the specific surface area are easier to reach the deep respiratory tract or even the alveoli through the respiratory barrier and cause lung injury. Therefore, it has been believed that ultrafine or nanometer particles with more toxic than those with larger particle sizes. Moreover, it was confirmed that CBNPs could induce inflammation, oxidative stress and changes in cell signaling and gene expression in mammalian cells and organs. However, the cytotoxicity mechanism of them has been uncertain so far. The aim of the present study was to explore the underlying mechanism of cytotoxicity induced by CBNPs on A549 cells. In the current research, the viabilities of A549 cells were detected by Cell Counting Kit-8 (CCK-8) assay. The further metabolomics studies were conducted to detect the cytotoxic effect of CBNPs on A549 cells with an IC50 value of 70 μg/mL for 48 h. Potential differential compounds were identified and quantified using a novel on-line acquisition method based on ultra-liquid chromatography quadrupole time-of-flight mass spectrometry(UHPLC-Q-TOF/MS). The cytotoxicity mechanism of CBNPs on A549 cells was evaluated by multivariate data analysis and statistics. As a result, a total of 32 differential compounds were identified between CBNPs exposure and control groups. In addition, pathway analysis showed the metabolic changes were involved in the tricarboxylic acid (TCA) cycle, alanine, aspartate and glutamate metabolism, histidine metabolism and so on. It is also suggested that CBNPs may induce cytotoxicity by affecting the normal process of energy metabolism and disturbing several vital signaling pathways and finally induce cell apoptosis.

A joint analysis of metabolomic profiles associated with muscle mass and strength in Caucasian women

Both loss of muscle mass and strength are important sarcopenia-related traits. In this study, we investigated both specific and shared serum metabolites associated with these two traits in 136 Caucasian women using a liquid chromatography-mass spectrometry method. A joint analysis of multivariate traits was used to examine the associations of individual metabolites with muscle mass measured by the body mass index-adjusted appendicular lean mass (ALM/BMI) and muscle strength measured by hand grip strength (HGS). After adjusting for multiple testing, nine metabolites including two amino acids (aspartic acid and glutamic acid) and an amino acid derive (pipecolic acid), one peptide (phenylalanyl-threonine), one carbohydrate (methyl beta-D-glucopyranoside), and four lipids (12S-HETRE, arachidonic acid, 12S-HETE, and glycerophosphocholine) were significant in the joint analysis. Of them, the two amino acids (aspartic acid and glutamic acid) and two lipids (12S-HETRE and 12S-HETE) were associated with both ALM/BMI and HGS, and the other five were only associated with ALM/BMI. The pathway analysis showed the amino acid metabolism pathways (aspartic acid and glutamic acid) might play important roles in the regulation of muscle mass and strength. In conclusion, our study identified novel metabolites associated with sarcopenia-related traits, suggesting novel metabolic pathways for muscle regulation.

Phenyl-γ-valerolactones and phenylvaleric acids, the main colonic metabolites of flavan-3-ols: synthesis, analysis, bioavailability, and bioactivity

Covering: 1958 to June 2018 Phenyl-γ-valerolactones (PVLs) and their related phenylvaleric acids (PVAs) are the main metabolites of flavan-3-ols, the major class of flavonoids in the human diet. Despite their presumed importance, these gut microbiota-derived compounds have, to date, in terms of biological activity, been considered subordinate to their parent dietary compounds, the flavan-3-ol monomers and proanthocyanidins. In this review, the role and prospects of PVLs and PVAs as key metabolites in the understanding of the health features of flavan-3-ols have been critically assessed. Among the topics covered, are proposals for a standardised nomenclature for PVLs and PVAs. The formation, bioavailability and pharmacokinetics of PVLs and PVAs from different types of flavan-3-ols are discussed, taking into account in vitro and animal studies, as well as inter-individual differences and the existence of putative flavan-3-ol metabotypes. Synthetic strategies used for the preparation of PVLs are considered and the methodologies for their identification and quantification assessed. Metabolomic approaches unravelling the role of PVLs and PVAs as biomarkers of intake are also described. Finally, the biological activity of these microbial catabolites in different experimental models is summarised. Knowledge gaps and future research are considered in this key area of dietary (poly)phenol research.

Effects of honey-extracted polyphenols on serum antioxidant capacity and metabolic phenotype in rats

It is generally known that honey polyphenols have antioxidant capacity and numerous biological functions. However, their metabolic phenotype after digestion has not been studied. In this study, the effects of honey-extracted polyphenols (HEPs) on serum antioxidant capacity and metabolic phenotype were revealed for the first time. Herein, sixteen male Sprague-Dawley rats were randomly divided into two groups: one group was administered HEPs (200 mg per kilogram body weight dose) and the other group was fed distilled water three times. Then, 1 h after the last gavage, the metabolic profiling of serum was analyzed by UHPLC-Orbitrap-HRMS and multivariate statistical analysis, and the following results were obtained. At first, twenty-five metabolites, including polyphenols, unsaturated fatty acids, and amino acids, were selected as potential biomarkers. Then, metabolic pathway analysis showed that several amino acid metabolism pathways, ubiquinone and other terpenoid-quinone biosynthesis, arachidonic acid metabolism, nicotinate and nicotinamide metabolism, and inositol phosphate metabolism were affected. Association analysis demonstrated that the alteration of metabolites may be responsible for the increased serum antioxidant capacity. This new insight into the effects on the metabolic phenotype after HEP intake prompted the study on the biological functions and emerging health benefits of HEPs.

Metabolomic Differentials in Women With and Without Fibromyalgia

A nontargeted plasma metabolomic analysis was conducted to compare differentially expressed metabolites in women with and without fibromyalgia (FM) using data and samples collected from two parent studies in women with FM (n = 20) and comparative data collected from newly recruited age‐matched women (n = 20). Blood plasma samples were analyzed for metabolite content using liquid chromatography mass spectrometry. Consolidation of positive and negative ion mode metabolomics data with fold change (>2 or <0.5) and variable importance of projection scores ≥1 revealed statistically significant metabolites comparing samples from women with and without FM. Metabolite profiles in patients with FM differed from the comparison group in energy, lipid and amino acid metabolites reflecting heightened oxidative stress, inflammation, and tryptophan degradation in patients with FM. Study results may contribute to further identification of unique metabolomic profiles enhancing understanding of the pathophysiology of FM and for the development of effective therapeutic options.

Characterization of serum metabolites as biomarkers of carbon black nanoparticles-induced subchronic toxicity in rats by hybrid triple quadrupole time-of-flight mass spectrometry with non-targeted metabolomics strategy

Carbon black nanoparticles (CBNPs) are one of atmospheric particles components and have been closely related with a series of lung diseases. It can reach the depths of the respiratory tract or even alveolar more easily than those micro-particles. Although some of its toxicities have been confirmed in animals or human bodies, the subchronic toxicity mechanism of CBNPs has been uncertain so far. Therefore, it is very necessary to establish a novel method and clarify the mechanism of subchronic toxicity caused by concentration adjustments of small molecule metabolites in vivo. In animal experiments, CB exposure, recovery and control group were set up. The concentration of CBNPs in chamber was 30.06 ± 4.42 mg/m³. We developed a UHPLC-Q-TOF-MS/MS-based non-targeted metabolomic analysis strategy to analyze serum samples of rats. Then, differential metabolites in serum were found by multivariate data analysis and 39 potential biomarkers were identified. It was showed that main metabolic pathways associated with CBNPs exposure were hormones metabolism, amino acid metabolism, nucleotide metabolism and lipid metabolism. It is worth noting that long-term exposure to CBNPs had the greatest impact on steroid hormones biosynthesis so that the risk of infertility could increase. The results provided a new mechanistic insight into the metabolic alterations owing to CBNPs induced subchronic toxicity.

Using a high-resolution mass spectrometry-based metabolomics strategy for comprehensively screening and identifying biomarkers of phthalate exposure: Method development and application

Di-(2-propylheptyl) phthalate (DPHP) is an alternative plasticizer that can replace other phthalates currently being scrutinized, and its use and production volumes are increasing. This study aimed to develop a high-resolution mass spectrometry (HRMS)-based metabolomics strategy to comprehensively screen urinary biomarkers of DPHP exposure and filter out potentially useful DPHP exposure markers for human exposure assessments. This strategy included three stages: screening of biomarkers, verification of dose-response relationships in laboratory animals, and application in human subjects. The multivariate data analysis method known as orthogonal partial least-squares discriminant analysis (OPLS-DA) was used to screen and find meaningful signals in an MS dataset generated from urine samples collected from DPHP-administered rats. Thirty-six MS signals were verified as exposure marker candidates by assessing dose-response relationships in an animal feeding study. A biotransformation product of DPHP, mono-(2-propyl-7-dihydroxy-heptyl) phthalate, was suggested as a DPHP exposure marker for general human exposure assessments after the human application study and chemical structure identification. Three previously oxidized DPHP biotransformation products might be suitable for human exposure assessments in high-level exposure groups but not in the general population due to their low sensitivity.

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 profiles associated with bone mineral density in US Caucasian women

BACKGROUND

Individuals' peak bone mineral density (BMD) achieved and maintained at ages 20-40 years is the most powerful predictor of low bone mass and osteoporotic fractures later in life. The aim of this study was to identify metabolomic factors associated with peak BMD variation in US Caucasian women.

METHODS

A total of 136 women aged 20-40 years, including 65 subjects with low and 71 with high hip BMD, were enrolled. The serum metabolites were assessed using a liquid chromatography-mass spectrometry (LC-MS) method. The partial least-squares discriminant analysis (PLS-DA) method and logistic regression models were used, respectively, to examine the associations of metabolomic profiles and individual metabolites with BMD.

RESULTS

The low and high BMD groups could be differentiated by the detected serum metabolites using PLS-DA (Ppermutation = 0.008). A total of 14 metabolites, including seven amino acids and amino acid derivatives, five lipids (including three bile acids), and two organic acids, were significantly associated with the risk for low BMD. Most of these metabolites are novel in that they have never been linked with BMD in humans earlier. The prediction model including the newly identified metabolites significantly improved the classification of the groups with low and high BMD. The area under the receiver operating characteristic curve without and with metabolites were 0.88 (95% CI: 0.83-0.94) and 0.97 (95% CI: 0.94-0.99), respectively (P for the difference = 0.0004).

CONCLUSION

Metabolomic profiling may improve the risk prediction of osteoporosis among Caucasian women. Our findings also suggest the potential importance of the metabolism of amino acids and bile acids in bone health.

Unraveling the metabolic response of Brassica oleracea exposed to Xanthomonas campestris pv. campestris

BACKGROUND

Brassica crops together with cereals represent the basis of world supplies. Due to their importance, the production losses caused by Xanthomonas campestris pv. campestris (Xcc) infection represent a high economic impact. Understanding molecular and biochemical mechanisms of plants is essential to develop resistant crops with durable protection against diseases. In this regard, metabolomics has emerged as a valuable technology to provide an overview of the biological status of a plant exposed to a disease. This study investigated the dynamic changes in the metabolic profile of Brassica oleracea plants during an Xcc infection from leaves collected at five different days post infection using a mass spectrometry approach.

RESULTS

Results showed that Xcc infection causes dynamic changes in the metabolome of B. oleracea. Moreover, induction/repression pattern of the metabolites implicated in the response follows a complex dynamics during infection progression, indicating a complex temporal response. Specific metabolic pathways such as alkaloids, coumarins or sphingolipids are postulated as promising key role candidates in the infection response.

CONCLUSION

This work tries to decipher the changes produced on Brassica crops metabolome under Xcc infection and represents a step forward in the understanding of B. oleracea-Xcc interaction. © 2018 Society of Chemical Industry.

Host: Microbiome co-metabolic processing of dietary polyphenols - An acute, single blinded, cross-over study with different doses of apple polyphenols in healthy subjects

Apples are one of the most commonly consumed fruits and their high polyphenol content is considered one of the most important determinants of their health-promoting activities. Here we studied the nutrikinetics of apple polyphenols by UHPLC-HRMS metabolite fingerprinting, comparing bioavailability when consumed in a natural or a polyphenol-enriched cloudy apple juice. Twelve men and women participated in an acute single blind controlled crossover study in which they consumed 250 mL of cloudy apple juice (CAJ), Crispy Pink apple variety, or 250 mL of the same juice enriched with 750 mg of an apple polyphenol extract (PAJ). Plasma and whole blood were collected at time 0, 1, 2, 3 and 5 h. Urine was collected at time 0 and 0-2, 2-5, 5-8, and 8-24 h after juice consumption. Faecal samples were collected from each individual during the study for 16S rRNA gene profiling. As many as 110 metabolites were significantly elevated following intake of polyphenol enriched cloudy apple juice, with large inter-individual variations. The comparison of the average area under the curve of circulating metabolites in plasma and in urine of volunteers consuming either the CAJ or the PAJ demonstrated a stable metabotype, suggesting that an increase in polyphenol concentration in fruit does not limit their bioavailability upon ingestion. Faecal bacteria were correlated with specific microbial catabolites derived from apple polyphenols. Human metabolism of apple polyphenols is a co-metabolic process between human encoded activities and those of our resident microbiota. Here we have identified specific blood and urine metabolic biomarkers of apple polyphenol intake and identified putative associations with specific genera of faecal bacteria, associations which now need confirmation in specifically designed mechanistic studies.

The influence of juicing on the appearance of blueberry metabolites 2 h after consumption: a metabolite profiling approach

The consumption of berries has been linked to decreased risk of degenerative disease. Berries are regularly processed into juices. It is largely unknown how the juicing process affects the bioavailability of metabolites. As metabolomics has shown to be a valuable nutritional tool to study global metabolite differences, the aim of this study was to investigate the effect of juicing on the relative appearance of blueberry metabolites in humans using metabolomics. Nine healthy subjects consumed 250 g of fresh blueberries either as the whole fruit or after juicing, and provided blood and urine samples before and 2 h after intake in a cross-over design. Samples underwent metabolite profiling using LCMS, and data were mined with multivariate analysis. Overall, <12 % of all ions detected were significantly influenced by blueberry treatment (P<0·05). Partial least-squared discriminant analysis models of post-treatment samples revealed good discrimination. In urinary samples, whole blueberry treatment resulted in 108 ions that were significantly higher compared with juiced treatment (positive and negative mode combined), whereas only eight were significantly higher after juiced treatment. Examples of putative annotations included metabolites of ferulic and caffeic acids, several phenolic metabolites conjugated to sulphate, glycoside or glucuronide and fatty acyl derivatives, which were of higher intensity after whole blueberry treatment. In conclusion, consumption of whole blueberries resulted in a higher range of phenolic and other metabolites in plasma and urine samples 2 h after consumption. Both whole and juiced blueberries resulted in very similar metabolite profiles at 2 h, although this was the only time point measured.