Five Easy Metrics of Data Quality for LC-MS-Based Global Metabolomics

Untargeted metabolomics reveals the effects of pre-analytic storage on serum metabolite profiles from healthy cats

Untargeted metabolomics investigations have characterized metabolic disturbances associated with various diseases in domestic cats. However, the pre-analytic stability of serum metabolites in the species is unknown. Our objective was to compare serum metabolomes from healthy cats stored at -20°C for up to 12 months to samples stored at -80°C. Serum samples from 8 adult, healthy cats were stored at -20°C for 6 months, -20°C for 12 months, or -80°C for 12 months. Untargeted liquid chromatography-mass spectrometry was used to generate serum metabolite profiles containing relative abundances of 733 serum metabolites that were compared among storage conditions. Unsupervised analysis with principal component analysis and hierarchical clustering of Euclidian distances revealed separation of samples from individual cats regardless of storage condition. Linear mixed-effects models identified 75 metabolites that differed significantly among storage conditions. Intraclass correlation analysis (ICC) classified most serum metabolites as having excellent (ICC ≥ 0.9; 33%) or moderate (ICC 0.75-0.89; 33%) stability, whereas 13% had poor stability (ICC < 0.5). Biochemicals that varied significantly among storage conditions and classified with poor stability included glutathione metabolites, amino acids, gamma-glutamyl amino acids, and polyunsaturated fatty acids. The benzoate; glycine, serine and threonine; tryptophan; chemical (xenobiotics); acetylated peptide, and primary bile acid sub pathways were enriched among highly stable metabolites, whereas the monohydroxy fatty acid, polyunsaturated fatty, and monoacylglycerol sub-pathways were enriched among unstable metabolites. Our findings suggest that serum metabolome profiles are representative of the cat of origin, regardless of storage condition. However, changes in specific serum metabolites, especially glutathione, gamma-glutamyl amino acid, and fatty acid metabolites were consistent with increased sample oxidation during storage at -20°C compared with -80°C. By investigating the pre-analytic stability of serum metabolites, this investigation provides valuable insights that could aid other investigators in planning and interpreting studies of serum metabolomes in cats.

Profiling of serum metabolome of breast cancer: multi-cancer features discriminate between healthy women and patients with breast cancer

Introduction

The progression of solid cancers is manifested at the systemic level as molecular changes in the metabolome of body fluids, an emerging source of cancer biomarkers.

Methods

We analyzed quantitatively the serum metabolite profile using high-resolution mass spectrometry. Metabolic profiles were compared between breast cancer patients (n=112) and two groups of healthy women (from Poland and Norway; n=95 and n=112, respectively) with similar age distributions.

Results

Despite differences between both cohorts of controls, a set of 43 metabolites and lipids uniformly discriminated against breast cancer patients and healthy women. Moreover, smaller groups of female patients with other types of solid cancers (colorectal, head and neck, and lung cancers) were analyzed, which revealed a set of 42 metabolites and lipids that uniformly differentiated all three cancer types from both cohorts of healthy women. A common part of both sets, which could be called a multi-cancer signature, contained 23 compounds, which included reduced levels of a few amino acids (alanine, aspartate, glutamine, histidine, phenylalanine, and leucine/isoleucine), lysophosphatidylcholines (exemplified by LPC(18:0)), and diglycerides. Interestingly, a reduced concentration of the most abundant cholesteryl ester (CE(18:2)) typical for other cancers was the least significant in the serum of breast cancer patients. Components present in a multi-cancer signature enabled the establishment of a well-performing breast cancer classifier, which predicted cancer with a very high precision in independent groups of women (AUC>0.95).

Discussion

In conclusion, metabolites critical for discriminating breast cancer patients from controls included components of hypothetical multi-cancer signature, which indicated wider potential applicability of a general serum metabolome cancer biomarker.

Molecular formula assignment of dissolved organic matter by ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry using two non-targeted data processing approaches: A case study from recirculating aquaculture systems

BACKGROUND

The accumulation of dissolved organic matter (DOM) poses an issue in the management of the water quality from recirculating aquaculture systems (RAS), but its characterization is often not detailed enough to understand the DOM transformations in RAS. In this study, we investigated the application of two distinct non-targeted data processing approaches using ultra-performance liquid chromatography (UPLC) with quadrupole time-of-flight mass spectrometry (QTOF-MS) and two software with different algorithmic designs: PetroOrg and Progenesis QI to accurately characterize the molecular composition of DOM in RAS by UPLC-QTOF-MS.

RESULTS

The UPLC-QTOF-MS resolution in combination with PetroOrg and Progenesis QI software successfully assigned 912 and 106 unique elemental compositions, respectively, including compounds containing carbon, hydrogen, and oxygen (CHO) and nitrogen-containing CHO compounds (CHON), in the DOM samples from RAS. The results of these two distinct data processing approaches were consistent with the list of DOM formulas from RAS identified by higher resolution mass spectrometry techniques confirming their reliability. PetroOrg approach revealed only compositional information in the DOM samples from RAS, while Progenesis QI in addition to identifying new elemental compositions, increased their chemical space by giving information about their polarity and their possible key structures. DOM samples from RAS were found to be rich in unsaturated CHO compounds, with tentatively key structures of terpenoids with medium polarity indicating natural origins in their composition. The analysis also revealed probable structures of sucrose fatty acid esters and polyethylene glycol, indicating anthropogenic sources.

SIGNIFICANCE AND NOVELTY

The combination of these two non-targeted data processing approaches significantly improves the characterization of the complex mixture of DOM from RAS by UPLC-QTOF-MS reporting for the first time accurate DOM results in terms of its composition, while proposing its key structures. The presented methods can also be used to analyze different DOM samples with other HRMS techniques and software.

Physicochemical, microbiological and metabolomics changes in yogurt supplemented with lactosucrose

Lactosucrose (LS) is a known prebiotic that has gained recognition for its low caloric content and various health benefits. However, its potential in food applications remains largely unexplored. In this study the effects of adding LS to milk at concentrations (0 %, 2 %, 5 % and 8 % w/v) for yogurt production, and the relevant changes in yogurt texture, microbial composition and metabolomics were investigated. Our findings revealed that LS played a role in promoting the formation of a structured gel during fermentation, resulting in increased elasticity and viscosity while reducing fluidity. Additionally incorporating high doses of LS into yogurt led to reduced post-acidification, enhanced survival of starter bacteria, improved water retention capacity and overall texture throughout a refrigerated storage period of 21 days. Notably higher concentrations of LS (8 % w/v) exhibited effects on enhancing yogurt quality. Furthermore, untargeted metabolomics analysis using UPLC Q TOF MS/MS revealed 45 differentially expressed metabolites, including up-regulated L-arginine, L-proline and L-glutamic acid along with the down-regulated glutathione, L-tyrosine, L-phenylalanyl and L-proline. These differential metabolites were primarily associated with amino acid metabolism such as thiamine metabolism, nicotinic acid salt and nicotinamide metabolism, and pyrimidine metabolism. As a result, the inclusion of LS in yogurt had an impact on the production of various beneficial metabolites in yogurt, highlighting the importance of combining prebiotic LS with probiotics to obtain desired physiological benefits of yogurt.

Quartet metabolite reference materials for inter-laboratory proficiency test and data integration of metabolomics profiling

BACKGROUND

Various laboratory-developed metabolomic methods lead to big challenges in inter-laboratory comparability and effective integration of diverse datasets.

RESULTS

As part of the Quartet Project, we establish a publicly available suite of four metabolite reference materials derived from B lymphoblastoid cell lines from a family of parents and monozygotic twin daughters. We generate comprehensive LC-MS-based metabolomic data from the Quartet reference materials using targeted and untargeted strategies in different laboratories. The Quartet multi-sample-based signal-to-noise ratio enables objective assessment of the reliability of intra-batch and cross-batch metabolomics profiling in detecting intrinsic biological differences among the four groups of samples. Significant variations in the reliability of the metabolomics profiling are identified across laboratories. Importantly, ratio-based metabolomics profiling, by scaling the absolute values of a study sample relative to those of a common reference sample, enables cross-laboratory quantitative data integration. Thus, we construct the ratio-based high-confidence reference datasets between two reference samples, providing "ground truth" for inter-laboratory accuracy assessment, which enables objective evaluation of quantitative metabolomics profiling using various instruments and protocols.

CONCLUSIONS

Our study provides the community with rich resources and best practices for inter-laboratory proficiency tests and data integration, ensuring reliability of large-scale and longitudinal metabolomic studies.

Nontargeted screening of aldehydes and ketones by chemical isotope labeling combined with ultra-high performance liquid chromatography-high resolution mass spectrometry followed by hybrid filtering of features

Aldehydes and ketones are important carbonyl compounds that are widely present in foodstuffs, biological organisms and human living environment. However, it is still challenging to comprehensively detect and capture them using liquid chromatography - mass spectrometry. In this work, a chemical isotope labeling (CIL) coupled with ultra-high performance liquid chromatography - high resolution mass spectrometry (UHPLC-HRMS) strategy was developed for the capture and detection of this class of compounds. 2,4-Dinitrophenylhydrazine (DNPH) and isotope-labeled DNPH (DNPH-d3) were utilized to selectively label the target analytes. To address the difficulties in processing UHPLC-HRMS data, a post-acquisition data processing method called MSFilter was proposed to facilitate the screening and identification aldehydes and ketones in complex matrices. The MSFilter consists of four independent filters, namely statistical characteristic-based filtering, mass defect filtering, CIL paired peaks filtering, and diagnostic fragmentation ion filtering. These filters can be used individually or in combination to eliminate unrelated interfering MS features and efficiently detect DNPH-labeled aldehydes and ketones. The results of a mixture containing 48 model compounds showed that although all individual filtering methods could significantly reduce more than 95% of the raw MS features with acceptable recall rates above 85%, but they had relatively high false positive ratios of over 90%. In comparison, the hybrid filtering method combining four filters is able to eliminate massive interfering features (> 99.5%) with a high recall rate of 81.25% and a much lower false positive ratio of 15.22%. By implementing the hybrid filtering method in MSFilter, a total of 154 features were identified as potential signals of CCs from the original 45,961 features of real tobacco samples, of which 70 were annotated. We believe that the proposed strategy is promising to analyze the potential CCs in complex samples by UHPLC-HRMS.

Metabolomic Evenness Underlies Intraspecific Differences Among Lineages of a Wetland Grass

The metabolome represents an important functional trait likely important to plant invasion success, but we have a limited understanding of whether the entire metabolome or targeted groups of compounds confer an advantage to invasive as compared to native taxa. We conducted a lipidomic and metabolomic analysis of the cosmopolitan wetland grass Phragmites australis. We classified features into metabolic pathways, subclasses, and classes. Subsequently, we used Random Forests to identify informative features to differentiate five phylogeographic and ecologically distinct lineages: European native, North American invasive, North American native, Gulf, and Delta. We found that lineages had unique phytochemical fingerprints, although there was overlap between the North American invasive and North American native lineages. Furthermore, we found that divergence in phytochemical diversity was driven by compound evenness rather than metabolite richness. Interestingly, the North American invasive lineage had greater chemical evenness than the Delta and Gulf lineages but lower evenness than the North American native lineage. Our results suggest that metabolomic evenness may represent a critical functional trait within a plant species. Its role in invasion success, resistance to herbivory, and large-scale die-off events common to this and other plant species remain to be investigated.

The changes in metabolomics profile induced by intermittent theta burst stimulation in major depressive disorder: an exploratory study

BACKGROUND

Recently, there has been an ongoing interest in the mechanism of intermittent theta burst stimulation (iTBS) in major depressive disorder. Studying the metabolite changes induced by iTBS may help to understand the mechanism.

METHODS

Eleven participants with major depressive disorder received 10 days iTBS treatment. Magnetic resonance imaging (MRI) was used to target the region of the left dorsolateral prefrontal cortex (DLPFC) in each participant. We analyzed the effects of iTBS on metabolites using high-throughput profiling and assessed its impact on depressive symptoms. These analyses were considered exploratory, and no correction for multiple comparisons was applied.

RESULTS

Among the 318 measured metabolites, a significant increase in cystine, asymmetric dimethylarginine (ADMA), 1-methylhistidine, indoleacetic acid (IAA), diethanolamine (DEA), dopa, riboflavin-5'-monophosphate (FMN), and a significant decrease in alphalinolenic acid (ALA), gamma-linolenic acid (GLA), serotonin, linoleic acid (LA) (p < 0.05) were detected in the patients after iTBS treatment. In Pearson correlation analysis, the plasma levels of LA, FMN and ADMA at baseline were significantly related to the reduction rate of the 17-item Hamilton Depression Rating Scale and the Patient Health Questionnaire-9 scores (p < 0.05).

CONCLUSIONS

Our study highlights that LA, FMN, ADMA and their relationship with oxidative stress, may be key factors in the antidepressant efficacy of iTBS.

Concordance-Based Batch Effect Correction for Large-Scale Metabolomics

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

Roles of the Fungal-Specific Lysine Biosynthetic Pathway in the Nematode-Trapping Fungus Arthrobotrys oligospora Identified through Metabolomics Analyses

In higher fungi, lysine is biosynthesized via the α-aminoadipate (AAA) pathway, which differs from plants, bacteria, and lower fungi. The differences offer a unique opportunity to develop a molecular regulatory strategy for the biological control of plant parasitic nematodes, based on nematode-trapping fungi. In this study, in the nematode-trapping fungus model Arthrobotrys oligospora, we characterized the core gene in the AAA pathway, encoding α-aminoadipate reductase (Aoaar), via sequence analyses and through comparing the growth, and biochemical and global metabolic profiles of the wild-type and Aoaar knockout strains. Aoaar not only has α-aminoadipic acid reductase activity, which serves fungal L-lysine biosynthesis, but it also is a core gene of the non-ribosomal peptides biosynthetic gene cluster. Compared with WT, the growth rate, conidial production, number of predation rings formed, and nematode feeding rate of the ΔAoaar strain were decreased by 40-60%, 36%, 32%, and 52%, respectively. Amino acid metabolism, the biosynthesis of peptides and analogues, phenylpropanoid and polyketide biosynthesis, and lipid metabolism and carbon metabolism were metabolically reprogrammed in the ΔAoaar strains. The disruption of Aoaar perturbed the biosynthesis of intermediates in the lysine metabolism pathway, then reprogrammed amino acid and amino acid-related secondary metabolism, and finally, it impeded the growth and nematocidal ability of A. oligospora. This study provides an important reference for uncovering the role of amino acid-related primary and secondary metabolism in nematode capture by nematode-trapping fungi, and confirms the feasibility of Aoarr as a molecular target to regulate nematode-trapping fungi to biocontrol nematodes.

Normalization Approach by a Reference Material to Improve LC-MS-Based Metabolomic Data Comparability of Multibatch Samples

Large cohorts of samples from multiple batches are usually required for global metabolomic studies to characterize the metabolic state of human disease. As such, it is critical to eliminate systematic variation and truly reveal the biologically associated alterations. In this study, we proposed a reference material-based approach (Ref-M) for data correction by liquid chromatography-mass spectrometry and represented by an analysis of multibatch human serum samples. The reference material was generated by mixing serum from healthy donors and distributed to each extraction batch of subject samples. Pooled quality control samples and isotopic internal standards were then applied in each acquisition batch for data quality control. Finally, each metabolite in subject samples was normalized by its counterpart in the reference serum. We demonstrated that Ref-M significantly enhanced the numbers of efficient features and effectively eliminated the batch variation of 522 serum samples of healthy individuals, benign pulmonary nodules, and lung cancer patients. Twenty differential metabolites were identified to distinguish lung cancer from healthy controls in the training set. The discriminant model was validated in an independent data set with an area under the receiver operating characteristics (ROC) curve (AUC) of 0.853. Another 40 serum samples further tested with Ref-M were achieved an AUC of 0.843 by the established model. Our results showed that the reference material-based approach presents the potential to improve the data comparability and precision for biomarker discovery in large-scale metabolomic studies.

Defining atherosclerotic plaque biology by mass spectrometry-based omics approaches

Atherosclerosis is the principal cause of vascular diseases and one of the leading causes of worldwide death. Even though several insights into its natural course, risk factors and interventions have been identified, it is still an ongoing global pandemic. Since the structure and biochemical composition of the plaques show high heterogeneity, a comprehensive understanding of the intraplaque composition, its microenvironment, and the mechanisms of the progression and instability across different vascular beds at their progression stages is crucial for better risk stratification and treatment modalities. Even though several cell-based studies, animal studies, and extensive multicentric population studies have been conducted concerning cardiovascular diseases for assessing the risk factors and plaque biology, the studies on human clinical samples are very limited. New novel approaches utilize samples from percutaneous coronary interventions, which could possibly gain more access to clinical samples at different stages of the diseases without complex invasive resections. As an emerging technological platform in disease discovery research, mass spectrometry-based omics technologies offer capabilities for a comprehensive understanding of the mechanisms linked to several vascular diseases. Here, we discuss the cellular and molecular processes of atherosclerosis, different mass spectrometry-based omics approaches, and the studies mostly done on clinical samples of atheroma plaque using mass spectrometry-based proteomics, metabolomics and lipidomics approaches.

Power of mzRAPP-Based Performance Assessments in MS1-Based Nontargeted Feature Detection

When performing chromatography-mass spectrometry-based nontargeted metabolomics, or exposomics, one of the key steps in the analysis is to obtain MS1-based feature tables. Inapt parameter settings in feature detection will result in missing or wrong quantitative values and might ultimately lead to downstream incorrect biological interpretations. However, until recently, no strategies to assess the completeness and abundance accuracy of feature tables were available. Here, we show that mzRAPP enables the generation of benchmark peak lists by using an internal set of known molecules in the analyzed data set. Using the benchmark, the completeness and abundance accuracy of feature tables can be assessed in an automated pipeline. We demonstrate that our approach adds to other commonly applied quality assurance methods such as manual or automatized parameter optimization techniques or removal of false-positive signals. Moreover, we show that as few as 10 benchmark molecules can already allow for representative performance metrics to further improve quantitative biological understanding.

PeakBot: Machine learning based chromatographic peak picking

Motivation

Chromatographic peak picking is among the first steps in data processing workflows of raw LC-HRMS datasets in untargeted metabolomics applications. Its performance is crucial for the holistic detection of all metabolic features as well as their relative quantification for statistical analysis and metabolite identification. Random noise, non-baseline separated compounds and unspecific background signals complicate this task.

Results

A machine-learning-based approach entitled PeakBot was developed for detecting chromatographic peaks in LC-HRMS profile-mode data. It first detects all local signal maxima in a chromatogram, which are then extracted as super-sampled standardized areas (retention-time versus m/z). These are subsequently inspected by a custom-trained convolutional neural network that forms the basis of PeakBot’s architecture. The model reports if the respective local maximum is the apex of a chromatographic peak or not as well as its peak center and bounding box. In training and independent validation datasets used for development, PeakBot achieved a high performance with respect to discriminating between chromatographic peaks and background signals (accuracy of 0.99). For training the machine-learning model a minimum of 100 reference features are needed to learn their characteristics to achieve high-quality peak-picking results for detecting such chromatographic peaks in an untargeted fashion. PeakBot is implemented in python (3.8) and uses the TensorFlow (2.5.0) package for machine-learning related tasks. It has been tested on Linux and Windows OSs.

Availability and implementation

The package is available free of charge for non-commercial use (CC BY-NC-SA). It is available at https://github.com/christophuv/PeakBot.

Supplementary information

Supplementary data are available at Bioinformatics online.

Lipidomics Profiling of HepG2 Cells and Interference by Mycotoxins Based on UPLC-TOF-IMS

Discovering the fungus-infected or mycotoxin-contaminated biomarkers is significant for systems biology since the metabolites in biological samples have significant polarity differences in both stochastic gene expression and microenvironmental change. Here, we aim to establish a comprehensive method for a lipidome by ion mobility mass spectrometry (IMS) merged with chemometrics to accurately find out the more scientific markers of cell interference by mycotoxins and for pathogenesis exploration and drug development. The differences in the abundances of several small molecules found in these metabolites were explored through multivariate statistical analysis, including principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA), to further screen biomarkers. Good applicability and predictability were demonstrated by R²(X) and Q² (R² = 0.959, Q² = 0.999). Five compounds with m/z values of 512.502 8, 540.5343, 722.525 8, 787.667 5, and 813.683 0 were selected as markers, and four of them were further confirmed by chemical standards (i.e., MSMS of m/z 813.683 0 covering m/z 86.0978, 125.0008, 184.0745, and 185.0781). In summary, we demonstrated the integration of UPLC-TOF-IMS and the chemometrics approach to elucidate identified biomarkers, which also provides a new way of thinking for covering lipid biomarkers or prognostic indicators for mycotoxins.

metaboprep: an R package for preanalysis data description and processing

MOTIVATION

Metabolomics is an increasingly common part of health research and there is need for preanalytical data processing. Researchers typically need to characterize the data and to exclude errors within the context of the intended analysis. Whilst some preprocessing steps are common, there is currently a lack of standardization and reporting transparency for these procedures.

RESULTS

Here, we introduce metaboprep, a standardized data processing workflow to extract and characterize high quality metabolomics datasets. The package extracts data from preformed worksheets, provides summary statistics and enables the user to select samples and metabolites for their analysis based on a set of quality metrics. A report summarizing quality metrics and the influence of available batch variables on the data are generated for the purpose of open disclosure. Where possible, we provide users flexibility in defining their own selection thresholds.

AVAILABILITY AND IMPLEMENTATION

metaboprep is an open-source R package available at https://github.com/MRCIEU/metaboprep.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Predictive Modeling of Alzheimer's and Parkinson's Disease Using Metabolomic and Lipidomic Profiles from Cerebrospinal Fluid

In recent years, metabolomics has been used as a powerful tool to better understand the physiology of neurodegenerative diseases and identify potential biomarkers for progression. We used targeted and untargeted aqueous, and lipidomic profiles of the metabolome from human cerebrospinal fluid to build multivariate predictive models distinguishing patients with Alzheimer's disease (AD), Parkinson's disease (PD), and healthy age-matched controls. We emphasize several statistical challenges associated with metabolomic studies where the number of measured metabolites far exceeds sample size. We found strong separation in the metabolome between PD and controls, as well as between PD and AD, with weaker separation between AD and controls. Consistent with existing literature, we found alanine, kynurenine, tryptophan, and serine to be associated with PD classification against controls, while alanine, creatine, and long chain ceramides were associated with AD classification against controls. We conducted a univariate pathway analysis of untargeted and targeted metabolite profiles and find that vitamin E and urea cycle metabolism pathways are associated with PD, while the aspartate/asparagine and c21-steroid hormone biosynthesis pathways are associated with AD. We also found that the amount of metabolite missingness varied by phenotype, highlighting the importance of examining missing data in future metabolomic studies.

Two untargeted metabolomics reveals yogurt-associated metabolic alterations in women with multiple metabolic disorders from a randomized controlled study

The beneficial role of yogurt on metabolic profile has been widely reported. Yet, few studies have intended to describe the integrated metabolic alterations in response to yogurt. Yogurt and milk (220 g/d) were given to 48 and 44 obese women with metabolic syndrome and nonalcoholic fatty liver disease for 24 weeks in a randomized controlled trial (registered at http://www.chictr.org.cn as ChiCTR-IPR-15006801). Fasting serum samples were collected before and after intervention for global, untargeted metabolomics based on ¹H nuclear magnetic resonance (NMR) and ultra-high-performance liquid chromatography coupled with electrospray ionization time-of-flight mass spectrometry (UPLC-Q-TOF-MS) (in positive and negative ion modes). Multivariable statistical analysis and pathway analysis were conducted. In both ¹H NMR and UPLC-Q-TOF-MS metabolomics, no clustering was observed between the two groups at baseline. While, a clear clustering was shown after intervention, and the yogurt group had significantly different metabolic status from the milk. The metabolites that contributed mostly to class separation were identified, and involved into pathway analysis. Pathways on amino acids metabolism, fatty acid oxidation, cholesterol catabolism and choline metabolism significantly changed after yogurt intervention. The study revealed the integrated metabolic alterations in response to yogurt via two metabolomics approaches, suggesting the potential mechanisms of yogurt against metabolic disorders. TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR-IPR-15006801. Registered 20 July 2015, http://www.chictr.org.cn/ ChiCTR-IPR-15006801. SIGNIFICANCE: Both review from prospective studies and our randomized clinical trial showed the protective role of yogurt against multiple metabolic disorders. However, they were focus on targeted glucose, lipid, and other metabolic indicators, which were only part of human metabolism, failing to show an integrated metabolic feature on yogurt. Therefore, two global, untargeted metabolomics were applied in our current randomized clinical trial, trying to uncover the significant metabolic alterations characterizing the effects of yogurt on obese women with multiple metabolic disorders, and to explore the potential biological mechanisms of yogurt. The finding will shed light on a more comprehensive picture of how yogurt affects host metabolism, and provide theoretical foundation for dietary prevention of chronic diseases.

Effect of circadian system disruption on the concentration and daily oscillations of cortisol, progesterone, melatonin, serotonin, growth hormone, and core body temperature in periparturient dairy cattle

Metabolic, circadian, sleep, and reproductive systems are integrated and reciprocally regulated, but the understanding of the mechanism is limited. To study this integrated regulation, the circadian timing system was disrupted by exposing late pregnant nonlactating (dry) cows to chronic shifts in the light-dark phase, and rhythms of body temperature and circulating cortisol (CORT), progesterone (P4), serotonin (5HT), melatonin (MEL), and growth hormone (GH) concentrations were measured. Specifically, across 2 identical studies (1 and 2), at 35 d before expected calving (BEC) multiparous cows were assigned to control (CON; n = 24) and exposed to 16 h light and 8 h dark or phase shift (PS; n = 24) treatments and exposed to 6-h light-dark phase shifts every 3 d until parturition. All cows were exposed to control lighting after calving. Blood samples were collected in the first study at 0600 h on d 35 BEC, d 21 BEC, and 2 d before calving, and d 0, 2, 9, 15, and 22 postpartum (PP). A subset of cows (n = 6/group) in study 1 was blood sampled every 4 h over 48 h beginning on d 23 BEC, 9 BEC, and 5 PP. Body temperature was measured every 30 min (n = 8-16/treatment) for 48 h at 23 BEC and 9 BEC in both studies; and at 14 PP and 60 PP only in study 2. Treatment did not affect levels of CORT, GH, or P4 at 0600 h, but overall level of 5HT was lower and MEL higher in PS cows across days sampled. A 2-component versus single-component cosinor model better described [>coefficient of determination (R²); <Akaike information criterion and <Bayesian information criterion] daily oscillations of all hormones and temperature for both treatments. Circadian rhythm fit (R²) of body temperature and MEL increased from 23 BEC to 9 BEC in CON and was marked by loss of feeding time influence on oscillations in both treatments. Both treatments exhibited circadian rhythms of CORT at 9 BEC, CON cows also exhibited circadian rhythms in P4 at 23 BEC, and 5HT at 9 BEC. Daily oscillations in temperature and hormones, except CORT, were affected by PS treatment in the prepartum and were associated with longer gestation. In the PP, circadian rhythmicity was lost or diminished for all hormones and body temperature in both treatments. Stronger rhythms of body temperature and multiple hormones at 1 wk prepartum may indicate a synchronizing cue to time parturition. Therefore, dairy systems may need to consider management factors that affect circadian clocks in late-gestation cows.

ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis

Genes are pleiotropic and getting a better knowledge of their function requires a comprehensive characterization of their mutants. Here, we generated multi-level data combining phenomic, proteomic and metabolomic acquisitions from plasma and liver tissues of two C57BL/6 N mouse models lacking the Lat (linker for activation of T cells) and the Mx2 (MX dynamin-like GTPase 2) genes, respectively. Our dataset consists of 9 assays (1 preclinical, 2 proteomics and 6 metabolomics) generated with a fully non-targeted and standardized approach. The data and processing code are publicly available in the ProMetIS R package to ensure accessibility, interoperability, and reusability. The dataset thus provides unique molecular information about the physiological role of the Lat and Mx2 genes. Furthermore, the protocols described herein can be easily extended to a larger number of individuals and tissues. Finally, this resource will be of great interest to develop new bioinformatic and biostatistic methods for multi-omics data integration.

Towards a comprehensive characterisation of the human internal chemical exposome: Challenges and perspectives

The holistic characterisation of the human internal chemical exposome using high-resolution mass spectrometry (HRMS) would be a step forward to investigate the environmental ætiology of chronic diseases with an unprecedented precision. HRMS-based methods are currently operational to reproducibly profile thousands of endogenous metabolites as well as externally-derived chemicals and their biotransformation products in a large number of biological samples from human cohorts. These approaches provide a solid ground for the discovery of unrecognised biomarkers of exposure and metabolic effects associated with many chronic diseases. Nevertheless, some limitations remain and have to be overcome so that chemical exposomics can provide unbiased detection of chemical exposures affecting disease susceptibility in epidemiological studies. Some of these limitations include (i) the lack of versatility of analytical techniques to capture the wide diversity of chemicals; (ii) the lack of analytical sensitivity that prevents the detection of exogenous (and endogenous) chemicals occurring at (ultra) trace levels from restricted sample amounts, and (iii) the lack of automation of the annotation/identification process. In this article, we discuss a number of technological and methodological limitations hindering applications of HRMS-based methods and propose initial steps to push towards a more comprehensive characterisation of the internal chemical exposome. We also discuss other challenges including the need for harmonisation and the difficulty inherent in assessing the dynamic nature of the internal chemical exposome, as well as the need for establishing a strong international collaboration, high level networking, and sustainable research infrastructure. A great amount of research, technological development and innovative bio-informatics tools are still needed to profile and characterise the "invisible" (not profiled), "hidden" (not detected) and "dark" (not annotated) components of the internal chemical exposome and concerted efforts across numerous research fields are paramount.

Defining Acute Coronary Syndrome through Metabolomics

As an emerging platform technology, metabolomics offers new insights into the pathomechanisms associated with complex disease conditions, including cardiovascular diseases. It also facilitates assessing the risk of developing the disease before its clinical manifestation. For this reason, metabolomics is of growing interest for understanding the pathogenesis of acute coronary syndromes (ACS), finding new biomarkers of ACS, and its associated risk management. Metabolomics-based studies in ACS have already demonstrated immense potential for biomarker discovery and mechanistic insights by identifying metabolomic signatures (e.g., branched-chain amino acids, acylcarnitines, lysophosphatidylcholines) associated with disease progression. Herein, we discuss the various metabolomics approaches and the challenges involved in metabolic profiling, focusing on ACS. Special attention has been paid to the clinical studies of metabolomics and lipidomics in ACS, with an emphasis on ischemia/reperfusion injury.

EVA: Evaluation of Metabolic Feature Fidelity Using a Deep Learning Model Trained With Over 25000 Extracted Ion Chromatograms

Extracting metabolic features from liquid chromatography-mass spectrometry (LC-MS) data relies on the recognition of extracted ion chromatogram (EIC) peak shapes using peak picking algorithms. Unfortunately, all peak picking algorithms present a significant drawback of generating a problematic number of false positives. In this work, we take advantage of deep learning technology to develop a convolutional neural network (CNN)-based program that can automatically recognize metabolic features with poor EIC shapes, which are of low feature fidelity and more likely to be false. Our CNN model was trained using 25095 EIC plots collected from 22 LC-MS-based metabolomics projects of various sample types, LC and MS conditions. Notably, we manually inspected all the EIC plots to assign good or poor EIC quality for accurate model training. The trained CNN model is embedded into a C#-based program, named EVA (short for evaluation). The EVA Windows Application is a versatile platform that can process metabolic features generated by LC-MS systems of various vendors and processed using various data processing software. Our comprehensive evaluation of EVA indicates that it achieves over 90% classification accuracy. EVA can be readily used in LC-MS-based metabolomics projects and is freely available on the Microsoft Store by searching "EVA Metabolomics".

A Metabolomic Aging Clock Using Human Cerebrospinal Fluid

Quantifying the physiology of aging is essential for improving our understanding of age-related disease and the heterogeneity of healthy aging. Recent studies have shown that, in regression models using "-omic" platforms to predict chronological age, residual variation in predicted age is correlated with health outcomes, and suggest that these "omic clocks" provide measures of biological age. This paper presents predictive models for age using metabolomic profiles of cerebrospinal fluid (CSF) from healthy human subjects and finds that metabolite and lipid data are generally able to predict chronological age within 10 years. We use these models to predict the age of a cohort of subjects with Alzheimer's and Parkinson's disease and find an increase in prediction error, potentially indicating that the relationship between the metabolome and chronological age differs with these diseases. However, evidence is not found to support the hypothesis that our models will consistently overpredict the age of these subjects. In our analysis of control subjects, we find the carnitine shuttle, sucrose, biopterin, vitamin E metabolism, tryptophan, and tyrosine to be the most associated with age. We showcase the potential usefulness of age prediction models in a small data set (n = 85) and discuss techniques for drift correction, missing data imputation, and regularized regression, which can be used to help mitigate the statistical challenges that commonly arise in this setting. To our knowledge, this work presents the first multivariate predictive metabolomic and lipidomic models for age using mass spectrometry analysis of CSF.

Toxoplasma gondii induces metabolic disturbances in the hippocampus of BALB/c mice

Toxoplasma gondii can cross the blood-brain barrier and infect different regions of the brain including the hippocampus. In the present study, we examined the impact of Toxoplasma gondii infection on the metabolism of the hippocampus of female BALB/c mice compared to control mice using ultra-high-performance liquid chromatography-tandem mass spectrometry. Multivariate analysis revealed significant differences between infected and control hippocampi and identified 25, 82, and 105 differential metabolites (DMs) in the infected hippocampi at 7, 14, and 21 days post-infection (dpi), respectively. One DM (sphingosyl-phosphocholine in the sphingolipid metabolism pathway) and 11 dysregulated pathways were detected at all time points post-infection, suggesting their important roles in the neuropathogenesis of T. gondii infection. These pathways were related to neural activity, such as inflammatory mediator regulation of TRP channels, retrograde endocannabinoid signaling, and arachidonic acid metabolism. Weighted correlation network analysis and receiver operating characteristic analysis identified 33 metabolites significantly associated with T. gondii infection in the hippocampus, and 30 of these were deemed as potential biomarkers for T. gondii infection. This study provides, for the first time, a global view of the metabolic perturbations that occur in the mouse hippocampus during T. gondii infection. The potential relevance of the identified metabolites and pathways to the pathogenesis of cognitive impairment and psychiatric disorders are discussed.

Effects of Dufulin on Oxidative Stress and Metabolomic Profile of Tubifex

Dufulin is a highly effective antiviral pesticide used in plants. In this study, a seven-day experiment was conducted to evaluate the effects of Dufulin at five different concentrations (1 × 10⁻⁴, 1 × 10⁻³, 1 × 10⁻², 0.1, and 1 mg/L) on Tubifex. LC-MS-based metabolome analysis detected a total of 5356 features in positive and 9110 features in negative, of which 41 showed significant changes and were identified as differential metabolites. Four metabolic pathways were selected for further study. Detailed analysis revealed that Dufulin exposure affected the urea cycle of Tubifex, probably via argininosuccinate lyase (ASL) inhibition. It also affected the fatty acid metabolism, leading to changes in the concentration of free fatty acids in Tubifex. Furthermore, the changes in metabolites after exposure to Dufulin at 1 × 10⁻² mg/L were different from those at the other concentrations.

Data Processing Thresholds for Abundance and Sparsity and Missed Biological Insights in an Untargeted Chemical Analysis of Blood Specimens for Exposomics

Background: An untargeted chemical analysis of bio-fluids provides semi-quantitative data for thousands of chemicals for expanding our understanding about relationships among metabolic pathways, diseases, phenotypes and exposures. During the processing of mass spectral and chromatography data, various signal thresholds are used to control the number of peaks in the final data matrix that is used for statistical analyses. However, commonly used stringent thresholds generate constrained data matrices which may under-represent the detected chemical space, leading to missed biological insights in the exposome research. Methods: We have re-analyzed a liquid chromatography high resolution mass spectrometry data set for a publicly available epidemiology study (n = 499) of human cord blood samples using the MS-DIAL software with minimally possible thresholds during the data processing steps. Peak list for individual files and the data matrix after alignment and gap-filling steps were summarized for different peak height and detection frequency thresholds. Correlations between birth weight and LC/MS peaks in the newly generated data matrix were computed using the spearman correlation coefficient. Results: MS-DIAL software detected on average 23,156 peaks for individual LC/MS file and 63,393 peaks in the aligned peak table. A combination of peak height and detection frequency thresholds that was used in the original publication at the individual file and the peak alignment levels can reject 90% peaks from the untargeted chemical analysis dataset that was generated by MS-DIAL. Correlation analysis for birth weight data suggested that up to 80% of the significantly associated peaks were rejected by the data processing thresholds that were used in the original publication. The re-analysis with minimum possible thresholds recovered metabolic insights about C19 steroids and hydroxy-acyl-carnitines and their relationships with birth weight. Conclusions: Data processing thresholds for peak height and detection frequencies at individual data file and at the alignment level should be used at minimal possible level or completely avoided for mining untargeted chemical analysis data in the exposome research for discovering new biomarkers and mechanisms.

Physiological extremes of the human blood metabolome: A metabolomics analysis of highly glycolytic, oxidative, and anabolic athletes

Human metabolism is highly variable. At one end of the spectrum, defects of enzymes, transporters, and metabolic regulation result in metabolic diseases such as diabetes mellitus or inborn errors of metabolism. At the other end of the spectrum, favorable genetics and years of training combine to result in physiologically extreme forms of metabolism in athletes. Here, we investigated how the highly glycolytic metabolism of sprinters, highly oxidative metabolism of endurance athletes, and highly anabolic metabolism of natural bodybuilders affect their serum metabolome at rest and after a bout of exercise to exhaustion. We used targeted mass spectrometry-based metabolomics to measure the serum concentrations of 151 metabolites and 43 metabolite ratios or sums in 15 competitive male athletes (6 endurance athletes, 5 sprinters, and 4 natural bodybuilders) and 4 untrained control subjects at fasted rest and 5 minutes after a maximum graded bicycle test to exhaustion. The analysis of all 194 metabolite concentrations, ratios and sums revealed that natural bodybuilders and endurance athletes had overall different metabolite profiles, whereas sprinters and untrained controls were more similar. Specifically, natural bodybuilders had 1.5 to 1.8-fold higher concentrations of specific phosphatidylcholines and lower levels of branched chain amino acids than all other subjects. Endurance athletes had 1.4-fold higher levels of a metabolite ratio showing the activity of carnitine-palmitoyl-transferase I and 1.4-fold lower levels of various alkyl-acyl-phosphatidylcholines. When we compared the effect of exercise between groups, endurance athletes showed 1.3-fold higher increases of hexose and of tetradecenoylcarnitine (C14:1). In summary, physiologically extreme metabolic capacities of endurance athletes and natural bodybuilders are associated with unique blood metabolite concentrations, ratios, and sums at rest and after exercise. Our results suggest that long-term specific training, along with genetics and other athlete-specific factors systematically change metabolite concentrations at rest and after exercise.

Systematic characterization of chemical constituents in Mahuang decoction by UHPLC tandem linear ion trap-Orbitrap mass spectrometry coupled with feature-based molecular networking

Comprehensive characterization of traditional Chinese medicine prescriptions has long been a hurdle due to the chemical complexity and the lack of analytical tools. Mahuang decoction is a well-known traditional Chinese medicine prescription widely used for sweating and relieving the exterior, relieving cough and asthma, but it was insufficiently chemically scrutinized. In this study, the chemical component information of Mahuang decoction was investigated by ultrahigh-performance liquid chromatography tandem linear ion trap-Orbitrap mass spectrometry. A new data processing tool, feature-based molecular networking, was introduced for grouping and elucidating the compounds. In this way, 156 chemical components were identified or tentatively characterized, including alkaloids, triterpenoid saponins, flavanone-O-glycosides, flavone-C-glycosides, and procyanidins. Thus, this research provides a solid foundation for further development of Mahuang decoction, and the adopted method is expected to be applied to other traditional Chinese medicine prescriptions.