A Support Vector Machine-Assisted Metabolomics Approach for Non-Targeted Screening of Multi-Class Pesticides and Veterinary Drugs in Maize

The contamination risks of plant-derived foods due to the co-existence of pesticides and veterinary drugs (P&VDs) have not been fully understood. With an increasing number of unexpected P&VDs illegally added to foods, it is essential to develop a non-targeted screening method for P&VDs for their comprehensive risk assessment. In this study, a modified support vector machine (SVM)-assisted metabolomics approach by screening eligible variables to represent marker compounds of 124 multi-class P&VDs in maize was developed based on the results of high-performance liquid chromatography-tandem mass spectrometry. Principal component analysis and orthogonal partial least squares discriminant analysis indicate the existence of variables with obvious inter-group differences, which were further investigated by S-plot plots, permutation tests, and variable importance in projection to obtain eligible variables. Meanwhile, SVM recursive feature elimination under the radial basis function was employed to obtain the weight-squared values of all the variables ranging from large to small for the screening of eligible variables as well. Pairwise t-tests and fold changes of concentration were further employed to confirm these eligible variables to represent marker compounds. The results indicate that 120 out of 124 P&VDs can be identified by the SVM-assisted metabolomics method, while only 109 P&VDs can be found by the metabolomics method alone, implying that SVM can promote the screening accuracy of the metabolomics method. In addition, the method's practicability was validated by the real contaminated maize samples, which provide a bright application prospect in non-targeted screening of contaminants. The limits of detection for 120 P&VDs in maize samples were calculated to be 0.3~1.5 µg/kg.

ALISTER - Application for lipid stability evaluation and research

BACKGROUND AND AIMS

In lipidomic and metabolomic studies, pre-analytical pitfalls enhance the risk of misusing resources such as time and money, as samples that are analyzed may not yield accurate or reliable data due to poor sample handling. Guidance and pre-analytic know-how are necessary for translation of omics technologies into routine clinical testing. The present work aims to enable decision making regarding sample stability in every phase of lipidomics- and metabolomics-centered studies.

MATERIALS AND METHODS

Data of multiple pre-analytic studies were aggregated into a database. Flexible approaches for evaluating these data were implemented in an RShiny-based web-application, tailored towards broad applicability in clinical and bioanalytic research.

RESULTS

Our "Application for lipid stability evaluation & research" - ALISTER facilitates decision making on blood sample stability during lipidomic and metabolomic studies, such as biomarker research, analysis of biobank samples and clinical testing. The interactive tool gives sampling recommendations when planning sample collection or aids in the assessment of sample quality of experiments retrospectively.

CONCLUSION

ALISTER is available for use under https://itmp.shinyapps.io/alister/. The application enables and simplifies data-driven decision making concerning pre-analytic blood sample handling and fits the needs of clinical investigations from multiple perspectives.

Spoilage markers for freshwater fish: A comprehensive workflow for non-targeted analysis of VOCs using DHS-GC-HRMS

Changes of volatile organic compounds (VOCs) patterns during 6 days of storage at +4 °C were investigated in different freshwater fish species, namely carp and trout, using dynamic headspace gas chromatography time-of-flight mass spectrometry (DHS-GC-TOFMS). DHS parameters were systematically optimized to establish optimum extraction and pre-concentration of VOCs. Moreover, different sample preparation methods were tested: mincing with a manual meat grinder, as well as mincing plus homogenization with a handheld homogenizer both without and with water addition. The addition of water during sample preparation led to pronounced changes of the volatile profiles, depending on the molecular structure and lipophilicity of the analytes, resulting in losses of up to 98 % of more lipophilic compounds (logP > 3). The optimized method was applied to trout and carp. Trout samples of different storage days were compared using univariate (Mann-Whitney U test, fold change calculation) and multivariate (OPLS-DA) statistics. 37 potential spoilage markers were selected; for 11 compounds identity could be confirmed via measurement of authentic standards and 10 compounds were identified by library spectrum match. 22 compounds were also found to be statistically significant spoilage markers in carp. Merging results of the different statistical approaches, the list of 37 compounds could be narrowed down to the 14 most suitable for trout spoilage assessment. This study comprises a systematic evaluation of the capabilities of DHS-GC coupled to high-resolution (HR) MS for studying spoilage in different freshwater fish species, including a comprehensive data evaluation workflow.

Metabolomics analysis of MnO2 nanosheets CDT for breast cancer cells and mechanism of cytotoxic action

Chemodynamic therapy (CDT) has received more and more attention as an emerging therapeutic strategy, especially transition metals with Fenton or Fenton-like activity have good effects in CDT research, manganese dioxide nanosheets (MnO2 NSs) and their complexes have become one of the most favored nanomaterials in CDT of tumors. CDT is mainly based on the role of reactive oxygen species (ROS) in tumor treatment, which have clear chemical properties and produce clear chemical reactions. However, their mechanism of interaction with cells has not been fully elucidated. Here, we performed CDT on mouse breast cancer cells (4T1) based on MnO2 NSs, extracted the metabolites from the 4T1 cells during the treatment, and analyzed the differences in metabolites by using high-resolution liquid chromatography-mass spectrometry (LC-MS). Untargeted metabolomics studies were conducted using the relevant data. This study mainly explored the changes in MnO2 NSs on the metabolite profile of 4T1 cells and their potential impact on tumor therapy, in order to determine the mechanism of action of MnO2 NSs in the treatment of breast cancer. The results of the study showed the presence of 11 different metabolites in MnO2 NSs CDT for 4T1 tumor cells, including phosphoserine, sphingine, phosphocholine, and stearoylcarnitine. These findings provide a deeper understanding of breast cancer treatment, and are beneficial for the further research and clinical application of CDT.

Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives

BACKGROUND

Analysis of the glutamine metabolic pathway has taken a special place in metabolomics research in recent years, given its important role in cell biosynthesis and bioenergetics across several disorders, especially in cancer cell survival. The science of metabolomics addresses the intricate intracellular metabolic network by exploring and understanding how cells function and respond to external or internal perturbations to identify potential therapeutic targets. However, despite recent advances in metabolomics, monitoring the kinetics of a metabolic pathway in a living cell in situ, real-time and holistically remains a significant challenge.

AIM

This review paper explores the range of analytical approaches for monitoring metabolic pathways, as well as physicochemical modeling techniques, with a focus on glutamine metabolism. We discuss the advantages and disadvantages of each method and explore the potential of label-free Raman microspectroscopy, in conjunction with kinetic modeling, to enable real-time and in situ monitoring of the cellular kinetics of the glutamine metabolic pathway.

KEY SCIENTIFIC CONCEPTS

Given its important role in cell metabolism, the ability to monitor and model the glutamine metabolic pathways are highlighted. Novel, label free approaches have the potential to revolutionise metabolic biosensing, laying the foundation for a new paradigm in metabolomics research and addressing the challenges in monitoring metabolic pathways in living cells.

Environmental pseudotargeted metabolomics: A high throughput and wide coverage method for metabolic profiling of 1000-year paddy soil chronosequences

Pseudotargeted metabolomics is achieved by introducing an algorithm designed to choose ions for selected ion monitoring from identified metabolites. This method integrates the advantages of both untargeted and targeted metabolomics. In this study, environmental pseudotargeted metabolomics was established to analyze the soil metabolites, based on microwave assisted derivatization followed by gas chromatography-mass spectrometry analysis. The method development included the optimization of extraction factors and derivatization conditions, evaluation of silylation reagent types and matrix-dependent behaviors. Under the optimal conditions, the microwave oximation and silylation were completed in 5 min and 9 min. A total of 184 metabolites from 26 chemical classifications were identified in soil matrices. The method validation demonstrated excellent performance in terms of linearity (correlation coefficient > 0.99), repeatability (relative standard deviation (RSD) < 20 %), reproducibility (RSD < 25 %), stability (relative difference < 10 % within 18 h), and sensitivity (16-110 times higher signal-to-noise ratio). This developed method was applied to characterize the metabolite compositions and metabolic profiling in a 1000-year paddy soil chronosequence. The relative abundance of trehalose was highest in 6-(40.3 %), 60-(55.8 %), 300-(67.7 %)and 1000-(61.7 %)years paddy soil, respectively, but long-chain fatty acids were most abundant in marine sediment (57.4 %). Forty-two characteristic metabolites were considered as primarily responsible for discriminating and characterizing the paddy soil chronosequences development and seven major metabolic pathways were altered. In addition, GC-MS metabolite profile presented better discriminating power in paddy soil ecosystem changes than phospholipid fatty acids (PLFAs). Overall, environmental pseudotargeted metabolomics can provide a high throughout and wide coverage approach for performing metabolic profiling in the soil research.

Metabolomics Approach on Non-Targeted Screening of 50 PPCPs in Lettuce and Maize

The metabolomics approach has proved to be promising in achieving non-targeted screening for those unknown and unexpected (U&U) contaminants in foods, but data analysis is often the bottleneck of the approach. In this study, a novel metabolomics analytical method via seeking marker compounds in 50 pharmaceutical and personal care products (PPCPs) as U&U contaminants spiked into lettuce and maize matrices was developed, based on ultrahigh-performance liquid chromatography-tandem mass spectrometer (UHPLC-MS/MS) output results. Three concentration groups (20, 50 and 100 ng mL-1) to simulate the control and experimental groups applied in the traditional metabolomics analysis were designed to discover marker compounds, for which multivariate and univariate analysis were adopted. In multivariate analysis, each concentration group showed obvious separation from other two groups in principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) plots, providing the possibility to discern marker compounds among groups. Parameters including S-plot, permutation test and variable importance in projection (VIP) in OPLS-DA were used for screening and identification of marker compounds, which further underwent pairwise t-test and fold change judgement for univariate analysis. The results indicate that marker compounds on behalf of 50 PPCPs were all discovered in two plant matrices, proving the excellent practicability of the metabolomics approach on non-targeted screening of various U&U PPCPs in plant-derived foods. The limits of detection (LODs) for 50 PPCPs were calculated to be 0.4~2.0 µg kg-1 and 0.3~2.1 µg kg-1 in lettuce and maize matrices, respectively.

Metabolomics in clinical and forensic toxicology, sports anti-doping and veterinary residues

Metabolomics is a multidisciplinary field providing workflows for complementary approaches to conventional analytical determinations. It allows for the study of metabolically related groups of compounds or even the study of novel pathways within the biological system. The procedural stages of metabolomics; experimental design, sample preparation, analytical determinations, data processing and statistical analysis, compound identification and validation strategies are explored in this review. The selected approach will depend on the type of study being conducted. Experimental design influences the whole metabolomics workflow and thus needs to be properly assessed to ensure sufficient sample size, minimal introduced and biological variation and appropriate statistical power. Sample preparation needs to be simple, yet potentially global in order to detect as many compounds as possible. Analytical determinations need to be optimised either for the list of targeted compounds or a universal approach. Data processing and statistical analysis approaches vary widely and need to be better harmonised for review and interpretation. This includes validation strategies that are currently deficient in many presented workflows. Common compound identification approaches have been explored in this review. Metabolomics applications are discussed for clinical and forensic toxicology, human and equine sports anti-doping and veterinary residues.

Tile-Based Pairwise Analysis of GC × GC-TOFMS Data to Facilitate Analyte Discovery and Mass Spectrum Purification

A new tile-based pairwise analysis workflow, termed 1v1 analysis, is presented to discover and identify analytes that differentiate two chromatograms collected using comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS). Tile-based 1v1 analysis easily discovered all 18 non-native analytes spiked in diesel fuel within the top 30 hits, outperforming standard pairwise chromatographic analyses. However, eight spiked analytes could not be identified with multivariate curve resolution-alternating least-squares (MCR-ALS) nor parallel factor analysis (PARAFAC) due to background contamination. Analyte identification was achieved with class comparison enabled-mass spectrum purification (CCE-MSP), which obtains a pure analyte spectrum by normalizing the spectra to an interferent mass channel (m/z) identified from 1v1 analysis and subtracting the two spectra. This report also details the development of CCE-MSP assisted MCR-ALS, which removes the identified interferent m/z from the data prior to decomposition. In total, 17 out of 18 spiked analytes had a match value (MV) > 800 with both versions of CCE-MSP. For example, MCR-ALS and PARAFAC were unable to decompose the pure spectrum of methyl decanoate (MVs < 200) due to its low 2D chromatographic resolution (∼0.34) and high interferent-to-analyte signal ratio (∼30:1). By leveraging information gained from 1v1 analysis, CCE-MSP and CCE-MSP assisted MCR-ALS obtained a pure spectrum with an average MV of 908 and 964, respectively. Furthermore, tile-based 1v1 analysis was applied to track moisture damage in cacao beans, where 86 analytes with at least a 2-fold concentration change were discovered between the unmolded and molded samples. This 1v1 analysis workflow is beneficial for studies where multiple replicates are either unavailable or undesirable to save analysis time.

Metabolomics-based non-targeted screening analysis of 34 PPCPs in bovine and piscine muscles

The metabolomics-based analytical strategy has showed superiority on the non-targeted screening of contaminants, especially for unknown and unexpected (U&U) contaminants in the field of food safety, but data analysis is often the bottleneck of the strategy. In this study, a novel metabolomics-based analytical method via searching for marker compounds was developed on the basis of ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) results to accurately, rapidly and comprehensively achieve the non-targeted screening of 34 pharmaceutical and personal care products (PPCPs) as U&U contaminants spiked in bovine and piscine muscle matrices. Three concentration groups (20, 50 and 100 ng mL^-1) were intentionally designed to simulate the control and experimental groups for the discovery of marker compounds, for which multivariate and univariate analyses were adopted. In multivariate analysis, each concentration group was fully separated from the other two groups in PCA and OPLS-DA plots, laying a foundation to distinguish marker compounds among groups. The S-plot, permutation and variable importance in projection (VIP) in OPLS-DA were employed to screen and identify marker compounds, which were further verified by pairwise t-test and fold change judgement in univariate analysis. The results indicate that 34 PPCPs spiked in two muscle matrices were all identified as marker compounds, proving the validity and practicability of this novel metabolomics-based non-targeted screening method, which will exhibit great superiority and broad application prospects, especially in the face of massive PPCPs and various animal matrices in the field of food safety control. In addition, the limits of detection (LODs) for 34 PPCPs were calculated to be 0.2-2.6 μg kg^-1 and 0.3-2.1 μg kg^-1 in bovine and piscine muscle matrices, respectively.

Metabolomics-based screening analysis of PPCPs in water pretreated with five different SPE columns

The selection of solid phase extraction (SPE) columns in the pretreatment process plays a decisive role in the screening and quantification of pharmaceutical and personal care products (PPCPs). As growing PPCPs have frequently been detected in the aquatic environment, it is a burdensome task through one-by-one recovery comparison to judge which column presents relatively ideal pretreatment results for PPCPs. In view of this, we developed a novel metabolomics-based screening method based on ultrahigh-performance liquid chromatography-tandem mass spectrometer (UHPLC-MS/MS) results to accurately, rapidly and comprehensively choose a suitable column from 5 different kinds to handle 64 PPCPs in two water environments (50 μg L^-1/pH ≅ 7.0/pure water and 1 μg L^-1/pH ≅ 7.0/reservoir water) through seeking 'biomarkers', for which multivariate and univariate analyses were adopted. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) play a crucial role in multivariate analysis, and the pairwise t-test and fold change judgement in univariate analysis. Each column group was fully separated from the other 4 groups in PCA and OPLS-DA plots, laying a foundation to distinguish 'biomarkers' between groups. The S-Plot, permutation and variable importance in projection (VIP) in OPLS-DA were employed to screen and identify 'biomarkers', which were further verified by a pairwise t-test and fold change judgement. Eventually, the 64 PPCPs as 'biomarkers' were divided into 5 groups, which correspond to 5 column groups, consistent with the findings of traditional PPCP recovery comparison, proving the validity of the metabolomics-based screening method. This novel method will exhibit greater superiority in choosing suitable SPE columns to handle a growing and larger number of PPCPs in water environments and beyond.

Bioaccumulation of therapeutic drugs by human gut bacteria

Bacteria in the gut can modulate the availability and efficacy of therapeutic drugs. However, the systematic mapping of the interactions between drugs and bacteria has only started recently¹ and the main underlying mechanism proposed is the chemical transformation of drugs by microorganisms (biotransformation). Here we investigated the depletion of 15 structurally diverse drugs by 25 representative strains of gut bacteria. This revealed 70 bacteria-drug interactions, 29 of which had not to our knowledge been reported before. Over half of the new interactions can be ascribed to bioaccumulation; that is, bacteria storing the drug intracellularly without chemically modifying it, and in most cases without the growth of the bacteria being affected. As a case in point, we studied the molecular basis of bioaccumulation of the widely used antidepressant duloxetine by using click chemistry, thermal proteome profiling and metabolomics. We find that duloxetine binds to several metabolic enzymes and changes the metabolite secretion of the respective bacteria. When tested in a defined microbial community of accumulators and non-accumulators, duloxetine markedly altered the composition of the community through metabolic cross-feeding. We further validated our findings in an animal model, showing that bioaccumulating bacteria attenuate the behavioural response of Caenorhabditis elegans to duloxetine. Together, our results show that bioaccumulation by gut bacteria may be a common mechanism that alters drug availability and bacterial metabolism, with implications for microbiota composition, pharmacokinetics, side effects and drug responses, probably in an individual manner.

The investigation of the volatile metabolites of lung cancer from the microenvironment of malignant pleural effusion

For malignant pleural effusions, pleural fluid cytology is a diagnostic method, but sensitivity is low. The pleural fluid contains metabolites directly released from cancer cells. The objective of this study was to diagnose lung cancer with malignant pleural effusion using the volatilomic profiling method. We recruited lung cancer patients with malignant pleural effusion and patients with nonmalignant diseases with pleural effusion as controls. We analyzed the headspace air of the pleural effusion by gas chromatography-mass spectrometry. We used partial least squares discriminant analysis (PLS-DA) to identify metabolites and the support vector machine (SVM) to establish the prediction model. We split data into a training set (80%) and a testing set (20%) to validate the accuracy. A total of 68 subjects were included in the final analysis. The PLS-DA showed high discrimination with an R² of 0.95 and Q² of 0.58. The accuracy of the SVM in the test set was 0.93 (95% CI 0.66, 0.998), the sensitivity was 83%, the specificity was 100%, and kappa was 0.85, and the area under the receiver operating characteristic curve was 0.96 (95% CI 0.86, 1.00). Volatile metabolites of pleural effusion might be used in patients with cytology-negative pleural effusion to rule out malignancy.

Novel Algorithms for Comprehensive Untargeted Detection of Doping Agents in Biological Samples

To address the limitations of current targeted analytical methods that can only detect known doping agents, a novel methodology that permits untargeted drug detection (UDD) has been developed to help in the fight against doping in sports. Fifty-seven drugs were spiked into blank equine plasma and were treated as unknowns since their exact masses and chromatographic retention times were not utilized for detection. The spiked drugs were extracted from the plasma samples and were analyzed using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). The acquired LC-HRMS raw data files were processed using metabolomic software for compound detection and identification. For UDD with the resultant data, a mathematical model was created, and two algorithms were generated to calculate the ratio of the mean (ROM) and outlier index (OLI). Using ROM and OLI, the majority of the 57 drugs were accurately detected by name (52 of 57) or chemical formula (1 of 57). The limit of detection for the drugs was from tens of picograms to nanograms per milliliter. Xenobiotics and endogenous substances relevant to doping control were also identified using this untargeted approach following their extraction from real-world race samples, thus validating the UDD methodology. To the authors' knowledge, this is the first completely UDD methodological approach and represents significant advance toward using artificial intelligence for the detection of both known and emerging doping agents in sports.

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

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

Transcriptional, metabolic, physiological and developmental responses of switchgrass to phosphorus limitation

Knowing how switchgrass (Panicum virgatum L.) responds and adapts to phosphorus (P)-limitation will aid efforts to optimize P acquisition and use in this species for sustainable biomass production. This integrative study investigated the impacts of mild, moderate, and severe P-stress on genome transcription and whole-plant metabolism, physiology and development in switchgrass. P-limitation reduced overall plant growth, increased root/shoot ratio, increased root branching at moderate P-stress, and decreased root diameter with increased density and length of root hairs at severe P-stress. RNA-seq analysis revealed thousands of genes that were differentially expressed under moderate and severe P-stress in roots and/or shoots compared to P-replete plants, with many stress-induced genes involved in transcriptional and other forms of regulation, primary and secondary metabolism, transport, and other processes involved in P-acquisition and homeostasis. Amongst the latter were multiple miRNA399 genes and putative targets of these. Metabolite profiling showed that levels of most sugars and sugar alcohols decreased with increasing P stress, while organic and amino acids increased under mild and moderate P-stress in shoots and roots, although this trend reversed under severe P-stress, especially in shoots.

Untargeted Screening in a Case Control Study Using Apples as a Matrix

Untargeted screening using high resolution mass spectrometry (HRMS) is a promising approach for screening the food supply for contaminants, but the sheer amount of information inherent to the HRMS data set presents analytical challenges. Red apples, collected during the U.S. FDA's Total Diet Study, were studied to determine whether bioinformatic software can be used to distinguish spiked model compounds from those native to apples. A workflow was created, in which initial data sets of over 44,000 features in each of the two spiked samples were reduced by several orders of magnitude to a scale suitable for visual inspection. After visual inspection to address degeneracy and data quality, the final data sets contained 30 and 2 suspect compounds, respectively. To the best of our knowledge, this is the largest scale case-control study on food matrices to date and the first use of market basket samples as references in an untargeted screening study.

UPLC-MS-based metabonomic analysis of intervention effects of Da-Huang-Xiao-Shi decoction on ANIT-induced cholestasis

ETHNOPHARMACOLOGICAL RELEVANCE

Cholestasis, caused by hepatic accumulation of bile acids, is a serious manifestation of liver diseases resulting in liver injury, fibrosis, and liver failure with limited therapies. Da-Huang-Xiao-Shi decoction (DHXSD) is a representative formula for treating jaundice and displays bright prospects in liver protective effect.

AIM OF THE STUDY

This study was designed to assess the effects and possible mechanisms of DHXSD against alpha-naphthylisothiocyanate-induced liver injury based on ultra-high performance liquid chromatography-hybrid quadrupole-Orbitrap mass spectrometry (UHPLC-Q-Orbitrap MS) metabonomic approach.

MATERIALS AND METHODS

The effects of DHXSD on serum indices (TBIL, DBIL, AST, ALT, ALP, TBA, and γ-GT) and the histopathology of the liver were analyzed. Moreover, UHPLC-Q-Orbitrap MS was performed to identify the possible effect of DHXSD on metabolites. The pathway analysis was conducted to illustrate the pathways and network by which DHXSD treats cholestasis.

RESULTS

The results demonstrated that DHXSD could significantly regulate serum biochemical indices and alleviate histological damage to the liver. Twelve endogenous components, such as glycocholic acid, taurocholic acid and indoleacetaldehyde, were identified as potential biomarkers of the therapeutic effect of DHXSD. A systematic network analysis of their corresponding pathways indicates that the anti-cholestatic effect of DHXSD on alpha-naphthylisothiocyanate-induced cholestasis rats occurs mainly through regulating primary bile acid biosynthesis, arginine and proline metabolism, and arachidonic acid metabolism.

CONCLUSIONS

DHXSD has exhibited favorable pharmacological effect on serum biochemical indices and pathological observation on cholestatic model by partially regulating the perturbed pathways. Moreover, these findings may help better understand the mechanisms of disease and provide a potential therapy for cholestasis.

Comprehensive assessment of measurement uncertainty in 13C-based metabolic flux experiments

In the field of metabolic engineering ¹³C-based metabolic flux analysis experiments have proven successful in indicating points of action. As every step of this approach is affected by an inherent error, the aim of the present work is the comprehensive evaluation of factors contributing to the uncertainty of nonnaturally distributed C-isotopologue abundances as well as to the absolute flux value calculation. For this purpose, a previously published data set, analyzed in the course of a ¹³C labeling experiment studying glycolysis and the pentose phosphate pathway in a yeast cell factory, was used. Here, for isotopologue pattern analysis of these highly polar metabolites that occur in multiple isomeric forms, a gas chromatographic separation approach with preceding derivatization was used. This rendered a natural isotope interference correction step essential. Uncertainty estimation of the resulting C-isotopologue distribution was performed according to the EURACHEM guidelines with Monte Carlo simulation. It revealed a significant increase for low-abundance isotopologue fractions after application of the necessary correction step. For absolute flux value estimation, isotopologue fractions of various sugar phosphates, together with the assessed uncertainties, were used in a metabolic model describing the upper part of the central carbon metabolism. The findings pinpointed the influence of small isotopologue fractions as sources of error and highlight the need for improved model curation.

Graphical abstract

Quality assurance procedures for mass spectrometry untargeted metabolomics. a review

Untargeted metabolomics, as a global approach, has already proven its great potential and capabilities for the investigation of health and disease, as well as the wide applicability for other research areas. Although great progress has been made on the feasibility of metabolomics experiments, there are still some challenges that should be faced and that includes all sources of fluctuations and bias affecting every step involved in multiplatform untargeted metabolomics studies. The identification and reduction of the main sources of unwanted variation regarding the pre-analytical, analytical and post-analytical phase of metabolomics experiments is essential to ensure high data quality. Nowadays, there is still a lack of information regarding harmonized guidelines for quality assurance as those available for targeted analysis. In this review, sources of variations to be considered and minimized along with methodologies and strategies for monitoring and improvement the quality of the results are discussed. The given information is based on evidences from different groups among our own experiences and recommendations for each stage of the metabolomics workflow. The comprehensive overview with tools presented here might serve other researchers interested in monitoring, controlling and improving the reliability of their findings by implementation of good experimental quality practices in the untargeted metabolomics study.