Untargeted Liquid Chromatography High-resolution Mass Spectrometry Research Articles

Untargeted metabolomics using liquid chromatography-high resolution mass spectrometry and chemometrics for analysis of non-halal meats adulteration in beef meat.

The adulteration of raw beef (BMr) with dog meat (DMr) and pork (PMr) becomes a serious problem because it is associated with halal status, quality, and safety of meats. This research aimed to develop an effective authentication method to detect non-halal meats (dog meat and pork) in beef using metabolomics approach. Liquid chromatography-high resolution mass spectrometry (LC-HRMS) using untargeted approach combined with chemometrics was applied for analysis non-halal meats in BMr. The untargeted metabolomics approach successfully identified various metabolites in BMr DMr, PMr, and their mixtures. The discrimination and classification between authentic BMr and those adulterated with DMr and PMr were successfully determined using partial least square-discriminant analysis (PLS-DA) with high accuracy. All BMr samples containing non-halal meats could be differentiated from authentic BMr. A number of discriminating metabolites with potential as biomarkers to discriminate BMr in the mixtures with DMr and PMr could be identified from the analysis of variable importance for projection value. Partial least square (PLS) and orthogonal PLS (OPLS) regression using discriminating metabolites showed high accuracy (R2>0.990) and high precision (both RMSEC and RMSEE <5%) in predicting the concentration of DMr and PMr present in beef indicating that the discriminating metabolites were good predictors. The developed untargeted LC-HRMS metabolomics and chemometrics successfully identified non-halal meats adulteration (DMr and PMr) in beef with high sensitivity up to 0.1% (w/w). A combination of LC-HRMS untargeted metabolomic and chemometrics promises to be an effective analytical technique for halal authenticity testing of meats. This method could be further standardized and proposed as a method for halal authentication of meats.

P1143 Per- and poly-fluoroalkyl substances (PFAS) exposure is associated with later occurrence of inflammatory bowel disease

Abstract Background Environmental chemical exposures are increasingly acknowledged as risk factors for inflammatory bowel disease (IBD). Per- and poly-fluoroalkyl substances (PFAS), a large class of persistent fluorinated organic chemicals, widely used in the modern environment, may be implicated in IBD etiology, but data are conflicting. Methods We investigated the association of PFAS mixture concentrations in pre-diagnostic serum with adult-onset IBD in a pilot study within the pre-clinical Proteomic Evaluation and Discovery in an IBD Cohort of Tri-service Subjects (PREDICTS) study. This is a nested case-control study of military personnel with Crohn’s disease (CD), ulcerative colitis (UC), and age-, sex-, and race-matched healthy controls with serum samples obtained at four-time points 1-10 years prior to IBD diagnosis. Using untargeted liquid-chromatography high-resolution mass spectrometry analytical approach, we conducted an untargeted metabolomic analysis and semiquantitative measurement of fluorinated compounds, including legacy PFAS and emerging PFAS chemicals. We used weighted quantile sum regression models adjusted for confounders to study the association of PFAS as a mixture with the odds of CD and UC at each time point. Results Our study sample included individuals with CD, UC, and matched healthy controls (n=25, each group, 4 samples per individual). Baseline characteristics were similar across cases and controls. We identified nine well known PFAS compounds and 27 other known fluorinated compounds. The chemical mixture of fluorinated compounds, including the known PFAS, in the serum samples within one year of diagnosis was associated with higher odds of CD and UC [odds ratio (OR) 2.13, 95% CI 1.33, 3.41, and 1.76, 95% CI 1.15, 2.68, respectively] per one unit increase in decile. These associations remained consistent for the PFAS mixture measured in serum at all four time points up to 10 years prior to diagnosis for both CD and UC (Figure). Major contributors to the overall mixture indices included legacy PFAS, as well as emerging PFAS chemicals. Conclusion We observed a substantial increase in the odds of CD and UC among individuals with higher serum PFAS mixture levels up to 10 years prior to diagnosis. Studies to understand underlying mechanisms are ongoing.

Scalable Analysis of Untargeted LC-HRMS Data by Means of SQL Database Archiving.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is widely used to detect chemicals with a broad range of physiochemical properties in complex biological samples. However, the current data analysis strategies are not sufficiently scalable because of data complexity and amplitude. In this article, we report a novel data analysis strategy for HRMS data founded on structured query language database archiving. A database called ScreenDB was populated with parsed untargeted LC-HRMS data after peak deconvolution from forensic drug screening data. The data were acquired using the same analytical method over 8 years. ScreenDB currently holds data from around 40,000 data files, including forensic cases and quality control samples that can be readily sliced and diced across data layers. Long-term monitoring of system performance, retrospective data analysis for new targets, and identification of alternative analytical targets for poorly ionized analytes are examples of ScreenDB applications. These examples demonstrate that ScreenDB makes a significant improvement to forensic services and that the concept has potential for broad applications for all large-scale biomonitoring projects that rely on untargeted LC-HRMS data.

Host Tree and Geography Induce Metabolic Shifts in the Epiphytic Liverwort Radula complanata.

Bryophytes are prolific producers of unique, specialized metabolites that are not found in other plants. As many of these unique natural products are potentially interesting, for example, pharmacological use, variations in the production regarding ecological or environmental conditions have not often been investigated. Here, we investigate metabolic shifts in the epiphytic Radula complanata L. (Dumort) with regard to different environmental conditions and the type of phorophyte (host tree). Plant material was harvested from three different locations in Sweden, Germany, and Canada and subjected to untargeted liquid chromatography high-resolution mass-spectrometry (UPLC/ESI-QTOF-MS) and data-dependent acquisition (DDA-MS). Using multivariate statistics, variable selection methods, in silico compound identification, and compound classification, a large amount of variation (39%) in the metabolite profiles was attributed to the type of host tree and 25% to differences in environmental conditions. We identified 55 compounds to vary significantly depending on the host tree (36 on the family level) and 23 compounds to characterize R. complanata in different environments. Taken together, we found metabolic shifts mainly in primary metabolites that were associated with the drought response to different humidity levels. The metabolic shifts were highly specific to the host tree, including mostly specialized metabolites suggesting high levels of ecological interaction. As R. complanata is a widely distributed generalist species, we found it to flexibly adapt its metabolome according to different conditions. We found metabolic composition to also mirror the constitution of the habitat, which makes it interesting for conservation measures.

Image classification combined with faster R–CNN for the peak detection of complex components and their metabolites in untargeted LC-HRMS data

Peak detection of untargeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) data is a key step to identify the metabolic status of the drugable chemicals and extracts from functional foods or herbs. Nevertheless, the existing approaches are difficult to obtain ideal results with low false positives and false negatives. In this paper, we proposed an automatic method based on convolutional neural network (CNN) for image classification and Faster R–CNN for peak location/classification in untargeted LC-HRMS data, and named it Peak_CF. It can achieve detection of target peaks with high accuracy and high recall (both >90%) as verified by an evaluation data-set. In terms of detecting the m/z peaks of known compounds, Peak_CF is better than Peakonly, and it can effectively have an overall peak shape judgment of split peaks. For the same evaluation data, the recall of MZmine2 (ADAP) is slightly higher than that of Peak_CF, however, the F1 score of Peak_CF is higher, indicating that it has higher accuracy. In addition, the Peak_ CF training model with strong generalization ability can be achieved and verified. At last, Peak_CF was applied in real metabolic fingerprints of total flavonoids from Glycyrrhiza uralensis Fisch, also a contrast was conducted based on 40 m/z peaks of 40 prototypes in serum data-set. The result showed that the recall rate of Peak_CF and Peakonly all reached 95%, higher than 70% of MZmine2 (ADAP), and Peak_CF is more accurate when detecting EIC that has serious drifts. In conclusion, Peak_CF provides a new route for data mining of LC-HRMS datasets of drug (or herbs, or functional foods) metabolites.

Genetic Architecture of Untargeted Lipidomics in Cardiometabolic-Disease Patients Combines Strong Polygenic Control and Pleiotropy.

Analysis of the genetic control of small metabolites provides powerful information on the regulation of the endpoints of genome expression. We carried out untargeted liquid chromatography–high-resolution mass spectrometry in 273 individuals characterized for pathophysiological elements of the cardiometabolic syndrome. We quantified 3013 serum lipidomic features, which we used in both genome-wide association studies (GWAS), using a panel of over 2.5 M imputed single-nucleotide polymorphisms (SNPs), and metabolome-wide association studies (MWAS) with phenotypes. Genetic analyses showed that 926 SNPs at 551 genetic loci significantly (q-value < 10−8) regulate the abundance of 74 lipidomic features in the group, with evidence of monogenic control for only 22 of these. In addition to this strong polygenic control of serum lipids, our results underscore instances of pleiotropy, when a single genetic locus controls the abundance of several distinct lipid features. Using the LIPID MAPS database, we assigned putative lipids, predominantly fatty acyls and sterol lipids, to 77% of the lipidome signals mapped to the genome. We identified significant correlations between lipids and clinical and biochemical phenotypes. These results demonstrate the power of untargeted lipidomic profiling for high-density quantitative molecular phenotyping in human-genetic studies and illustrate the complex genetic control of lipid metabolism.

Modification of Early Response of Vitis vinifera to Pathogens Relating to Esca Disease and Biocontrol Agent Vintec® Revealed By Untargeted Metabolomics on Woody Tissues.

Esca disease is one of the most destructive grapevine trunk diseases. Phaeoacremonium minimum and Phaeomoniella chlamydospora are two of the known fungal pathogens associated with this disease. Today, biocontrol agents against Esca are mainly based on the use of the strain of the mycoparasite fungal genus Trichoderma such as the Vintec® product. The aim of this study was to investigate early response of woody tissues to Esca pathogens and identify metabolites that could be correlated with a biocontrol activity within a complex woody matrix. An untargeted liquid chromatography–high-resolution mass spectrometry metabolomic approach coupled to a spectral similarity network was used to highlight clusters of compounds associated with the plant response to pathogens and biocontrol. Dereplication highlighted the possible role of glycerophospholipids and polyphenol compounds, the latest mainly belonging to stilbenoids. Antifungal activity of some relevant biomarkers, evaluated in vitro on Phaeomoniella chlamydospora and Botrytis cinerea, suggests that some of these compounds can play a role to limit the development of Esca pathogens in planta.

Metabolite discovery through global annotation of untargeted metabolomics data.

Liquid chromatography-high resolution mass spectrometry (LC-MS)-based metabolomics aims to identify and quantitate all metabolites, but most LC-MS peaks remain unidentified. Here, we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. The approach aims to generate, for all experimentally observed ion peaks, annotations that match the measured masses, retention times, and (when available) MS/MS fragmentation patterns. Peaks are connected based on mass differences reflecting adducting, fragmentation, isotopes, or feasible biochemical transformations. Global optimization generates a single network linking most observed ion peaks, enhances peak assignment accuracy, and produces chemically-informative peak-peak relationships, including for peaks lacking MS/MS spectra. Applying this approach to yeast and mouse data, we identified five previously unrecognized metabolites (thiamine derivatives and N-glucosyl-taurine). Isotope tracer studies indicate active flux through these metabolites. Thus, NetID applies existing metabolomic knowledge and global optimization to substantially improve annotation coverage and accuracy in untargeted metabolomics datasets, facilitating metabolite discovery.

MetaClean: a machine learning-based classifier for reduced false positive peak detection in untargeted LC-MS metabolomics data.

Despite the availability of several pre-processing software, poor peak integration remains a prevalent problem in untargeted metabolomics data generated using liquid chromatography high-resolution mass spectrometry (LC-MS). As a result, the output of these pre-processing software may retain incorrectly calculated metabolite abundances that can perpetuate in downstream analyses. To address this problem, we propose a computational methodology that combines machine learning and peak quality metrics to filter out low quality peaks. Specifically, we comprehensively and systematically compared the performance of 24 different classifiers generated by combining eight classification algorithms and three sets of peak quality metrics on the task of distinguishing reliably integrated peaks from poorly integrated ones. These classifiers were compared to using a residual standard deviation (RSD) cut-off in pooled quality-control (QC) samples, which aims to remove peaks with analytical error. The best performing classifier was found to be a combination of the AdaBoost algorithm and a set of 11 peak quality metrics previously explored in untargeted metabolomics and proteomics studies. As a complementary approach, applying our framework to peaks retained after filtering by 30% RSD across pooled QC samples was able to further distinguish poorly integrated peaks that were not removed from filtering alone. An R implementation of these classifiers and the overall computational approach is available as the MetaClean package at https://CRAN.R-project.org/package=MetaClean . Our work represents an important step forward in developing an automated tool for filtering out unreliable peak integrations in untargeted LC-MS metabolomics data.

Assessment of endocrine disruptors effects on zebrafish (Danio rerio) embryos by untargeted LC-HRMS metabolomic analysis

Bisphenol A (BPA), perfluorooctane sulfonate (PFOS), and tributyltin (TBT) are emerging endocrine disruptors (EDCs) with still poorly defined mechanisms of toxicity and metabolic effects in aquatic organisms. We used an untargeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) metabolomic approach to study the effects of sub-lethal doses of these three EDCs on the metabolic profiles of zebrafish embryos exposed from 48 to 120hpf (hours post fertilization). Advanced chemometric data analysis methods were used to reveal effects on the subjacent regulatory pathways. EDC treatments induced changes in concentrations of about 50 metabolites for TBT and BPA, and of 25 metabolites for PFOS. The analysis of the corresponding metabolic changes suggested the presence of similar underlying zebrafish responses to BPA, TBT and PFOS affecting the metabolism of glycerophospholipids, amino acids, purines and 2-oxocarboxylic acids. We related the changes in glycerophospholipid metabolism to alterations in absorption of the yolk sack, the main source of nutrients (including lipids) for the developing embryo, linking the molecular markers with adverse phenotypic effects. We propose a general mode of action for all three chemical compounds, probably related to their already described interaction with the PPAR/RXR complex, combined with specific effects on different signaling pathways resulting in particular alterations in the zebrafish embryos metabolism.

Validation of highly sensitive simultaneous targeted and untargeted analysis of keto-steroids by Girard P derivatization and stable isotope dilution-liquid chromatography-high resolution mass spectrometry

A multiplexed quantitative method for the analysis of three major unconjugated steroids in human serum by stable isotope dilution liquid chromatography-high resolution mass spectrometry (LC-HRMS) was developed and validated on a Q Exactive Plus hybrid quadrupole/Orbitrap mass spectrometer. This quantification utilized isotope dilution and Girard P derivatization on the keto-groups of testosterone (T), androstenedione (AD) and dehydroepiandrosterone (DHEA) to improve ionization efficiency using electrospray ionization. Major isomeric compounds to T and DHEA; the inactive epimer of testosterone (epiT), and the metabolite of AD, 5α-androstanedione (5α-AD) were completely resolved on a biphenyl column within an 18min method. Inter- and intra-day method validation using LC-HRMS with qualifying product ions was performed and acceptable analytical performance was achieved. The method was further validated by comparing steroid levels from 100μL of serum from young vs older subjects. Since this approach provides high-dimensional HRMS data, untargeted analysis by age group was performed. DHEA and T were detected among the top analytes most significantly different across the two groups after untargeted LC-HRMS analysis, as well as a number of other still unknown metabolites, indicating the potential for combined targeted/untargeted analysis in steroid analysis.