Untargeted Metabolomics Data Research Articles

Similarities on the mode of action of the terpenoids citral and farnesene in Arabidopsis seedlings involve interactions with DNA binding proteins

The sesquiterpene farnesene and the monoterpene citral are phytotoxic natural compounds characterized by a high similarity in macroscopic effects, suggesting an equal or similar mechanism of action when assayed at IC50 concentration. In the present study, a short-time experiment (24 and 48 h) using an imaging spectrofluorometer allowed us to monitor the in-vivo effects of the two molecules, highlighting that both terpenoids were similarly affecting all PSII parameters, even when the effects of citral were quicker in appearing than those of farnesene. The multivariate, univariate, and pathway analyses, carried out on untargeted-metabolomic data, confirmed a clear separation of the plant metabolome in response to the two treatments, whereas similarity in the affected pathways was observed. The main metabolites affected were amino acids and polyamine, which significantly accumulated in response to both treatments. On the contrary, a reduction in sugar content (i.e. glucose and sucrose) was observed. Finally, the in-silico studies demonstrated a similar mechanism of action for both molecules by interacting with DNA binding proteins, although differences concerning the affinity with the proteins with which they could potentially interact were also highlighted. Despite the similarities in macroscopic effects of these two molecules, the metabolomic and in-silico data suggest that both terpenoids share a similar but not equal mechanism of action and that the similar effects observed on the photosynthetic machinery are more imputable to a side effect of molecules-induced oxidative stress.

DBDIpy: a Python library for processing of untargeted datasets from real-time plasma ionization mass spectrometry.

Plasma ionization is rapidly gaining popularity for mass spectrometry (MS)-based studies of volatiles and aerosols. However, data from plasma ionization are delicate to interpret as competing ionization pathways in the plasma create numerous ion species. There is no tool for detection of adducts and in-source fragments from plasma ionization data yet, which makes data evaluation ambiguous. We developed DBDIpy, a Python library for processing and formal analysis of untargeted, time-sensitive plasma ionization MS datasets. Its core functionality lies in the identification of in-source fragments and identification of rivaling ionization pathways of the same analytes in time-sensitive datasets. It further contains elementary functions for processing of untargeted metabolomics data and interfaces to an established ecosystem for analysis of MS data in Python. DBDIpy is implemented in Python (Version ≥ 3.7) and can be downloaded from PyPI the Python package repository (https://pypi.org/project/DBDIpy) or from GitHub (https://github.com/leopold-weidner/DBDIpy). Supplementary data are available at Bioinformatics online.

Benchmarking Outlier Detection Methods for Detecting IEM Patients in Untargeted Metabolomics Data.

Untargeted metabolomics (UM) is increasingly being deployed as a strategy for screening patients that are suspected of having an inborn error of metabolism (IEM). In this study, we examined the potential of existing outlier detection methods to detect IEM patient profiles. We benchmarked 30 different outlier detection methods when applied to three untargeted metabolomics datasets. Our results show great differences in IEM detection performances across the various methods. The methods DeepSVDD and R-graph performed most consistently across the three metabolomics datasets. For datasets with a more balanced number of samples-to-features ratio, we found that AE reconstruction error, Mahalanobis and PCA reconstruction error also performed well. Furthermore, we demonstrated the importance of a PCA transform prior to applying an outlier detection method since we observed that this increases the performance of several outlier detection methods. For only one of the three metabolomics datasets, we observed clinically satisfying performances for some outlier detection methods, where we were able to detect 90% of the IEM patient samples while detecting no false positives. These results suggest that outlier detection methods have the potential to aid the clinical investigator in routine screening for IEM using untargeted metabolomics data, but also show that further improvements are needed to ensure clinically satisfying performances.

OpenNAU: An open-source platform for normalizing, analyzing, and visualizing cancer untargeted metabolomics data.

As an important part of metabolomics analysis, untargeted metabolomics has become a powerful tool in the study of tumor mechanisms and the discovery of metabolic markers with high-throughput spectrometric data which also poses great challenges to data analysis, from the extraction of raw data to the identification of differential metabolites. To date, a large number of analytical tools and processes have been developed and constructed to serve untargeted metabolomics research. The different selection of analytical tools and parameter settings lead to varied results of untargeted metabolomics data. Our goal is to establish an easily operated platform and obtain a repeatable analysis result. We used the R language basic environment to construct the preprocessing system of the original data and the LAMP (Linux+Apache+MySQL+PHP) architecture to build a cloud mass spectrum data analysis system. An open-source analysis software for untargeted metabolomics data (openNAU) was constructed. It includes the extraction of raw mass data and quality control for the identification of differential metabolic ion peaks. A reference metabolomics database based on public databases was also constructed. A complete analysis system platform for untargeted metabolomics was established. This platform provides a complete template interface for the addition and updating of the analysis process, so we can finish complex analyses of untargeted metabolomics with simple human-computer interactions. The source code can be downloaded from https://github.com/zjuRong/openNAU.

The Combination of Bioinformatics Analysis and Untargeted Metabolomics Reveals Potential Biomarkers and Key Metabolic Pathways in Asthma.

Asthma is a complex chronic airway inflammatory disease that seriously impacts patients' quality of life. As a novel approach to exploring the pathogenesis of diseases, metabolomics provides the potential to identify biomarkers of asthma host susceptibility and elucidate biological pathways. The aim of this study was to screen potential biomarkers and biological pathways so as to provide possible pharmacological therapeutic targets for asthma. In the present study, we merged the differentially expressed genes (DEGs) of asthma in the GEO database with the metabolic genes obtained by Genecard for bioinformatics analysis and successfully screened out the metabolism-related hub genes (HIF1A, OCRL, NNMT, and PER1). Then, untargeted metabolic techniques were utilized to reveal HDM-induced metabolite alterations in 16HBE cells. A total of 45 significant differential metabolites and 5 differential metabolic pathways between the control group and HDM group were identified based on the OPLS-DA model. Finally, three key metabolic pathways, including glycerophospholipid metabolism, galactose metabolism, and alanine, aspartate, and glutamate metabolism, were screened through the integrated analysis of bioinformatics data and untargeted metabolomics data. Taken together, these findings provide valuable insights into the pathophysiology and targeted therapy of asthma and lay a foundation for further research.

How Low Can You Go? Selecting Intensity Thresholds for Untargeted Metabolomics Data Preprocessing

Untargeted mass spectrometry (MS) metabolomics is an increasingly popular approach for characterizing complex mixtures. Recent studies have highlighted the impact of data preprocessing for determining the quality of metabolomics data analysis. The first step in data processing with untargeted metabolomics requires that signal thresholds be selected for which features (detected ions) are included in the dataset. Analysts face the challenge of knowing where to set these thresholds; setting them too high could mean missing relevant features, but setting them too low could result in a complex and unwieldy dataset. This study compared data interpretation for an example metabolomics dataset when intensity thresholds were set at a range of feature heights. The main observations were that low signal thresholds (1) improved the limit of detection, (2) increased the number of features detected with an associated isotope pattern and/or an MS-MS fragmentation spectrum, and (3) increased the number of in-source clusters and fragments detected for known analytes of interest. When the settings of parameters differing in intensities were applied on a set of 39 samples to discriminate the samples through principal component analyses (PCA), similar results were obtained with both low- and high-intensity thresholds. We conclude that the most information-rich datasets can be obtained by setting low-intensity thresholds. However, in the cases where only a qualitative comparison of samples with PCA is to be performed, it may be sufficient to set high thresholds and thereby reduce the complexity of the data processing and amount of computational time required.

Multivariate equivalence testing for food safety assessment.

Products for food and feed derived from genetically modified (GM) crops are only allowed on the market when they are deemed to be safe for human health and the environment. The European Food Safety Authority (EFSA) performs safety assessment including a comparative approach: the compositional characteristics of a GM genotype are compared to those of reference genotypes that have a history of safe use. Statistical equivalence tests are used to carry out such a comparative assessment. These tests are univariate and therefore only consider one measured variable at a time. Phenotypic data, however, often comprise measurements on multiple variables that must be integrated to arrive at a single decision on acceptance in the regulatory process. The surge of modern molecular phenotyping platforms further challenges this integration, due to the large number of characteristics measured on the plants. This paper presents a new multivariate equivalence test that naturally extends a recently proposed univariate equivalence test and allows to assess equivalence across all variables simultaneously. The proposed test is illustrated on plant compositional data from a field study on maize grain and on untargeted metabolomic data of potato tubers, while its performance is assessed on simulated data.

Standardized multi-omics of Earth’s microbiomes reveals microbial and metabolite diversity

Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth’s environments (for example, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus). This Resource provides insight into the taxa and metabolites within microbial communities from diverse habitats across Earth, informing both microbial and chemical ecology, and provides a foundation and methods for multi-omics microbiome studies of hosts and the environment.

A non-targeted metabolomics study on serum of occupational people exposed with nanometer titanium dioxide particles (TiO(2)-NPs)

Objective: To study the changes of serum metabolic profile of occupational people exposed with nanometer titanium dioxide particles (TiO(2)-NPs), and to explore the biomarkers and injury mechanism of TiO(2)-NPs health effects. Methods: From June 2020 to June 2021, a TiO(2)-NPs production enterprise was selected as the research site by a typical sampling method, 64 people in the TiO(2)-NPs exposure group were selected from the enterprise, and 62 people of the logistics administrative staff in the same enterprise were selected as the control group, and blood samples were collected using non-anticoagulant blood collection tubes. After the samples were methanol-precipitated, the untargeted metabolomic data was collected by ultra-high performance liquid chromatography time-of-flight mass spectrometry, and biomarkers were screened and metabolic pathway analysis was performed. Results: 46 different metabolites were screened out by P<0.05 and variable importance projection index (VIP) value >1, mainly including glycerides, sphingomyelin, glycerophospholipid, fatty acyl, etc.; By ROC analysis to determine 3-hydroxy-4, 5-dimethyl-2 (5H) - furanone, 4-aminobiphenyl, heptanoylcarnitine, Hexadecanedioic acid mono-L-carnitine ester, Ibutilide, LysoPA (18∶1 (9Z) /0∶0), LysoPC (18∶0), PC (16∶0/16∶0), PC (16∶0/20: 4 (5Z, 8Z, 11Z, 14Z) ), PC (P-18∶1 (9Z) /P-18∶1 (9Z) ) 10 candidate biomarkers; involving changes in 4 metabolic pathways, namely glycerophospholipid metabolism, sphingolipid metabolism, phenylalanine, tyrosine and tryptophan acid biosynthesis, linoleic acid metabolism. Conclusion: Occupational exposure to TiO(2)-NPs has a significant impact on serum metabolic profiles.

Genome-wide metabolite quantitative trait loci analysis (mQTL) in red blood cells from volunteer blood donors

The Red Blood Cell (RBC)-Omics study, part of the larger NHLBI-funded Recipient Epidemiology and Donor Evaluation Study (REDS-III), aims to understand the genetic contribution to blood donor RBC characteristics. Previous work identified donor demographic, behavioral, genetic, and metabolic underpinnings to blood donation, storage, and (to a lesser extent) transfusion outcomes, but none have yet linked the genetic and metabolic bodies of work. We performed a Genome-Wide Association (GWA) analysis using RBC-Omics study participants with generated untargeted metabolomics data to identify metabolite quantitative trait loci (mQTL) in RBCs. We performed GWA analyses of 382 metabolites in 243 individuals imputed using the 1000 Genomes Project phase 3 all-ancestry reference panel. Analyses were conducted using ProbABEL and adjusted for sex, age, donation center, number of whole blood donations in the past two years, and first ten principal components of ancestry. Our results identified 423 independent genetic loci associated with 132 metabolites (p < 5x10-8). Potentially novel locus-metabolite associations were identified for the region encoding heme transporter FLVCR1 and choline, and for lysophosphatidylcholine acetyltransferase LPCAT3 and lysophosphatidylserine 16.0, 18.0, 18.1, and 18.2; these associations are supported by published rare disease and mouse studies. We also confirmed previous metabolite GWA results for associations including N(6)-Methyl-L-lysine and protein PYROXD2, and various carnitines and transporter SLC22A16. Association between pyruvate levels and G6PD polymorphisms was validated in an independent cohort and novel murine models of G6PD deficiency (African and Mediterranean variants). We demonstrate that it is possible to perform metabolomics-scale GWA analyses with a modest, trans-ancestry sample size.

TMET-08. METABOLOMIC CHANGES IN GLIOBLASTOMA PATIENTS UNDERGOING CONCURRENT CHEMORADIATION THERAPY

Abstract OBJECTIVE To illustrate changes of untargeted metabolomics in patients with glioblastoma IDH wildtype undergoing concurrent radiation therapy (RT) with temozolomide (TMZ). This study implemented machine learning (ML) algorithms to predict treatment phase: pre-surgery, post-surgery, pre-radiation, and post radiation based on untargeted metabolomics data. METHODS Thirty-six patients with glioblastoma IDH wildtype (18 methylguanine methyltransferase [MGMT] methylated, 16 MGMT unmethylated, 2 MGMT status unknown) were enrolled into this study. Serum samples obtained from patients on the same day before surgery, 2 days after surgery, before starting their concurrent chemoradiation, and after concluding this phase of treatment. Blood samples were obtained via antecubital phlebotomy without regard for time of the day, diet, or fasting status. Untargeted metabolomics by GC-TOF mass spectrometry were obtained and compared. The proposed ML models analyzed 105 samples from 36 patients utilizing 157 structurally identified blood metabolites. Multinomial Logistic Regression (MLR) and GradientBoostingClassifier (GB Classifier) were used to classify patient samples based on detected changes in blood metabolites. The classification performance of these models was evaluated using performance metrics and AUC scores. RESULTS Post radiation; significant increase in the following metabolites: glycine, serine, threonine, oxoproline, 6-deoxyglucose, gluconic acid, glycerol-alpha-phosphate, ethanolamine, propyleneglycol, triethanolamine, xylitol, and fumaric acid were noted while significant decrease in 3-aminopiperidine 2,6-dione was noted post radiation. MLR produced 78% accuracy, 75% precision, and AUC = 0.89, and GB Classifier produced 75% accuracy, 77% precision and AUC = 0.91. Finally, we presented a pattern of metabolites changes per clinical stage based on pairwise correlations. CONCLUSIONS This study represent the first serum metabolic signature associated with RT in patients with glioblastoma. The results from the classification algorithms and pairwise correlations showed that blood metabolites have the potential to predict phase of treatment and potentially enable to evaluate response to treatment in patients with glioblastoma in a relatively small cohort.

The metabolomics approach revealed a distinctive metabolomics pattern associated with hyperthyroidism treatment

Hyperthyroidism is characterized by increased thyroid hormone production, which impacts various processes, including metabolism and energy expenditure. Yet, the underlying mechanism and subsequent influence of these changes are unknown. Metabolomics is a broad analytical method that enables qualitative and quantitative examination of metabolite level changes in biological systems in response to various stimuli, pathologies, or treatments. This study uses untargeted metabolomics to explore the potential pathways and metabolic patterns associated with hyperthyroidism treatment. The study consisted of 20 patients newly diagnosed with hyperthyroidism who were assessed at baseline and followed up after starting antithyroid treatment. Two blood samples were taken from each patient, pre (hyperthyroid state) and post-treatment (euthyroid state). Hyperthyroid and euthyroid states were identified based on thyroxine and thyroid-stimulating hormone levels. The metabolic alteration associated with antithyroid therapy was investigated using liquid chromatography- high-resolution mass spectrometry. The untargeted metabolomics data was analyzed using both univariate and multivariate analyses using MetaboAnalyst v5.0. The significant metabolic pattern was identified using the lab standard pipeline, which included molecular annotation in the Human Metabolome Database, LipidMap, LipidBlast, and METLIN. The identified metabolites were examined using pathway and network analyses and linked to cellular metabolism. The results revealed a strong group separation between the pre- and post-hyperthyroidism treatment (Q2 = 0.573, R2 = 0.995), indicating significant differences in the plasma metabolome after treatment. Eighty-three mass ions were significantly dysregulated, of which 53 and 30 characteristics were up and down-regulated in the post-treatment compared to the pre-treatment group, respectively. The medium-chain acylcarnitines, octanoylcarnitine, and decanoylcarnitine, previously found to rise in hyperthyroid patients, were among the down-regulated metabolites, suggesting that their reduction could be a possible biomarker for monitoring euthyroid restoration. Kynurenine is a downregulated tryptophan metabolite, indicating that the enzyme kynurenine 3-hydroxylase, inhibited in hyperthyroidism, is back functioning. L-cystine, a cysteine dimer produced from cysteine oxidation, was among the down-regulated metabolites, and its accumulation is considered a sign of oxidative stress, which was reported to accompany hyperthyroidism; L-cystine levels dropped, this suggests that the plasma level of L-cystine can be used to monitor the progress of euthyroid state restoration. The plasma metabolome of patients with hyperthyroidism before and after treatments revealed differences in the abundance of several small metabolites. Our findings add to our understanding of hyperthyroidism's altered metabolome and associated metabolic processes and shed light on acylcarnitines as a new biomarker for treatment monitoring in conjunction with thyroxine and thyroid-stimulating hormone.

Discovery of Homogentisic Acid as a Precursor in Trimethoprim Metabolism and Natural Product Biosynthesis.

Opportunistic infections by Burkholderia cenocepacia are life threatening for patients suffering from cystic fibrosis and chronic granulomatous disease. These infections are often associated with variable clinical outcomes, prompting an interest in molecular investigations of phenotypes associated with disease severity. The production of the pyomelanin pigment is one such phenotype, which was recently linked to the ability of clinical strains to carry out biotransformation of the antibiotic trimethoprim. However, this biotransformation product was not identified, and differences in metabolite production associated with pyomelanin pigmentation are poorly understood. Here, we identify several key metabolites produced exclusively by the pyomelanin-producing strains. To provide insight into the structures and biosynthetic origin of these metabolites, we developed a mass spectrometry-based strategy coupling unsupervised in silico substructure prediction with stable isotope labeling referred to as MAS-SILAC (Metabolite Annotation assisted by Substructure discovery and Stable Isotope Labeling by Amino acids in Cell culture). This approach led to discovery of homogentisic acid as a precursor for biosynthesis of several natural products and for biotransformation of trimethoprim, representing a previously unknown mechanism of antibiotic tolerance. This work presents application of computational methods for analysis of untargeted metabolomic data to link the chemotype of pathogenic microorganisms with a specific phenotype. The observations made in this study provide insights into the clinical significance of the melanated phenotype.

The use of ecological analytical tools as an unconventional approach for untargeted metabolomics data analysis: the case of Cecropia obtusifolia and its adaptive responses to nitrate starvation

Plant metabolomics studies haves revealed new bioactive compounds. However, like other omics disciplines, the generated data are not fully exploited, mainly because the commonly performed analyses focus on elucidating the presence/absence of distinctive metabolites (and/or their precursors) and not on providing a holistic view of metabolomic changes and their participation in organismal adaptation to biotic and abiotic stress conditions. Therefore, spectral libraries generated from Cecropia obtusifolia cell suspension cultures in a previous study were considered as a case study and were reanalyzed herein. These libraries were obtained from a time-course experiment under nitrate starvation conditions using both electrospray ionization modes. The applied methodology included the use of ecological analytical tools in a systematic four-step process, including a population analysis of metabolite α diversity, richness, and evenness (i); a chemometrics analysis to identify discriminant groups (ii); differential metabolic marker identification (iii); and enrichment analyses and annotation of active metabolic pathways enriched by differential metabolites (iv). Our species α diversity results referring to the diversity of metabolites represented by mass-to-charge ratio (m/z) values detected at a specific retention time (rt) (an uncommon way to analyze untargeted metabolomic data) suggest that the metabolome is dynamic and is modulated by abiotic stress. A total of 147 and 371 m/z_rt pairs was identified as differential markers responsive to nitrate starvation in ESI− and ESI+ modes, respectively. Subsequent enrichment analysis showed a high degree of completeness of biosynthetic pathways such as those of brassinosteroids, flavonoids, and phenylpropanoids.

Homologue series detection and management in LC-MS data with homologueDiscoverer.

Untargeted metabolomics data analysis is highly labour intensive and can be severely frustrated by both experimental noise and redundant features. Homologous polymer series is a particular case of features that can either represent large numbers of noise features or alternatively represent features of interest with large peak redundancy. Here, we present homologueDiscoverer, an R package that allows for the targeted and untargeted detection of homologue series as well as their evaluation and management using interactive plots and simple local database functionalities. homologueDiscoverer is freely available at GitHub https://github.com/kevinmildau/homologueDiscoverer. Supplementary data are available at Bioinformatics online.

Untargeted Metabolomics Analysis of Crocus cancellatus subsp. damascenus (Herb.) B. Mathew Stigmas and Their Anticarcinogenic Effect on Breast Cancer Cells.

Safranal, crocin, crocetin, and picrocrocin are major known compounds in the stigma extract of Crocus sativus with various medicinal properties. Crocus cancellatus is another Crocus species that grows extensively in Iran's various regions, such as the Kurdistan province. The predominant metabolites and biological properties of C. cancellatus have not yet been investigated. The ingredients of the stigma ethanol extract of C. cancellatus were investigated using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography with tandem mass spectrometry (LC-MS). The ROIMCR approach was performed to analyze the LC-MS full scan data sets. This method searches the MS regions of interest (ROI) data in the m/z domain and analyses the results using the multivariate curve-resolution alternating least squares (MCR-ALS) chemometrics technique for simultaneous resolution of two extracts. Also, the antiproliferative properties of C. cancellatus against MDA-MB-231 and MCF-7 cancer cells were examined by MTT, dual acridine orange/ethidium bromide test, Annexin V-FITC/PI, and zymography. The GC-MS and LC-MS untargeted metabolomics data analysis of the extract indicated the presence of cytotoxic agents including safranal, crocin, picrocrocin, and crocetin in the stigma ethanol extract of C. cancellatus. Biological tests showed that the viability of MDA-MB-231 and MCF-7 cancer cells is decreased following C. cancellatus treatment in a time- and dose-dependent way in both monolayer and 3D cell cultures. The MCF-7 cell spheroids had greater resistance to the cytotoxic activity of the extract in 3D cell culture than the MDA-MB-231 cell spheroids. The morphological changes of the cells treated with C. cancellatus stigmas extract were indicative of apoptosis. Zymography analysis revealed a similar trend of matrix metallopeptidase-2 (MMP-2) and matrix metallopeptidase-9 (MMP-9) activity in the treated cells with C. cancellatus extract in comparison with doxorubicin treatment as a positive control. The findings of this research indicate that the ethanolic extract of C. cancellatus stigmas was a good source of bioactive metabolites with anticancer activity.

Finding CYP2D6 activity biomarkers by correlating MDMA pharmacokinetic parameters to untargeted metabolomics data

Cytochrome P450 (CYP) enzymes play a major role in drug metabolism. Many of these enzymes exhibit a huge inter-individual variation in activity, not only attributable to the individual's genotype. Such environmental influences are hardly measurable, let alone their outcome predictable. The actual phenotype is usually determined through administration of well-studied CYP probe substrates – an approach that is obviously unsuitable for forensic investigations, particularly postmortem. Therefore, we aimed at finding alternative biomarkers of the CYP phenotype. Application of metabolomics techniques in theory should reveal endogenous metabolites processed by CYP isoenzymes, which in turn could be indicative for a person's phenotype for particular CYP isoenzymes (e.g. 2D6). In a standard metabolomics approach, features (unidentified metabolites) could be compared between different known CYP genotype/phenotypes. While this approach is most promising, a controlled study needs to be conducted including genotyping and probe-substrate phenotyping. Instead of a new phenotyping study, we aimed to explore the intended approach with a previous crossover study with a typical CYP2D6 substrate (MDMA) and a known inhibitor of CYP2D6 (bupropion) for the general suitability to find endogenous correlates of CYP2D6 activity. Plasma samples of a controlled MDMA/bupropion crossover study with four experimental test sessions (pretreatment – study day as follows: placebo-placebo, bupropion – placebo, placebo-MDMA, and bupropion-MDMA, n = 16 each), initially intended to study pharmacological questions, were reused. Plasma from timepoint 9 from the placebo-placebo and bupropion-placebo session were worked-up by simple protein precipitation and measured with a previously published untargeted metabolomics method (Boxler. Drug Test Anal 2019;11(5):678–696). Briefly, LC-qTOF-MS (Sciex 6600) with reversed-phase (RP) and HILIC chromatography was used in both positive and negative ESI mode. Untargeted data was analysed using msDial 4.8 and a pre-release version of SIRIUS (5.4.1) for structure elucidation and comparison to common databases. Feature peak areas were correlated (spearman) with the ratio of the area under the curve (AUC) of MDMA metabolites/AUC MDMA as determined as the best CYP2D6 correlate in a former targeted analysis (Steuer. Plos One 2016;11:e0150955). Further filter criteria were: fold change between groups of > 2 or < 0.5 and mean S/N > 150. Out of 24858 features found in RP positive mode, by correlation analysis, 27 features with a spearman correlation greater than 0.55 and with assigned MS/MS and 109 features without MS/MS information were found, respectively. As expected, 3 of the features could be unambiguously identified to be bupropion and its known metabolites. Further 10 features indicated a chlorine cluster, not expected endogenously, which pointed to further fragments/metabolites or artifacts of bupropion. The remaining 14 features could be considered as promising biomarker candidates not related to bupropion and as such as tentative indicators of the respective CYP2D6 phenotype. Using the pharmacokinetic parameters of MDMA, acting as a CYP2D6 substrate, with and without CYP2D6 inhibition by bupropion and corresponding metabolomics data, we could show, that the metabolome approach is able to find potential biomarkers for actual CYP2D6 status. As unfortunately typical for untargeted metabolomics, final identification of features still remains the bottleneck and requires further investigation. In addition, the applicability of the found biomarkers of course needs to be tested in a larger independent cohort. Ideally, a ratio of features that are substrate and product of the enzyme could be formed to increase the predictive power of this approach.

Addressing Missing Data in GC × GC Metabolomics: Identifying Missingness Type and Evaluating the Impact of Imputation Methods on Experimental Replication.

Missing data is a significant issue in metabolomics that is often neglected when conducting data preprocessing, particularly when it comes to imputation. This can have serious implications for downstream statistical analyses and lead to misleading or uninterpretable inferences. In this study, we aim to identify the primary types of missingness that affect untargeted metabolomics data and compare strategies for imputation using two real-world comprehensive two-dimensional gas chromatography (GC × GC) data sets. We also present these goals in the context of experimental replication whereby imputation is conducted in a within-replicate-based fashion─the first description and evaluation of this strategy─and introduce an R package MetabImpute to carry out these analyses. Our results conclude that, in these two GC × GC data sets, missingness was most likely of the missing at-random (MAR) and missing not-at-random (MNAR) types as opposed to missing completely at-random (MCAR). Gibbs sampler imputation and Random Forest gave the best results when imputing MAR and MNAR compared against single-value imputation (zero, minimum, mean, median, and half-minimum) and other more sophisticated approaches (Bayesian principal component analysis and quantile regression imputation for left-censored data). When samples are replicated, within-replicate imputation approaches led to an increase in the reproducibility of peak quantification compared to imputation that ignores replication, suggesting that imputing with respect to replication may preserve potentially important features in downstream analyses for biomarker discovery.

Graph Properties of Mass-Difference Networks for Profiling and Discrimination in Untargeted Metabolomics.

Untargeted metabolomics seeks to identify and quantify most metabolites in a biological system. In general, metabolomics results are represented by numerical matrices containing data that represent the intensities of the detected variables. These matrices are subsequently analyzed by methods that seek to extract significant biological information from the data. In mass spectrometry-based metabolomics, if mass is detected with sufficient accuracy, below 1 ppm, it is possible to derive mass-difference networks, which have spectral features as nodes and chemical changes as edges. These networks have previously been used as means to assist formula annotation and to rank the importance of chemical transformations. In this work, we propose a novel role for such networks in untargeted metabolomics data analysis: we demonstrate that their properties as graphs can also be used as signatures for metabolic profiling and class discrimination. For several benchmark examples, we computed six graph properties and we found that the degree profile was consistently the property that allowed for the best performance of several clustering and classification methods, reaching levels that are competitive with the performance using intensity data matrices and traditional pretreatment procedures. Furthermore, we propose two new metrics for the ranking of chemical transformations derived from network properties, which can be applied to sample comparison or clustering. These metrics illustrate how the graph properties of mass-difference networks can highlight the aspects of the information contained in data that are complementary to the information extracted from intensity-based data analysis.

Prediction of degradation pathways of phenolic compounds in the human gut microbiota through enzyme promiscuity methods

The relevance of phenolic compounds in the human diet has increased in recent years, particularly due to their role as natural antioxidants and chemopreventive agents in different diseases. In the human body, phenolic compounds are mainly metabolized by the gut microbiota; however, their metabolism is not well represented in public databases and existing reconstructions. In a previous work, using different sources of knowledge, bioinformatic and modelling tools, we developed AGREDA, an extended metabolic network more amenable to analyze the interaction of the human gut microbiota with diet. Despite the substantial improvement achieved by AGREDA, it was not sufficient to represent the diverse metabolic space of phenolic compounds. In this article, we make use of an enzyme promiscuity approach to complete further the metabolism of phenolic compounds in the human gut microbiota. In particular, we apply RetroPath RL, a previously developed approach based on Monte Carlo Tree Search strategy reinforcement learning, in order to predict the degradation pathways of compounds present in Phenol-Explorer, the largest database of phenolic compounds in the literature. Reactions predicted by RetroPath RL were integrated with AGREDA, leading to a more complete version of the human gut microbiota metabolic network. We assess the impact of our improvements in the metabolic processing of various foods, finding previously undetected connections with output microbial metabolites. By means of untargeted metabolomics data, we present in vitro experimental validation for output microbial metabolites released in the fermentation of lentils with feces of children representing different clinical conditions.