Metabolic Pathway Extension Approach for Metabolomic Biomarker Identification

Identification of Xenobiotic Biotransformation Products Using Mass Spectrometry-Based Metabolomics Integrated with a Structural Elucidation Strategy by Assembling Fragment Signatures

The identification of xenobiotic biotransformation products is crucial for delineating toxicity and carcinogenicity that might be caused by xenobiotic exposures and for establishing monitoring systems for public health. However, the lack of available reference standards and spectral data leads to the generation of multiple candidate structures during identification and reduces the confidence in identification. Here, a UHPLC-HRMS-based metabolomics strategy integrated with a metabolite structure elucidation approach, namely, FragAssembler, was proposed to reduce the number of false-positive structure candidates. biotransformation product candidates were filtered by mass defect filtering (MDF) and multiple-group comparison. FragAssembler assembled fragment signatures from the MS/MS spectra and generated the modified moieties corresponding to the identified biotransformation products. The feasibility of this approach was demonstrated by the three biotransformation products of di(2-ethylhexyl)phthalate (DEHP). Comprehensive identification was carried out, and 24 and 13 biotransformation products of two xenobiotics, DEHP and 4'-Methoxy-α-pyrrolidinopentiophenone (4-MeO-α-PVP), were annotated, respectively. The number of 4-MeO-α-PVP biotransformation product candidates in the FragAssembler calculation results was approximately 2.1 times lower than that generated by BioTransformer 3.0. Our study indicates that the proposed approach has great potential for efficiently and reliably identifying xenobiotic biotransformation products, which is attributed to the fact that FragAssembler eliminates false-positive reactions and chemical structures and distinguishes modified moieties on isomeric biotransformation products. The FragAssembler software and associated tutorial are freely available at https://cosbi.ee.ncku.edu.tw/FragAssembler/ and the source code can be found at https://github.com/YuanChihChen/FragAssembler.

Deep Characterization of Serum Metabolome Based on the Segment-Optimized Spectral-Stitching Direct-Infusion Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Approach

Direct-infusion Fourier transform ion cyclotron resonance mass spectrometry (DI-FTICR MS) shows great promise for metabolomic analysis due to ultrahigh mass accuracy and resolution. However, most of the DI-FTICR MS approaches focused on high-throughput metabolomics analysis at the expense of sensitivity and resolution and the potential for metabolome characterization has not been fully explored. Here, we proposed a novel deep characterization approach of serum metabolome using a segment-optimized spectral-stitching DI-FTICR MS method integrated with high-confidence and database-independent formula assignments. With varied acquisition parameters for each segment, a highly efficient acquisition was achieved for the whole mass range with sub-ppm mass accuracy. In a pooled human serum sample, thousands of features were assigned with unambiguous formulas and possible candidates based on highly accurate mass measurements. Furthermore, a reaction network was used to select confidently unique formulas from possible candidates, which was constructed by unambiguous formulas and possible candidates connected by the formula differences resulting from biochemical and MS transformation. Compared with full-range and conventional segment acquisition, 8- and 1.2-fold increases in observed features were achieved, respectively. Assignment accuracy was 93-94% for both a standard mixture containing 190 metabolites and a spiked serum sample with the root mean square mass error of 0.15-0.16 ppm. In total, 3534 unequivocal neutral molecular formulas were assigned in the pooled serum sample, 35% of which are contained in the HMDB. This method offers great enhancement in the deep characterization of serum metabolome by DI-FTICR MS.

Highly accurate and large-scale collision cross sections prediction with graph neural networks

The collision cross section (CCS) values derived from ion mobility spectrometry can be used to improve the accuracy of compound identification. Here, we have developed the Structure included graph merging with adduct method for CCS prediction (SigmaCCS) based on graph neural networks using 3D conformers as inputs. A model was trained, evaluated, and tested with >5,000 experimental CCS values. It achieved a coefficient of determination of 0.9945 and a median relative error of 1.1751% on the test set. The model-agnostic interpretation method and the visualization of the learned representations were used to investigate the chemical rationality of SigmaCCS. An in-silico database with 282 million CCS values was generated for three different adduct types of 94 million compounds. Its source code is publicly available at https://github.com/zmzhang/SigmaCCS . Altogether, SigmaCCS is an accurate, rational, and off-the-shelf method to directly predict CCS values from molecular structures.

Time-Series-Dependent Global Data Filtering Strategy for Mining and Profiling of Xenobiotic Metabolites in a Dynamic Complex Matrix: Application to Biotransformation of Flavonoids in the Extract of Ginkgo biloba by Gut Microbiota

Efficient characterization of xenobiotic metabolites and their dynamics in a changing complex matrix remains difficult. Herein, we proposed a time-series-dependent global data filtering strategy for the rapid and comprehensive characterization of xenobiotic metabolites and their dynamic variation based on metabolome data. A set of data preprocessing methods was used to screen potential xenobiotic metabolites, considering the differences between the treated and control groups and the fluctuations over time. To further identify metabolites of the target, an in-house accurate mass database was constructed by potential metabolic pathways and applied. Taking the extract of Ginkgo biloba (EGB) co-incubated with gut microbiota as an example, 107 compounds were identified as flavonoid-derived metabolites (including 67 original from EGB and 40 new) from 7468 ions. Their temporal metabolic profiles and regularities were also investigated. This study provided a systematic and feasible method to elucidate and profile xenobiotic metabolism.

GC-MS based metabolite fingerprinting of serous ovarian carcinoma and benign ovarian tumor

The aim of this study is to identify urinary metabolomic profile of benign and malign ovarian tumors patients. Samples were analyzed using gas chromatography-mass spectrometry (GC-MS) and metabolomic tools to define biomarkers that cause differentiation between groups. 7 metabolites were found to be different in patients with ovarian cancer (OC) and benign tumors (BT). R2Y and Q2 values were found to be 0.670 and 0.459, respectively. L-tyrosine, glycine, stearic acid, turanose and L-threonine metabolites were defined as prominent biomarkers. The sensitivity of the model was calculated as 90.72% and the specificity as 82.09%. In the pathway analysis, glutathione metabolism, aminoacyl-tRNA biosynthesis, glycine serine and threonine metabolic pathway, primary bile acid biosynthesis pathways were found to be important. According to the t-test, 29 metabolites were found to be significant in urine samples of OC patients and healthy controls (HC). R2Y and Q2 values were found to be 0.8170 and 0.749, respectively. These results showed that the model has high compatibility and predictive power. Benzoic acid, L-threonine, L-pyroglutamic acid, creatinine and 3,4-dihydroxyphenylacetic acid metabolites were determined as prominent biomarkers. The sensitivity of the model was calculated as 93.81% and the specificity as 98.59%. Glycine serine and threonine metabolic pathway, glutathione metabolism and aminoacyl-tRNA biosynthesis pathways were determined important in OC patients and HC. The R2Y, Q2, sensitivity and specificity values in the urine samples of BT patients and HC were found to be 0.869, 0.794, 91.75, 97.01% and 97.18%, respectively. L-threonine, L-pyroglutamic acid, benzoic acid, creatinine and pentadecanol metabolites were determined as prominent biomarkers. Valine, leucine and isoleucine biosynthesis and aminoacyl-tRNA biosynthesis were significant. In this study, thanks to the untargeted metabolomic approach and chemometric methods, every group was differentiated from the others and prominent biomarkers were determined.

Emerging targetome and signalome landscape of gut microbial metabolites

The gut microbiome produces chemically diverse small molecules to interact with the host, conveying signals from the gut to the whole system. The microbial metabolites feature several unique modes of interaction with host targets, which fits well into the balanced and networked fashion of biological regulation. Hence, fully unveiling the targetome of signaling microbial metabolites may offer new insights into host health and disease, expand the repertoire of druggable targets, and enlighten a bioinspired path to drug design and discovery. In this review, we present an updated understanding of how microbial metabolite interaction with host targets finely orchestrates and integrates multiple signals to pathophysiological phenotypes, contributing new insights into organ crosstalk and holistic homeostasis maintenance in biological systems. We discuss strategies and open questions for mining and biomimicking the microbial metabolite-targetome interactions for pharmacological manipulation, which may lead to a new paradigm of drug discovery.

Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches

Covering: up to the end of 2020Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions.

Functional Metabolomics and Chemoproteomics Approaches Reveal Novel Metabolic Targets for Anticancer Therapy

Cancer cells exhibit different metabolic patterns compared to their normal counterparts. Although the reprogrammed metabolism has been indicated as strong biomarkers of cancer initiation and progression, increasing evidences suggest that metabolic alteration tuned by oncogenic drivers contributes to the occurrence and development of cancers rather than just being a hallmark of cancer. With this notion, targeting cancer metabolism holds promise as a novel anticancer strategy and is embracing its renaissance during the past two decades. Herein we have summarized the most recent developments in omics technology, including both metabolomics and proteomics, and how the combined use of these analytical tools significantly impacts this field by comprehensively and systematically recording the metabolic changes in cancer and hence reveals potential therapeutic targets that function by modulating the disrupted metabolic pathways.

A high-resolution MS/MS based strategy to improve xenobiotic metabolites analysis by metabolic pathway extension searching combined with parallel reaction monitoring: Flavonoid metabolism in wound site as a case

For the analysis of xenobiotic metabolism, metabolites are commonly qualified by high-resolution mass spectrometry such as orbitrap or time-of-flight mass spectrometers, and quantified by triple-quadrupole (QQQ) mass spectrometer based multiple reaction monitoring. While this workflow shows drawback in the difficulty for instrumental parameters transfer, and QQQ provides less specificity. In this work, we constructed a high-resolution MS/MS (HR-MS/MS) based strategy to improve the discovery and quantification of unknown xenobiotic metabolites by metabolic pathway extension (MPE) searching combined with parallel reaction monitoring (PRM). Taking the flavonoid metabolism in diabetes wound S9 incubates as a test case. Firstly, MPE approach was used to screen all potential metabolites. In this step, an m/z value library of all theoretic flavonoid metabolites were constructed based on predefined flavonoid structures through 21 common xenobiotic metabolic reactions, and this library was matched with all features extracted from raw data (MS1 scan) of flavonoid-S9 co-incubate, then the matched features were exported into target list for MS2 fragmentation for structure validation. Secondly, the metabolites were relatively quantified by PRM mode based on their characteristic product ions. As a result, 131 metabolites of 9 different kinds of flavonoids in the skin and muscle were identified. To our best knowledge, this is the first report on the metabolism of flavonoids in the skin or muscle tissue. The results also validated the proposed HR-MS/MS-based strategy provided high specificity throughout both discovery and quantitation process of unknown xenobiotic metabolites without need of instrumental parameter transfer.

Uterine Metabolomics Reveals Protection of Taohong Siwu Decoction Against Abnormal Uterine Bleeding

Incomplete abortion, a procedure for terminating pregnancy, will lead to abnormal uterine bleeding (AUB), infections, and even death. Taohong Siwu decoction (TSD) is a traditional Chinese medicine (TCM) formula, which has been developed to treat AUB for hundreds of years. However, the mechanism of the protective effect of TSD against AUB is not clear. We performed mass spectrometry (MS) of uterine samples to observe metabolic profile resulting from the treatment with TSD. An integrated gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry based untargeted metabolomics approach combined with multivariate statistical analyses were used to investigate the metabolic profile of TSD against AUB. There was clear separation between pregnant and incomplete aborting rats as well as incomplete aborting and TSD administered rats. Based on random forest algorithm and receiver operator characteristic analysis, 12 biomarkers were optimized related to TSD administered. The effect of TSD on AUB are related to several pathways, such as AA metabolism, glyoxylate and dicarboxylate metabolism, alanine, aspartate, and glutamate metabolism. To our knowledge, this is the first uterine metabolomics study focusing on TSD on AUB and provide a new perspective for explaining the mechanism of TSD on AUB.

Diagnostic fragmentation-assisted mass spectral networking coupled with in silico dereplication for deep annotation of steroidal alkaloids in medicinal Fritillariae Bulbus

Fully understanding the chemicals in an herbal medicine remains a challenging task. Molecular networking (MN) allows to organize tandem mass spectrometry (MS/MS) data in complex samples by mass spectral similarity, which yet suffers from low coverage and accuracy of compound annotation due to the size limitation of available databases and differentiation obstacle of similar chemical scaffolds. In this work, an enhanced MN-based strategy named diagnostic fragmentation-assisted molecular networking coupled with in silico dereplication (DFMN-ISD) was introduced to overcome these obstacles: the rule-based fragmentation patterns provide insights into similar chemical scaffolds, the generated in silico candidates based on metabolic reactions expand the available natural product databases, and the in silico annotation method facilitates the further dereplication of candidates by computing their fragmentation trees. As a case, this approach was applied to globally profile the steroidal alkaloids in Fritillariae bulbus, a commonly used antitussive and expectorant herbal medicine. Consequently, a total of 325 steroidal alkaloids were discovered, including 106 cis-D/E-cevanines, 142 trans-D/E-cevanines, 29 jervines, 23 veratramines, and 25 verazines. And 10 of them were confirmed by available reference standards. Approximately 70% of the putative steroidal alkaloids have never been reported in previous publications, demonstrating the benefit of DFMN-ISD approach for the comprehensive characterization of chemicals in a complex plant organism.

An improved detection and identification strategy for untargeted metabolomics based on UPLC-MS

Untargeted metabolomics provides a comprehensive investigation of metabolites and enables the discovery of biomarkers. Improvements in sample preparation, chromatographic separation and raw data processing procedure greatly enhance the metabolome coverage. In addition, database-dependent software identification is also essential, upon which enhances the identification confidence and benefits downstream biological analysis. Herein, we developed an improved detection and identification strategy for untargeted metabolomics based on UPLC-MS. In this work, sample preparation was optimized by considering chemical properties of different metabolites. Chromatographic separation was done by two different columns and MS detection was performed under positive and negative ion modes regarding to the different polarities of metabolites. According to the characteristics of the collected data, an improved identification and evaluation strategy was developed involving fragment simulation and MS/MS library search based on two commonly used databases, HMDB and METLIN. Such combination integrated information from different databases and was aimed to enhance identification confidence by considering the rationality of fragmentation, biological sources and functions comprehensively. In addition, decision tree analysis and lab-developed database were also introduced to assist the data processing and enhance the identification confidence. Finally, the feasibility of the developed strategy was validated by liver samples of obesity mice and controls. 238 metabolites were accurately detected, which was beneficial for the subsequent biomarker discovery and downstream pathway analysis. Therefore, the developed strategy remarkably facilitated the identification accuracy and the confirmation of metabolites in untargeted metabolomics.

The crucial role of metabolic regulation in differential hepatotoxicity induced by furanoids in Dioscorea bulbifera

Diterpenoid lactones (DLs), a group of furan-containing compounds found in Dioscorea bulbifera L. (DB), have been reported to be associated with hepatotoxicity. Different hepatotoxicities of these DLs have been observed in vitro, but reasonable explanations for the differential hepatotoxicity have not been provided. Herein, the present study aimed to confirm the potential factors that contribute to varied hepatotoxicity of four representative DLs (diosbulbins A, B, C, F). In vitro toxic effects were evaluated in various cell models and the interactions between DLs and CYP3A4 at the atomic level were simulated by molecular docking. Results showed that DLs exhibited varied cytotoxicities, and that CYP3A4 played a modulatory role in this process. Moreover, structural variation may cause different affinities between DLs and CYP3A4, which was positively correlated with the observation of cytotoxicity. In addition, analysis of the glutathione (GSH) conjugates indicated that reactive intermediates were formed by metabolic oxidation that occurred on the furan moiety of DLs, whereas, GSH consumption analysis reflected the consistency between the reactive metabolites and the hepatotoxicity. Collectively, our findings illustrated that the metabolic regulation played a crucial role in generating the varied hepatotoxicity of DLs.

Mesophilic and thermophilic anaerobic digestion of aqueous phase generated from hydrothermal liquefaction of cornstalk: Molecular and metabolic insights

The critical challenge of hydrothermal liquefaction (HTL) for bio-oil production from biomass is the production of large amounts of aqueous products (HTL-AP) with high organic contents. The present study investigated the anaerobic digestion (AD) performances of HTL-AP under both thermophilic and mesophilic conditions, and molecular and metabolic analysis were conducted to provide insights into the different performances. The results showed that thermophilic AD had lower COD removal efficiency compared to mesophilic AD (45.0% vs. 61.6%). Liquid chromatography coupled with organic carbon detection and organic nitrogen (LC-OCD-OND) analysis showed that both high molecular weight (HMW) and low molecular weight (LMW) compounds were degraded to some extent and more LMW acids (LMWA) and recalcitrant aromatic compounds were degraded in the mesophilic reactor, which was the main reason of higher COD removal efficiency. Phenyl compounds (e.g. phenol and 2 methoxyphenol), furans and pyrazines were the recalcitrant chemicals detected through GC-MS analysis. Fourier transform ion cyclone resonance mass spectrometry (FT-ICR-MS) analysis demonstrated the complexity of HTL-AP and the proportions of phenolic or condensed aromatic compounds increased especially in the thermophilic effluents. Metabolites analysis showed that the reasons contributing to the differences of mesophilic and thermophilic AD were not only related to the degradation of organic compounds (e.g. benzoate degradation via CoA ligation) in HTL-AP but also related to the microbial autogenesis (e.g. fatty acid biosynthesis) as well as the environmental information processing. In addition, the enrichment of Mesotoga, responsible for the high degradation efficiency of LMWA, and Pelolinea, involved in the degradation of phenyl compounds, were found in mesophilic reactor, which was consistent with higher removal of corresponding organics.

Application of metabolomics for unveiling the therapeutic role of traditional Chinese medicine in metabolic diseases

ETHNOPHARMACOLOGICAL RELEVANCE

Traditional medicine has been practiced for thousands of years in China and some Asian countries. Traditional Chinese Medicine (TCM) is characterized as multi-component and multiple targets in disease therapy, and it is a great challenge for elucidating the mechanisms of TCM.

AIM OF THE REVIEW

Comprehensively summarize the application of metabolomics in biomarker discovery, stratification of TCM syndromes, and mechanism underlying TCM therapy on metabolic diseases.

METHODS

This review systemically searched the publications with key words such as metabolomics, traditional Chinese medicine, metabolic diseases, obesity, cardiovascular disease, diabetes mellitus in "Title OR Abstract" in major databases including PubMed, the Web of Science, Google Scholar, Science Direct, CNKI from 2010 to 2019.

RESULTS

A total of 135 papers was searched and included in this review. An overview of articles indicated that metabolic characteristics may be a hallmark of different syndromes/models of metabolic diseases, which provides a new perspective for disease diagnosis and therapeutic optimization. Moreover, TCM treatment has significantly altered the metabolic perturbations associated with metabolic diseases, which may be an important mechanism for the therapeutic effect of TCM.

CONCLUSIONS

Until now, many metabolites and differential biomarkers related to the pathogenesis of metabolic diseases and TCM therapy have been discovered through metabolomics research. Unfortunately, the biological role and mechanism of disease-related metabolites were largely unclarified so far, which warrants further investigation.

Butyrate Suppresses the Proliferation of Colorectal Cancer Cells via Targeting Pyruvate Kinase M2 and Metabolic Reprogramming

Butyrate is a short chain fatty acid present in a high concentration in the gut lumen. It has been well documented that butyrate, by serving as an energetic metabolite, promotes the proliferation of normal colonocytes while, by serving as a histone deacetylase inhibitor, epigenetically suppressing the proliferation of cancerous counterparts undergoing the Warburg effect. However, how butyrate interrupts the metabolism of colorectal cancer cells and ultimately leads to the suppression of cell proliferation remains unclear. Here, we employed a metabolomics-proteomics combined approach to explore the link between butyrate-mediated proliferation arrest and cell metabolism. A metabolomics study revealed a remodeled metabolic profile with pronounced accumulation of pyruvate, decreased glycolytic intermediates upstream of pyruvate and reduced levels of nucleotides in butyrate-treated HCT-116 cells. Supplementation of key metabolite intermediates directly affected cancer-cell metabolism and modulated the suppressive effect of butyrate in HCT-116 cells. By a Drug Affinity Responsive Target Stability (DARTS)-based quantitative proteomics approach, we revealed the M2 isoform of a pyruvate kinase, PKM2, as a direct binding target of butyrate. Butyrate activates PKM2 via promoting its dephosphorylation and tetramerization and thereby reprograms the metabolism of colorectal cancer cells, inhibiting the Warburg effect while favoring energetic metabolism. Our study thus provides a mechanistic link between PKM2-induced metabolic remodeling and the antitumorigenic function of butyrate and demonstrates a widely applicable approach to uncovering unknown protein targets for small molecules with biological functions.

Non-targeted glycosidic profiling of international wines using neutral loss-high resolution mass spectrometry

Many metabolites naturally occur as glycosides, since sugar moieties can be crucial for their biological activity and increase their water solubility. In the plant kingdom they may occur as glycosides or sugar esters, depending on precursor chemical structure, and in wine they have traditionally attracted attention due to their organoleptic properties, such as astringency and bitterness, and because they affect the colour and aroma of wines. A new approach directed at detailed description of glycosides in a large selection of monovarietal wines (8 samples each of Pinot Blanc, Muller Thurgau, Riesling, Traminer, Merlot, Pinot Noir and Cabernet Sauvignon) was developed by combining high performance liquid chromatography with high resolution tandem mass spectrometry. Analytical separation was performed on an Accucore™ Polar Premium LC column, while mass analysis was performed in negative ion mode with an non-targeted screening approach, using a Full MS/AIF/NL dd-MS² experiment at a resolving power of 140,000 FWHM. Over 280 glycoside-like compounds were detected, of which 133 (including low-molecular weight phenols, flavonoids and monoterpenols) were tentatively identified in the form of pentose (6), deoxyhexose (17), hexose (73), hexose-pentose (16), hexose-deoxyhexose (7), dihexose (5) and hexose ester (9) derivatives. It was not possible to univocally define the corresponding chemical structure for the remaining 149 glycosides. Non-parametric statistical analysis showed it was possible to well characterise the glycosylated profile of all red and Traminer wines, while the identified glycosides were almost entirely lacking in Pinot Blanc, Riesling and Muller Thurgau wines. Also Tukey's Honestly Significant Difference test (p < 0.05) and Principal Component Analysis confirmed that it was possible to almost entirely distinguish the selected red wines from each other according to their glycosylated profile.

Metabolism of five diterpenoid lactones from Dioscorea bulbifera tubers in zebrafish

Diterpenoid lactones (DLs) have been reported to be the main hepatotoxic constituents in Dioscorea bulbifera tubers (DBT), a traditional Chinese medicinal herb. The acquisition of early information regarding its metabolism is critical for evaluating the potential hepatotoxicity of DLs. We investigated, for the first time, the main metabolites of diosbulbin A (DIOA), diosbulbin C (DIOC), diosbulbin (DIOG), diosbulbin (DIOM) and diosbulbin (DIOF) in adult zebrafish. By using ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF MS), 6, 2, 7, 5 and 4 metabolites of DIOA, DIOC, DIOF, DIOM and DIOG were identified in the zebrafish body and the aqueous solution, respectively. Both phase-I and phase-II metabolites were observed in the metabolic profiles and the metabolic pathways involved in hydroxyl reduction, glucuronidation, glutathione conjugation and sulfation. The above results indicated that hepatocytic metabolism might be the major route of clearance for DLs. This study provided important information for the understanding of the metabolism of DLs in DBT.