MSE with mass defect filtering for in vitro and in vivo metabolite identification

Dilute and shoot approach for toxicology testing

Toxicology testing is performed in clinical settings, forensic settings, and for controlling doping. Drug screening is a toxicology test to determine if drugs are present in biological samples. The most common specimen type for drug testing is urine, as drugs and/or their metabolites are often more concentrated in the urine, extending the detection window of drugs. The dilute-and-shoot method is a simple procedure used in toxicology testing, where a sample is diluted before being directly injected into the liquid chromatography-mass spectrometry (LC-MS) system. This method is easy, quick, and cost-saving, and can be used for protein-poor liquid specimens such as urine. Thus, it is reasonable and attractive for busy toxicology laboratories to combine the dilute-and-shoot method with high-resolution hyphenated-MS for urine drug screening. This method has several disadvantages, including a suboptimal detection capability for certain analytes, as well as interference from co-eluting matrix components called matrix effects, in which co-eluting matrix molecules alter the ionization efficiency of the analyte molecules at the ionization source in LC-MS, altering (mostly reducing) the analyte detection capability. The matrix effect testing is essential for the validation of LC-MS-based assays. A reasonable approach to addressing these undesirable effects would be to minimize these components. The most straightforward approach is to reduce the amounts of matrix components by using a higher dilution of the specimen and a lower volume for specimen injection. Optimization of the chromatographic separation is another reasonable approach for reducing co-eluting matrix components with the analyte.

Expanding Per- and Polyfluoroalkyl Substances Coverage in Nontargeted Analysis Using Data-Independent Analysis and IonDecon

Per- and polyfluoroalkyl substances (PFAS) are widespread, persistent environmental contaminants that have been linked to various health issues. Comprehensive PFAS analysis often relies on ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC HRMS) and molecular fragmentation (MS/MS). However, the selection and fragmentation of ions for MS/MS analysis using data-dependent analysis results in only the topmost abundant ions being selected. To overcome these limitations, All Ions fragmentation (AIF) can be used alongside data-dependent analysis. In AIF, ions across the entire m/z range are simultaneously fragmented; hence, precursor-fragment relationships are lost, leading to a high false positive rate. We introduce IonDecon, which filters All Ions data to only those fragments correlating with precursor ions. This software can be used to deconvolute any All Ions files and generates an open source DDA formatted file, which can be used in any downstream nontargeted analysis workflow. In a neat solution, annotation of PFAS standards using IonDecon and All Ions had the exact same false positive rate as when using DDA; this suggests accurate annotation using All Ions and IonDecon. Furthermore, deconvoluted All Ions spectra retained the most abundant peaks also observed in DDA, while filtering out much of the artifact peaks. In complex samples, incorporating AIF and IonDecon into workflows can enhance the MS/MS coverage of PFAS (more than tripling the number of annotations in domestic sewage). Deconvolution in complex samples of All Ions data using IonDecon did retain some false fragments (fragments not observed when using ion selection, which were not isotopes or multimers), and therefore DDA and intelligent acquisition methods should still be acquired when possible alongside All Ions to decrease the false positive rate. Increased coverage of PFAS can inform on the development of regulations to address the entire PFAS problem, including both legacy and newly discovered PFAS.

An integrated analytical strategy to decipher the metabolic profile of alkaloids in Compound Kushen injection based on UHPLC-ESI-QTOF/MSE

1. Compound Kushen injection (CKI) is a kind of sterilized water-soluble traditional Chinese medicine preparation that has been used for the clinical treatment of a variety of cancers (hepatocellular carcinoma, lung cancer, etc.) for nineteen years. However, to date, the metabolism-related study on CKI in vivo has not been conducted.2. An integrated analytical strategy was established to investigate the metabolic profile of alkaloids of CKI in rat plasma, urine and feces based on ultra-high performance liquid chromatography-electrospray quadrupole time-of-flight mass spectrometry in MS^E mode (UHPLC-ESI-QTOF/MS^E).3. Nineteen prototype alkaloids (including 12 matrine-type alkaloids, 2 cytisine-type alkaloids, 3 lupinine-type alkaloids, and 2 aloperine-type alkaloids) of CKI were identified in vivo. Furthermore, seventy-one metabolites of alkaloids (including 11 of lupanine-related metabolites, 14 of sophoridine-related metabolites, 14 of lamprolobine-related metabolites and 32 of baptifoline-related metabolites) were tentatively characterized. Metabolic pathways involved in the metabolism of phase I (include oxidation, reduction, hydrolysis, and desaturation), phase II (mainly include glucuronidation, acetylcysteine or cysteine conjugation, methylation, acetylation and sulfation) and associated combination reactions.4. The integrated analytical strategy was successfully used to characterize the prototype alkaloids and their metabolites in CKI, and the results laying a foundation for further study its pharmacodynamic substances in vivo.

Bacterial growth monitored by two-dimensional tandem mass spectrometry

The growth of the bacterium E. coli was monitored by targeting the phospholipid constituents through desorption electrospray ionization and characterizing individual sets of isomers by recording the full 2D MS/MS data domain in a single scan of a modified quadrupole ion trap mass spectrometer. The experiments tested the applicability of the new instrumental capabilities which include sample interrogation at the molecular level for multiple components at speeds of <10 seconds/sample. The major lipids observed were phosphatidylethanolamines and phosphatidylglycerols and the growth experiment showed fatty acid chain modification from alkene to cyclopropyl groups over time. Notably, these novel MS scans were also performed using desorption electrospray ionization (DESI) to quickly sample complex mixtures without pre-separation. This demonstration experiment has implications for the value of ambient ionization mass spectrometry for monitoring biological systems on physiologically relevant timescales.

Acylated saponins and flavonoid glycosides from the fruits of Stewartia koreana

Three acylated saponins and three flavonoid glycosides, along with nine known flavonoids, were isolated from the fruits of Stewartia koreana Nakai ex Rehder (Theaceae) using relative mass defect filtering analysis. The structures of these compounds were determined by performing spectroscopic analyses and using chemical methods. Furthermore, all the isolates were evaluated for their effects on the mRNA expression of the genes for proprotein convertase subtilisin/kexin type 9 (PCSK9) and low-density lipoprotein receptor (LDLR) as well as their inhibitory activities on PCSK9 and LDLR binding. None of the isolates was deemed to be active in PCSK9-LDLR binding inhibition. However, (+)-catechin was found to inhibit PCSK9 expression and increase LDLR expression, suggesting the potential of (+)-catechin to lower cholesterol level via the downregulation of PCSK9 expression.

Ultra-high pressure liquid chromatography coupled to travelling wave ion mobility-time of flight mass spectrometry for the screening of pharmaceutical metabolites in wastewater samples: Application to antiretrovirals

The presence of pharmaceutical compounds in the aquatic environment is a significant environmental health concern, which is exacerbated by recent evidence of the contribution of drug metabolites to the overall pharmaceutical load. In light of a recent report of the occurrence of metabolites of antiretroviral drugs (ARVDs) in wastewater, we investigate in the present work the occurrence of further ARVD metabolites in samples obtained from a domestic wastewater treatment plant in the Western Cape, South Africa. Pharmacokinetic data indicate that ARVDs are biotransformed into several positional isomeric metabolites, only two of which have been reported wastewater samples. Given the challenges associated with the separation and identification of isomeric species in complex wastewater samples, a method based on liquid chromatography hyphenated to ion mobility spectrometry-high resolution mass spectrometry (LC-IMS-HR-MS) was implemented. Gradient LC separation was achieved on a sub-2 µm reversed phase column, while the quadrupole-time-of-flight MS was operated in data independent acquisition (DIA) mode to increase spectral coverage of detected features. A mass defect filter (MDF) template was implemented to detect ARVD metabolites with known phase I and phase II mass shifts and fractional mass differences and to filter out potential interferents. IMS proved particularly useful in filtering the MS data for co-eluting species according to arrival time to provide cleaner mass spectra. This approach allowed us to confirm the presence of two known hydroxylated efavirenz and nevirapine metabolites using authentic standards, and to tentatively identify a carboxylate metabolite of abacavir previously reported in literature. Furthermore, three hydroxylated-, two sulphated and one glucuronidated metabolite of efavirenz, two hydroxylated metabolites of nevirapine and one hydroxylated metabolite of ritonavir were tentatively or putatively identified in wastewater samples for the first time. Assignment of the metabolites is discussed in terms of high resolution fragmentation data, while collisional cross section (CCS) values measured for the detected analytes are reported to facilitate further work in this area.

Review on in vivo profiling of drug metabolites with LC-MS/MS in the past decade

Metabolite profiling is an indispensable part of drug discovery and development, enabling a comprehensive understanding of the drug's metabolic behavior. Liquid chromatography-mass spectrometry facilitates metabolite profiling by reducing sample complexity and providing high sensitivity. This review discusses the in vivo metabolite profiling involving LC-MS/MS and the utilization of QTOF, QQQ mass analyzers with a particular emphasis on a mass filter. Further, a summary of sample extraction procedures in biological matrices such as plasma, urine, feces, serum and hair as in vivo samples are outlined. toward the end, we present 15 case studies in biological matrices and their LC-MS/MS conditions to understand the metabolic disposition.

Insight into chemical basis of traditional Chinese medicine based on the state-of-the-art techniques of liquid chromatography-mass spectrometry

Traditional Chinese medicine (TCM) has been an indispensable source of drugs for curing various human diseases. However, the inherent chemical diversity and complexity of TCM restricted the safety and efficacy of its usage. Over the past few decades, the combination of liquid chromatography with mass spectrometry has contributed greatly to the TCM qualitative analysis. And novel approaches have been continuously introduced to improve the analytical performance, including both the data acquisition methods to generate a large and informative dataset, and the data post-processing tools to extract the structure-related MS information. Furthermore, the fast-developing computer techniques and big data analytics have markedly enriched the data processing tools, bringing benefits of high efficiency and accuracy. To provide an up-to-date review of the latest techniques on the TCM qualitative analysis, multiple data-independent acquisition methods and data-dependent acquisition methods (precursor ion list, dynamic exclusion, mass tag, precursor ion scan, neutral loss scan, and multiple reaction monitoring) and post-processing techniques (mass defect filtering, diagnostic ion filtering, neutral loss filtering, mass spectral trees similarity filter, molecular networking, statistical analysis, database matching, etc.) were summarized and categorized. Applications of each technique and integrated analytical strategies were highlighted, discussion and future perspectives were proposed as well.

Hybrid organic/inorganic hybrid surface technology for increasing the performance of LC/MS(MS)-based drug metabolite identification studies: Application to gefitinib and metabolites in mouse plasma and urine

The detection, identification and quantification of drug metabolites plays a key role in drug discovery and development. Liquid chromatography (LC) coupled to mass spectrometry (MS) has become the primary technology for these studies due to its sensitivity and specificity. However, the presence of transition metals in the chromatography system and columns can result in non-specific and unwanted interactions with the drug and/or its metabolites, via electron-pair donation, leading to poor chromatography and analyte loss. The use of a hybrid organic/inorganic surface applied to the metal surfaces of the chromatography system and column has been demonstrated to reduce or eliminate these effects. When employed for the analysis of mouse urine, derived from the oral dosing of mice with the EGFR inhibitor gefitinib, we observed more symmetrical LC peaks. This resulted in a 33 % improvement in peak capacity for a 10 min reversed - phase gradient separation, a two-fold increase in MS response, cleaner MS spectra and improved peak response reproducibility. This hybrid surface barrier appears to offer significant advantages in the analysis of low-concentration metabolites, potentially facilitating the accurate determination of the elimination phase of the pharmacokinetic (PK) curve and detection of drug metabolites in microdosing or microsampling studies.

Synthetic drugs of abuse

Synthetic drugs of abuse contain various psychoactive substances. These substances have recently emerged as novel drugs of abuse in public; thus, they are known as novel psychoactive substances (NPS). As these compounds are artificially synthesized in a laboratory, they are also called designer drugs. Synthetic cannabinoids and synthetic cathinones are the two primary classes of NPS or designer drugs. Synthetic cannabinoids, also known as "K2" or "Spice," are potent agonists of the cannabinoid receptors. Synthetic cathinones, known as "Bath salts," are beta-keto amphetamine derivatives. These compounds can cause severe intoxication, including overdose deaths. NPS are accessible locally and online. NPS are scheduled in the US and other countries, but the underground chemists keep modifying the chemical structure of these compounds to avoid legal regulation; thus, these compounds have been evolving rapidly. These drugs are not detectable by traditional drug screening, and thus, these substances are mainly abused by young individuals and others who wish to avoid drug detection. These compounds are analyzed primarily by mass spectrometry.

Advances in drugs of abuse testing

Drugs of abuse testing is widely used clinically and forensically. Urine is the preferred type of specimen for drugs of abuse screening, but saliva, sweat, hair, and meconium are emerging types of specimens. GC-MS has been used as a gold standard for confirmatory drug testing, but LC-tandem-MS can analyze more diverse types of analytes than GC-MS. Thus, LC-tandem-MS becomes a new gold standard for confirmatory drug testing. Unlike GC-MS, LC-tandem-MS is not suited for non-targeted comprehensive drug screening. But with the advent of high-resolution-MS such as Tof-MS, which can discriminate the compounds of similar molecular masses but with different formulas, LC-hybrid-Tof-MS is usable for non-targeted comprehensive drug screening. Another technical advancement is the advent of miniature ambient ionization MS, which can analyze biological specimens including urine within one minute. Thus these mass spectrometers are promising for rapid drugs of abuse testing in a POC setting.

Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma

A discovery-based lipid profiling study of serum samples from a cohort that included patients with clear cell renal cell carcinoma (ccRCC) stages I, II, III, and IV (n = 112) and controls (n = 52) was performed using ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry and machine learning techniques. Multivariate models based on support vector machines and the LASSO variable selection method yielded two discriminant lipid panels for ccRCC detection and early diagnosis. A 16-lipid panel allowed discriminating ccRCC patients from controls with 95.7% accuracy in a training set under cross-validation and 77.1% accuracy in an independent test set. A second model trained to discriminate early (I and II) from late (III and IV) stage ccRCC yielded a panel of 26 compounds that classified stage I patients from an independent test set with 82.1% accuracy. Thirteen species, including cholic acid, undecylenic acid, lauric acid, LPC(16:0/0:0), and PC(18:2/18:2), identified with level 1 exhibited significantly lower levels in samples from ccRCC patients compared to controls. Moreover, 3α-hydroxy-5α-androstan-17-one 3-sulfate, cis-5-dodecenoic acid, arachidonic acid, cis-13-docosenoic acid, PI(16:0/18:1), PC(16:0/18:2), and PC(O-16:0/20:4) contributed to discriminate early from late ccRCC stage patients. The results are auspicious for early ccRCC diagnosis after validation of the panels in larger and different cohorts.

Mass Spectrometry-Based Metabolic Fingerprinting Contributes to Unveil the Role of RSUME in Renal Cell Carcinoma Cell Metabolism

Clear cell renal cell carcinoma (ccRCC) is a heterogeneous disease with 50-80% patients exhibiting mutations in the von Hippel-Lindau (VHL) gene. RSUME (RWD domain (termed after three major RWD-containing proteins: RING finger-containing proteins, WD-repeat-containing proteins, and yeast DEAD (DEXD)-like helicases)-containing protein small ubiquitin-related modifier (SUMO) enhancer) acts as a negative regulator of VHL function in normoxia. A discovery-based metabolomics approach was developed by means of ultraperformance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (MS) for fingerprinting the endometabolome of a human ccRCC cell line 786-O and three other transformed cell systems (n = 102) with different expressions of RSUME and VHL. Cross-validated orthogonal projection to latent structures discriminant analysis models were built on positive, negative, and a combination of positive- and negative-ion mode MS data sets. Discriminant feature panels selected by an iterative multivariate classification allowed differentiating cells with different expressions of RSUME and VHL. Fifteen identified discriminant metabolites with level 1, including glutathione, butyrylcarnitine, and acetylcarnitine, contributed to understand the role of RSUME in ccRCC. Altered pathways associated with the RSUME expression were validated by biological and bioinformatics analyses. Combined results showed that in the absence of VHL, RSUME is involved in the downregulation of the antioxidant defense system, whereas in the presence of VHL, it acts in rerouting energy-related pathways, negatively modulating the lipid utilization, and positively modulating the fatty acid synthesis, which may promote deposition in droplets.

Metabolic stability studies of lead compounds supported by separation techniques and chemometrics analysis

With metabolism being one of the main routes of drug elimination from the body (accounting for removal of around 75% of known drugs), it is crucial to understand and study metabolic stability of drug candidates. Metabolically unstable compounds are uncomfortable to administer (requiring repetitive dosage during therapy), while overly stable drugs increase risk of adverse drug reactions. Additionally, biotransformation reactions can lead to formation of toxic or pharmacologically active metabolites (either less-active than parent drug, or even with different action). There were numerous approaches in estimating metabolic stability, including in vitro, in vivo, in silico, and high-throughput screening to name a few. This review aims at describing separation techniques used in in vitro metabolic stability estimation, as well as chemometric techniques allowing for creation of predictive models which enable high-throughput screening approach for estimation of metabolic stability. With a very low rate of drug approval, it is important to understand in silico methods that aim at supporting classical in vitro approach. Predictive models that allow assessment of certain biological properties of drug candidates allow for cutting not only cost, but also time required to synthesize compounds predicted to be unstable or inactive by in silico models.

Fast Evaluation of Herbal Substance Class Composition by Relative Mass Defect Plots

A holistic, nontargeted mass spectrometric analysis of any herbal material and preparation is intimately connected to fast chemical profiling and visualization of secondary plant metabolite classes or single compounds. High-resolution mass spectral data enable a broad variety of analytical possibilities. Often a fast and comprehensive overview on compound classes (phytochemical profiling) is needed before single-substance considerations. We present a fast approach for the initial characterization and substance class profiling using relative mass defect plots for the visualization of herbal compositions. From a dataset of 1160 common plant metabolites that represent a varied mixture of molecular classes in polarity, glycosylation, and alkylation, manually annotated for substance classes, the relative mass defects were calculated using theoretical molecular masses. For the calculation of the relative mass defect, a new approach incorporating two correction functions to obtain correct relative mass defect results also for large hydrocarbons, and a multitude of polyhalogenated molecules was developed. Using the Khachyan algorithm, elliptical areas clustering substance classes within the relative mass defect plots were calculated. The resulting novel relative mass defect plots provide a quick way of two-dimensional substance class mapping directly from high-resolution mass spectral data and may be considered as a unique fingerprint for herbals, part of them or herbal preparations. We show that adding the retention time as a third dimension improves the resolution power of the two-dimensional relative mass defect plot and offers the possibility for a more detailed substance class mapping.

Metabolite identification using an ion mobility enhanced data-independent acquisition strategy and automated data processing

RATIONALE

Liquid chromatography/mass spectrometry is an essential tool for efficient and reliable quantitative and qualitative analysis and underpins much of contemporary drug metabolism and pharmacokinetics. Data-independent acquisition methods such as MS^E have reduced the potential to miss metabolites, but do not formally generate quadrupole-resolved product ion spectra. The addition of ion mobility separation to these approaches, for example, in High-Definition MS^E (HDMS^E ) has the potential to reduce the time needed to set up an experiment and maximize the chance that all metabolites present can be resolved and characterized. We compared High-Definition Data-Dependent Acquisition (HD-DDA), MS^E and HDMS^E approaches using automated software processing with Mass-MetaSite and WebMetabase.

METHODS

Metabolite identification was performed on incubations of glucagon-like peptide-1 (7-37) (GLP-1) and verapamil hydrochloride. The HD-DDA, MS^E and HDMS^E experiments were conducted on a Waters ACQUITY UPLC I-Class LC system with a VION IMS quadrupole time-of-flight (QTOF) mass spectrometer operating under UNIFI control. All acquired data were processed using MassMetaSite able to read data from UNIFI 1.9.4. WebMetabase was used to review the detected chromatographic peaks and the spectral data interpretations.

RESULTS

A comparison of outcomes obtained for MS^E and HDMS^E data demonstrated that the same structures were proposed for metabolites of both verapamil and GLP-1. The ratio of structurally matched to mismatched product ions found by MassMetaSite was slightly greater for HDMS^E than for MS^E , and HD-DDA, thus improving confidence in the structures proposed through the addition of ion mobility based data acquisitions_. CONCLUSIONS: HDMS^E data acquisition is an effective approach for the elucidation of metabolite structures for both small molecules and peptides, with excellent accuracy and quality, requiring minimal tailoring for the compound under investigation.

SWATH: A Data-Independent Tandem Mass Spectrometry Method to Quantify 13C Enrichment in Cellular Metabolites and Fragments

Recently, the sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH) method coupled with liquid chromatography has been demonstrated for the quantification of isotopic ¹³C enrichment in a large number of cellular metabolites and fragments. SWATH, a data-independent acquisition (DIA) method, alleviates the need for data deconvolution and shows greater accuracy in the quantification of low abundance isotopologs of fragments thereby resulting in a lower systematic error. Here we provide a detailed protocol for the design of Q1 mass isolation windows and the post-acquisition data analysis with emphasis on the untargeted nature of SWATH.

A classification of liquid chromatography mass spectrometry techniques for evaluation of chemical composition and quality control of traditional medicines

Natural products (NPs) and traditional medicines (TMs) are used for treatment of various diseases and also to develop new drugs. However, identification of drug leads within the immense biodiversity of living organisms is a challenging task that requires considerable time, labor, and computational resources as well as the application of modern analytical instruments. LC-MS platforms are widely used for both drug discovery and quality control of TMs and food supplements. Moreover, a large dataset generated during LC-MS analysis contains valuable information that could be extracted and handled by means of various data mining and statistical tools. Novel sophisticated LC-MS based approaches are being introduced every year. Therefore, this review is prepared for the scientists specialized in pharmacognosy and analytical chemistry of NPs as well as working in related areas, in order to navigate them in the world of diverse LC-MS based techniques and strategies currently employed for NP discovery and dereplication, quality control, pattern recognition and sample comparison, and also in targeted and untargeted metabolomic studies. The suggested classification system includes the following LC-MS based procedures: elemental composition determination, isotopic fine structure analysis, mass defect filtering, de novo identification, clustering of the compounds in Molecular Networking (MN), diagnostic fragment ion (or neutral loss) filtering, manual dereplication using MS/MS data, database-assisted peak annotation, annotation of spectral trees, MS fingerprinting, feature extraction, bucketing of LC-MS data, peak profiling, predicted metabolite screening, targeted quantification of biomarkers, quantitative analysis of multi-component system, construction of chemical fingerprints, multi-targeted and untargeted metabolite profiling.

Challenges in Identifying the Dark Molecules of Life

Metabolomics is the study of the metabolome, the collection of small molecules in living organisms, cells, tissues, and biofluids. Technological advances in mass spectrometry, liquid- and gas-phase separations, nuclear magnetic resonance spectroscopy, and big data analytics have now made it possible to study metabolism at an omics or systems level. The significance of this burgeoning scientific field cannot be overstated: It impacts disciplines ranging from biomedicine to plant science. Despite these advances, the central bottleneck in metabolomics remains the identification of key metabolites that play a class-discriminant role. Because metabolites do not follow a molecular alphabet as proteins and nucleic acids do, their identification is much more time consuming, with a high failure rate. In this review, we critically discuss the state-of-the-art in metabolite identification with specific applications in metabolomics and how technologies such as mass spectrometry, ion mobility, chromatography, and nuclear magnetic resonance currently contribute to this challenging task.

High-resolution MS/MS metabolomics by data-independent acquisition reveals urinary metabolic alteration in experimental colitis

INTRODUCTION

Traditional high-resolution MS1 based untargeted metabolomics suffers from low sensitivity, while low-resolution MS/MS based multiple reaction monitoring increases sensitivity at the cost of metabolite coverage and the mass accuracy.

OBJECTIVES

To evaluate and apply the high-resolution MS/MS level untargeted metabolomics.

METHODS

SWATH based data-independent acquisition (DIA) was optimized to obtain MS/MS of all precursor ions.

RESULTS

SWATH-MS/MS could rescue MS1 obscured or saturated metabolites and potentially provide diagnostic fragments to differentiate isomers. For SWATH-MS/MS, 4944 out of 21492 (23.0%) and 2289 out of 12831 (17.8%) fragment ion features significantly changed (Fold change > 1.5, P < 0.05) between Normal and experimental acute ulcerative colitis (UC) groups in positive and negative ion mode, respectively. For SWATH-MS1, 1022 out of 4818 (21.2%) and 353 out of 2266 (15.6%) features significantly changed in positive and negative ion mode, respectively. By deciphering the metabolite profiles with high-resolution MS/MS, it allows versatile post-acquisition data mining such as open detection of different sub-metabolome. The method revealed a global urinary metabolic alteration and increased glucuronide and sulfate sub-metabolome in UC. The major limitation of untargeted SWATH-MS/MS is increased interferences derived from wider Q1 isolation window.

CONCLUSIONS

SWATH-MS/MS is a versatile metabolomics strategy, merging the coverage of high-resolution untargeted metabolomics and the sensitivity of MS/MS.

Hybrid multidimensional data acquisition and data processing strategy for comprehensive characterization of known, unknown and isomeric compounds from the compound Dan Zhi Tablet by UPLC-TWIMS-QTOFMS

The compound Dan Zhi Tablet (DZT), a reputable traditional Chinese medicine prescription, is widely used for the treatment of ischemic stroke in clinic. However, its systematic chemical constituents have rarely been elucidated, which hampers its quality evaluation, the study of bioactive constituents and the mechanism of action interpretation. In this study, we developed a combination of multidimensional data acquisition and data processing strategy with the aim to globally and comprehensively identify the chemical constituents in DZT based on UPLC-TWIMS-QTOFMS. First, multidimensional acquisition modes (MSE, Fast DDA and HDMSE) were performed on UPLC-TWIMS-QTOFMS. Second, targeted characterizations of the known compounds and their analogues present in DZT were carried out on the basis of the corresponding commercial standards or Mass2Motifs. Third, untargeted identification of unknown compounds in DZT was performed by extracting shared Mass2Motifs from the raw fragmentation spectra. Finally, the coeluting isomers were characterized using a precursor and/or product ion mobility. Consequently, 202 compounds were detected from DZT: 29 of them were unambiguously identified by comparison with reference compounds, 29 unknown compounds were discovered in specific medicinal materials, and ten pairs of coeluting isomers, which could not be distinguished using conventional MSE or Fast-DDA, were resolved using HDMSE only. This strategy was successfully used for the rapid and global identification of complex compounds including known, unknown and coeluting isomeric compounds in DZT and provided helpful chemical information for further quality control, pharmacology and active mechanism research on DZT.

Metabolic Footprinting of a Clear Cell Renal Cell Carcinoma in Vitro Model for Human Kidney Cancer Detection

A protocol for harvesting and extracting extracellular metabolites from an in vitro model of human renal cell lines was developed to profile the exometabolome by means of a discovery-based metabolomics approach using ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry. Metabolic footprints provided by conditioned media (CM) samples ( n = 66) of two clear cell Renal Cell Carcinoma (ccRCC) cell lines with different genetic backgrounds and a nontumor renal cell line, were compared with the human serum metabolic profile of a pilot cohort ( n = 10) comprised of stage IV ccRCC patients and healthy individuals. Using a cross-validated orthogonal projection to latent structures-discriminant analysis model, a panel of 21 discriminant features selected by iterative multivariate classification, allowed differentiating control from tumor cell lines with 100% specificity, sensitivity, and accuracy. Isoleucine/leucine, phenylalanine, N-lactoyl-leucine, and N-acetyl-phenylalanine, and cysteinegluthatione disulfide (CYSSG) were identified by chemical standards, and hydroxyprolyl-valine was identified with MS and MS/MS experiments. A subset of 9 discriminant features, including the identified metabolites except for CYSSG, produced a fingerprint of classification value that enabled discerning ccRCC patients from healthy individuals. To our knowledge, this is the first time that N-lactoyl-leucine is associated with ccRCC. Results from this study provide a proof of concept that CM can be used as a serum proxy to obtain disease-related metabolic signatures.

Identification and Quantification of Novel Major Metabolites of the Steroidal Aromatase Inhibitor, Exemestane

Exemestane (EXE) is an aromatase inhibitor used for the prevention and treatment of estrogen receptor–positive breast cancer. Although the known major metabolic pathway for EXE is reduction to form the active 17β-dihydro-EXE (17β-DHE) and subsequent glucuronidation to 17β-hydroxy-EXE-17-O-β-D-glucuronide (17β-DHE-Gluc), previous studies have suggested that other major metabolites exist for exemestane. In the present study, a liquid chromatography–mass spectrometry (LC-MS) approach was used to acquire accurate mass data in MS^E mode, in which precursor ion and fragment ion data were obtained simultaneously to screen novel phase II EXE metabolites in urine specimens from women taking EXE. Two major metabolites predicted to be cysteine conjugates of EXE and 17β-DHE by elemental composition were identified. The structures of the two metabolites were confirmed to be 6-methylcysteinylandrosta-1,4-diene-3,17-dione (6-EXE-cys) and 6-methylcysteinylandrosta-1,4-diene-17β-hydroxy-3-one (6-17β-DHE-cys) after comparison with their chemically synthesized counterparts. Both underwent biosynthesis in vitro in three stepwise enzymatic reactions, with the first involving glutathione conjugation. The cysteine conjugates of EXE and 17β-DHE were subsequently quantified by liquid chromatography–mass spectrometry in the urine and matched plasma samples of 132 subjects taking EXE. The combined 6-EXE-cys plus 6-17β-DHE-cys made up 77% of total EXE metabolites in urine (vs. 1.7%, 0.14%, and 21% for EXE, 17β-DHE, and 17β-DHE-Gluc, respectively) and 35% in plasma (vs. 17%, 12%, and 36% for EXE, 17β-DHE, and 17β-DHE-Gluc, respectively). Therefore, cysteine conjugates of EXE and 17β-DHE appear to be major metabolites of EXE in both urine and plasma.

Metabonomics delineates allergic reactions induced by Shuang-huang-lian injection in rats using ultra performance liquid chromatography-mass spectrometry

Shuang-huang-lian Injection (SHLI) is the first successfully developed drug from traditional Chinese medicine (TCM) powder for injection, since its use for the treatment of acute respiratory tract infection, pneumonia, influenza, etc. At the same time, its allergic reactions have also emerged, which limits clinical applications. However, few scholars pay attention to the mechanism of allergic reactions. In this present study, metabonomics technology was used to explore the changes in endogenous metabolites in urine of the rat model of SHLI induced allergic reaction; we and analyzed the metabolites, metabolic pathway, and the mechanism which were closely related to the allergic reactions. The levels of serum histamine and tryptase were examined and changes in histomorphology were also observed. Based on the UPLC-Q-TOF/MS metabonomics, we carried out the pattern recognition analysis, selected potential biomarkers associated with allergic reactions, and explored the pathological mechanism for SHLI induced allergic reaction, which laid the foundation for the safety research of SHLI. Our results showed that SHLI increased the levels of serum histamine and tryptase in rats with allergic reaction; we determined 15 biomarkers in rat allergic reaction model induced by SHLI and found multiple metabolic pathways involved, such as metabolism of linolenic acid, phenylalanine, amino acid, 2-oxo acid, and purine and other metabolic pathways.

A tutorial in small molecule identification via electrospray ionization-mass spectrometry: The practical art of structural elucidation

The identification of unknown molecules has been one of the cornerstone applications of mass spectrometry for decades. This tutorial reviews the basics of the interpretation of electrospray ionization-based MS and MS/MS spectra in order to identify small-molecule analytes (typically below 2000 Da). Most of what is discussed in this tutorial also applies to other atmospheric pressure ionization methods like atmospheric pressure chemical/photoionization. We focus primarily on the fundamental steps of MS-based structural elucidation of individual unknown compounds, rather than describing strategies for large-scale identification in complex samples. We critically discuss topics like the detection of protonated and deprotonated ions ([M + H]+ and [M - H]- ) as well as other adduct ions, the determination of the molecular formula, and provide some basic rules on the interpretation of product ion spectra. Our tutorial focuses primarily on the fundamental steps of MS-based structural elucidation of individual unknown compounds (eg, contaminants in chemical production, pharmacological alteration of drugs), rather than describing strategies for large-scale identification in complex samples. This tutorial also discusses strategies to obtain useful orthogonal information (UV/Vis, H/D exchange, chemical derivatization, etc) and offers an overview of the different informatics tools and approaches that can be used for structural elucidation of small molecules. It is primarily intended for beginning mass spectrometrists and researchers from other mass spectrometry sub-disciplines that want to get acquainted with structural elucidation are interested in some practical tips and tricks.

An LC-MS-based lipidomics pre-processing framework underpins rapid hypothesis generation towards CHO systems biotechnology

INTRODUCTION

Given a raw LC-MS dataset, it is often required to rapidly generate initial hypotheses, in conjunction with other 'omics' datasets, without time-consuming lipid verifications. Furthermore, for meta-analysis of many datasets, it may be impractical to conduct exhaustive confirmatory analyses. In other cases, samples for validation may be difficult to obtain, replicate or maintain. Thus, it is critical that the computational identification of lipids is of appropriate accuracy, coverage, and unbiased by a researcher's experience and prior knowledge.

OBJECTIVES

We aim to prescribe a systematic framework for lipid identifications, without usage of their characteristic retention-time by fully exploiting their underlying mass features.

RESULTS

Initially, a hybrid technique, for deducing both common and distinctive daughter ions, is used to infer parent lipids from deconvoluted spectra. This is followed by parent confirmation using basic knowledge of their preferred product ions. Using the framework, we could achieve an accuracy of ~ 80% by correctly identified 101 species from 18 classes in Chinese hamster ovary (CHO) cells. The resulting inferences could explain the recombinant-producing capability of CHO-SH87 cells, compared to non-producing CHO-K1 cells. For comparison, a XCMS-based study of the same dataset, guided by a user's ad-hoc knowledge, identified less than 60 species of 12 classes from thousands of possibilities.

CONCLUSION

We describe a systematic LC-MS-based framework that identifies lipids for rapid hypothesis generation.

An optimized solvent extraction and characterization of unidentified flavonoid glucuronide derivatives from spinach by UHPLC-HR-QTOF-MS

A rapid, sensitive analytical method using ultra-high-pressure liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-HR-QTOF-MS) was developed for the identification and quantification of flavonoids from spinach. The extraction efficiency of flavonoids was evaluated by different solvents such as acetone, ethanol, methanol, acetone: water (70:30), ethanol: water (70:30) and methanol: water (70:30). Flavonoid identification was achieved by UV spectra, high resolution accurate mass and their fragmentation pattern. The precursor and product ions were recorded by both broadband collision ion dissociation (bbCID) and multiple reaction monitoring (MRM) techniques. Different collision energies (5, 10, 15, 20, 40, and 70 eV) were optimized to obtain the mass spectra of flavonoids in positive and negative ionization modes. For the first time, five minor flavonoid glucuronide derivatives were identified in spinach. MRM and bbCID provided glucuronide fingerprint ions at m/z 175.0278 and m/z 113.0257 respectively in negative ionization mode. The quantification of identified flavonoids was achieved by 5,3',4'-trihydroxy-3-methoxy-6:7-methylen-dioxyflavone-4'-β-D-glucuronide which was purified by semi-preparatory HPLC. The purity of the isolated compound was confirmed by NMR analysis. The identified 5,3',4'-trihydroxy-3-methoxy-6:7-methylen-dioxyflavone-4'-β-D-(2'-O-feurloyl-glucuronide) was the prominent flavonoid and the level was significantly higher in the acetone fraction (2.95 ± 0.16 µg/g FW). This study demonstrates the systematic identification of potential bioactive compounds especially glucuronide derivatives from spinach.

SWATH Tandem Mass Spectrometry Workflow for Quantification of Mass Isotopologue Distribution of Intracellular Metabolites and Fragments Labeled with Isotopic 13C Carbon

Accurate quantification of mass isotopologue distribution (MID) of metabolites is a prerequisite for ¹³C-metabolic flux analysis. Currently used mass spectrometric (MS) techniques based on multiple reaction monitoring (MRM) place limitations on the number of MIDs that can be analyzed in a single run. Moreover, the deconvolution step results in amplification of error. Here, we demonstrate that SWATH MS/MS, a data independent acquisition (DIA) technique allows quantification of a large number of precursor and product MIDs in a single run. SWATH sequentially fragments all precursor ions in stacked mass isolation windows. Co-fragmentation of all precursor isotopologues in a single SWATH window yields higher sensitivity enabling quantification of MIDs of fragments with low abundance and lower systematic and random errors. We quantify the MIDs of 53 precursor and product ions corresponding to 19 intracellular metabolites from a dynamic ¹³C-labeling of a model cyanobacterium, Synechococcus sp. PCC 7002. The use of product MIDs resulted in an improved precision of many measured fluxes compared to when only precursor MIDs were used for flux analysis. The approach is truly untargeted and allows additional metabolites to be quantified from the same data.

A Complete Pipeline for Generating a High-Resolution LC-MS-Based Reference Mass Spectra Library

Databases containing mass spectrometry (MS) spectral data (i.e., MS reference libraries) are currently the most reliable and widely accepted approach to annotate unknown features in MS-based metabolomics. While for gas chromatography (GC)-MS data, a strategy for collecting, storing, and comparing to raw data has been established, this is not the case for liquid chromatography (LC)-MS data. Here, we present our approach for high-throughput data collection and automated MS reference library generation, as applied recently in the WEIZMASS library of plant metabolites. Methodologies to experimentally generate pools of chemical standards and computationally convert them into a unique source of reference data are detailed.

Simultaneous structural identification of diacylglyceryl-N-trimethylhomoserine (DGTS) and diacylglycerylhydroxymethyl-N,N,N-trimethyl-β-alanine (DGTA) in microalgae using dual Li+ /H+ adduct ion mode by ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry

RATIONALE

Diacylgycerol-N-trimethylhomoserine (DGTS) and diacylglycerylhydroxymethyl-N,N,N-trimethyl-β-alanine (DGTA) are structural isomers that are the most commonly described betaine lipids in microalgae. The structural differentiation and precise identification of DGTS and DGTA in microalgae need to be established during mass spectrometry analysis.

METHODS

Total lipid was extracted from Amphora spp. with CHCl₃ /CH₃ OH (1:1, v/v). The qualitative analysis of DGTS and DGTA in Amphora spp. was carried out using Li⁺ /H⁺ dual mode by ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry operating in MS^E mode (UPLC/QTOF MS^E ).

RESULTS

Characteristic fragment ions [C₁₀ H₂₂ O₅ N]⁺ at m/z 236.15 and [C₇ H₁₄ O₂ N]⁺ at m/z 144.10 from the [M + H]⁺ precursor ion can be used for the qualitative analysis of both DGTA and DGTS, whereas the loss of m/z 87 and 74 from the [M + Li]⁺ precursor ion are specific for DGTS, and the loss of m/z 103 from the [M + Li]⁺ precursor ion is only for DGTA. As a result, 9 DGTSs and 16 DGTAs with different fatty acids were identified simultaneously in Amphora spp. Semi-quantitative analysis of DGTS and DGTA in Amphora spp. showed that the contents of DGTS ranged from 0.003 to 0.438 nmol mg^-1 dw, and that of DGTA from 0.004 to 0.414 nmol mg^-1 dw.

CONCLUSIONS

This is the first report to achieve the ambiguous structural identification of DGTS and DGTA by UPLC/QTOF MS^E using dual Li⁺ /H⁺ adduct ion mode, which has remained a challenge in the past. It could provide new insights into their phylogeny and be helpful to characterize the natural phytoplankton communities as intact polar lipid biomarkers. Copyright © 2017 John Wiley & Sons, Ltd.

Implementation of a novel ultra fast metabolic stability analysis method using exact mass TOF-MS

AIM

Increasing numbers of compounds requiring stability data means highly optimized methods capable of rapid turnaround are desirable during early discovery. Materials and methods/results: An advanced, generic analytical workflow for metabolic stability has been developed that utilizing ballistic gradient LC (sub 1 min run times), exact mass TOF-MS (Waters Xevo-G2-XS Q-TOF) and automated data processing (Waters UNIFI software) allowed for rapid integration and interpretation of all data produced, eliminating the need for method development and manual processing. We can analyze and process 96 compounds across two species in quadruplicate in a 24-h period with no method development.

CONCLUSION

An advanced bioanalytical workflow has increased our capacity threefold and reduced our instrument/processing needs threefold.

Dynamic Cluster Analysis: An Unbiased Method for Identifying A + 2 Element Containing Compounds in Liquid Chromatographic High-Resolution Time-of-Flight Mass Spectrometric Data

Dynamic cluster analysis (DCA) is an automated, unbiased technique which can identify Cl, Br, S, and other A + 2 element containing metabolites in liquid chromatographic high-resolution mass spectrometric data. DCA is based on three features, primarily the previously unutilized A + 1 to A + 2 isotope cluster spacing which is a strong classifier in itself but improved with the addition of the monoisotopic mass, and the well-known A:A+2 intensity ratio. Utilizing only the A + 1 to A + 2 isotope cluster spacing and the monoisotopic mass it was possible to filter a chromatogram for metabolites which contain Cl, Br, and S. Screening simulated isotope patterns of the Antibase Natural Products Database it was determined that the A + 1 to A + 2 isotope cluster spacing can be used to correctly classify 97.4% of molecular formulas containing these elements, only misclassifying a few metabolites which were either over 2800 u or metabolites which contained other A + 2 elements, such as Cu, Ni, Mg, and Zn. It was determined that with an interisotopic mass accuracy of 1 ppm, in a fully automated process, using all three parameters, it is possible to specifically filter a chromatogram for S containing metabolites with monoisotopic masses less than 825 u. Furthermore, it was possible to specifically filter a chromatogram for Cl and Br containing metabolites with monoisotopic masses less than 1613 u. Here DCA is applied on (i) simulated isotope patterns of the Antibase natural products databases, (ii) LC-QTOF data of reference standards, and (iii) LC-QTOF data of crude extracts of 10 strains of laboratory grown cultures of the microalga Prymnesium parvum where it identified known metabolites of the prymnesin series as well as over 20 previously undescribed prymnesin-like molecular features.

The WEIZMASS spectral library for high-confidence metabolite identification

Annotation of metabolites is an essential, yet problematic, aspect of mass spectrometry (MS)-based metabolomics assays. The current repertoire of definitive annotations of metabolite spectra in public MS databases is limited and suffers from lack of chemical and taxonomic diversity. Furthermore, the heterogeneity of the data prevents the development of universally applicable metabolite annotation tools. Here we present a combined experimental and computational platform to advance this key issue in metabolomics. WEIZMASS is a unique reference metabolite spectral library developed from high-resolution MS data acquired from a structurally diverse set of 3,540 plant metabolites. We also present MatchWeiz, a multi-module strategy using a probabilistic approach to match library and experimental data. This strategy allows efficient and high-confidence identification of dozens of metabolites in model and exotic plants, including metabolites not previously reported in plants or found in few plant species to date.

Unambiguous metabolite annotation is a critical, yet problematic step, in mass spectrometry based metabolomics. Here, Shahaf et al. present WEIZMASS, a platform consisting of a diverse spectral library of more than 3500 plant metabolites and software to aid their identification in biological samples.

Recent developments in software tools for high-throughput in vitro ADME support with high-resolution MS

The last several years have seen the rapid adoption of the high-resolution MS (HRMS) for bioanalytical support of high throughput in vitro ADME profiling. Many capable software tools have been developed and refined to process quantitative HRMS bioanalysis data for ADME samples with excellent performance. Additionally, new software applications specifically designed for quan/qual soft spot identification workflows using HRMS have greatly enhanced the quality and efficiency of the structure elucidation process for high throughput metabolite ID in early in vitro ADME profiling. Finally, novel approaches in data acquisition and compression, as well as tools for transferring, archiving and retrieving HRMS data, are being continuously refined to tackle the issue of large data file size typical for HRMS analyses.

An Integrated Approach for Studying Exposure, Metabolism, and Disposition of Multiple Component Herbal Medicines Using High-Resolution Mass Spectrometry and Multiple Data Processing Tools

A typical prescription of traditional Chinese medicine (TCM) contains up to a few hundred prototype components. Studying their absorption, metabolism, distribution, and elimination (ADME) presents great challenges. The objective of this study was to develop a practical approach for investigating ADME of individual prototypes in TCM. An active fraction of Xiao-Xu-Ming decoction (AF-XXMD) as a model TCM prescription was orally administered to rats. AF-XXMD-related components in plasma, urine, bile, and feces were detected using high-resolution mass spectrometry and background subtraction, an untargeted data-mining tool. Components were then structurally characterized on the basis of MS(n) spectral data. Connection of detected AF-XXMD metabolites to their precursor species, either prototypes or upstream metabolites, were determined on the basis of mass spectral similarity and the matching of biotransformation reactions. As a result, 247 AF-XXMD-related components were detected and structurally characterized in rats, 134 of which were metabolites. Among 198 AF-XXMD prototypes dosed, 65 were fully or partially absorbed and 13 prototypes and 34 metabolites were found in the circulation. Glucuronidation, isomerization, and deglycosylation followed by biliary and urinary excretions and direct elimination of prototypes via kidney and liver were the major clearance pathways of AF-XXMD prototypes. As an example, the ADME profile of H56, the single major AF-XXMD component in rat plasma, was elucidated on the basis of profiles of H56-related components in plasma and excreta. The results demonstrate that the new analytical approach is a useful tool for rapid and comprehensive detection and characterization of TCM components in biologic matrix in a TCM ADME study.

Primaquine pharmacology in the context of CYP 2D6 pharmacogenomics: Current state of the art

Primaquine is the only antimalarial drug available to clinicians for the treatment of relapsing forms of malaria. Primaquine development and usage dates back to the 1940s and has been administered to millions of individuals to treat and eliminate malaria infections. Primaquine therapy is not without disadvantages, however, as it can cause life threatening hemolysis in humans with glucose-6-phosphate dehydrogenase (G6PD) deficiency. In addition, the efficacy of primaquine against relapsing malaria was recently linked to CYP 2D6 mediated activation to an active metabolite, the structure of which has escaped definitive identification for over 75years. CYP 2D6 is highly polymorphic among various human populations adding further complexity to a comprehensive understanding of primaquine pharmacology. This review aims to discuss primaquine pharmacology in the context of state of the art understanding of CYP 2D6 mediated 8-aminoquinoline metabolic activation, and shed light on the current knowledge gaps of 8-aminoquinoline mechanistic understanding against relapsing malaria.

In vivo drug metabolite identification in preclinical ADME studies by means of UPLC/TWIMS/high resolution-QTOF MS(E) and control comparison: cost and benefit of vehicle-dosed control samples

1. Liquid chromatography (LC)-high resolution mass spectrometry (HRMS) techniques proved to be well suited for the identification of predicted and unexpected drug metabolites in complex biological matrices. 2. To efficiently discriminate between drug-related and endogenous matrix compounds, however, sophisticated postacquisition data mining tools, such as control comparison techniques are needed. For preclinical absorption, distribution, metabolism and excretion (ADME) studies that usually lack a placebo-dosed control group, the question arises how high-quality control data can be yielded using only a minimum number of control animals. 3. In the present study, the combination of LC-traveling wave ion mobility separation (TWIMS)-HRMS(E) and multivariate data analysis was used to study the polymer patterns of the frequently used formulation constituents polyethylene glycol 400 and polysorbate 80 in rat plasma and urine after oral and intravenous administration, respectively. 4. Complex peak patterns of both constituents were identified underlining the general importance of a vehicle-dosed control group in ADME studies for control comparison. Furthermore, the detailed analysis of administration route, blood sampling time and gender influences on both vehicle peak pattern as well as endogenous matrix background revealed that high-quality control data is obtained when (i) control animals receive an intravenous dose of the vehicle, (ii) the blood sampling time point is the same for analyte and control sample and (iii) analyte and control samples of the same gender are compared.

Metabolism elucidation of BJ-B11 (a heat shock protein 90 inhibitor) by human liver microsomes: identification of main contributing enzymes

OBJECTIVE

The aim of this article is to elucidate the metabolic pathways of BJ-B11, a heat shock protein 90 inhibitor, in human liver microsomes (HLM) and determine the main enzymes responsible for formation of each metabolite.

METHODS

Metabolites of BJ-B11 were identified using the ultra performance liquid chromatography- quadrupole time-of-flight/mass spectrometry (UPLC-QTOF/MS) method. Esterase contributing to the hydrolysis of BJ-B11 was identified by chemical inhibition and activity correlation assays. Reaction phenotyping and kinetic studies using expressed cytochrome P450 (CYP) enzymes were performed to determine the contributions of CYP isozymes to BJ-B11 metabolism.

RESULTS

BJ-B11 was rapidly hydrolyzed to generate a deacetylated product M1-1. M1-1 was subsequently metabolized to form eight metabolites. Hydrolysis of BJ-B11 was markedly inhibited by vinblastine (a dual inhibitor of arylacetamide deacetylase and carboxylesterase 2). By contrast, digitonin and telmisartan (the specific inhibitors for carboxylesterase 1 and carboxylesterase 2, respectively) did not inhibit BJ-B11 hydrolysis at all. Further, BJ-B11 hydrolysis was significantly correlated with hydrolysis of phenacetin (an activity marker of arylacetamide deacetylase). Moreover, reaction phenotyping revealed that metabolism of M1-1 in HLM was attributable to several CYP enzymes, including CYP1A1, 1B1, 3A4 and 3A5.

CONCLUSION

BJ-B11 was subjected to efficient metabolism in the liver, generating nine metabolites. BJ-B11 metabolism was contributed mainly by arylacetamide deacetylase and multiple CYP enzymes.

Software-aided cytochrome P450 reaction phenotyping and kinetic analysis in early drug discovery

RATIONALE

Cytochrome P450 (CYP450) reaction phenotyping (CRP) and kinetic studies are essential in early drug discovery to determine which metabolic enzymes react with new drug entities. A new semi-automated computer-assisted workflow for CRP is introduced in this work. This workflow provides not only information regarding parent disappearance, but also metabolite identification and relative metabolite formation rates for kinetic analysis.

METHODS

Time-course experiments based on incubating six probe substrates (dextromethorphan, imipramine, buspirone, midazolam, ethoxyresorufin and diclofenac) with recombinant human enzymes (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) and human liver microsomes (HLM) were performed. Liquid chromatography/high-resolution mass spectrometry (LC/HRMS) analysis was conducted with an internal standard to obtain high-resolution full-scan and MS/MS data. Data were analyzed using Mass-MetaSite software. A server application (WebMetabase) was used for data visualization and review.

RESULTS

CRP experiments were performed, and the data were analyzed using a software-aided approach. This automated-evaluation approach led to (1) the detection of the CYP450 enzymes responsible for both substrate depletion and metabolite formation, (2) the identification of specific biotransformations, (3) the elucidation of metabolite structures based on MS/MS fragment analysis, and (4) the determination of the initial relative formation rates of major metabolites by CYP450 enzymes.

CONCLUSIONS

This largely automated workflow enabled the efficient analysis of HRMS data, allowing rapid evaluation of the involvement of the main CYP450 enzymes in the metabolism of new molecules during drug discovery.

Profiling of Altered Metabolomic States in Nicotiana tabacum Cells Induced by Priming Agents

Metabolomics has developed into a valuable tool for advancing our understanding of plant metabolism. Plant innate immune defenses can be activated and enhanced so that, subsequent to being pre-sensitized, plants are able to launch a stronger and faster defense response upon exposure to pathogenic microorganisms, a phenomenon known as priming. Here, three contrasting chemical activators, namely acibenzolar-S-methyl, azelaic acid and riboflavin, were used to induce a primed state in Nicotiana tabacum cells. Identified biomarkers were then compared to responses induced by three phytohormones-abscisic acid, methyljasmonate, and salicylic acid. Altered metabolomes were studied using a metabolite fingerprinting approach based on liquid chromatography and mass spectrometry. Multivariate data models indicated that these inducers cause time-dependent metabolic perturbations in the cultured cells and revealed biomarkers of which the levels are affected by these agents. A total of 34 metabolites were annotated from the mass spectral data and online databases. Venn diagrams were used to identify common biomarkers as well as those unique to a specific agent. Results implicate 20 cinnamic acid derivatives conjugated to (i) quinic acid (chlorogenic acids), (ii) tyramine, (iii) polyamines, or (iv) glucose as discriminatory biomarkers of priming in tobacco cells. Functional roles for most of these metabolites in plant defense responses could thus be proposed. Metabolites induced by the activators belong to the early phenylpropanoid pathway, which indicates that different stimuli can activate similar pathways but with different metabolite fingerprints. Possible linkages to phytohormone-dependent pathways at a metabolomic level were indicated in the case of cells treated with salicylic acid and methyljasmonate. The results contribute to a better understanding of the priming phenomenon and advance our knowledge of cinnamic acid derivatives as versatile defense metabolites.

Lipidomics: Novel insight into the biochemical mechanism of lipid metabolism and dysregulation-associated disease

The application of lipidomics, after genomics, proteomics and metabolomics, offered largely opportunities to illuminate the entire spectrum of lipidome based on a quantitative or semi-quantitative level in a biological system. When combined with advances in proteomics and metabolomics high-throughput platforms, lipidomics provided the opportunity for analyzing the unique roles of specific lipids in complex cellular processes. Abnormal lipid metabolism was demonstrated to be greatly implicated in many human lifestyle-related diseases. In this review, we focused on lipidomic applications in brain injury disease, cancer, metabolic disease, cardiovascular disease, respiratory disease and infectious disease to discover disease biomarkers and illustrate biochemical metabolic pathways. We also discussed the analytical techniques, future perspectives and potential problems of lipidomic applications. The application of lipidomics in disease biomarker discovery provides the opportunity for gaining novel insights into biochemical mechanism.