An integrated strategy to improve data acquisition and metabolite identification by time-staggered ion lists in UHPLC/Q-TOF MS-based metabolomics

Studies on the metabolism and mechanism of acteoside in treating chronic glomerulonephritis

ETHNOPHARMACOLOGICAL RELEVANCE

Acteoside (ACT) is the main ingredient derived from the leaves of Rehmannia glutinosa (Dihuangye). Dihuangye has the function of clearing heat, replenishing qi and activating blood, nourishing yin and tonifying kidney in traditional Chinese medicine. Recent studies have demonstrated that Dihuangye can be used to treat nephritis and ACT is a promising antinephritic agent.

AIM OF THE STUDY

To clarify the metabolites of ACT in biological samples and investigate the renoprotective effect and mechanism of ACT in rats with chronic glomerulonephritis (CGN).

MATERIALS AND METHODS

In this study, the biotransformation of ACT in rat biological samples was clarified by quadrupole time-of-flight tandem mass spectrometry. The metabolites were validated by urine samples in nephropathy model rats. The effect of ACT and its metabolites was evaluated by glomerular podocyte injury due to high glucose. Based on an analysis of the ingredients in vivo, the potential therapeutic targets in the treatment of CGN were investigated by using network pharmacological analysis and molecular docking. Then, the renoprotective effect and mechanism of ACT were determined in rats in a passive Heymann nephritis (PHN) model.

RESULTS

A total of 49 metabolites of ACT were detected and identified. Meanwhile, 21 metabolites were detected in nephropathy model rats. ACT was absorbed rapidly and transferred from the kidney, and the metabolites were eliminated via urine. The whole process lasted approximately 8 h. ACT had a significant protective effect on glomerular podocytes damaged by high glucose and 3,4-dihydroxyphenylacetic acid might be the main metabolite of ACT underlying its functions in vivo. The network pharmacology and molecular docking results showed 84 ACT-CGN targets, among which MAPK1, HRAS, AKT1, EGFR, and others were a highly correlated. In the PHN rat model, ACT significantly reduced the 24-h urine protein and serum creatinine concentrations, suppressed the leukocyte CD18 expression levels, decreased the serum tumor necrosis factor α (TNF-α) levels and tended to reduce serum interleukin 6 (IL-6) levels. ACT significantly reduced the platelet aggregation rate and inhibited the proliferative activity of splenic lymphocytes in response to the mitogen concanavalin A. Meanwhile, ACT inhibited transforming growth factor-β and fibronectin expression in renal tissues and dose-dependently inhibited TNF-α and IL-6 production in RAW264.7 mouse macrophages at doses ranging from 1.8 to 1330 μg/mL.

CONCLUSIONS

ACT had therapeutic effects on PHN rats, and its mechanism might be related to the inhibition of intercellular or intercellular-matrix adhesion, suppression of inflammatory response, regulation of immune function, improvement of tissue hemodynamics and hemorheology, and relief of fibrotic lesions.

An integrated strategy for comprehensive characterization of metabolites and metabolic profiles of bufadienolides from Venenum Bufonis in rats

Comprehensive characterization of metabolites and metabolic profiles in plasma has considerable significance in determining the efficacy and safety of traditional Chinese medicine (TCM) in vivo. However, this process is usually hindered by the insufficient characteristic fragments of metabolites, ubiquitous matrix interference, and complicated screening and identification procedures for metabolites. In this study, an effective strategy was established to systematically characterize the metabolites, deduce the metabolic pathways, and describe the metabolic profiles of bufadienolides isolated from Venenum Bufonis in vivo. The strategy was divided into five steps. First, the blank and test plasma samples were injected into an ultra-high performance liquid chromatography/linear trap quadrupole-orbitrap-mass spectrometry (MS) system in the full scan mode continuously five times to screen for valid matrix compounds and metabolites. Second, an extension-mass defect filter model was established to obtain the targeted precursor ions of the list of bufadienolide metabolites, which reduced approximately 39% of the interfering ions. Third, an acquisition model was developed and used to trigger more tandem MS (MS/MS) fragments of precursor ions based on the targeted ion list. The acquisition mode enhanced the acquisition capability by approximately four times than that of the regular data-dependent acquisition mode. Fourth, the acquired data were imported into Compound Discoverer software for identification of metabolites with metabolic network prediction. The main in vivo metabolic pathways of bufadienolides were elucidated. A total of 147 metabolites were characterized, and the main biotransformation reactions of bufadienolides were hydroxylation, dihydroxylation, and isomerization. Finally, the main prototype bufadienolides in plasma at different time points were determined using LC-MS/MS, and the metabolic profiles were clearly identified. This strategy could be widely used to elucidate the metabolic profiles of TCM preparations or Chinese patent medicines in vivo and provide critical data for rational drug use.

•

Extension-mass defect filter model could reduce about 39% interfering ions.

•

The optimized acquisition mode enhanced about 4 times acquisition capability than regular DDA mode.

•

147 metabolites were characterized with metabolic network prediction, and the metabolic pathways were deduced in plasmas.

•

The quantitative method of 14 prototypes was established by LC-MS/MS for metabolic profiles study.

A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies

Unwanted experimental/biological variation and technical error are frequently encountered in current metabolomics, which requires the employment of normalization methods for removing undesired data fluctuations. To ensure the 'thorough' removal of unwanted variations, the collective consideration of multiple criteria ('intragroup variation', 'marker stability' and 'classification capability') was essential. However, due to the limited number of available normalization methods, it is extremely challenging to discover the appropriate one that can meet all these criteria. Herein, a novel approach was proposed to discover the normalization strategies that are consistently well performing (CWP) under all criteria. Based on various benchmarks, all normalization methods popular in current metabolomics were 'first' discovered to be non-CWP. 'Then', 21 new strategies that combined the 'sample'-based method with the 'metabolite'-based one were found to be CWP. 'Finally', a variety of currently available methods (such as cubic splines, range scaling, level scaling, EigenMS, cyclic loess and mean) were identified to be CWP when combining with other normalization. In conclusion, this study not only discovered several strategies that performed consistently well under all criteria, but also proposed a novel approach that could ensure the identification of CWP strategies for future biological problems.

Recent advances in analytical strategies for mass spectrometry-based lipidomics

Lipids are vital biological molecules and play multiple roles in cellular function of mammalian organisms such as cellular membrane anchoring, signal transduction, material trafficking and energy storage. Driven by the biological significance of lipids, lipidomics has become an emerging science in the field of omics. Lipidome in biological systems consists of hundreds of thousands of individual lipid molecules that possess complex structures, multiple categories, and diverse physicochemical properties assembled by different combinations of polar headgroups and hydrophobic fatty acyl chains. Such structural complexity poses a huge challenge for comprehensive lipidome analysis. Thanks to the great innovations in chromatographic separation techniques and the continuous advances in mass spectrometric detection tools, analytical strategies for lipidomics have been highly diversified so that the depth and breadth of lipidomics have been greatly enhanced. This review will present the current state of mass spectrometry-based analytical strategies including untargeted, targeted and pseudotargeted lipidomics. Recent typical applications of lipidomics in biomarker discovery, pathogenic mechanism and therapeutic strategy are summarized, and the challenges facing to the field of lipidomics are also discussed.

A green and efficient pseudotargeted lipidomics method for the study of depression based on ultra-high performance supercritical fluid chromatography-tandem mass spectrometry

The pseudotargeted lipidomics method integrates the advantages of untargeted andtargeted lipidomics methods as a novel emerging approach. In this study, a green andefficient pseudotargeted lipidomics method based on ultra-high performancesupercritical fluid chromatography-tandem mass spectrometry (UHPSFC-MS/MS) wasdeveloped. The tandem mass spectra of the analytes were obtained by using UHPSFCwith quadrupole-time of flight MS (Q-TOF MS) in MS E mode and the multiplereaction monitoring (MRM) transitions of the lipidome were defined. Then, thecandidate MRM transitions were verified by UHPSFC with triple quadrupole massspectrometry (QqQ MS) in the scheduled MRM mode. In total, 758 potential lipidscorresponding to 509 and 249 MRM transitions were detected within 8 min in positiveand negative modes, respectively. The established pseudotargeted lipidomics methodwas validated to have excellent analytical characteristics. Compared with thepseudotargeted method based on ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS), the UHPSFC-MS/MS-basedpseudotargeted method not only reduced the analytical time by half but also improvedthe sensitivity and resolution for most analytes, especially had better separation forlipid isomers. Besides, the UHPSFC-MS/MS-based pseudotargeted method showedhigher sensitivity and better repeatability for most analytes than the UHPSFC-MS/MS-based untargeted method. The established method was finally applied to investigatingthe lipid profiles of the plasma from the depressed rats and 33 differential variableswere screened, which related to three metabolic pathways. The results indicated thatthe UHPSFC-MS/MS-based pseudotargeted method is reliable and efficient and couldbe used in the lipidomics studies.

Bridging Targeted and Untargeted Mass Spectrometry-Based Metabolomics via Hybrid Approaches

This mini-review aims to discuss the development and applications of mass spectrometry (MS)-based hybrid approaches in metabolomics. Several recently developed hybrid approaches are introduced. Then, the overall workflow, frequently used instruments, data handling strategies, and applications are compared and their pros and cons are summarized. Overall, the improved repeatability and quantitative capability in large-scale MS-based metabolomics studies are demonstrated, in comparison to either targeted or untargeted metabolomics approaches alone. In summary, we expect this review to serve as a first attempt to highlight the development and applications of emerging hybrid approaches in metabolomics, and we believe that hybrid metabolomics approaches could have great potential in many future studies.

Development of a plasma pseudotargeted metabolomics method based on ultra-high-performance liquid chromatography-mass spectrometry

Untargeted methods are typically used in the detection and discovery of small organic compounds in metabolomics research, and ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) is one of the most commonly used platforms for untargeted metabolomics. Although they are non-biased and have high coverage, untargeted approaches suffer from unsatisfying repeatability and a requirement for complex data processing. Targeted metabolomics based on triple-quadrupole mass spectrometry (TQMS) could be a complementary tool because of its high sensitivity, high specificity and excellent quantification ability. However, it is usually applicable to known compounds: compounds whose identities are known and/or are expected to be present in the analyzed samples. Pseudotargeted metabolomics merges the advantages of untargeted and targeted metabolomics and can act as an alternative to the untargeted method. Here, we describe a detailed protocol of pseudotargeted metabolomics using UHPLC-TQMS. An in-depth, untargeted metabolomics experiment involving multiple UHPLC-HRMS runs with MS at different collision energies (both positive and negative) is performed using a mixture obtained using small amounts of the analyzed samples. XCMS, CAMERA and Multiple Reaction Monitoring (MRM)-Ion Pair Finder are used to find and annotate peaks and choose transitions that will be used to analyze the real samples. A set of internal standards is used to correct for variations in retention time. High coverage and high-performance quantitative analysis can be realized. The entire protocol takes ~5 d to complete and enables the simultaneously semiquantitative analysis of 800-1,300 metabolites.

Comparing Targeted vs. Untargeted MS2 Data-Dependent Acquisition for Peak Annotation in LC-MS Metabolomics

One of the most widely used strategies for metabolite annotation in untargeted LCMS is based on the analysis of MSⁿ spectra acquired using data-dependent acquisition (DDA), where precursor ions are sequentially selected from MS scans based on user-selected criteria. However, the number of MSⁿ spectra that can be acquired during a chromatogram is limited and a trade-off between analytical speed, sensitivity and coverage must be ensured. In this research, we compare four different strategies for automated MS² DDA, which can be easily implemented in the frame of standard QA/QC workflows for untargeted LC–MS. These strategies consist of (i) DDA in the MS working range; (ii) iterated DDA split into several m/z intervals; (iii) dynamic iterated DDA of (pre)selected potentially informative features; and (iv) dynamic iterated DDA of (pre)annotated metabolic features using a reference database. Their performance was assessed using the analysis of human milk samples as model example by comparing the percentage of LC–MS features selected as the precursor ion for MS², the number, and class of annotated features, the speed and confidence of feature annotation, and the number of LC runs required.