Nontargeted diagnostic ion network analysis (NINA): A software to streamline the analytical workflow for untargeted characterization of natural medicines

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.

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

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

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

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

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.

Offline two-dimensional liquid chromatography coupled with ion mobility-quadrupole time-of-flight mass spectrometry enabling four-dimensional separation and characterization of the multicomponents from white ginseng and red ginseng

Inherent complexity of plant metabolites necessitates the use of multi-dimensional information to accomplish comprehensive profiling and confirmative identification. A dimension-enhanced strategy, by offline two-dimensional liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (2D-LC/IM-QTOF-MS) enabling four-dimensional separations (2D-LC, IM, and MS), is proposed. In combination with in-house database-driven automated peak annotation, this strategy was utilized to characterize ginsenosides simultaneously from white ginseng (WG) and red ginseng (RG). An offline 2D-LC system configuring an Xbridge Amide column and an HSS T3 column showed orthogonality 0.76 in the resolution of ginsenosides. Ginsenoside analysis was performed by data-independent high-definition MSE (HDMSE) in the negative ESI mode on a Vion™ IMS-QTOF hybrid high-resolution mass spectrometer, which could better resolve ginsenosides than MSE and directly give the CCS information. An in-house ginsenoside database recording 504 known ginsenosides and 58 reference compounds, was established to assist the identification of ginsenosides. Streamlined workflows, by applying UNIFI™ to automatedly annotate the HDMSE data, were proposed. We could separate and characterize 323 ginsenosides (including 286 from WG and 306 from RG), and 125 thereof may have not been isolated from the Panax genus. The established 2D-LC/IM-QTOF-HDMSE approach could also act as a magnifier to probe differentiated components between WG and RG. Compared with conventional approaches, this dimension-enhanced strategy could better resolve coeluting herbal components and more efficiently, more reliably identify the multicomponents, which, we believe, offers more possibilities for the systematic exposure and confirmative identification of plant metabolites.

Intestinal mucosal metabolites-guided detection of trace-level ginkgo biloba extract metabolome

The characterization of metabolome for poorly absorptive natural medicines is challenging. Previous identification strategy often relies on nontargeted scanning biological samples from animals administered with natural medicines in a data-dependent acquisition (DDA) mode by LC-MS/MS. Substances that displayed significant increases following drug administration are thus assigned as potential metabolites. The accurate m/z of precursors and the corresponding MS/MS fragment ions are used to match with herbal ingredients and to infer possible metabolic reactions. Nevertheless, the low concentration of these metabolites within complex biological matrices has often hampered the detection. Herein we developed a strategy termed intestinal mucosal metabolome-guided detection (IMMD) to tackle this challenge using ginkgo biloba (GBE) as an example. The rationale is that poorly absorptive natural products are usually concentrated and extensively metabolized by enterocytes before they enter the blood stream and distribute to other organs. Therefore, we firstly identified the metabolites from intestinal mucosa of GBE-treated rats, and then used the identified intestinal mucosal GBE metabolome as targeted repository for MRM analysis. The presences of these metabolites were subsequently examined in rat plasma, liver and brain. The resultant GBE metabolome showed significantly improved coverage with 39, 45 and 6 metabolites identified in plasma, liver and brain compared to 22, 16 and 0 metabolites from the corresponding regions via the DDA-based strategy. In addition, we integrated the previously reported nontargeted diagnostic ion network analysis to facilitate the characterization of GBE components, and a chemicalome-metabolome matching approach (CMMA) to assist the identity assignment of GBE metabolome with IMMD. Combinatorially, we establish a multi-faceted platform to streamline the workflow of metabolome characterization for herbal medicines of low bioavailability. The metabolome information is expected to shed light on the elucidation of metabolic pathways for natural products, and the underlying mechanisms of their biological efficacies.

Review of emerging metabolomic tools and resources: 2015-2016

Data processing and analysis are major bottlenecks in high-throughput metabolomic experiments. Recent advancements in data acquisition platforms are driving trends toward increasing data size (e.g., petabyte scale) and complexity (multiple omic platforms). Improvements in data analysis software and in silico methods are similarly required to effectively utilize these advancements and link the acquired data with biological interpretations. Herein, we provide an overview of recently developed and freely available metabolomic tools, algorithms, databases, and data analysis frameworks. This overview of popular tools for MS and NMR-based metabolomics is organized into the following sections: data processing, annotation, analysis, and visualization. The following overview of newly developed tools helps to better inform researchers to support the emergence of metabolomics as an integral tool for the study of biochemistry, systems biology, environmental analysis, health, and personalized medicine.

An enhanced targeted identification strategy for the selective identification of flavonoid O-glycosides from Carthamus tinctorius by integrating offline two-dimensional liquid chromatography/linear ion-trap-Orbitrap mass spectrometry, high-resolution diagnostic product ions/neutral loss filtering and liquid chromatography-solid phase extraction-nuclear magnetic resonance

Targeted identification of potentially bioactive molecules from herbal medicines is often stymied by the insufficient chromatographic separation, ubiquitous matrix interference, and pervasive isomerism. An enhanced targeted identification strategy is presented and validated by the selective identification of flavonoid O-glycosides (FOGs) from Carthamus tinctorius. It consists of four steps: (i) enhanced separation and detection by offline two-dimensional liquid chromatography/LTQ-Orbitrap MS (offline 2D-LC/LTQ-Orbitrap MS) using collision-induced dissociation (CID) and high-energy C-trap dissociation (HCD); (ii) improved identification of the major aglycones by acid hydrolysis and LC-SPE-NMR; (iii) simplified spectral elucidation by high-resolution diagnostic product ions/neutral loss filtering; and (iv) more convincing structural identification by matching an in-house library. An offline 2D-LC system configuring an Acchrom XAmide column and a BEH Shield RP-18 UPLC^® column enabled much better separation of the easily co-eluting components. Combined use of CID and HCD could produce complementary fragmentation information. The intensity ratios of the aglycone ion species ([Y₀-H]^-/Y₀^- and [Y₀-2H]^-/Y₀^-) in the HCD-MS² spectra were found diagnostic for discriminating the aglycone subtypes and characterizing the glycosylation patterns. Five aglycone structures (kaempferol, 6-hydroxykaempferol, 6-methoxykaempferol, carthamidin, and isocarthamidin) were identified based on the ¹H-NMR data recorded by LC-SPE-NMR. Of the 107 characterized flavonoids, 80 FOGs were first reported from C. tinctorius. Unknown aglycones, pentose, and novel acyl substituents were discovered. A new compound thereof was isolated and fully identified, which could partially validate the MS-oriented identification. This integral strategy can improve the potency, efficiency, and accuracy in the detection of new compounds from medicinal herbs and other natural sources.