Feature-based Molecular Networking Research Articles

Targeted isolation of ursane-type triterpenoids from Tragopogon porrifolius based on Feature-Based Molecular Networking

Targeted isolation of ursane-type triterpenoids from Tragopogon porrifolius based on Feature-Based Molecular Networking

Mining Xanthine Oxidase Inhibitors from an Edible Seaweed Pterocladiella capillacea by Using In Vitro Bioassays, Affinity Ultrafiltration LC-MS/MS, Metabolomics Tools, and In Silico Prediction.

The prevalence of gout and the adverse effects of current synthetic anti-gout drugs call for new natural and effective xanthine oxidase (XOD) inhibitors to target this disease. Based on our previous finding that an edible seaweed Pterocladiella capillacea extract inhibits XOD, XOD-inhibitory and anti-inflammatory activities were used to evaluate the anti-gout potential of different P. capillacea extract fractions. Through affinity ultrafiltration coupled with liquid chromatography tandem mass spectrometry (LC-MS/MS), feature-based molecular networking (FBMN), and database mining of multiple natural products, the extract's bioactive components were traced and annotated. Through molecular docking and ADMET analysis, the possibility and drug-likeness of the annotated XOD inhibitors were predicted. The results showed that fractions F4, F6, F4-2, and F4-3 exhibited strong XOD inhibition activity, among which F4-3 reached an inhibition ratio of 77.96% ± 4.91% to XOD at a concentration of 0.14 mg/mL. In addition, the P. capillacea extract and fractions also displayed anti-inflammatory activity. Affinity ultrafiltration LC-MS/MS analysis and molecular networking showed that out of the 20 annotated compounds, 8 compounds have been previously directly or indirectly reported from seaweeds, and 4 compounds have been reported to exhibit anti-gout activity. Molecular docking and ADMET showed that six seaweed-derived compounds can dock with the XOD activity pocket and follow the Lipinski drug-like rule. These results support the value of further investigating P. capillacea as part of the development of anti-gout drugs or related functional foods.

Molecular networking-guided isolation strategy of a new C-glycosyl flavone rotamer from Stellaria alsine and evaluation of anti-inflammatory and antioxidant activities.

Stellaria alsine has traditionally been used as both a famine relief food and an alternative medicine in East Asia. Modern pharmacological studies have revealed that S. alsine has various biological effects such as anticancer, anti-hepatoma, anti-inflammatory, and antioxidative effects. However, the anti-inflammatory properties of chemical constituents derived from this plant have not been studied well. To identify potential therapeutic candidate for treating inflammatory diseases such as inflammatory bowel disease (IBD). The distribution of chemical compounds was investigated by Global Natural Product Social (GNPS)-based molecular networking (MN) analysis using UPLC-Orbitrap tandem mass spectrometry. The anti-inflammatory and antioxidative effects of S. alsine extracts and fractions were evaluated by measuring interleukin (IL)-8 and reactive oxygen species (ROS) productions. The active EA layer of S. alsine showed the highest percentage of major compounds by feature-based molecular networking. The top candidate structures of EA fraction were rapidly annotated as flavone C- or O-glycosides via an advanced analysis tool, Network Annotation Propagation (NAP). With the GNPS molecular networking-guided isolation strategy, a new C-glycosyl flavone rotamer (1) was isolated. The structures of the major (1a) and minor (1b) rotational isomers were determined by extensive NMR analysis and MS/MS fragmentation. Finally, the anti-inflammatory activity of 1 was predicted by molecular docking simulations with IL-8 protein. These results suggested that the compound 1 is a potential therapeutic candidate for inflammatory bowel disease (IBD).

Non-target screening and identification of the significant quality markers in the wild and cultivated Cordyceps sinensis using OPLS-DA and feature-based molecular networking

Non-target screening and identification of the significant quality markers in the wild and cultivated Cordyceps sinensis using OPLS-DA and feature-based molecular networking

Discovery of Naphthol Tetramers from Endolichenic Fungus Daldinia childiae 047219 Based on MS/MS Molecular Networking.

Feature-based molecular networking analysis suggested the presence of naphthol tetramers in Daldinia childae 047219, the same species but a different strain from one used previously for the discovery of naphthol trimers promoting adiponectin synthesis. The new tetramers were composed of 5-methoxy-4-naphthol, each of which was connected to one another in various positions. Targeted isolation afforded six previously unreported naphthol tetramers (1-6) together with 13 known polyketides (7-19) including naphthol monomers, dimers, and trimers. Structures of the isolated compounds were established by using NMR and mass spectroscopic analysis. Nodulisporin A (13), nodulisporin B (14), and 1,1',3',3″-ternaphthalene-5,5',5″-trimethoxy-4,4',4″-triol (16) demonstrated anti-inflammatory activities against NO production, but the new compounds were less active.

Evaluation of Data-Dependent MS/MS Acquisition Parameters for Non-Targeted Metabolomics and Molecular Networking of Environmental Samples: Focus on the Q Exactive Platform.

Non-targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a widely used tool for metabolomics analysis, enabling the detection and annotation of small molecules in complex environmental samples. Data-dependent acquisition (DDA) of product ion spectra is thereby currently one of the most frequently applied data acquisition strategies. The optimization of DDA parameters is central to ensuring high spectral quality, coverage, and number of compound annotations. Here, we evaluated the influence of 10 central DDA settings of the Q Exactive mass spectrometer on natural organic matter samples from ocean, river, and soil environments. After data analysis with classical and feature-based molecular networking using MZmine and GNPS, we compared the total number of network nodes, multivariate clustering, and spectrum quality-related metrics such as annotation and singleton rates, MS/MS placement, and coverage. Our results show that automatic gain control, microscans, mass resolving power, and dynamic exclusion are the most critical parameters, whereas collision energy, TopN, and isolation width had moderate and apex trigger, monoisotopic selection, and isotopic exclusion minor effects. The insights into the data acquisition ergonomics of the Q Exactive platform presented here can guide new users and provide them with initial method parameters, some of which may also be transferable to other sample types and MS platforms.

Application of feature-based molecular networking and MassQL for the MS/MS fragmentation study of depsipeptides.

The Feature-based Molecular Networking (FBMN) is a well-known approach for mapping and identifying structures and analogues. However, in the absence of prior knowledge about the molecular class, assessing specific fragments and clusters requires time-consuming manual validation. This study demonstrates that combining FBMN and Mass Spec Query Language (MassQL) is an effective strategy for accelerating the decoding mass fragmentation pathways and identifying molecules with comparable fragmentation patterns, such as beauvericin and its analogues. To accomplish this objective, a spectral similarity network was built from ESI-MS/MS experiments of Fusarium oxysporum at various collision energies (CIDs) and paired with a MassQL search query for conserved beauvericin ions. FBMN analysis revealed that sodiated and protonated ions clustered differently, with sodiated adducts needing more collision energy and exhibiting a distinct fragmentation pattern. Based on this distinction, two sets of particular fragments were discovered for the identification of these hexadepsipeptides: ([M + H]+) m/z 134, 244, 262, and 362 and ([M + Na]+) m/z 266, 284 and 384. By using these fragments, MassQL accurately found other analogues of the same molecular class and annotated beauvericins that were not classified by FBMN alone. Furthermore, FBMN analysis of sodiated beauvericins at 70eV revealed subclasses with distinct amino acid residues, allowing distinction between beauvericins (beauvericin and beauvericin D) and two previously unknown structural isomers with an unusual methionine sulfoxide residue. In summary, our integrated method revealed correlations between adduct types and fragmentation patterns, facilitated the detection of beauvericin clusters, including known and novel analogues, and allowed for the differentiation between structural isomers.

Ecdysteroids from the Korean Endemic Species Ajuga spectabilis with Activities against Glucocorticoid Receptors and 11β-Hydroxysteroid Dehydrogenase Type 1.

Two new ecdysteroids, spectasterone A (1) and spectasterone B (2), together with four known ecdysteroids, breviflorasterone (3), ajugalactone (4), 20-hydroxyecdysone (5), and polypodine B (6) were isolated from the Korean endemic plant Ajuga spectabilis using feature-based molecular networking analysis. The chemical structures of 1 and 2 were determined based on the interpretation of NMR and mass spectrometric data. Their absolute configurations were established using 3JH,H coupling constants, NOESY interactions, Mosher's method, and ECD and DP4+ calculations. To identify their biological target, a machine learning-based prediction system was applied, and the results indicated that ecdysteroids may have 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1)-related activity, which was further supported by molecular docking results of ecdysteroids with 11β-HSD1. Following this result, all the isolated ecdysteroids were tested for their ability to affect the expression of 11β-hydroxysteroid dehydrogenase type 1 and glucocorticoid receptors (GRs) in HaCaT cells irradiated with UVB. Compounds 2-5 exhibited inhibition of 11β-HSD1 expression and increases in GR activity.

Structural annotation, semi-quantification and toxicity prediction of pyrrolizidine alkaloids from functional food: In silico and molecular networking strategy.

Many traditional Chinese herbs contain pyrrolizidine alkaloids (PAs), which have been reported to be toxic to livestock and humans. However, the lack of PAs standards makes it difficult to effectively conduct a risk assessment in the varied components of traditional Chinese medicine. It is necessary to propose a suitable strategy to obtain the representative occurrence data of PAs in complex systems. A comprehensive approach for annotating the structures, concentration, and mutagenicity of PAs in three Chinese herbs has been proposed in this article. First, feature-based molecular networking (FBMN) combined with network annotation propagation (NAP) on the Global Natural Products Social Molecular Networking web platform speeds up the process of annotating PAs found in Chinese herbs. Second, a semi-quantitative prediction model based on the quantitative structure and ionization intensity relationship (QSIIR) is used to forecast the amounts of PAs in complex substrates. Finally, the T.E.S.T. was used to provide predictions regarding the mutagenicity of annotated PAs. The goal of this study was to develop a strategy for combining the results of several computer models for PA screening to conduct a comprehensive analysis of PAs, which is a crucial step in risk assessment of unknown PAs in traditional Chinese herbal preparations.

An integrated strategy combining network toxicology and feature-based molecular networking for exploring hepatotoxic constituents and mechanism of Epimedii Folium-induced hepatotoxicity in vitro.

Epimedii Folium (EF), a commonly used herbal medicine to treat osteoporosis, has caused serious concern due to potential hepatotoxicity. Until now, its intrinsic hepatotoxic mechanism and hepatotoxic ingredients remain unclear. Here, a novel high-throughput approach was designed to investigate the intrinsic hepatotoxic of EF. High-content screen imaging (HCS) and biochemical tests were first performed to obtain the cytotoxicity parameter matrix of 17 batch EF samples. EF-treated alpha mouse liver 12 (AML12) cells showed increased reactive oxygen species (ROS), reduced glutathione (GSH) and mitochondrial membrane potential (MMP), and apoptosis and cholestasis were further observed. Network toxicology predicted that EF-triggered hepatotoxiciy was involved in transcription factor (TF) activity. The FXR expression, screened by a TF PCR array, exhibited down-regulation following EF extract administration. Moreover, EF inhibited bile acid (BA) metabolism pathway in an FXR-dependent manner. Pearson correlation between the cytotoxicity parameter matrix and quantification feature table obtained from UHPLC-QTOF data of EF suggested 7 prenylated flavonoids possessed potent hepatotoxicities and their cytotoxicity order was further summarized. The transcriptional repression effects of them on FXR were also verified. Collectively, our findings indicate that FXR is probably responsible for EF-induced hepatotoxicity and prenylated flavonoids may be a major class of hepatotoxic constituents in EF.

Shades of Fine Dark Chocolate Colors: Polyphenol Metabolomics and Molecular Networking to Enlighten the Brown from the Black.

High-quality dark chocolates (70% cocoa content) can have shades from light to dark brown color. This work aimed at revealing compounds that discriminate black and brown chocolates. From 37 fine chocolate samples from years 2019 and 2020 provided by Valrhona,8 dark black samples and 8 light brown samples were selected. A non-targeted metabolomics study was performed based on ultra-high performance liquid chromatography-high resolution mass spectrometry/mass spectrometry experiments, univariate, multivariate, and feature-based molecular networking analyses. Twenty-seven overaccumulated discriminating compounds were found for black chocolates. Among them, glycosylated flavanols including monomers and glycosylated A-type procyanidin dimers and trimers were highly representative. Fifty overaccumulated discriminating compounds were found for brown chocolates. Most of them were B-type procyanidins (from trimers to nonamers). These phenolic compounds may be partially related to the chocolate colors as precursors of colored compounds. This study increases the knowledge on the chemical diversity of dark chocolates by providing new information about the phenolic profiles of black and brown chocolates.

UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis

Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and accurate data processing and dereplication are required. Here, we present UmetaFlow, a computational workflow for untargeted metabolomics that combines algorithms for data pre-processing, spectral matching, molecular formula and structural predictions, and an integration to the GNPS workflows Feature-Based Molecular Networking and Ion Identity Molecular Networking for downstream analysis. UmetaFlow is implemented as a Snakemake workflow, making it easy to use, scalable, and reproducible. For more interactive computing, visualization, as well as development, the workflow is also implemented in Jupyter notebooks using the Python programming language and a set of Python bindings to the OpenMS algorithms (pyOpenMS). Finally, UmetaFlow is also offered as a web-based Graphical User Interface for parameter optimization and processing of smaller-sized datasets. UmetaFlow was validated with in-house LC–MS/MS datasets of actinomycetes producing known secondary metabolites, as well as commercial standards, and it detected all expected features and accurately annotated 76% of the molecular formulas and 65% of the structures. As a more generic validation, the publicly available MTBLS733 and MTBLS736 datasets were used for benchmarking, and UmetaFlow detected more than 90% of all ground truth features and performed exceptionally well in quantification and discriminating marker selection. We anticipate that UmetaFlow will provide a useful platform for the interpretation of large metabolomics datasets.Graphical

Carboxyl-containing components delineation via feature-based molecular networking: A key to processing conditions of fermented soybean

Carboxyl-containing components (CCCs) was the key chemical markers for fermented soybean, whose composition and content would be dramatically changed during the fermentation processes. To select the optimal fermented conditions, a rapid and sensitive 5-(diisopropylamino)amylamine derivatization, ultra-high performance liquid chromatography-quadrupole-time-of-flight/mass spectrometry and feature-based molecular networking method was established for determination CCCs and the developed method was successfully applied to compare the dynamic changes of CCCs under different processing conditions. A total of 120 components were identified, the optimum fermentation conditions were the temperature at 30 °C, 50% humidity, with a 2-hour steaming time. Sixteen chemical markers with significant differences were screened. Furthermore, molecular docking technology was used to verify the antiperoxidative effect of these chemical markers. Accordingly, we focused on the high-content, little-studied active CCCs rather than the low-content and well-studied flavonoids. This study was helpful for the nutraceutical development and contributed scientific basis to further exploring quality evaluation of fermented food.

The screening for marine fungal strains with high potential in alkaloids production by in situ colony assay and LC-MS/MS based secondary metabolic profiling.

Alkaloids are the second primary class of secondary metabolites (SMs) from marine organisms, most of which have antioxidant, antitumor, antibacterial, anti-inflammatory, and other activities. However, the SMs obtained by traditional isolation strategies have drawbacks such as highly reduplication and weak bioactivity. Therefore, it is significantly important to establish an efficient strategy for screening strains and mining novel compounds. In this study, we utilized in situ colony assay combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify the strain with high potential in alkaloids production. The strain was identified by genetic marker genes and morphological analysis. The secondary metabolites from the strain were isolated by the combine use of vacuum liquid chromatography (VLC), ODS column chromatography, and Sephadex LH-20. Their structures were elucidated by 1D/2D NMR, HR-ESI-MS, and other spectroscopic technologies. Finally, these compounds bioactivity were assay, including anti-inflammatory and anti-β aggregation. Eighteen marine fungi were preliminarily screened for alkaloids production by in situ colony assay using Dragendorff reagent as dye, and nine of them turned orange, which indicated abundant alkaloids. By thin-layer chromatography (TLC), LC-MS/MS, and multiple approaches assisted Feature-Based Molecular Networking (FBMN) analysis of fermentation extracts, a strain ACD-5 (Penicillium mallochii with GenBank accession number OM368350) from sea cucumber gut was selected for its diverse alkaloids profiles especially azaphilones. In bioassays, the crude extracts of ACD-5 in Czapek-dox broth and brown rice medium showed moderate antioxidant, acetylcholinesterase inhibitory, anti-neuroinflammatory, and anti-β aggregation activities. Three chlorinated azaphilone alkaloids, compounds 1-3 (sclerotioramine, isochromophilone VI, and isochromophilone IX, respectively), were isolated from the fermentation products of ACD-5 in brown rice medium guided by bioactivities and mass spectrometry analysis. Compound 1 had shown remarkable anti-neuroinflammatory activity in liposaccharide induced BV-2 cells. In summary, in situ colony screening together with LC-MS/MS, multi-approach assisted FBMN can act as an efficient screening method for strains with potential in alkaloids production.

Cytotoxic Metabolites from Calophyllum tacamahaca Willd.: Isolation and Detection through Feature-Based Molecular Networking

Isocaloteysmannic acid (1), a new chromanone, was isolated from the leaf extract of the medicinal species Calophyllum tacamahaca Willd. along with 13 known metabolites belonging to the families of biflavonoids (2), xanthones (3-5, 10), coumarins (6-8) and triterpenes (9, 11-14). The structure of the new compound was characterized based on nuclear magnetic resonance (NMR), high-resolution electrospray mass spectrometry (HRESIMS), ultraviolet (UV) and infrared (IR) data. Its absolute configuration was assigned through electronic circular dichroism (ECD) measurements. Compound (1) showed a moderate cytotoxicity against HepG2 and HT29 cell lines, with IC50 values of 19.65 and 25.68 µg/mL, respectively, according to the Red Dye method. Compounds 7, 8 and 10-13 exhibited a potent cytotoxic activity, with IC50 values ranging from 2.44 to 15.38 µg/mL, against one or both cell lines. A feature-based molecular networking (FBMN) approach led to the detection of a large amount of xanthones in the leaves extract, and particularly analogues of the cytotoxic isolated xanthone pyranojacareubin (10).

Feature-Based Molecular Networking Facilitates the Comprehensive Identification of Differential Metabolites in Diabetic Cognitive Dysfunction Rats.

Cognitive dysfunction is a frequent complication of type 2 diabetes mellitus (T2DM), usually accompanied by metabolic disorders. However, the metabolic changes in diabetic cognitive dysfunction (DCD) patients, especially compared to T2DM groups, are not fully understood. Due to the subtle differences in metabolic alterations between DCD groups and T2DM groups, the comprehensive detection of the untargeted metabolic profiles of hippocampus and urine samples of rats was conducted by LC-MS, considering the different ionization modes and polarities of the examined compounds, and feature-based molecular networking (FBMN) was performed to help identify differential metabolites from a comprehensive perspective in this study. In addition, an association analysis of the differential metabolites in hippocampus and urine was conducted by the O2PLS model. Finally, a total of 71 hippocampal tissue differential metabolites and 179 urine differential metabolites were identified. The pathway enrichment results showed that glutamine and glutamate metabolism, alanine, aspartate, and glutamate metabolism, glycerol phospholipid metabolism, TCA cycle, and arginine biosynthesis in the hippocampus of DCD animals were changed. Seven metabolites (AUC > 0.9) in urine appeared as key differential metabolites that might reflect metabolic changes in the target tissue of DCD rats. This study showed that FBMN facilitated the comprehensive identification of differential metabolites in DCD rats. The differential metabolites may suggest an underlying DCD and be considered as potential biomarkers for DCD. Large samples and clinical experiments are needed for the subsequent elucidation of the possible mechanisms leading to these alterations and the verification of potential biomarkers.

Highlighting the Phototherapeutical Potential of Fungal Pigments in Various Fruiting Body Extracts with Informed Feature-Based Molecular Networking.

Fungal pigments are characterized by a diverse set of chemical backbones, some of which present photosensitizer-like structures. From the genus Cortinarius, for example, several biologically active photosensitizers have been identified leading to the hypothesis that photoactivity might be a more general phenomenon in the kingdom Fungi. This paper aims at testing the hypothesis. Forty-eight fruiting body-forming species producing pigments from all four major biosynthetic pathways (i.e., shikimate-chorismate, acetate-malonate, mevalonate, and nitrogen heterocycles) were selected and submitted to a workflow combining in vitro chemical and biological experiments with state-of-the-art metabolomics. Fungal extracts were profiled by high-resolution mass spectrometry and subsequently explored by spectral organization through feature-based molecular networking (FBMN), including advanced metabolite dereplication techniques. Additionally, the photochemical properties (i.e., light-dependent production of singlet oxygen), the phenolic content, and the (photo)cytotoxic activity of the extracts were studied. Different levels of photoactivity were found in species from all four metabolic groups, indicating that light-dependent effects are common among fungal pigments. In particular, extracts containing pigments from the acetate-malonate pathway, e.g., extracts from Bulgaria inquinans, Daldinia concentrica, and Cortinarius spp., were not only efficient producers of singlet oxygen but also exhibited photocytotoxicity against three different cancer cell lines. This study explores the distribution of photobiological traits in fruiting body forming fungi and highlights new sources for phototherapeutics.

New Phenylspirodrimanes from the Sponge-Associated Fungus Stachybotrys chartarum MUT 3308

Two phenylspirodrimanes, never isolated before, stachybotrin J (1) and new stachybocin G (epi-stachybocin A) (2), along with the already reported stachybotrin I (3), stachybotrin H (4), stachybotrylactam (5), stachybotrylactam acetate (6), 2α-acetoxystachybotrylactam acetate (7), stachybotramide (8), chartarlactam B (9), and F1839-J (10) were isolated from the sponge-associated fungus Stachybotrys chartarum MUT 3308. Their structures were established based on extensive spectrometric (HRMS) and spectroscopic (1D and 2D NMR) analyses. Absolute configurations of the stereogenic centers of stachybotrin J (1), stachybocin G (2), and stachybotrin I (3), were determined by comparison of their experimental circular dichroism (CD) spectra with their time-dependent density functional theory (TD-DFT) circular dichroism (ECD) spectra. The putative structures of seventeen additional phenylspirodrimanes were proposed by analysis of their respective MS/MS spectra through a Feature-Based Molecular Networking approach. All the isolated compounds were evaluated for their cytotoxicity against five aggressive cancer cell lines (MP41, 786, 786R, CAL33, and CAL33RR), notably including two resistant human cancer cell lines (786R, CAL33RR), and compounds 5, 6, and 7 exhibited cytotoxicity with IC50 values in the range of 0.3-2.2 µM.

Bioactivity Profiling and Untargeted Metabolomics of Microbiota Associated with Mesopelagic Jellyfish Periphylla Periphylla

The marine mesopelagic zone extends from water depths of 200 m to 1000 m and is home to a vast number and diversity of species. It is one of the least understood regions of the marine environment with untapped resources of pharmaceutical relevance. The mesopelagic jellyfish Periphylla periphylla is a well-known and widely distributed species in the mesopelagic zone; however, the diversity or the pharmaceutical potential of its cultivable microbiota has not been explored. In this study, we isolated microorganisms associated with the inner and outer umbrella of P. periphylla collected in Irminger Sea by a culture-dependent approach, and profiled their chemical composition and biological activities. Sixteen mostly gram-negative bacterial isolates were selected and subjected to an OSMAC cultivation regime approach using liquid and solid marine broth (MB) and glucose-yeast-malt (GYM) media. Their ethyl acetate (EtOAc) extracts were assessed for cytotoxicity and antimicrobial activity against fish and human pathogens. All, except one extract, displayed diverse levels of antimicrobial activities. Based on low IC50 values, four most bioactive gram-negative strains; Polaribacter sp. SU124, Shewanella sp. SU126, Psychrobacter sp. SU143 and Psychrobacter sp. SU137, were prioritized for an in-depth comparative and untargeted metabolomics analysis using feature-based molecular networking. Various chemical classes such as diketopiperazines, polyhydroxybutyrates (PHBs), bile acids and other lipids were putatively annotated, highlighting the biotechnological potential in P. periphylla-associated microbiota as well as gram-negative bacteria. This is the first study providing an insight into the cultivable bacterial community associated with the mesopelagic jellyfish P. periphylla and, indeed, the first to mine the metabolome and antimicrobial activities of these microorganisms.

UHPLC Q-Orbitrap Mass Spectrometry-Based Molecular Networking for Identification of Chemical Constituents in the Multi-Herb Formula Runyan Mixture.

Traditional Chinese medicine (TCM) in-hospital preparations are approved for use only in the hospital where they are prepared. They are widely used in China because of their efficacy and affordable price. However, few researchers focused on their quality controls and treatment mechanisms, for which a key consideration is the elucidation of their chemical composition. Runyan mixture (RY) is a typical in-hospital TCM preparation comprising a formula of eight herbal drugs used for adjuvant therapy of upper respiratory tract infections. The chemical constituents of formulated RY have not yet been elucidated. In the present work, RY was analyzed by a ultrahigh-performance liquid chromatography system equipped with high-resolution orbitrap mass spectrometry (MS). The acquired MS data were processed by MZmine and a feature-based molecular networking was constructed to identify the metabolites of RY. 165 compounds including 41 flavonoid O-glycosides, 11 flavonoid C-glycosides, 18 quinic acids, 54 coumaric acids, 11 iridoids, and 30 others were identified. This study demonstrates an efficient method to identify compounds in complex herbal drug mixtures using high-resolution MS and molecular networking tools which will support future research into quality controls and treatment mechanisms of in-hospital TCM preparations.