Discovery of markers for discriminating the age of cultivated ginseng by using UHPLC-QTOF/MS coupled with OPLS-DA

Metabolomics analysis of Astragali Radix in Shanxi Province: Investigating the impact of various cultivation methods and growth years on metabolite profiles

Astragali radix (HQ) is a herb with rich medicinal and edible value. Wild-simulated HQ (FYS) and Transplanted HQ (PZ) are its currently two primary forms available in the market. Metabolomics was employed to investigate their intricate metabolic variations under various cultivation methods and growth years. Notable similarities were observed in their metabolic changes across various growth years. Specifically, saponins was higher in the early growth phase, while flavonoids increased in the later. Additionally, comparative analysis of HQ samples from different cultivation methods indicated that FYS generally exhibited different chemical characteristics compared to PZ within the same market circulation period, and Calycosin-7-O-Glc-6"-O-acetate and Cycloastragenol-H2O might be used to discriminant them (the content of Calycosin-7-O-Glc-6"-O-acetate and Cycloastragenol-H2O was higher in FYS than in PZ). This approach elucidates the dynamic change pattern of characteristic metabolites and pinpoints potential biomarkers for both FYS and PZ, thereby enhancing our understanding of these medicinal materials.

Metabolomics analysis of bioactive compositions of Michelia macclurei Dany and its antioxidant and enzyme inhibitory activities

BACKGROUND

Michelia macclurei Dandy is a traditional Chinese medicinal plant, but little is understood about the bioactive compositions and biological potential of its different parts, limiting their applications. This study aims to identify the bioactive compositions and analyze differences in accumulation patterns from different parts of Michelia macclurei (heartwood, sapwood, bark, root, leaf, and fruit) using metabolomics. It also seeks to explore their biological potential and analyze the relationship between the bioactive compositions and biological potential.

RESULTS

A total of 63 volatile metabolites (VMs) were identified by gas chromatography-mass spectrometry (GC-MS) in six parts, and the VMs in each part were dominated by sesquiterpenes and their derivatives (71.40-88.32%). Six parts of Michelia macclurei contained structurally diverse non-volatile metabolites (NVMs) with a total of 207 bioactive compounds, including 92 alkaloids, 30 flavonoids, 19 lignans, and 18 organic acids, utilizing ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) analysis. Multivariate statistical analysis showed that the accumulation patterns of bioactive compositions differed significantly among the different parts, and the 25 VMs and 72 NVMs could be considered potential markers for distinguishing the different parts of Michelia macclurei. The excellent antioxidant and enzyme inhibitory capacity of extracts of all six parts was indicated by in vitro bioactivity assays. Pearson's correlation analysis showed that the bioactive compositions in the six parts were significantly correlated with antioxidant and enzyme inhibitory activities.

CONCLUSION

This study offers helpful information on the distribution of bioactive compositions in different parts of Michelia macclurei and confirms the excellent antioxidant, and enzyme inhibitory potential of its extracts, which could provide scientific evidence for its potential applications in the pharmaceutical industry, cosmetics, and functional foods. © 2024 Society of Chemical Industry.

Lincomycin as a growth-promoting antibiotic induces metabolic and immune dysregulation in animals

Since animal growth promoters (AGPs) are used in large quantities and commonly released to the environment from animal farms, it is necessary to determine whether such agents should be regarded as an environmental toxin that poses a threat to the ecosystem and health risk to wildlife. In this study, a multi-omics approach was employed to explore the effects of a representative AGP, lincomycin, on key metabolic and physiological functions of animals, using a mouse model. The results indicated that exposure to lincomycin resulted in a significant increase in growth rate of mice (50.11 %) over an 8 weeks period, during which significant decrease (61.94 %) and increase (68.64 %) in the relative abundance of Firmicutes and Escherichia coli, respectively, was observed in the gut microbiota, indicating that the gut microbiota structure has been altered. Moreover, the mice exhibited altered lipid profiles and liver damage suggestive of early-stage non-alcoholic fatty liver disease (NAFLD). Disruptions in blood glucose and insulin levels associated with type 2 diabetes mellitus (T2DM) were also observed. Furthermore, lincomycin was found to cause suppression in inflammatory responses, as evidenced by the downregulation of related genes and elevated inflammatory mediators, potentially resulting in increased susceptibility to microbial infection. Our findings underscore the detrimental effects of lincomycin on animal health and highlight the necessity for comprehensive toxicological assessments of lincomycin and other AGPs before their environmental release.

Spatial Metabolomic Profiling of Pinelliae Rhizoma from Different Leaf Types Using Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

Pinelliae Rhizoma (PR), a highly esteemed traditional Chinese medicinal herb, is widely applied in clinical settings due to its diverse pharmacological effects, including antitussive, expectorant, antiemetic, sedative-hypnotic, and antitumor activities. Pinellia ternata exhibits morphological variation in its leaves, with types resembling peach, bamboo, and willow leaves. However, the chemical composition differences among the corresponding rhizomes of these leaf phenotypes remain unelucidated. This pioneering research employed Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) to conduct the in situ identification and spatial profiling of 35 PR metabolites in PR, comprising 12 alkaloids, 4 organic acids, 12 amino acids, 5 flavonoids, 1 sterol, and 1 anthraquinone. Our findings revealed distinct spatial distribution patterns of secondary metabolites within the rhizome tissues of varying leaf types. Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) effectively differentiated between rhizomes associated with different leaf morphologies. Furthermore, this study identified five potential differential biomarkers-methylophiopogonanone B, inosine, cytidine, adenine, and leucine/isoleucine-that elucidate the biochemical distinctions among leaf types. The precise tissue-specific localization of these secondary metabolites offers compelling insights into the specialized accumulation of bioactive compounds in medicinal plants, thereby enhancing our comprehension of PR's therapeutic potential.

Rational analysis of data from LC-MS/MS: new insights in acylcarnitines as biomarkers for brain disorders or neurotoxicity

Objective

LC-MS/MS-based metabolomics is an important tool for studying disease-related biomarkers. Conventionally, different strategies have been used to screen biomarkers. However, many studies for biomarker screening by different strategies have ignored the dose-response relationship between the biomarker level and exposure level, and no relevant studies have described and compared different strategies in detail. Phenobarbital (PHB) which belongs to the barbiturates, was selected as the typical representative of neurotoxins. Acylcarnitines have been promising candidates for diagnostic biomarkers for several neurological disorders and neurotoxicity. In this work, we aimed to use an acute PHB poisoning animal model to clarify PHB poisoning effects on plasma and brain acylcarnitine changes and how to rationally analyze data from LC-MS/MS.

Methods

The acylcarnitine profiles in plasma and brain regions in an actuate PHB poisoning animal model were utilized. The dose-response relationship between plasma PHB and carnitine and acylcarnitines (CARs) in plasma and brain were assessed by the variance analysis trend test and Spearman's rank correlation test. In different strategies, principal component analysis (PCA) and partial least squares discriminant analysis (OPLS-DA) screened the differential CARs, variable importance plots (VIPs) were utilized to select putative biomarkers for PHB-induced toxicity, and receiver operating characteristic (ROC) curve analysis then illustrated the reliability of biomarkers.

Results

Under the first strategy, 14 potential toxicity biomarkers were obtained including eight downregulated CARs with AUC >0.8. Under the second strategy, 11 potential toxicity biomarkers were obtained containing five downregulated CARs with AUC >0.8. Only when the dose-response relationship was fully considered, different strategies screen for the same biomarkers (plasma acetyl-carnitine (C2) and plasma decanoyl-carnitine (C10)), which indicated plasma acylcarnitines might serve as toxicity biomarkers. In addition, the plasma CAR level changes showed differences from brain CAR level changes, and correlations between plasma CARs and their brain counterparts were weak.

Conclusion

We found that plasma C2 and C10 might serve as toxicity biomarkers for PHB poisoning disorders, and PHB poisoning effects on changes in plasma CARs may not be fully representative of changes in brain CARs.

In-depth characterization of cycloartane triterpenoids and discovery of species-specific markers from three Cimicifuga species guided by a strategy that integrates in-source fragment elimination, diagnostic ion recognition, and feature-based molecular networking

Characterization studies of the plant metabolome are crucial for revealing plant physiology, developing functional foods, and controlling quality. Mass spectrometry-based metabolite profiling allows unprecedented qualitative coverage of complex biological extract composition. However, the electrospray ionization used in metabolite profiling generates multiple artifactual signals for a single analyte, which makes it challenging to filter out redundant signals and organize the signals corresponding to abundant constituents. This study proposed a strategy integrating in-source fragments elimination, diagnostic ions recognition, and feature-based molecular networking (ISFE-DIR-FBMN) to simultaneously characterize cycloartane triterpenoids (CTs) from three medicinal Cimicifuga species. The results showed that 63.1 % of the measured ions were redundant. A total of 184 CTs were annotated, with 27.1 % being reported for the first time. It presents a promising approach to assess the composition of natural extracts, thus facilitating new ingredient registrations or natural-extracts-based drug discovery campaigns. Besides, chemometrics analysis of the three Cimicifuga species identified 32 species-specific markers, highlighting significant differences among them. The valuable information can enhance the sustainable utilization and further development of Cimicifuga resources. The codes involved in ISFE-DIR-FBMN are freely available on GitHub (https://github.com/LHJ-Group/ISFE-DIR-FBMN.git).

Application of machine learning for high-throughput tumor marker screening

High-throughput sequencing and multiomics technologies have allowed increasing numbers of biomarkers to be mined and used for disease diagnosis, risk stratification, efficacy assessment, and prognosis prediction. However, the large number and complexity of tumor markers make screening them a substantial challenge. Machine learning (ML) offers new and effective ways to solve the screening problem. ML goes beyond mere data processing and is instrumental in recognizing intricate patterns within data. ML also has a crucial role in modeling dynamic changes associated with diseases. Used together, ML techniques have been included in automatic pipelines for tumor marker screening, thereby enhancing the efficiency and accuracy of the screening process. In this review, we discuss the general processes and common ML algorithms, and highlight recent applications of ML in tumor marker screening of genomic, transcriptomic, proteomic, and metabolomic data of patients with various types of cancers. Finally, the challenges and future prospects of the application of ML in tumor therapy are discussed.

The isolation strategy and chemical analysis of oil cells from Asari Radix et Rhizoma

BACKGROUND

Single-cell analysis, a rapidly evolving field, encounters significant challenges in detecting individual cells within complex plant tissues, particularly oil cells (OCs). The intricate process of single-cell isolation, coupled with the inherent chemical volatility of oil cells, necessitates a comprehensive methodology.

RESULTS

This study presents a method for obtaining intact OC from Asari Radix et Rhizoma (ARR), a traditional herbal medicine. The developed approach facilitates both qualitative and quantitative analysis of diverse OCs. To determine the most reliable approach, four practical methods-laser capture microdissection, micromanipulation capturing, micromanipulation piping, and cell picking-were systematically compared and evaluated, unequivocally establishing cell picking as the most effective method for OC isolation and chemical analysis. Microscopic observations showed that OCs predominantly distribute in the cortex of adventitious and fibrous roots, as well as the pith and cortex of the rhizome, with distinct morphologies-oblong in roots and circular in rhizomes. Sixty-three volatile constituents were identified in OCs, with eighteen compounds exhibiting significant differences. Safrole, methyleugenol, and asaricin emerged as the most abundant constituents in OCs. Notably, cis-4-thujanol and tetramethylpyrazine were exclusive to rhizome OCs, while isoeugenol methyl ether was specific to fibrous root OCs based on the detections. ARR roots and rhizomes displayed marked disparities in OC distribution, morphology, and constituents.

CONCLUSION

The study highlights the efficacy of cell picking coupled with HS-SPME-GC-MS as a flexible, reliable, and sensitive method for OC isolation and chemical analysis, providing a robust methodology for future endeavors in single-cell analyses.

Effect of indoor dehydration on the chemical composition of Marselan grapes in Heyang of China

Marselan is a red wine grape variety with great brewing prospects. The aim of this study was to investigate the effect of postharvest indoor dehydration on the quality of Marselan grapes. For two consecutive years, the harvested grapes were dehydrated naturally indoors (24-28°C). Fresh grapes were used as a control, and dehydrated samples were taken every 7 days during the period of dehydration until ending at day 28. Dehydration treatment increased degrees Brix, reducing sugars, glycerol, and malic acid. On day 7, there was an increase in protocatechuic acid, p-coumaric acid, and total tannin of 26.00%-27.73%, 11.43%-52.52%, and 39.74%-70.45%, respectively. With increasing dehydration time, total phenols, total flavonoids and total flavanols in the skins were decreased by 17.05%-38.13%, 24.32%-57.38%, and 17.05%-59.48%, respectively, with an increase in pH, citric acid, and ascorbic acid contents of grape juice by 7.66%-21.43%, 100%-137.50%, and 61.29%-258.82%, respectively. On day 21, the esters were increased by 1.10-1.75 factors. Partial least square-discriminant analysis result of volatile compounds showed that ethyl acetate, 1-propanol, 1-propanol, 2-methyl-, 1-hexanol, and 1-butanol, 3-methyl- were the predominant characteristic flavor compounds during dehydration of Marselan grapes. The effect of indoor dehydration on Marselan grape quality offered application value for China's later dehydration wine production.

Mass spectrometry-based ginsenoside profiling: Recent applications, limitations, and perspectives

Ginseng, the roots of Panax species, is an important medicinal herb used as a tonic. As ginsenosides are key bioactive components of ginseng, holistic chemical profiling of them has provided many insights into understanding ginseng. Mass spectrometry has been a major methodology for profiling, which has been applied to realize numerous goals in ginseng research, such as the discrimination of different species, geographical origins, and ages, and the monitoring of processing and biotransformation. This review summarizes the various applications of ginsenoside profiling in ginseng research over the last three decades that have contributed to expanding our understanding of ginseng. However, we also note that most of the studies overlooked a crucial factor that influences the levels of ginsenosides: genetic variation. To highlight the effects of genetic variation on the chemical contents, we present our results of untargeted and targeted ginsenoside profiling of different genotypes cultivated under identical conditions, in addition to data regarding genome-level genetic diversity. Additionally, we analyze the other limitations of previous studies, such as imperfect variable control, deficient metadata, and lack of additional effort to validate causation. We conclude that the values of ginsenoside profiling studies can be enhanced by overcoming such limitations, as well as by integrating with other -omics techniques.

Discovery of bioactive-chemical Q-markers of Acanthopanax sessiliflorus leaves: An integrated strategy of plant metabolomics, fingerprint and spectrum-efficacy relationship research

An integrated bioactive-chemical quality markers (Q-markers) discovery strategy, which was based on the LC-MS plant metabolomics, HPLC fingerprint as well as the chemical spectrum-efficacy relationships, was designed to develop a methodology for accurate and comprehensive evaluation of the quality of Acanthopanax sessiliflorus leaves (ASL). Firstly, a high resolution and sensitivity UHPLC-Q-Orbitrap MS method was used for plant metabolomics analysis to obtain component characterization and screen potential chemical markers that differentiate between different harvesting periods. A total of 53 chemical components were identified, and 8 potential chemical markers were discovered, such as sucrose, maltol and phenylalanine. Secondly, a selective HPLC fingerprint analysis of ASL and its pancreatic lipase activity assay method was successfully investigated in vitro. In the study of chemical spectrum-efficacy relationships, neochlorogenic acid, chlorogenic acid, caffeic acid and hyperoside were screened and showed the inhibited pancreatic lipase activity with IC50 values, 0.16 ± 0.01, 0.13 ± 0.01, 0.31 ± 0.01, and 0.44 ± 0.02 mg/mL, respectively, which indicated the above four constituents were selected as the bioactive-chemical Q-markers of ASL. Finally, an accurate and reliable quantitative HPLC assay was developed and validated for simultaneous determination of four bioactive-chemical Q-markers in ASL, and their content levels in ASL varied widely in different harvesting periods. The systematic and efficient screening strategy for bioactive-chemical Q-markers in this study, based on " LC-MS plant metabolomics, HPLC fingerprint, and spectrum-efficacy relationships," could have effectively improved the quality assessment level of ASL.

Classification and characterization on sorghums based on HS-GC-IMS combined with OPLS-DA and GA-PLS

Headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) detected 206 and 186 samples of fresh and stored sorghums respectively with three major types in Baijiu industry. The fingerprints showed the differences of volatile compounds among fresh sorghum types by qualitative analysis and artificial recognition. Organic waxy sorghums had more contents of nonanal and 2-ethyl-1-hexanol but fewer ketones. The contents of acetoin in non-glutinous sorghums and organic non-glutinous sorghums were high. On the other hand, genetic algorithm-partial least squares (GA-PLS) selected 19 and 32 characteristic volatile compounds in fresh and stored sorghums. After centering and auto scaling to unit variance, the classification models with three major types of organic waxy sorghum, non-glutinous sorghum and organic non-glutinous sorghum were established based on orthogonal partial least squares-discriminant analysis (OPLS-DA). The goodness-of-fit (R2Y) and the goodness-of-prediction in cross-validation (Q2) in the model of fresh sorghum types all exceeded 0.9, in stored were over 0.8, the correct classification rates of external prediction were 95 % and 100 %, which revealed good performance and prediction. On this basis, the correct classification rates reached 87 % in organic waxy sorghums adulterated over 10 % ratio. GC-IMS combined with chemometrics is applicable in practical production for rapid identification of sorghum types and adulterations.

Metabolomics reveal root differential metabolites of different root-type alfalfa under drought stress

Introduction

Alfalfa (Medicago sativa L.) is the favored premium feed ingredient in animal husbandry production which is in serious jeopardy due to soil moisture shortages. It is largely unknown how different root types of alfalfa respond to arid-induced stress in terms of metabolites and phytohormones.

Methods

Therefore, rhizomatous rooted M. sativa 'Qingshui' (or QS), tap-rooted M. sativa 'Longdong' (or LD), and creeping rooted M. varia 'Gannong No. 4' (or GN) were investigated to identify metabolites and phytohormones responses to drought conditions.

Results

We found 164, 270, and 68 significantly upregulated differential metabolites were categorized into 35, 38, and 34 metabolic pathways in QS, LD, and GN within aridity stress, respectively. Amino acids, organic acids, sugars, and alkaloids were the four categories of primary differential metabolites detected, which include 6-gingerol, salicylic acid (SA), indole-3-acetic acid (IAA), gibberellin A4 (GA4), abscisic acid (ABA), trans-cinnamic acid, sucrose, L-phenylalanine, L-tyrosine, succinic acid, and nicotinic acid and so on, turns out these metabolites are essential for the resistance of three root-type alfalfa to aridity coercing.

Discussion

The plant hormone signal transduction (PST) pathway was dramatically enriched after drought stress. IAA and ABA were significantly accumulated in the metabolites, indicating that they play vital roles in the response of three root types of alfalfa to water stress, and QS and LD exhibit stronger tolerance than GN under drought stress.

Metabolomic profiling combined with network analysis of serum pharmacochemistry to reveal the therapeutic mechanism of Ardisiae Japonicae Herba against acute lung injury

Introduction: Acute lung injury (ALI) is a common and devastating respiratory disease associated with uncontrolled inflammatory response and transepithelial neutrophil migration. In recent years, a growing number of studies have found that Ardisiae Japonicae Herba (AJH) has a favorable anti-inflammatory effect. However, its serum material basis and molecular mechanism are still unknown in ALI treatment. In this study, metabolomics and network analysis of serum pharmacochemistry were used to explore the therapeutic effect and molecular mechanism of AJH against lipopolysaccharide (LPS)-induced ALI. Methods: A total of 12 rats for serum pharmacochemistry analysis were randomly divided into the LPS group and LPS + AJH-treated group (treated with AJH extract 20 g/kg/d), which were administered LPS (2 mg/kg) by intratracheal instillation and then continuously administered for 7 days. Moreover, 36 rats for metabolomic research were divided into control, LPS, LPS + AJH-treated (5, 10, and 20 g/kg/d), and LPS + dexamethasone (Dex) (2.3 × 10-4 g/kg/d) groups. After 1 h of the seventh administration, the LPS, LPS + AJH-treated, and LPS + Dex groups were administered LPS by intratracheal instillation to induce ALI. The serum pharmacochemistry profiling was performed by UPLC-Orbitrap Fusion MS to identify serum components, which further explore the molecular mechanism of AJH against ALI by network analysis. Meanwhile, metabolomics was used to select the potential biomarkers and related metabolic pathways and to analyze the therapeutic mechanism of AJH against ALI. Results: The results showed that 71 serum components and 18 related metabolites were identified in ALI rat serum. We found that 81 overlapping targets were frequently involved in AGE-RAGE, PI3K-AKT, and JAK-STAT signaling pathways in network analysis. The LPS + AJH-treated groups exerted protective effects against ALI by reducing the infiltration of inflammatory cells and achieved anti-inflammatory efficacy by significantly regulating the interleukin (IL)-6 and IL-10 levels. Metabolomics analysis shows that the therapeutic effect of AJH on ALI involves 43 potential biomarkers and 14 metabolic pathways, especially phenylalanine, tyrosine, and tryptophan biosynthesis and linoleic acid metabolism pathways, to be influenced, which implied the potential mechanism of AJH in ALI treatment. Discussion: Our study initially elucidated the material basis and effective mechanism of AJH against ALI, which provided a solid basis for AJH application.

Pseudotargeted metabolomics-based random forest model for tracking plant species from herbal products

BACKGROUND

The "one-to-multiple" phenomenon is prevalent in medicinal herbs. Accurate species identification is critical to ensure the safety and efficacy of herbal products but is extremely challenging due to their complex matrices and diverse compositions.

PURPOSE

This study aimed to identify the determinable chemicalome of herbs and develop a reasonable strategy to track their relevant species from herbal products.

METHODS

Take Astragali Radix-the typical "one to multiple" herb, as a case. An in-house database-driven identification of the potentially bioactive chemicalome (saponins and flavonoids) in AR was performed. Furthermore, a pseudotargeted metabolomics method was first developed and validated to obtain high-quality semi-quantitative data. Then based on the data matrix, the random forest algorithm was trained to predict Astragali Radix species from commercial products.

RESULTS

The pseudotargeted metabolomics method was first developed and validated to obtain high-quality semi-quantitative data (including 56 saponins and 49 flavonoids) from 26 batches of AR. Then the random forest algorithm was well-trained by importing the valid data matrix and showed high performance in predicting Astragalus species from ten commercial products.

CONCLUSION

This strategy could learn species-special combination features for accurate herbal species tracing and could be expected to promote the traceability of herbal materials in herbal products, contributing to manufacturing standardization.

HS-SPME/GC×GC-TOFMS-Based Flavoromics and Antimicrobial Properties of the Aroma Components of Zanthoxylum motuoense

Zanthoxylum motuoense Huang, native to Tibet, China, is a newly discovered Chinese prickly ash, which, recently, has increasingly attracted the attention of researchers. In order to understand its volatile oil compositions and flavor characteristics, and to explore the flavor difference between Z. motuoense and the common Chinese prickly ash sold in the market, we analyzed the essential oils of Z. motuoense pericarp (MEO) using HS-SPME/GC×GC-TOFMS coupled with multivariate data and flavoromics analyses. The common commercial Chinese prickly ash in Asia, Zanthoxylum bungeanum (BEO), was used as a reference. A total of 212 aroma compounds from the 2 species were identified, among which alcohols, terpenoids, esters, aldehydes, and ketones were the major compounds. The predominant components detected from MEO were citronellal, (+)-citronellal, and β-phellandrene. Six components-citronellal, (E,Z)-3,6-nonadien-1-ol, allyl methallyl ether, isopulegol, 3,7-dimethyl-6-octen-1-ol acetate, and 3,7-dimethyl-(R)-6-octen-1-ol-could be used as the potential biomarkers of MEO. The flavoromics analysis showed that MEO and BEO were significantly different in aroma note types. Furthermore, the content differences of several numb taste components in two kinds of prickly ash were quantitatively analyzed using RP-HPLC. The antimicrobial activities of MEO and BEO against four bacterial strains and nine plant pathogenic fungi were determined in vitro. The results indicated that MEO had significantly higher inhibitory activities against most microbial strains than BEO. This study has revealed the fundamental data in respect of the volatile compound properties and antimicrobial activity of Z. motuoense, offering basic information on valuable natural sources that can be utilized in the condiment, perfume, and antimicrobial sectors.

Identification of Daphne genkwa and Its Vinegar-Processed Products by Ultraperformance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry and Chemometrics

Crude herbs of Daphne genkwa (CHDG) are often used in traditional Chinese medicine to treat scabies baldness, carbuncles, and chilblain owing to their significant purgation and curative effects. The most common technique for processing DG involves the use of vinegar to reduce the toxicity of CHDG and enhance its clinical efficacy. Vinegar-processed DG (VPDG) is used as an internal medicine to treat chest and abdominal water accumulation, phlegm accumulation, asthma, and constipation, among other diseases. In this study, the changes in the chemical composition of CHDG after vinegar processing and the inner components of the changed curative effects were elucidated using optimized ultrahigh-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). Untargeted metabolomics, based on multivariate statistical analyses, was also used to profile differences between CHDG and VPDG. Eight marker compounds were identified using orthogonal partial least-squares discrimination analysis, which indicated significant differences between CHDG and VPDG. The concentrations of apigenin-7-O-β-d-methylglucuronate and hydroxygenkwanin were considerably higher in VPDG than those in CHDG, whereas the amounts of caffeic acid, quercetin, tiliroside, naringenin, genkwanines O, and orthobenzoate 2 were significantly lower. The obtained results can indicate the transformation mechanisms of certain changed compounds. To the best of our knowledge, this study is the first to employ mass spectrometry to detect the marker components of CHDG and VPDG.

Combination of chemical profiling and network pharmacology analysis to investigate the potential mechanism of Li-Zhong-Xiao-Pi granules in the treatment of gastric precancerous lesions

Li-Zhong-Xiao-Pi granules (LZXP) are effective for treating gastric precancerous lesions (GPL) in traditional Chinese medicine. However, the active compounds of LZXP and their potential therapeutic mechanism in GPL remained unclarified. The purpose of this study is to investigate the chemical composition and potential targets of LZXP. Based on the accurate masses, ion fragments, and literature data, a total of 128 compounds were identified in the LZXP sample using ultra-performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) in both positive and negative ion modes, and 28 of these compounds were exactly determined by comparison with authentic reference standards. Meanwhile, 11 typical components were quantified via UPLC during a 24 min period. The linearity, accuracy, stability and recovery of the method were all proven. Through the network pharmacological analysis, six chemicals (quercetin, 4'-hydroxywogonin, sinensetin, 5, 7, 8, 3', 4'-pentamethoxyflavanone, 8-gingerdione and quercetin) were identified as the active ingredients, and five LZXP targets (AKT1, CYP1B1, PTGS2, MMP9 and EGFR) were found to be the crucial molecules in the treatment of GPL. This study provides a systematic and applicable method for the rapid screening and identification of the chemical constituents from LZXP, and an effective understanding the mechanism of LZXP in the treatment of GPL.

Quality assessment of traditional Chinese medicine based on data fusion combined with machine learning: A review

The authenticity and quality of traditional Chinese medicine (TCM) directly impact clinical efficacy and safety. Quality assessment of traditional Chinese medicine (QATCM) is a global concern due to increased demand and shortage of resources. Recently, modern analytical technologies have been extensively investigated and utilized to analyze the chemical composition of TCM. However, a single analytical technique has some limitations, and judging the quality of TCM only from the characteristics of the components is not enough to reflect the overall view of TCM. Thus, the development of multi-source information fusion technology and machine learning (ML) has further improved QATCM. Data information from different analytical instruments can better understand the connection between herbal samples from multiple aspects. This review focuses on the use of data fusion (DF) and ML in QATCM, including chromatography, spectroscopy, and other electronic sensors. The common data structures and DF strategies are introduced, followed by ML methods, including fast-growing deep learning. Finally, DF strategies combined with ML methods are discussed and illustrated for research on applications such as source identification, species identification, and content prediction in TCM. This review demonstrates the validity and accuracy of QATCM-based DF and ML strategies and provides a reference for developing and applying QATCM methods.

A Comprehensive Analysis to Elucidate the Effects of Spraying Mineral Elements on the Accumulation of Flavonoids in Epimedium sagittatum during the Harvesting Period

The harvesting period is a critical period for the accumulation of flavonoids in the leaves of the important medicinal plant Epimedium sagittatum. In this study, we conducted an experiment on E. sagittatum leaves sprayed with mineral elements with the aim of improving the quality of the herbal leafage during the harvesting period. We elucidated the changes in flavonoids (icariin, epimedin A, epimedin B, and epimedin C) in E. sagittatum leaves. The sum of main flavonoids content reached a maximum (11.74%) at 20 days after the high-concentration Fe²⁺ (2500 mg·L^-1) treatment. We analyzed the FT-IR spectra characteristics of E. sagittatum leaf samples using the FT-IR technique, and constructed an OPLS-DA model and identified characteristic peaks to achieve differentiated identification of E. sagittatum. Further, widely untargeted metabolomic analysis identified different classes of metabolites. As the most important characteristic flavonoids, the relative contents of icariin, icaritin, icariside I, and icariside II were found to be up-regulated by high-Fe²⁺ treatment. Our experimental results demonstrate that high-concentration Fe²⁺ treatment is an effective measure to increase the flavonoids content in E. sagittatum leaves during the harvesting period, which can provide a scientific basis for the improvement of E. sagittatum leaf cultivation agronomic measures.

Discrimination of chrysanthemum varieties using lipidomics based on UHPLC-HR-AM/MS/MS

BACKGROUND

Chrysanthemum is one of the most important and popular ornamentals over the world. Chrysanthemum drink is a type of traditional healthy drink like Chinese tea. Owing to the differences in the chemical compositions, different chrysanthemum varieties have different medicinal effects on human health. Thus, the identification of different chrysanthemum varieties is very important and necessary. This study aims to distinguish seven chrysanthemum varieties that are widely used in China. First, total lipids were obtained from chrysanthemums. After that, lipid profiles were characterized using ultra-high-performance liquid chromatography hyphenated with a Q Exactive™ high resolution-accurate-mass mass spectrometer.

RESULTS

A total of 163 lipid molecular species from 17 types of lipid classes in seven varieties of chrysanthemums were determined. Principal component analysis indicated that three lipid molecules, lysophosphatidylethanolamine(18:2) (LPE(18:2)), LPE(16:0), and phosphatidic acid(18:2/18:3) (variable importance in projection >3, P < 0.001), can be used as potential biomarkers to distinguish seven chrysanthemum varieties. Hierarchical cluster analysis showed that the lipid molecular profiles of 'Gongju' were most similar to 'Jinzijianju', followed by 'Huaibaiju', 'Boju', 'Hangbaiju', 'Chuju', and 'Fubaiju'.

CONCLUSION

This comprehensive analysis provided a new method to identify chrysanthemum varieties through the perspective of lipidomics combined with chemometrics. © 2022 Society of Chemical Industry.

Establishment of a NIR-based methodology for tracking the blend homogeneity of HTPB propellant slurry in the mixing process

The hydroxyl-terminated polybutadiene (HTPB) propellants with high level of solid loadings from 80 wt% to 90 wt% consist of aluminum (Al) powder, ammonium perchlorate (AP) and HTPB. The Al/AP/HTPB adhesive system full of solid grains appears high viscosity against flow. Therefore, the mixing is a crucial procedure in the production as it directly affects the structural integrity of the finished product. This work focused on the feasibility of tracking the blend homogeneity of Al/AP/HTPB adhesive system in the mixing process through using the near-infrared (NIR) spectroscopy and orthogonal partial least squares discrimination analysis (OPLS-DA). The OPLS-DA classification models were created by variable selection, spectral pretreatment and latent variables (LVs) optimization. It had been demonstrated that the developed models presented an excellent predictability with the root mean square error of cross-validation (RMSECV) for slurries in Ⅰ, Ⅱ groups of 0.1261 and 0.0789, respectively. Meanwhile, the well-fitted models for slurries in Ⅰ, Ⅱ groups with the squared correlation coefficient (R²) of 0.806 and 0.980, exhibited separately an acceptable predictive capability with the predictive squared correlation coefficient (Q²) > 0.5. Furthermore, Euclidean distance and move block standard deviation (MBSD) as reference methods were used to validate the predictive performance of the developed models with respect to the blend homogeneity of HTPB propellant slurry. The experimental results showed that the terminal time for each batch of slurry reaching to ideal uniformity predicted by Euclidean distance/MBSD and OPLS-DA were both at 26-30 min. Therefore, it had been proved that the method we proposed was a potential tool to monitor the variation of the uniform state of HTPB propellant slurry in the mixing process.

Analysis of Metabolic Differences in the Water Extract of Shenheling Fermented by Lactobacillus fermentum Based on Nontargeted Metabolomics

Objective: To explore the characteristics of metabolites in Shenheling (SHL) fermented by Lactobacillus fermentum. Methods: In this study, ultrahigh-performance liquid chromatography-quadrupole electrostatic field orbit trap mass spectrometry (UHPLC-QE-MS) was used to qualitatively, quantitatively, and differentially analyze the metabolites of SHL before and after fermentation. Results: A total of 102 significant differential metabolites in nine categories were analyzed before and after fermentation. It mainly includes 29 terpenoids, 17 alkaloids, 14 organic acids and derivatives, 10 flavonoids, 9 phenylpropanoids, 6 phenols, 3 aromaticity, and 3 amino acid derivatives. Further screening found that the content of most active substances, such as alkaloids, organic acids, and flavonoids, increased significantly. These metabolites play an important role in improving the taste and efficacy of SHL. After fermentation, the contents of differential metabolites, such as panaquinquecol 2, ginsenoside Rh3, ginsenoside Rg3, dehydronuciferin, nicotinic acid, 5-hydroxytryptophan, azelaic acid, dihydrokaempferol, and chrysin, were increased, which increased the effects of antioxidation, anti-obesity, hypoglycemic, antibacterial, and improved immunity compared with those before fermentation. KEGG pathway analysis identified 10 metabolic pathways. Isoquinoline alkaloid biosynthesis, vitamin B6 metabolism, beta-alanine metabolism, nicotinate, and nicotinamide metabolism, purine metabolism, pantothenate and CoA biosynthesis, glyoxylate and dicarboxylate metabolism, tyrosine metabolism, citrate cycle (TCA cycle), phenylpropanoid biosynthesis, etc. Conclusions: Fermentation significantly changed the metabolites in SHL and played an important role in improving its taste, aroma quality, antioxidant, anti-obesity, and other health care functional components.

Effect of Lactobacillus plantarum Fermentation on Metabolites in Lotus Leaf Based on Ultra-High-Performance Liquid Chromatography–High-Resolution Mass Spectrometry

The lotus leaf is a raw material commonly used in slimming herbal products, but the deep processing technology is insufficient. Lactic acid bacteria (LAB) fermentation is an effective method to improve the efficacy of plant materials. In this study, ultra-high-performance liquid chromatography–high-resolution mass spectrometry (UHPLC–HR-MS) was used to explore the differential metabolites of a lotus leaf aqueous extract before and after fermentation. Information about the metabolites in the water extract of lotus leaves before and after fermentation was collected in positive- and negative-ion modes, and the metabolites identified before and after fermentation were screened by multivariate statistical analysis. A total of 91 different metabolites were obtained. They included flavonoids, alkaloids, phenylpropanoids, organic acids and derivatives, terpenoids, fatty acids and fatty acyls, phenols, amino acid derivatives and others. Compared with the metabolites’ levels before fermentation, the relative contents of 68 metabolites were upregulated after fermentation, and the relative contents of 23 metabolites were downregulated. A Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis identified 25 metabolic pathways, of which flavone and flavonol biosynthesis, citrate cycle and flavonoid biosynthesis were the main metabolic pathways. The results of this study can provide a basis for further research and the development of products containing lotus leaves fermented by LAB.

Discrimination and Characterization of the Volatile Organic Compounds in Schizonepetae Spica from Six Regions of China Using HS-GC-IMS and HS-SPME-GC-MS

Volatile organic compounds (VOCs) are the main chemical components of Schizonepetae Spica (SS), which have positive effects on the quality evaluation of SS. In this study, HS-SPME-GC-MS (headspace solid-phase microextraction-gas chromatography-mass spectrometry) and HS-GC-IMS (headspace-gas chromatography-ion mobility spectrometry) were performed to characterize the VOCs of SS from six different regions. A total of 82 VOCs were identified. In addition, this work compared the suitability of two instruments to distinguish SS from different habitats. The regional classification using orthogonal partial least squares discriminant analysis (OPLS-DA) shows that the HS-GC-IMS method can classify samples better than the HS-SPME-GC-MS. This study provided a reference method for identification of the SS from different origins.

Study on Closely Related Citrus CMMs based on Chemometrics and Prediction of Components-Targets-Diseases Network by Ingenuity Pathway Analysis

As the representatives of closely related Chinese medicinal materials (CMMs) originated from Rutaceae family, Aurantii fructus immaturus (AFI), Aurantii fructus (AF), Citri reticulatae pericarpium viride (CRPV), and Citri reticulatae pericarpium (CRP) have better functions in regulating qi and promoting gastrointestinal motility. However, differences in the quality of closely related Citrus CMMs have not yet been revealed until now. Herein, this study focused on the systematic differentiation and in-depth understanding of closely related Citrus CMMs by a strategy integrating chemometrics and network pharmacology. Determined by ultra performance liquid chromatography, the content of nine flavonoids showed obvious fluctuations in the decoction pieces from different species (Citrus aurantium Linnaeus and Citrus reticulate Blanco) with decreasing levels in the samples of ripe fruits. Decoction pieces from the different species and ripening stages were well distinguished by orthogonal projection to latent structure-discriminate analysis (OPLS-DA) and cluster analysis. As a result, four active components including narirutin, naringenin, hesperidin, and 3,5,6,7,8,3′,4′-heptemthoxyflavone were filtered out by variable importance for the projection (VIP) value (VIP > 1.0), which were regarded as chemotaxonomic markers. Furthermore, a components-targets-diseases network was constructed via ingenuity pathway analysis (IPA), and the correlations were predicted between four chemotaxonomic markers, 223 targets, and three diseases including colitis, breast cancer, and colorectal cancer. The obtained results will be of great significance for identifying closely related Citrus CMMs and conduce to improving the resource utilization of CMMs.

Screening of specific quantitative peptides of beef by LC-MS/MS coupled with OPLS-DA

A rapid, simple, and efficient analysis methodology for screening specific quantitative peptides of beef was established based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) coupled with orthogonal partial least squares-discriminant analysis (OPLS-DA). The OPLS-DA model was built to select species-specific peptides that make a significant contribution to classification. Peptides with statistical significance were selected based on the variable importance in the projection (VIP) values and univariate P values. After the workflow of the statistical process, three specific quantitative peptides were identified by using homemade products with different beef contents. A quantification method for selected specific quantitative peptides was established by using LC-MS/MS. The quantitative results were applied to commercialized beef products. The developed method has high sensitivity, specificity, and repeatability. The results of this study proved that the integration of LC-MS/MS coupled with OPLS-DA is an efficient method for screening specific quantitative peptides and identification of the authenticity of meat products.

Exploration of habitat-related chemomarkers for Magnoliae officinalis cortex applying both global and water-soluble components-based metabolomics method

BACKGROUND

The quality of traditional Chinese medicines (TCMs) has been closely related to their growth regions. The geo-herbalism of TCMs is just like the protected destination of origin on foodstuffs and wines, telling us the specific geographic regions could yield TCMs with superior quality. However, the impact of habitat on TCMs could hardly been indicated in current quality evaluation, defects were as follows: (1) few studies involved the effect of environmental factors, (2) more attentions were paid to several abundant compounds, while global components especially water-soluble compounds were prone to be ignored.

PURPOSE

A new integrated metabolomics analysis based on global and water-soluble components was proposed aiming to explore habitat-related chemomarkers for TCMs combined with correlation analysis to environmental factors. The geo-herbalism of Magnoliae officinalis cortex (MOC) was studied as an example.

METHODS

Multi-metabolomics approach based on UPLC/Q-TOF-MS and GC-MS combined with LC-2ECD were employed to analyze global components and accurately quantified water-soluble compounds, respectively. Meanwhile, decision tree, partial least squares discriminant analysis (PLS-DA) as well as hierarchical clustering analysis (HCA) heat map was applied to classify different samples and explore habitat-related chemomarkers. In addition, support vector machines model was used to verify the importance of screened out chemomarkers in predicting sample classification, and the impact of environmental factors on the markers were also demonstrated by correlation analysis.

RESULTS

By analyzing 148 batches of MOC samples from 21 habitats, 238 variables were picked and 84 of them were identified by UNIFI, meanwhile, seven water-soluble compounds were accurately quantified. Among them, thirteen markers including Var.1, magnolignan E, magnoloside N isomer, α-agarofuran, γ-eudesmol, β-eudesmol, magnolosides A, B, D, F, H, L and M were suggested importance in grouping Chuan-po and the other MOC samples. Support vector machines model also indicated well prediction performance with an accuracy of 96.97%. Most markers belong to water-soluble compounds and temperature and precipitation contributed to such chemical differences.

CONCLUSIONS

The proposed strategy based on multi-metabolomics analysis could aid exploration of habitat-related chemomarkers for TCMs. Meanwhile, the screened out water-soluble compounds could perform equivalent functions in recognition of Daodi medicinal materials (DMMs) and non-DMM samples compared to the global components to some extent.

Identification and quantification of fox meat in meat products by liquid chromatography-tandem mass spectrometry

Over the years, food adulteration has become an important global problem, threatening public health safety and the healthy development of food industry. This study established a liquid chromatography-tandem mass (LC-MS/MS) method for accurate identification and quantitative analysis of fox meat products. High-resolution mass was used for data collection, and Proteome Discoverer was used for data analysis to screen fox-specific peptides. Multivariate statistical analysis was conducted using the data obtained from the label-free analysis of different contents of simulated samples. Samples with different contents were distinguished without interfering with each other, suggesting the feasibility of quantitative analysis of fox meat content. The linear correlation coefficient and recovery rate were calculated to determine the fox peptides that can be used for accurate quantification. The established LC-MS/MS method can be used for the accurate identification and quantification of actual samples. In addition, this method can provide technical support for law enforcement departments.

Advances and challenges in ginseng research from 2011 to 2020: the phytochemistry, quality control, metabolism, and biosynthesis

Covering: 2011 to the end of 2020Panax species (Araliaceae), particularly P. ginseng, P. quinquefolius, and P. notoginseng, have a long history of medicinal use because of their remarkable tonifying effects, and currently serve as crucial sources for various healthcare products, functional foods, and cosmetics, aside from their vast clinical preparations. The huge market demand on a global scale prompts the continuous prosperity in ginseng research concerning the discovery of new compounds, precise quality control, ADME (absorption/disposition/metabolism/excretion), and biosynthesis pathways. Benefitting from the ongoing rapid development of analytical technologies, e.g. multi-dimensional chromatography (MDC), personalized mass spectrometry (MS) scan strategies, and multi-omics, highly recognized progress has been made in driving ginseng analysis towards "systematicness, integrity, personalization, and intelligentization". Herein, we review the advances in the phytochemistry, quality control, metabolism, and biosynthesis pathway of ginseng over the past decade (2011-2020), with 410 citations. Emphasis is placed on the introduction of new compounds isolated (saponins and polysaccharides), and the emerging novel analytical technologies and analytical strategies that favor ginseng's authentic use and global consumption. Perspectives on the challenges and future trends in ginseng analysis are also presented.

Comprehensive profiling of Platycodonis radix in different growing regions using liquid chromatography coupled with mass spectrometry: from metabolome and lipidome aspects

Platycodon grandiflorus (Jacq.) A. DC. is widely cultivated across the south and north of China. Its root, Platycodonis radix, is commonly used as a vegetable, functional food, and traditional herbal medicine with various biological benefits. It is critical to fully clarify the chemical composition of Platycodonis radix for the sake of the food industry and traditional herb markets. In this study, a strategy of metabolome and lipidome profiling based on ultra-high performance liquid chromatography coupled to ion mobility-quadrupole time of flight mass spectrometry (UPLC-IM-QTOF-MS) was developed to reveal the overall chemical composition of Platycodonis radix. IN particular, comprehensive lipidome profiling was first performed for Platycodonis radix, in which 170 lipid molecular species including 55.9% glycerophospholipids, 31.2% glycerolipids, and 12.9% sphingolipids were identified. Platycodonis radix from two major production regions in China, Inner Mongolia and Anhui province, were collected and analyzed by the MS based approach combined with multivariate statistical analysis from both the metabolome and lipidome aspects. This study threw focus on the profiling investigations of Platycodonis radix from different growing regions and provided new potential in the lipidome analysis of medicinal food.

Multivariate Statistical Analysis Uncovers Spectrum–Effect Relationship between HPLC Fingerprints and Antioxidant Activity of Saffron

Crocus sativus L. is commonly used as functional food and medicinal herb in traditional Chinese medicine. In this study, the spectrum–effect relationship was established between HPLC fingerprints and in vitro antioxidant activity of saffron to improve the quality evaluation method of saffron. The fingerprints of 21 batches of saffron collected from different regions were assessed, and the data were further analyzed by chemometric methods, including similarity analysis, hierarchical clustering analysis, principal component analysis, and orthogonal partial least squares discriminant analysis. The spectrum–effect relationship between fingerprints and antioxidant effect of saffron was analyzed by grey relational analysis and partial least square methods to figure out the antioxidant component of saffron. Thirteen common peaks of 21 batches of saffron were included in the analysis, and peak 3 (picrocrocin), peak 7 (crocin I), and peak 10 (crocin II) were identified as the main active components responsible for antioxidant efficacy. Besides, a multi-index quality control method was developed for simultaneous determination of these three antioxidant components in saffron. Taken together, this study provided new strategies for the quality control and the development of new bioactive products of saffron in the future.

Comprehensive phytochemical profiling of American ginseng in Jilin province of China based on ultrahigh-performance liquid chromatography quadrupole time-of-flight mass spectrometry

American ginseng (AG), the underground part of Panax quinquefolium L., is composed of four morphological regions, including main root (MR), lateral root (LR), fibrous root (FR), and rhizome (RH). In the clinical, MR is the main medicinal region, other regions are rarely attention. Aiming at revealing the chemical composition of AG and making better use of AG, screening analysis and metabolomic analysis were both performed to profile MR, LR, FR, and RH. First, in the systematical screening analysis, a total of 134 compounds (including 122 shared components) with various structural patterns were identified and tentatively characterized. The results indicated that the phytochemicals with various structural types were rich in MR, LR, FR, and RH. Second, 6, 4, 8, and 11 chemical markers were identified from MR, LR, FR, and RH, respectively. Seven triterpene saponins (protopanaxatriol, quinquenoside R1 , ginsenoside Rc, Rk1 , Rg1 , Re, and vinaginsenoside R1 ) might be used for rapid differentiation of four morphological regions. This comprehensive profile study of metabolites illustrated the similarities and differences of phytochemicals in four morphological regions of AG. The results could be used for the quality control of AG and furnish a basis for the further development and utilization of AG sources.

Study on changes in pigment composition during the blooming period of safflower based on plant metabolomics and semi-quantitative analysis

Red and yellow pigments are the major ingredients of safflower, often used to color food and cosmetics. Carthamin was the main component of red pigment and hydroxysafflor yellow A and anhydrosafflower yellow B were representative components of yellow pigment. Plant metabolomics and semi-quantitative analysis were used to analyze the changes of pigment composition during the blooming period, especially these characteristic components. Carthamin, hydroxysafflor yellow A, anhydrosafflower yellow B, and other components were screened out as differential metabolites based on plant metabolomics. Then semi-quantitative analysis was used to quantify these three representative components of pigments. Experimental results showed that the content of pigments has dynamic changes along with flowering, in the early blooming period, yellow pigment accumulated much and red pigment was low in content. In the middle period, the accumulation rate of the yellow pigment slowed down and content was stabilized. In the next step, the content of yellow pigments gradually decreased, and the content of red pigments gradually increased. Later, the level of yellow pigment decreased significantly, and the accumulation rate of red pigment increased significantly. Last, the appearance color of safflower was red, with yellow parts barely visible, and accumulation of red pigment was the highest and of the yellow pigment was the lowest in content.

LINT-Web: A Web-Based Lipidomic Data Mining Tool Using Intra-Omic Integrative Correlation Strategy

Lipidomics is a younger member of the "omics" family. It aims to profile lipidome alterations occurring in biological systems. Similar to the other "omics", lipidomic data is highly dimensional and contains a massive amount of information awaiting deciphering and data mining. Currently, the available bioinformatic tools targeting lipidomic data processing and lipid pathway analysis are limited. A few tools designed for lipidomic analysis perform only basic statistical analyses, and lipid pathway analyses rely heavily on public databases (KEGG, Reactome, and HMDB). Due to the inadequate understanding of lipid signaling and metabolism, the use of public databases for lipid pathway analysis can be biased and misleading. Instead of using public databases to interpret lipidomic ontology, the authors introduce an intra-omic integrative correlation strategy for lipidomic data mining. Such an intra-omic strategy allows researchers to unscramble and predict lipid biological functions from correlated genomic ontological results using statistical approaches. To simplify and improve the lipidomic data processing experience, they designed an interactive web-based tool: LINT-web (http://www.lintwebomics.info/) to perform the intra-omic analysis strategy, and validated the functions of LINT-web using two biological systems. Users without sophisticated statistical experience can easily process lipidomic datasets and predict the potential lipid biological functions using LINT-web.

Metabolomic analysis of untargeted bovine uterine secretions in dairy cows with endometritis using ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry

Endometritis is among the most common bovine uterine diseases; as a cause of infertility, it affects the progress of the cattle industry. In this study, we used a novel technique based on ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry for comparative metabolomics of uterine secretions in healthy cows and cows with endometritis, classified based on clinical symptoms. Univariate and multivariate statistical analyses revealed significant differences between the two groups (n = 12). Compared with healthy uterine secretion samples, in the clinical endometritis samples, coumaric acid, benzoic acid, and equol were downregulated. However, l-phenylalanine, glutamine, succinic acid, linoleate, arachidonic acid, and other metabolites were upregulated, revealing variations between healthy cows and cows with endometritis (p < 0.05). This metabolomic approach may provide an in-depth understanding of endometritis pathobiology, along with a theoretical framework for the diagnosis and treatment of this bovine disease.

An Integrated Strategy for Rapid Discovery and Identification of Quality Markers in Gardenia Fructus Using an Omics Discrimination-Grey Correlation-Biological Verification Method

Background: Gardenia Fructus (GF), a traditional Chinese medicine of Gardenia Ellis in Rubiaceae family, has the potential to clear heat and purge fire and has been widely used to treat multiple infection-related diseases. However, the quality markers (Q-Markers) of GF have not been revealed comprehensively.

Methods: In this experiment, the transgenic zebrafish lines, Tg (l-fabp:EGFP) and Tg (lyz:EGFP), were used to evaluate two main kinds of traditional efficacies of GF, hepatoprotective and anti-inflammatory effects. All the GF samples from different production areas were tested their anti-liver injury and anti-inflammantory activities. High-performance liquid chromatography-quadrupole time-of-flight mass spectrometry method (HPLC-Q-TOF/MS) was employed for herbal metabonomic analysis of GF samples. Gray correlation analysis (GCA) was utilized to screen out the components closely associated with the activities. Finally, the zebrafish model was applied to verify the bioactivity of the crucial components to determine the Q-Markers of GF.

Results: The zebrafish models were established by inducing with hydrogen peroxide or copper sulfate and applied to quickly evaluate the hepatoprotective effect and inflammation of GF samples. 27 potentially active components for liver protection and 21 potentially active components with anti-inflammatory properties were identified by herbal metabolomic analysis based on HPLC-Q-TOF/MS. The GCA result showed that five of the 27 components were highly correlated with liver protection, 15 of the 21 components were highly correlated with anti-inflammatory activity. Among them, geniposide and crocin-1 were confirmed their bioactivities on zebrafish experiment to be responsible for the protective effects of GF against liver injury, and genipin-1-β-D-gentiobioside, quinic acid, gardenoside, d-glucuronic acid, l-malic acid, mannitol, rutin, and chlorogenic acid were confirmed to be responsible for the anti-inflammatory effects. Finally, according to the screening principles of Q-Markers, genipin-1-β-D-gentiobioside, geniposide, and gardenoside were preliminarily identified to be the Q-Markers of GF.

Conclusion: This study established an effective research strategy of “Omics Discrimination-Grey Correlation-Biological Verification,” which enabled the rapid identification of key pharmacological components of GF. These markers have provided a scientific basis for constructing a modern quality evaluation system for GF.

Particulate Nitrosamines and Nitramines in Seoul and Their Major Sources: Primary Emission versus Secondary Formation

Seven nitrosamines and three nitramines in particulate matter with an aerodynamic diameter of less than or equal to 2.5 μm (PM_2.5) collected in 2018 in Seoul, South Korea, were quantified. Annual mean concentrations of the sum of nitrosamines and nitramines were 9.81 ± 18.51 and 1.12 ± 0.70 ng/m³, respectively, and nitrosodi-methylamine (NDMA) and dimethyl-nitramine (DMN) comprised the largest portion of nitrosamines and nitramines, respectively. Statistical analyses such as non-parametric correlation analysis, positive matrix factorization, analysis of covariance, and orthogonal partial least squared discrimination analysis were carried out to identify contribution of the atmospheric reactions in producing NDMA and DMN. In addition, kinetic calculation using reaction information obtained from the previous chamber studies was performed to estimate concentrations of NDMA and DMN that might be produced from the atmospheric reactions. It was concluded that (1) the atmospheric reactions contributed to the concentrations of NDMA more than they did for those of DMN, (2) the contribution of atmospheric reactions to the concentrations of NDMA and DMN was significant due to high NO₂ concentrations in winter, and (3) primary emissions predominantly affected the ambient concentrations of NDMA and DMN in spring, summer, and autumn.

Molecular-networking-guided discovery of species-specific markers for discriminating five medicinal Paris herbs

BACKGROUND

Paridis Rhizoma (PR) is a famous traditional herbal medicine. Apart from two officially recorded species, viz. Paris polyphylla Smith var. yunnanensis (Franch.) Hand. - Mazz. (PPY) and P. polyphylla Smith var. chinensis (Franch.) Hara (PPC), there are still many other species used as folk medicine. It is necessary to understand the metabolic differences among Paris species.

PURPOSE

To establish a strategy that can discover species-specific steroidal saponin markers to distinguish closely-related Paris herbs for quality and safety control.

METHODS

A new strategy of molecular-networking-guided discovery of species-specific markers was proposed. Firstly, the ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) was applied to obtain the MS and MS/MS data of all samples. Then, molecular networking (MN) was created using MS/MS data to prescreen the steroidal saponins for subsequent analysis. Next, the principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLS-DA) models were established to discover potential markers. Finally, the verification, identification and distribution of chemical markers were performed.

RESULTS

A total of 126 steroidal saponins were screened out from five species using MN. Five species were classified successfully by OPLS-DA model, and 18 species-specific markers were discovered combining the variable importance in the projection (VIP) value, P value (one-way ANOVA) and their relative abundance. These markers could predict the species of Paris herbs correctly.

CONCLUSION

These results revealed that this new strategy could be an efficient way for chemical discrimination of medicinal herbs with close genetic relationship.

Plasma metabolome alteration in dairy cows with left displaced abomasum before and after surgical correction

Left displaced abomasum (LDA) leads to substantial changes in the metabolism of dairy cows. Surgical correction of LDA can rapidly improve the health of cows; however, changes in metabolism following surgery are rarely described. To investigate the changes of plasma metabolome in cows with LDA before and after surgical correction, blood samples were collected from 10 healthy postpartum cows and 10 cows with LDA on the day of diagnosis, then again from the LDA cows 14 d after surgery. Serum nonesterified fatty acid, β-hydroxybutyric acid, cortisol and histamine concentration, and antioxidant enzyme (superoxide dismutase and glutathione peroxidase) activities were evaluated, and the metabolic profile in plasma was analyzed using ultra-high-performance liquid chromatography time-of-flight mass spectrometry. The results demonstrated that cows with LDA experienced severe negative energy balance and oxidative stress, which can be improved by surgical correction. The metabolic profile was analyzed using multidimensional and univariate statistical analyses, and different metabolites were identified. In total, 102 metabolites differed between cows with LDA and healthy cows. After surgical correction, 65 metabolites changed in cows with LDA, compared with these cows during the LDA event. Following surgical correction, AA levels tended to increase, and lipid levels tended to decrease in cows with LDA. Pathway analysis indicated marked changes in linoleic acid metabolism, Arg biosynthesis, and Gly, Ser, and Thr metabolism in cows at the onset of LDA and following surgical correction. Surgical treatment reversed the changes in AA and lipid metabolism in cows with LDA.