UPLC-Q-TOF-MS and UPLC-QQQ-MS were used for the qualitative and quantitative analysis of oligosaccharides in Fufang Ejiao Syrup

Spectrum-effect relationship between HPLC fingerprints and antioxidant activity of Qi-Fu-Yin based on multiple statistical correlation analysis

INTRODUCTION

Qi-Fu-Yin has been used to treat Alzheimer's disease (AD) in China. Oxidative stress has been recognized as a factor in AD progress. To date, there is no quality control method to ensure batch-to-batch consistency of Qi-Fu-Yin, and the potential antioxidant compounds in Qi-Fu-Yin remain uncertain.

OBJECTIVES

The aim of this study is to identify the potential antioxidant compounds of Qi-Fu-Yin and establish quality control standards for Qi-Fu-Yin.

METHODS

High-performance liquid chromatography was used to establish and quantify the fingerprints of Qi-Fu-Yin from various batches. Ultrahigh-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UHPLC-Q-TOF/MS) was used to identify the common peaks. Bivariate correlation analysis, partial least squares regression analysis, and gray correlation analysis were used to establish the spectrum-effect relationship.

RESULTS

Forty-nine common peaks were determined through the establishment of fingerprints. Among them, 35 common peaks were preliminarily characterized. The multiple statistical correlation analysis methods identified six compounds as potential antioxidant constituents of Qi-Fu-Yin, and their antioxidant activities were validated in vitro. All six antioxidant compounds derived from two herbs. Therefore, three chemical index compounds derived from other three herbs were added to the quantitative analysis, while for two herbs, no peaks could be included. Eventually, six antioxidant constituents and three index compounds were quantitatively determined to provide a relatively comprehensive quality control for Qi-Fu-Yin.

CONCLUSIONS

The study elucidated the antioxidant substance basis of Qi-Fu-Yin and provided a relatively comprehensive approach for the assay of Qi-Fu-Yin, which is a promising advance in the quality control of Qi-Fu-Yin.

Catalytic Mode and Product Specificity of an α-Agarase Reveal Its Direct Catalysis for the Production of Agarooligosaccharides

Many α-agarases have been characterized and are utilized for producing agarooligosaccharides through the degradation of agar and agarose, which are considered valuable for applications in the food and medicine industries. However, the catalytic mechanism and product transformation process of α-agarase remain unclear, limiting further enzyme engineering for industrial applications. In this study, an α-agarase from Catenovulum maritimus STB14 (Cm-AGA) was employed to degrade agarose oligosaccharides (AGOs) with varying degrees of polymerization (DPs) to investigate the catalytic mechanism of α-agarases. The results demonstrated that Cm-AGA could degrade agarose into agarotetraose and agarohexaose. The reducing ends of agarotetraose and agarohexaose spontaneously release unstable 3,6-anhydro-α-l-galactose molecules, which were further degraded into agarotriose and agaropentose. Cm-AGA cannot act on α-1,3-glucoside bonds in agarotriose, agarotetraose, neoagarobiose, and neoagarotetraose but can act on AGOs with a DP greater than four. The product analysis was further verified by β-galactosidase hydrolysis, which specifically cleaves the non-reducing glycosidic bond of agarooligosaccharides. Multiple sequence alignment results showed that two conserved residues, Asp994 and Glu1129, were proposed as catalytic residues and were further identified by site-directed mutagenesis. Molecular docking of Cm-AGA with agaroheptose revealed the potential substrate binding mode of the α-agarase. These findings enhance the understanding of Cm-AGA's catalytic mode and could guide enzyme engineering for modulating the production of agarooligosaccharides.

Chemical constituent characterization and determination of Quisqualis fructus based on UPLC-Q-TOF-MS and HPLC combined with fingerprint and chemometric analysis

Quisqualis fructus (QF) is a traditional Chinese medicine (TCM) that it has a long history in the therapeutic field of killing parasites, eliminating accumulation, and stopping diarrhea. However, the therapeutic material basis of QF is remaining ambiguous nowadays. The geographical origin differences of QF are also usually ignored in the process of medication. In this study, the alcohol-aqueous soluble constituents in QF from different origins were systematically characterized and accurately measured by ultra-high performance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF-MS) and high-performance liquid chromatography (HPLC) respectively. Chemometric analysis was performed for origin differentiation and screening of potential quality marker (Q-marker). Finally, A total of 106 constituents were tentatively characterized in positive and negative ion modes, including 29 fatty acids, 26 organic acids, 11 amino acids and derivatives, 10 glycosides, 9 alkaloids and derivatives, and 21 other constituents. QF from different origins were effectively distinguished and 16 constituents were selected as the potential Q-markers subsequently. Four representative components (trigonelline, adenosine, ellagic acid, and 3,3'-di-O-methylellagic acid) in QF samples were simultaneously determined. HPLC fingerprint analysis indicated that the similarity between 16 batches of QF was in the range of 0.870-0.999. The above results provide some insights for the research on the pharmacodynamic constituents, quality control, and geographical discrimination of QF.

Danggui Jixueteng decoction for the treatment of myelosuppression after chemotherapy: A combined metabolomics and network pharmacology analysis

Objective

This study aimed to explore the mechanism of the Danggui Jixueteng decoction (DJD) in treating Myelosuppression after chemotherapy (MAC) through network pharmacology and metabolomics.

Methods

We obtained the chemical structures of DJD compounds from TCMSP and PubMed. SwissTargetPrediction, STITCH, CTD, GeneCards, and OMIM were utilized to acquire component targets and MAC-related targets. We identified the key compounds, core targets, main biological processes, and signaling pathways related to DJD by constructing and analyzing related networks. The main active compounds and key proteins of DJD in treating AA were confirmed by molecular docking. A MAC rat model was established through intraperitoneal injection of cyclophosphamide to confirm DJD's effect on the bone marrow hematopoietic system. Untargeted metabolomics analyzed serum metabolite differences between MAC rats and the control group, and before and after DJD treatment, to explore DJD's mechanism in treating MAC.

Results

Of the 93 active compounds identified under screening conditions, 275 compound targets and 3113 MAC-related targets were obtained, including 95 intersecting targets; AKT1, STAT3, CASP3, and JUN were key proteins in MAC treatment. The phosphatidylinositol-3-kinase/RAC-alpha serine/threonine-protein kinase (PI3K/AKT) signaling pathway may play a crucial role in MAC treatment with DJD. Molecular docking results showed good docking effects of key protein AKT1 with luteolin, β-sitosterol, kaempferol, and glycyrrhizal chalcone A. In vivo experiments indicated that, compared to the model group, in the DJD group, levels of WBCs, RBCs, HGB, and PLTs in peripheral blood cells, thymus index increased, spleen index decreased, serum IL-3, GM-CSF levels increased, and IL-6, TNF-α, and VEGF levels decreased (p < 0.01); the pathological morphology of femoral bone marrow improved. Eleven differential metabolites were identified as differential serum metabolites, mainly concentrated in phenylalanine, tyrosine, and tryptophan biosynthesis pathways, phenylalanine metabolism, and arachidonic acid metabolism.

Conclusion

This study revealed that DJD's therapeutic effects are due to multiple ingredients, targets, and pathways. DJD may activate the PI3K/AKT signaling pathway, promote hematopoietic-related cytokine production, regulate related metabolic pathways, and effectively alleviate cyclophosphamide-induced myelosuppression after chemotherapy in rats.

Identification of raffinose family oligosaccharides in processed Rehmannia glutinosa Libosch using matrix-assisted laser desorption/ionization mass spectrometry image combined with machine learning

RATIONALE

Currently, research on oligosaccharides primarily focuses on the physiological activity and function, with a few studies elaborating on the spatial distribution characterization and variation in the processing of Rehmannia glutinosa Libosch. Thus, imaging the spatial distributions and dynamic changes in oligosaccharides during the steaming process is significant for characterizing the metabolic networks of R. glutinosa. It will be beneficial to characterize the impact of steaming on the active ingredients and distribution patterns in different parts of the plant.

METHODS

A highly sensitive matrix-assisted laser desorption/ionization mass spectrometry image (MALDI-MSI) method was used to visualize the spatial distribution of oligosaccharides in processed R. glutinosa. Furthermore, machine learning was used to distinguish the processed R. glutinosa samples obtained under different steaming conditions.

RESULTS

Imaging results showed that the oligosaccharides in the fresh R. glutinosa were mainly distributed in the cortex and xylem. As steaming progressed, the tetra- and pentasaccharides were hydrolyzed and diffused gradually into the tissue section. MALDI-MS profiling combined with machine learning was used to identify the processed R. glutinosa samples accurately at different steaming intervals. Eight algorithms were used to build classification machine learning models, which were evaluated for accuracy, precision, recall, and F1 score. The linear discriminant analysis and random forest models performed the best, with prediction accuracies of 0.98 and 0.97, respectively, and thus can be considered for identifying the steaming durations of R. glutinosa.

CONCLUSIONS

MALDI-MSI combined with machine learning can be used to visualize the distribution of oligosaccharides and identify the processed samples after steaming for different durations. This can enhance our understanding of the metabolic changes that occur during the steaming process of R. glutinosa; meanwhile, it is expected to provide a theoretical reference for the standardization and modernization of processing in the field of medicinal plants.