Predicting the grades of Astragali radix using mass spectrometry-based metabolomics and machine learning

Nontargeted metabolomics and enzyme inhibitory and antioxidant activities for chemical and biological characterization of jujube (Ziziphus jujuba) extracts

The chemical and biologically active characterization of jujube samples (fruits, cores, and leaves) were carried out by the integrated nontargeted metabolomics and bioassay. Firstly, collision cross-section values of active compounds in jujubes were determined by ultrahigh-performance liquid chromatography coupled with ion mobility quadrupole time-of-flight mass spectrometry. Then, a multidimensional statistical analysis that contained principal component analysis, partial least squares-discriminant analysis and hierarchical clustering analysis was employed to effectively cluster different tissues and types of jujubes, making identification more scientific. Furthermore, angiotensin-converting enzyme (ACE) and 2, 2-diphenyl-1-picrylhydrazyl (DPPH) were used to evaluate the quality of jujubes from a double activity dimension. The analytical results obtained by using ACE and DPPH to evaluate the quality of jujube were different from multivariate statistics, providing a reference for the application of jujube. Therefore, integrating chemical and biological perspectives to evaluate the quality of jujube provided a more comprehensive evaluation and effective reference for clinical needs.

A Robust TabNet-Based Multi-Classification Algorithm for Infrared Spectral Data of Chinese Herbal Medicine with High-Dimensional Small Samples

Robust classification algorithms for high-dimensional, small-sample datasets are valuable in practical applications. Faced with the infrared spectroscopic dataset with 568 samples and 3448 wavelengths (features) to identify the origins of Chinese medicinal materials, this paper proposed a novel embedded multiclassification algorithm, ITabNet, derived from the framework of TabNet. Firstly, a refined data pre-processing (DP) mechanism was designed to efficiently find the best adaptive one among 50 DP methods with the help of Support Vector Machine (SVM). Following this, an innovative focal loss function was designed and joined with a cross-validation experiment strategy to mitigate the impact of sample imbalance on algorithm. Detailed investigations on ITabNet were conducted, including comparisons of ITabNet with SVM for the conditions of DP and Non-DP, GPU and CPU computer settings, as well as ITabNet against XGBT (Extreme Gradient Boosting). The numerical results demonstrate that ITabNet can significantly improve the effectiveness of prediction. The best accuracy score is 1.0000, and the best Area Under the Curve (AUC) score is 1.0000. Suggestions on how to use models effectively were given. Furthermore, ITabNet shows the potential to apply the analysis of medicinal efficacy and chemical composition of medicinal materials. The paper also provides ideas for multi-classification modeling data with small sample size and high-dimensional feature.

Decoding active compounds and molecular targets of herbal medicine by high-throughput metabolomics technology: A systematic review

Clinical experiences of herbal medicine (HM) have been used to treat a variety of human intractable diseases. As the treatment of diseases using HM is characterized by multi-components and multi-targets, it is difficult to determine the bio-active components, explore the molecular targets and reveal the mechanisms of action. Metabolomics is frequently used to characterize the effect of external disturbances on organisms because of its unique advantages on detecting changes in endogenous small-molecule metabolites. Its systematicity and integrity are consistent with the effective characteristics of HM. After HM intervention, metabolomics can accurately capture and describe the behavior of endogenous metabolites under the disturbance of functional compounds, which will be used to decode the bioactive ingredients of HM and expound the molecular targets. Metabolomics can provide an approach for explaining HM, addressing unclear clinical efficacy and undefined mechanisms of action. In this review, the metabolomics strategy and its applications in HM are systematically introduced, which offers valuable insights for metabolomics methods to characterizing the pharmacological effects and molecular targets of HM.

Ononin promotes radiosensitivity in lung cancer by inhibiting HIF-1α/VEGF pathway

BACKGROUND

In our previous study, we provided evidence that Astragalus mongholicus Bunge(AM) and its extracts possess a protective capability against radiation-induced damage, potentially mediated through the reduction of reactive oxygen species (ROS) and nitric oxide (NO). However, we were pleasantly surprised to discover during our experimentation that AM not only offers protection against radiation damage but also exhibits a radiation sensitization effect. This effect may be attributed to a specific small molecule present in AM known as ononin. Currently, radiation sensitizers are predominantly found in nitrazole drugs and nanomaterials, with no existing reports on the radiation sensitization properties of ononin, nor its underlying mechanism.

PURPOSE

This study aims to investigate the sensitization effect of the small molecule ononin derived from AM on lung cancer radiotherapy, elucidating its specific molecular mechanism of action. Additionally, the safety profile of combining astragalus small molecule ononin with radiation therapy will be evaluated.

METHODS

The effective concentration of ononin was determined through cell survival experiments, and the impact of ononin combined with varying doses of radiation on lung cancer cells was observed using CCK-8 and cell cloning experiments. The apoptotic effect of ononin combined with radiation on lung cancer cells was assessed using Hochester staining, flow cytometry, and WB assay. Additionally, WB and immunofluorescence analysis were conducted to investigate the influence of ononin on HIF-1α/VEGF pathway. Furthermore, Molecular Dynamics Simulation was employed to validate the targeted binding ability of ononin and HIF-1α. A lung cancer cell line was established to investigate the effects of knockdown and overexpression of HIF-1α. Subsequently, the experiment was repeated using tumor bearing nude mice and C57BL/6 mouse models in an in vivo study. Tumor volume was measured using a vernier caliper, while HE, immunohistochemistry, and immunofluorescence techniques were employed to observe the effects of ononin combined with radiation on tumor morphology, proliferation, and apoptosis. Additionally, Immunofluorescence was employed to examine the impact of ononin on HIF-1α/VEGF pathway in vivo, and its effect on liver function in mice was assessed through biochemistry analysis.

RESULTS

At a concentration of 25 μM, ononin did not affect the proliferation of lung epithelial cells but inhibited the survival of lung cancer cells. In vitro experiments demonstrated that the combination of ononin and radiation could effectively inhibit the growth of lung cancer cells, induce apoptosis, and suppress the excessive activation of the Hypoxia inducible factor 1 alpha/Vascular endothelial growth factor pathway. In vivo experiments showed that the combination of ononin and radiation reduced the size and proliferation of lung cancer tumors, promoted cancer cell apoptosis, mitigated abnormal activation of the Hypoxia inducible factor 1 alpha pathway, and protected against liver function damage.

CONCLUSION

This study provides evidence that the combination of AM and its small molecule ononin can enhance the sensitivity of lung cancer to radiation. Additionally, it has been observed that this combination can specifically target HIF-1α and exert its effects. Notably, ononin exhibits the unique ability to protect liver function from damage while simultaneously enhancing the tumor-killing effects of radiation, thereby demonstrating a synergistic and detoxifying role in tumor radiotherapy. These findings contribute to the establishment of a solid basis for the development of novel radiation sensitizers derived from traditional Chinese medicine.

Identify production area, growth mode, species, and grade of Astragali Radix using metabolomics "big data" and machine learning

BACKGROUND

Astragali Radix (AR) is a widely used herbal medicine. The quality of AR is influenced by several key factors, including the production area, growth mode, species, and grade. However, the markers currently used to distinguish these factors primarily focus on secondary metabolites, and their validation on large-scale samples is lacking.

PURPOSE

This study aims to discover reliable markers and develop classification models for identifying the production area, growth mode, species, and grade of AR.

METHODS

A total of 366 batches of AR crude slices were collected from six provinces in China and divided into learning (n = 191) and validation (n = 175) sets. Three ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) methods were developed and validated for determining 22 primary and 10 secondary metabolites in AR methanol extract. Based on the quantification data, seven machine learning algorithms, such as Nearest Neighbors and Gradient Boosted Trees, were applied to screen the potential markers and build the classification models for identifying the four factors associated with AR quality.

RESULTS

Our analysis revealed that secondary metabolites (e.g., astragaloside IV, calycosin-7-O-β-D-glucoside, and ononin) played a crucial role in evaluating AR quality, particularly in identifying the production area and species. Additionally, fatty acids (e.g., behenic acid and lignoceric acid) were vital in determining the growth mode of AR, while amino acids (e.g., alanine and phenylalanine) were helpful in distinguishing different grades. With both primary and secondary metabolites, the Nearest Neighbors algorithm-based model was constructed for identifying each factor of AR, achieving good classification accuracy (>70%) on the validation set. Furthermore, a panel of four metabolites including ononin, astragaloside II, pentadecanoic acid, and alanine, allowed for simultaneous identification of all four factors of AR, offering an accuracy of 86.9%.

CONCLUSION

Our findings highlight the potential of integrating large-scale targeted metabolomics and machine learning approaches to accurately identify the quality-associated factors of AR. This study opens up possibilities for enhancing the evaluation of other herbal medicines through similar methodologies, and further exploration in this area is warranted.

Exploring potential targets of HPV&BC based on network pharmacology and urine proteomics

BACKGROUND

Bladder cancer (BC) caused by Human papillomavirus (HPV) infection remains a complex public health problem in developing countries. Although the HPV vaccine effectively prevents HPV infection, it does not benefit patients with BC who already have HPV.

METHODS

Firstly, the differential genes of HPV-related BC patients were screened by transcriptomics, and then the prognostic and clinical characteristics of the differential genes were analyzed to screen out the valuable protein signatures. Furthermore, the compound components and targets of Astragali Radix (AR) were analyzed by network pharmacology, and the intersection targets of drug components and HPV_BC were screened out for pathway analysis. In addition, the binding ability of the compound to the Astragali-HPV_BC target was verified by molecular docking and virtual simulation. Finally, to identify potential targets in BC patients through urine proteomics and in vitro experiments.

RESULTS

Eleven HPV_BC-related protein signatures were screened out, among which high expression of EGFR, CTNNB1, MYC, GSTM1, MMP9, CXCR4, NOTCH1, JUN, CXCL12, and KRT14 had a poor prognosis, while low expression of CASP3 had a poor prognosis. In the analysis of clinical characteristics, it was found that high-risk scores, EGFR, MMP9, CXCR4, JUN, and CXCL12 tended to have higher T stage, pathological stage, and grade. Pharmacological and molecular docking analysis identified a natural component of AR (Quercetin) and it corresponding core targets (EGFR). The OB of the natural component was 46.43, and the DL was 0.28, respectively. In addition, EGFR-Quercetin has high affinity. Urine proteomics and RT-PCR showed that EGFR was expressed explicitly in BC patients. Mechanism analysis revealed that AR component targets might affect HPV_BC patients through Proteoglycans in the cancer pathway.

CONCLUSION

AR can target EGFR through its active component (Quercetin), and has a therapeutic effect on HPV_BC patients.

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.

Pseudotargeted Metabolomics Approach Enabling the Classification-Induced Ginsenoside Characterization and Differentiation of Ginseng and Its Compound Formulation Products

The use of diversified ginseng extracts in health-promoting foods is difficult to differentiate, as they share bioactive ginsenosides among different Panax species (e.g., P. ginseng, P. quinquefolius, P. notoginseng, and P. japonicus) and different parts (e.g., root, leaf, and flower). This work was designed to develop a pseudo-targeted metabolomics approach to discover ginsenoside markers facilitating the precise authentication of ginseng and its use in compound formulation products (CFPs). Versatile mass spectrometry experiments on the QTrap mass spectrometer achieved classified characterization of the neutral, malonyl, and oleanolic acid-type ginsenosides, with 567 components characterized. A pseudo-targeted metabolomics approach by multiple reaction monitoring (MRM) of 262 ion pairs could assist to establish key identification points for 12 ginseng species. The simultaneous detection of 14 markers enabled the identification of ginseng from 15 ginseng-containing CFPs. The pseudo-targeted metabolomics strategy enabled better performance in differentiating among multiple ginseng, compared with the full-scan high-resolution mass spectrometry approach.

Evaluation of the chemical profile from four germplasms sources of Pruni Semen using UHPLC-LTQ-Orbitrap-MS and multivariate analysis

Pruni Semen, the seed of several unique Prunus plants, is a traditional purgative herbal material. To determine the authentic sources of Pruni Semen, 46 samples from four species were collected and analyzed. Ten compounds including multiflorin A (Mul A), a notable purative compound, were isolated and identified by chemical separation and nuclear magnetic resonance spectroscopy. Seventy-six communal components were identified by ultra-high performance liquid chromatography with linear ion trap-quadrupole Orbitrap mass spectrometry, and acetyl flavonoid glycosides were recognized as characteristic constituents. The flavonoids were distributed in the seed coat and cyanogenic glycosides in the kernel. Based on this, methods for identifying Pruni Semen from different sources were established using chemical fingerprinting, quantitative analysis of the eight principal compounds, hierarchical cluster analysis, principal component analysis, and orthogonal partial least squares discriminant analysis. The results showed that the samples were divided into two categories: one is the small seeds from Prunus humilis (Ph) and Prunus japonica (Pj), and the other is the big seeds from Prunus pedunculata (Pp) and Prunus triloba (Pt). The average content of Mul A was 3.02, 6.93, 0.40, and 0.29 mg/g, while the average content of amygdalin was 18.5, 17.7, 31.5, and 30.9 mg/g in Ph, Pj, Pp, and Pt, respectively. All the above information suggests that small seeds might be superior sources of Pruni Semen. This is the first comprehensive report on the identification of chemical components in Pruni Semen from different species.

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Chemical constituents of Pruni semen from four Prunus species were compared.

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Acetyl flavonoid glycosides were identified as the characteristic components.

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Flavonoids were present in the seed coat and cyanogenic glycosides in the kernel.

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The content of acetyl flavonoid in small seeds is significant higher than those in big ones.

Attenuation of doxorubicin-induced oxidative damage in rat brain by regulating amino acid homeostasis with Astragali Radix

The nervous system disorders caused by doxorubicin (DOX) are among the severe adverse effects that dramatically reduce the quality of life of cancer survivors. Astragali Radix (AR), a popular herbal drug and dietary supplement, is believed to help treat brain diseases by reducing oxidative stress and maintaining metabolic homeostasis. Here we show the protective effects of AR against DOX-induced oxidative damage in rat brain via regulating amino acid homeostasis. By constructing a clinically relevant low-dose DOX-induced toxicity rat model, we first performed an untargeted metabolomics analysis to discover specific metabolic features in the brain after DOX treatment and AR co-treatment. It was found that the amino acid (AA) metabolism pathways altered most significantly. To accurately characterize the brain AA profile, we established a sensitive, fast, and reproducible hydrophilic interaction chromatography-tandem mass spectrometry method for the simultaneous quantification of 22 AAs. The targeted analysis further confirmed the changes of AAs between different groups of rat brain. Specifically, the levels of six AAs, including glutamate, glycine, serine, alanine, citrulline, and ornithine, correlated (Pearson |r| >  0.47, p < 0.05) with the brain oxidative damage that was caused by DOX and rescued by AR. These findings present that AAs are among the regulatory targets of DOX-induced brain toxicity, and AR is a promising therapeutic agent for it.