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

ABSTRACT: Astragali radix (AR, the dried root of Astragalus) is a popular herbal remedy in both China and the United States. The commercially available AR is commonly classified into premium graded (PG) and ungraded (UG) ones only according to the appearance. To uncover novel sensitive and specific markers for AR grading, we took the integrated mass spectrometry-based untargeted and targeted metabolomics approaches to characterize chemical features of PG and UG samples in a discovery set (n=16 batches). A series of five differential compounds were screened out by univariate statistical analysis, including arginine, calycosin, ononin, formononetin, and astragaloside Ⅳ, most of which were observed to be accumulated in PG samples except for astragaloside Ⅳ. Then, we performed machine learning on the quantification data of five compounds and constructed a logistic regression prediction model. Finally, the external validation in an independent validation set of AR (n=20 batches) verified that the five compounds, as well as the model, had strong capability to distinguish the two grades of AR, with the prediction accuracy > 90%. Our findings present a panel of meaningful candidate markers that would significantly catalyze the innovation in AR grading. Graphical abstract Image 1 Highlights • Five specific and reliable markers are uncovered to distinguish two grades of AR.• A logistic regression prediction model is built, with an accuracy of > 90%.• Most markers, but not astragaloside Ⅳ, exhibit accumulation in premium graded AR.• The findings are validated in an independent set by targeted LC-MS/MS analysis.

SUBMITTER: Yu X

PROVIDER: S-EPMC8572717 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:According to current research, the primary active ingredients of Radix Astragali (RA), such as saponins, flavonoids, and polysaccharides, play an important role in anti-inflammatory effects. However, the exact molecular mechanism underlying the action was not elucidated to date. Our research attempted to determine the active components in RA and to investigate the interaction between the active components and targets involved in the anti-inflammation activity by network pharmacology and molecular docking. The active components and targets of RA were screened out by TCMSP. Thereafter, through the "anti-inflammation effect" and "inflammation" as the keywords, disease targets were obtained from the GeneCards database. The PPI network was constructed with Cytoscape 3.8.0 software to screen core targets. The GO function and KEGG analysis were enriched and analyzed through the Metascape platform, obtaining the 3-dimensional view of the core targets from the PDB database, and then, performing molecular docking in AutoDock Vina, a heatmap was constructed using the binding free energies in GraphPad Prism 8. The Discovery Studio software was used for docking analysis, and eventually, the docking results were visualized. We also explored the targets and signaling pathways of Astragaloside IV acting on anti-inflammatory effects via constructing compound-disease-target-pathway network. 18 active components and 45 targets of RA were screened out. The main anti-inflammatory active components of RA were quercetin, Astragaloside IV, kaempferol, 7-O-methylisomucronulatol, and formononetin, and the strongly interacting core proteins were TNF, IL6, IL1B, TLR4, CXCL8, CCL2, IL10, VEGFA, and MMP9. The signal pathways mainly involved include Lipid and atherosclerosis, IL-17 signaling pathway, Chagas disease, leishmaniasis, and TNF signaling pathway. Moreover, molecular docking showed that the 2 most active compounds, Astragaloside IV and kaempferol, could efficiently bind with the targets TNF, TLR4, and IL10. Astragaloside IV may play a part in anti-inflammatory effects through pathways such as HIF-1 signaling pathway, Inflammatory bowel disease and Hepatitis B ect. RA exhibits the characteristic of multicomponent and multitarget synergistic effects in exerting anti-inflammatory effects and the effective component of RA is Astragaloside IV, targeting TNF, TLR4, and IL10.

Project description:Advances in the field of targeted proteomics and mass spectrometry have significantly improved assay sensitivity and multiplexing capacity. The high-throughput nature of targeted proteomics experiments has increased the rate of data production, which requires development of novel analytical tools to keep up with data processing demand. Currently, development and validation of targeted mass spectrometry assays require manual inspection of chromatographic peaks from large datasets to ensure quality, a process that is time consuming, prone to inter- and intra-operator variability and limits the efficiency of data interpretation from targeted proteomics analyses. To address this challenge, we have developed TargetedMSQC, an R package that facilitates quality control and verification of chromatographic peaks from targeted proteomics datasets. This tool calculates metrics to quantify several quality aspects of a chromatographic peak, e.g. symmetry, jaggedness and modality, co-elution and shape similarity of monitored transitions in a peak group, as well as the consistency of transitions' ratios between endogenous analytes and isotopically labeled internal standards and consistency of retention time across multiple runs. The algorithm takes advantage of supervised machine learning to identify peaks with interference or poor chromatography based on a set of peaks that have been annotated by an expert analyst. Using TargetedMSQC to analyze targeted proteomics data reduces the time spent on manual inspection of peaks and improves both speed and accuracy of interference detection. Additionally, by allowing the analysts to customize the tool for application on different datasets, TargetedMSQC gives the users the flexibility to define the acceptable quality for specific datasets. Furthermore, automated and quantitative assessment of peak quality offers a more objective and systematic framework for high throughput analysis of targeted mass spectrometry assay datasets and is a step towards more robust and faster assay implementation.

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

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