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.

[Regulatory effect of bone marrow mesenchymal stem cells on polarization of macrophages]

Objective: To examine the regulatory effect of bone marrow mesenchymal stem cells (BM-MSCs) on the polarization of bone marrow-derived macrophages, and to provide a theoretical support for the application of mesenchymal stem cells in the treatment of liver fibrosis. Methods: MSCs and macrophages were first isolated from the bone marrow of mice. Macrophages were polarized to M1 macrophages with lipopolysaccharide (LPS) and interferon-γ (IFN-γ), and to M2 macrophages with interleukin-4 (IL-4). The macrophages were then co-cultured with BM-MSCs in a Transwell for 24 h, and changes in the percentages of M1 and M2 macrophages were examined using flow cytometry. The mRNA levels of the M1 macrophage-associated cytokines, tumor necrosis factor-α (TNF-α) and interleukin-23a (IL-23a), and M2 macrophage-associated molecules, arginase-1 (Arg-1) and CD163, were measured by real-time quantitative PCR. The two samples were compared using the t test, and P < 0.05 was considered as statistically significant. Results: Flow cytometry showed that the percentage of M1 macrophages was significantly lower in the (macrophage + LPS + IFN-γ + BM-MSC) co-culture group than in the (macrophage + LPS + IFN-γ) group (62.5% ± 4.6% vs 86.6% ± 6.9%, t = 5.034, P = 0.0073). In addition, the relative mRNA expression of TNF-α and IL-23a was also significantly reduced in the co-culture group compared with those in the macrophage control group as measured by RT-qPCR (t = 11.57 and 10.57, respectively, P < 0.05). Compared with that in the macrophage control group, the percentage of M2 macrophages in the (macrophage+BM-MSC) co-culture group was significantly increased (89.5% ± 5.8% vs 70.1% ± 6.3%, t = 3.924, P = 0.0172), along with significantly elevated relative mRNA expression of Arg1 (14.35±1.05 vs 1.00±0.03, t = 21.96, P < 0.05) and CD163 (3.04 ± 0.27 vs 1.00 ± 0.03, t = 13.14, P < 0.05). Conclusion: BM-MSCs can inhibit LPS + IFN-γ-induced polarization to M1 macrophages and promote polarization to M2 macrophages through the release of paracrine factors.