Introduction
The World Health Organization estimates that about 80% population in the developing countries apply herbal medicines for their primary health care (Li et al., 2017). Additionally, herbal medicines also play an important role in the treatment of some major diseases, such as COVID-19 (Yang et al., 2020), cardiovascular diseases (Hao et al., 2017), and cancers (Wang et al., 2020), etc. Recently, plenty of experimental studies have been carried out on the mechanism of action of herbal medicines, and the herb targets have been identified. With the massive accumulation of the previous researches (Chen et al., 2019; Liu et al., 2018b; Liu et al., 2018c), the analysis of the active ingredients that can regulate these targets will be the research focus in the future. The active ingredients analysis of herbal medicines is crucial for their quality control and efficacy/safety evaluation. However, the present methods and strategies are still challenging, owing to the complex disease pathophysiology and herbal composition. Here, we propose a new strategy for effectively and rapidly analyzing the active ingredients of herbal medicines based on the public bioinformatics platforms. This strategy may extend and develop the achievements of the previous herb targets researches, and also provide the references for the later development and clinical application of herbal medicines, which serves as a link between the preceding and the following.
PubChem BioAssay and STRING are the public bioinformatics platforms, both of which are open-accessed and user-friendly. PubChem BioAssay database contains the bioactive targets information of small molecules, which is generated through the experiments and literatures (Wang et al., 2017). STRING database aims to achieve a comprehensive and objective protein-protein interaction networks and allows users to visualize the interaction networks (Szklarczyk et al., 2019). Both public resources can be applied in this strategy. At present, there are mainly two ways to obtain the information of herbal compounds. One method is directly based on the compound analysis techniques (Li et al., 2020; Liu et al., 2018a; Wang et al., 2019); the other method is based on the retrieval from the database and literature (Ding et al., 2020; Li et al., 2017; Zhang et al., 2017). Herbal medicine contains multiple compounds with different polarities. Using the different solvents for the preparation of herbal extracts will affect their composition of compounds. Therefore, the herbal compounds in each extract may not be exactly the same as those in the database and literature. Compared with the retrieval from the database and literature, the results of the compound analysis techniques (eg. LC-MS/MS analysis) may better reflect the true composition of herbal extracts (Liu et al., 2018a; Wang et al., 2019). Previous research has shown that Herba Lysimachiae (HL), the dried entire plant of Lysimachia paridiformis Franch. var.stenophylla Franch. (Primulaceae), can regulate the synovial platelet aggregation through the biolabels (integrin alpha 2b (Itga2b) and integrin beta 3 (Itgb3)) (Li et al., 2020). This is beneficial to alleviate synovial injury in osteoarthritis (OA) (Li et al., 2020). The prevalence rate of OA is 2-6% around the world and exceeds 40% in people over 70 (Hsu and Siwiec, 2019). Biolabels, also the herb targets, reflect the holistic effects of herbal medicines on the living organisms (Li et al., 2020; Li et al., 2019). Biolabels can be used not only to position the therapeutic effects of herbal medicines, but also to guide the analysis of their active ingredients (Li et al., 2020; Li et al., 2017; Zhang et al., 2017). Currently, the relevant active ingredients are still unknown. Based on previous research (Li et al., 2020), we may further use this strategy to carry out the subsequent analysis of the active ingredients. Firstly, LC-MS/MS technique is used to analyze the herbal compounds; subsequently, the compound targets are searched in PubChem BioAssay database; then, STRING database is used to analyze the association network between the biolabels and compound targets. Finally, based on the association network, the links between herbal compounds and biolabels are established, from which the herbal compounds with the potential to regulate the biolabels are screened (Figure 1). These compounds may be the active ingredients of herbal medicines in treating diseases. In the current study, the treatment of OA by HL was used as an example to practice this strategy. OA model was used to confirm the analysis results.