Expanding potential targets of herbal chemicals by node2vec based on herb-drug interactions.

BACKGROUND: The identification of chemical-target interaction is key to pharmaceutical research and development, but the unclear materials basis and complex mechanisms of traditional medicine (TM) make it difficult, especially for low-content chemicals which are hard to test in experiments. In this research, we aim to apply the node2vec algorithm in the context of drug-herb interactions for expanding potential targets and taking advantage of molecular docking and experiments for verification. METHODS: Regarding the widely reported risks between cardiovascular drugs and herbs, Salvia miltiorrhiza (Danshen, DS) and Ligusticum chuanxiong (Chuanxiong, CX), which are widely used in the treatment of cardiovascular disease (CVD), and approved drugs for CVD form the new dataset as an example. Three data groups DS-drug, CX-drug, and DS-CX-drug were applied to serve as the context of drug-herb interactions for link prediction. Three types of datasets were set under three groups, containing information from chemical-target connection (CTC), chemical-chemical connection (CCC) and protein-protein interaction (PPI) in increasing steps. Five algorithms, including node2vec, were applied as comparisons. Molecular docking and pharmacological experiments were used for verification. RESULTS: Node2vec represented the best performance with average AUROC and AP values of 0.91 on the datasets "CTC, CCC, PPI". Targets of 32 herbal chemicals were identified within 43 predicted edges of herbal chemicals and drug targets. Among them, 11 potential chemical-drug target interactions showed better binding affinity by molecular docking. Further pharmacological experiments indicated caffeic acid increased the thermal stability of the protein GGT1 and ligustilide and low-content chemical neocryptotanshinone induced mRNA change of FGF2 and MTNR1A, respectively. CONCLUSIONS: The analytical framework and methods established in the study provide an important reference for researchers in discovering herb-drug interactions, alerting clinical risks, and understanding complex mechanisms of TM.

[Mutational Spectrum and Prognosis Analysis of Young Patients with Diffuse Large B-Cell Lymphoma Based on Next-Generation Sequencing].

OBJECTIVE: To investigate the mutational spectrum in young patients with diffuse large B-cell lymphoma (DLBCL) based on next generation sequencing (NGS), and to provide a basis for in-depth understanding of the molecular biological characteristics and accurate prognosis of young DLBCL. METHODS: From March 2009 to March 2021, 68 young DLBCL patients with complete initial diagnosis data from the Department of Hematology, The People's Hospital Xinjiang Uygur Autonomous Region were retrospectively analyzed, and their paraffin-embedded tissues were subjected to targeted sequencing analysis by NGS technology (including 475 Target genes), and the differences in gene mutation profiles and signaling pathways between high-risk patients with aaIPI ≥2 and low-intermediate risk patients with aaIPI <2 were compared. RESULTS: A total of 44 high-frequency mutation genes were detected in 68 young DLBCL patients. By comparing the high-frequency mutation genes in aaIPI high-risk group and low-intermediate risk group, it was found that CARD11 mutation in aaIPI high-risk group was significantly higher than that in low-intermediate risk group (P =0.002), while MGA mutation (P =0.037) only appeared in the aaIPI high-risk group, and SPEN mutation (P =0.004) only appeared in the aaIPI low-intermediate risk group. The high-frequency mutation genes and clinical indicators of the aaIPI high-risk group were included in the survival analysis, and the results showed that TP53 (P =0.009, P =0.027), POU2AF1 (P =0.003, P =0.006) and CCND3 (P =0.040, P =0.014) genes mutations were associated with worse PFS and OS, while B2M was associated with better PFS (P =0.014) and OS (P =0.013). Multivariate COX regression analysis showed that the TP53, POU2AF1 and CCND3 were independent risk factors for PFS（P ＝0.021，P =0.005，P =0.020） and OS（P ＝0.042，P =0.010，P =0.013）. CONCLUSION: The aaIPI staging combination with molecular biology markers is more conducive to accurately judging the prognosis of young DLBCL patients. TP53, POU2AF1 and CCND3 mutations predict worse survival in the patients with the aaIPI high-risk group.

A network pharmacology-based study on the quality control markers of antithrombotic herbs: Using Salvia miltiorrhiza - Ligusticum chuanxiong as an example.

ETHNOPHARMACOLOGICAL RELEVANCE: Salvia miltiorrhiza (Danshen, DS), the dried root and rhizome of Salvia miltiorrhiza Bunge and Ligusticum chuanxiong (Chuanxiong, CX), the dried rhizomes of Ligusticum striatum DC are effective in invigorating blood circulation and eliminating stasis which is highly related with cardiovascular disease (CVD). AIM OF STUDY: The identification of activity-based chemical markers is very important, but the complex mechanism of "multi-component, multi-target, and multi-effect" within traditional Chinese medicine (TCM) poses a great challenge to this work. In this study, we combined network pharmacological prediction with experimental validation of the DS and CX to explore an effective method for discovering quality control (QC) of antithrombotic herbs by clarifying the intermediate layer "module/cluster" between the whole complex system and a single component. MATERIALS AND METHODS: Based on structural similarity analysis of compound and the thrombosis network published before, we firstly modularized two layers called chemical cluster (CC) network and functional module (FM) network respectively and linked them into one bilayer modularized compound target (BMCT) network. "Two-step" calculation was applied on identifying the significant compounds as the potential QC markers from CC. The in vitro inhibitory activity of selected QC marker compounds on thrombin was evaluated to partially verify their pharmacological activities. HPLC was used to determine contents. RESULTS: According to the network-based analysis, nine compounds with great importance in the BMCT network were identified as QC markers of DS-CX, including tanshinone I, tanshinone IIA, cryptotanshinone, salvianolic acid B, ferulic acid, salvianolic acid A, rosmarinic acid, chlorogenic acid, and coniferyl ferulate. Enzyme inhibitory test partially verified the activity of tanshinone I and tanshinone IIA. Chemical profiling indicated that the nine marker compounds are the main components in the herbal pair. CONCLUSIONS: This study identified activity-based QC markers of DS-CX herbal pair and provided a new methodology that can be used in the QC of other herbs, herbal pairs, or formulas.