Comparative Analysis of Active Ingredients and Potential Bioactivities of Essential Oils from Artemisia argyi and A. verlotorum.

Artemisia argyi H. Lév. and Vaniot is a variety of Chinese mugwort widely cultured in central China. A. verlotorum Lamotte, another variety of Chinese mugwort, has been used in the southern region of China since ancient times. Despite their similar uses in traditional medicine, little is known about the differences in their active ingredients and potential benefits. Herein, the chemical compositions of the essential oils (EOs) from both varieties were analyzed using chromatography-mass spectrometry (GC-MS). A series of databases, such as the Traditional Chinese Medicine Systems Pharmacology database (TCMSP), SuperPred database and R tool, were applied to build a networking of the EOs. Our results revealed significant differences in the chemical compositions of the two Artemisia EOs. However, we found that they shared similar ingredient-target-pathway networking with diverse bioactivities, such as neuroprotective, anti-cancer and anti-inflammatory. Furthermore, our protein connection networking analysis showed that transcription factor p65 (RELA), phosphatidylinositol 3-kinase regulatory subunit alpha (PIK3R1) and mitogen-activated protein kinase 1 (MAPK1) are crucial for the biological activity of Artemisia EOs. Our findings provided evidence for the use of A. verlotorum as Chinese mugwort in southern China.

Essential oil of lemon myrtle (Backhousia citriodora) induces S-phase cell cycle arrest and apoptosis in HepG2 cells.

ETHNOPHARMACOLOGICAL RELEVANCE: Lemon myrtle (Backhousia citriodora F.Muell.) leaves, whether fresh or dried, are used traditionally in folk medicine to treat wounds, cancers, skin infections, and other infectious conditions. However, the targets and mechanisms related to anti-cancer effect of lemon myrtle are unavailable. In our study, we found that the essential oil of lemon myrtle (LMEO) showed anti-cancer activity in vitro, and we initially explored its mechanism of action. MATERIALS AND METHODS: We analyzed the chemical compositions of LMEO by GC-MS. We tested the cytotoxicity of LMEO on various cancer cell lines using the MTT assay. Network pharmacology was used also to analyze the targets of LMEO. Moreover, the mechanisms of LMEO were investigated through scratch assay, flow cytometry analysis, and western blot in the HepG2 liver cancer cell line. RESULTS: LMEO showed cytotoxicity on various cancer cell lines with values of IC50 40.90 ± 2.23 (liver cancer HepG2 cell line), 58.60 ± 6.76 (human neuroblastoma SH-SY5Y cell line), 68.91 ± 4.62 (human colon cancer HT-29 cell line) and 57.57 ± 7.61 µg/mL (human non-small cell lung cancer A549 cell line), respectively. The major cytotoxic chemical constituent in LMEO was identified as citrals, which accounted for 74.9% of the content. Network pharmacological analysis suggested that apurinic/apyrimidinic endodeoxyribonuclease 1 (APEX1), androgen receptor (AR), cyclin-dependent kinases 1 (CDK1), nuclear factor erythroid 2-related factor 2 (Nrf-2), fatty acid synthase (FASN), epithelial growth factor receptor (EGFR), estrogen receptor 1 (ERα) and cyclin-dependent kinases 4 (CDK4) are potential cytotoxic targets of LMEO. These targets are closely related to cell migration, cycle and apoptosis. Notley, the p53 protein had the highest confidence to co-associate with the eight common targets, which was further confirmed by scratch assay, flow cytometry analysis, and western blot in the HepG2 liver cancer cell line. LMEO significantly inhibited the migration of HepG2 cells in time-dependent and dose-dependent manner. Moreover, LMEO caused a S-phase blocking on HepG2 cells and promoted apoptosis in the meanwhile. Western blot results indicated that p53 protein, Cyclin A2 and Bax proteins were up-regulated, while Cyclin E1 and Bcl-2 proteins were down-regulated. CONCLUSION: LMEO showed cytotoxicity in various cancer cell lines in vitro. Pharmacological networks showed LMEO to have multi-component and multi-targeting effects that are related to inhibit migration of HepG2 cells, and affect cell cycle S-phase arrest and apoptosis through modulation of p53 protein.

The genome of medicinal leech (Whitmania pigra) and comparative genomic study for exploration of bioactive ingredients.

BACKGROUND: Leeches are classic annelids that have a huge diversity and are closely related to people, especially medicinal leeches. Medicinal leeches have been widely utilized in medicine based on the pharmacological activities of their bioactive ingredients. Comparative genomic study of these leeches enables us to understand the difference among medicinal leeches and other leeches and facilitates the discovery of bioactive ingredients. RESULTS: In this study, we reported the genome of Whitmania pigra and compared it with Hirudo medicinalis and Helobdella robusta. The assembled genome size of W. pigra is 177 Mbp, close to the estimated genome size. Approximately about 23% of the genome was repetitive. A total of 26,743 protein-coding genes were subsequently predicted. W. pigra have 12346 (46%) and 10295 (38%) orthologous genes with H. medicinalis and H. robusta, respectively. About 20 and 24% genes in W. pigra showed syntenic arrangement with H. medicinalis and H. robusta, respectively, revealed by gene synteny analysis. Furthermore, W. pigra, H. medicinalis and H. robusta expanded different gene families enriched in different biological processes. By inspecting genome distribution and gene structure of hirudin, we identified a new hirudin gene g17108 (hirudin_2) with different cysteine patterns. Finally, we systematically explored and compared the active substances in the genomes of three leech species. The results showed that W. pigra and H. medicinalis exceed H. robusta in both kinds and gene number of active molecules. CONCLUSIONS: This study reported the genome of W. pigra and compared it with other two leeches, which provides an important genome resource and new insight into the exploration and development of bioactive molecules of medicinal leeches.

A strategy to find novel candidate anti-Alzheimer's disease drugs by constructing interaction networks between drug targets and natural compounds in medical plants.

BACKGROUND: Alzheimer' disease (AD) is an ultimately fatal degenerative brain disorder that has an increasingly large burden on health and social care systems. There are only five drugs for AD on the market, and no new effective medicines have been discovered for many years. Chinese medicinal plants have been used to treat diseases for thousands of years, and screening herbal remedies is a way to develop new drugs. METHODS: We used molecular docking to screen 30,438 compounds from Traditional Chinese Medicine (TCM) against a comprehensive list of AD target proteins. TCM compounds in the top 0.5% of binding affinity scores for each target protein were selected as our research objects. Structural similarities between existing drugs from DrugBank database and selected TCM compounds as well as the druggability of our candidate compounds were studied. Finally, we searched the CNKI database to obtain studies on anti-AD Chinese plants from 2007 to 2017, and only clinical studies were included. RESULTS: A total of 1,476 compounds (top 0.5%) were selected as drug candidates. Most of these compounds are abundantly found in plants used for treating AD in China, especially the plants from two genera Panax and Morus. We classified the compounds by single target and multiple targets and analyzed the interactions between target proteins and compounds. Analysis of structural similarity revealed that 17 candidate anti-AD compounds were structurally identical to 14 existing approved drugs. Most of them have been reported to have a positive effect in AD. After filtering for compound druggability, we identified 11 anti-AD compounds with favorable properties, seven of which are found in anti-AD Chinese plants. Of 11 anti-AD compounds, four compounds 5,862, 5,863, 5,868, 5,869 have anti-inflammatory activity. The compound 28,814 mainly has immunoregulatory activity. The other six compounds have not yet been reported for any biology activity at present. DISCUSSION: Natural compounds from TCM provide a broad prospect for the screening of anti-AD drugs. In this work, we established networks to systematically study the connections among natural compounds, approved drugs, TCM plants and AD target proteins with the goal of identifying promising drug candidates. We hope that our study will facilitate in-depth research for the treatment of AD in Chinese medicine.

The identification and molecular mechanism of anti-stroke traditional Chinese medicinal compounds.

Stroke is a worldwide epidemic disease with high morbidity and mortality. The continuously exploration of anti-stroke medicines and molecular mechanism has a long way to go. In this study, in order to screen candidate anti-stroke compounds, more than 60000 compounds from traditional Chinese medicine (TCM) database were computationally analyzed then docked to the 15 known anti-stroke targets. 192 anti-stroke plants for clinical therapy and 51 current anti-stroke drugs were used to validate docking results. Totally 2355 candidate anti-stroke compounds were obtained. Among these compounds, 19 compounds are structurally identical with 16 existing drugs in which part of them have been used for anti-stroke treatment. Furthermore, these candidate compounds were significantly enriched in anti-stroke plants. Based on the above results, the compound-target-plant network was constructed. The network reveals the potential molecular mechanism of anti-stroke for these compounds. Most of candidate compounds and anti-stroke plants are tended to interact with target NOS3, PSD-95 and PDE5A. Finally, using ADMET filter, we identified 35 anti-stroke compounds with favorable properties. The 35 candidate anti-stroke compounds offer an opportunity to develop new anti-stroke drugs and will improve the research on molecular mechanism of anti-stroke.

Pharmacophore-Map-Pick: A Method to Generate Pharmacophore Models for All Human GPCRs.

GPCR-based drug discovery is hindered by a lack of effective screening methods for most GPCRs that have neither ligands nor high-quality structures. With the aim to identify lead molecules for these GPCRs, we developed a new method called Pharmacophore-Map-Pick to generate pharmacophore models for all human GPCRs. The model of ADRB2 generated using this method not only predicts the binding mode of ADRB2-ligands correctly but also performs well in virtual screening. Findings also demonstrate that this method is powerful for generating high-quality pharmacophore models. The average enrichment for the pharmacophore models of the 15 targets in different GPCR families reached 15-fold at 0.5 % false-positive rate. Therefore, the pharmacophore models can be applied in virtual screening directly with no requirement for any ligand information or shape constraints. A total of 2386 pharmacophore models for 819 different GPCRs (99 % coverage (819/825)) were generated and are available at http://bsb.kiz.ac.cn/GPCRPMD.

In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database.

There is a constant demand to develop new, effective, and affordable anti-cancer drugs. The traditional Chinese medicine (TCM) is a valuable and alternative resource for identifying novel anti-cancer agents. In this study, we aim to identify the anti-cancer compounds and plants from the TCM database by using cheminformatics. We first predicted 5278 anti-cancer compounds from TCM database. The top 346 compounds were highly potent active in the 60 cell lines test. Similarity analysis revealed that 75% of the 5278 compounds are highly similar to the approved anti-cancer drugs. Based on the predicted anti-cancer compounds, we identified 57 anti-cancer plants by activity enrichment. The identified plants are widely distributed in 46 genera and 28 families, which broadens the scope of the anti-cancer drug screening. Finally, we constructed a network of predicted anti-cancer plants and approved drugs based on the above results. The network highlighted the supportive role of the predicted plant in the development of anti-cancer drug and suggested different molecular anti-cancer mechanisms of the plants. Our study suggests that the predicted compounds and plants from TCM database offer an attractive starting point and a broader scope to mine for potential anti-cancer agents.