As a Traditional Chinese Medicine, Eucommia ulmoides Oliv. has been used for the treatment of various diseases since ancient times, involving lumbar pain, knee pain, osteoporosis, hepatoprotection, paralysis, intestinal haemorrhoids, vaginal bleeding, abortion, spermatorrhoea, foot fungus, anti-aging etc. With the developing discovery of E. ulmoides extracts and its active components in various pharmacological activities, E. ulmoides has gained more and more attention. Up to now, E. ulmoides has been revealed to show remarkable therapeutic effects on hypertension, hyperglycemia, diabetes, obesity, osteoporosis, Parkinson’s disease, Alzheimer’s disease, sexual dysfunction. E. ulmoides has also been reported to possess antioxidant, anti-inflammatory, neuroprotective, anti-fatigue, anti-aging, anti-cancer and immunoregulation activities etc. Along these lines, this review summarizes the traditional application and modern pharmacological research of E. ulmoides, providing novel insights of E. ulmoides in the treatment of various diseases.

Cornus officinalis Sieb et. Zucc and Paeonia lactiflora Pall. have exhibited favorable therapeutic effects against rheumatoid arthritis (RA), but the specific mechanisms of their active compounds remain unclear. The aim of this study was to comprehensively analyze the therapeutic mechanisms of selected active compounds in Cornus officinalis (loganin, ursolic acid, and morroniside) and Paeonia lactiflora (paeoniflorin and albiflorin) via network pharmacology. The pharmacological properties of the five active compounds were evaluated and their potential target genes were identified by database screening. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional analysis were performed to determine the enriched molecular pathways associated with the active compounds. Using network pharmacology tools, eight genes (IL1β, VEGFA, STAT3, TP53, IL6, TNF, FOS, and LGALS3) were identified as common targets between RA and the five active compounds. Molecular docking simulation revealed the compound-target relationship between the five active compounds and three selected targets from the eight common ones (LGALS3, STAT3, and VEGFA). The compound-target relationships were subsequently validated via preliminary in vivo experiments in a rat model of collagen-induced arthritis. Rats subjected to collagen-induced arthritis showed increased protein expression of LGALS3, STAT3, and VEGFA in synovial tissues. However, treatment using Cornus officinalis or/and Paeonia lactiflora, as well as their most drug-like active compounds (ursolic acid or/and paeoniflorin, respectively, identified based on pharmacological properties), attenuated the expression of these three targets, as previously predicted. Collectively, network pharmacology allowed the pharmacological and molecular roles of Cornus officinalis and Paeonia lactiflora to be systematically revealed, further establishing them as important candidate drugs in the treatment and management of RA.

Rheumatoid arthritis (RA) is a chronic autoimmune disease that leads to cartilage destruction and bone erosion. In-depth exploration of the pathogenesis of RA and the development of effective therapeutic drugs are of important clinical and social value. Herein, we explored the medicinal value of Cornus officinalis Sieb. and Paeonia lactiflora Pall. in RA treatment using a rat model of collagen-induced arthritis (CIA). We compared the therapeutic effect of Cornus officinalis and Paeonia lactiflora with that of their main active compounds, ursolic acid and paeoniflorin, respectively. We demonstrated that the combination of Cornus officinalis and Paeonia lactiflora effectively inhibited the release of factors associated with oxidative stress and inflammation during RA, therein ameliorating the symptoms and suppressing the progression of RA. We further showed that the underlying mechanisms may be related to the regulation of apoptosis in synovial tissues, and we investigated the potential involvement of AMPK-mediated mitochondrial dynamics in the therapeutic action of the two drugs and their active components.

We present a deep clustering-based MS data visualization strategy (MCnebula), integrated with the influential open-source automatic MS annotation platform SIRIUS and in vivo and in vitro methods, to screen and validate potential lead compounds from natural products.

Euphorbia pekinensis (EP) is a commonly used Chinese medicine treating edema with potential hepatorenal toxicity. However, its toxic mechanism and prevention are remained to be explored. Oleanolic acid (OA) is a triterpene acid with potential hepatorenal protective activities. We investigated the protective effect and potential mechanism of OA on EP‐induced hepatorenal toxicity. In this study, rats were given total diterpenes from EP (TDEP, 16 mg/kg) combined with OA (10, 20, 40 mg/kg) by gavage for 4 weeks. The results showed that TDEP administration could lead to a 3–4‐fold increasement in hepatorenal biochemical parameters with histopathological injuries, while OA treatment could ameliorate them in a dose‐dependent manner. At microbial and metabolic levels, intestinal flora and host metabolism were perturbed after TDEP administration. The disturbance of bile acid metabolism was the most significant metabolic pathway, with secondary bile acids increasing while conjugated bile acids decreased. OA treatment can improve the disorder of intestinal flora and metabolic bile acid spectrum. Further correlation analysis screened out that Escherichia‐Shigella, Phascolarctobacterium, Acetatifactor, and Akkermansia were closely related to the bile acid metabolic disorder. In conclusion, oleanolic acid could prevent hepatorenal toxicity induced by EP by regulating bile acids metabolic disorder via intestinal flora improvement.

No abstract

Untargeted mass spectrometry is a robust tool for biology, but it usually requires a large amount of time on data analysis, especially for system biology. A framework called Multiple-Chemical nebula (MCnebula) was developed herein to facilitate the LC−MS data analysis process by focusing on critical chemical classes and visualization in multiple dimensions. This framework consists of three vital steps as follows: (1) abundance-based classes (ABC) selection algorithm, (2) critical chemical classes to classify "features" (corresponding to compounds), and (3) visualization as multiple Child-Nebulae (network graph) with annotation, chemical classification, and structure. Notably, MCnebula can be used to explore the classification and structural characteristic of unknown compounds beyond the limit of the spectral library. Moreover, it is intuitive and convenient for pathway analysis and biomarker discovery because of its function of ABC selection and visualization. MCnebula was implemented in the R language. A series of tools in R packages were provided to facilitate downstream analysis in an MCnebula-featured way, including feature selection, homology tracing of top features, pathway enrichment analysis, heat map clustering analysis, spectral visualization analysis, chemical information query, and output analysis reports. The broad utility of MCnebula was illustrated by a human-derived serum data set for metabolomics analysis. The results indicated that "Acyl carnitines" were screened out by tracing structural classes of biomarkers, which was consistent with the reference. A plant-derived data set was investigated to achieve a rapid annotation and discovery of compounds in E. ulmoides.