Nintedanib inhibits keloid fibroblast functions by blocking the phosphorylation of multiple kinases and enhancing receptor internalization.

Keloid is a benign skin tumor characterized by its cell hyperproliferative activity, invasion into normal skin, uncontrolled growth, overproduction and deposition of extracellular matrices and high recurrence rate after various therapies. Nintedanib is a receptor tyrosine kinase inhibitor targeting VEGF, PDGF, FGF, and TGF-ß receptors with proved efficacy in anti-angiogenesis and in treating various types of cancers. In this study, we investigated the effects of nintedanib on keloid fibroblasts in both in vitro and ex vivo models. Keloid fibroblasts were prepared from 54 keloid scar samples in active stages collected from 49 patients. We found that nintedanib (1-4 µM) dose-dependently suppressed cell proliferation, induced G0/G1 cell cycle arrest, and inhibited migration and invasion of keloid fibroblasts. The drug also significantly inhibited the gene and protein expression of collagen I (COL-1) and III (COL-3), fibronectin (FN), and connective growth factor (CTGF), as well as the gene expression of other pathological factors, such as alpha smooth muscle actin (α-SMA), plasminogen activator inhibitor-1 (PAI-1), FK506-binding protein 10 (FKBP10), and heat shock protein 47 (HSP47) in keloid fibroblasts. Furthermore, nintedanib treatment significantly suppressed the phosphorylation of p38, JNK, ERK, STAT3, and Smad, enhanced endocytosis of various growth factor receptors. Using an ex vivo tissue explant model, we showed that nintedanib significantly suppressed cell proliferation, migration, and collagen production. The drug also significantly disrupted microvessel structure ex vivo. In summary, our results demonstrate that nintedanib is likely to become a potential targeted drug for keloid systemic therapy.

[Effects of stereoscopic cultivation on photosynthetic characteristics and growth of Tulipa edulis].

The effect of stereoscopic cultivation on the growth, photosynthetic characteristics and yield of Tulipa edulis was studied to explore the feasibility of stereoscopic cultivation on efficient cultivation of T.edulis. Total leaf area and photosynthetic parameters of T.edulis under stereoscopic cultivation (the upper, middle and the lower layers ) and the control were measured using LI-3100 leaf area meter and LI-6400XT photosynthesis system in the growing peak period of T.edulis.Plant biomass and biomass allocation were also determined.In addition, the bulb regeneration and yield of T.edulis were measured in the harvesting time.The results indicated that in the middle layer of stereoscopic cultivation, leaf biomass proportion was the highest, but total bulb fresh and dry weight and output growth (fresh weight) were the lowest among the treatments.And total bulb fresh weight in the middle of stereoscopic cultivation reduced significantly, by 22.84%, compared with the control.Light intensity in the lower layer of stereoscopic cultivation was moderate, in which T.edulis net photosynthetic rate and water use efficiency were higher than those of the other layers of stereoscopic cultivation, and bulb biomass proportion was the highest in all the treatments.No significant difference was detected in the total bulb fresh weight, dry weight and output growth (fresh weight) between the middle layer of stereoscopic cultivation and the control.In general, there was no significant difference in the growth status of T.edulis between stereoscopic cultivation and the control.Stereoscopic cultivation increased the yield of T.edulis by 161.66% in fresh weight and 141.35% in dry weight compared with the control in the condition of the same land area, respectively.In conclusion, stereoscopic cultivation can improve space utilization, increase the production, and achieve the high density cultivation of T.edulis.

Uncovering the Complexity Mechanism of Different Formulas Treatment for Rheumatoid Arthritis Based on a Novel Network Pharmacology Model.

Traditional Chinese medicine (TCM) with the characteristics of "multi-component-multi-target-multi-pathway" has obvious advantages in the prevention and treatment of complex diseases, especially in the aspects of "treating the same disease with different treatments". However, there are still some problems such as unclear substance basis and molecular mechanism of the effectiveness of formula. Network pharmacology is a new strategy based on system biology and poly-pharmacology, which could observe the intervention of drugs on disease networks at systematical and comprehensive level, and especially suitable for study of complex TCM systems. Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease, causing articular and extra articular dysfunctions among patients, it could lead to irreversible joint damage or disability if left untreated. TCM formulas, Danggui-Sini-decoction (DSD), Guizhi-Fuzi-decoction (GFD), and Huangqi-Guizhi-Wuwu-Decoction (HGWD), et al., have been found successful in controlling RA in clinical applications. Here, a network pharmacology-based approach was established. With this model, key gene network motif with significant (KNMS) of three formulas were predicted, and the molecular mechanism of different formula in the treatment of rheumatoid arthritis (RA) was inferred based on these KNMSs. The results show that the KNMSs predicted by the model kept a high consistency with the corresponding C-T network in coverage of RA pathogenic genes, coverage of functional pathways and cumulative contribution of key nodes, which confirmed the reliability and accuracy of our proposed KNMS prediction strategy. All validated KNMSs of each RA therapy-related formula were employed to decode the mechanisms of different formulas treat the same disease. Finally, the key components in KNMSs of each formula were evaluated by in vitro experiments. Our proposed KNMS prediction and validation strategy provides methodological reference for interpreting the optimization of core components group and inference of molecular mechanism of formula in the treatment of complex diseases in TCM.

A Novel Network Pharmacology Strategy to Decode Mechanism of Lang Chuang Wan in Treating Systemic Lupus Erythematosus.

Complex disease is a cascade process which is associated with functional abnormalities in multiple proteins and protein-protein interaction (PPI) networks. One drug one target has not been able to perfectly intervene complex diseases. Increasing evidences show that Chinese herb formula usually treats complex diseases in the form of multi-components and multi-targets. The key step to elucidate the underlying mechanism of formula in traditional Chinese medicine (TCM) is to optimize and capture the important components in the formula. At present, there are several formula optimization models based on network pharmacology has been proposed. Most of these models focus on the 2D/3D similarity of chemical structure of drug components and ignore the functional optimization space based on relationship between pathogenetic genes and drug targets. How to select the key group of effective components (KGEC) from the formula of TCM based on the optimal space which link pathogenic genes and drug targets is a bottleneck problem in network pharmacology. To address this issue, we designed a novel network pharmacological model, which takes Lang Chuang Wan (LCW) treatment of systemic lupus erythematosus (SLE) as the case. We used the weighted gene regulatory network and active components targets network to construct disease-targets-components network, after filtering through the network attribute degree, the optimization space and effective proteins were obtained. And then the KGEC was selected by using contribution index (CI) model based on knapsack algorithm. The results show that the enriched pathways of effective proteins we selected can cover 96% of the pathogenetic genes enriched pathways. After reverse analysis of effective proteins and optimization with CI index model, KGEC with 82 components were obtained, and 105 enriched pathways of KGEC targets were consistent with enriched pathways of pathogenic genes (80.15%). Finally, the key components in KGEC of LCW were evaluated by in vitro experiments. These results indicate that the proposed model with good accuracy in screening the KGEC in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.