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.

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.

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-β 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 G 0 /G 1 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.

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Keloid is a chronic inflammation-associated skin fibrosis with overabundant immune cells and extracellular matrix (ECM). 1 Keloids appear to share similar features with tumors such as progressive uncontrolled growth, invasive ability and high rates of recurrence. 2,3 Topological, environmental and patient-related factors have been considered as the main pathogenetic factors of keloids. 4 In recent years, mechanical microenvironment, an important topological factor, has received increased attention for mediating cell dysfunctions in keloids. 5 The characteristic mechanical environment of keloid is high stretching tension and stiffness. 6 In recent years, it has been found that the mechanical microenvironment can affect the function of immune cells. The persistence of a number of inflammatory immune cells including lymphocytes, mast cells and primarily macrophages, is crucial for the continued cell proliferation and ECM deposition in keloids. 7

The pathogenesis of diabetic nephropathy (DN) is accompanied by alterations in biological function and signaling pathways regulated through complex molecular mechanisms. A number of regulatory factors, including transcription factors (TFs) and non-coding RNAs (ncRNAs, including lncRNAs and miRNAs), have been implicated in DN; however, it is unclear how the interactions among these regulatory factors contribute to the development of DN pathogenesis. In this study, we developed a network-based analysis to decipher interplays between TFs and ncRNAs regulating progression of DN by combining omics data with regulatory factor-target information. To accomplish this, we identified differential expression programs of mRNAs and miRNAs during early DN (EDN) and established DN. We then uncovered putative interactive connections among miRNA-mRNA, lncRNA-miRNA, and lncRNA-mRNA implicated in transcriptional control. This led to the identification of two lncRNAs (MALAT1 and NEAT1) and the three TFs (NF-κB, NFE2L2, and PPARG) that likely cooperate with a set of miRNAs to modulate EDN and DN target genes. The results highlight how crosstalk among TFs, lncRNAs, and miRNAs regulate the expression of genes both transcriptionally and post-transcriptionally, and our findings provide new insights into the molecular basis and pathogenesis of progressive DN.

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