Conjugated linoleic acid attenuates 2,4-dinitrofluorobenzene-induced atopic dermatitis in mice through dual inhibition of COX-2/5-LOX and TLR4/NF-κB signaling.

Conjugated linoleic acid (CLA), commonly found in beef, lamb and dairy products, has been reported to exhibit anti-inflammatory and antipruritus effects and to inhibit the release of chemical mediators such as histamine and eicosanoid in laboratory rodents. The chief objective of the study is to assess the efficacy of CLA on atopic dermatitis (AD) in mice and to explore possible mechanisms with CLA treatments. To develop a new therapy for AD, the anti-AD potential of CLA was investigated by inducing AD-like skin lesions in mice using 2,4-dinitrofluorobenzene. We evaluated dermatitis severity; histopathological changes; serum levels of T helper (Th) cytokines (interferon-γ, interleukin-4); changes in protein expression by western blotting and immunohistochemistry staining for cyclooxygenase-2 (COX-2), 5-lipoxygenase (5-LOX), toll like receptor 4 (TLR-4), myeloid differentiation factor 88 (MyD88), nuclear factor-κB (NF-κB) and tumor necrosis factor α (TNF-α); and production of the proinflammatory lipid mediators, such as prostaglandin E2 and leukotriene B4, in the skin lesions. Treatment with CLA ameliorated the development of AD-like clinical symptoms and effectively inhibited epidermal hyperplasia and infiltration of mast cells and CD4+ T cells in the AD mouse skin. Total serum immunoglobulin E levels and the expression levels of Th1/Th2 cytokines and lipid mediators in dorsal skin were dramatically suppressed by CLA. Furthermore, CLA down-regulated the expressions of COX-2, 5-LOX, TLR4, MyD88, NF-κB and TNF-α. Taken together, our findings demonstrate the potential usefulness of CLA as an anti-inflammatory dietary supplement or drug for the prevention and management of AD skin diseases by modulating the COX-2/5-LOX and TLR4/MyD88/NF-κB signaling pathways.

Genome-scale screening of drug-target associations relevant to Ki using a chemogenomics approach.

The identification of interactions between drugs and target proteins plays a key role in genomic drug discovery. In the present study, the quantitative binding affinities of drug-target pairs are differentiated as a measurement to define whether a drug interacts with a protein or not, and then a chemogenomics framework using an unbiased set of general integrated features and random forest (RF) is employed to construct a predictive model which can accurately classify drug-target pairs. The predictability of the model is further investigated and validated by several independent validation sets. The built model is used to predict drug-target associations, some of which were confirmed by comparing experimental data from public biological resources. A drug-target interaction network with high confidence drug-target pairs was also reconstructed. This network provides further insight for the action of drugs and targets. Finally, a web-based server called PreDPI-Ki was developed to predict drug-target interactions for drug discovery. In addition to providing a high-confidence list of drug-target associations for subsequent experimental investigation guidance, these results also contribute to the understanding of drug-target interactions. We can also see that quantitative information of drug-target associations could greatly promote the development of more accurate models. The PreDPI-Ki server is freely available via: http://sdd.whu.edu.cn/dpiki.