Obesity-induced dysregulation of skin-resident PPARγ+ Treg cells promotes IL-17A-mediated psoriatic inflammation.

Obesity is a major risk factor for psoriasis, but how obesity disrupts the regulatory mechanisms that keep skin inflammation in check is unclear. Here, we found that skin was enriched with a unique population of CD4+Foxp3+ regulatory T (Treg) cells expressing the nuclear receptor peroxisome proliferation-activated receptor gamma (PPARγ). PPARγ drove a distinctive transcriptional program and functional suppression of IL-17A+ γδ T cell-mediated psoriatic inflammation. Diet-induced obesity, however, resulted in a reduction of PPARγ+ skin Treg cells and a corresponding loss of control over IL-17A+ γδ T cell-mediated inflammation. Mechanistically, PPARγ+ skin Treg cells preferentially took up elevated levels of long-chain free fatty acids in obese mice, which led to cellular lipotoxicity, oxidative stress, and mitochondrial dysfunction. Harnessing the anti-inflammatory properties of these PPARγ+ skin Treg cells could have therapeutic potential for obesity-associated inflammatory skin diseases.

Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications.

Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.