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Objective: The aim of this study was to investigate the role and mechanisms of miR-155 in chronic spontaneous urticaria (CSU). Methods: The expression level of miR-155 in the skin tissues of patients with CSU and experimental rats were detected by RT-qPCR, followed by the measurement of the histamine release rate in the serum through the histamine release test. Besides, hematoxylin & eosin staining was used to observe the pathological changes of the skin tissues; Corresponding detection kits and flow cytometry to measure the changes of immunoglobulins, inflammatory cytokines and T cell subsets in the serum of rats in each group; and western blot to check the expression level of proteins related to JAK/STAT signaling pathway in the skin tissues. Results: Knockdown of miR-155 reduced the number and duration of pruritus, alleviated the skin damage, and decreased the number of eosinophils in CSU rats. Moreover, knockdown of miR-155 elevated the serum levels of IgG and IgM, decreased the levels of IgA and inflammatory cytokines, and reduced the proportion of CD4 + and CD4 + CD25 + T cells, as well as the CD4+/CD8 + ratio in CSU rats. However, Tyr705 intervention could reverse the effects of knockdown of miR-155 on CSU model rats. Furthermore, we found that knockdown of miR-155 significantly reduced the protein expression of IRF-9, as well as the P-JAK2/JAK2 and P-STAT3/STAT3 ratios in the skin tissues of CSU rats. Conclusion: Knockdown of miR-155 can alleviate skin damage and inflammatory responses and relieve autoimmunity in CSU rats by inhibiting the JAK/STAT3 signaling pathway. [ABSTRACT FROM AUTHOR]

Identification and validation of bioactive small-molecule targets is a significant challenge in drug discovery. In recent years, various in-silico approaches have been proposed to expedite time- and resource-consuming experiments for target detection. Herein, we developed several chemogenomic models for target prediction based on multi-scale information of chemical structures and protein sequences. By combining the information of a compound with multiple protein targets together and putting these compound-target pairs into a well-established model, the scores to indicate whether there are interactions between compounds and targets can be derived, and thus a target prediction task can be completed by sorting the outputted scores. To improve the prediction performance, we constructed several chemogenomic models using multi-scale information of chemical structures and protein sequences, and the ensemble model with the best performance was used as our final model. The model was validated by various strategies and external datasets and the promising target prediction capability of the model, i.e., the fraction of known targets identified in the top-k (1 to 10) list of the potential target candidates suggested by the model, was confirmed. Compared with multiple state-of-art target prediction methods, our model showed equivalent or better predictive ability in terms of the top-k predictions. It is expected that our method can be utilized as a powerful computational tool to narrow down the potential targets for experimental testing., (© 2023. The Author(s).)