Evaluation of the antiviral activity of naringenin, a major constituent of Typha angustifolia, against white spot syndrome virus in crayfish Procambarus clarkii.

White spot syndrome virus (WSSV) is a serious pathogen threatening global crustacean aquaculture with no commercially available drugs. Herbal medicines widely used in antiviral research offer a rich reserve for drug discovery. Here, we investigated the inhibitory activity of 13 herbal medicines against WSSV in crayfish Procambarus clarkii and discovered that naringenin (NAR) has potent anti-WSSV activity. In the preliminary screening, the extracts of Typha angustifolia displayed the highest inhibitory activity on WSSV replication (84.62%, 100 mg/kg). Further, NAR, the main active compound of T. angustifolia, showed a much higher inhibition rate (92.85%, 50 mg/kg). NAR repressed WSSV proliferation followed a dose-dependent manner and significantly improved the survival of WSSV-challenged crayfish. Moreover, pre- or post-treatment of NAR displayed a comparable inhibition on the viral loads. NAR decreased the transcriptional levels of vital genes in viral life cycle, particularly for the immediately early-stage gene ie1. Further results showed that NAR could decrease the STAT gene expression to block ie1 transcription. Besides, NAR modulated immune-related gene Hsp70, antioxidant (cMnSOD, mMnSOD, CAT, GST), anti-inflammatory (COX-1, COX-2) and pro-apoptosis-related factors (Bax and BI-1) to inhibit WSSV replication. Overall, these results suggest that NAR may have the potential to be developed as preventive or therapeutic agent against WSSV.

In silico prediction of spleen tyrosine kinase inhibitors using machine learning approaches and an optimized molecular descriptor subset generated by recursive feature elimination method.

We tested four machine learning methods, support vector machine (SVM), k-nearest neighbor, back-propagation neural network and C4.5 decision tree for their capability in predicting spleen tyrosine kinase (Syk) inhibitors by using 2592 compounds which are more diverse than those in other studies. The recursive feature elimination method was used for improving prediction performance and selecting molecular descriptors responsible for distinguishing Syk inhibitors and non-inhibitors. Among four machine learning models, SVM produces the best performance at 99.18% for inhibitors and 98.82% for non-inhibitors, respectively, indicating that the SVM is potentially useful for facilitating the discovery of Syk inhibitors.