Inhibition of influenza a virus infection by natural stilbene piceatannol targeting virus hemagglutinin.

BACKGROUND: Given the magnitude of influenza pandemics as a threat to the global population, it is crucial to have as many prevention and treatment options as possible. Piceatannol (PIC) is a tetrahydroxylated stilbenoid (trans-3,4,3',5'-tetrahydroxystilbene), also known as 3'- hydroxy resveratrol, which has demonstrated many different biological activities such as anti-inflammatory and antiviral activities. PURPOSE: In this study, the anti-influenza A virus (IAV) activities and mechanisms of PIC in vitro and in vivo were investigated in order to provide reference for the development of novel plant-derived anti-IAV drugs. METHODS: The viral plaque assay, RT-PCR and western blot assay were used to evaluate the anti-IAV effects of PIC in vitro. The anti-IAV mechanism of PIC was determined by HA syncytium assay, DARTS assay and Surface Plasmon Resonance assay. The mouse pneumonia model combined with HE staining were used to study the anti-IAV effects of PIC in vivo. RESULTS: PIC shows inhibition on the multiplication of both H1N1 and H3N2 viruses, and blocks the infection of H5N1 pseudovirus with low toxicity. PIC may directly act on the envelope of IAV to induce the rupture and inactivation of IAV particles. PIC can also block membrane fusion via binding to HA2 rather than HA1 and cleavage site of HA0. PIC may interact with the two residues (HA2-T68 and HA2-I75) of HA2 to block the conformational change of HA so as to inhibit membrane fusion. Importantly, oral therapy of PIC also markedly improved survival and reduced viral titers in IAV-infected mice. CONCLUSION: PIC possesses significant anti-IAV effects both in vitro and in vivo and may block IAV infection mainly through interaction with HA to block membrane fusion. Thus, PIC has the potential to be developed into a new broad-spectrum anti-influenza drug for the prevention and treatment of influenza.

Design, synthesis, and biological evaluation of novel penindolone derivatives as potential inhibitors of hemagglutinin-mediated membrane fusion.

Development and design of anti-influenza drugs with novel mechanisms is of great significance to combat the ongoing threat of influenza A virus (IAV). Hemagglutinin (HA) is regarded as a potential target for the therapy of IAV. Our previous research led to the discovery of penindolone (PND), a new diclavatol indole adduct, as an HA targeting leading compound exhibited anti-IAV activity. To enhance the bioactivity and understand the structure-activity relationships (SARs), 65 PND derivatives were designed and synthesized, and the anti-IAV activities as well as the HA targeting effects were systematically investigated in this study. Among them, compound 5g possessed high affinity to HA and was more effective than PND in terms of inhibiting HA-mediated membrane fusion. Compound 5g may act on the trypsin cleavage site of HA to exhibit a strong inhibition on membrane fusion. In addition, oral administration of 5g can significantly reduce the pulmonary virus titer, attenuate the weight loss, and improve the survival of IAV-infected mice, superior to the effects of PND. These findings suggest that the HA inhibitor 5g has potential to be developed into a novel broad-spectrum anti-IAV agent in the future.

Inhibition of influenza A virus replication by a marine derived quinolone alkaloid targeting virus nucleoprotein.

Owing to the emergence of drug resistance and high morbidity and mortality, the need for novel anti-influenza A virus (IAV) drugs with divergent targets is highly sought after. Herein, a novel quinolone alkaloid (QLA) derived from marine fungus was discovered with broad-spectrum anti-IAV activities with low toxicity. Distinct from current anti-IAV drugs, QLA may block virus replication and viral RNA (vRNA) export from the nucleus by targeting virus nucleoprotein (NP). QLA can block the binding of chromosome region maintenance 1 to nuclear export signal 3 of NP to inhibit the nuclear export of NP and vRNP. QLA may also affect vRNP assembly by interfering with the binding of NP to RNA rather than NP oligomerization. Arg305 and Phe488-Gly490 may be required for the interaction between QLA and NP, and the binding pocket around these amino acids may be a promising target for anti-IAV drugs. Importantly, oral administration of QLA can protect the mice against IAV-induced death and weight loss, superior to the effects of the clinical drug oseltamivir. In summary, the marine derived compound QLA has the potential to be developed into a novel anti-IAV agent targeting virus NP protein in the future.

The inhibition effects and mechanisms of sulfated chitooligosaccharides on influenza A virus in vitro and in vivo.

Sulfated chitooligosaccharide was reported to possess inhibition effect on human immunodeficiency virus (HIV) entry into host cells. Herein, we prepared chitooligosaccharide COS and its sulfate derivative SCOS and explored whether the sulfation modification can enhance the anti-influenza A virus (IAV) activity of COS. Interestingly, we discovered that SCOS possessed broad-spectrum anti-IAV effects with low toxicity, while the non-sulfated chitooligosaccharide COS had very low inhibition on IAV, verifying that the sulfation modification is essential for the anti-IAV actions of chitooligosaccharide. SCOS may target virus hemagglutinin (HA) protein to block both virus adsorption and membrane fusion processes. Oral administration of SCOS significantly decreased pulmonary viral titers and improved survival rate in IAV infected mice, comparable to the effects of Oseltamivir. Therefore, our findings support further studies on the use of SCOS as a novel entry inhibitor for IAV and as a supplement to current therapeutics for influenza.

Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network.

Landslide susceptibility prediction (LSP) modeling is an important and challenging problem. Landslide features are generally uncorrelated or nonlinearly correlated, resulting in limited LSP performance when leveraging conventional machine learning models. In this study, a deep-learning-based model using the long short-term memory (LSTM) recurrent neural network and conditional random field (CRF) in cascade-parallel form was proposed for making LSPs based on remote sensing (RS) images and a geographic information system (GIS). The RS images are the main data sources of landslide-related environmental factors, and a GIS is used to analyze, store, and display spatial big data. The cascade-parallel LSTM-CRF consists of frequency ratio values of environmental factors in the input layers, cascade-parallel LSTM for feature extraction in the hidden layers, and cascade-parallel full connection for classification and CRF for landslide/non-landslide state modeling in the output layers. The cascade-parallel form of LSTM can extract features from different layers and merge them into concrete features. The CRF is used to calculate the energy relationship between two grid points, and the extracted features are further smoothed and optimized. As a case study, the cascade-parallel LSTM-CRF was applied to Shicheng County of Jiangxi Province in China. A total of 2709 landslide grid cells were recorded and 2709 non-landslide grid cells were randomly selected from the study area. The results show that, compared with existing main traditional machine learning algorithms, such as multilayer perception, logistic regression, and decision tree, the proposed cascade-parallel LSTM-CRF had a higher landslide prediction rate (positive predictive rate: 72.44%, negative predictive rate: 80%, total predictive rate: 75.67%). In conclusion, the proposed cascade-parallel LSTM-CRF is a novel data-driven deep learning model that overcomes the limitations of traditional machine learning algorithms and achieves promising results for making LSPs.