Influenza A viruses (IAVs) cause seasonal pandemics and epidemics with high morbidity and mortality, which calls for effective anti-IAV agents. The glycoprotein hemagglutinin of influenza virus plays a crucial role in the initial stage of virus infection, making it a potential target for anti-influenza therapeutics development. Here we found that quercetin inhibited influenza infection with a wide spectrum of strains, including A/Puerto Rico/8/34 (H1N1), A/FM-1/47/1 (H1N1), and A/Aichi/2/68 (H3N2) with half maximal inhibitory concentration (IC₅₀) of 7.756 ± 1.097, 6.225 ± 0.467, and 2.738 ± 1.931 μg/mL, respectively. Mechanism studies identified that quercetin showed interaction with the HA2 subunit. Moreover, quercetin could inhibit the entry of the H5N1 virus using the pseudovirus-based drug screening system. This study indicates that quercetin showing inhibitory activity in the early stage of influenza infection provides a future therapeutic option to develop effective, safe and affordable natural products for the treatment and prophylaxis of IAV infections.

Background/Objectives: The development of anti-drug antibodies (ADA) significantly diminishes the clinical efficacy of infliximab (IFX) in Crohn’s disease (CD). This study aimed to develop and validate an interpretable machine learning (ML) framework for predicting ADA risk during IFX induction therapy using multidimensional clinical and laboratory data. Methods: We conducted a retrospective analysis of 606 CD patients who initiated IFX induction between January 2023 and August 2024 at the Sixth Affiliated Hospital of Sun Yat-sen University. Predictor selection was performed through univariate analysis and least absolute shrinkage and selection operator (LASSO) regression, with significant features further evaluated via multivariate logistic regression. Seven ML models were developed and evaluated mainly based on area under the curve (AUC), F1 score, and Brier score. Model interpretability was enhanced using SHapley Additive exPlanations (SHAP). Results: Among the 606 CD patients, 145 (23.93%) developed ADA during IFX induction. Independent predictors included serum trough levels of IFX (TLI), erythrocyte sedimentation rate (ESR), history of delayed treatment, prior exposure to anti-TNF agents, and concomitant use of immunosuppressants (IMM). The XGBoost algorithm outperformed others, with an AUC of 0.899, accuracy of 0.851, F1 score of 0.640, and Brier score of 0.102 in validation. SHAP analysis identified TLI and ESR as the most influential predictors, with history of delayed treatment and prior exposure to anti-TNF agents showing moderate impact, while concomitant use of IMM was associated with a protective effect. Conclusions: We developed an interpretable ML model that effectively predicts ADA formation in CD patients undergoing IFX induction therapy, facilitating early risk stratification and personalized treatment planning. This approach integrates advanced analytics with clinical practice to support precision medicine in CD management.

Drug-resistant influenza demands novel antiviral treatments. Non-structural protein 1 (NS1) of influenza A virus (IAV) regulates the viral life cycle and host immune response, thus becoming a promising therapeutic target. The atractylenolide (ACT) -Ⅲ exhibits notable anti-IAV efficacy; however, its in vivo anti-IAV activity and the underlying mechanisms need further exploration. We explored the binding affinity of ACT-Ⅲ with NS1 and elucidated the in vivo anti-influenza activity and underlying mechanism of ACT-Ⅲ. Surface plasmon resonance (SPR) analysis was utilized to determine the binding affinity of ACT-Ⅲ to NS1. The anti-IAV activity of ACT-Ⅲ was further tested in vitro using IAV-infected lung epithelial cells and in vivo in IAV-challenged mice. The evaluation criteria included the assessment of virus-associated cytopathies, viral protein and gene expression, virus titer, mice weight loss, survival curve, and lung pathology. Transcriptomic sequencing was conducted to evaluate the inflammation regulatory function of ACT-Ⅲ in IAV-infected Raw264.7 cells. Quantitative real-time PCR (qRT-PCR) was used to detect the target gene expression. Flow cytometry was utilized to assess macrophages polarization. Co-immunoprecipitation was employed to examine the impact of ACT-Ⅲ on the interaction between NS1 and cleavage and polyadenylation-specific factor 4 (CPSF4). ACT-Ⅲ exhibited anti-IAV activity in vitro and in vivo, mitigating virus-induced lung injury. Mechanistically, ACT-Ⅲ impeded IAV infection and viral induced inflammation by binding to viral NS1, which reduced the activation of pro-inflammatory signaling, and disrupted the NS1-CPSF4 interaction that mediates alternative polyadenylation (APA). ACT-Ⅲ inhibits IAV replication and viral pneumonia by binding to NS1, showing potential as a therapeutic agent for IAV treatment.

The infection cycle of the Influenza A Virus (IAV) typically requires host factors to regulate replication and proliferation. However, the roles of these factors remain undiscovered. This study focuses on Sorting Nexin 10 (SNX10), which is involved in regulating membrane trafficking and endosomal stabilization. Our previous study identified that SNX10 facilitates the replication of human coronavirus OC43 through enhancing clathrin-mediated endocytosis. In our present study, we found that SNX10 significantly promoted IAV infection in host cells. The conditional knockout of Snx10 in mice lungs prolonged survival following IAV challenge. Mechanistically, SNX10 facilitated the production of acidic endosomal vesicles and promoted the accumulation of pro-viral autophagic structures, a process supported by the specific interaction between SNX10 and the viral NP and M2 protein of IAV. Blocking SNX10-mediated acidic endosomal vesicles and autophagosome formation demonstrated antiviral effects. Moreover, IAV infection increased SNX10 protein levels by suppressing its ubiquitination, suggesting that SNX10 could serve as a potential host-derived antiviral drug target.

Coiled coils (CCs) are protein structural motifs universally found in proteins and mediate a plethora of biological interactions, and thus their reliable annotation is crucial for studies of protein structure and function. Autographa californica multiple nucleopolyhedrovirus (AcMNPV) is a large double-stranded DNA (dsDNA) virus and encodes 154 proteins. In this study, genome-wide scans of previously uncharacterized CC motifs throughout AcMNPV was conducted using CC prediction software. In total, 24 CC motifs in 19 CC proteins with high confidence were identified. The characteristic of viral CC motifs were analyzed. The CC proteins could be divided into 12 viral structural proteins and 7 non-structural proteins, including viral membrane fusion proteins, enzymes, and transcription factors. Moreover, CC motifs are conserved in the baculoviral orthologs of 14 of the 19 proteins. It is noted that five CC proteins, including Ac51, Ac66, Exon0, Ac13, and GP16, were previously identified to function in the nuclear egress of nucleocapsids, and Ac66 contains multiple CC motifs, the longest of which comprises 252 amino acids, suggesting a role of CC motifs in this process. Taken together, the CC motifs identified in this study are valuable resource for studying protein function and protein interaction networks during virus replication.