Neuraminidase is a host-directed approach to regulate neutrophil responses in sepsis and COVID-19.

BACKGROUND AND PURPOSE: Neutrophil overstimulation plays a crucial role in tissue damage during severe infections. Because pathogen-derived neuraminidase (NEU) stimulates neutrophils, we investigated whether host NEU can be targeted to regulate the neutrophil dysregulation observed in severe infections. EXPERIMENTAL APPROACH: The effects of NEU inhibitors on lipopolysaccharide (LPS)-stimulated neutrophils from healthy donors or COVID-19 patients were determined by evaluating the shedding of surface sialic acids, cell activation, and reactive oxygen species (ROS) production. Re-analysis of single-cell RNA sequencing of respiratory tract samples from COVID-19 patients also was carried out. The effects of oseltamivir on sepsis and betacoronavirus-induced acute lung injury were evaluated in murine models. KEY RESULTS: Oseltamivir and zanamivir constrained host NEU activity, surface sialic acid release, cell activation, and ROS production by LPS-activated human neutrophils. Mechanistically, LPS increased the interaction of NEU1 with matrix metalloproteinase 9 (MMP-9). Inhibition of MMP-9 prevented LPS-induced NEU activity and neutrophil response. In vivo, treatment with oseltamivir fine-tuned neutrophil migration and improved infection control as well as host survival in peritonitis and pneumonia sepsis. NEU1 also is highly expressed in neutrophils from COVID-19 patients, and treatment of whole-blood samples from these patients with either oseltamivir or zanamivir reduced neutrophil overactivation. Oseltamivir treatment of intranasally infected mice with the mouse hepatitis coronavirus 3 (MHV-3) decreased lung neutrophil infiltration, viral load, and tissue damage. CONCLUSION AND IMPLICATIONS: These findings suggest that interplay of NEU1-MMP-9 induces neutrophil overactivation. In vivo, NEU may serve as a host-directed target to dampen neutrophil dysfunction during severe infections.

De Novo Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization.

Artificial intelligence and multiobjective optimization represent promising solutions to bridge chemical and biological landscapes by addressing the automated de novo design of compounds as a result of a humanlike creative process. In the present study, we conceived a novel pair-based multiobjective approach implemented in an adapted SMILES generative algorithm based on recurrent neural networks for the automated de novo design of new molecules whose overall features are optimized by finding the best trade-offs among relevant physicochemical properties (MW, logP, HBA, HBD) and additional similarity-based constraints biasing specific biological targets. In this respect, we carried out the de novo design of chemical libraries targeting neuraminidase, acetylcholinesterase, and the main protease of severe acute respiratory syndrome coronavirus 2. Several quality metrics were employed to assess drug-likeness, chemical feasibility, diversity content, and validity. Molecular docking was finally carried out to better evaluate the scoring and posing of the de novo generated molecules with respect to X-ray cognate ligands of the corresponding molecular counterparts. Our results indicate that artificial intelligence and multiobjective optimization allow us to capture the latent links joining chemical and biological aspects, thus providing easy-to-use options for customizable design strategies, which are especially effective for both lead generation and lead optimization. The algorithm is freely downloadable at https://github.com/alberdom88/moo-denovo and all of the data are available as Supporting Information.