Drug susceptibility of influenza A/H3N2 strains co-circulating during 2009 influenza pandemic: first report from Mumbai

Genotypic and phenotypic susceptibility of emerging avian influenza A viruses to neuraminidase and cap-dependent endonuclease inhibitors

Avian influenza outbreaks, including ones caused by highly pathogenic A(H5N1) clade 2.3.4.4b viruses, have devastated animal populations and remain a threat to humans. Risk elements assessed for emerging influenza viruses include their susceptibility to approved antivirals. Here, we screened >20,000 neuraminidase (NA) or polymerase acidic (PA) protein sequences of potentially pandemic A(H5Nx), A(H7Nx), and A(H9N2) viruses that circulated globally in 2010-2023. The frequencies of NA or PA substitutions associated with reduced inhibition (RI) or highly reduced inhibition (HRI) by NA inhibitors (NAIs) (oseltamivir, zanamivir) or a cap-dependent endonuclease inhibitor (baloxavir) were low: 0.60% (137/22,713) and 0.62% (126/20,347), respectively. All tested subtypes were susceptible to NAIs and baloxavir at sub-nanomolar concentrations. A(H9N2) viruses were the most susceptible to oseltamivir, with IC50s 3- to 4-fold lower than for other subtypes (median IC50: 0.18 nM; n = 22). NA-I222M conferred RI of A(H5N1) viruses by oseltamivir (with a 26-fold IC50 increase), but NA-S246N did not reduce inhibition. PA-E23G, PA-K34R, PA-I38M/T, and the previously unreported PA-A36T caused RI by baloxavir in all subtypes tested. Avian A(H9N2) viruses endemic in Egyptian poultry predominantly acquired PA-I38V, which causes only a <3-fold decrease in the baloxavir EC50 and fails to meet the RI criteria. PA-E199A/D in A(H7Nx) and A(H9N2) viruses caused a 2- to 4-fold decrease in EC50 (close to the borderline for RI) and should be closely monitored. Our data indicate antiviral susceptibility is high among avian influenza A viruses with pandemic potential and present novel markers of resistance to existing antiviral interventions.

Network spectra for drug-target identification in complex diseases: new guns against old foes

The fundamental understanding of altered complex molecular interactions in a diseased condition is the key to its cure. The overall functioning of these molecules is kind of jugglers play in the cell orchestra and to anticipate these relationships among the molecules is one of the greatest challenges in modern biology and medicine. Network science turned out to be providing a successful and simple platform to understand complex interactions among healthy and diseased tissues. Furthermore, much information about the structure and dynamics of a network is concealed in the eigenvalues of its adjacency matrix. In this review, we illustrate rapid advancements in the field of network science in combination with spectral graph theory that enables us to uncover the complexities of various diseases. Interpretations laid by network science approach have solicited insights into molecular relationships and have reported novel drug targets and biomarkers in various complex diseases.