Pandemic Avian Influenza and Intra/Interhaemagglutinin Subtype Electrostatic Variation among Viruses Isolated from Avian, Mammalian, and Human Hosts

Evolution of highly pathogenic H5N1 influenza A virus in the central nervous system of ferrets

Central nervous system (CNS) disease is the most common extra-respiratory tract complication of influenza A virus infections in humans. Remarkably, zoonotic highly pathogenic avian influenza (HPAI) H5N1 virus infections are more often associated with CNS disease than infections with seasonal influenza viruses. Evolution of avian influenza viruses has been extensively studied in the context of respiratory infections, but evolutionary processes in CNS infections remain poorly understood. We have previously observed that the ability of HPAI A/Indonesia/5/2005 (H5N1) virus to replicate in and spread throughout the CNS varies widely between individual ferrets. Based on these observations, we sought to understand the impact of entrance into and replication within the CNS on the evolutionary dynamics of virus populations. First, we identified and characterized three substitutions-PB1 E177G and A652T and NP I119M - detected in the CNS of a ferret infected with influenza A/Indonesia/5/2005 (H5N1) virus that developed a severe meningo-encephalitis. We found that some of these substitutions, individually or collectively, resulted in increased polymerase activity in vitro. Nevertheless, in vivo, the virus bearing the CNS-associated mutations retained its capacity to infect the CNS but showed reduced dispersion to other anatomical sites. Analyses of viral diversity in the nasal turbinate and olfactory bulb revealed the lack of a genetic bottleneck acting on virus populations accessing the CNS via this route. Furthermore, virus populations bearing the CNS-associated mutations showed signs of positive selection in the brainstem. These features of dispersion to the CNS are consistent with the action of selective processes, underlining the potential for H5N1 viruses to adapt to the CNS.

Comparative Surface Electrostatics and Normal Mode Analysis of High and Low Pathogenic H7N7 Avian Influenza Viruses

Influenza A viruses are rarely symptomatic in wild birds, while representing a higher threat to poultry and mammals, where they can cause a variety of symptoms, including death. H5 and H7 subtypes of influenza viruses are of particular interest because of their pathogenic potential and reported capacity to spread from poultry to mammals, including humans. The identification of molecular fingerprints for pathogenicity can help surveillance and early warning systems, which are crucial to prevention and protection from such potentially pandemic agents. In the past decade, comparative analysis of the surface features of hemagglutinin, the main protein antigen in influenza viruses, identified electrostatic fingerprints in the evolution and spreading of H5 and H9 subtypes. Electrostatic variation among viruses from avian or mammalian hosts was also associated with host jump. Recent findings of fingerprints associated with low and highly pathogenic H5N1 viruses, obtained by means of comparative electrostatics and normal modes analysis, prompted us to check whether such fingerprints can also be found in the H7 subtype. Indeed, evidence presented in this work showed that also in H7N7, hemagglutinin proteins from low and highly pathogenic strains present differences in surface electrostatics, while no meaningful variation was found in normal modes.

Normal modes analysis and surface electrostatics of haemagglutinin proteins as fingerprints for high pathogenic type A influenza viruses

Background

Type A influenza viruses circulate and spread among wild birds and mostly consist of low pathogenic strains. However, fast genome variation timely results in the insurgence of high pathogenic strains, which when infecting poultry birds may cause a million deaths and strong commercial damage. More importantly, the host shift may concern these viruses and sustained human-to-human transmission may result in a dangerous pandemic outbreak. Therefore, fingerprints specific to either low or high pathogenic strains may represent a very important tool for global surveillance.

Results

We combined Normal Modes Analysis and surface electrostatic analysis of a mixed strain dataset of influenza A virus haemagglutinins from high and low pathogenic strains in order to infer specific fingerprints. Normal Modes Analysis sorted the strains in two different, homogeneous clusters; sorting was independent of clades and specific instead to high vs low pathogenicity. A deeper analysis of fluctuations and flexibility regions unveiled a special role for the 110-helix region. Specific sorting was confirmed by surface electrostatics analysis, which further allowed to focus on regions and mechanisms possibly crucial to the low-to-high transition.

Conclusions

Evidence from previous work demonstrated that changes in surface electrostatics are associated with the evolution and spreading of avian influenza A virus clades, and seemingly involved also in the avian to mammalian host shift. This work shows that a combination of electrostatics and Normal Modes Analysis can also identify fingerprints specific to high and low pathogenicity. The possibility to predict which specific mutations may result in a shift to high pathogenicity may help in surveillance and vaccine development.

Protein electrostatics: From computational and structural analysis to discovery of functional fingerprints and biotechnological design

Computationally driven engineering of proteins aims to allow them to withstand an extended range of conditions and to mediate modified or novel functions. Therefore, it is crucial to the biotechnological industry, to biomedicine and to afford new challenges in environmental sciences, such as biocatalysis for green chemistry and bioremediation. In order to achieve these goals, it is important to clarify molecular mechanisms underlying proteins stability and modulating their interactions. So far, much attention has been given to hydrophobic and polar packing interactions and stability of the protein core. In contrast, the role of electrostatics and, in particular, of surface interactions has received less attention. However, electrostatics plays a pivotal role along the whole life cycle of a protein, since early folding steps to maturation, and it is involved in the regulation of protein localization and interactions with other cellular or artificial molecules. Short- and long-range electrostatic interactions, together with other forces, provide essential guidance cues in molecular and macromolecular assembly. We report here on methods for computing protein electrostatics and for individual or comparative analysis able to sort proteins by electrostatic similarity. Then, we provide examples of electrostatic analysis and fingerprints in natural protein evolution and in biotechnological design, in fields as diverse as biocatalysis, antibody and nanobody engineering, drug design and delivery, molecular virology, nanotechnology and regenerative medicine.