Astrovirus replication is dependent on induction of double-membrane vesicles through a PI3K-dependent, LC3-independent pathway

Antigenic determinants of HAstV-VA1 neutralization and their relevance in the human immune response

Astroviruses are highly divergent and infect a wide variety of animal hosts. In 2009, a genetically divergent human astrovirus (HAstV) strain VA1 was first identified in an outbreak of acute gastroenteritis. This strain has also been associated with fatal central nervous system disease. In this work, we report the isolation of three high-affinity neutralizing monoclonal antibodies (Nt-MAbs) targeting the capsid spike domain of HAstV-VA1. These antibodies (7C8, 2A2, 3D8) were used to select individual HAstV-VA1 mutants resistant to their neutralizing activity and also select a HAstV-VA1 triple mutant that escapes neutralization from all three Nt-MAbs. Sequencing of the virus genome capsid region revealed escape mutations that map to the surface of the capsid spike domain, define three potentially independent neutralization epitopes, and help delineate four antigenic sites in rotaviruses. Notably, two of the escape mutations were found to be present in the spike sequence of the HAstV-VA1-PS strain isolated from an immunodeficient patient with encephalitis, suggesting that those mutations arose as a result of the immune pressure generated by the patient's immunotherapy. In accordance with this observation, human serum samples exhibiting strong neutralization activity against wild-type HAstV-VA1 had a 2.6-fold reduction in neutralization titer when evaluated against the triple-escape HAstV-VA1 mutant, indicating shared neutralization epitopes between the mouse and human antibody response. The isolated Nt-MAbs reported in this work will help characterize the functional sites of the virus during cell entry and have the potential for developing a specific antibody therapy for the neurological disease associated with HAstV-VA1.

Importance

Human astroviruses (HAstVs) have been historically associated with acute gastroenteritis. However, the genetically divergent HAstV-VA1 strain has been associated with central nervous system disease. This work isolated high-affinity neutralizing monoclonal antibodies directed to HAstV-VA1. The proposed binding sites for these antibodies, together with previously reported sites for neutralizing antibodies against classical HAstVs, suggest the existence of at least four neutralization sites on the capsid spike of astroviruses. Our data show that natural infection with human astrovirus VA1 elicits a robust humoral immune response that targets the same antigenic sites recognized by the mouse monoclonal antibodies and strongly suggests the emergence of a variant HAstV-VA1 virus in an immunodeficient patient with prolonged astrovirus infection. The isolated Nt-MAb reported in this work will be helpful in defining the functional sites of the virus involved in cell entry and hold promise for developing a specific antibody therapy for the neurological disease associated with HAstV-VA1.

Leveraging machine learning for taxonomic classification of emerging astroviruses

Astroviruses are a family of genetically diverse viruses associated with disease in humans and birds with significant health effects and economic burdens. Astrovirus taxonomic classification includes two genera, Avastrovirus and Mamastrovirus. However, with next-generation sequencing, broader interspecies transmission has been observed necessitating a reexamination of the current host-based taxonomic classification approach. In this study, a novel taxonomic classification method is presented for emergent and as yet unclassified astroviruses, based on whole genome sequence k-mer composition in addition to host information. An optional component responsible for identifying recombinant sequences was added to the method's pipeline, to counteract the impact of genetic recombination on viral classification. The proposed three-pronged classification method consists of a supervised machine learning method, an unsupervised machine learning method, and the consideration of host species. Using this three-pronged approach, we propose genus labels for 191 as yet unclassified astrovirus genomes. Genus labels are also suggested for an additional eight as yet unclassified astrovirus genomes for which incompatibility was observed with the host species, suggesting cross-species infection. Lastly, our machine learning-based approach augmented by a principal component analysis (PCA) analysis provides evidence supporting the hypothesis of the existence of human astrovirus (HAstV) subgenus of the genus Mamastrovirus, and a goose astrovirus (GoAstV) subgenus of the genus Avastrovirus. Overall, this multipronged machine learning approach provides a fast, reliable, and scalable prediction method of taxonomic labels, able to keep pace with emerging viruses and the exponential increase in the output of modern genome sequencing technologies.