Influenza passaging annotations: what they tell us and why we should listen

Seasonal antigenic prediction of influenza A H3N2 using machine learning

Antigenic characterization of circulating influenza A virus (IAV) isolates is routinely assessed by using the hemagglutination inhibition (HI) assays for surveillance purposes. It is also used to determine the need for annual influenza vaccine updates as well as for pandemic preparedness. Performing antigenic characterization of IAV on a global scale is confronted with high costs, animal availability, and other practical challenges. Here we present a machine learning model that accurately predicts (normalized) outputs of HI assays involving circulating human IAV H3N2 viruses, using their hemagglutinin subunit 1 (HA1) sequences and associated metadata. Each season, the model learns an updated nonlinear mapping of genetic to antigenic changes using data from past seasons only. The model accurately distinguishes antigenic variants from non-variants and adaptively characterizes seasonal dynamics of HA1 sites having the strongest influence on antigenic change. Antigenic predictions produced by the model can aid influenza surveillance, public health management, and vaccine strain selection activities.

[Mathematical model for assessing the level of cross-immunity between strains of influenza virus subtype H3N2]

INTRODUCTION

The WHO regularly updates influenza vaccine recommendations to maximize their match with circulating strains. Nevertheless, the effectiveness of the influenza A vaccine, specifically its H3N2 component, has been low for several seasons. The aim of the study is to develop a mathematical model of cross-immunity based on the array of published WHO hemagglutination inhibition assay (HAI) data.

MATERIALS AND METHODS

In this study, a mathematical model was proposed, based on finding, using regression analysis, the dependence of HAI titers on substitutions in antigenic sites of sequences. The computer program we developed can process data (GISAID, NCBI, etc.) and create real-time databases according to the set tasks.

RESULTS

Based on our research, an additional antigenic site F was identified. The difference in 1.6 times the adjusted R2, on subsets of viruses grown in cell culture and grown in chicken embryos, demonstrates the validity of our decision to divide the original data array by passage histories. We have introduced the concept of a degree of homology between two arbitrary strains, which takes the value of a function depending on the Hamming distance, and it has been shown that the regression results significantly depend on the choice of function. The provided analysis showed that the most significant antigenic sites are A, B, and E. The obtained results on predicted HAI titers showed a good enough result, comparable to similar work by our colleagues.

CONCLUSION

The proposed method could serve as a useful tool for future forecasts, with further study to confirm its sustainability.

Predicting Egg Passage Adaptations to Design Better Vaccines for the H3N2 Influenza Virus

Seasonal H3N2 influenza evolves rapidly, leading to an extremely poor vaccine efficacy. Substitutions employed during vaccine production using embryonated eggs (i.e., egg passage adaptation) contribute to the poor vaccine efficacy (VE), but the evolutionary mechanism remains elusive. Using an unprecedented number of hemagglutinin sequences (n = 89,853), we found that the fitness landscape of passage adaptation is dominated by pervasive epistasis between two leading residues (186 and 194) and multiple other positions. Convergent evolutionary paths driven by strong epistasis explain most of the variation in VE, which has resulted in extremely poor vaccines for the past decade. Leveraging the unique fitness landscape, we developed a novel machine learning model that can predict egg passage substitutions for any candidate vaccine strain before the passage experiment, providing a unique opportunity for the selection of optimal vaccine viruses. Our study presents one of the most comprehensive characterizations of the fitness landscape of a virus and demonstrates that evolutionary trajectories can be harnessed for improved influenza vaccines.

Sequence dynamics of three influenza A virus strains grown in different MDCK cell lines, including those expressing different sialic acid receptors

Viruses are often cultured in cell lines for research and vaccine development, and those often differ from the natural hosts or tissues. Cell lines can also differ in the presence of virus receptors, such as the sialic acid (Sia) receptors used by influenza A viruses (IAV), which can vary in linkage (α2,3- or α2,6-linkage) and form (N-glycolylneuraminic acid [Neu5Gc] or N-acetylneuraminic acid [Neu5Ac]). The selective pressures resulting from passaging viruses in cell types with host-specific variations in viral receptors are still only partially understood. IAV are commonly cultured in MDCK cells which are both derived from canine kidney tubule epithelium and inherently heterogeneous. MDCK cells naturally present Neu5Ac and α2,3-linked Sia forms. Here, we examine natural MDCK variant lineages, as well as engineered variants that synthesize Neu5Gc and/or α2,6-linkages. We determined how viral genetic variation occurred within human H3N2, H1N1 pandemic and canine H3N2 IAV populations when serially passaged in MDCK cell lines that vary in cell type (MDCK-Type I or MDCK-Type II clones) and in Sia display. Deep sequencing of viral genomes showed small numbers of consensus-level mutations, mostly within the hemagglutinin (HA) gene. Both human IAV showed variants in the HA stem and the HA receptor-binding site of populations passaged in cells displaying Neu5Gc. Canine H3N2 showed variants near the receptor-binding site when passaged in cells displaying Neu5Gc or α2,6-linkages. Viruses replicated to low titres in MDCK-Type II cells, suggesting that not all cell types in heterogeneous MDCK cell populations are equally permissive to infection.

Drivers of recombinant soluble influenza A virus hemagglutinin and neuraminidase expression in mammalian cells

Recombinant soluble trimeric influenza A virus hemagglutinins (HA) and tetrameric neuraminidases (NAs) have proven to be excellent tools to decipher biological properties. Receptor binding and sialic acid cleavage by recombinant proteins correlate satisfactorily compared to whole viruses. Expression of HA and NA can be achieved in a plethora of different laboratory hosts. For immunological and receptor interaction studies however, insect and mammalian cell expressed proteins are preferred due to the presence of N-linked glycosylation and disulfide bond formation. Because mammalian-cell expression is widely applied, an increased expression yield is an important goal. Here we report that using codon-optimized genes and sfGFP fusions, the expression yield of HA can be significantly improved. sfGFP also significantly increased expression yields when fused to the N-terminus of NA. In this study, a suite of different hemagglutinin and neuraminidase constructs are described, which can be valuable tools to study a wide array of different HAs, NAs and their mutants.

Human Organs-on-Chips for Virology

While conventional in vitro culture systems and animal models have been used to study the pathogenesis of viral infections and to facilitate development of vaccines and therapeutics for viral diseases, models that can accurately recapitulate human responses to infection are still lacking. Human organ-on-a-chip (Organ Chip) microfluidic culture devices that recapitulate tissue–tissue interfaces, fluid flows, mechanical cues, and organ-level physiology have been developed to narrow the gap between in vitro experimental models and human pathophysiology. Here, we describe how recent developments in Organ Chips have enabled re-creation of complex pathophysiological features of human viral infections in vitro.

Microfluidic Organ Chip culture devices are emerging alternatives to conventional in vitro and animal models due to their ability to replicate many structural and functional features of human physiology and disease states.

Recent innovations demonstrate that Organ Chip technology is a promising strategy for virology studies where there have been successes in reproducing various viral disease phenotypes.

Organ Chips have enabled investigation of many aspects of viral infection, including virus–host interactions, viral therapy-resistance evolution, and development of new antiviral therapeutics, as well as underlying pathogenesis.

As Organ Chip-based assays provide accessibility to study virus-induced diseases in real time and at high resolution, they can open new avenues to uncover viral pathogenesis in a human-relevant environment and may eventually enable development of novel therapeutics and vaccines.

Influenza A Hemagglutinin Passage Bias Sites and Host Specificity Mutations

Animal studies aimed at understanding influenza virus mutations that change host specificity to adapt to replication in mammalian hosts are necessarily limited in sample numbers due to high cost and safety requirements. As a safe, higher-throughput alternative, we explore the possibility of using readily available passage bias data obtained mostly from seasonal H1 and H3 influenza strains that were differentially grown in mammalian (MDCK) and avian cells (eggs). Using a statistical approach over 80,000 influenza hemagglutinin sequences with passage information, we found that passage bias sites are most commonly found in three regions: (i) the globular head domain around the receptor binding site, (ii) the region that undergoes pH-dependent structural changes and (iii) the unstructured N-terminal region harbouring the signal peptide. Passage bias sites were consistent among different passage cell types as well as between influenza A subtypes. We also find epistatic interactions of site pairs supporting the notion of host-specific dependency of mutations on virus genomic background. The sites identified from our large-scale sequence analysis substantially overlap with known host adaptation sites in the WHO H5N1 genetic changes inventory suggesting information from passage bias can provide candidate sites for host specificity changes to aid in risk assessment for emerging strains.