HA N193D substitution in the HPAI H5N1 virus alters receptor binding affinity and enhances virulence in mammalian hosts

During the 2021/2022 winter season, we isolated highly pathogenic avian influenza (HPAI) H5N1 viruses harbouring an amino acid substitution from Asparagine(N) to Aspartic acid (D) at residue 193 of the hemagglutinin (HA) receptor binding domain (RBD) from migratory birds in South Korea. Herein, we investigated the characteristics of the N193D HA-RBD substitution in the A/CommonTeal/Korea/W811/2021[CT/W811] virus by using recombinant viruses engineered via reverse genetics (RG). A receptor affinity assay revealed that the N193D HA-RBD substitution in CT/W811 increases α2,6 sialic acid receptor binding affinity. The rCT/W811-HA193N virus caused rapid lethality with high virus titres in chickens compared with the rCT/W811-HA193D virus, while the rCT/W811-HA193D virus exhibited enhanced virulence in mammalian hosts with multiple tissue tropism. Surprisingly, a ferret-to-ferret transmission assay revealed that rCT/W811-HA193D virus replicates well in the respiratory tract, at a rate about 10 times higher than that of rCT/W811-HA193N, and all rCT/W811-HA193D direct contact ferrets were seroconverted at 10 days post-contact. Further, competition transmission assay of the two viruses revealed that rCT/W811-HA193D has enhanced growth kinetics compared with the rCT/W811-HA193N, eventually becoming the dominant strain in nasal turbinates. Further, rCT/W811-HA193D exhibits high infectivity in primary human bronchial epithelial (HBE) cells, suggesting the potential for human infection. Taken together, the HA-193D containing HPAI H5N1 virus from migratory birds showed enhanced virulence in mammalian hosts, but not in avian hosts, with multi-organ replication and ferret-to-ferret transmission. Thus, this suggests that HA-193D change increases the probability of HPAI H5N1 infection and transmission in humans.

Topological data analysis to model the shape of immune responses during co-infections

Co-infections by multiple pathogens have important implications in many aspects of health, epidemiology and evolution. However, how to disentangle the non-linear dynamics of the immune response when two infections take place at the same time is largely unexplored. Using data sets of the immune response during influenza-pneumococcal co-infection in mice, we employ here topological data analysis to simplify and visualise high dimensional data sets. We identified persistent shapes of the simplicial complexes of the data in the three infection scenarios: single viral infection, single bacterial infection, and co-infection. The immune response was found to be distinct for each of the infection scenarios and we uncovered that the immune response during the co-infection has three phases and two transition points. During the first phase, its dynamics is inherited from its response to the primary (viral) infection. The immune response has an early shift (few hours post co-infection) and then modulates its response to react against the secondary (bacterial) infection. Between 18 and 26 h post co-infection the nature of the immune response changes again and does no longer resembles either of the single infection scenarios.

Meta-analysis of cell- specific transcriptomic data using fuzzy c-means clustering discovers versatile viral responsive genes

BACKGROUND

Despite advances in the gene-set enrichment analysis methods; inadequate definitions of gene-sets cause a major limitation in the discovery of novel biological processes from the transcriptomic datasets. Typically, gene-sets are obtained from publicly available pathway databases, which contain generalized definitions frequently derived by manual curation. Recently unsupervised clustering algorithms have been proposed to identify gene-sets from transcriptomics datasets deposited in public domain. These data-driven definitions of the gene-sets can be context-specific revealing novel biological mechanisms. However, the previously proposed algorithms for identification of data-driven gene-sets are based on hard clustering which do not allow overlap across clusters, a characteristic that is predominantly observed across biological pathways.

RESULTS

We developed a pipeline using fuzzy-C-means (FCM) soft clustering approach to identify gene-sets which recapitulates topological characteristics of biological pathways. Specifically, we apply our pipeline to derive gene-sets from transcriptomic data measuring response of monocyte derived dendritic cells and A549 epithelial cells to influenza infections. Our approach apply Ward's method for the selection of initial conditions, optimize parameters of FCM algorithm for human cell-specific transcriptomic data and identify robust gene-sets along with versatile viral responsive genes.

CONCLUSION

We validate our gene-sets and demonstrate that by identifying genes associated with multiple gene-sets, FCM clustering algorithm significantly improves interpretation of transcriptomic data facilitating investigation of novel biological processes by leveraging on transcriptomic data available in the public domain. We develop an interactive 'Fuzzy Inference of Gene-sets (FIGS)' package (GitHub: https://github.com/Thakar-Lab/FIGS ) to facilitate use of of pipeline. Future extension of FIGS across different immune cell-types will improve mechanistic investigation followed by high-throughput omics studies.