Elucidating the dynamics of host-virus interaction. 


Sagi Shapira, Raul Rabadan, Barry Honig


In Progress


Influenza pandemics are the result of the emergence of a virus from a non-human host and rapid spread in the human population. It has been estimated that 50 million people died in 1918 as a result of the influenza pandemic. Since 1918, influenza pandemics have occurred every 30 years, the last one in 2009. Another influenza pandemic threat is inevitable and much effort is being placed on disease surveillance and monitoring of transmission across species, but still little is known about what are the molecular factors that allow interspecies transmission and spread [1-3]. 

As obligate intracellular pathogens, viruses depend on interactions with host cell molecules for their replication. Thus, there is immense pressure on viruses to maintain proper interactions with the cells they infect. With viruses that can jump across species (zoonotic viruses) this pressure is even more pronounced, because there is a requirement to interact with those cellular factors that are conserved across taxa, or to have the physiological flexibility to interact with non-conserved factors that allow, nonetheless, for replication and host-to-host transmission. Identifying host and viral factors that contribute to jump species could provide a rationale to assess the epidemic risk of zoonotic viruses and to elucidate key host factors that are necessary for viral replication and transmission.

In this DBP, we propose to take advantage to the computational framework and infrastructure generated by the MAGNet center to predict and experimentally validate changes in the physical network that arise during influenza evolution. In short, we hypothesize that by uncovering the dynamics of the influenza-host protein-protein interactions we will be able to identify key elements of this network that control inter-species and intra-species transmission. Specifically, the aims of this DBP are:

Aim 1Apply the PREPPI algorithm [4] to predict protein-protein interactions between influenza virus and human, avian, and swine hosts based on available genomic sequence data. Rather than provide the evolutionary structure of influenza viruses and their pattern of transmission through human and animal populations that is based on genomic sequence alone, we propose to implement PREPPI in different viral strains and hosts to identify key host factors of viral replication and transmission.

Aim 2Elucidate the determinant host factors driving the dynamics of the PPI network. We propose to functionally validate conserved and unique protein-protein interactions using a high-throughput loss and gain of function experimental pipeline. Experimental validation will provide invaluable feedback for optimizing the current algorithms.


In the past we published our findings on the complete topological features of influenza sequences [5]. The tree structure is currently the accepted paradigm to represent evolutionary relationships between organisms, species or other taxa. However, horizontal, or reticulate, genomic exchanges are pervasive in nature and confound characterization of phylogenetic trees. Drawing from algebraic topology, we have developed a unique evolutionary framework that comprehensively captures both clonal and reticulate evolution. Our method effectively characterizes clonal evolution, reassortment, and recombination in RNA viruses. Beyond detecting reticulate evolution, it can succinctly recapitulate the history of complex genetic exchanges involving more than two parental strains, such as the triple reassortment of H7N9 avian influenza and the formation of circulating HIV-1 recombinants. In addition, we identify recurrent, large-scale patterns of reticulate evolution, including frequent PB2-PB1-PA-NP cosegregation during avian influenza reassortment. Finally, we bound the rate of reticulate events (i.e., 20 reassortments per year in avian influenza). Our method provides an evolutionary perspective that not only captures reticulate events precluding phylogeny, but also indicates the evolutionary scales where phylogenetic inference could be accurate.

We also defined major phylogenetic classes of pandemic influenza strains that infected avian, swine, and human hosts. We used this information to identify domains in influenza proteins that are associated with human infection. These domains were then used to identify human interacting partners with the use of the PrePPI algorithm. Our initial results lead to the identification of several hundred human proteins predicted to interact with influenza. The majority of these are novel and their interactions with influenza have not been previously described. 

To functionally interrogate the results of the PrePPI predictions, we chose several for further analysis. Specifically, we used RNAi in primary human bronchial epithelial cells (HBEC) to knock down human proteins predicted to interact with influenza. Ongoing work is focusing on interrogating the precise function of several functionally validated interactions in modulating cellular innate immune responses to influenza. The complementary in silico and experimental tools we are using are enabling the discovery of changes in the influenza-host interactome that are associated with adaptation of influenza viruses to human cells.


  1. Khiabanian H, Trifonov V, Rabadan R. Reassortment patterns in Swine influenza viruses. PLoS One. 2009;4(10):e7366.
  2. Trifonov V, Khiabanian H, Rabadan R. Geographic dependence, surveillance, and origins of the 2009 influenza A (H1N1) virus. N Engl J Med. 2009 Jul 9;361(2):115-9.
  3. Trifonov V, Khiabanian H, Greenbaum B, Rabadan R. The origin of the recent swine influenza A(H1N1) virus infecting humans. Euro Surveill. 2009 Apr 30;14(17). pii: 19193.
  4. Zhang QC, Petrey D, Deng L, Qiang L, Shi Y, Thu CA, Bisikirska B, Lefebvre C, Accili D, Hunter T, Maniatis T, Califano A, Honig B. Structure-based prediction of protein-protein interactions on a genome-wide scale. Nature. 2012 Oct 25;490(7421):556-60. 
  5. Chan JM, Carlsson G, Rabadan R. Topology of viral evolution. Proc Natl Acad Sci U S A. 2013 Nov 12;110(46):18566-71.