(468b) In Silico Modeling of the Immune Response Network: Influenza Immune-Escape Strategies and Implications in Influenza Drug Research

                Recent evidence suggests that an overly
ambitious immune response may be the primary factor that determines influenza
severity [1], yet the definition of what is a reasonable immune response remains vague.  Seasonal influenza, alone, is responsible for approximately 30,000 deaths in the US each year.  With the recent swine origin influenza virus (SOIV) pandemic and the fear that highly pathogenic avian influenza strains (H5N1) could attain human-human transmissibility, there is a growing interest in identifying host-viral interactions. A network understanding of the viral-host interaction will greatly facilitate the identification of immune escape strategies employed by influenza viruses. Furthermore, these in silico models may be employed to identify drug targets which may selectively alter viral replication capacity or mediate collateral damage from an aberrant immune response [2].  To date, we have created models of viral replication in epithelial cells and inter-cellular communication between lymphocytes against which microarray and protein data can be interpreted. Applying mRNA and protein data to these models, down-regulation of the chemokines responsible for directing lymphocyte chemotaxis has been identified as a potential immune-escape strategy for highly-pathogenic influenzas.

                In silico models of viral replication
and immune signaling were constructed to predict the chemokines released from
infected cells and the impending effect of the chemokines on the recruitment
and chemotaxis of lymphocyte cells, respectively. Currently, the viral
replication consists of approximately 200 proteins and 320 host-viral,
protein-protein/protein-RNA interactions.  Upon infection, the virion fuses
with the cell membrane, resulting in transportation of the viral
ribonucleoprotein (vRNP) complex to the nucleus where viral replication begins.
While several intra-cellular, host defense pathways are circumvented by the
viral proteins, infection still results in the production of chemokines.  These
chemokines act as the input into the immune signaling model and are responsible
for the migration of lymphocyte cells (T/B cells, natural killer (NK) cells) as
well as directing lymphocytes to the site of infection. Chemokines emanating
from infected epithelial cells attract NK cells and macrophages. As macrophages
approach the site of infection, they release additional chemokines which
attract T and B cells. In an ideal immune-response situation, this
communication results in the recognition and destruction (via apoptosis) of
viral-infected cells. The viral replication model has been constructed using
CellDesigner, and equations are being developed to better explain to complex
behaviors observed in immune-related, intra-cellular signaling. The immune
signaling model currently consists of 8 states in which transport to the site
of infection is modeled as different states.

                To explore differences in the immune
response to particular influenza strains, mRNA expression and protein levels
were measured in bronchial epithelial swabs and sera, respectively, for monkeys
exposed to a seasonal influenza, the SOIV, and an avian H5N1 strain. Data was
collected from 12 monkeys at days 1, 3, 5, and 7 post infection (p.i.).
Clustering of the microarray data, interpreted using GO analysis, showed that
the behavior of key biological process, such as cell cycle and RNA production,
hijacked by the viruses during infection, is highly conserved between the
strains. Furthermore, immune related genes are up regulated day 1 p.i. for all
three strains.  Protein data in the blood also showed little difference between
the infection types with one notable exception. Key interleukins known to
increase immunoglobin secretion could not be detected in SOIV samples. This may
explain the why SOIV can achieve higher viral loads that seasonal influenza in
lung tissue.

                Microarray data reveals a possible
immune-escape mechanism for highly pathogenic influenza infections that's
physiological effects can be correlated with the immune signaling model. At
days 5 and 7 p.i., the H5N1 infected data shows a sudden down-regulation of many
of the immune signaling genes. Interpreted through the immune signaling model,
the effect of this observed regulation would be the continued induction of
lymphocyte recruitment but a strong inhibition of the coordinated chemotaxis
required to bring the lymphocytes to the site of infection. As viral
replication continues, large populations of lymphocytes can accumulate, leading
to apoptosis and inflammation in the tissue without abating the infection. This
prediction from the model is consistent with several experimental studies [3] and histopathological studies are underway to provide direct observational
evidence.

                The models of the intra and inter-cellular
immune response provide several advantages to drug research and development. As
more viral-host interactions become known, it is hoped that pathway redundancy
may allow for the identification of host pathways which are critical for viral
replication yet temporarily dispensable for the host. Furthermore, drug regimen
strategies which mediate the immune-response instead of the viral-replication
mechanism can be explored. Should similar immune-escape strategies be observed
in other highly pathogenic strains, these drug regimens may proof to be more
robust than many modern treatments as the virus itself is not being targeted.

                In conclusion, in silico models of
the immune response provide an invaluable platform for elucidating viral
infection strategies and for drug target exploration/strategy development.
Currently, several projects in our lab focus on many aspects of modeling the
immune response. Data-driven and literature-driven methods of protein-protein
interaction network elucidation along with protein pull-down essays are being
used to explore for additional host-viral interactions. Furthermore, better
modeling platforms are being designed that can account for both biochemical
modeling and mechanical motion (endocytosis and chemotaxis). As predictive
capacity increases, these models will provide valuable insights into the
workings of the immune response and the immune-escape strategies of other
viral/bacterial infections.                  

1.            Peiris JS, Cheung CY, Leung
CY, Nicholls JM: Innate immune responses to influenza A H5N1: friend or foe?
Trends Immunol 2009, 30(12):574-584.

2.            Kobasa D, Jones SM, Shinya
K, Kash JC, Copps J, Ebihara H, Hatta Y, Kim JH, Halfmann P, Hatta M et al:
Aberrant innate immune response in lethal infection of macaques with the
1918 influenza virus. Nature 2007, 445(7125):319-323.

3.            Rowe T, Leon AJ, Crevar CJ,
Carter DM, Xu L, Ran L, Fang Y, Cameron CM, Cameron MJ, Banner D et al: Modeling
host responses in ferrets during A/California/07/2009 influenza infection. Virology
2010.