4.7 Article

Kinetics of Coinfection with Influenza A Virus and Streptococcus pneumoniae

Journal

PLOS PATHOGENS
Volume 9, Issue 3, Pages -

Publisher

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.ppat.1003238

Keywords

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Funding

  1. U.S. Department of Energy [DE-AC52-06NA25396]
  2. NIH [HHSN272201000055C, AI028433, AI100946, P20-GM103452]
  3. National Center for Research Resources
  4. Office of Research Infrastructure Programs (ORIP) [OD011095]
  5. Los Alamos National Laboratory LDRD Program
  6. National Science Foundation [DMS-0354259]
  7. 21st Century Science Initiative Grant from the James S. McDonnell Foundation
  8. FCT Portugal [PCOFUND-GA-2009-246542]
  9. Direct For Mathematical & Physical Scien
  10. Division Of Mathematical Sciences [1148230] Funding Source: National Science Foundation

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Secondary bacterial infections are a leading cause of illness and death during epidemic and pandemic influenza. Experimental studies suggest a lethal synergism between influenza and certain bacteria, particularly Streptococcus pneumoniae, but the precise processes involved are unclear. To address the mechanisms and determine the influences of pathogen dose and strain on disease, we infected groups of mice with either the H1N1 subtype influenza A virus A/Puerto Rico/8/34 (PR8) or a version expressing the 1918 PB1-F2 protein (PR8-PB1-F2(1918)), followed seven days later with one of two S. pneumoniae strains, type 2 D39 or type 3 A66.1. We determined that, following bacterial infection, viral titers initially rebound and then decline slowly. Bacterial titers rapidly rise to high levels and remain elevated. We used a kinetic model to explore the coupled interactions and study the dominant controlling mechanisms. We hypothesize that viral titers rebound in the presence of bacteria due to enhanced viral release from infected cells, and that bacterial titers increase due to alveolar macrophage impairment. Dynamics are affected by initial bacterial dose but not by the expression of the influenza 1918 PB1-F2 protein. Our model provides a framework to investigate pathogen interaction during coinfections and to uncover dynamical differences based on inoculum size and strain.

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