Published in

American Society for Microbiology, Journal of Virology, 19(78), p. 10420-10432, 2004

DOI: 10.1128/jvi.78.19.10420-10432.2004

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Integration of Clinical Data, Pathology, and cDNA Microarrays in Influenza Virus-Infected Pigtailed Macaques (Macaca nemestrina)†

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

ABSTRACT For most severe viral pandemics such as influenza and AIDS, the exact contribution of individual viral genes to pathogenicity is still largely unknown. A necessary step toward that understanding is a systematic comparison of different influenza virus strains at the level of transcriptional regulation in the host as a whole and interpretation of these complex genetic changes in the context of multifactorial clinical outcomes and pathology. We conducted a study by infecting pigtailed macaques ( Macaca nemestrina ) with a genetically reconstructed strain of human influenza H1N1 A/Texas/36/91 virus and hypothesized not only that these animals would respond to the virus similarly to humans, but that gene expression patterns in the lungs and tracheobronchial lymph nodes would fit into a coherent and complete picture of the host-virus interactions during infection. The disease observed in infected macaques simulated uncomplicated influenza in humans. Clinical signs and an antibody response appeared with induction of interferon and B-cell activation pathways, respectively. Transcriptional activation of inflammatory cells and apoptotic pathways coincided with gross and histopathological signs of inflammation, with tissue damage and concurrent signs of repair. Additionally, cDNA microarrays offered new evidence of the importance of cytotoxic T cells and natural killer cells throughout infection. With this experiment, we confirmed the suitability of the nonhuman primate model in the quest for understanding the individual and joint contributions of viral genes to influenza virus pathogenesis by using cDNA microarray technology and a reverse genetics approach.