Abstract
To identify gene expression responses common to multiple pulmonary diseases we collected microarray data for acute lung inflammation models from 12 studies and used these in a meta-analysis. The data used include exposures to air pollutants; bacterial, viral, and parasitic infections; and allergic asthma models. Hierarchical clustering revealed a cluster of 383 up-regulated genes with a common response. This cluster contained five subsets, each characterized by more specific functions such as inflammatory response, interferon-induced genes, immune signaling, or cell proliferation. Of these subsets, the inflammatory response was common to all models, interferon-induced responses were more pronounced in bacterial and viral models, and a cell division response was more prominent in parasitic and allergic models. A common cluster containing 157 moderately down-regulated genes was associated with the effects of tissue damage. Responses to influenza in macaques were weaker than in mice, reflecting differences in the degree of lung inflammation and/or virus replication. The existence of a common cluster shows that in vivo lung inflammation in response to various pathogens or exposures proceeds through shared molecular mechanisms.
Highlights
Microarray gene expression profiling has become a common method for gaining insight into molecular disease mechanisms that are involved in host-pathogen interaction, and the outcome of the infection process, in terms of development of disease
The increasing public availability of microarray data allows for combining data in a meta-analysis, to identify common clusters of genes induced upon infection
It has been shown that under controlled in vitro conditions macrophages respond to a broad range of bacteria with a shared gene expression pattern [1] and similar findings have been described for dendritic cells [2] and peripheral blood mononuclear cells [3]
Summary
Microarray gene expression profiling has become a common method for gaining insight into molecular disease mechanisms that are involved in host-pathogen interaction, and the outcome of the infection process, in terms of development of disease. The meta-analysis of in vitro data by Jenner and Young [4] revealed a common infection cluster across a larger number of pathogens and studies, that included several genes for which this role was not recognized in the underlying studies Whether these findings in in vitro infection models are representative for what happens in vivo is unknown. Our laboratory recently published data on in vivo lung infection responses to respiratory syncytial virus (RSV) [5] and Bordetella pertussis [6,7] Comparing these data sets showed several similarities as well as differences in the genes involved, the kinetics of the responses was completely different [5,6,7]. Our aim was to employ a meta-analysis to identify gene expression changes in in vivo lung inflammatory models that are common to all, or subsets of, inducers of lung inflammatory lesions and pathogens
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