Comparative transcriptional profiling of the lung reveals shared and distinct features of Streptococcus pneumoniae and influenza A virus infection

Abstract

Pneumonia is the most common cause of death from infectious disease in the western hemisphere. Pathophysiological and protective processes are initiated by pattern recognition of microbial structures. To provide the molecular framework for a better understanding of processes relevant to host defence in pneumonia, we performed pulmonary transcriptome analysis in mice infected with the major bacterial and viral agents of community-acquired pneumonia, Streptococcus pneumoniae and influenza A virus. We detected differential expression of 1300 genes after infection with either pathogen. Of these, approximately 36% or 30% were specific for pneumococcal or influenza infection, respectively, yielding pathogen-specific as well as shared inflammatory transcriptional signatures. These results not only reveal a differential response on the cytokine and chemokine levels but also emphasize the important role of genes implicated in regulation and fine tuning of inflammation. As one, albeit unexpected, key feature of pneumococcal pneumonia we discovered down-regulation of B-cell responses, probably reflecting a pneumococcal virulence strategy. The pathophysiological consequences of influenza A virus infection were reflected by the emerging protective T-cell response and differential induction of genes involved in tissue regeneration and proliferation. These data provide new insights into pathogenesis of the most common forms of pneumonia, highlighting the value of transcriptional profiling for the elucidation of underlying mechanisms.

Figure 1

Inflammatory influx of leucocytes and granulocytes into the lung, histopathological consequence and lung bacterial burden after infection with S. pneumoniae. Mice transnasally infected with S. pneumoniae were killed 12 hr, 24 hr or 48 hr after infection. Control mice were challenged with 20 μl PBS, and killed after 48 hr. Lung cells were counted using a haemocytometer and leucocytes were further differentiated by FACS analysis of CD45 and Gr-1 expression. Data are presented as mean leucocyte (a) or granulocyte (b) numbers ± SEM, *P < 0·05, **P < 0·01 versus control, n = 5 each. The representative haematoxylin & eosin-stained cryosection of a blood-free mouse lung 48 hr after pneumococcal challenge confirms severe pneumonia (c). For the quantification of bacterial load, mice were killed 12 hr, 24 hr or 48 hr after infection. Blood-free lungs were homogenized, plated on blood agar and incubated at 37° for 16 hr before colony counting. Data are depicted as log colony forming units (CFU) ± SEM, **P < 0·001 versus control (non-infected mice), n = 5 each (d).

Figure 2

Pulmonary transcription signatures of mice infected with S. pneumoniae and influenza A virus. Different time-points of the S. pneumoniae and influenza A virus infections were analysed independently using the two criteria (i) more than two-fold differential expression of the combined colour-swap ratio profiles as ratio experiments and (ii) anti-correlation of the respective colour-swap ratio profiles. The set union of all analysis results comprised 1286 genes which were differentially expressed at least at one time-point during either infection. (a) This set union was used to generate a two-dimensional cluster containing a gene-level cluster and an experiment-level cluster in one graph by applying an agglomerative algorithm with the heuristic criterion ‘average link’ and the similarity measure ‘Manhattan distance’ for both classifications. The hierarchical structure of the time-points and infections is illustrated as a dendrogram while the fold change values are colour encoded with green for down-regulated genes, black for unregulated genes and red for up-regulated genes. (b) The set union of the different time-points of the S. pneumoniae infection comprised 902 genes and the set union of the different time-points of the influenza A virus infection included 820 genes. These two sets were compared and the Venn diagram shows the numbers of transcripts that were differentially expressed in a pathogen-specific or common manner.

Figure 3

Quantitative PCR analysis of assorted gene products. Real-time quantitative PCR analysis of total lung mRNA from infected and mock-infected mice was performed to verify and extend the microarray data. A positive fold change of gene expression on the x-axis reflects increased expression in infected versus control mice and a negative value reflects the opposite. Averaged values from three animals performed in triplicate are depicted. Groups of differentially expressed genes at given time-points after S. pneumoniae (a) or influenza A virus infection (b) are shown. Columns of genes depicted in (a) and (b) are shown in a temporally consecutive manner.

Figure 4

Disappearance of B lymphocytes in pneumococcal pneumonia. Mice transnasally infected with S. pneumoniae were killed 12 hr, 24 hr or 48 hr after infection. Control mice were challenged with 20 μl PBS and killed after 48 hr. Lung, liver and spleen cells were counted using a haemocytometer and B cells were further differentiated by FACS analysis of CD45 and CD19 expression. Blood B cells were quantified by FACS using BD Truecount in combination with CD45 and CD19 labelling. Data are presented as mean B-cell numbers ±SEM, *P < 0·05, **P < 0·01 versus control, n = 5 each experiment.