Background
Mucosal infections elicit inflammatory responses via regulated signaling pathways. Infection outcome depends strongly on early events occurring immediately when bacteria start interacting with cells in the mucosal membrane. Hitherto reported transcription profiles on host-pathogen interactions are strongly biased towards in vitro studies. To detail the local in vivo genetic response to infection, we here profiled host gene expression in a recent experimental model that assures high spatial and temporal control of uropathogenic Escherichia coli (UPEC) infection within the kidney of a live rat.
Conclusion
Our hypothesis-free approach identified components of infection-associated multi-cellular tissue responses and demonstrated how a comparative analysis allows retrieval of relevant information from lower-quality data sets. The data further define marked representation of IFN-γ responsive genes and a prompt inter-organ communication as a hallmark of an early local tissue response to infection.
Results
Transcriptional profiling of tissue biopsies from UPEC-infected kidney tissue revealed 59 differentially expressed genes 8 h post-infection. Their relevance for the infection process was supported by a Gene Ontology (GO) analysis. Early differential expression at 3 h and 5 h post-infection was of low statistical significance, which correlated to the low degree of infection. Comparative transcriptomics analysis of the 8 h data set and online available studies of early local infection and inflammation defined a core of 80 genes constituting a "General tissue response to early local bacterial infections". Among these, 25% were annotated as interferon-γ (IFN-γ) regulated. Subsequent experimental analyses confirmed a systemic increase of IFN-γ in rats with an ongoing local kidney infection, correlating to splenic, rather than renal Ifng induction and suggested this inter-organ communication to be mediated by interleukin (IL)-23. The use of comparative transcriptomics allowed expansion of the statistical data handling, whereby relevant data could also be extracted from the 5 h data set. Out of the 31 differentially expressed core genes, some represented specific 5 h responses, illustrating the value of comparative transcriptomics when studying the dynamic nature of gene regulation in response to infections.