Cluster:

´´Next generation transcriptomics´´ for bacterial infections

Coordinator:
  • Prof. Dr. Jörg Vogel, Institute for Molecular Infection Biology, University of Würzburg

Project Partners:
  • Dr. Cynthia Sharma, Zentrum für Infektionsforschung, Universität Würzburg

Description:
A comprehensive understanding of infection requires the knowledge of gene expression changes in both pathogen and host. Traditional gene expression profiling was based on probe-dependent approaches, using hybridization techniques or RT-PCR for individual transcripts or microarrays for the profiling of all known protein-coding genes. Tiling arrays extended the analysis to the non-coding part of the genome, and captured transcripts at higher resolution. Nonetheless, a major caveat of all the above probe-dependent approaches was that for reasons of cross-hybridization, pathogen and host had to be physically separated prior to RNA preparation. Thus, the transcriptomes of pathogen and host had to be investigated individually when using probe-dependent approaches and the extensive manipulation during sample preparation was likely to introduce additional biases.
Massively parallel sequencing of cDNA has been revolutionizing the study of transcriptomes, and provided a probe-independent means to capture the RNA profile of a cell. The technology is now well-established, and was recently used successfully to profile prokaryotic transcriptomes, including those of model pathogens, and of eukaryotic organisms, including the host of many pathogens such as humans and mice. The probe-independent transcriptomics by next-generation sequencing, in principle, obviates the need to separate pathogen and host. It therefore is an attractive candidate approach for transcriptome sequencing of the mixed host-pathogen populations of infected cells and animals. However, such parallel transcriptomics has not been attempted thus far. Thus, the goal of this project is to establish next-generation sequencing as a novel tool for unbiased gene expression profiling during infections, and thereby promote a paradigm change in the way infection processes are studied.
Using the model pathogen Salmonella typhimurium, we will address issues of the dynamic range of ratios of prokaryotic versus eukaryotic RNA to be profiled, and develop a pipeline from preparation of suitable cDNA libraries to bioinformatics annotation of the transcriptome. The ultimate goal is to monitor the transcriptional landscape during infection at the single cell level and within infected animal tissue


 
A new approach to study gene expression changes based on RNA-seq.

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