PROPHECY: Translational control in infection biology: riboproteogenomics of bacterial pathogens (PROPHECY)

My recent findings revealed translation of numerous previously unidentified (small) open reading frames and expression of alternative N-terminal proteoforms when studying bacterial translation. This proposal aims at unraveling the repertoire of bacterial pathogen proteoforms employed to establish a successful infection in a mammalian host cell. While deep sequencing has enabled the study of gene expression at the transcript level in both pathogen and host simultaneously, the depth of sequencing has so far proven to be unsatisfactory. Moreover, the study of bacterial proteome changes upon infection remains highly unexplored because of the higher proteome complexity of the host cell compared to the pathogen. These challenges clearly stresses the need for novel strategies based on complementary proteogenomics approaches enabling translation control studies in bacterial pathogens in a host context . I here propose the development and application of a complementary cutting-edge proteogenomic toolset which will enable for the first time targeted systematic genome- and proteome-wide surveys of bacterial transcriptional and translational activity during actual host cell infection. This ambitious endeavor will lead to: I) Establishment of dual Ribo-seq that allows the selective isolation of host or bacterial ribosomes, enabling to study the bacterial translatome in a host cell context. II) Development of tailored proteomics strategies permitting the selective isolation of (nascent) bacterial protein Ntermini and enrichment of bacterial small ORF-encoded polypeptides (SEPs). Further, proteome-wide subcellular localization and protein stability studies will provide a dynamic view on bacterial protein expression. II) Bacterial proteoform interaction maps by the development of an innovative proxeome strategy. The identification of new pathogen virulence factors will contribute to the development of therapeutics and diagnostics for multiple models of infectious diseases.