Figure 1. Bacterial tssRNA identification. A. Methodology to detect tssRNAs. B. tssRNAs map preferentially to the promoter regions. Upper panel: the mapping locations of various tssRNAs; middle panel: mRNA levels of the genomic region measured by DSSS (Vivancos et al.); lower panel: the coexpression matrix of the nine genes composing an operon located on the shown genome area. tssRNAs are located at the TSS of the different alternative transcripts of this operon.
2. Signal transduction and disease Understanding signal transduction pathways is capital for human health. Different cell types share many of their signaling molecules yet can respond specifically to the same stimuli, through ways not fully understood. With increasing information available from large-scale ‘–omics’ experiments in recent years, the representation of signaling systems has changed from the traditional depiction of linear pathways to complex network maps (“everything does everything to everything”, Dumont et al., 2002; Am. J. Physiol. Cell. Physiol. 283, C2-C28). Therefore, it is difficult to elucidate when knocking out a protein or blocking an activity with a drug what is the relationship between phenotype and the interaction affected. On the other hand cells respond specifically to a large number of different external signaling molecules and stimuli although they frequently sharing downstream signaling modules. In principle, one simple way to explain the different responses in different cell lines to the same signal could be that some critical proteins in the network are differentially expressed. It is clear that a central ‘hub’protein in the network cannot interact with all of its partner proteins simultaneously and that some interactions are mutually exclusive (Kiel et al., 2011; Mol. Syst. Biol. 7, 551). One could envisage that differences in protein concentrations between cell lines could change cellular output if there is competition at a critical branching point of the network, e.g., if an upstream hub is expressed at a low concentration, then the concentrations (and affinities) of competing binding partners could determine the signaling pathway taken. In our group we want to understand signal transduction in a quantitative way to the point that we can model accurately the response of different cells to drugs or mutations. As a scientific target we have selected signal transduction in vision and the MAPK pathway and our final goal is to obtain a global quantitative understanding with the idea of designing better therapies for diseases involving its deregulation. To understand that pathway in a quantitative predictive way we are building the interaction network at structural level mapping all known diseases mutations, determining the concentrations of all proteins in different cell lines and applying different perturbations to understand how the network is regulated. The results of these analyses plus data from the literature regarding Kds, kons, koffs is used to model mathematically the network. In 2012 we have developed a new webserver tool (SAPIN) that can be used to determine competing interactions in a protein network using structural reconstruction (Yang et al., 2012) (Figure 2). We have also shown how changing protein specificity in promiscuous protein interactions can increase signal
154 . Annual Report 2012
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