Arias et al. BMC Genomics 2011, 12:390 http://www.biomedcentral.com/1471-2164/12/390 under cell wall stress, and the coding sequence of the NAT1 gene, which encodes resistance to the antibiotic nourseothricin. In other words, the expression of NAT1 is controlled by the MLP1 promoter; therefore, yeast mutants with a constitutive activation of the CWI pathway transformed with this construction should be able to grow in the presence of higher concentrations of the antibiotic than a wild-type strain. Following this approach, we have designed a large- scale screening to identify yeast deletions associated with Slt2-driven transcriptional activation. To achieve this goal, we used the collection of haploid mutant strains in all non-essential genes of Saccharomyces cerevisiae (~4800 strains). As represented in Figure 1, we proceeded to transform this collection into a 96- well microplate format with the plasmid containing MLP1P-NAT1, and the transformed strains were screened for their ability to grow in the presence of 300 μg/ml nourseothricin. This inhibitory concentra- tion of antibiotic was previously determined using selected mutants in which basal levels of Slt2 activa- tion were similar to those found in a wild-type strain (data not shown). After 48-72 hours of growth in a selective medium, 174 mutant strains with hyper-resis- tance to nourseothricin were identified and selected for further characterization. Figure 1 Overview of the screening strategy designed to identify yeast mutants with constitutive CWI pathway activation. Yeast mutant strains, transformed with the plasmid pJS05 (MLP1P-NAT1), in which the expression of MLP1 is higher compared to the wild-type strain, were identified as resistant to nourseothricin. Page 3 of 17 Slt2 phosphorylation levels in nourseothricin-resistant mutants identified in the screening A reliable method for monitoring signaling through the CWI pathway is to follow the activation state of the MAPK Slt2 by using commercially available antibodies that recognize the dual phosphorylation of conserved threonine and tyrosine residues within the activation loop of Slt2, analogous to Thr202/Tyr204 of mammalian p44/p42 MAP Kinase (ERK) [39]. In order to associate the phos- phorylation status of Slt2 to MLP1 up-regulation, the 174 deletant strains formerly selected were examined following this approach. After densitometric quantification of the phospho-Slt2 bands obtained by Western blotting analysis of the total protein extracts from each mutant strain, 64 mutants recorded higher Phospho-Slt2 levels (at least twofold) than the wild-type strain. The relative amount of phospho-Slt2 in these mutants was distributed over a wide range of values (from twice to thirtyfold) (Table 1). Repre- sentative examples of Slt2 activation in different mutants are shown in Figure 2. The complete data set (Western blots) for all the selected mutants is presented in Addi- tional file 1. These data indicate different levels of CWI pathway activation for each mutant, suggesting that yeast cells modulate pathway activation as required by specific stimuli. Remarkably, more than 20% of the mutations identified in a large-scale analysis revealing synthetic lethal interaction with slt2 Δ [40] have also been isolated in our screening (Table 1). As shown in Table 1, the functional categorization of mutants identified for constitutive Slt2 activation revealed the most representative functional groups according to the BIOBASE Knowledge Library Proteome and Saccharomyces Genome Database classifications, being those involved in cell wall organization and mor- phogenesis (28%), genes of unknown function (17%), signal transduction (17%), transport (11%) and transcrip- tion (9%). The remaining functional categories comprise mutants linked to metabolism (principally of RNA and proteins) and other cellular functions with lower repre- sentation in the screening (see Table 1). This distribu- tion is consistent with putative inputs of the pathway, namely, cell wall alterations or regulatory proteins. An analysis was conducted of predicted and known interactions between the whole set of genes identified in the screening using the STRING web resource (http:// string-db.org) [41]. This tool is very useful for the retrie- val of an overall perspective of interacting genes/proteins. As shown in Figure 3, a large number of genes (38 out of 64) showed functional interactions between them. Inter- estingly, the interaction network was clustered again in the three main functional nodes (cell wall and morpho- genesis, signal transduction and transcription) described above. From the large group of 18 mutants related to cell wall and morphogenesis, different subgroups can be