matrixscience.com). Molecular weight and pI were calculated based on amino acid sequence and compared with gel location. Functional annotation of the identified protein was carried out using the gene ontology (GO) database (http://geneontology.org) and UniProtKB (http://www.uniprot.org). Bacterial sample preparation was same as described for proteomic analysis. Extraction of intracellular metabolites was performed as previously described with slight modifications (Frimmersdorf et al., 2010). Four replicates were used in each group. The compounds were derivatized with methoxyamine hydrochloride and N-methyl-N-trimethylsilyltrifluoracetamide. A set of alkane standards were

added to calculate retention indices. The derivatized extracts were analyzed with a GC-MS-QP-5050A (Shimadzu). Spectral deconvolution, calibration and identification of metabolites

were performed using amdis software from NIST (Natural Institute of Standards and Technology). EPZ-6438 clinical trial Prior to statistical analysis, each compound was normalized by the peak area of the standard (ribitol) and the optical density of each bacterial culture (Strelkov et al., 2004). These relative ratios can be compared directly among different groups without knowledge of the absolute compound concentrations. Hierarchical cluster analysis with Pearson correlation as the distance measure and a one-way PD-0332991 cost anova test was performed with tigr mev 4.7.4. Significant differences in the metabolite level were determined by comparing the P values (P < 0.05). The metabolites

with significant changes were submitted to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg/pathway.html) to obtain the compound IDs and then submitted to Metabolite Pathway Enrichment Analysis (MPEA) Analysis (http://ekhidna.biocenter.helsinki.fi/poxo/mpea/mpea) to determine which metabolic pathways are most likely to be involved with these compounds. P-values were calculated by Monte Carlo simulation. Pseudomonas Silibinin sp. TLC6-6.5-4 is a rod-shaped bacterial strain with an average length of 6.52 ± 1.60 μm when grown in LB broth without copper (Fig. 1b, d and e). However, when the bacterial isolate was exposed to 4 mM copper, about 70% reduction in bacterial cell length was observed. The mean cell length was 1.92 ± 0.38 μm, which is significantly (P < 0.05) shorter than cells grown in LB broth without copper (Fig. 1a, c and e). A comprehensive genome-wide analysis of Pseudomonas sp. TLC6-6.5-4 was performed to identify the genes involved in copper resistance using random transposon mutagenesis. A total of 5023 colonies with transposon insertions were screened for copper-sensitive mutants, which resulted in the identification of three mutants with a decrease in resistance to copper. These mutants were designated CSM1 (Copper Sensitive Mutant), CSM2 and CSM3. Growth of these mutants in the medium without copper was not affected (Fig. 2).