03 search engine were used OICR-9429 manufacturer to automate database searching. Both MS/MS and MS data were used for the identification of proteins with the following selection criteria: NCBInr database (release 20110409, 13655082 sequences; 4686307983 residues), taxonomy of all entries followed by ‘Bacteria’ or ‘Fungi’ database, trypsin of the digestion enzyme, one missed cleavage site, parent ion mass tolerance at 100 ppm, MS/MS mass tolerance of 0.5 Da, carbamidomethylation of cysteine (global modification), and methionine oxidation (variable modification).
The probability score (95% confidence interval) calculated by the software was used as criteria for correct identification . Due to the vast varieties of soil sample sources, the mass spectra were searched against databases step by step. Firstly, ‘all entries’ in NCBInr, which include all organisms, was selected for the search. Then, the ‘Bacteria’ and ‘Fungi’ databases were separately selected to avoid the failed matching when ‘all entries’ was used. AZD2281 manufacturer The above strategy alleviated the problem of missing some of the mass spectra for matches in searching against ‘all entries’, and allowed significant matching results by searching against ‘Bacteria’ and
‘Fungi’ databases. Both MS/MS and MS data were used for the identification of proteins. The proteins sharing equal searches by MS/MS and MS were preferentially selected. Furthermore, the proteins that matched at least two MS/MS peptides MG-132 ic50 or six peptide mass fingerprintings (PMFs) were subjected to further identification. Only the proteins with the highest score and similar predicted molecular mass were selected. Gene ontology and KEGG pathway analysis Gene Ontology (GO) annotations for the identified soil proteins were assigned according to those reported in the uniprot database . WEGO (Web Gene Ontology Annotation Plotting)
tool  was used for plotting GO annotation results at GO level 2 as described by Ye et al. . Furthermore, these proteins were used to search KEGG database  to obtain reference pathways. Acknowledgements This work was supported by the National Natural Science Foundation of China (Grant nos. 30772729, 30671220, 31070403), the National Key Basic Research Program of China (Grant nos. 2012CB126309, U1205021) and the earmarked fund for Modern Agro-industry Technology Research System projected by Ministry of Agriculture, China. Electronic supplementary material Additional file 1: Table S1: Most discriminant eight carbon substrates as determined by PCA on the data of community level carbon source utilization using BIOLOG Eco microplates by different soil communities. (DOC 36 KB) Additional file 2: Figure S1: Silver stained 2-D gel of proteins extracted from the control soil (a), plant cane soil (b) and ratoon cane soil (c). Arrows in (a) point at proteins with differential expression.