Acquired raw data were processed using the MaxQuant software suite (version (55). Raw files of particular experiments were processed separately (table S3). In total, we used 449 raw files, of which 249 belong to phosphopeptide enrichment fractions. The derived peak list was searched using Andromeda search engine integrated in MaxQuant (56) against a reference E. coli K-12 proteome (taxonomy ID 83333) obtained from UniProt (4313 protein entries, released in October 2015), protein sequence of HipA7, HipA mutants, and a file containing 245 common laboratory contaminants. During the first search, peptide mass tolerance was set to 20 ppm (parts per million) and, in the main search, to 4.5 ppm. For triple-label SILAC experiments, multiplicity was set to three with Lys4 and Lys8 specified as medium and heavy labels, respectively. Methionine oxidation, protein N-terminal acetylation, and Ser-Thr-Tyr phosphorylation were defined as variable modifications, and carbamidomethylation of cysteines was set as a fixed modification.

The minimum required peptide length was set to seven amino acids with the maximum of two missed cleavages allowed for endoproteinase Lys-C that was set to specifically cleave at lysine C terminus. Chymotrypsin was set to specifically cleave at phenylalanine, tryptophan, tyrosine, leucine, and methionine C terminus with maximum five missed cleavages allowing for maximum of four labeled amino acids. All (phospho)peptide and protein identifications were filtered using a target-decoy approach with a false discovery rate (FDR) set to 0.01 at peptide and protein level (57). Proteins identified by the same set of peptides were combined to a single protein group. Protein groups identified by a single peptide were kept in the data set. For protein quantification, a minimum of two peptide ratio counts was required. To increase the number of quantified features, the “match between runs” option was enabled with a match time window set to 0.7 min. This allows the transfer of peptide identifications across LC-MS/MS runs based on the mass and the retention time of the peptide identified by MS/MS. Requantify option was enabled to allow for quantification of SILAC pairs that result in extreme ratio values.

Statistical analysis of MaxQuant output data was performed manually or by using Perseus software (version (58), and figures were edited in Adobe Illustrator. All contaminants and reverse hits were removed. Phosphorylation sites were additionally filtered for posterior error probability scores of <0.01. Minimal score of 40 was required for phosphorylation site and 20 for protein identifications. Changes in phosphorylation events were normalized to differences in protein abundances, unless otherwise stated. For that, phosphorylation site SILAC ratios were divided with the protein SILAC ratios of corresponding proteins. Normalized phosphorylation site ratios were log2-transformed and plotted against the log10-transformed phosphopeptide intensities summed for each of two SILAC channels observed. Statistically significantly regulated phosphorylation sites were determined by applying an arbitrary ratio threshold of 2 in log2 scale (fourfold). In the experiment with hipA7 on the chromosome in which no plasmids were used, statistically significantly regulated phosphorylation sites were determined by using significance B test with a P value of 0.01. Statistically significantly regulated proteins were determined by using significance B test with a P value of 0.001. For Volcano plots, log2-transformed ratios of three independent experiments were grouped into one group and compared to the group containing only zero values using t test with FDR of 0.01 or 0.001 and the minimal fold change S0 of 1. Phosphorylation site occupancies were determined as the proportion between the phosphorylated peptide and corresponding unmodified peptide using the algorithm implemented in MaxQuant based on the calculation described by Olsen et al. (32). The calculation of occupancies requires SILAC ratio of a phosphorylated peptide, the SILAC ratio of the corresponding unmodified peptide, and the SILAC protein ratio. For RplK and SeqA, occupancy values were calculated manually using M/L and H/M ratios, giving a and b values between 0 and 1 in three independent experiments. For SeqA in “light” and RplK in “heavy” labeling state, occupancy was determined only from two independent experiments.

To identify significantly represented temporal protein profiles, we used Short Time-series Expression Miner (STEM) program (P value of 0.05 after Bonferroni multiple testing correction) (59). Gene annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) tool (version 6.7) with default parameters (60). UniProt IDs were used as an input for the enrichment. Kinase motif analysis was performed using motif-x software (61) with the reference E. coli proteome used as a background and 15–amino acid–slong sequences (seven amino acids on both sides around phosphorylation site) of all identified phosphorylation sites with the localization probability higher than 0.75 as an input. The parameters of motif-x analysis were as follows: S or T as a foreground and background central residue, width of 15, 40 occurrences, and significance threshold of 0.00000001.

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