Project PXD015051: "Identification of an RNA sponge that controls the levels, processing and efficacy of the RoxS riboregulator of central metabolism in B. subtilis"

Data collection method:

Strains were grown to O.D. 600 nm 1.0 in LB. 20 O.D. units were harvested and washed 3 X with PBS to remove media components. Cells were resuspended in 200 µl urea buffer (8M urea, 50 mM Tris and 75 mM NaCl). 200 µl of urea buffer washed 0.1 µM beads were added to the cells before being disrupted using three rounds of bead beating for 40 seconds using a FastPrep. Cells were placed on ice between the three rounds of bead beating. The disrupted cells were then sonicated in a water bath for 15 minutes. Cell extracts were centrifuged at 15,000 x g, 5 min and supernatants used for protein quantification (Qubit protein assay kit). Protein reduction and alkylation was conducted by mixing 150 µg of total protein with 10 mM TCEP and 40 mM CAA, at 600 rpm, for 20 min at room temperature. After, proteins were predigested with 1.5 µg of rLysC (Promega) for 3 h at room temperature and samples diluted with 50 mM ammonium bicarbonate, 2 M urea final concentration. Protein digestion was performed with 1.5 µg of Trypsin (Promega) overnight at room temperature. The reaction was stopped by adding 1% TFA and 10 µg of peptides were desalted using StageTip (Rappsilber et al. Nat Protoc. 2007; DOI: 10.1038/nprot.2007.261). Reversed phase chromatography was used to separate 1 µg of tryptic peptides prior to mass spectrometric analysis. The cell proteomes were analysed with two columns, an Acclaim PepMap µ-precolumn cartridge 300 µm i.d. x 5 mm, 5 μm, 100 Å and an Acclaim PepMap RSLC 75 µm i.d. x 50 cm, 2 µm, 100 Å (Thermo Scientific). The columns were installed on an Ultimate 3000 RSLCnano system (Dionex) at 40ᵒC. Mobile phase buffer A was composed of 0.1% formic acid and mobile phase B was composed of acetonitrile containing 0.1% formic acid. Samples were loaded onto the µ-precolumn equilibrated in 2% aqueous acetonitrile containing 0.1% trifluoroacetic acid for 8 min at 10 µL min-1 after which peptides were eluted onto the analytical column at 250 nL min-1 by increasing the mobile phase B concentration from 8% B to 25% over 90 min, then to 35% B over 12 min, followed by a 3 min wash at 90% B and a 15 min re-equilibration at 4% B. Eluting peptides were converted to gas-phase ions by means of electrospray ionization and analysed on a Thermo Orbitrap Fusion (Thermo Scientific). Survey scans of peptide precursors from 375 to 1500 m/z were performed at 120K resolution (at 200 m/z) with a 2x105 ion count target. The maximum injection time was set to 150 ms. Tandem MS was performed by isolation at 1.2 Th using the quadrupole, HCD fragmentation with normalized collision energy of 33, and rapid scan MS analysis in the ion trap. The MS2 ion count target was set to 3x103 and maximum injection time was 200 ms. Precursors with charge state 2–6 were selected and sampled for MS2. The dynamic exclusion duration was set to 60 s with a 10 ppm tolerance around the selected precursor and its isotopes. Monoisotopic precursor selection was turned on and instrument was run in top speed mode. Thermo-Scientific raw files were analysed using MaxQuant software v1.6.0.16 (Tyanova et al. 2016, The MaxQuant computational platform for mass-spectrometry based shotgun proteomics, Nature Protocols 11, 2301-2319; DOI: 10.1038/nprot.2016.136) against the UniProtKB B. subtilis database (UP000001570, 4,260 entries). Peptide sequences were assigned to MS/MS spectra using the following parameters: cysteine carbamidomethylation as a fixed modification and protein N-terminal acetylation and methionine oxidations as variable modifications. The FDR was set to 0.01 for both proteins and peptides with a minimum length of 7 amino acids and was determined by searching a reversed database. Enzyme specificity was trypsin with a maximum of two missed cleavages. Peptide identification was performed with an initial precursor mass deviation of 7 ppm and a fragment mass deviation of 20 ppm. The MaxQuant feature ‘match between runs’ was enabled. Label-free protein quantification (LFQ) was done with a minimum ratio count of 2. Data processing was performed using the Perseus module of MaxQuant v1.6.0.16 (Tyanova, S., Temu, T., Sinitcyn, P., Carlson, A., et al., The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods 2016, 13, 731-740). Proteins identified by the reverse, contaminant and only by site hits were discarded. Only protein groups identified with at least two assigned peptides were accepted and LFQ intensities were log2 transformed.