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

Only a few small regulatory RNAs (sRNAs) have been characterized in B. subtilis, the paradigm of Gram-positive bacteria, and one of the major challenges is target identification. Here we use global in vivo RNA psoralen cross-linking to identify RNA-RNA partners in Bacillus subtilis. Two sRNAs, RoxS and FsrA, play key roles in balancing the metabolic state of the cell in response to carbon sources and iron limitation, respectively. In this study, we identify new mRNA targets for both RoxS and FsrA, and a small RNA (S345/RosA) that is able to interact with both sRNAs. We report that RosA controls the maturation and degradation of RoxS and acts as a sponge to limit the efficacy of RoxS on its targets. Expression of RosA is catabolically repressed by the transcription factor CcpA. We provide evidence that the RosA/RoxS interaction plays a key role in regulating metabolism in response to a switches in carbon source.

Microbial sciences
Omic sciences and technologies

Cite this dataset as:
Durand, S., Callan-Sidat, A., McKeown, J., Li, S., Kostova, G., Hernandez-Fernaud, J., Alam, M., Millard, A., Allouche, D., Constantinidou, C., Condon, C., Denham, E., 2021. Project PXD015051: "Identification of an RNA sponge that controls the levels, processing and efficacy of the RoxS riboregulator of central metabolism in B. subtilis". PRIDE Archive. Available from:


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Sylvain Durand
Centre national de la recherche scientifique

Adam Callan-Sidat
University of Warwick

Josie McKeown
University of Warwick

Stephen Li
University of Warwick

Gergana Kostova
Centre national de la recherche scientifique

Juan R Hernandez-Fernaud
Data Curator
University of Warwick; Fundación Canaria Instituto de Investigación Sanitaria de Canarias

Andrew Millard
University of Warwick

Delphine Allouche
Centre national de la recherche scientifique

Ciarán Condon
Centre national de la recherche scientifique

Emma Denham
Data Curator
University of Bath


University of Bath
Rights Holder


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.


Biotechnology and Biological Sciences Research Council (BBSRC)

Understanding the role of small regulatory RNAs in the Gram-positive model organism Bacillus subtilis

University of Warwick (Warwick)

Noreen Murray Award

Medical Research Council (MRC)

PhD studentship

Centre National de la Recherche Scientifique (CNRS)

Université de Paris (University of Paris)


Agence Nationale de la Recherche (ANR)

Publication details

Publication date: 8 September 2021
by: PRIDE Archive

Version: 1

Official landing page URL:

URL for this record:

Related papers and books

Durand, S., Callan-Sidat, A., McKeown, J., Li, S., Kostova, G., Hernandez-Fernaud, J. R., Alam, M. T., Millard, A., Allouche, D., Constantinidou, C., Condon, C., and Denham, E. L., 2021. Identification of an RNA sponge that controls the RoxS riboregulator of central metabolism in Bacillus subtilis. Nucleic Acids Research, 49(11), 6399-6419. Available from:

Contact information

Please contact the Research Data Service in the first instance for all matters concerning this item.

Contact person: Emma Denham


Life Sciences
Biology & Biochemistry