New RoxS sRNA targets identified in B. subtilis by pulsed SILAC
Allouche, D.; Kostova, G.; Hamon, M.; Marchand, C. H.; Caron, M.; Belhocine, S.; Christol, N.; Charteau, V.; Condon, C.; DURAND, S.
Show abstract
Non-coding RNAs (sRNA) play a key role in controlling gene expression in bacteria, typically by base-pairing with ribosome binding sites to block translation. The modification of ribosome traffic along the mRNA generally affects its stability. However, a few cases have been described in bacteria where sRNAs can affect translation without a major impact on mRNA stability. To identify new sRNA targets in B. subtilis potentially belonging to this class of mRNAs, we used pulsed-SILAC (stable isotope labeling by amino acids in cell culture) to label newly synthesized proteins after short expression of the RoxS sRNA, the best characterized sRNA in this bacterium. RoxS sRNA was previously shown to interfere with the expression of genes involved in central metabolism, permitting control of the NAD+/NADH ratio in B. subtilis. In this study, we confirmed most of the known targets of RoxS, showing the efficiency of the method. We further expanded the number of mRNA targets encoding enzymes of the TCA cycle and identified new targets primarily regulated at the translational level. One of these is YcsA, a tartrate dehydrogenase that uses NAD+ as co-factor, in excellent agreement with the proposed role of RoxS in Firmicutes. ImportanceNon-coding RNA (sRNA) play an important role in bacterial adaptation and virulence. The identification of the most complete set of targets for these regulatory RNAs is key to fully identify the perimeter of its function(s). Most sRNAs modify both the translation (directly) and mRNA stability (indirectly) of their targets. However, sRNAs can also influence the translation efficiency of the target primarily, with little or no impact on mRNA stability. The characterization of these targets is challenging. We describe here the application of the pulsed SILAC method to identify these targets and obtain the most complete list of targets for a defined sRNA.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A 3' UTR-derived small RNA connecting nitrogen and carbon metabolism in enteric bacteria 97%
- Staphylococcal aconitase expression during iron deficiency is controlled by an sRNA-driven feedforward loop and moonlighting activity 97%
- The DEAD-box RNA helicases RhlE2 is a global regulator of Pseudomonas aeruginosa lifestyle and pathogenesis 96%
Similar papers in this journal
- SR7 - a dual function antisense RNA from Bacillus subtilis 96%
- Identification of RNAs bound by Hfq reveals widespread RNA partners and a sporulation regulator in the human pathogen Clostridioides difficile 96%
- Early posttranscriptional response to tetracycline exposure in a gram-negative soil bacterium reveals unexpected attenuation mechanism of a DUF1127 gene 95%
Similar papers in this journal
- FrlP, an ABC type I importer component of Bacillus subtilis: regulation and impact in bacterial fitness 96%
- A Novel Family of RNA-Binding Proteins Regulate Polysaccharide Metabolism in Bacteroides thetaiotaomicron 96%
- The proteomic and transcriptomic landscapes altered by Rgg2/3 activity in Streptococcus pyogenes 95%
Similar papers in this journal
- A Mycobacterium tuberculosis Mbox controls a conserved, small upstream ORF via a translational expression platform and rho-dependent termination of transcription 94%
- Balanced cell division is secured by two different regulatory sites in OxyS RNA 94%
- Ribosomal RNA degradation induced by the bacterial RNA polymerase inhibitor rifampicin. 94%
Similar papers in this journal
- The LysR-type transcriptional regulator BsrA (PA2121) controls vital metabolic pathways in Pseudomonas aeruginosa 96%
- Systems-wide analysis of the GATC-binding nucleoid-associated protein Gbn and its impact on Streptomyces development 95%
- The stringent stress response controls proteases and global regulators under optimal growth conditions in Pseudomonas aeruginosa 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.