TheS subunit of RNA polymerase, the product of the rpoS gene, controls the expression of genes responding to starvation and cellular stresses. Using gene array technology, we investigated rpoS-dependent expression at the onset of stationary phase in Escherichia coli grown in rich medium. Forty-one genes were expressed at significantly lower levels in an rpoS mutant derived from the MG1655 strain; for 10 of these, we also confirmed rpoS and stationary-phase dependence by reverse transcription-PCR. Only seven genes (dps, osmE, osmY, sodC, rpsV, wrbA, and yahO) had previously been recognized as rpoS dependent. Several newly identified rpoSdependent genes are involved in the uptake and metabolism of amino acids, sugars, and iron. Indeed, the rpoS mutant strain shows severely impaired growth on some sugars such as fructose and N-acetylglucosamine. The rpoS gene controls the production of indole, which acts as a signal molecule in stationary-phase cells, via regulation of the tnaA-encoded tryptophanase enzyme. Genes involved in protein biosynthesis, encoding the ribosome-associated protein RpsV (sra) and the initiation factor IF-1 (infA), were also induced in an rpoSdependent fashion. Using primer extension, we determined the promoter sequences of a selection of rpoSregulated genes representative of different functional classes. Significant fractions of these promoters carry sequence features specific for E S recognition of the ؊10 region, such as cytosines at positions ؊13 (70%) and ؊12 (30%) as well as a TG motif located upstream of the ؊10 region (50%), thus supporting the TGN 0-2 C(C/ T)ATA(C/A)T consensus sequence recently proposed for S .

Bacterial cells often face hostile environmental conditions, to which they adapt by activation of stress responses. In Escherichia coli, environmental stresses resulting in significant reduction in growth rate stimulate the expression of the rpoS gene, encoding the alternative σ factor σ(S). The σ(S) protein associates with RNA polymerase, and through transcription of genes belonging to the rpoS regulon allows the activation of a 'general stress response', which protects the bacterial cell from harmful environmental conditions. Each step of this process is finely tuned in order to cater to the needs of the bacterial cell: in particular, selective promoter recognition by σ(S) is achieved through small deviations from a common consensus DNA sequence for both σ(S) and the housekeeping σ(70). Recognition of specific DNA elements by σ(S) is integrated with the effects of environmental signals and the interaction with regulatory proteins, in what represents a fascinating example of multifactorial regulation of gene expression. In this report, we discuss the function of the rpoS gene in the general stress response, and review the current knowledge on regulation of rpoS expression and on promoter recognition by σ(S).

The ability to control growth is essential for fundamental studies of bacterial physiology and biotechnological applications. We have engineered an Escherichia coli strain in which the transcription of a key component of the gene expression machinery, RNA polymerase, is under the control of an inducible promoter. By changing the inducer concentration in the medium, we can adjust the RNA polymerase concentration and thereby switch bacterial growth between zero and the maximal growth rate supported by the medium. We show that our synthetic growth switch functions in a medium‐independent and reversible way, and we provide evidence that the switching phenotype arises from the ultrasensitive response of the growth rate to the concentration of RNA polymerase. We present an application of the growth switch in which both the wild‐type E. coli strain and our modified strain are endowed with the capacity to produce glycerol when growing on glucose. Cells in which growth has been switched off continue to be metabolically active and harness the energy gain to produce glycerol at a twofold higher yield than in cells with natural control of RNA polymerase expression. Remarkably, without any further optimization, the improved yield is close to the theoretical maximum computed from a flux balance model of E. coli metabolism. The proposed synthetic growth switch is a promising tool for gaining a better understanding of bacterial physiology and for applications in synthetic biology and biotechnology.

The alternative sigma factor S , mainly active in stationary phase of growth, recognizes in vitro a ؊10 promoter sequence almost identical to the one for the main sigma factor, 70 , thus raising the problem of how specific promoter recognition by S -RNA polymerase (E S ) is achieved in vivo. We investigated the promoter features involved in selective recognition by E S at the strictly S -dependent aidB promoter. We show that the presence of a C nucleotide as first residue of the aidB ؊10 sequence (؊12C), instead of the T nucleotide canonical for 70 -dependent promoters, is the major determinant for selective recognition by E S . The presence of the ؊12C does not allow formation of an open complex fully proficient in transcription initiation by E 70 . The role of ؊12C as specific determinant for promoter recognition by E S was confirmed by sequence analysis of known E S -dependent promoters as well as site-directed mutagenesis at the promoters of the csgB and sprE genes. We propose that E S , unlike E 70 , can recognize both C and T as the first nucleotide in the ؊10 sequence. Additional promoter features such as the presence of a C nucleotide at position ؊13, contributing to open complex formation by E S , and a TG motif found at the unusual ؊16/؊15 location, possibly contributing to initial binding to the promoter, also represent important factors for S -dependent transcription. We propose a new sequence, TG(N) 0 -2 CCATA(c/a)T, as consensus ؊10 sequence for promoters exclusively recognized by E S .Bacterial cells adapt to changing environmental and physiological conditions by modulating gene expression. Sigma ( ) factors of RNA polymerase, as the subunits responsible for promoter recognition, play a major role in programming gene expression. At least seven different subunits have been identified in Escherichia coli; 70 is the main subunit and can carry out transcription from the majority of E. coli promoters. The alternative subunits can direct transcription toward specific sets of genes (i.e. heat-shock, extracellular proteins, etc.) whose transcription is directed by -specific promoter sequences. A partial exception to the typical role for alternative subunits is represented by S , the product of the rpoS gene, mainly expressed in the stationary phase of growth (1-3). Unlike the other subunits, S -RNA polymerase (E S ) 1 can initiate transcription from several promoters also recognized by E 70 , suggesting that they recognize similar promoter sequences (4). The recognition of similar promoter sequences by S and 70 is reflected by their strong similarity in the DNA binding domains (5). The alignment of E S -dependent promoters and the search for an optimal promoter for E S in vitro using the systematic evolution of ligands by exponential enrichment (SELEX) procedure have pointed to a Ϫ10 consensus sequence for E S , CTATA(c/a)T that is very similar to the canonical TATAAT sequence for 70 (6 -10). These results suggest that promoter selectivity between 70 and S might be determined by factors other than promote...

In bacteria, selective promoter recognition by RNA polymerase is achieved by its association with σ factors, accessory subunits able to direct RNA polymerase “core enzyme” (E) to different promoter sequences. Using Chromatin Immunoprecipitation-sequencing (ChIP-seq), we searched for promoters bound by the σS-associated RNA polymerase form (EσS) during transition from exponential to stationary phase. We identified 63 binding sites for EσS overlapping known or putative promoters, often located upstream of genes (encoding either ORFs or non-coding RNAs) showing at least some degree of dependence on the σS-encoding rpoS gene. EσS binding did not always correlate with an increase in transcription level, suggesting that, at some σS-dependent promoters, EσS might remain poised in a pre-initiation state upon binding. A large fraction of EσS-binding sites corresponded to promoters recognized by RNA polymerase associated with σ70 or other σ factors, suggesting a considerable overlap in promoter recognition between different forms of RNA polymerase. In particular, EσS appears to contribute significantly to transcription of genes encoding proteins involved in LPS biosynthesis and in cell surface composition. Finally, our results highlight a direct role of EσS in the regulation of non coding RNAs, such as OmrA/B, RyeA/B and SibC.

Background In bacterial genomes, there are two mechanisms to terminate the DNA transcription: the “intrinsic” or Rho-independent termination and the Rho-dependent termination. Intrinsic terminators are characterized by a RNA hairpin followed by a run of 6–8 U residues relatively easy to identify using one of the numerous available prediction programs. In contrast, Rho-dependent termination is mediated by the Rho protein factor that, firstly, binds to ribosome-free mRNA in a site characterized by a C > G content and then reaches the RNA polymerase to induce its release. Conversely on intrinsic terminators, the computational prediction of Rho-dependent terminators in prokaryotes is a very difficult problem because the sequence features required for the function of Rho are complex and poorly defined. This is the reason why it still does not exist an exhaustive Rho-dependent terminators prediction program. Results In this study we introduce RhoTermPredict, the first published algorithm for an exhaustive Rho-dependent terminators prediction in bacterial genomes. RhoTermPredict identifies these elements based on a previously proposed consensus motif common to all Rho-dependent transcription terminators. It essentially searches for a 78 nt long RUT site characterized by a C > G content and with regularly spaced C residues, followed by a putative pause site for the RNA polymerase. We tested RhoTermPredict performances by using available genomic and transcriptomic data of the microorganism Escherichia coli K-12, both in limited-length sequences and in the whole-genome, and available genomic sequences from Bacillus subtilis 168 and Salmonella enterica LT2 genomes. We also estimated the overlap between the predictions of RhoTermPredict and those obtained by the predictor of intrinsic terminators ARNold webtool. Our results demonstrated that RhoTermPredict is a very performing algorithm both for limited-length sequences (F 1 -score obtained about 0.7) and for a genome-wide analysis. Furthermore the degree of overlap with ARNold predictions was very low. Conclusions Our analysis shows that RhoTermPredict is a powerful tool for Rho-dependent terminators search in the three analyzed genomes and could fill this gap in computational genomics. We conclude that RhoTermPredict could be used in combination with an intrinsic terminators predictor in order to predict all the transcription terminators in bacterial genomes. Electronic supplementary material The online version of this article (10.1186/s12859-019-2704-x) contains supplementary material, which is available to authorized users.

e Escherichia coli adapts its lifestyle to the variations of environmental growth conditions, swapping between swimming motility or biofilm formation. The stationary-phase sigma factor RpoS is an important regulator of this switch, since it stimulates adhesion and represses flagellar biosynthesis. By measuring the dynamics of gene expression, we show that RpoS inhibits the transcription of the flagellar sigma factor, FliA, in exponential growth phase. RpoS also partially controls the expression of CsgD and CpxR, two transcription factors important for bacterial adhesion. We demonstrate that these two regulators repress the transcription of fliA, flgM, and tar and that this regulation is dependent on the growth medium. CsgD binds to the flgM and fliA promoters around their ؊10 promoter element, strongly suggesting direct repression. We show that CsgD and CpxR also affect the expression of other known modulators of cell motility. We propose an updated structure of the regulatory network controlling the choice between adhesion and motility.M icroorganisms adapt to changes in their environment in many different ways. Among others, they adjust gene expression, modify their metabolism, and change their surface properties by displaying particular surface proteins. The latter phenomenon is directly linked to the choice of a particular "lifestyle," adhesion (biofilm formation) or motility (planktonic growth). In Escherichia coli, expression of flagella at the cell surface predestines the cell for motility. More than 50 genes are involved in the synthesis of flagella. These genes are classified into three groups according to their temporal expression sequence. The master regulator of flagellar synthesis, FlhDC, is expressed first, and the corresponding genes constitute the class 1 flagellar operon. FlhDC activates the expression of class 2 genes, encoding the inner part of the flagellum as well as the flagellar sigma factor FliA ( 28 or F ) and the FlgM protein (anti-28 ). The class 3 genes are transcribed by 28 -RNA polymerase (RNAP) and encode the outer components of the flagellum as well as chemotaxis proteins (for a review, see reference 1).Flagellar synthesis is tightly controlled by several environmental conditions, including osmolarity (2) and temperature (3). Many of these environmental influences affect the transcription of flagellar genes and control in particular the expression of the master regulator, FlhDC. The regulators of the flhDC operon include cyclic AMP (cAMP)-cAMP receptor protein (CRP), H-NS (4), OmpR (2), LrhA (5), integration host factor (IHF) (2, 6), RpoN (7), and Fur (8). Other regulators affect cell motility by controlling fliA expression. These factors include NsrR (9), signaling molecules such as cyclic diguanylic acid (c-di-GMP) (reviewed in reference 10), the alarmone polyphosphate guanosine [(p)ppGpp] (11), and quorum-sensing molecules such as autoinducer-2 (AI-2) (12)(13)(14).Transcriptomic data show that the stationary sigma factor RpoS ( 38 ) represses the transcription of flagellar gene...

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