7.19D: Regulation of Sigma Factor Activity - Biology

7.19D:  Regulation of Sigma Factor Activity - Biology

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Learning Objectives

  • Analyze the regulation of sigma factor activation

Sigma factors are proteins that function in transcription initiation. Specifically, in bacteria, sigma factors are necessary for recognition of RNA polymerase to the gene promoter site. The sigma factor allows the RNA polymerase to properly bind to the promoter site and initiate transcription which will result in the production of an mRNA molecule. The type of sigma factor that is used in this process varies and depends on the gene and on the cellular environment. The sigma factors identified to date are characterized based on molecular weight and have shown diversity between bacterial species as well. Once the role of the sigma factor is completed, the protein leaves the complex and RNA polymerase will continue with transcription.

The regulation of sigma factor activity is critical and necessary to ensure proper initiation of transcription. The activity of sigma factors within a cell is controlled in numerous ways. Sigma factor synthesis is controlled at the levels of both transcription and translation. Often times, sigma factor expression or activity is dependent on specific growth phase transitions of the organism. If transcription of genes involved in growth is necessary, the sigma factors will be translated to allow for transcription initiation to occur. However, if transcription of genes is not required, sigma factors will not be active.

In specific instances when transcriptional activity needs to be inhibited, there are anti-sigma factors which perform this function. The anti-sigma factors will bind to the RNA polymerase and prevent its binding to sigma factors present at the promoter site. The anti-sigma factors are responsible for regulating inhibition of transcriptional activity in organisms that require sigma factor for proper transcription initiation.

Key Points

  • Sigma factor proteins promote binding of RNA polymerase to promoter sites within DNA sequences to allow for initiation of transcription.
  • Sigma factors are specific for the gene and are affected by the cellular environment.
  • Sigma factors can regulate at both a transcription and translational level.
  • Anti-sigma factors are responsible for inhibiting sigma factor function thus, inhibiting transcription.

Key Terms

  • growth phase transitions: The various phases required for bacterial growth include: lag, exponential, and stationary phases.

SIG1, a Sigma Factor for the Chloroplast RNA Polymerase, Differently Associates with Multiple DNA Regions in the Chloroplast Chromosomes in Vivo

500–600 bp on average). Then, immunoprecipitation with a specific antibody is performed to purify the protein of interest, together with cross-linked target DNA. Finally, cross-links are removed with heat treatment, and DNA is purified. Consequently, the genomic regions bound to the protein of interest at the moment of formaldehyde cross-linking can be specifically enriched. Finally, the levels of purified genomic regions can be measured by quantitative PCR (qPCR) and their signals analyzed as the percent recovery against the amount of input DNA.

Multiple pathways for regulation of sigmaS (RpoS) stability in Escherichia coli via the action of multiple anti-adaptors

SigmaS, the stationary phase sigma factor of Escherichia coli and Salmonella, is regulated at multiple levels. The sigmaS protein is unstable during exponential growth and is stabilized during stationary phase and after various stress treatments. Degradation requires both the ClpXP protease and the adaptor RssB. The small antiadaptor protein IraP is made in response to phosphate starvation and interacts with RssB, causing sigmaS stabilization under this stress condition. IraP is essential for sigmaS stabilization in some but not all starvation conditions, suggesting the existence of other anti-adaptor proteins. We report here the identification of new regulators of sigmaS stability, important under other stress conditions. IraM (inhibitor of RssB activity during Magnesium starvation) and IraD (inhibitor of RssB activity after DNA damage) inhibit sigmaS proteolysis both in vivo and in vitro. Our results reveal that multiple anti-adaptor proteins allow the regulation of sigmaS stability through the regulation of RssB activity under a variety of stress conditions.

Global regulation of a sigma 54-dependent flagellar gene family in Caulobacter crescentus by the transcriptional activator FlbD.

Biosynthesis of the Caulobacter crescentus polar flagellum requires the expression of a large number of flagellar (fla) genes that are organized in a regulatory hierarchy of four classes (I to IV). The timing of fla gene expression in the cell cycle is determined by specialized forms of RNA polymerase and the appearance and/or activation of regulatory proteins. Here we report an investigation of the role of the C. crescentus transcriptional regulatory protein FlbD in the activation of sigma 54-dependent class III and class IV fla genes of the hierarchy by reconstituting transcription from these promoters in vitro. Our results demonstrate that transcription from promoters of the class III genes flbG, flgF, and flgI and the class IV gene fliK by Escherichia coli E sigma 54 is activated by FlbD or the mutant protein FlbDS140F (where S140F denotes an S-to-F mutation at position 140), which we show here has a higher potential for transcriptional activation. In vitro studies of the flbG promoter have shown previously that transcriptional activation by the FlbD protein requires ftr (ftr for flagellar transcription regulation) sequence elements. We have now identified multiple ftr sequences that are conserved in both sequence and spatial architecture in all known class III and class IV promoters. These newly identified ftr elements are positioned ca. 100 bp from the transcription start sites of each sigma 54-dependent fla gene promoter, and our studies indicate that they play an important role in controlling the levels of transcription from different class III and class IV promoters. We have also used mutational analysis to show that the ftr sequences are required for full activation by the FlbD protein both in vitro and in vivo. Thus, our results suggest that FlbD, which is encoded by the class II flbD gene, is a global regulator that activates the cell cycle-regulated transcription from all identified sigma 54-dependent promoters in the C. crescentus fla gene hierarchy.

Regulation of Sigma Factor Activity

The sigma factor is responsible for proper transcriptional initiation.

Learning Objectives

Analyze the regulation of sigma factor activation

Key Takeaways

Key Points

  • Sigma factor proteins promote binding of RNA polymerase to promoter sites within DNA sequences to allow for initiation of transcription.
  • Sigma factors are specific for the gene and are affected by the cellular environment.
  • Sigma factors can regulate at both a transcription and translational level.
  • Anti-sigma factors are responsible for inhibiting sigma factor function thus, inhibiting transcription.

Key Terms

  • growth phase transitions: The various phases required for bacterial growth include: lag, exponential, and stationary phases.

Sigma factors are proteins that function in transcription initiation. Specifically, in bacteria, sigma factors are necessary for recognition of RNA polymerase to the gene promoter site. The sigma factor allows the RNA polymerase to properly bind to the promoter site and initiate transcription which will result in the production of an mRNA molecule. The type of sigma factor that is used in this process varies and depends on the gene and on the cellular environment. The sigma factors identified to date are characterized based on molecular weight and have shown diversity between bacterial species as well. Once the role of the sigma factor is completed, the protein leaves the complex and RNA polymerase will continue with transcription.

Sigma factor SigR: Structure of sigma factor.

The regulation of sigma factor activity is critical and necessary to ensure proper initiation of transcription. The activity of sigma factors within a cell is controlled in numerous ways. Sigma factor synthesis is controlled at the levels of both transcription and translation. Often times, sigma factor expression or activity is dependent on specific growth phase transitions of the organism. If transcription of genes involved in growth is necessary, the sigma factors will be translated to allow for transcription initiation to occur. However, if transcription of genes is not required, sigma factors will not be active.

In specific instances when transcriptional activity needs to be inhibited, there are anti-sigma factors which perform this function. The anti-sigma factors will bind to the RNA polymerase and prevent its binding to sigma factors present at the promoter site. The anti-sigma factors are responsible for regulating inhibition of transcriptional activity in organisms that require sigma factor for proper transcription initiation.

Function of plastid sigma factors in higher plants: regulation of gene expression or just preservation of constitutive transcription?

Plastid gene expression is rather complex. Transcription is performed by three different RNA polymerases, two of them are nucleus-encoded, monomeric, of the phage-type (named RPOTp and RPOTmp) and one of them is plastid-encoded, multimeric, of the eubacterial-type (named PEP). The activity of the eubacterial-type RNA polymerase is regulated by up to six nucleus-encoded transcription initiation factors of the sigma-type. This complexity of the plastid transcriptional apparatus is not yet well understood and raises the question of whether it is subject to any regulation or just ensures constitutive transcription of the plastid genome. On the other hand, considerable advances have been made during the last years elucidating the role of sigma factors for specific promoter recognition and selected transcription of some plastid genes. Sigma-interacting proteins have been identified and phosphorylation-dependent functional changes of sigma factors have been revealed. The present review aims to summarize these recent advances and to convince the reader that plastid gene expression is regulated on the transcriptional level by sigma factor action.

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The availability of rickettsial genome sequences enables large-scale investigations including DNA microarrays-based experiments. Such a functional post-genomic application is very useful to understand how pathogens adapt to their environment and has provided some insights into the pathogenic strategies displayed by several microorganisms during the infectious process (Boyce et al., 2004, Jansen and Yu, 2006). However, DNA microarray-based gene expression profiling for the study of bacterial infection have been hampered by several challenges (Hinton et al., 2004). In contrast to eukaryotic mRNAs, bacterial mRNAs are devoid of poly-A tails and have a reduced half-life estimated to be about 15 min for Rickettsia prowazekii (Winkler, 1995), which make them difficult to isolate. When starting from eukaryotic infected cells, as it is the case when studying obligate intracellular bacteria, it is even more challenging to obtain high-quality prokaryotic mRNAs (Hinton et al., 2004). The mRNA stability can be ensured by the co-extraction of eukaryotic and prokaryotic nucleic acids, as usually performed for RT-PCR assays. Therefore, because eukaryotic RNA can compete with bacterial RNA during cDNA synthesis and fluorochrome labeling, it has to be eliminated from such samples to succeed in microarray hybridization (Belland et al., 2003, Di Cello et al., 2005). Beyond quality, another major limitation in labeling and hybridization reactions is the low amount of available RNA (Hinton et al., 2004). To our knowledge, the only accurate global transcriptomic analysis of obligate intracellular bacteria which circumvents both the eukaryotic RNA contamination and the low amount of template, was that described by Belland et al. (2003). Their strategy, applied on Chlamydia trachomatis, combined the removal of eukaryotic RNA from total RNA followed by a cDNA amplification step with a complete set of oligonucleotides specifically designed for this microorganism. Another non-biased amplification method based on the use of random primers was recently delineated and demonstrated to be compatible even for AT-rich microorganisms (Francois et al., 2007).

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The work presented in this paper was performed with Rickettsia conorii, the causative agent of the Mediterranean spotted fever transmitted to humans by Rhipicephalus sanguineus, the brown dog tick (Raoult and Roux, 1997). To assess both the feasibility and the accuracy of the proposed approach in monitoring the transcriptional changes of rickettsiae by microarrays, bacteria were exposed to a nutrient stress. From RT-PCR-based experiments, we noted that under such conditions, R. conorii does undergo some transcriptional modifications (Rovery et al., 2005a, Rovery et al., 2005b). A small microarray composed of a limited number of targets, including the R. conorii ORFs previously found up- or down-regulated was constructed. Other targets, including transcription factors, chaperones or virB genes, are susceptible to improve the survival of bacteria during prolonged periods of starvation. Given their putative regulation, they were also amplified and spotted on our microarray. Differentially expressed genes identified through the microarray-based experiments were then validated, ensuring that the proposed protocol that combine removal of eukaryotic contaminants and amplification of purified bacterial RNA preserves the information content of original samples.

REVIEW article

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Forty years after the initial discovery of light-dependent protein phosphorylation at the thylakoid membrane system, we are now beginning to understand the roles of chloroplast phosphorylation networks in their function to decode and mediate information on the metabolic status of the organelle to long-term adaptations in plastid and nuclear gene expression. With the help of genetics and functional genomics tools, chloroplast kinases and several hundred phosphoproteins were identified that now await detailed functional characterization. The regulation and the target protein spectrum of some kinases are understood, but this information is fragmentary with respect to kinase and target protein crosstalk in a changing environment. In this review, we will highlight the most recent advances in the field and discuss approaches that might lead to a comprehensive understanding of plastid signal integration by protein phosphorylation.


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Unless defined otherwise, all technical and scientific terms used herein have the meaning commonly understood by a person skilled in the art to which this invention belongs. The following references provide one of skill with a general definition of many of the terms used in this invention: Singleton et al., Dictionary of Microbiology and Molecular Biology (2nd ed. 1994) The Cambridge Dictionary of Science and Technology (Walker ed., 1988) The Glossary of Genetics, 5th Ed., R. Rieger et al. (eds.), Springer Verlag (1991) and Hale & Marham, The Harper Collins Dictionary of Biology (1991). As used herein, the following terms have the meanings ascribed to them unless specified otherwise.

When introducing elements of the present disclosure or the preferred embodiments(s) thereof, the articles “a”, “an”, “the” and “said” are intended to mean that there are one or more of the elements. The terms “comprising”, “including” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements.

As used herein, the term “endogenous sequence” refers to a chromosomal sequence that is native to the cell.

The term “exogenous,” as used herein, refers to a sequence that is not native to the cell, or a chromosomal sequence whose native location in the genome of the cell is in a different chromosomal location.

A “gene,” as used herein, refers to a DNA region (including exons and introns) encoding a gene product, as well as all DNA regions which regulate the production of the gene product, whether or not such regulatory sequences are adjacent to coding and/or transcribed sequences. Accordingly, a gene includes, but is not necessarily limited to, promoter sequences, terminators, translational regulatory sequences such as ribosome binding sites and internal ribosome entry sites, enhancers, silencers, insulators, boundary elements, replication origins, matrix attachment sites, and locus control regions.

The term “heterologous” refers to an entity that is not endogenous or native to the cell of interest. For example, a heterologous protein refers to a protein that is derived from or was originally derived from an exogenous source, such as an exogenously introduced nucleic acid sequence. In some instances, the heterologous protein is not normally produced by the cell of interest.

The terms “nucleic acid” and “polynucleotide” refer to a deoxyribonucleotide or ribonucleotide polymer, in linear or circular conformation, and in either single- or double-stranded form. For the purposes of the present disclosure, these terms are not to be construed as limiting with respect to the length of a polymer. The terms can encompass known analogs of natural nucleotides, as well as nucleotides that are modified in the base, sugar and/or phosphate moieties (e.g., phosphorothioate backbones). In general, an analog of a particular nucleotide has the same base-pairing specificity i.e., an analog of A will base-pair with T.

The term “nucleotide” refers to deoxyribonucleotides or ribonucleotides. The nucleotides may be standard nucleotides (i.e., adenosine, guanosine, cytidine, thymidine, and uridine) or nucleotide analogs. A nucleotide analog refers to a nucleotide having a modified purine or pyrimidine base or a modified ribose moiety. A nucleotide analog may be a naturally occurring nucleotide (e.g., inosine) or a non-naturally occurring nucleotide. Non-limiting examples of modifications on the sugar or base moieties of a nucleotide include the addition (or removal) of acetyl groups, amino groups, carboxyl groups, carboxymethyl groups, hydroxyl groups, methyl groups, phosphoryl groups, and thiol groups, as well as the substitution of the carbon and nitrogen atoms of the bases with other atoms (e.g., 7-deaza purines). Nucleotide analogs also include dideoxy nucleotides, 2′-O-methyl nucleotides, locked nucleic acids (LNA), peptide nucleic acids (PNA), and morpholinos.

The terms “polypeptide” and “protein” are used interchangeably to refer to a polymer of amino acid residues.

Techniques for determining nucleic acid and amino acid sequence identity are known in the art. Typically, such techniques include determining the nucleotide sequence of the mRNA for a gene and/or determining the amino acid sequence encoded thereby, and comparing these sequences to a second nucleotide or amino acid sequence. Genomic sequences can also be determined and compared in this fashion. In general, identity refers to an exact nucleotide-to-nucleotide or amino acid-to-amino acid correspondence of two polynucleotides or polypeptide sequences, respectively. Two or more sequences (polynucleotide or amino acid) can be compared by determining their percent identity. The percent identity of two sequences, whether nucleic acid or amino acid sequences, is the number of exact matches between two aligned sequences divided by the length of the shorter sequences and multiplied by 100. An approximate alignment for nucleic acid sequences is provided by the local homology algorithm of Smith and Waterman, Advances in Applied Mathematics 2:482-489 (1981). This algorithm can be applied to amino acid sequences by using the scoring matrix developed by Dayhoff, Atlas of Protein Sequences and Structure, M. O. Dayhoff ed., 5 suppl. 3:353-358, National Biomedical Research Foundation, Washington, D.C., USA, and normalized by Gribskov, Nucl. Acids Res. 14(6):6745-6763 (1986). An exemplary implementation of this algorithm to determine percent identity of a sequence is provided by the Genetics Computer Group (Madison, Wis.) in the “BestFit” utility application. Other suitable programs for calculating the percent identity or similarity between sequences are generally known in the art, for example, another alignment program is BLAST, used with default parameters. For example, BLASTN and BLASTP can be used using the following default parameters: genetic code=standard filter=none strand=both cutoff=60 expect=10 Matrix=BLOSUM62 Descriptions=50 sequences sort by=HIGH SCORE Databases=non-redundant, GenBank+EMBL+DDBJ+PDB+GenBank CDS translations+Swiss protein+Spupdate+PIR. Details of these programs can be found on the GenBank website.

As various changes could be made in the above-described cells and methods without departing from the scope of the invention, it is intended that all matter contained in the above description and in the examples given below, shall be interpreted as illustrative and not in a limiting sense.


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  4. Mwita

    I accept it with pleasure. In my opinion, this is relevant, I will take part in the discussion. Together we can come to the right answer.

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