It is not understood whether these elements function collectively to confer transcriptional regulation, or individually to control specific aspects of activation or repression, such as initiation versus maintenance. Here, we have analyzed cis element regulation of the critical hematopoietic factor Gata2, which is expressed in early precursors and repressed as GATA-1 levels rise during terminal differentiation. We engineered mice lacking a single cis element −1.8 kb upstream of the Gata2 transcriptional start site. Although Gata2 is normally repressed in late-stage erythroblasts, the −1.8 kb mutation unexpectedly resulted in reactivated Gata2 transcription, blocked differentiation, and an aberrant lineage-specific gene expression pattern. Our findings demonstrate that the −1.8 kb site selectively maintains repression, confers a specific histone modification pattern and expels RNA Polymerase II from the locus. These studies reveal how an individual cis element establishes a normal developmental program via regulating specific steps in the mechanism by which a critical transcription factor is repressed.In development, lineage-restricted transcription factors simultaneously promote differentiation while repressing alternative fates. Molecular dissection of this process has been challenging as transcription factor loci are regulated by many  Gata2, by the transcription factor GATA1. In the DNA sequence upstream of Gata2, there are several sites that GATA1 is known to bind to; however, it is unclear whether these binding sites work together or independently to control expression of Gata2. To study this, we engineered mice to specifically remove one of these GATA1-binding sites. We found that removal of this single site reactivated expression of Gata2 in a specific stage of red blood cell development where Gata2 is normally not expressed, caused a block in differentiation of these cells, and changed the histone modification pattern specifically in the region upstream of Gata2. This work supports a model in which individual transcription factor binding sites within regions of multiple binding sites can independently and distinctly regulate gene expression during development.Different cell types are formed and maintained by proteins called transcription factors that directly bind to specific DNA sequences to activate or repress gene expression. While numerous DNA sequences bound by transcription factors are established, many questions remain unanswered regarding how they function at specific sites located at distinct chromosomal regions. As a model to study this process, we examined the regulation of a gene controlling red blood cell development,  The covalent modification of histones to yield specific histone marks promotes either the activation or repression of transcription cis elements residing within clusters of cis elements to the transcriptional control of endogenous loci is incompletely understood.Metazoan development is characterized by complex transcriptional programs specified by gene regulatory networks cis elements to the initiation and maintenance of transcriptional repression. The GATA family of transcription factors plays diverse roles in multiple developmental contexts Gata2 transcription via displacing GATA-2 from chromatin sites at its own locus, a process termed a “GATA Switch” GATA factor cross-regulation represents an instructive model system for investigating the contribution of individual Gata2 knock-out mice resulting in lethality during midgestation Gata2 through specific cis elements is required for differentiation. Genome-wide studies revealed GATA-1 occupancy at only a small subset of cis elements in the genome cis elements exist as single or more complex GATA motifs, although the functionality of different permutations of GATA motifs at endogenous loci has not been investigated.GATA factor function has been extensively studied in the context of hematopoiesis, where GATA-1, GATA-2, and GATA-3 are key regulators. GATA-2 has a broad role in hematopoietic development, as demonstrated by impaired hematopoiesis in Gata2 locus, where several conserved GATA motif-containing regions span approximately 100 kb of the locus Gata2 expression, and to investigate the underlying mechanisms, we generated mice lacking one of these regulatory regions residing −1.8 kb upstream of the Gata2 promoter. We find that while this site is not essential for Gata2 expression in hematopoietic progenitors or initiation of Gata2 repression during erythropoiesis, it maintains Gata2 repression in erythroblasts. Molecular analyses demonstrate that loss of the −1.8 kb site reduces GATA-1 binding, allows for increased RNA Polymerase II (Pol II) occupancy at the locus, and results in changes in select histone marks. Further, elimination of the −1.8 kb site dysregulates Gata2 transcriptional control, disrupts the GATA-2-dependent genetic network, and interferes with red blood cell maturation. These results highlight the qualitatively distinct activities of individual cis elements in specific aspects of gene repression during development.The role of individual GATA-binding sites in gene regulation has been investigated extensively at the Gata2 locus   , S1. Mic allele) ., implyi allele) , and aduGata2 expression. Using fluorescence-activated cell sorting with the erythroid markers CD71 and Ter119 loTer119− , CD71hiTer119− , CD71hiTer119+ , and CD71loTer119+    . In wild, and IV . Gata2 e(p≤0.05) . Thus, tGata2 is reactivated  and ∼50% (p≤0.05), respectively, in the late erythroblasts of Δ-1.8 versus wild-type mice. In contrast to Gata1 and Eraf, most late erythroid genes examined, including the transcription factors Scl, Eklf, and the heme synthesis enzyme Alas2, were expressed at similar levels, indicating that erythroid genes are differentially sensitive to Gata2 reactivation. Whereas expression of β-like globin genes  was normal (Hba-a1) was reduced by 50% (p≤0.05) and ζ-globin (Hba-x) was increased by 2-fold (p≤0.05) (cMyb and the established GATA-2 target cKit were upregulated 4-fold (p≤0.05)   . We also(p≤0.05) . In mast(p≤0.05) . These rGata2 expression (data not shown), indicating that Gata2 transcription is differentially regulated during fetal and adult erythropoiesis. Adult erythropoiesis has several unique attributes relative to the fetal process, including differences in proliferative capacity and rate of transit through the differentiation program Gata2 reactivation. We reasoned that stress erythropoiesis in the adult, which resembles fetal liver erythropoiesis Gata2 expression to a state mimicking that in the fetus. To establish stress erythropoietic conditions, peripheral anemia was induced through phenylhydrazine-mediated red blood cell lysis. Examination of erythropoietic recovery in Δ-1.8 mice revealed no differences in hematocrit, implying that there is no deficiency in recovery from acute anemia in these mice (data not shown). However, analysis of erythropoietic progenitor production in the bone marrow during recovery revealed that the absolute number of Stage III erythroid progenitors was significantly increased in Δ-1.8 mice (p≤0.05) (Gata2 transcription is increased significantly (p≤0.05) in Stage IV cells from Δ-1.8 mice  did not reveal differences in (p≤0.05) , while t1.8 mice . These r1.8 mice , indicatGata2 is transcribed from two alternate promoters, termed 1S and 1G, leading to two transcripts with different first exons Gata2 repression in Δ-1.8 erythroid cells (Gata2 transcripts expressed in Stage I were derived from the 1G promoter and were repressed in Stage II–IV similarly in wild-type and Δ-1.8 cells (id cells  reflects.8 cells . mRNA ex.8 cells . Quantit.8 cells . TogetheRPII215) did not change upon loss of the −1.8 kb site  analysis of E14.5 fetal liver cells demonstrated reduction of GATA-1 occupancy at the −1.75 kb (used as a surrogate for measuring occupancy at the deleted −1.8 kb site) and the −2.8 kb sites (p = 0.057 and 0.058 respectively) and the proximal GATA-binding regions at −3.9 kb, (p≤0.01); occupancy was not significantly altered at the distal −77 and +9.5 kb sites . ChIP an kb site , providiGata2 locus cis element contributes to the maintenance of the dimeH3K4 mark in erythroid cells.To analyze histone modifications within the erythroid populations in which we observed an altered phenotype on removal of the −1.8 kb site , we perfContribution of the dimethylH3K4 modification to transcriptional regulation is incompletely understood cis element leads to decreased GATA-1 occupancy at sites up to several kilobases away, reductions in dimeH3K4 and monomeH4K4 marks in the regulatory regions, and increased RNA Pol II occupancy. We propose a model in which this altered nucleoprotein structure favors a transcriptionally active locus, thereby permitting Gata2 reactivation.These data indicate that loss of GATA-1 binding at the deleted −1.8 kb Gata2 locus contains four CpG islands Gata2 has been correlated with promoter methylation in some tissues cis element maintains repression through such a mechanism. Bisulfite sequencing was utilized to quantitate promoter methylation of a 3′ section of the 1S CpG island within sorted populations of Stage II–IV erythroid progenitors from wild-type and mutant mice. In wild-type mice, the CpG island located at the Gata2 1S promoter was largely unmethylated in Stage II, Stage III, and Stage IV progenitors, with an average methylation of 5.2%, 8.9%, and 7.1%, respectively  with two SalI sites in order to clone in a Neo resistance cassette flanked by loxP sites. Once the neo cassette is removed, the locus contains a single loxP site flanked by SalI sites. The new sequence does not contain any binding motifs known to be involved in hematopoietic development. In more detail, we first inserted a HpaI site into pBSK between NotI and SacI with an oligo. Then, we cloned a −7.2 kb to intron 1S fragment of the Gata2 locus into pBSK with KpnI and HpaI. We then replaced the two palindromic WGATAR sites with a Sal I site via PCR and replaced the wild-type XbaI to NdeI fragment with this mutated version. Then, we cloned HSV-TK cloned into the SacII site of pBSK. Following this, we cloned a second SalI site into the XbaI site of pflox21 with an oligo and used the flanking SalI sites to clone this loxP-PGKneo-loxP cassette into the SalI site of the Gata2-containing pBSK  or Phosphate Buffered Saline (PBS) supplemented with 2% fetal calf serum . Single cell suspensions of fetal livers and spleens were made by passage through 70 micron nylon mesh  in PBS supplemented 2% fetal calf serum . Cells were kept on ice until use and counts were performed using a Beckman Coulter AcT10 hematological analyzer.RNA was prepared from the described populations using the Trizol Kit , DNAseI treated by RQ1 RNase-Free DNase  and quantified. cDNA was synthesized using 1 µg of RNA with the iScript cDNA Synthesis Kit . Typically, 1 µl of cDNA was then used as a template for quantitative PCR using the iQ SYBR Green Supermix  in an iCycler thermocycler . Primer sequences can be found in 4Cl) lysis buffer.Whole PB was analyzed on a Beckman Coulter AcT10 hematological analyzer. White blood cell and progenitor subsets were analyzed from peripheral blood by staining with Gr-1 and Mac1 or CD3 and B220 after red blood cell lysis using ACK  or eBiosciences , and the following clones were used; Ly-76 (Ter-119), CD71 (C2), CD117 (2B8). Antibodies to surface markers of interest were used at 1∶60 dilution and after 30–60 minutes unbound antibody was washed away. In the case of biotinylated antibodies, streptavidin conjugated to various fluorochromes was added for the last 15–30 minutes of antibody incubation at 1∶100 dilution. For cell sorting experiments of erythroid progenitor subsets, fetal liver cells were stained with antibodies to CD71 and Ter119, and 7AAD was added to allow for exclusion of dead cells during sorting. For examination of enucleation, cells were stained with CD71 and Ter119 as above and incubated with Draq5  as per manufacturers instructions before analysis.Rabbit anti-GATA-1 and anti-GATA-2 polyclonal antibodies have been described previously Gata2 1S promoter  and transformed into DH5α E. coli cells. Miniprep plasmid DNA was verified by EcoRI digestion and positive clones were sequenced using M13 forward (−20) or reverse primers.Bisulfite treatment of genomic DNA was performed as previously described using the Qiagen EpiTect Bisulfite Kit as per the manufacter's instructions. Sequence-specific PCR of the bisulfite-treated DNA was performed using primers specific to the murine Data are presented as mean ± SEM. Statistical significance was assessed by two-sided Student's t-test.Figure S1Δ-1.8 targeting construct generation. Graphical representation of the generation of the targeting construct to replace the palindromic GATA-binding site −1.8 kb upstream of the 1S transcriptional start site with a loxP-flanked PGK-neomycin cassette (A). Southern blot strategy outlining the HindIII/SalI digested fragment sizes for the wild-type and targeted alleles, and probe hybridization sites (B). Southern blot of Δ-1.8 germline mice and wild-type (WT) littermates from tail tip genomic DNA (C).(0.27 MB PDF)Click here for additional data file.Figure S24 wild-type and Δ-1.8 E12.5 fetal liver cells (C). FACS histograms showing the proportion of enucleated cells from Stage IV erythroblasts within representative wild-type and Δ-1.8 fetal livers (D).E12.5 hematopoiesis in Δ-1.8 mice. Representative E12.5 wildtype (WT) and Δ-1.8 embryos (A). Cytospins of embryonic peripheral blood and fetal liver cells from E12.5 WT and Δ-1.8 embryos (B). Number of CFU-GEMM, CFU-GM, BFU-E, and CFU-E colonies per 10(0.99 MB PDF)Click here for additional data file.Figure S3Gata2 locus. Quantitative ChIP analysis across the Gata2 locus using antibodies to trimethylH3K4 (A) and monomethylH3K4 (B) in whole fetal liver cells from wild-type and Δ-1.8 embryos at E14.5. Calculations were derived as above. Bisulfite sequencing of the 3′ region of the Gata2 1S promoter CpG island in WT and Δ-1.8 Stage II, Stage III, and Stage IV erythroid progenitors. Each line represents an individual sequenced clone; white circles denote unmethylated CpG dinucleotides, black circles denote methylated CpGs (C).Loss of the −1.8 kb site leads to altered nucleoprotein architecture of the (0.49 MB PDF)Click here for additional data file.Text S1Supporting Materials and Methods.(0.05 MB DOC)Click here for additional data file.


Salmonella RNAs that are targeted by the common bacterial Sm-like protein, Hfq. Initial transcriptomic analysis revealed that Hfq controls the expression of almost a fifth of all Salmonella genes, including several horizontally acquired pathogenicity islands , two sigma factor regulons, and the flagellar gene cascade. Subsequent HTPS analysis of 350,000 cDNAs, derived from RNA co-immunoprecipitation (coIP) with epitope-tagged Hfq or control coIP, identified 727 mRNAs that are Hfq-bound in vivo. The cDNA analysis discovered new, small noncoding RNAs (sRNAs) and more than doubled the number of sRNAs known to be expressed in Salmonella to 64; about half of these are associated with Hfq. Our analysis explained aspects of the pleiotropic effects of Hfq loss-of-function. Specifically, we found that the mRNAs of hilD (master regulator of the SPI-1 invasion genes) and flhDC (flagellar master regulator) were bound by Hfq. We predicted that defective SPI-1 secretion and flagellar phenotypes of the hfq mutant would be rescued by overexpression of HilD and FlhDC, and we proved this to be correct. The combination of epitope-tagging and HTPS of immunoprecipitated RNA detected the expression of many intergenic chromosomal regions of Salmonella. Our approach overcomes the limited availability of high-density microarrays that have impeded expression-based sRNA discovery in microorganisms. We present a generic strategy that is ideal for the systems-level analysis of the post-transcriptional regulons of RNA-binding proteins and for sRNA discovery in a wide range of bacteria.Recent advances in high-throughput pyrosequencing (HTPS) technology now allow a thorough analysis of RNA bound to cellular proteins, and, therefore, of post-transcriptional regulons. We used HTPS to discover the  Salmonella enterica serovar Typhimurium. This new strategy used Next Generation pyrosequencing to identify 727 Hfq-bound mRNAs. The numbers of sRNAs expressed in Salmonella was doubled to 64; half are associated with Hfq. We defined the exact coordinates of sRNAs, and confirmed that they are expressed at significant levels. We also determined the Hfq regulon in Salmonella, and reported the role of Hfq in controlling transcription of major pathogenicity islands, horizontally acquired regions, and the flagellar cascade. Hfq is reported to be a global regulator that affects the expression of almost a fifth of all Salmonella genes. Our new approach will allow sRNAs and mRNAs to be characterized from different genetic backgrounds, or from bacteria grown under particular environmental conditions. It will be valuable to scientists working on genetically tractable bacteria who are interested in the function of RNA-binding proteins and the identification of sRNAs.The past decade has seen small regulatory RNA become an important new mediator of bacterial mRNA regulation. This study describes a rapid way to identify novel sRNAs that are expressed, and should prove relevant to a variety of bacteria. We purified the epitope-tagged RNA-binding protein, Hfq, and its bound RNA by immunoprecipitation from the model pathogen,  Until now, global gene expression control studies have generally focussed on the transcriptional regulation exerted by the specific action of DNA binding proteins, and on the post-translational regulation governed by specific protein–protein interactions. In comparison, little is known about how RNA binding proteins facilitate the global control of gene expression at the post-transcriptional level. However, the latest discoveries of many small noncoding RNAs (sRNAs) in both pro- and eukaryotes have shown that the interaction of RNA with proteins plays a prominent role in the regulation of cellular processes. In bacteria, the majority of the sRNAs basepair with target mRNAs to regulate their translation and/or decay Escherichia coli as a host factor required for phage Qβ RNA replication ∼40 years ago Hfq is one of the most abundant RNA-binding proteins in bacteria hfq deletion mutation was first apparent from the multiple stress response-related phenotypes in E. colirpoS mRNA, encoding the major stress sigma factor, σS hfq attenuates the ability of the model pathogen Salmonella enterica serovar Typhimurium (S. Typhimurium) to infect mice, to invade epithelial cells, to secrete virulence factors and to survive inside cultured macrophages Salmonella and the deregulation of >70 abundant proteins, including the accumulation of outer membrane proteins (OMPs); the latter is accompanied by a chronic activation of the σE (σ24)-mediated envelope stress response Salmonella gene expression changes induced by the low gravity condition experienced during spaceflight The pleiotropy of an Salmonella gene expression at the post-transcriptional level requires the identification of its sRNA and mRNA ligands. In a pioneering global study in E. coli, Zhang et al. (2003) used co-immunoprecipitation (coIP) with Hfq-specific antisera and direct detection of the bound RNAs on genomic high-density oligonucleotide microarrays. Although this method proved highly effective for detecting diverse sRNAs and mRNAs in E. coli, the requirement for high-density microarrays and specialized antibodies has hampered similar studies in other bacteria. An alternate approach identified individual abundant Hfq-associated RNAs by cDNA cloning or direct sequencing Understanding how Hfq controls Salmonella, we have now used high-throughput pyrosequencing  of RNA associated with an epitope-tagged Hfq protein  was determined. We used two different conditions for the comparison; aerobic growth in L-broth to early stationary phase  was chosen because the hfq mutation causes drastic protein pattern changes in ESP SalmonellaSalmonella virulence genes required for host cell invasion hfq strain grown to ESP , and in the flagellar and chemotaxis pathways, >60% of genes were down-regulated, which explains the previously observed invasion and motility phenotypes of ΔhfqSalmonella at the mRNA level (ESP) and at the protein level (ESP and SPI-1 inducing conditions). σE mRNA and protein levels were strongly elevated in Δhfq under both conditions tested . Of these “Control cDNAs” which ranged in length from 1 to 290 bp  were abundant rRNA, tRNA, and housekeeping RNA transcripts . This browser allows the visualization of both whole genomes and individual genomic regions. invR locus located at the right-hand SPI-1 border  and all major five pathogenicity islands for expression patterns of IGRs indicative of new sRNA genes, and for possible enrichment by Hfq coIP. Using criteria similar to E. coli. Of the latter two classes of candidates  was unaffected in the absence of Hfq. STnc500, which is only detected in samples originating from cultures at OD600 of 1, and STnc580, which seems to be specifically expressed under the SPI-1 inducing condition, were not detected on these blots.We determined whether 8 of the new  binding . The STn binding , by factSalmonella genome annotation ypfM and yneM mRNA-encoding genes of E. coliSalmonella loci yielded signals of stable short mRNAs which are expressed in a growth phase-dependent manner  shown in hfq strain is necessarily a “direct” Hfq target, i.e. its mRNA bound by Hfq. Second, there may be a considerable number of Hfq-associated mRNAs below our very stringent cut-off for Hfq-association; increasing sequencing depth will overcome this problem. Third, the precise borders of most 5′/3′ UTRs are unknown in Salmonella ; consequently, calculations of Hfq enrichment were limited to the CDS of an mRNA. As outlined further below  was required to be represented by ≥10 cDNAs to be considered significant, which identified 727 Hfq-bound mRNAs (cistrons) for further analysis. er below , this caS. Typhimurium oligonucleotide microarray. Analysis of this coIP-on-Chip experiment with SAM-software  identified 365 enriched mRNAs. Nearly half (45%) of these mRNAs corresponded to regions identified by the deep sequencing approach (P<10e-10). The overlap increased to 67% when genes that showed enrichment values above 5 were taken into consideration. Although coIP-on-Chip displays a lower sensitivity than deep sequencing these two independent methods do generate comparable results for the identification of mRNA-protein interactions.To validate our cDNA sequencing approach for the detection of Hfq-bound mRNAs by the conventional approach, we hybridized the RNA obtained from Hfq and control coIP to a ompA cDNAs does not drastically differ in the two libraries , which would question whether ompA is an Hfq-bound mRNA. However, up to 12-fold enrichment is seen in some sections of the ompA mRNA, e.g., around the AUG and in the central CDS . Whilst tral CDS . Note thi) cDNAs of Hfq-bound mRNAs of hilD , fliC (which encodes a major flagellin), or flhDC  were distributed over the entire length of the relevant gene, including the ∼300 nt 3′ UTR in the case of hilD. Either Hfq does target such a large number of sites on these three mRNAs, or alternatively, given that Hfq is a ribosome-bound protein, these cDNAs may derive from polysome-bound mRNAs. ii) cDNAs of ompD were also distributed over the entire ompD locus, and abruptly ended 50 nt downstream of the ompD stop codon, at the predicted Rho-independent terminator; a major cDNA cluster was observed around the ompD AUG start codon, i.e. the −70 to +19 region  coding region. The Hfq-dependent sRNA, GcvB, is known to target the dppA 5′ UTR dppA region could enhance GcvB action. iii) cDNA clones of the ∼10kb flgBCDEFGHIJKL mRNA (flagellar components) were almost exclusively derived from the terminal, 80 nt region downstream of the flgL stop codon which includes the terminator. It is possible that Hfq controls flagellar operon mRNA expression through modulation of mRNA decay initiating at the 3′ end. iv) Almost all of the 48 cDNAs of the dicistronic glmUS mRNA mapped in two clusters to the glmUS IGR (188 nt). cDNAs of the upstream cluster start with the adenosine of the glmU UGA stop codon and span the first 73 nt of the IGR. In E. coli, glmUS mRNA undergoes RNaseE-dependent cleavage within the glmU UGA to generate a monocistronic glmS mRNA glmS mRNA is activated by the GlmZ sRNA, which binds Hfq glmUS IGR dppA, Hfq is likely to aid the binding of GlmZ to the glmUS mRNA in the region of the two clusters of cDNAs.Salmonella Hfq coIP (data not shown). We believe that the sequenced cDNAs were synthesized from a mixture of shorter cDNA fragments, rather than from intact transcripts of several kb in length. The short cDNAs that were prepared from Hfq coIP have the advantage of favoring the primary Hfq binding region.It is worth noting that the extended steps of lysate preparation and antibody incubation involved in the Hfq coIP protocol do cause some mRNA degradation dppA, glmUS, flhD and hilD mRNAs were in vitro-synthesized, and analyzed in gel mobility shift assays  secretion. If so, ectopic HilD overexpression should restore SPI-1 effector secretion to Δhfq. As predicted, overproduction of HilD from a PBAD expression plasmid restored SPI-1 effector secretion to almost wild-type levels in the absence of Hfq . Thus, by identifying the hilD mRNA as a direct Hfq target, we have revealed the mechanism of part of the pleiotropic virulence defect of the Δhfq strain.Our analyses revealed an intriguing relationship between the transcriptomic and deep sequencing data; the genes belonging to some regulons were consistently down-regulated in the Δhfq mutant, yet Hfq primarily bound to the fhlDC (class I genes), flgMN, flgKL, fliAZ, fliD, fliI and fliP mRNAs (class II genes) and fliC mRNA , presumably because the FlhD2C2-independent chemotaxis genes required for full motility are also down-regulated in the absence of Hfq . Moreover, the 24 cDNAs recovered from the control library, i.e. without Hfq coIP, suggest the exciting possibility that deep sequencing of total RNA, without prior enrichment or size-fractionation, will prove to be a successful approach for sRNA discovery. Like any other global method for RNA identification Regarding the sensitivity of our approach, it is remarkable that RyeB sRNA was found in 653 Hfq cDNAs and 24 Control cDNAs ; RyeB isSalmonella gene expression. Transcriptome analysis under two different growth conditions suggests that Hfq regulates the expression of nearly a fifth of all Salmonella genes. This proportion of Hfq-dependent genes is similar to Pseudomonas aeruginosa . Our current data set comprises several hundred such candidate mRNAs . In the SPI-1 invasion gene island, HilD acts at the top of a transcription factor cascade to activate SPI-1 genes, and to mediate secretion of effector proteins by the SPI-1 type III secretion system  argues against direct transcriptional control by Hfq. Rather, the 7.5-fold enrichment of hilD cDNAs by Hfq coIP . The FlhDC expression plasmid, pAS-0081, was constructed by inserting a PCR fragment obtained with primers JVS-2152/-2153 into plasmid pBAD/Myc-His A (Invitrogen) by NcoI/XhoI cloning. All cloning procedures where carried out in E. coli strain Top10 (Invitrogen). The Growth in Lennox (L) broth  or on L-plates at 37°C was used throughout this study. Antibiotics (where appropriate) were used at the following concentrations: 50 µg/ml ampicillin, 30 µg/ml chloramphenicol. For early stationary phase (ESP) cultures, 30 ml L-broth in 100 ml flasks were inoculated 1/100 from overnight cultures and incubated at 37°C, 220 rpm to an optical density of 2. For SPI-1 induced cultures, 5 ml L-broth containing NaCl  was inoculated from single colonies; incubation was carried out for 12 hours at 37°C, 220 rpm in tightly closed 15 ml Falcon tubes. For SPI-2 induced cultures, 70 ml SPI-2 medium hfq) were grown in L-broth either to an OD600 of 2 (ESP aerobic growth), or for 12 hours under SPI-1 inducing conditions. RNA extraction and data generation were carried out as described with SALSA microarrays Strain SL1344 and JVS-0255 (ΔProteins were resolved by SDS PAGE (12% gels). For Coomassie stain or Western analysis, proteins equivalent to 0.1 OD or 0.05 OD, respectively, were loaded per lane. For FliC detection, strains SL1344 and JVS-0255 carrying the indicated plasmids were grown to an OD of 1, and induced with L-arabinose. Growth continued for one hour, and whole cell and secreted protein fractions were analyzed as described in RNA was prepared by hot phenol extraction dppA oligonucleotides JVO-1034/1035 (the fragment covers the dppA region from positions −163 to +73 relative to the start codon) were used. For the PCR of the intergenic region of glmUS primer JVO-2471/2472 were used, resulting in a product starting 38 nucleotides upstream of the glmU stop codon and extending to nucleotide 113 in the intergenic region. For flhD, oligonucleotides JVO-2284/-2285 were used, to yield a fragment that covers flhD from position −59 to +38 relative to the start codon. The hilD fragment (oligonucleotides JVO-2286/-2287) spans region +400 to +600 relative to the start codon.DNA templates carrying a T7 promoter sequence were generated by PCR using genomic DNA and primers as listed in In vitro transcription was performed using the MEGAscript High Yield Transcription Kit , followed by DNase I digestion . Following extraction with phenol:chloroform:isopropanol (25∶24∶1 v/v), the RNA was precipitated overnight at -20°C with 1 vol of isopropanol. RNA integrity was checked on a denaturing polyacrylamide gel. RNA was 5′ end-labeled and purified as described in Gel mobility shift assays were carried out as described in hfqFLAG) were grown in L-broth under normal aeration at 37°C to ESP. Co-immunoprecipitation was carried out using the protocol published in S. Typhimurium SL1344 NCTC13347 genome and 636 control oligonucleotides. The SL1344 sequence was obtained from the Sanger Institute  website . As this genome is not yet fully annotated, the oligonucleotides were associated with corresponding S. Typhimurium LT2 genes or intergenic regions, if conserved in both organisms. Full description of the microarray and protocols used for generating and analysing the data are associated with the dataset deposited in the GEO data repository (http://www.ncbi.nlm.nih.gov/geo/) under accession number GSE10149. For detailed description of data analysis using the Integrated Genome Browser see the Supplementary Salmonella chromosome and hits per nucleotide were calculated along the entire genome. To calculate enrichment factors for Hfq coIP, the Hfq cDNA number was divided by Control cDNA number at each position of the genome, following normalization to the total number of mapped reads. Upon upload of the Salmonella genome sequence and annotation from Genbank (NC_003197.fna and NC_003197.gff), the two graphs for each library were loaded into the Integrated Genome Browser (IGB) of Affymetrix (version IGB-4.56), which can be directly launched by Java Web Start at http://www.affymetrix.com/support/developer/tools/download_igb.affx or downloaded from http://genoviz.sourceforge.net/.Strains SL1344 and JVS-1338  or for 12 hours under SPI-1 inducing conditions, respectively. (A) Analysis of mRNA level by real time PCR for rpoE, degP, and rpoS mRNA. (B) Whole cell proteins were separated by 12% SDS PAGE and sigma factors detected via Western blot. Expression levels of each protein were determined by densitometry and are given as a percentage of the wild-type level of expression below each gel.Expression levels of RpoE and RpoS in wild-type and Δ(0.29 MB TIF)Click here for additional data file.Figure S2Salmonella at OD600 of 2. Northern blots based on agarose gel for detection of long transcripts showing the detection of six mRNAs.Northern detection of Hfq bound mRNAs. Total RNA was isolated from (1.29 MB TIF)Click here for additional data file.Figure S3Salmonella. RNA samples were either taken from wild-type or hfq mutant Salmonella at different growth stages Click here for additional data file.Figure S4Hfq binds significantly to a few but not all mRNAs of the SPI-1 and the flagellar regulon. Shown are all genes belonging to the SPI-1 and the flagellar regulon. The level of Hfq-dependent gene regulation is shown as fold-change below each gene Click here for additional data file.Figure S5Salmonella. Northern analysis of istR transcripts. Total RNA was extracted from of E. coli K12 and Salmonella Typhimurium SL1344 cells grown to an OD600 of 2, exposed to Mitomycin C (0.5 µg/ml) for 30 min as described by Expression of IstR-1 and IstR-2 in (0.28 MB TIF)Click here for additional data file.Table S1hfq at ESP.Deregulated genes in Δ(0.95 MB DOC)Click here for additional data file.Table S2hfq after 12 hrs SPI-inducing conditions.Deregulated genes in Δ(0.21 MB DOC)Click here for additional data file.Table S3Salmonella sRNA loci in pyrosequencing data.Coverage of known and candidate (0.26 MB DOC)Click here for additional data file.Table S4mRNAs in Hfq CoIP identified by ≥10 of 170,000 inserts in pyrosequencing data.(0.81 MB DOC)Click here for additional data file.Table S5Genes that were significantly enriched in coIP-on-Chip and were identified by pyrosequencing.(0.32 MB DOC)Click here for additional data file.Table S6Oligodeoxynucleotides used in this study.(0.06 MB DOC)Click here for additional data file.Table S7Oligodeoxynucleotides used for Northern detection.(0.05 MB DOC)Click here for additional data file.Text S1Supplementary material and methods.(0.31 MB DOC)Click here for additional data file.


RNA polymerases frequently deal with a number of obstacles during transcription elongation that need to be removed for transcription resumption. One important type of hindrance consists of DNA lesions, which are removed by transcription-coupled repair (TC-NER), a specific sub-pathway of nucleotide excision repair. To improve our knowledge of transcription elongation and its coupling to TC-NER, we used the yeast library of non-essential knock-out mutations to screen for genes conferring resistance to the transcription-elongation inhibitor mycophenolic acid and the DNA-damaging agent 4-nitroquinoline-N-oxide. Our data provide evidence that subunits of the SAGA and Ccr4-Not complexes, Mediator, Bre1, Bur2, and Fun12 affect transcription elongation to different extents. Given the dependency of TC-NER on RNA Polymerase II transcription and the fact that the few proteins known to be involved in TC-NER are related to transcription, we performed an in-depth TC-NER analysis of a selection of mutants. We found that mutants of the PAF and Ccr4-Not complexes are impaired in TC-NER. This study provides evidence that PAF and Ccr4-Not are required for efficient TC-NER in yeast, unraveling a novel function for these transcription complexes and opening new perspectives for the understanding of TC-NER and its functional interconnection with transcription elongation. Saccharomyces cerevisiae for genes that affect this type of repair that is coupled to transcription. Our study has permitted us to identify and demonstrate new roles in DNA repair for factors with a previously known function in transcription, opening new perspectives for the understanding of DNA repair and its functional interconnection with transcription.Dealing with DNA lesions is one of the most important tasks of both prokaryotic and eukaryotic cells. This is particularly relevant for damage occurring inside genes, in the DNA strands that are actively transcribed, because transcription cannot proceed through a damaged site and the persisting lesion can cause either genome instability or cell death. Cells have evolved specific mechanisms to repair these DNA lesions, the malfunction of which leads to severe genetic syndromes in humans. Despite many years of intensive research, the mechanisms underlying transcription-coupled repair is still poorly understood. To gain insight into this phenomenon, we undertook a genome-wide screening in the model eukaryotic organism  Several lines of evidence indicate that transcript elongation by RNAPII involves frequent pausing and stalling, and an important role of the many accessory factors may be to minimize the negative impact of such events on transcription Synthesis of an RNA transcript by RNA polymerase II (RNAPII) requires the successful completion of at least four steps in the transcription cycle: promoter binding and initiation, promoter clearance, elongation, and termination. While many studies have focused on the regulation of initiation, more recent studies have demonstrated that transcription elongation is a dynamic and highly regulated stage of the transcription cycle capable of coordinating downstream events. Numerous factors have been identified that specifically target elongation Nucleotide excision repair (NER) is an evolutionarily conserved DNA repair pathway that deals with severely distorting DNA lesions including intrastrand crosslinks such as UV-induced pyrimidine dimers (CPDs) and DNA bulky adducts such as those generated by the model carcinogen 4-nitroquinoline-N-oxide (4-NQO). Within NER two damage-sensing pathways are recognized: one for the entire genome, global genome repair (GG-NER), and the other for transcribed strand of active genes, transcription coupled repair (TC-NER). As ongoing transcription is required for TC-NER, damage recognition is likely triggered by the elongating RNAP itself, whose progression gets obstructed at the site of damage. RNAP arrests at injuries in the template strand initiating, likely via additional specific factors, the recruitment of the DNA repair machinery in vitro reconstitution of the TC-NER initiation steps, in which an elongating RNAPII arrested at a DNA lesion was shown to mediate an ATP-dependent incision of the damaged DNA only in the presence of CSB In eukaryotes, the precise mechanism of TC-NER remains poorly understood. Mutations in proteins required for NER lead to severe disorders known as Xeroderma pigmentosum and Cockayne's syndrome. One of these proteins, Cockayne syndrome B protein (CSB), and its yeast orthologue Rad26, share conserved functions In addition, RNAPII is subject to ubiquitylation and proteasome-mediated degradation in response to UV damage To improve our knowledge of gene products that function at the interface between transcription and DNA repair, we used a yeast mutant library covering the yeast non-essential genes for a genome-wide analysis of the genes conferring resistance to the inosine monophosphate dehydrogenase inhibitor mycophenolic acid (MPA), and the DNA-damaging agent 4-NQO. This approach allowed us to identify new putative candidates for genes involved in transcription and TC-NER. Our study unravels a new function for known transcription complexes in TC-NER and open new perspectives for the understanding of TC-NER and its functional interconnection with transcription elongation.www.ncbi.nlm.nih.gov/geo/ under the access number GSE11561). Among these genes, mRNA levels that were at least 2-fold above or below mock treated cells were found for 376 genes in cells treated with 4-NQO and for 295 genes in cells treated with MPA  of yeast non-essential genes, were grown in SC medium supplemented either with 150 ng/ml 4-NQO or 25 µg/ml MPA. Cells were incubated at 30°C and growth monitored after 48, 72, and 120 for 4-NQO and 72 hours for MPA. Analysis of the strains showing at least 80% growth inhibition by 4-NQO at the indicated time led to a classification of sensitive strains into three groups : group ACorrespondingly, 85 MPA-sensitive deletions showing at least 50% growth inhibition were found , of whicvs. short transcription units PHO5-lacZ or PHO5-LAC4) vs. a short transcription unit (PHO5), which is taken as an approximate measurement of Gene Length Accumulation of mRNA (GLAM) were determined (rpb4Δ and rpb9Δ), THO (tho2Δ), Spt4-Spt5 (spt4Δ), and PAF (rtf1Δ and cdc73Δ). 7 mutants carried deletions of genes encoding proteins affecting transcription, for which an implication in transcription elongation has been proposed in the past. These included subunits of SAGA (spt3Δ and spt20Δ) bur2Δ) ccr4Δ) bre1Δ, rad6Δ, and lge1Δ) med2Δ and med15Δ) and Ccr4-Not (not5Δ) complexes as well as the Uvs1 putative transcription factor (YPL230wΔ). The remaining 7 mutants affected proteins whose function has not been previously linked to transcription  or is unknown (YJR018wΔ). To evaluate the significance of these putative new links with RNAPII elongation, and because some mutants of the SAGA (spt7Δ and spt8Δ), CDK (ctk1Δ and ctk3Δ), and Mediator (med12Δ) complexes did not exhibit expression deficiencies, we extended our analysis to deletions of other functionally related genes.We have previously shown that mutants impaired in transcription elongation display lower efficiency in the transcription of long termined . GLAM-ratermined . Out of spt20Δ and spt3Δ, the absence of three other subunits  showed a clear gene expression defect (GLAM<0.5) while the remaining 5 viable deletions  were only poorly or not affected, as observed for spt7Δ and spt8Δ.Gene expression was analyzed in all viable SAGA deletions . We founctk1Δ and ctk3Δ being MPA-sensitive, CTDK-I might be dispensable for the expression of long genes.Since the H2B-ubiquitylases Rad6, Bre1, and Lge1, two PAF subunits (Cdc73 and Rtf1) as well as Bur2 belonged to the MPA-sensitive mutants exhibiting low GLAM-ratios, we extended the analysis to mutants of COMPASS, the complex responsible for H3-K4 methylation. Low GLAM values were observed for some of the COMPASS and H2B ubiquitylation mutants , suggestnot4Δ showed low expression of long genes, as observed for not5Δ and ccr4Δ, whereas this was not the case for caf1Δ and not3Δ.Most viable deletions lacking Mediator subunits and the viable deletions lacking other subunits of the Ccr4-Not complex were also assayed . The restpd3Δ, all other deletions lacking subunits of the PP2A complex were assayed. Finally, since the only known function of Fun12 is related to translation initiation, we assayed other viable deletions of translational machinery elements playing a role during initiation . To understand the low GLAM-ratios of in vitro elongation assay. This assay is based on a plasmid (pGCYG1-402) in which a hybrid GAL4-CYC1 promoter containing a Gal4 binding site is fused to a 1.88-kb DNA fragment coding for two G-less cassettes. The first cassette is right downstream of the promoter and is 84-nt-long. The second is located 1.48-kb from the promoter and is 376-nt-long. The efficiency of elongation is determined in whole cell extracts (WCEs) by the values of the ratio of accumulation of the 376- versus the 84-nt-long G-less RNA fragments after RNase T1 digestion Since the analysis of GLAM-ratios relies on measurement of enzymatic activities, we decided to assess directly the efficiency of transcription of representative mutants using an med15Δ), CTK (bur2Δ), Bre1-Rad6 (bre1Δ), SAGA (spt20Δ), Ccr4-Not (not5Δ), and PP2A (tpd3Δ) complexes as well as the translation initiation mutant fun12Δ were analyzed. As can be seen in bre1Δ, spt20Δ, and not5Δ WCEs transcribed the 376-nt G-less cassette with efficiencies around or below 60% of the wild-type levels. These results indicate severe defects in transcription elongation in those subunits of the Bre1-Rad6, SAGA, and Ccr4-Not complexes. Strikingly, fun12Δ cell extracts also led to a clear transcription elongation phenotype in our assay (62%). WCEs extracts of bur2 and med15 mutants were moderately affected in transcription elongation, with efficiencies ranging from 68 to 77% of wild-type levels. Transcription elongation efficiencies of tpd3Δ WCEs reached wild-type levels, indicating that this mutant was fully transcription elongation-proficient in this assay.WCEs from representative mutants of Mediator  and 4 mutants as slightly more sensitive to UV than rad7Δ , this effect being more obvious when higher UV doses were used (data not shown). The remaining 9 strains were much more sensitive to UV than rad7Δ . Rad6 and Rpb9 are known to be involved in post-replication repair of UV-damaged DNA and TC-NER, respectively med2Δ and med15Δ), SAGA (spt3Δ and spt20Δ), PAF (rtf1Δ and cdc73Δ), and Ccr4-Not (not5Δ) complexes. Given the fact that other subunits of the Mediator and SAGA complexes were represented in the moderately UV-sensitive strains , we focused on the PAF and Ccr4-Not complexes for a more detailed analysis.Given the strong dependency of TC-NER on RNAPII transcription and the fact that the few proteins known to be involved in TC-NER are related to transcription, we made use of the results of our MPA-sensitivity screen to select 18 mutants encoding for transcription factors, protein de-ubiquitylase, H2B-ubiquitylase, subunits of the CDK, SWI/SNF, SAGA, Mediator, PAF, Ccr4-Not complexes, and RNAPII and look for those possibly involved in TC-NER. For this purpose, we abolished GG-NER by deleting the op assay . Growth rtf1Δ, cdc73Δ, paf1Δ, leo1Δ, not5Δ, not4Δ, not3Δ, caf1Δ, ccr4Δ) alone or in combination with the rad7Δ mutation  digestion -resulting in ssDNA cleavage at CPD sites- followed by alkaline electrophoresis and indirect end-labeling with strand-specific probes  and non-transcribed (NTS) strands of the constitutively expressed c probes . Non-irrf GG-NER , not5Δ crft1Δ with both the def1Δ and the rpb9Δ mutants, which are deficient in RNAPII degradation in response to UV in yeast rft1Δ def1Δ and the rft1Δ rpb9Δ mutants being more sensitive to UV than the corresponding single mutants. Similarly, the rft1Δ def1Δ rad7Δ and the rft1Δ rpb9Δ rad7Δ triple mutants were more sensitive to UV irradiation than the corresponding double mutants. These results suggest that the TC-NER phenotype of PAF mutants is not due to an alteration of the Def1- or Rpb9-mediated degradation of UV damage-stalled RNAPII.A number of factors have been implicated in the repair of DNA lesions encountered by the RNAPII in eukaryotes, but our knowledge on the mechanisms of TC-NER is scarce. Since proteasome-mediated degradation of UV damage-stalled RNAPII complexes is believed to be alternatively required for DNA repair, we tested whether the effect of PAF and Ccr4-Not effect on TC-NER was dependent on RNAPII degradation. For this, we performed an epistatic analysis of the PAF mutant Taken together, our results place the PAF and the Ccr4-Not5 complexes as new factors needed for efficient TC-NER.in vitro transcription assay, but not by RNAPII occupancy. Notably, we provide genetic and molecular evidence that PAF and Ccr4-Not are required for efficient TC-NER in yeast. Sensitivity analysis of viable deletion mutants showed that the 4-NQO sensitive mutants were enriched in DNA repair genes, while the MPA sensitive mutants were enriched in both DNA repair and transcription genes, in agreement with the known effect of MPA on transcription elongation and 4-NQO on DNA repair. Out of the 25 mutations conferring sensitivity to both compounds, 13 correspond to genes involved in RNAPII transcription and mRNP biogenesis  or head  domains not3). Importantly, we show that not5 cells are impaired in transcription elongation in vitro and show RNAPII elongation defects in vivo, as determined by ChIP analysis.Although transcription regulation by Ccr4-Not mostly occurs during the initiation stage, it seems likely that this complex control gene expression at several levels, including regulation of transcription initiation, mRNA deadenylation and degradation, and protein turnover in vivo and deletion of BRE1 leads to elongation defects in vitro, suggesting that this complex has a role in transcription elongation. However, no significant alterations of RNAPII occupancy on a transcribed gene were observed by ChIP. Similar findings were obtained in bur2 mutants, although in this case ChIP analyses also showed an RNAPII distribution consistent with an in vivo transcription elongation defect, alluding that the Bur1-Bur2 complex might also be involved in transcription elongation. These findings are in agreement with the previous report that Bur1 and Bur2 are both recruited to transcription elongation complexes Ubiquitylation of histone H2B by Rad6-Bre1 is associated with active transcription in vitro transcription elongation assay but not in RNAPII ChIP assays have been reported previously, as it is the case of PAF complex mutants in vitro and in vivo. Further development of novel transcription elongation assays may be required to solve such cases. In any case, the putative implication of a number of known transcription-initiation factors and other factors in transcription elongation opens new perspectives about the function of these proteins in transcription that demand further studies.Finally, the Fun12 translation initiation factor also affects transcription elongation in all assays but RNAPII occupancy measurement, although we show that this is not a general feature of translation initiation factors. Further studies would be required to better understand the function of Fun12 and how this might influence transcription elongation. In this sense, it is worth noticing that mutants showing elongation deficiencies in the in vitro assay, the fact that PAF and Ccr4-Not work during transcription elongation makes it plausible that both complexes affect TC-NER directly. The PAF complex is present at promoters and coding regions of all active genes tested and has been involved in transcription initiation and elongation, as well as in the 3′-end formation of polyadenylated and nonpolyadenylated RNAPII transcripts bre1Δ cells were not sensitive to UV irradiation in the absence of GG-NER  tail length regulation, which in turn regulates the expression of a number of DNA-damage inducible genes. Furthermore, Ccr4 and Caf1 show complex genetic interactions with a number of DNA damage checkpoint genes in response to HU and MMS In addition to genetic interactions  between the PAF and Ccr4-Not complexes rad9Δ cells revealed that a functional DNA damage checkpoint response is important for viability upon UV irradiation both in repair proficient and in GG-NER deficient cells  with BUR1 and BUR2 as well as 6-AU sensitivity as NOT2 and NOT4 mutants do caf1 and ccr4 share the TC-NER phenotype of not mutants  or 50 µg/ml (MPA). Cell harvesting, purification of total RNA and transcriptional profiling were performed using DNA microarrays containing PCR-amplified fragments of S. cerevisiae ORFs as previously described Yeast cells were grown at 28°C in 10 ml YPD medium to an ODThe screenings of the complete collection of yeast haploid mutant strains were performed as described Gene Length Accumulation of mRNA, GLAM ratios were determined by measuring acid phosphatase of plasmid gene expression constructs sharing the same “short” transcription unit (GAL1pr::PHO5), but differing in the 3′UTR of the “long” one (GAL1pr::PHO5-lacZ and GAL1pr::PHO5-LAC4) as described An indirect measurement of 600 of 1 and WCEs were prepared as described in vitro transcription reaction was performed with 100 mg WCEs and 100 ng of purified Gal4-VP16, using as template the pCYC1-402 plasmid containing the two G-less cassette used to assay transcription elongation.Yeast cells were grown in rich YEPD medium at 30°C to an OD600 of 0.5. Samples were taken and ChIP assay were performed as described previously Yeast cells harboring pCM184-LAUR were grown in synthetic complete medium (SC) medium at 30°C to an OD600 of 0.7. Serial dilutions  were dropped on YPAD plates, irradiated with 15 J/m2 UV light, and incubated in the dark at 30°C for 3 days.UV survival curves were performed as described RPB2 gene, mapping of CPDs, and analysis and quantification of membranes were carried out as described Irradiation and repair at the Figure S1Correlation between PHO5-lacZ/PHO5 and PHO5-LAC4/PHO5 gene expression ratios (Pearson coefficient of 0.75). Values plotted are those shown in (0.45 MB EPS)Click here for additional data file.Table S1http://www.ncbi.nlm.nih.gov/geo/ under the access number GSE11561.Analysis of genes whose expression is affected by 4-NQO and MPA. Expression of a total of 2374 genes was determined by microarray analysis after treating wild-type cells with either 75 ng/ml of 4-NQO or 50 µg/ml of MPA for 30 min each. The genes showing mRNA levels that were at least 2-fold above or below mock treated cells for each treatment are listed. The complete microarray data are available at (0.06 MB PDF)Click here for additional data file.Table S2Analysis of mutant strains leading to 4-NQO sensitivity. Growth was monitored after 48, 72, and 120 hours in 4-NQO-containing media (150 ng/ml) and the sensitive strains classified into three groups. Group A contains 189 strains whose sensitivity to 4-NQO is observed from early on (48 h) and is maintained over the course of the experiment , group B contains 315 strains whose sensitivity was observed only at early time points (48 h), and group C contains 100 slow-growing strains which showed 4-NQO sensitivity only at later time points (from 72 h on and up to 120 h). The strains of group B were not considered as significantly inhibited by 4-NQO and excluded from further analyses. Values between 60% and 80% and above 80% inhibition are highlighted .(0.07 MB PDF)Click here for additional data file.Table S32. Plates were grown for 3 days at 30°C or 37°C and cell viability for each condition was scored as + for growth as wild-type, as +/− for moderate growth defects, and as - for severe growth defects. Most of the UV sensitive strains were also sensitive to 4-NQO, consistent with the fact that the bulky adduct produced by 4-NQO get repaired by NER . However, 9 of the 4-NQO sensitive strains were not sensitive to UV , indicating that 4-NQO is more than a ‘UV-mimetic’ agent. Those strains appeared mainly sensitive to Mnd, reflecting their deficiencies in the presence of oxidative damage. Comparison of strain sensitivity to 4-NQO, MMS, Mnd, and heat stress did not lead to significant clustering of cross-resistance, indicating that each drug leads to its own response, as observed previously for 15 DNA-damaging agents including MMS and 4-NQO Genetic analysis of sensitivity to DNA-damaging agents. Thirty 4-NQO-sensitive strains of group A that encompassed mutations of the SAGA, PAF, CDKs, and Mediator complexes as well as proteins involved in RNAPII transcription, mRNA processing and degradation, chromatin remodeling, DNA-damage response, and translation initiation were analyzed for drug sensitivity. The indicated strains were spotted as 10-fold serial dilutions on complete minimal medium (SC) and minimal medium containing 0.1 µg/ml 4-nitroquinoline-N-oxide (4-NQO), 0,015% methyl methane sulfonate (MMS), or 0.1 nM menadione (Mnd). UV sensitivity (UV) was assessed following irradiation with 70 J/m(0.09 MB DOC)Click here for additional data file.Table S4Analysis of mutant strains leading to MPA sensitivity. Growth was monitored after 72 hours in MPA-containing media (25 µg/ml). Strains showing at least 50% growth inhibition are listed together with their GLAM-ratios. Values above 60% inhibition and ratios below 0.5 are highlighted in red. n.d., not determined.(0.03 MB PDF)Click here for additional data file.


Promoters process signals through recruitment of transcription factors and RNA polymerase, and dynamic changes in promoter activity constitute a major noise source in gene expression. However, it is barely understood how complex promoter architectures determine key features of promoter dynamics. Here, we employ prototypical promoters of yeast ribosomal protein genes as well as simplified versions thereof to analyze the relations among promoter design, complexity, and function. These promoters combine the action of a general regulatory factor with that of specific transcription factors, a common motif of many eukaryotic promoters. By comprehensively analyzing stationary and dynamic promoter properties, this model-based approach enables us to pinpoint the structural characteristics underlying the observed behavior. Functional tradeoffs impose constraints on the promoter architecture of ribosomal protein genes. We find that a stable scaffold in the natural design results in low transcriptional noise and strong co-regulation of target genes in the presence of gene silencing. This configuration also exhibits superior shut-off properties, and it can serve as a tunable switch in living cells. Model validation with independent experimental data suggests that the models are sufficiently realistic. When combined, our results offer a mechanistic explanation for why specific factors are associated with low protein noise in vivo. Many of these findings hold for a broad range of model parameters and likely apply to other eukaryotic promoters of similar structure. Combinatorial regulation of gene expression is an important mechanism for signal integration in prokaryotes and eukaryotes. Typically, this regulation is established by transcription factors that bind to DNA or to other regulatory proteins. Modifications of the DNA structure provide another layer of control, for instance, in gene silencing. However, it is barely understood how complex promoter architectures determine key features of promoter dynamics such as gene expression levels and noise. Here, we employ realistic mathematical models for prototypical promoters of yeast ribosomal protein genes as well as simplified versions thereof to analyze the relations among promoter design, complexity, and function. By comprehensively analyzing stationary and dynamic promoter properties, we find that functional tradeoffs impose constraints on the promoter architecture. More specifically, a stable configuration in the natural design results in low transcriptional noise and strong co-regulation of target genes in the presence of gene silencing. Combined, our results offer a mechanistic explanation for why specific factors are associated with low protein noise in vivo. We expect that many of these findings apply to other promoters of similar structure. These regulators typically serve as converging points for signaling pathways, they possess variable activation/repression domains, and often act in concert with coactivators/corepressors and general regulatory factors (GRFs)  genes. The set includes the in vivo design and three functionally related, but progressively simpler synthetic architectures. We integrate selected information from large-scale studies and from targeted experiments to provide the necessary quantitative basis for these models, and to comprehensively characterize the stationary and dynamic regulation of promoter activities using deterministic and stochastic simulations. This enables us to pinpoint structural features underlying the observed behavior and to identify functional tradeoffs that impose constraints on promoter architecture.To develop kinetic promoter models, we start from elementary interactions between transcription factors and DNA. Typical RP gene promoters contain paired binding sites for the GRF Rap1 Model 1 represents the wild-type scenario as follows : Sequentsingle TF such as Ifh1. Note that more complex promoters in terms of combinatorial control exist even in yeast Since most physiological stimuli appear to regulate Ifh1 binding while Rap1 and Fhl1 serve as scaffold, it is unclear if the seemingly complex architecture of the natural RP gene promoter yields any functional advantage. In principle, one could envision the same coordinated regulation by controlling the activity, localization, or DNA binding affinity of a To investigate differences in function and regulatory performance of structurally related, but simpler architectures, we developed three alternative promoter models (Models 2–4). They are progressive simplifications of the natural promoter configuration . By contfunctional equivalence, but not structural identity to Ifh1, which itself cannot bind to DNA Although the simplified models are synthetic, they correspond to promoter architectures encountered in vivo. Model 2 with its cooperative activation by homodimeric TFs resembles regulation by cI repressor in phage λ Stochastic binding and dissociation events of TFs and of RNA polymerase determine whether a given RP gene is transcribed. We represented control events by sets of elementary chemical reactions and mass-action kinetics To address how upstream signaling pathways—through variation in Ifh1 levels—modulate RP gene transcription, and how this is influenced by the ambivalent coactivator/repressor Fhl1, we compared model predictions of stationary promoter activity without chromatin remodeling. For realistic parameter values, promoter activities are very similar for all models  because,η), Fhl1 binds the majority of Rap12-RP gene complexes in design 1 and thereby efficiently quenches basal transcriptional activity when Ifh1 is absent . Next, we therefore investigated the dynamic promoter responses to varying external conditions. More specifically, we investigated the dynamic promoter performance with and without gene inactivation due to chromatin modifications, which may lower the concentration of accessible genes at any given time point. For the steady-state analysis above, silencing could be mimicked by decreasing the affinity of TFs for DNA, but this does not hold for the dynamic behavior.Specifically, we mimicked environmental changes by applying a sinusoidal time-varying input of free Ifh1 with fixed amplitude and frequency. Such a periodic forcing function is the standard choice in frequency response analysis f = 1 Hz) and approximately 27 hours (f = 10−5 Hz) when gene silencing is neglected. Along with the phase shift between input and output activity, this representation is related to the well-known Bode plot for linear systems in control theory. It combines the ratio of output and input amplitudes in a double logarithmic plot and the phase shift between output and input in a semi-logarithmic plot as function of the input frequency. As our models are nonlinear, the predicted promoter activities deviate from the sinusoidal shape of the input and show a more switch-like behavior, but the predominant frequency contribution to the output was always identical to the input frequency (see f<10−3 Hz), while it rejects fast Ifh1 oscillations with a period below 15 min (f>10−3 Hz) for the chosen parameter settings. The frequency response of activity oscillations and the phase shift  in the absence of TF binding see . With a In contrast, chromatin closure has almost no effect on the dynamics of Rap1-containing promoter architectures (Models 1–3), apart from a slight reduction in average promoter activity. Similarly, promoter activity in models 1–3 resists noise even for large, physiologically plausible fluctuations in Rap1 concentrations regardless of chromatin compaction .poised state for rapid reactivation even after prolonged absence of TF binding. This obviates the need to first reactivate the genes in a sequence of—potentially slow—chromatin modification steps Altogether, stably bound dimeric GRFs, in general, can protect the promoter from noise propagation due to unspecific chromatin modifications. GRFs ensure that the promoter remains in a The deterministic analysis suggested that Rap1-containing promoters are more resistant to noise from random chromatin modifications. To further investigate noise propagation, we analyzed the ‘extreme’ models 1 and 4 by stochastic simulations. In addition to chromatin modification, we considered inherent fluctuations of TF levels as noise sources see . A priorIn particular, we focused on the stationary noise in RP mRNA levels as a function of four factors see . This isaK≥2.51, i.e. when RP genes are active more than 70% of the time in the absence of any TFs). We conclude that, despite its complexity, the natural design specifically prevents random fluctuations caused by chromatin remodeling.To investigate gene expression noise more systematically, we explored the combined effects of variations for pairs of the above influence factors. Noise was quantified by calculating the CV of mRNA numbers for simulated trajectories in steady-state. In the presence of chromatin remodeling, the natural promoter architecture (model 1) exhibits lower mRNA noise than the simple design in all conditions investigated . Notably2-RP gene complex (not shown)—it almost exclusively results from the promoter structure.What are the sources for the lower noise in gene expression of the natural design? Apparently, scenario 4 produces fewer mRNA molecules than scenario 1 because constitutive chromatin compaction inactivates a higher fraction of promoters. However, a systematic comparison of relative variability for a range of mRNA levels demonstrates that promoter configuration 1 is consistently associated with less noise than design 4 . The majHence, noise creation and propagation at complex promoters is not solely determined by the binding strength of a particular TF, but also critically depends on the order of recruitment and on dynamic interactions with other TFs. Consequently, the domains mediating protein–protein interactions among cooperating TFs are selectable targets for the evolution of noise traits.More specifically, Rap1-containing promoters achieve low intrinsic noise in gene expression because they minimize stochastic noise induced by unspecific remodeling events, especially for realistic kinetic values and molecule numbers in yeast. Importantly, the simulation results in −36, Welch's t-test). Hence, the natural promoter architecture is clearly superior in keeping absolute mRNA levels within tight bounds for large gene sets simultaneously. It enables efficient production of molecular machine precursors in stoichiometric quantities despite short mRNA half-lives that are required for quick adaptation to changes in external conditions. For ribosomal proteins, these features are essential because, when environmental conditions deteriorate, resource-intensive ribosome synthesis must be stopped immediately to reroute building blocks and energy to processes critical for survival.The regulation of RP genes is intricate because transcriptional co-regulation is particularly strong IFH1 overexpression to evaluate if model structure and parameters would yield reliable predictions for a regulator contained in the model.Finally, to critically test the predictive capabilities of the most realistic model (Model 1) we used two independent data sets for model validation. In both cases, except for experiment-specific settings, model structures and parameters remained unchanged. More specifically, we compared model predictions with the experimentally observed dynamic response to IFH1 expression with experimental data IFH1 mRNA profile GAL1-IFH1 construct (GAL1: SGD S000000224). Since the absolute level of basal GAL1-IFH1 expression under non-inducing conditions was not determined, we performed simulations for a range of plausible values. For basal IFH1 mRNA expression at 12% of the wild-type level on glucose we obtained good qualitative and quantitative agreement between model and experimental data (η (data not shown). The model does not capture the decrease in measured mRNA levels at the last time point. However, no such reduction was observed in a similar experiment IFH1 mRNA basal levels in absolute terms; more comprehensive experiments are required to evaluate model performance more stringently.First, we compared the predicted dynamics of Ifh1 binding to RP promoters and RP mRNA production for galactose-inducible tal data , indepent-test, 95% confidence level) in any of the three conditions. We obtained the same results for predicted Rap1 occupancies and for Fhl1 occupancy under osmotic shock (p = 0.0055) and rapamycin addition (p = 0.0084). Underestimation of Fhl1 binding in these conditions may reflect the influence of additional regulators. Such quantitative discrepancies highlight which model aspects require improvement; they identify possible settings under which alternative regulators can be studied in future experiments. We conclude that Model 1, which considers only Ifh1 and Fhl1 as dominating dynamic regulators, correctly captures salient features of RP gene promoter and mRNA behavior under stationary and dynamic conditions. Biologically meaningful and partly quantitative predictions are, hence, possible already with our simple model.To assess potential structural model uncertainties, we simulated the relation between Ifh1 promoter occupancy (which is the key control variable in the model) and RP mRNA production for stresses that might involve unmodeled regulators. In the model, stationary RP mRNA levels depend linearly on the fraction of Ifh1-bound RP promoters. This assumption leads to qualitatively correct predictions of changes in RP mRNA levels  in respoic shock . The difComplex promoters are involved in many cellular processes where correct timing of expression or precise and coherent regulation of gene sets is required. Their architectures, however, prevent intuitive explanations of promoter functions and advantages for controlling gene expression dynamics. Using the well-characterized yeast RP gene promoter as example, we derive a set of quantitative kinetic models for the natural and for three simplified synthetic promoter configurations. Our model comparison encompasses a broad range of performance characteristics, including dynamic responsiveness and noise transmission, which are not commonly covered in more traditional promoter models For the specific example of yeast RP gene promoters, we conclude that the natural design is particularly suited to combine tunable regulation of gene expression with a fast response to external signals, strong co-regulation of target genes, and low mRNA noise in the presence of chromatin remodeling. These are partially contradicting objectives, and a quantitative analysis is required to evaluate the corresponding trade-offs. In particular, the natural promoter can serve as switch between activating and repressing modes with tunable upper and lower activity bounds. Despite the limited quantitative data available for model development, the most realistic models' qualitative—and to a certain extent quantitative—features and predictions comply with our knowledge on RP gene regulation in yeast. Importantly, several predictions are new and experimentally testable: (i) the importance of Forkhead proteins for superior shut-off properties and (ii) the differential regulation of Fhl1 and Ifh1 required for tunable switch function. Specifically, the role of Rap1-Fhl1 scaffolds in achieving low transcriptional noise mechanistically explain why RP promoters recruiting these factors are associated with low protein noise in vivo poised state for rapid (re-) activation even during prolonged absence of the main activating TF. Therefore, complex promoters can contribute much less noise to mRNA levels than simpler designs. This is particularly relevant for highly expressed and unstable proteins, where forced mRNA fluctuations dominate intrinsic noise This study's more general results on complex promoter architectures primarily concern the relations between promoter structure and noise resistance. Importantly, GRF-containing architectures render promoter activity robust to influences of unspecific chromatin remodeling, independent of the compaction efficiency and speed. They maintain genes in a Two important aspects warrant further investigation. First, TFs frequently interact with histones and histone (de)acetylases that co-regulate promoter activity Our analysis relies on a realistic biological example and, correspondingly, some quantitative model features may be specific for the RP gene system. However, robustness analysis and model optimizations demonstrate that many stationary and qualitative dynamic features are inherent properties of the promoter structure; they are preserved within a broad range typical of physiological parameter values and TF levels. These findings may apply to structurally related promoter architectures, especially those involving certain Forkhead proteins We modeled molecular interactions of transcription factors at the promoter using chemical reaction kinetics, which lead to ordinary differential equation (ODE) models. All deterministic simulations were performed in MATLAB 7 R14 . For stochastic simulations, we employed extended promoter models that also account for the noise in transcription factor levels, synthesis and degradation of RPmRNAs, and competitive binding of Rap1 to non-RP target genes. Stochastic simulations were performed on a PC cluster using a C-based implementation of the approximate R-leaping algorithm of Auger et al. −1 and 101 nM−1. Performing the same analysis for a range from 10−2 to 102 nM−1 did not alter the relative sensitivity of promoter activity qualitatively (data not shown).To assess the influence of TF levels on steady-state promoter activity, total concentrations of the TF under question were varied and the model was simulated until it reached steady state. We assigned activity levels to the resulting complexes between RP gene and TFs depending on composition . We inveTo establish Bode-type plots for the frequency responses of the promoters, we first simulated the ODE models into steady state in the absence of the stimulant (either Ifh1 or Rap1). Subsequently, a sinusoidal input in the free stimulant concentration was applied such that the concentration oscillated between its total concentration and zero for 50 cycles at the respective frequency. For each model, 4096 points of the simulated trajectories of the relevant molecular species were used to determine their corresponding frequency, amplitude, and phase values by Fast Fourier Transformation. Despite the nonlinear nature of the models, the predominant frequency contribution to the output was always identical to the input frequency. From this data, the associated promoter activities and phase shifts between input and output were calculated.aK value constant. Similarly, the gene inactivation rate constant was kept at its nominal value while varying the probability of an RP gene being active in the absence of TF binding between af = 1% and af = 99% by adjusting aK  and (C) Model 2, (D) Model 3, (E) Model 4. Here, promoter activity is shown as a function of parameter KIfh1. All equilibrium constants K are in units of µM−1.Robustness of promoter activity against random perturbations in binding constants. Color-coded relative stationary promoter activity for n = 10,000 simulations of log uniformly, randomly sampled parameter combinations. (A) Model 1, as in (12.16 MB EPS)Click here for additional data file.Figure S2−1.Complex promoter architectures are less robust at maintaining high, but more robust in ensuring basal promoter activity. Distribution of stationary promoter activities for 10,000 vectorially-perturbed parameter sets per model. The parameters were varied according to a log uniform distribution. Distributions of those n parameter combinations out of 10,000 samples that resulted in >80% of maximal activity are shown. (A) and (B) Model 2 (n = 577), (C) and (D) Model 3 (n = 1736), (E) Model 4 (n = 2226). In (A) and (C), open symbols represent the first, filled symbols the second Rap1 binding step. In (B), open symbols represent the first, filled symbols the second Ifh1 binding step. In (D) and (E), open symbols represent Ifh1. All equilibrium constants K are in units of µM(0.58 MB EPS)Click here for additional data file.Figure S3−3 Hz). Color codes for (B): Model 1: red, Model 2: green, Model 3: blue, and Model 4: black.The shape of the output amplitude (promoter activity) deviates from the input shape. Phase profiles of input ((A), concentration of free Ifh1) and output ((B), relative activity of the RP promoter) for a sinusoidal input with a period of ∼17 min (f = 10(0.37 MB EPS)Click here for additional data file.Figure S4Promoters serve as low-pass filters and differ in resistance to chromatin remodeling. Bode-type plot comparing model responses to a sinusoidal input in the concentration of free Ifh1 with (A) or without (B) gene inactivation. (A) and (B): phase shift between input and output. Color codes: Model 1 - red, Model 2 - green, Model 3 - blue, Model 4 - black. Models 3 and 4 exhibit essentially identical phase shifts for Ifh1 oscillations and hence cannot be discerned in (A). (C) and (D): normalized amplitude of promoter activity oscillations for the Rap1 containing models 1–3 in the absence (C) or presence (D) of gene silencing with free Rap1 as oscillating input. Solid vertical markers indicate the physiologically relevant frequency range.(0.62 MB EPS)Click here for additional data file.Figure S5T) for the different models  in the absence (A) and presence (B) of gene inactivation. (C) and (D) Normalized deviation between output and ideal step response shape as a function of input step height without (C) and with (D) random gene inactivation. Symbols: Model 1 - filled circles, Model 2 - open squares, Model 3 - filled triangles, Model 4 - open diamonds.Rap1-containing promoters exhibit superior input tracking in the presence of gene inactivation. (A) and (B) Promoter activity response (output) to a series of step inputs in total Ifh1 (10–100% Ifh1(2.76 MB EPS)Click here for additional data file.Figure S6RPmRNA = 35%. (B) Simple architecture lacking Rap1 (Model 4) with a mean of 45.2 mRNA molecules and CVRPmRNA = 58%. Note the markedly higher frequency of complete mRNA absence in Model 4 (B).Rap1-containing promoters produce less noisy mRNA distributions. Stationary distributions of RP mRNA levels obtained by stochastic simulation. (A) Rap1 containing promoter (Model 1) with a mean of 46.2 mRNA molecules and CV(0.29 MB EPS)Click here for additional data file.Figure S7Relative changes in RP mRNA levels can depend non-monotonically on basal IFH1 expression for promoters with at least two states of nonzero transcriptional activity. Stationary RP mRNA levels before (dashed lines) and after (dash-dotted lines) stimulation of IFH1 expression Click here for additional data file.Figure S8Predicted TF promoter occupancies in response to stress. Comparison of simulated and measured Rap1 (upper row) and Fhl1 occupancies (lower row) under various stress conditions Click here for additional data file.Protocol S1Supporting methods(0.88 MB DOC)Click here for additional data file.Protocol S2SBML file for deterministic version of Model 1(3.00 KB ZIP)Click here for additional data file.Protocol S3SBML file for stochastic version of Model 1(3.00 KB ZIP)Click here for additional data file.Protocol S4SBML file for deterministic version of Model 4(2.00 KB ZIP)Click here for additional data file.Protocol S5SBML file for stochastic version of Model 4(2.00 KB ZIP)Click here for additional data file.