Drosophila combined with microarray and bioinformatics analyses to tackle this challenge. The nuclear adaptor CHIP/LDB is a major developmental regulator capable of forming tissue-specific transcription complexes with various types of transcription factors and cofactors, making it a valuable model to study the intricacies of gene regulation. To date only few CHIP/LDB complexes target genes have been identified, and possible tissue-dependent crosstalk between these complexes has not been rigorously explored. SSDP proteins protect CHIP/LDB complexes from proteasome dependent degradation and are rate-limiting cofactors for these complexes. By using mutations in SSDP, we identified 189 down-stream targets of CHIP/LDB and show that these genes are enriched for the binding sites of APTEROUS (AP) and PANNIER (PNR), two well studied transcription factors associated with CHIP/LDB complexes. We performed extensive genetic screens and identified target genes that genetically interact with components of CHIP/LDB complexes in directing the development of the wings (28 genes) and thoracic bristles (23 genes). Moreover, by in vivo RNAi silencing we uncovered novel roles for two of the target genes, xbp1 and Gs-alpha, in early development of these structures. Taken together, our results suggest that loss of SSDP disrupts the normal balance between the CHIP-AP and the CHIP-PNR transcription complexes, resulting in down-regulation of CHIP-AP target genes and the concomitant up-regulation of CHIP-PNR target genes. Understanding the combinatorial nature of transcription complexes as presented here is crucial to the study of transcription regulation of gene batteries required for development.It is increasingly clear that transcription factors play versatile roles in turning genes “on” or “off” depending on cellular context via the various transcription complexes they form. This poses a major challenge in unraveling combinatorial transcription complex codes. Here we use the powerful genetics of  Different cell types in multi-cellular organisms are determined by the repertoire of genes active in each cell. This repertoire, or transcriptome, is established by the coordinated activity of transcription factors and cofactors that form modular transcription complexes. The modular nature of transcription complexes complicates our understanding of how transcription factors shape the transcriptome. CHIP/LDB transcription complexes direct formation of various cell types including blood and nerve cells. CHIP/LDB malfunction leads to developmental defects and cancer. The function of these complexes depends critically on the docking of specific transcription factors and co-factors at a specific time and in a specific cell type, making them outstanding models for intricate transcriptional regulation. Here we demonstrate that loss of SSDP, a key regulatory component of CHIP/LDB transcription complexes, alters transcription of a large group of genes. We used bioinformatics tools and genetic tests to examine the function of additional components of CHIP/LDB transcription complexes and their target genes during the development of specific organs. We demonstrate how differences in the availability of transcription factors in different cells can affect the function and composition of CHIP/LDB transcription complexes. The intricate regulation of gene expression in multi-cellular organisms involves an elaborate collaboration between repertoires of cis-regulatory sequences and modular, multi-protein transcription complexes that bind them  proteins that have been well preserved in evolution all the way from Caenorhabditis elegans to man. These multi-adaptor proteins mediate interactions between different classes of transcription factors and additional co-regulators of transcription  protein . We used two different heteroallelic combinations of ssdp hypomorphic alleles, neo48/ssdpBG1663ssdp and 31/ssdpBG1663ssdp, which allow survival up to the pupal stage ssdp on different genetic backgrounds rather than homozygotes, in order to minimize inadvertent homozygosity for extraneous mutations. The heteroallelic mutant pairs were compared to each of the corresponding single heterozygotes  collagen gene We searched for enrichment for putative SSDP binding sites in the 500 bp upstream region of the 189 candidate genes identified in the microarray work, using two algorithms, PRIMA ssdp mutants is in agreement with SSDP functioning as a positive cofactor of the CHIP-AP complex. In contrast, the PNR binding sites were significantly enriched in the genes up-regulated in the mutants (PRIMA: p = 2.59E-05 and DEMON: p = 6.1E-06) but not in the genes down-regulated in the mutants (PRIMA: p = 0.085 and DEMON: p = 0.27). This suggests that a significant number of the genes up-regulated in ssdp mutants are direct targets of PNR. Given that AP and PNR bind to CHIP competitively during Drosophila thorax formation stripe, a key gene regulating development of the wing imaginal disc In order to determine whether SSDP target genes are also likely CHIP/LDB target genes, we searched the same upstream regions for binding sites of AP and PNR, transcription factors known to function in the CHIP/LDB complex. Binding site matrices for all available insect transcription factors (including the AP binding site) were obtained from TransFac In addition to the expected enrichment for the SSDP, AP and PNR binding sites upstream of the candidate target genes, we found enrichment for several other binding sites in the upstream regions of these genes see .The fact that the 189 putative SSDP target genes identified in our microarray experiments are enriched for binding sites of SSDP itself and its known partners in transcription is an independent orthogonal validation of the microarray results. These data encouraged us to ask if these putative targets have a genetic function in developmental events mediated by CHIP/LDB.pnr is not expressed The analysis of SSDP target genes suggested that they are targeted by both CHIP-AP and CHIP-PNR complexes. To simplify the interpretation of genetic tests, we chose to begin looking for functional interactions between SSDP target genes and the CHIP-AP complex in the wing, where BxDlmo which encodes a negative regulator of the CHIP-AP complex.In the wing imaginal disc the CHIP-AP complex is involved in determination of the dorsal compartment. The edge of the CHIP-AP domain is the dorsal/ventral (D/V) boundary which will later give rise to the adult wing margin. Subtle disruption of the transcription activity of the CHIP-AP complex causes irregularities in the D/V boundary, which are evident as notches in the adult wing margin BxDlmo mutants have been previously shown to genetically interact with various ssdp loss-of-function alleles BxDlmo2 allele provides a sensitized background to determine whether SSDP target genes function in D/V boundary formation. An example of the assay is depicted in Dlmo resides on the X chromosome, heterozygous females have a considerably less severe notching than hemizygous males  and Class 6 represents the most severe wing notching. The control distributions were of Bx2Dlmo/+ females and Bx2Dlmo/Y males , the wing notching phenotype was enhanced, as evidenced by a shift of the distribution towards the more severe phenotypic classes in the double-heterozygous flies. Flies heterozygous for UGO35ap alone (UGO35ap/+) had normal wings. As expected due to the lack of pnr expression in this tissue, the pnr loss of function allele, V1pnr, did not interact genetically with Bx2Dlmo in our assay  might have a similar effect on the Bx2Dlmo wing notching phenotype. This is indeed the case with fringe (fng), a known CHIP-AP target gene in the wing disc Bx2Dlmo mutant larvae dorsal wing pouch cells Bx2Dlmo and 80fngBx2Dlmo alone, just as observed for the interaction of Bx2Dlmo and UGO35ap  enhanced the wing notching phenotype of Bx2Dlmo and only three (11%) suppressed it. In comparison, the interactions observed with the random set of deletions always suppressed Bx2Dlmo. Thus, loss-of-function mutations in SSDP target genes have a similar effect on Bx2Dlmo as loss of function mutations in ap and in its previously known target gene fng. This is consistent with a negative regulatory role for dLMO with respect to the CHIP-AP complex Bx2Dlmo may be targets that are not dose sensitive, interact in different temporal or spatial contexts, or false positives.We tested 39 genes from our original set of 189 SSDP candidate target genes in this genetic interaction assay with the mutation . These gmutation  in our amutation . StrikinDlmoBx2 . This isssdp and Chip or BxDlmo in a double heterozygous state are readily detected in the wing ssdp and loss of function alleles of ap are not. Therefore, to study the interactions between SSDP and CHIP-AP we needed another assay. We therefore explored using the only available dominant allele of ap, Xaap, as a sensitized background. This mutant exhibits severe wing notching in a heterozygous state. We examined Xaap/+ versus Xaap/+; L7ssdp/+ flies, and observed augmentation of wing notching phenotype in the double heterozygous flies . Mutations in the katanin-60, CG12163 and Myofilin genes ameliorated the wing notching phenotype of Xaap/+ flies, whereas CG11893 and Xbp1 mutations exacerbated wing notching (CG1518 and Cyp6d4 did not show an overt genetic interaction with Xaap). These data indicate that both SSDP and SSDP target genes interact with AP and are therefore likely to act in a common pathway. Interestingly, the SSDP target genes enhanced the Xaap wing notching less severely than ssdp itself, suggesting that the effect of SSDP is distributed among a large number of SSDP targets.We tested seven of the SSDP target genes in the Bx2Dlmo and Xaap demonstrated that the SSDP target genes we identified are likely regulated by the CHIP-AP complex. Next we used genetic interactions to directly test our hypothesis that loss of SSDP disrupts the balance between the CHIP-AP and CHIP-PNR complexes in favor of the latter. To look at this balance between complexes, we examined thoracic bristles where both complexes function The genetic interactions with ac/sc enhancer. This CHIP-PNR function should be antagonized by AP since PNR and AP compete for binding of CHIP L7ssdp and e5.5Chip mutants display duplication of scutellar bristles as heterozygotes , a phenotype similar to gain of function alleles of pnrVX6pnr alone have normal number of scutellar bristles. We therefore expected that double heterozygous flies  would exhibit reduced occurrence of scutellar bristle duplications due to the lower levels of pnr. Indeed, duplicated scutellar bristles phenotype was abolished in L7ssdp/+; VX6pnr/+ flies. Thus, reduced levels of pnr rescued the duplicated bristle phenotype of a loss of function ssdp mutant, supporting the antagonistic model for CHIP-PNR and CHIP-AP complex formation.The CHIP-PNR complex positively regulates formation of thoracic sensory bristles via direct binding to the L7ssdp and e5.5Chip mutations as a sensitized background to screen the SSDP target genes for modifiers of scutellar bristle formation . Given the opposing roles of the CHIP-AP and CHIP-PNR complexes in this tissue we expected SSDP target genes to either enhance or suppress the duplicated scutellar bristles phenotype of L7ssdp/+ and e5.5Chip/+ depending on which of the two complexes regulates that particular SSDP target.This model predicts that mutations in the SSDP target genes will have a similar phenotypic effect as altering the balance between CHIP-PNR and CHIP-AP complexes. To test this prediction we used the L7ssdp or e5.5Chip  interacted with both. This impressive rate of interaction suggests that SSDP targets are regulated by either or both CHIP complexes. The rate of interaction with CHIP and SSDP mutations in bristles is somewhat lower than what we observed for interaction with Bx2Dlmo in the wing. However, this is not surprising as SSDP target genes may be regulated by either AP or PNR or both, which might make bristles more robust to perturbation and thus make it harder to detect genetic interaction in the thoracic bristles compared with the wing, where only AP is present.Mutations in twenty eight SSDP target genes were tested as double heterozygotes with either Chipe5.5 . Fourteee5.5Chip bristle phenotype and only five affected the L7ssdp bristle phenotype (three genes suppressed both e5.5Chip and L7ssdp phenotypes). In contrast, nine of the enhancers affected the L7ssdp bristle phenotype while only four enhanced the e5.5Chip phenotype (two genes enhanced both Chip and ssdp bristle phenotypes). Thus, it appears that loss of ssdp has a predominant effect on genes that negatively regulate scutellar bristle formation. This finding is consistent with our microarray and transcription factor binding site enrichment analyses which showed that loss of ssdp function resulted in down regulation of the CHIP-AP target genes, and with the antagonistic effect of AP on bristle formation. In contrast, although CHIP functions as a cofactor for both AP and PNR, the e5.5Chip mutation was more useful than the L7ssdp mutation for identifying genes that are positive regulators of scutellar bristle formation. The reason for this difference is unknown, but given the complexity evident when comparing the interactions and function of CHIP/LDB complex in just two tissues, it is likely that further complexity remains to be discovered in other contexts. The salient point is that our genetic interaction results demonstrate a clear modularity of the regulation of SSDP target genes by CHIP/LDB complexes in different tissues. Understanding this type of context-dependent component shuffling in transcription complexes will be required for a full understanding of transcriptional networks.Among the 23 interacting SSDP target genes, mutations in 12 were found to partially suppress the duplicated scutellar bristle phenotype suggesting that they are positive regulators of scutellar bristle formation . ConversBx2Dlmo, Xa, ssdpL7ap and e5.5Chip in the wing and scutellar bristles, respectively. Next we wished to determine whether the SSDP target genes identified are essential for proper development of these structures. The simplest way is to examine mutations in SSDP target genes in a homozygous state. Unfortunately, those mutations which were homozygous viable did not exhibit any wing or thorax morphological defects. For example, the EY05974CG2604 mutation enhanced the Bx2Dlmo wing notching in a double heterozygous state . It is possible that these genes participate in, but are not essential for, wing and thorax formation, or that the mutations used to test for function were weak hypomorphs. For example, EY05974CG2604/Df(3R)ED5147 exhibit ectopic wing veins  we silenced SSDP target genes in either ap- or pnr-expressing cells. We knocked down nine SSDP target genes that interacted with Bx2Dlmo, Xa, ssdpL7ap and e5.5Chip. Silencing of two of them had profound effects.Several of SSDP target gene mutations we used were homozygous lethal prior to adulthood, precluding examination of wing or thorax phenotypes. To avoid difficulties due to pleotyropic affects on viability, we utilized the transgenic GAL4/UAS system for targeted silencing of the SSDP target genes Xbp1 (a.k.a. CG9415) in ap-expressing cells resulted in semi-lethality. Survivors reaching adulthood developed severely disrupted wings which appeared as small amorphic inflated structures, accompanied by marked excess of bristles on the wing and scutum, while the scutellum was not affected  in ap-expressing cells caused a curled wing phenotype .SSDP proteins directly bind DNA ssdp enhances the wing notching phenotype of a dominant allele of ap. Additionally, over-expression of Dlmo, whose product negatively regulates the CHIP-AP complex, also interacts with mutants of SSDP target genes, demonstrating that SSDP target genes are involved in the CHIP-AP pathway. The efficiency of finding genetic interactions among the genes differentially expressed in the microarray experiments, demonstrated the power of this approach. Specifically, 72% of the loci we tested with Bx2Dlmo is more than an order of magnitude higher than an EP insertion screen (1.3% interacting) in a Bx1Dlmo sensitized background Dlmo-interacting genes by screening a much smaller group of putative target genes CG1943 was found in the 189 genes identified in our microarray screen. Our study specifically identified down-stream targets of SSDP, while those researchers searched for any modifiers of the Dlmo wing notching phenotype and thus uncovered genes that function in other regulatory pathways or genes that are upstream of the CHIP-AP complexes. This may explain the limited overlap between their results and ours.Consistent with the involvement of SSDP with the CHIP-AP complex, we found that upstream regulatory regions of the SSDP putative target genes are also enriched for the AP binding site ssdp mutants we found the PNR binding site to be enriched specifically in the genes up-regulated in the ssdp mutants. We therefore present a model in which loss of SSDP disrupts the balance between the CHIP-AP and CHIP-PNR complexes. Mammalian SSDP proteins protect LDB, LHX and LMO proteins from ubiquitination and subsequent proteasome-mediated degradation by interfering with the interaction between LDB and the E3 ubiquitin ligase, RLIM. It is therefore possible that in the absence of SSDP proteins, CHIP/LDB and LMO can escape degradation by interacting with GATA and beta-HLH proteins that are not subjected to proteasome-mediated regulation In contrast to the enrichment of SSDP binding sites in the genes down-regulated in L7ssdp/+ flies display duplications of scutellar sensory bristles, similar to gain of function mutations in pnr. In addition, lowered levels of pnr in L7ssdp/+; VX6pnr/+ flies suppresses scutellar bristle duplication. This indicates that the duplicated scutellar bristle phenotype of L7ssdp/+ flies depends on the presence of PNR. As predicted by our model, since both AP and PNR regulate bristle formation, the functional interactions between SSDP target genes and L7ssdp and/or e5.5Chip resulted in either suppression or enhancement of the duplicated scutellar bristle phenotype.According to our model, in cells where both the CHIP-AP and CHIP-PNR complexes are active, loss of SSDP should result in the same phenotype as over-expression of PNR. Indeed, we found that Our results in flies indicate that SSDP contributes differentially to CHIP/LDB complexes containing AP versus PNR. By contrast, mouse SSDP proteins positively contribute to the transcription activity and assembly of both LDB-GATA and LDB-LHX complexes Drosophila homolog of the yeast SWI2/SNF2 gene) complex Our search for enrichment of transcription factor binding sites upstream of the putative SSDP target genes identified additional transcription factors that may warrant future study. Some of these factors are associated with SSDP and CHIP/LDB complexes. For example, the binding sites for PNR and ZESTE (Z) were both enriched in the up-regulated putative SSDP target genes. This is in agreement with previous studies showing that Z can recruit the BRAHMA  gene. G protein coupled receptors are important regulators of development by for example, signaling via the protein kinase A (PKA) pathway ap-expressing cells Bx2Dlmo flies. Moreover, we found that mutant alleles of Gs-alpha60A enhanced the wing blistering phenotype of Bx2Dlmo (data not shown). Silencing of G-salpha60A in ap-expressing cells caused a curled wing phenotype. Such a phenotype can result from differences in the size of the dorsal and ventral wing blade surfaces. In addition, silencing of this gene in pnr-expressing cells caused the posterior pair of scutellar bristles to form in reversed orientation. Bristle orientation have been proposed to be regulated by planar cell polarity genes Additional novel functions for CHIP/LDB complexes are implied by our results regarding the ssdp mutant larvae, combined with genetic screens enabled us to gain insight into the intricate context-dependent transcriptional regulation by CHIP/LDB complexes. We were able to identify 28 putative SSDP target genes that are involved in wing development and 23 putative SSDP target genes that play a role in scutellar bristle formation. Examination of two of these, xbp1 and Gs-alpha60A, suggests novel aspects of developmental regulation such as the involvement of SSDP and CHIP/LDB complexes in ER function and PKA signaling. Furthermore, we showed for the first time that SSDP proteins contribute differentially to transcription activity, and probably to the balance in formation of CHIP-AP and CHIP-PNR complexes. Furthermore we identified potential novel partners of SSDP in regulating transcription of downstream genes during fly development. It stands to reason that an extension of our genetic analysis to mammals and other vertebrates will reveal a host of additional functions of SSDP and CHIP/LDB during the multifaceted process of transcriptional regulation that underlies the development of multicellular organisms.Our genome-wide expression profiling and bioinformatics analysis of ssdp mutant strains  used for the microarray experiment were previously described neo48ssdp) line is a precise excision of the P element inserted in neo48ssdp. UAS-RNAi lines 18873, 38686, 38186, 24959, 24959, 6367, 40871, 9026, 12823 and 15347 were obtained from VDRC http://flystocks.bio.indiana.edu). Oregon-R flies were used as wild type.Unless otherwise stated, flies were grown on a standard medium containing cornmeal, yeast, molasses, and propionic acid at 25°C. The BG1663ssdp/neo48ssdp was obtained by crossing BG1663ssdp/TM3-GFP virgin females to neo48ssdp/TM3-GFP males. The trans-heteroallelic combination BG1663ssdp/31ssdp was obtained by crossing BG1663ssdp/TM3-GFP virgin females to 31ssdp/TM3-GFP males. The control single heterozygotes BG1663ssdp/+ and 31ssdp/+ were obtained by crossing virgin females from each mutant strain to wild type Oregon-R males. The control single heterozygote, neo48ssdp/+, was obtained by crossing virgin wild type females to mutant males. An additional control, BG1663ssdp/rev (neo48ssdp), was used instead of BG1663ssdp/+ for comparison to the trans-heteroallelic combination BG1663ssdp/neo48ssdp since they share more genetic background. The BG1663ssdp/rev (neo48ssdp) combination was obtained by crossing BG1663ssdp virgin females to rev(neo48ssdp) males. For detailed genotypes of microarray samples see The transheteroallelic combination Bx2Dlmo females were crossed to males harboring a mutation in a single target gene. Each cross was set up in 50 ml vials with 10 females and 7-10 males in each vial. All resultant F1 phenotypic classes were counted. The double heterozygote offspring  were counted according to their wing notching severity class. Class 1 representing flies with wild type wings; class 2 represents flies that have anterior notching of one wing; class 3 represents flies that have anterior notching of both wings; class 4 represents flies that have anterior notching of both wings and posterior notching of one wing; class 5 represents flies that have anterior and posterior notching of both wings and finally class 6 representing flies displaying partial detachment of the dorsal and ventral wing layers. Most crosses were set up in three vials and results were pooled. An average of 100 double heterozygote females and 114 double heterozygote males were counted for each target gene tested. The control Bx2Dlmo/+ females and Bx2Dlmo/Y males were obtained by crossing the Bx2Dlmo females to wild type Oregon-R males. A control cross was set up parallel to each set of test crosses. Rarely crosses were discarded if the control distribution was not consistent with previous control crosses. Finally, data from all the control crosses was combined to a single distribution for females and a single distribution for males and all the test distributions were compared to these two master controls. Significance was determined according to the Wilcoxon signed-rank test Bx2Dlmo/1118w female and Bx2Dlmo/Y male flies obtained by crossing the Bx2Dlmo virgin females to males of the isogenic 1118w line used to create the chromosomal deletions.Virgin TapXa/In(2R)Gla Bc Elp females were crossed to L7ssdp/TM6- Tb Sb e males or males caring insertion mutations in different SSDP target genes. Each cross was set up in three 30 ml vials with 5 females and 3–5 males in each vial. The control XaTap/+ flies were obtained by crossing the TapXa/In(2R)Gla Bc Elp females to wild type Oregon-R males. A control cross was set up parallel to each set of test crosses. Each test cross was compared to the control cross done in parallel. The resultant F1 of genotypes XaTap/+, XaTap/+; L7ssdp/+ or TapXa/+; -target genes/+ were counted according to their phenotypic severity class. Flies of genotypes XaTap/+ or XaTap/+; target genes−/+ exhibited two severity classes, class 1 being the least severe. Flies of the genotype XaTap/+; L7ssdp/+ exhibited three severity classes which were different then those observed for XaTap/+ or XaTap/+; target genes−/+ flies and were therefore classified independently. Class 1 being the list severe and class 3 being the most severe.Virgin L7ssdp/TM6-Tb Sb e or e5.5Chip/CyO-GFP females were crossed to males harboring a mutation in any single target gene. Each cross was set up in 50 ml vials with 10 females and 7–10 males in each vial. All resultant F1 phenotypic classes were counted. The double heterozygote offspring  were counted and monitored for duplications of scutellar bristles. A control cross was set up parallel to each set of test crosses. The control L7ssdp/+ or e5.5Chip/+ flies were obtained by crossing the L7ssdp/TM6-Tb Sb e or e5.5Chip/CyO-GFP females to wild type Oregon-R males. Each test cross was compared to the control cross done in parallel. For the control cross the frequency of appearance of the duplicated bristle phenotype was calculated as p = (d+1)/(n+1) where n is the total number of flies and d is the number of flies displaying the duplicated bristle phenotype. Significance was determined using binomial cumulative distribution function with parameters p and m, m being the total number of flies in the test cross. The p-values calculated were corrected for multiple hypotheses testing using the false discovery rate procedure Virgin MD544ap and MD237pnr were used as ap-Gal4 and pnr-Gal4 respectively. Each cross was set up in three 30 ml vials with 5 females and 3–5 males in each vial and results were pulled. The control ap-Gal4/+ flies were obtained by crossing the ap-Gal4/In(2R)Gla Bc Elp females to wild type Oregon-R males. A control cross was set up parallel to each set of test crosses. Each test cross was compared to the control cross done in parallel.The insertion alleles RNA handling was performed exactly as described The FlyGem platform is available under GEO accession GPL20 Microarray experiments were conducted exactly as described http://www.r-project.org). The raw intensity data normalized within-arrays using the PrintTipLoess algorithm The array data was analyzed using R, which is an integrated suite of software facilities for data manipulation; calculation and graphical display  procedure All 22 hybridizations were used to select for candidate target genes that were significantly differentially expressed between http://genome.ucsc.edu/, assembly Apr. 2006 for the D. melanogaster genome) Drosophila).All genomic sequences were obtained from the UCSC genome browser  of promoter sequences regulated by a given transcription factor that take into account multiple binding sites of varying affinities in a promoter. DEMON builds an HMM for each one of the transcription factors binding sites and scores each pair of HMM-promoter for all the HMMs and the promoters in the data set. The score reflects how likely it is that the motif modeled by this HMM appears in this promoter. The scores are then utilized to obtain a p-value for each transcription factor binding site that reflects the probability that the binding site is enriched in the given set of promoters compared to a background set.database for annotation, visualization and integrated discovery (DAVID) Analysis for enrichment of GO functions was conducted using the ssdp was PCR amplified, cloned into pZEX plasmid and expressed with a GST tag in E.coli BL-21. Crude cell extract or purified GST-SSDP fusion protein was used for binding assays. GST-SSDP was purified on a glutathione agarose column (Sigma G4510). The ssdp single stranded CT oligonucleotide nd generation  according to the manufacturer instruction in a final volume of 20 µl containing labeled DNA (150 fmoles), 1 µl of poly-L-lysine and 3 µl poly-[d(I-C)], 140 ng cell extract. For competition experiments 90 or 360 ng of unlabeled probe were added. Following a 20 min incubation at room temperature, the binding reaction products were separated on a native 6% polyacrylamide gel in 0.5% TBE (pH = 8). The gel was contact blotted onto a Hybond-N+ membrane (Amersham Biosciences). The chemiluminescent detection was performed following the manufacturer's instructions . The membrane was exposed to X-ray film (FUJI) for 15 min at 37°C.Fly Figure S1Dlmo and pnr or fng. A&B V1pnr does not interact genetically with Bx2Dlmo in the wing. (A) Genotype of the test group (in gray) is Bx2Dlmo/+; V1pnr/+ and the control group (in black) is Bx2Dlmo/+. (B) Genotype of the test group (in gray) is Bx2Dlmo/Y; V1pnr/+ and the control group (in black) is Bx2Dlmo/Y. C&D 80fng enhances the Bx2Dlmo wing phenotype. (C) Genotype of the test group (in gray) is Bx2Dlmo/+;80fng/+ and the control group (in black) is Bx2Dlmo/+. (B) Genotype of the test group (in gray) is Bx2Dlmo/Y; 80fng/+ and the control group (in black) is Bx2Dlmo/Y.Genetic interactions between (0.07 MB PPT)Click here for additional data file.Figure S2Bx2Dlmo are evenly distributed. Each gene is plotted against the FDR transformed p-value generated by the ANOVA based statistical test used to determine the statistically significant genes that are differentially expressed between ssdp trans-heteroallels and their corresponding heterozygotes.The genes selected for the genetic interaction screen with (0.03 MB PPT)Click here for additional data file.Figure S3EY05974CG2604 is a hypomorphic mutation. (A) Wild-type wing. (B) Wing of a EY05974CG2604/Df(3R)ED5147 fly, ectopic wing veins are indicated by arrows.(0.22 MB PPT)Click here for additional data file.Table S1Sample genotypes of microarray hybridizations.(0.02 MB XLS)Click here for additional data file.Table S2SSDP putative target genes identified by microarray analysis.(0.06 MB XLS)Click here for additional data file.Table S3Transcription factors binding sites enriched in up stream regulatory sequences of SSDP target genes.(0.07 MB XLS)Click here for additional data file.Table S4Bx2Dlmo.Allele specific genetic interactions between SSDP target genes and (0.02 MB XLS)Click here for additional data file.Table S5L7ssdp and e5.5Chip.Genetic interaction with (0.02 MB XLS)Click here for additional data file.Table S6Biological proceses enriched in SSDP target genes as analysed by DAVID.(0.02 MB XLS)Click here for additional data file.


Escherichia coli a major fraction of its Transcription Start Sites (TSSs) are still unknown, limiting therefore our understanding of the regulatory circuits that control gene expression in this model organism. RegulonDB (http://regulondb.ccg.unam.mx/) is aimed at integrating the genetic regulatory network of E. coli K12 as an entirely bioinformatic project up till now. In this work, we extended its aims by generating experimental data at a genome scale on TSSs, promoters and regulatory regions. We implemented a modified 5′ RACE protocol and an unbiased High Throughput Pyrosequencing Strategy (HTPS) that allowed us to map more than 1700 TSSs with high precision. From this collection, about 230 corresponded to previously reported TSSs, which helped us to benchmark both our methodologies and the accuracy of the previous mapping experiments. The other ca 1500 TSSs mapped belong to about 1000 different genes, many of them with no assigned function. We identified promoter sequences and type of σ factors that control the expression of about 80% of these genes. As expected, the housekeeping σ70 was the most common type of promoter, followed by σ38. The majority of the putative TSSs were located between 20 to 40 nucleotides from the translational start site. Putative regulatory binding sites for transcription factors were detected upstream of many TSSs. For a few transcripts, riboswitches and small RNAs were found. Several genes also had additional TSSs within the coding region. Unexpectedly, the HTPS experiments revealed extensive antisense transcription, probably for regulatory functions. The new information in RegulonDB, now with more than 2400 experimentally determined TSSs, strengthens the accuracy of promoter prediction, operon structure, and regulatory networks and provides valuable new information that will facilitate the understanding from a global perspective the complex and intricate regulatory network that operates in E. coli.Despite almost 40 years of molecular genetics research in  These efforts have provided a vast amount of information regarding gene content, and thus the physiological traits of these organisms. Transcriptomic, proteomic and metabolomic experiments have greatly enriched our global understanding of the general metabolic potential of some of these bacterial species. Certainly, whole genome-expression profiles have made outstanding contributions to understand global gene expression patterns http://regulondb.ccg.unam.mx/) is the primary reference database offering curated knowledge of the transcriptional regulatory network of Escherichia coli K12, currently the most used, electronically encoded, database of the genetic regulatory network of any free-living organism cis-acting DNA regulatory elements as possible.Since the mid-1990s, the number of completely sequenced bacterial genomes has grown to more than 1350 and many more are in progress  promoter identification is straightforward once the TSS has been experimentally determined.The promoter element defines the DNA site directing the RNAP holoenzyme for transcription initiation, and it is a crucial element to understand gene expression in bacteria. Promoters differ at their consensus sequence depending on the interchangeable polymerase σ factor used, which provides DNA recognition specificity E. coli, the genetically and physiologically most studied organism so far, more than 700 TSSs have been determined E. coli using a tiled array approach Caulobacter crescentus with 5 nucleotide resolution E. coli using chromatin immunoprecipitation and microarrays (ChIP-chip). These experiments provided further evidence of active promoters but they were not designed to precisely map TSSs The bacterial TSSs have been mainly identified through two experimental procedures: primer extension E. coli K-12 genome. 317 putative TSSs were determined by DMTSS and about 1500 more by HTPS, constituting about two and a half times the TSSs mapped in more than 40 years of molecular genetic studies in this organism. Control experiments with genes for which the TSSs have been previously mapped showed that both methodologies are robust and accurate, and for many of them, additional TSSs not previously mapped were discovered, indicating that those genes are subject to a more complex genetic control than previously thought. Scrutiny of the promoter region of all the newly mapped TSSs helped us to identify their putative sequence and the most likely σ factor recognizing them. Remarkably, we found more than 2300 additional 5′ RNA ends by HTPS (with more than one replicate) within the coding regions, about 600 (26%) of them in antisense orientation, very likely with regulatory functions. In conclusion, our large-scale TSSs mapping effort adds substantial new information to the catalogue of experimentally determined promoters and DNA regulatory sites in E. coli that will encourage the experimental biologists to investigate the gene expression mechanisms underlying some of these genes, particularly for those with no biological function assigned. The methodologies described here can be applied to other less studied bacteria to gain functional insights into the transcriptional regulatory mechanisms that govern the spatio-temporal regulation of gene expression.In this report we implemented a simple procedure based on a modification of the Rapid Amplification of 5′ complementary DNA ends 5′ RACE  named DiE. coli K-12 transcriptional units (TUs). The precise mapping of the TSSs is critical to unambiguously identify promoters and gene expression regulatory sequences. We implemented two methodologies to map TSSs, HTPS (see below), and DMTSS. For the latter, total mRNA is randomly amplified, and the resulting cDNA is labeled at the 3′ end by incorporating a homopolynucleotide. This pool of cDNA can be used in independent experiments to map hundreds of TSSs using gene-specific oligonucleotides as primers in a PCR reaction  were carried out for these control genes, and always the same TSSs were detected  we detected an additional TSSs not previously reported . Putative promoters with significant scores (see below) were detected in front of many of these new TSSs , http://www.ccg.unam.mx/Computational_Genomics/SupMaterial/TSS/index.html. In total, 623 TUs were analyzed: gene-specific oligonucleotide primers were designed for the first gene of each of these TUs , and we ybbA with partial complementarity with the gdhA and sstT genes. This limited base pair complementary was sufficient to generate extension products and to solve the TSSs for these two genes . The other 19 TSSs were annotated in RegulonDB after we started this work (this database is continuously updated). Detailed examination of these controls confirmed that the DMTSS methodology is very robust. 67% coincided precisely with the previously mapped TSSs; 29% had a small difference in the position of the TSSs of up to two nucleotides; and the remaining 4% had a difference of up to 3 nucleotides, but there was some uncertainty of where is the 5′ end due to “ambiguity by tailing”. This ambiguity arises when the last nucleotide of the sequence is the same as the polynucleotide tail that is used to label the 3′end of the cDNA library, such that it is not possible to determine where exactly the tailing begins. This problem is easy to overcome if a different nucleotide is used to generate the 3′ tail of the cDNA. Of the 317 TSSs mapped by DMTSS, 263 did not have previous experimental evidence, while 54 had been reported and served us as controls . As discE. coli. Our unbiased genomic approach identified the TSSs, promoters, and in some cases even regulatory binding sites (see below) for a large number of genes with no precise function assigned. We expect that this new data will contribute in some cases to narrow down the function of these genes.Since our strategy did not select for any particular class of genes other than the most highly expressed ones, we ended up mapping the TSSs of 130 genes for which function is not known  shorter than the E. coli putative protein; thus, using this as the genuine initiation codon then the TSS determined by us maps 21 nucleotides upstream of it. For YbjX, all proteobacterial orthologs, including the closely related Salmonella species, the protein initiates at a methionine that is at position 13 of the putative E. coli protein. Therefore, it is also very likely that in E. coli, the YbjX protein initiates further downstream and therefore the TSS is now at position −18. These results indicate that TSS mapping can also help in some cases to properly annotate ORFs. On the other hand, if the rest of the TSSs are bona fide transcription initiation events, they must come from a promoter. Therefore, we analyzed the region upstream of each TSSs and for 16 of them clear σ70 or σ38 −10 recognition elements with high scores from two different prediction methods (see below) were detected at the right position upstream of some 5′ ends described in this section  TSSs upstream of the ATG , that is located 1 nucleotide upstream of the −10 element, and the UP element, usually positioned 4 nucleotides upstream of the −35 promoter region 70 promoters. On average, σ70-depended promoters preserve eight of the 12 canonical nucleotides of the −35 and −10 hexamers Transcription initiation occurs when the RNAP, associated with one of the seven σ factors present in 70, followed by those depended on σ38. To annotate the most likely promoters and the σ factors recognizing them we used the Position-Weight Matrices (PWM), generated by Huerta and Collado-Vides 70 and for σ38 we generated a new PWM using the WCONSENSUS program. Each PWM was scanned with the PATSER program, in order to search for patterns in regions upstream of the TSSs obtained by DMTSS 70 and 14 σ38 promoters with high levels of confidence , we investigated the TSSs and the promoter structure of all the genes involved in the glycolytic pathway in E. coli wild type and in some mutant derivatives lacking the phosphotransferase system. The great majority of these genes have overlapping promoters for σ70 and σ38, and thus can be transcribed by both RNAP holoenzymes depending on the growth or stress conditions of the cell.In the accompanying article  that graphically displays each sequence below their corresponding position in the E coli genome, so that it is very easy to detect where the cDNA ends are located in the genome , our data set contained 13,181 unique 3′ cDNA ends. Next, we analyzed the sequences corresponding to genes with previously determined TSSs, both from the literature and our DMTSS methodology described above, to evaluate the robustness of the HTPS methodology. We observed the same or very close initiation sites for 225 3′ cDNA ends; 133 of them (59%) mapped exactly or within one nucleotide, 67 (30%) mapped between 2 to 4 nucleotides, while only 25 (11%) mapped with up to 7 nucleotides difference to the previously reported TSS. TSSs mapped further apart where considered products of different promoters. The exact coincidence of the TSSs mapped by DMTSS and HTPS (56 TSSs) was 67%, while 64% of the previously mapped by other methodologies (121 TSSs) coincided precisely to the HTPS data, confirming the precision of our DMTSS methodology. These results indicate that the HTPS strategy developed here is robust and can be used to map TSSs.Once the rRNA sequences were removed, we ended up with more than 33,000 sequences . We realigned each sequence to the bona fide TSSs or of mRNA degradation processes. If multiple sequences pointing to the same TSS were obtained it is more likely that they represented authentic TSSs rather than random mRNA degradation products. Furthermore, if they were from more than one growth condition, the probability of being bona fide TSSs increases. Alternatively, they could represent very specific mRNA processing products. Thus, a way to discern between these possibilities is to associate the 5′ mRNA ends to promoter DNA sequences. We searched in the DNA region immediately upstream of each 3′ cDNA end for promoter elements recognized by E-σ70 and E-σ38 using the strategy described above. Promoter elements with highly significant scores ], within [14% (171)], or in antisense [9.7% (119)] orientation of the coding region  were in the same orientation than the ORF, while 538 (26%) were in antisense orientation. Although we have detected bona fide TSSs within the coding regions, the lack of recognizable promoter elements in front of these sequences precluded us to call these 3′ cDNA ends TSSs until we have additional supporting information.2079 additional 3′ cDNA ends, with two or more identical sequences obtained independently, were detected within the coding region of 854 genes without obvious σThe unbiased results of the HTPS revealed a great number of transcripts in antisense orientation. Even when it is uncertain that the 5′ of the antisense sequences detected here are the result of transcription initiation processes, the fact that they are not distributed randomly suggests that they represent actual transcription events of the cell. They may be involved in gene expression control by duplex formation of their complementary transcripts, as detected in several other systems csrB and cspA. The 178 sequences obtained from csrBP1 initiate at two adjacent nucleotides almost in equal proportions, while cspA is more variable, having a preferred site but using three other adjacent sites. In this context, it seems that the +1 position should rather be defined as the site where the majority of the transcription events initiate, although the RNA polymerase actually initiates around this site. It is likely that the RNA polymerase docks to different promoter regions with different strengths, so that some initiate in a more relaxed form than others, resembling message slippage during elongation. These results indicate that HTPS for TSS mapping also provides relevant data to understand the basic mechanisms of transcription initiation. It will be interesting to relate different promoter properties to the strictness of the TSS.Previous methodologies used to identify TSSs did not quantify the frequency of initiation at each position in respect to the promoter, as the TSS was simply taken as the major band in a gel. The advantage of the HTPS method is that each sequence represents a single transcription event, so that it is now possible to quantify the relative number of transcriptions initiation events. The high redundancy of data obtained by HTPS for highly expressed genes, allowed us to observe that the transcription initiation site is not an exact location downstream of the promoter. With our dataset of more than 1700 TSSs detected by DMTSS and by HTPS, we calculated the frequency at which each nucleotide initiated transcription. As shown in E. coli , which can certainly help to better understand transcriptional regulation of the newly mapped promoters, we screened the regions around each TSS as described in E. coli . It is nbona fide TF binding sites.The distance between the TSSs and the putative TF binding sites is shown in E. coli adding further support to proper TSS determination by our genome-wide methodology thrA, ybaB, rib, alx, lysC, thiM, ribD, rpsO and rpsM, with a long 5′ UTR, very probably have a riboswitch or another RNA regulatory element.Next, we determined the 5′ UTR length (from the TSS to the first ATG codon of the product) for each of the about 1000 TUs located upstream of ATG. Attenuators are regulatory RNA structures that modify gene expression by altering transcription or translation. Transcriptional attenuators require the formation of one of two mutually exclusive RNA-secondary structures in the leader sequence of a transcript, the terminator and the antiterminator. Antiterminators are RNA structures that block the formation of terminators and make possible the transcription of downstream genes, while terminators block transcription of these genes when the products are not necessary. A translational attenuator is also an RNA structure that works by blocking, by base pair complementary, the Shine-Dalgarno ribosomal-binding motif required to properly initiate translation 70 and σ38 promoters, and also in the 237 TSSs upstream of ORFs for which we did not detect promoter elements with high confidence. We found 76 attenuator sequences, almost half of them translational attenuators  around 60 to 64°C, and most importantly, for each primer the last eight nucleotides of the 3′ end region were unique within the E. coli genome. Finally, from the collection of output primers generated by the GPA program we selected the best one for each gene, by imposing another stringent filter, which consisted in that the sequence of the last 6 nucleotides of the 3′ region of the desired primer should not be found in the highly expressed 16S and 23S ribosomal genes. This criterion was followed after we detected several products that originated from rRNAs instead of the specific gene for which the primers were designed, due to local 3′ match of eight or more nucleotides  with the aim of precisely mapping as many TSSs as possible. Accurate PCR amplification of the individual genes from the cDNA library was favored by designing primers, which allow specific annealing with the non-template strand of its own target gene. For this purpose, we developed software called “Genome Primer Analysis” (GPA) in which the following criteria were followed to achieve optimal primer design: oligonucleotide length, G+C content, and minimizing palindromic sequence formation. Candidate primer sites were evaluated within a gene coding-sequence window between 100 to 300 nucleotides downstream of the translation start codon, taking into account how many consecutive nucleotides at the 3′ end of each primer were present elsewhere in the leotides .We used as experimental platform the 5′ RACE methodology E. coli MG1665 (K-12) strain was purified from three different growth conditions: (1) Luria Broth (LB), 30°C, OD 600 nm 0.8; (2) Minimal Medium (M9) supplemented with 0.2% glucose as the solely carbon source, 30°C, OD 600 nm 0.8; and (3) M9 supplemented with 0.2% glycerol, 30°C, OD 600 nm 0.5 was isolated. These growth conditions exactly reproduced the conditions used in previous gene expression profile microarray experiments obtained for this E. coli strain Total RNA from the wild type At this step, and contrasting to the standard 5′ RACE protocol that uses a specific primer per gene, DMTSS employs a random hexamer primer to generate cDNA products for each RNA source. Briefly, 3 µl (approximately 1.5 µg) of purified RNA sample was mixed with 700 pmol of random primer at 70°C for 10 minutes. The reaction was cooled on ice for 5 min, and immediately a reaction mix containing: 5 µl of dNTPs (2.5 mM), 1 µl of DTT, 4 µl of 5X reaction buffer and 1 µl of SuperScript III reverse transcriptase (200 U/µl) in a 20/µl final volume was added. All these reagents were purchased from Invitrogen . To allow cDNA synthesis the reactions were incubated in a Stratagene RoboCycler PCR instrument  under the following program: . The final cDNA product was purified using the Roche's High Pure PCR Product Purification Kit , according to the manufacturer's instructions.The purified cDNA libraries were enzymatically labeled at the 3′ terminal end with a homo polynucleotide tail. Briefly, a 20 µl purified cDNA sample was mixed with 1 µl of Terminal Deoxynucleotidyl Transferase (20 U/µl), 0.2 mM final concentration of the appropriate dNTP and 1X reaction buffer in a 25 µl final reaction volume. The reagents were purchased from Fermentas . The reaction was incubated at 37°C for 30 min, following by enzyme inactivation by heating at 70°C for 10 min.GAC-TCG-AGT-CGA-CAT-CGA-NNN-NNN-NNN-NNN-NNN-NN-3′5′-; N is the complementary nucleotide to the homopolymer tail), which has an adaptor sequence at its 5′ terminal end (underlined), was allowed to anneal to the poly homonucleotide tract of the tagged cDNA, and used to linearly expand the library in a standard PCR amplification reaction under the following conditions: 1 cycle, 94°C for 10 min; 30°cycles of 94°C for 1 min, 45°C for 2 min, 72°C for 3 min, and finally one last extension cycle at 72°C for 5 min).Next, and differing from the standard 5′ RACE protocol, we performed a linear PCR amplification in order to enrich the yield of the cDNA complementary strand. Briefly, 20 pmol of DMTSS-1 primer (GAC-TCG-AGT-CGA-CAT-CGA-TT-3′5′-), which carry the adaptor sequence and a primer that specifically anneal with the cDNA complementary strand of their target gene . We generated cDNA libraries by reverse transcription using SuperScript III reverse transcriptase together with an hexamer random primer-adaptor B (5′GCCTTGCCAGCCCGCTCANNNNNN3′). The cDNA synthesis reactions were incubated in a RoboCycler equipment  under the following program: 28°C for 20 min, 45°C for 40 min, 70°C for 10 min. The cDNA final products were purified using the High Pure PCR product purification kit , according to the manufacturer instructions. A double stranded adaptor A was ligated to purified cDNA libraries (tagged). One of the oligonucleotides of this double stranded adaptor has a randomized sequence of six nucleotides that match with the 3′ end of the cDNA products.Total RNA from GCCTCCCTCGCGCCATCAGNNNNNN3′5′CGGAGGGAGCGCGGTAGTC5′3′5 µl of purified cDNA sample was mixed with T4 DNA ligase (1 Weiss U/µl), 1X reaction buffer, 35 pmol of adaptor A, in a final reaction volume of 25 µl. The reaction was incubated at 16°C overnight, following by enzyme inactivation at 70°C for 10 minutes. All the reagents were purchased from Fermentas .5′GCCTCCCTCGCGCCATCAG3′) and B (5′GCCTTGCCAGCCCGCTC3′), PCR amplicons were generated by Fast Start High Fidelity PCR System , purified with MiniElute PCR purification Kit , and quantified using the NanoDrop spectrophotometer . At least 3 µg of cDNA were obtained for each sample. The quality of the DNA was evaluated by capillary electrophoresis using the Agilent Bioanalyser 2100 . For pyrosequencing, samples were prepared according to 454 Roche GS20 DNA Amplicon Library Preparation Kit user manual. Each amplicon mix was sequenced independently using the GS emPCR Kit II . The sequence was done at Laboratorio Nacional de Genomica para la Biodiversidad (LANGEBIO) Cinvestav, Irapuato, México.Using primers complementary to adaptors A . For the −10 region, we selected those results where the search pattern was located within the first 20 nucleotides upstream of the 5′ end. We choose this distance because it is well known that the −10 element for σIn order to predict regulatory sites, the regions from −400 to +200 of each TSS were scanned for the known TFs binding sites annotated in RegulonDB , using the program Matrix-Scan Figure S1rpsP gene. All the sequences pointed to the same TSS that is identical to the reported Electropherograms from multiple experiments of TSS mapping for the (0.58 MB PDF)Click here for additional data file.Figure S2E. coli K12. a) gene pepN, b) gene ydaN, and c) gene gdhA.Experimental results of TSS mapping for three different genes showing the PCR product(s), electropherogram, and the DNA alignment with (0.96 MB PDF)Click here for additional data file.Figure S3gdhA and sstT genes. A) PCR products obtained with oligonucleotide ybbA, designed for ybbA gene. B) Partial base-pair complementarily of this oligonucleotide with gdhA and sstT regions. Products 1 and 2 correspond to the upstream region of gdhA, while product 3 corresponds to the upstream region of sstT. No product corresponding to gene ybbA, for which the oligonucleotide was designed, was detected.Unspecific priming of oligonucleotide ybbA into (0.17 MB PDF)Click here for additional data file.Figure S4ydfH gene, the ambiguity was for only one nucleotide (adenine or guanine). By using a different nucleotide for tailing, for instance thymine instead of adenine, as we did in this case, the ambiguity is solved. This change shows that the guanine nucleotide was indeed the TSS of the ydfH gene under the conditions tested. A) Incorporation of dTTP at the 3′ end of the cDNA. B) Incorporation of dATP at the 3′ end of the cDNA.Solving the TSS ambiguity by using a different polynucleotide for the 3′ end labeling. In the case of the (0.35 MB PDF)Click here for additional data file.Figure S5csrB, tff and sroG small RNAs.Gene context of the (0.11 MB PDF)Click here for additional data file.Figure S6http://www.ccg.unam.mx/Computational_Genomics/SupMaterial/TSS/index.htmlLocation of the top TF's binding sites predicted in the regulatory region of each TU with promoter prediction. The complete data set is stored at (0.11 MB PDF)Click here for additional data file.Table S170 and σ38 with the program PATSER Results for prediction of −10 and −35 elements recognized by σ(2.63 MB XLS)Click here for additional data file.Table S2Oligonucleotides utilized for DMTSS.(0.22 MB XLS)Click here for additional data file.Table S3Genes with known, predicted, conserved, putative, hypothetical or unknown function.(0.07 MB XLS)Click here for additional data file.Table S4Putative binding sites for 55 transcription factors were searched in the regions from −400 to +200 for each TSS with promoter prediction, using the Matriz-Scan program.(0.16 MB XLS)Click here for additional data file.Table S5Transcriptional factors binding sites associated to each TSS. The information in RegulonDB for each of these TFs and their binding sites is shown.(0.05 MB DOC)Click here for additional data file.Table S6Genes with associated attenuator identified in this work.(0.04 MB XLS)Click here for additional data file.


Salmonella enterica ssp. I serovar Typhimurium (S. Typhimurium). We created a genomic plasmid library fused to a green fluorescent protein (GFP) reporter and subjected replicate populations harboring this library to fluctuating selection for GFP expression using fluorescent-activated cell sorting (FACS). After seven rounds of fluctuating selection, the populations were strongly enriched for promoters that showed a high amount of noise in gene expression. Our results indicate that the activity of some promoters of S. Typhimurium varies on such a short time scale that these promoters can absorb rapid fluctuations in the direction of selection, as imposed during our experiment. The genomic fragments that conferred the highest levels of phenotypic variation were promoters controlling the synthesis of flagella, which are associated with virulence and host–pathogen interactions. This confirms earlier reports that phenotypic noise may play a role in pathogenesis and indicates that these promoters have among the highest levels of noise in the S. Typhimurium genome. This approach can be applied to many other bacterial and eukaryotic systems as a simple method for identifying genes with noisy expression.Genetically identical populations of unicellular organisms often show marked variation in some phenotypic traits. To investigate the molecular causes and possible biological functions of this phenotypic noise, it would be useful to have a method to identify genes whose expression varies stochastically on a certain time scale. Here, we developed such a method and used it for identifying genes with high levels of phenotypic noise in  Salmonella typhimurium that vary at a time scale of one day. We found that traits involved in interaction with the host are particularly noisy, suggesting that phenotypic noise might be important in pathogenesis. This method can be readily adopted for other organisms and might contribute to elucidating the role of noise in biology.According to the conventional view, the characteristics of an organism are determined by nature and nurture—by its genes and by the environment it lives in. Consequently, one would expect that two organisms that share the same genes and live in the same environment have identical characteristics. Recently it has become clear that this expectation is often not borne out; clonal families of simple organisms living under constant conditions often show variation in biological traits and sometimes even have markedly different properties and do different things. In order to investigate molecular causes and possible biological functions of such phenotypic noise, it would be very valuable to have a simple and fast method for identifying biological traits that are particularly noisy. Here, we developed such a method, and screened for noisy traits in the bacterial pathogen  Clonal populations of unicellular organisms growing under constant conditions often show substantial variation in phenotypic traits. The rate at which some of these traits vary is so high that it cannot result from mutational change. Rather, this phenotypic noise has been shown to result from chance events in the cells, namely random fluctuation in the transcription and translation of genes S. Typhimurium that switch between active and inactive over the course of a few generations.To further our understanding of the biological significance of phenotypic noise, it would be helpful to have a simple method to identify genes whose expression varies stochastically at a given timescale and under specific environmental conditions. So far, most of the research on phenotypic noise was based on the detailed analysis of individual biological traits The method is based on subjecting a promoter library to selection for high levels of random variation on a short time scale. The screen was initiated with a genomic library consisting of short genomic fragments upstream of a gene encoding green fluorescent protein (GFP). Cells carrying a fragment with an active promoter expressed GFP. In order to select for promoters with a high level of phenotypic noise, we used fluorescence-activated cell sorting to select cells based on the cellular concentration of GFP, and alternated between selecting for high levels of GFP, and selecting for low levels of GFP. There was no signal indicating the direction of selection during a given round of the selection experiment; one would thus expect that promoters that randomly switch between expressing and not expressing GFP would increase in frequency.S. Typhimurium. Our screen demonstrates that these promoters stand out in terms of the level of noise, and that they vary on a very short timescale. This method thus offers a simple and powerful approach to identify genes with high levels of noise, and allows for easily modulating timescale and environmental conditions under which such phenotypic noise manifests.This screen led to a strong enrichment of promoters with high levels of noise. The promoters that showed the highest levels of noise were found to be flagellar promoters, which are involved in the interaction with the host. These promoters have previously been reported to be heterogeneously expressed in clonal populations of 6S. Typhimurium clones containing a library of genomic fragments ranging in size from 400 bp to 1200 bp linked to a GFP reporter . After the seven rounds of selection, clones from all populations were plated onto agar plates. Twenty-four clones from each of the ten populations were randomly selected for future analyses.5 cells per clone . This shows that the level of phenotypic noise is a consistent property of a clone (presumably reflecting the noise of the promoter on the genomic fragment it contains), and that this property is stably maintained in clonal populations that are repeatedly grown from an individual cell.Selection for increased phenotypic noise can only be successful if the level of variation is a stable property of a clone. We thus first asked whether the level of phenotypic noise in GFP expression was a stable and consistent trait in these clonal isolates. We used the 240 frozen clonal stocks described above to seed fresh cultures of cells, and analyzed GFP concentration for about 5×10lone see . We repelone see . We founNext, we asked whether fluctuating selection had led to an enrichment of clones exhibiting larger amounts of stochastic phenotypic variation. We compared the clones from the five selected populations to the clones from the five control populations. Among the clones from the selected populations, a sizable fraction showed high coefficients of variation in fluorescence, which is a measure of stochastic phenotypic variation. In contrast, the control population did not contain any clones with high coefficients of variation . An analfliC and to a lesser extent flgK . We found that the clones exhibiting the highest levels of variation were dominated by two promoter sequences that regulate genes involved in flagellar synthesis, namely ent flgK . On the fliC for two tests of the robustness of our results. First, we tested whether the fliC promoter is also noisy in the native chromosomal context. To do so, we constructed a transcriptional fusion of gfp to the fliC promoter at its native location in the chromosome. Clones from this chromosomal construction showed very similar levels of phenotypic noise to the plasmid-based fliC promoter . Western blot analysis with anti-FliC, anti-fljK antibodies on these three cell fractions confirmed that GFP expression is positively correlated with FliC protein production. . This demonstrates that phase variation is not the main reason for the phenotypic noise observed here, and is most likely not involved.As a direct test of the effect of phase variation on stochastic phenotypic variation, we transformed the plasmid with the Having identified promoters that are particularly variable, one can then ask whether variability in these promoters might serve a biological function. This question can be addressed by functional studies of the genes whose expression is particularly noisy. However, first insights can be gained from looking at the types of promoters that showed the highest levels of stochastic phenotypic variation.fliC. This supports a previous report that the expression of FliC is heterogeneous in clonal populations of S. Typhimurium By far the highest level of phenotypic noise observed in our experiment comes from flagellar promoters, most notably, S. Typhimurium, flagella are instrumental for swimming towards the host's epithelial mucus layer S. Typhimurium promotes the division of labor between two phenotypically different subpopulations. One subpopulation invades the gut tissue and elicits an inflammation of the gut; the other subpopulation remains in the gut and benefits from the fact that the inflammation reduces competition from commensal bacteria The advantage that is usually postulated is mediation between conflicting selection pressures on flagellar expression in the host. During initial stages of gut infection by S. Typhimurium promoters varies on such a short time scale that these promoters can absorb rapid fluctuations in the direction of selection, as imposed during our experiment. This is an important experimental test of one of the main ideas for why phenotypic noise can be adaptive: variation in the phenotypes encoded by a single genotype can increase the long-term growth rate of this genotype in fluctuating environments Two main insights emerge from this study. The first insight is that the activity of some The second insight is methodological: fluctuating selection is a simple and fast tool to screen large pools of individuals in order to identify variable promoters in unicellular organisms, and thus complements exhaustive characterizations of individual genes One particular advantage of this tool is that the time-scale at which the direction of selection changes can be varied. By changing the direction of selection every few cell divisions, on can impose selection for promoters that switch at a very high rate; changing the direction of selection less frequently selects for promoters that switch at lower rates. It should thus be possible to identify promoters that vary at different time scales, and to investigate whether they might be associated with responses to environmental conditions that vary at different frequencies.Once noisy promoters are identified, functional studies are needed to investigate the biological consequences of their variation. This might lead toward new answers to one of the fundamental and most challenging questions about the biology of noise – whether phenotypic noise is beneficial, and what its possible benefits might be.Strains were grown at 37°C on LB agar plates or in 1 ml of liquid LB broth in 5ml polystyrene round bottom tubes , with shaking at 200 rpm until mid-exponential phase. Ampicillin (Sigma) was used at a concentration of 100 µg/ml in strains containing plasmid pM968 or its derivatives.6 clones) was constructed by partially digesting S. Typhimurium SL1344 wildtype Bsp143I. Fragments within a size range of 400 bp to 1200 bp were ligated into BamHI digested pM968. This plasmid is low copy number promoter-less derivative of pBAD24 containing promoterless gfpmut2, described in E. coli Χ6060, re-isolated by standard methods and electrotransformed into S. Typhimurium M324 . These were grown overnight in 1ml of LB containing Ampicillin and frozen at −80°C in 15% glycerol.Cells were sorted using fluorescence-activated cell sorting (FACS) with FACS–Diva sorting software . Immediately prior to sorting, 5×104 cells were analyzed for GFP expression on the FACS Calibur .One day prior to analysis, the 240 frozen clonal stocks were used to inoculate 1ml of medium in 5ml polystyrene round bottom tubes  and prepared in the same manner as described above (Growth for cytometry and cell sorting). For each clone, 5×10Raw data was exported from FlowJo 4.6.1 software  into custom software. The software was used to exclude data deemed to be extraneous and for performing calculations relating to noise in fluorescence intensity.The following conventions were applied to calculate variation in GFP expression and to limit the influence from cellular aggregates, cell detritus, and undefined values. Modified from Newman et al All SSC, FSC, and fluorescence zero values were excluded.Data was excluded that fell within the forward scatter (FSC) and side scatter (SSC) region where significant counts appeared in “buffer only” controls.Extreme values of FSC and SSC were excluded (the highest and lowest 2.5% of events) from total counts in order to limit influence from cell detritus and cell aggregates.FSC and SSC medians were calculated and a series of circular gates expanding out from the FSC and SSC medians were applied. For each gate size the coefficient of variation (CV) was calculated for fluorescence. A single gate size was then chosen for all analyses; this gate resulted in the lowest average CV (in order to maintain a conservative estimate of noise) yet contained enough cells for robust analysis (a minimum of 950 cells).Extreme values of the fluorescence channel (FL1) (the highest 1.0% of events) were excluded to limit only a very small number of cells having undue effects on the values of the mean and CV.When calculating the correlation between the coefficients of variation in fluorescence on two consecutive days, two data points were excluded from the analyses because they were more than 3 standard deviations away from expected values.5′ GTCAGAGGTTTTCACCGTCATCAC 3′. R: 5′CAAGAATTGGGACAACTCCAGTG 3′) were used to PCR amplify the genomic segments inserted into plasmid pM968. Both primers anneal to regions on pM968 that flank the insert region. Inserts were sequenced using the reverse primer. The insert sequences were blasted against the genomic sequence of Salmonella typhimurium LT2 genomic and plasmid sequence (accession numbers NC_003197 and NC_003277), and the single best hit was retained as a hypothetical promoter. For each of these hypothetical promoters, the two nearest downstream genes were checked to see if either were oriented in the same direction as the hypothetical promoter. If either of these genes were oriented in the correct direction, the name and distance to the closest gene was noted. If neither of these genes were oriented in the correct direction, we concluded that it was unlikely that the insert sequence was actively driving transcription.The following primers  and a rpsM promoter fused to gfp+fliC promoter on the plasmid pM968 (blue lines) and in the native location on the chromosome (green lines). These two strains differ in the average expression level and in the pattern of distribution of the expression levels in the population. Strain M557 containing no gfp gene (black line) and a rpsM promoter fused to gfp+ (red line) inserted in the chromosome of strain M557 serve as controls.Comparison of noise in expression of chromosomal-based and plasmid-based (0.61 MB TIF)Click here for additional data file.Figure S2FliC. Cells containing the pfliC::gfp construct in plasmid pM968 were sorted based on expression of GFP using the FACS. Cells were sorted into three fractions, each containing the same number of cells: The first fraction contained cells with high levels of fluorescence; the second fraction contained cells whose fluorescence did not exceed background; the third fraction was a random sample of cells, chosen irrespective of their level of fluorescence. Cells were subjected to western blot analysis with staining using anti-FliC, -FljB antibodies and reprobed with anti-OmpC as a loading control. Only cells with high levels of GFP expression of GFP showed a band when stained with anti-FliC, indicating that GFP expression positively correlates with production of FliC protein. It is unclear why the fraction containing all cells does not also show a band; however, the lower intensity of the anti-OmpC band of this fraction and the fact that this fraction contains many cells that do not express gfp suggests that the anti-FliC band might be too faint to see.Western blot analysis shows that GFP expression correlates with the expression of (1.34 MB TIF)Click here for additional data file.Figure S3dcm promoter. GFP expression is plotted in grey , illustrating the temporal pattern of switching of the dcm promoter, isolated from a control population. The image and the lineage tree are based on Lineage tree of microcolony growth and expression pattern of the (0.56 MB TIF)Click here for additional data file.Table S1Sequenced inserts from selected and control populations. Sequence data from the 240 clones used for analysis.(0.07 MB XLS)Click here for additional data file.Text S1Supporting information containing supplementary materials and methods as well as supplementary figure legends.(0.06 MB DOC)Click here for additional data file.Movie S1fliC promoter during the growth of a microcolony. GFP is under the control of fliC promoter on plasmid M956. This movie lasts for 106 minutes in real time. The phase and fluorescent images have been merged; a lineage reconstruction of this movie can be seen in Time-lapse movie showing GFP expression under the control of the (0.39 MB MOV)Click here for additional data file.Movie S2dcm promoter during the growth of a microcolony. GFP is under the control of the dcm (a DNA cytosine methylase) promoter on plasmid M956. This clone was isolated from a control population. This movie lasts for 178 minutes in real time. The phase and fluorescent images have been merged; a lineage reconstruction of this movie can be seen in Time-lapse movie showing GFP expression under the control of the (1.37 MB MOV)Click here for additional data file.


Saccharomyces cerevisiae indicates that High Mobility Group (HMGB) proteins, non-sequence-specific chromosomal proteins, participate in mediating responses to growth signals. As HMGB proteins are distinguished by their ability to alter DNA topology, they frequently function in the assembly of higher-order nucleoprotein complexes. We review here recent evidence, which suggests that HMGB proteins may function to coordinate TOR-dependent regulation of rRNA and RP gene expression.Cells grow in response to nutrients or growth factors, whose presence is detected and communicated by elaborate signaling pathways. Protein kinases play crucial roles in processes such as cell cycle progression and gene expression, and misregulation of such pathways has been correlated with various diseased states. Signals intended to promote cell growth converge on ribosome biogenesis, as the ability to produce cellular proteins is intimately tied to cell growth. Part of the response to growth signals is therefore the coordinate expression of genes encoding ribosomal RNA (rRNA) and ribosomal proteins (RP). A key player in regulating cell growth is the Target of Rapamycin (TOR) kinase, one of the gatekeepers that prevent cell cycle progression from G1 to S under conditions of nutritional stress. TOR is structurally and functionally conserved in all eukaryotes. Under favorable growth conditions, TOR is active and cells maintain a robust rate of ribosome biogenesis, translation initiation and nutrient import. Under stress conditions, TOR signaling is suppressed, leading to cell cycle arrest, while the failure of TOR to respond appropriately to environmental or nutritional signals leads to uncontrolled cell growth. Emerging evidence from  Eukaryotic cell growth is regulated by intricate signaling pathways in response to nutrient levels, environmental stress and the presence of growth factors. Several protein kinases contribute to such signaling, including the target of rapamycin (TOR) kinase, a highly conserved serine/threonine kinase that plays a central role in the control of cell growth (reviewed in -3). TOR Saccharomyces cerevisiae is dedicated to synthesis of ribosomal components - rRNA by RNA polymerase (Pol) I, ribosomal proteins by Pol II and 5S ribosomal RNA and tRNA by Pol III - while in the proliferating mammalian cell, as much as 50% of nuclear transcription is committed to this goal. What has also becomes increasingly evident is that ribosome biogenesis is an important determinant of cell growth; to maintain steady growth in response to favorable conditions, about 2000 ribosomes need to be synthesized per minute. While the role of TOR in regulating the synthesis of ribosomal components is well established, the mechanism by which all three polymerases are coordinately regulated in response to appropriate stimuli remains to be fully elucidated. As reviewed here, emerging evidence points to a role for high mobility group (HMGB) proteins in mediating such coordinated regulation in response to TOR signaling.Up to 80% of the transcriptional machinery in via a rapamycin-sensitive signaling pathway [Both yeast and mammals contain two functionally and structurally distinct TOR complexes, TOR complex 1 (TORC1) and TOR complex 2 (TORC2), each composed of TOR as well as subunits that determine downstream substrates . TORC1 c pathway -11 while pathway -15. Whil pathway , 16, 17.Huntington, EF3, A subunit of PP2A, TOR1), required for proper subcellular localization [FRAP, an earlier name for mammalian TOR, ATM, TRAP) may mediate protein-protein interactions [1); [The ~280 kDa TOR proteins share 40-60% sequence identity and contain several domains. The N-terminal domain contains a number of HEAT motifs ; , 20).Streptomyces hygroscopicus as an antifungal agent. Treatment of yeast cells with rapamycin phenocopies nitrogen starvation or inactivation of TOR by leading to down-regulation of protein synthesis and upregulation of macroautophagy (delivery of cytoplasmic contents to lysosomes and vacuoles). The mechanism by which FKBP12-rapamycin inhibits TORC1 is not known but has been proposed to include blockage of substrate access or dissociation of TOR from associated proteins [1)); KOG1 deficiency in yeast resembles the phenotype of rapamycin treatment, suggesting that KOG1 is a positive regulator of TOR, an inference also reached with regard to LST8 [The response to rapamycin requires that the drug first combine with the intracellular receptor FKBP12 , 21. FKBproteins , 23. Pro to LST8 , 24. Kno to LST8 , 25, 26.Several upstream signaling cues, including growth factors in mammals, nutrients, and environmental stress such as hypoxia and DNA damage regulate TORC1 signaling. Major targets of TORC1 include components of the translation machinery (reviewed in ). Such dIn addition to numerous roles in regulation of translation, TORC1 regulates transcription of all genes whose products are involved in ribosome biogenesis. Production of rRNA accounts for at least 60% of total yeast transcriptional activity during normal growth, so it is a highly energy- and nutrient-consuming process. To conserve resources, cells must therefore limit the production of new ribosomes during nutrient starvation. Accordingly, both synthesis and the subsequent processing of 35S precursor rRNA is regulated by the TOR signaling pathway in both mammalian and yeast cells , 34.rDNA transcription is confined to the nucleolus, where rDNA genes are arranged as hundreds of head-to-tail repeats. The primary rRNA transcript is then processed into the mature rRNAs in the nucleolus, which is also the site of ribosome biogenesis. A recent study showed that rapamycin and nutrient starvation causes rapid delocalization of RNA Pol I from the nucleolus, suggesting a possible mechanism for the regulation of rDNA transcription by TOR . These aLike Pol II and Pol III, Pol I requires auxiliary factors to mediate promoter recognition; for mammalian Pol I, preinitiation complex formation requires the upstream binding factor UBF and the promoter selectivity factor TIF-IB/SL1  -42. Yeasvia the transcription factor TIF-IA/Rrn3, as inactivation of TOR impairs formation of the transcription initiation complex [In both yeast and mammals, TOR controls transcription by RNA polymerase I  complex , 51-53.  complex . Thus, tChanges in UBF phosphorylation are key to modulating rDNA activity during cell cycle progression; for example, phosphorylation of the C-terminal activation domain of UBF by casein kinase II (CKII) facilitates the interaction between UBF and TIF-IB/SL1 . StimulaIn addition to signaling functions, including the control of cellular localization of specific transcription factors as discussed below, TOR1 itself is dynamically distributed in the cytoplasm and nucleus in yeast. TOR1 nuclear localization is nutrient-dependent and rapamycin sensitive, and starvation or treatment with rapamycin causes TOR1 to exit from the nucleus. TOR1 nuclear localization is critical for 35S rRNA synthesis, but not for the expression of ribosomal protein genes . Thus, eRibosomal protein (RP) genes in eukaryotes are also regulated in response to growth stimuli and environmental stress. Global transcription profiling revealed that RP gene transcription is drastically repressed in response to rapamycin , 57. As Co-repressor with FHL1) or the coactivator IFH1 (Interacts with Forkhead). This decision is made based on the subcellular localization of CRF1, which is in turn determined by its phosphorylation state. TOR controls the subcellular localization of protein kinase A (PKA) and the PKA-regulated kinase YAK1 for which CRF1 is a substrate [2)). That additional factors must contribute to TOR-mediated regulation of RP gene activity was indicated by the observation that CRF1 may not be essential for rapamycin-dependent down-regulation of RP gene activity, depending on strain background, and that simply tethering of FHL1 to RP gene promoters is insufficient to cause transcriptional activation, in spite of the fact that such tethered FHL1 does recruit IFH1 [A number of transcription factors have been implicated in regulation of RP gene expression. Genome-wide detection of transcription factor binding revealed that the Forkhead-like transcription factor FHL1, originally isolated as a multicopy suppressor of RNA Pol III, is found at yeast RP gene promoters , 61. Sububstrate -64; in aubstrate . The essubstrate , 64, 65;uit IFH1 .in vitro [Although yeast FHL1 has a domain that is related to the DNA binding domain of the Drosophila Forkhead protein, FHL1 was reported not to bind DNA in vitro . Thus, tin vitro , 69. Howin vitro , suggestvia FHL1 and IFH1, and that the association of IFH1 and FHL1 with RP gene promoters was reduced by approximately four-fold and two-fold, respectively, in sfp1Δ cells [The complexity of the TOR pathway is further underscored by the reported involvement of yet another transcription factor, Sfp1, a key cell size regulator, in regulating RP gene expression in response to nutrients and stress . Under o1Δ cells . As for 1Δ cells . Thus, wrpa49Δ mutation, as RPA49 corresponds to the mammalian PAF53 which interacts directly with the HMG-box-containing transcription factor UBF; as noted above, yeast does not encode a UBF homolog [As summarized above, an effect of TOR inactivation on rRNA and RP gene expression has been well established, although the molecular basis for the response remains to be fully elucidated. Notably, recent evidence has finally furnished a link that may provide a clue to the mechanism by which transcriptional activity of the different RNA polymerase machineries may be coordinated in response to TOR signaling. This link comes in the form of the yeast HMGB homolog HMO1. HMGB proteins are conserved architectural proteins that bind DNA without sequence specificity, but with preference for DNA with structural distortions. As for mammalian HMGB homologs, HMO1 is composed of two HMG boxes, box A and box B. Box A has low affinity for DNA but is required for DNA structural specificity and for DNA bending, whereas box B is required for high affinity DNA binding , 75. Gen homolog , 48, 49. homolog , 47.fhl1Δ strain, while inactivation of HMO1 completely eliminates association of FHL1 and IFH1 with RP genes [A seminal study by Hall and coworkers demonstrated that HMO1 associates throughout the rDNA locus . Their sRP genes . These det al. subsequently reported that HMO1 specifically interacts with the Pol I-transcribed region of the rDNA locus, pointing to a role in elongation, and confirmed its association with a subset of RP gene promoters [Using a global genetic approach, Berger romoters . This isromoters . Notablyromoters . These rThe molecular mechanisms underlying the role of HMO1 in coordinating rRNA and RP gene transcription in response to TOR-mediated signals remain to be determined. In the case of rDNA, HMO1 has been proposed to serve roles akin to those fulfilled by UBF in mammalian transcriptional initiation as well as in linking transcriptional elongation with pre-rRNA processing. Regardless of its specific function(s), it is clear that HMO1 abandons the rDNA locus when TORC1 is inhibited . For UBF2)). At most RP gene promoters, HMO1 appears to associate in a Rap1-dependent fashion, followed or accompanied by FHL1; however, at the promoters to which HMO1 only associates modestly, FHL1 binding appears independent of HMO1 [CKII), Utp22, Rrp7 and IFH1), a complex with which FHL1 also loosely associates [At RP gene promoters, several transcription factors have been implicated in mediating downstream effects of TOR signaling. While the role of Sfp1 appears particularly uncertain, a consensus is emerging based on consistent reports from several groups (Fig. ( of HMO1 . The tra of HMO1 . One possociates . Notablysociates . Whethersociates , consistn vitro, even in the presence of Rap1 [As HMO1 is non-sequence-specific in its DNA binding , 75, it  of Rap1 , combine of Rap1 , suggest of Rap1 , 78; suc of Rap1 . As for