Drosophila melanogaster. Using chromatin-immunoprecipitation combined with microarray hybridisation, we show that Nup153 and Megator (Mtor) bind to 25% of the genome in continuous domains extending 10 kb to 500 kb. These Nucleoporin-Associated Regions (NARs) are dominated by markers for active transcription, including high RNA polymerase II occupancy and histone H4K16 acetylation. RNAi–mediated knock-down of Nup153 alters the expression of ∼5,700 genes, with a pronounced down-regulatory effect within NARs. We find that nucleoporins play a central role in coordinating dosage compensation—an organism-wide process involving the doubling of expression of the male X chromosome. NARs are enriched on the male X chromosome and occupy 75% of this chromosome. Furthermore, Nup153-depletion abolishes the normal function of the male-specific dosage compensation complex. Finally, by extensive 3D imaging, we demonstrate that NARs contribute to gene expression control irrespective of their sub-nuclear localization. Therefore, we suggest that NAR–binding is used for chromosomal organization that enables gene expression control.Transcriptional regulation is one of the most important processes for modulating gene expression. Though much of this control is attributed to transcription factors, histones, and associated enzymes, it is increasingly apparent that the spatial organization of chromosomes within the nucleus has a profound effect on transcriptional activity. Studies in yeast indicate that the nuclear pore complex might promote transcription by recruiting chromatin to the nuclear periphery. In higher eukaryotes, however, it is not known whether such regulation has global significance. Here we establish nucleoporins as a major class of global regulators for gene expression in  The eukaryotic genome is spatially distributed in a highly organized manner, with chromosomal regions localizing to well-defined sub-nuclear positions. This organization could have a profound effect on chromatin accessibility and transcriptional activity on a genome-wide level. Using high-resolution, genome-wide, chromatin-binding profiles we show that the nuclear pore components Nup153 and Megator bind to quarter of the Drosophila genome in form of chromosomal domains. These domains represent active regions of the genome. Interestingly, comparison of male and female cells revealed enrichment of these domains on the male X chromosome, which represents an exceptionally active chromosome that is under dosage compensation control to equalize gene expression due to differences in X chromosome number between males and females. Based on extensive 3D image analysis, we show that these chromosomal domains are contributed by both peripheral as well as intranuclear pool of these proteins. We suggest that chromosomal organization by nucleoporins could contribute to global gene expression control. The spatial organisation of DNA, both at the nucleotide and chromosomal levels, allows efficient storage of genetic information inside the nucleus. However, DNA-dependent processes such as transcription, require the chromosomal structure to be modified in order to allow access to this information.The regulation of chromatin accessibility is an intensely studied subject Drosophila melanogaster and humans established that lamins—proteins lining the nuclear membrane The eukaryotic genome is spatially distributed in a highly organised manner, with entire chromosomal regions localising to well-defined sub-nuclear positions Saccharomyces cerevisiae identified subunits of the nuclear pore complex that preferentially bound transcriptionally active genes Although the nuclear periphery has been primarily associated with repression, recent evidence has also suggested a role for membrane components in transcriptional activation Since there are differences in the composition of the nuclear envelope—such as the lack of lamins—it is important to also study the contribution of nuclear envelope components in gene regulation in higher organisms Drosophila, the Male Specific Lethal (MSL) complex offsets the imbalance in the number of sex chromosomes in males and females by doubling the expression of genes on the male X chromosome Recently, we revealed a biochemical association between nucleoporins and the dosage compensation apparatus in higher eukaryotes including humans Here, we present the first genome-wide study of nucleoporin-binding in a higher eukaryote. Using chromatin immunoprecipitation followed by hybridisation to high resolution tiling microarrays, we show that Nup153 and Mtor interact with 25% of the Drosophila genome in large domains spanning 10–500 kb in size. These regions—which we term nucleoporin associated regions (NARs)—contain large numbers of highly expressed genes, and are enriched for markers of active transcription including RNA polymerase-binding and histone H4 lysine 16 acetylation. Additionally, we reveal a remarkably high density of NARs on the male X chromosome, which correlate extremely well with the binding pattern of the dosage compensation complex. Finally, we demonstrate that chromosomal regions bound by these nucleoporins are composed of peripheral as well as non-peripheral pools of these proteins but interestingly the X chromosomal target regions are preferentially localised closer to the nuclear periphery. In summary, we firmly establish nucleoporins as a major class of chromatin-binding proteins in higher eukaryotes, with a general role in transcriptional regulation and three-dimensional chromosomal organisation. Finally we show for the first time, the importance of nucleoporin-binding not only as a mechanism for transcriptional control, but also in maintaining a complex organism-level biological system namely dosage compensation.Drosophila male SL-2 and female KC cell lines using chromatin immunoprecipitation followed by hybridisation to Affymetrix tiling arrays We produced DNA-binding profiles for nuclear pore components Mtor and Nup153 in r = 0.77 and 0.88 for SL-2 and KC cells respectively; r = 0.73), and we validated binding at 18 control genes by real-time PCR in triplicate . Thus nucleoporins represent a new class of global chromatin-binding proteins for higher eukaryotes.Both Mtor and Nup153 exhibit extensive binding across the whole genome, and together they bind to 42% of the Visual inspection of the ChIP-chip profiles reveals that Nup153 and Mtor interact with the genome in a manner not observed for traditional transcription factors  [33]. InIn order to analyse the visual observations in a statistically rigorous fashion, we quantified binding that takes place within a 10 kb sliding window that was scanned along the genome see . WindowsDrosophila genome (25Mb and 29Mb for Nup153 and Mtor respectively) and in female Kc cells 1,865 NARs occupy a similar proportion of the genome . This observation is supported by data quantifying RNA polymerase II-occupancy .We measured gene expression levels using Affymetrix GeneChips see . Using tccupancy , S2; by p-value <2.2×10−16). In contrast, histone H3 lysine 27 tri-methylation p-value <2.2e−16).Recent publications demonstrated that histone modifications, MOF acetyltransferase- and lamin-binding are robust genome-wide indicators of transcriptional activity. In both SL-2 and KC cells, acetylated histone H4 lysine (H4K16Ac) and MOF-binding p-value <0.05). Moreover, there is a large enrichment of down-regulated genes within NARs  compared with non-NARs . We obtain similar enrichments for cells collected five days after RNAi-treatment, and also upon Mtor-depletion (data not shown). These observations strongly indicate that nucleoporin-binding promotes a high-level of transcriptional activity, which may be due to the formation of an open chromatin environment.Finally, we confirmed a causal link between nucleoporin-binding and transcriptional regulation by measuring gene expression levels following RNAi-mediated knock-down of Nup153 , S2. TheDrosophila—in which females have two X chromosomes but males possess only a single X—the dosage compensation complex offsets the imbalance in gene content by doubling the expression of the male X chromosome. Thus, the chromosome represents an outstanding example of an exceptionally highly transcribed genomic region.One of the most important manifestations of gene expression control in higher eukaryotes is dosage compensation for different number of sex chromosomes between the two sexes. In In order to explore the association of Nup153 and Mtor with the dosage compensation complex further, we compared the patterns of nucleoporin-binding in male SL-2 and female Kc cells . There iHaving established that the nucleoporins are enriched on the male X chromosome, we explored the association with the dosage compensation system further. Recently, we demonstrated that the members of the dosage compensation complex—MSL1, MSL3 and MOF—preferentially bind to the male X chromosome We also tested the effects of Nup153-depletion on MSL1 and MOF-binding to 10 known target loci using chromatin-immunoprecipitation followed by qPCR. X-chromosomal binding is severely reduced for both proteins , and theAlthough nucleoporins are primarily located at the nuclear periphery, some display dynamic association with the nuclear pore complex First we checked the localisation of Nup153 and Mtor themselves . Immunosn<91). Size differences between nuclei were normalised by representing distances as a percentage of the nuclear radius. In Next, we used DAPI and lamin protein-immunostaining to assess the nuclear localisation of our target loci. We display a selection of images in in vivo controls representing peripheral and non-peripheral localisation.The lamin-bound L105 locus displays a distribution that is heavily skewed towards the periphery ; howeverMany NAR-target distributions show almost perfect overlap with L105, demonstrating that they are preferentially situated at the periphery ; interesIn fact, we find that NARs from chromosome X tend to reside at the periphery , but in contrast there was no significant change for any of the non-peripheral loci . These data suggest that the sub-nuclear positioning of peripheral NARs—specifically those on the male X—depends on the presence of Nup153, whereas the localisation of intra-nuclear loci is independent regardless of whether they are bound by nucleoporins.Finally to confirm the influence of nucleoporins on localisation, we tested the effects of RNAi-mediated Nup153-knockdown for six loci: three peripheral X chromosomal NARs , a non-peripheral X chromosomal NAR (T11), a non-peripheral autosomal NAR (T9) and the non-peripheral control (N2). For each we compared the distribution of Nup153-depleted samples against a mock EGFP RNAi-treatment . All thrThe classical view of transcriptional regulation describes the interplay of transcription factors, histones and associated enzymes with DNA in order to recruit the transcriptional machinery to the appropriate genomic loci. However, it has become increasingly clear that these interactions explain only one level at which gene expression is controlled. At a genome-wide level, the spatial organisation of chromosomes within the nucleus is increasingly considered to have a profound effect on chromatin structure and transcriptional activity In this study, we established conclusively that nucleoporins play a central role in mediating transcriptional regulation in a complex, multicellular organism. For the first time in any higher eukaryote, we generated a genome-wide profile of nucleoporin-binding; contrary to preliminary observations, binding is widespread, occurring across 40% of the genome. Thus, we reveal that nucleoporins—Nup153 and Mtor in particular—represent a major new class of global chromatin-binding proteins.Drosophila genome. Moreover, NARs are functionally important as they demarcate regions of open chromatin and transcriptional activity, which is lost on depletion of Nup153. It is significant that the male X chromosome—a prime example for hyper-transcription—is almost entirely occupied by NARs. Therefore, we suggest that Nup153 and Mtor may stimulate transcription by promoting the formation of an open chromatin environment.Intriguingly, these proteins interact with the genome differently to traditional transcription factors. Rather than associate with individual loci, nucleoporins bind continuous sections of chromosomes at very high density. Termed NARs, these regions extend up to 500kb in length and occupy 25% of the entire In dramatic contrast to expectations, nucleoporin-binding does not automatically lead to localisation at the nuclear periphery, though the male X chromosome is an exception in this regard. Since Nup153 and Mtor are known to be dynamic components of the nuclear pore complex, it appears likely that both peripheral and intra-nuclear pools of nucleoporins contribute to chromatin-binding. Given the dynamic nature of chromatin-localisation, it is also possible that NARs are located at the periphery in a very transient manner, and further developments in imaging techniques will help clarify this. Where NAR-formation and peripheral localisation do coincide however, Nup153 is necessary for sustained positioning.Chromosomal domains have been implicated in the formation of three-dimensional sub-nuclear structures to coordinate the expression of otherwise distant loci Finally, the special link with dosage compensation confirms the importance of nucleoporin-binding not only as a molecular mechanism for transcriptional control, but also in maintaining a complex, organism-level biological system.Chromatin immunoprecipitation combined with microarray hybridisation (ChIP-chip), and qPCR experiments were performed as described previously in Kind et al Drosophila Tiling 2.0R Arrays (Dm35b_MR_v02) were processed as in Kind et al Numerical data from Affymetrix Chromosomal regions with high densities of Nup153- and Mtor-binding were identified by sliding a 10 kb window along each chromosome, centred on the start position of each probe. NARs were defined as continuous chromosomal regions containing positive binding signal  for more than 70% of probes. We also implemented the two-stage domain-finding method described by Guelen et al Nup153 and Nup50 were depleted as previously described in Mendjan et al Drosophila2 GeneChips in triplicate for each condition. Data analysis was performed using publicly available packages in the BioConductor Software Suite Gene expression was measured using Affymetrix p-values were corrected for multiple-testing using FDR p-value<0.05 was selected.In control (EGFP-treated) cells, expressed genes were identified as those outputting MAS5.0 ‘present’ cells in all three replicates Drosophila genome provided by the Ensembl database (v. 41) We compared the overlap between NARs and genomic features. For ease of comparison, all data were mapped onto the −16; t-test): processed ChIP-chip profiles obtained from Kind et al −16; Fisher test): processed ChIP-chip profiles obtained from Kind et al −16; Fisher test): PolII-bound genes obtained from Muse et al p-value <2.2e−16; Wilcoxon test): number of genes as annotated by the Ensembl database within a 20kb sliding window with a 1 kb offset. (v) Expressed genes : gene expression measured using Affymetrix Drosophila2 GeneChips as described above. (vi) Down-regulated genes upon Nup153-depletion : differentially expressed genes in RNAi-treated cells compared with untreated cells as described above. (vii) Lamin-binding : processed ChIP-chip data were obtained from Pickersgill et al p-value <2.2e−16; Fisher test): processed ChIP-chip profiles obtained from Schwartz et al (i) Histone H4 lysine K16 acetylation . After rinsing with PBS, the cells were fixed with 4% paraformaldehyde in PBT  for 10 min at room temperature. Cells were then washed three times with PBT, incubated for 1 h at room temperature with RNAse A (100 µg/ml in PBT). After rinsing with PBS, cells were incubated with 0.5% Triton in PBS for 10 min at room temperature. Cells were rinsed again with PBS and incubated with 20% glycerol in PBS for 30 min at room temperature. Cells were then frozen in liquid nitrogen, thawed at room temperature and soaked in 20% glycerol in PBS, repeatedly four times. After washing the cells again with PBS three times, they were incubated for 5 min in 0.1N HCl, briefly rinsed in 2XSSC twice, and stored in 50% formamide, 2XSSC, 10% dextransulphate, pH 7.0. Fluorescent probes were prepared with the FISH Tag DNA Kit , dissolved in the hybridization mixture , applied to cells and sealed under coverslips with rubber cement. Probe and cellular DNA were denatured simultaneously on a hot block at 78°C for 3 min. Hybridization was carried out in a humid atmosphere at 37°C for 1 d. After hybridization, slides were washed in 2XSSC three times for 5 min at 37°C, and in 0.1XSSC three times for 5 min at 45°C, rinsed in PBS twice and counter-stained with DAPI.DNA FISH on SL-2 cells was performed as previously described by Lanzuolo et al For immuno-FISH, the following procedure is added after washing with 0.1XSSC at 45°C. Wash twice with 2XSSC 5 min each at RT. Blocking with  for 1 h at RT. Anti-lamin antibody is incubated for overnight at 4°C in TNT buffer, wash with wash buffer three times for 5 min. Second antibody is applied in TNT buffer for 2–3 h at RT, wash with wash buffer , including DAPI staining as described above. Cells were mounted on the glass slide with FluoromountG . Three-dimensional image stacks were taken with Leica SP5 confocal microscope  using an x63 oil immersion objective with a numerical aperture of 1.4, and zoom 3.2±0.2.To perform DNA FISH on target and non-target probes, approximately 15 kb region were chosen, except for the repeated sequence, in the genome and amplified by PCR from genomic DNA with 5–10 primers pairs, each covering around 0.5–3 kb. Primer sequences are available on request.To determine quantitatively the three-dimensional position of the FISH signal within the nucleus, we used the ImageJ software http://www.optinav.com/Local_Thickness.htm). We thresholded the FISH images to identify voxels within the nucleus that corresponded to the FISH signal and we measured the distances between all such voxels and the closest point on the nuclear boundary. For each nucleus we calculated the mean distance, and then for each test locus, we use the set of mean distances for all nuclei to plot the distance distribution. Similar results were obtained when we used the centre of mass of the FISH signal as the reference point instead of the mean distances for individual voxels (data not shown).Next we calculated the distances between the FISH signal and the nuclear boundary , S3, S4.In total, we examined 1,712 nuclei . For a given target, we compiled all distance measurements from all relevant nuclei to produce a distribution of distances as shown in in vivo controls with representative distributions for peripheral and non-peripheral sub-nuclear localisation. We compared the localisation of each target locus by comparing its distance measurements against the L105 and N2 controls separately. Statistical significance was calculated using the Wilcoxon test, with a FDR-corrected threshold of p <0.05. Briefly, a non-significant p-value  compared with the L105 distribution is indicative of peripheral localisation, whereas a non-significant p-value  compared with the N2 distribution is indicative of non-peripheral localisation.The lamin L105 and N2 non-NAR targets were selected as Microarray data are available in the ArrayExpress databaset Figure S1Processing of ChIP-chip data and NAR determination for Nup153. All ChIP-chip assays were performed in triplicate. Raw data were GCRMA-normalised. Triplicates were averaged and binding ratios were calculated relative to average intensities from triplicates of 10% input DNA. Data were then smoothened by using averaging of intensities within a 500bp sliding window centred on each probe. We then calculated the density of positively probes in 10 Kb windows centred on each probe, and used a cut-off of 70% to determine Nucleoporin Associated Regions (NARs). Profiles of the different analysis steps are illustrated for a 200 Kb region of chromosome X in SL-2 cells: GCRMA-normalised intensities for individual probes across three biological replicates (light orange); mean intensity values of the three biological replicates for Nup153 binding (orange); GCRMA-normalised intensities and mean values for the input DNA control (light and dark grey); ratios of Nup153-binding and control mean intensity signals (light blue); smoothed ratios using a 500-bp sliding window centred on each probe (dark blue); density of positively bound probes in 10 Kb windows centred on each probe (solid black line) and 70% threshold for detection of NARs (dotted red line); Nup153 NARs (dark red boxes); FlyBase genes in the forward and reverse strand are represented in light grey; coordinates represent the position on the corresponding chromosome. A similar procedure was used to determine NARs in male and female samples for Nup153 and Mtor.(0.6 MB PDF)Click here for additional data file.Figure S2Validation of Nup153 and Mtor target and non-target genes by ChIP-QPCR. Chromatin prepared from SL-2 cells was used for immunoprecipitation using Nup153 (blue) and Mtor (grey) antibodies. Recovered DNA (% Input) was analysed by Q-PCR using primers in the beginning (P1), middle (P2) and end (P3) of genes as shown. Error bars represent standard deviation obtained from three independent experiments.(0.04 MB PDF)Click here for additional data file.Figure S3Nup153 and Mtor NARs in Kc cells. (A) Karyotype representation of Nup153 and Mtor NARs across the genome in Kc cells. (B) Magnified view of a 1Mb region of chromosomal arm 2L. Tracks represent the smoothened binding ChIP/input ratio for Nup153 and Mtor (dark grey), the density of positively bound probes calculated in 10 Kb windows centred on each probe (solid grey line), and NARs (red boxes), for regions with a density of positively bound probes above 70%. (C) Magnified view of a 100 kb region in (B).(2.11 MB PDF)Click here for additional data file.Figure S4Correlation between Nup153 and Mtor binding in Kc cells. (A) Smoothed scatter plot displaying the ChIP/input binding ratios for Nup153 and Mtor (Pearson r = 0.88). (B) Bar chart representing the overlap in NARs defined by Nup153 and Mtor binding profiles. (C) Histogram of Nup153 and Mtor NAR length distributions.(0.56 MB PDF)Click here for additional data file.Figure S5H4K16Ac and H3K27me3 are mutually exclusive throughout the genome. (A) Detail view of H3K27me3 and H4K16Ac modifications in a 1 Mb region of chromosome X in SL-2 cells. H3K27me3 data were obtained from Schwartz et al (2006) (1.39 MB PDF)Click here for additional data file.Figure S6Immunostaining of Nup153 and Mtor in salivary glands. Immunostaining of Nup153 and Mtor in salivary glands isolated from 3rd instar male larvae. Salivary glands were co-immunostained with either MSL1 antibody or pre-immune serum  and serum (Mtor and Nup153). Both Nup153 and Mtor show predominantly nuclear rim staining but there is also some diffuse staining within the nucleus. X chromosomal territory is observed with MSL1 staining.(0.23 MB PDF)Click here for additional data file.Figure S7RNAi-mediated depletion of Nup153 in SL-2 cells. (A) Whole extracts were obtained from cells treated with EGFP or Nup153 dsRNA for 0, 3, 5, or 7 days, and separated on SDS PAGE followed by western blot analysis using Nup153 and Tubulin antibodies. Size markers (kDa) are indicated on the right side. (B) Cells treated with EGFP or Nup153 dsRNA were used for immunofluorescence confocal microscopy. Nup153, Nup50, and Lamin antibodies were used for triple-immunostaining and pseudo colours were added using the ImageJ software. A similar strategy was used for MOF, MSL1 and Lamin triple immunostaining. Arrows indicate residual MSL1- or MOF-staining in Nup153-depleted cells.(0.75 MB PDF)Click here for additional data file.Figure S8MOF-binding to autosomal promoters is affected in Nup153-depleted cells. Chromatin prepared from cells treated with EGFP (black) or Nup153 (grey) dsRNA was used for immunoprecipitation using MOF antibody. MOF-binding was scored on six autosomal target promoters. Recovered DNA was analysed by qPCR and is shown as percentage of input DNA (% Input). Error bars represent standard deviations obtained from three independent experiments.(0.33 MB PDF)Click here for additional data file.Figure S9Control RNAi-mediated depletion of Nup50 in SL-2 cells. (A) Whole cells extracts were made from cells treated with EGFP or Nup50 dsRNA for 0, 5, or 7 days, and separated on SDS PAGE followed by western blot analysis using Nup50 and Tubulin antibodies. Size markers (kDa) are indicated on the right side. (B) Cells treated with EGFP or Nup153 dsRNA were used for immunofluorescence confocal microscopy using antibodies against Nup153, Nup50, Mtor, MOF, and MSL1 (shown in red). Nup153, Mtor antibodies, and Hoechst were used for triple-immunostaining and pseudo-colours were added using the ImageJ software. A similar strategy was used for MOF, MSL1, and Lamin triple immunostaining.(0.43 MB PDF)Click here for additional data file.Figure S10Segmentation of DAPI image. (Top left) Nucleus labelled with lamin and stained with DAPI were used for the development of segmentation method . (Top centre) Lamin signal was thresholded and then reduced to single pixel rim (green). Detected rim was overlaid to the original lamin image. (Top right) The segmentation strategy were verified by measuring the deviation between DAPI segmented image (black/white) and the lamin rim (green). Pink lines show how these deviations were measured. (Bottom) Probability density distributions of the mean radii of 62 individual nuclei calculated using the DAPI or lamin signals. The median radius for the DAPI segmented edge was 2.45±0.33 µm and for the lamin signal was 2.41±0.35 µm.(0.05 MB PDF)Click here for additional data file.Figure S11Measurement of three-dimensional distance of FISH signals from the nuclear periphery. Three-dimensional brightest-point projection images of a simulated nucleus showing (A) peripheral localisation and (B) non-peripheral localisation. Outlines of nuclear periphery in each z-slice  and FISH signal  are shown. Nucleus is rotated on the x-axis with 30 degree increments from top-left to bottom-right panel. Three-dimensional brightest-point projection images of real nuclei with NAR locus (C) T4 and (D) control locus N2. Bar = 5 µm. See also (0.55 MB PDF)Click here for additional data file.Table S1Enrichment of active and repressive markers in NARs and non-NARs in SL-2 and Kc cells.(0.05 MB PDF)Click here for additional data file.Table S2Enrichment of H4K16Ac and gene density in NARs versus non-NARs for SL-2 and Kc cells.(0.05 MB PDF)Click here for additional data file.Table S3Drosophila melanogaster genome (R5.11). Genes in each probe set are also indicated. Individual genes within these regions, which were further tested by Q-PCR in this study, are indicated in red.Target (T) and non-target (N) regions used for FISH analysis. Start and end show the chromosomal localization coordinates according to release 3 of the (0.08 MB PDF)Click here for additional data file.Table S4This table accompanies (0.06 MB PDF)Click here for additional data file.Text S1Primer sequences for quantitative PCR; primer sequences for RNAi.(0.09 MB PDF)Click here for additional data file.Video S13D projection movie of a simulated nucleus with FISH signal at nuclear periphery. Nuclear envelope is shown as blue contours and FISH signal is shown in red. Montages of the movies are shown in (0.73 MB MOV)Click here for additional data file.Video S23D projection movie of a simulated nucleus with FISH signal located between the periphery and nuclear centre.(0.73 MB MOV)Click here for additional data file.Video S33D projection movie of real nucleus with NAR locus T4 localised to the periphery.(0.62 MB MOV)Click here for additional data file.Video S43D projection movie of real nucleus with control locus N2 localised at the interior.(0.83 MB MOV)Click here for additional data file.


Salmonella enterica serovar Typhimurium is a common food-borne pathogen that induces inflammatory diarrhea and invades intestinal epithelial cells using a type three secretion system (T3SS) encoded within Salmonella pathogenicity island 1 (SPI1). The genes encoding the SPI1 T3SS are tightly regulated by a network of interacting transcriptional regulators involving three coupled positive feedback loops. While the core architecture of the SPI1 gene circuit has been determined, the relative roles of these interacting regulators and associated feedback loops are still unknown. To determine the function of this circuit, we measured gene expression dynamics at both population and single-cell resolution in a number of SPI1 regulatory mutants. Using these data, we constructed a mathematical model of the SPI1 gene circuit. Analysis of the model predicted that the circuit serves two functions. The first is to place a threshold on SPI1 activation, ensuring that the genes encoding the T3SS are expressed only in response to the appropriate combination of environmental and cellular cues. The second is to amplify SPI1 gene expression. To experimentally test these predictions, we rewired the SPI1 genetic circuit by changing its regulatory architecture. This enabled us to directly test our predictions regarding the function of the circuit by varying the strength and dynamics of the activating signal. Collectively, our experimental and computational results enable us to deconstruct this complex circuit and determine the role of its individual components in regulating SPI1 gene expression dynamics. Salmonella is a causative agent for a wide range of diseases in humans, including gastroenteritis and enteric fever. A key step in the infection process occurs when Salmonella invades intestinal epithelial cells using a molecular hypodermic needle. Salmonella uses these needles to inject proteins into host cells that enable the bacterium to enter and replicate within them. The production of these needles, and the corollary decision to invade the host, is tightly controlled by a complex network of interacting regulatory proteins that, when studied individually, seemingly have either redundant or antagonizing effects. To understand how this ensemble of regulators dynamically controls the expression of these invasion genes, we systematically deconstructed the network and then used this information to analyze their composite behavior by computer simulation. Our analysis demonstrates that this regulatory network ensures that the invasion genes are expressed only when the invasion signals, a combination of environmental and cellular cues, exceed a defined threshold. Once induced, this network further amplifies and accelerates the expression of the invasion genes. These results further our understanding of this important pathogen by unraveling a key mechanism during infection, namely the decision to invade. Salmonella enterica is a common food-borne pathogen that causes an array of diseases in humans, ranging from self-limiting gastroenteritis to life-threatening systemic infections Salmonella Pathogenicity Island 1 (SPI1) Salmonella as anatomical cues for initiating invasion and also for coordinating SPI1 gene expression with other cellular processes, most notably adhesion and motility hilC and hilD are encoded within SPI1 whereas rtsA is encoded elsewhere on the chromosome. These three transcription factors can independently activate HilA expression. They can also activate each others' and their own expression hilA PhilC, PhilD, and PrtsA promoters. These auto-regulatory interactions result in three coupled positive feedback loops comprising HilC, HilD, and RtsA, the output of each capable of activating HilA expression  medium using 1% NaCl as the inducing signal. Growth in low-oxygen and high-salt conditions has previously been shown to induce SPI1 gene expression The advantage of using the luciferase reporter system is that it is sensitive to dynamic changes in promoter activity, particularly at low levels of expression hilD and PhilA promoters were activated in a sequential manner, consistent with HilD being necessary for HilA expression . These results indicate that there is a temporal hierarchy in SPI1 gene expression, with HilC, HilD, and RtsA at the top of the transcriptional cascade and HilA at the bottom. A similar hierarchy has also been observed in the activation of the downstream promoters regulating the expression of the genes encoding the T3SS and secreted effector proteins We measured gene expression dynamics in wild-type cells using the luciferase reporter system. After a brief lag following subculture, we found that the PhilA promoter, the dynamics were not continuous; rather, individual cells transitioned from an “off” to the “on” state in a switch-like manner , consistent with previous reports hilD promoter, we observed a weak, step-like increase in activity in the absence of HilD , indicating that the transient switch in PhilD promoter activity is not due to any SPI1 regulator but rather factors external to SPI1.HilD is necessary for HilA expression. Even though HilC and RtsA can independently activate HilA expression when constitutively expressed from ectopic promoters, these two regulators are incapable of doing so in the absence of HilD when expressed from their native promoters hilD promoter activity. This signal is then amplified by a positive feedback loop involving HilD. As discussed below, HilC and RtsA serve to further amplify this signal. Interestingly, the heterogeneity in SPI1 activation is not due to the interacting positive feedback loops within the circuit but rather is intrinsic to the activating signal. The signals activating the PhilD promoter, however, are unknown. While multiple global regulators are known to affect SPI1 gene expression These results demonstrate that the SPI1 gene circuit is activated by a step increase in PhilC promoter was active in absence of HilD, though at a reduced level, whereas the PrtsA promoter was effectively off . However, even though the PhilC promoter is active in the absence of HilD, HilA is not expressed. These results suggest that activation of the PhilD promoter is the trigger mechanism for induction of SPI1 gene expression. Interestingly, when we assayed PhilC promoter activity in a ΔhilD mutant using flow cytometry, we found that the dynamics were not switch-like but rather continuous and rheostatic . This homogeneity within the population indicates that the signal activating the PhilC promoter is fundamentally different than the one activating the PhilD promoter.With regards to HilC and RtsA, we found that the PhilA promoter when constitutively expressed from an ectopic promoter hilC ΔrtsA mutant using the luciferase reporters . When we measured gene expression at single-cell resolution using flow cytometry, we again observed a switch-like response in the ΔhilC ΔrtsA mutant . Rather, we observed only a decrease in the level of PhilD promoter activity associated with the “on” state. Similar results for both promoters are observed in the single deletion mutants, though the overall effect is small, indicating that HilC and RtsA additively contribute to SPI1 gene expression . Based on these results, we conclude that HilC and RtsA serve two functions in the SPI1 circuit. First, HilC and RtsA amplify HilA and HilD expression, in the sense that HilA and HilD expression is reduced the absence of HilC and RtsA. Second, HilC and RtsA accelerate the transition of HilA expression from the “off” to the “on” state.Unlike HilD, the HilC and RtsA proteins are not absolutely required for HilA expression. Yet, these two proteins can independently induce transcription from the PhilD, PhilC, PrtsA, and PhilA promoters We next investigated the role of HilE in the SPI1 gene circuit. HilE binds to HilD and prevents it from activating the PhilE mutant . Similar results were observed in the flow cytometry experiments for the PhilD and PhilA promoters ( and S4B) and the PhilC and PrtsA promoters (data not shown). These data suggest that HilE serves to dampen SPI1 gene expression by reducing the maximal level of promoter activity.We compared gene expression using the luciferase assay in wild type and a ΔThe defining feature of the SPI1 gene circuit is the presence of three coupled positive feedback loops. An immediate question then is why are multiple loops present when most bacterial circuits employing feedback have just one. To explore this question in more detail, we constructed a simple mathematical model of the SPI1 gene circuit based on our understanding of how it functions , the PhilC promoter behaves homogenously in a ΔhilD mutant . Our model is also able to capture this behavior .In constructing this model, we assumed that asynchronous activation of the POur goal in constructing this model was not simply to recapitulate our experimental results but rather to explore the behavior of the circuit by simulating it over a range of different parameter values. In particular, we employed the model to explore the roles of coupled positive feedback and HilE in regulating SPI1 gene expression. When performing this parametric analysis, we found it most informative to focus on the steady-state behavior of the SPI1 gene circuit. This enabled us to explore the effect of a limited number of model parameters two at a time and also bypass the issue of stochasticity. As a consequence, our analysis is confined to the parameters characterizing the regulatory topology of the circuit and not those defining the dynamics (e.g. degradation and protein-protein association/disassociation rates).We first considered the role of positive feedback on HilD expression, given the central role of this SPI1 regulator. To perform this analysis, we varied the degree by which the SPI1 regulators - HilC, HilD, and RtsA - could activate HilD expression by simulating the model at different values for the parameter Next, we explored the effect of HilC and RtsA on SPI1 gene expression by varying the strength of their connectivity within the circuit. Specifically, we varied the degree by which the SPI1 regulators – HilD, HilC and RtsA - could enhance both HilC and RtsA gene expression, given respectively by the parameters  and S6C). In addition, when expressed at a sufficiently high rate, HilE effectively shuts off the expression of HilD and HilA, a result that we also observe experimentally (data not shown). Most notably, our model predicts that HilE sets the threshold for SPI1 activation - as the rate of HilE expression increases so does the threshold for activation and vice versa. The exogenous factors regulating HilE expression, therefore, may serve to tune this activation threshold. However, we note that HilE alone does not endow the SPI1 circuit with a threshold. Rather, the threshold results from the complex interplay between HilE and the HilD positive feedback loop .Last, we explored the effect of HilE on SPI1 gene expression. Unlike the other SPI1 regulators, HilE is not known to be involved in any feedback loops with the other SPI1 regulators. Rather, its expression is controlled by exogenous factors. In our simulations, we varied the rate of HilE expression, given by the parameter Taken together, these results allow us to assign putative function to the interacting regulators and associated feedback loops comprising the SPI1 gene circuit. When viewed as a whole, the circuit appears to serve two functions. The first is to place a threshold on SPI1 activation, ensuring that the assembly of the needle complex is initiated only in response to the appropriate combination of environmental and cellular cues. The second is to amplify SPI1 gene expression.hilD promoter with the weaker PhilC promoter at its native chromosomal locus in an otherwise ΔhilC background. In this mutant, (ΔPhilD::PhilC ΔhilC), hilD is transcriptionally regulated in a manner similar to hilC. If the circuit does indeed function to place a threshold on activation, then we expect that this mutant will be unable to induce HilA expression if the activating signal for the PhilC promoter is too weak to overcome the threshold.Our computational analysis predicts that the SPI1 gene circuit functions as a gene expression amplifier with a variable activation threshold. While our experimental results directly support the conclusion regarding gene amplification 3, the ohilA promoter is not active in this strain   using flow cytometry, we no longer observed the transient heterogeneity found in wild type. Rather, we found that the population responded homogeneously . These results further support our hypothesis that the switch-like dynamics observed in wild type is due to asynchronous activation of the PhilD promoter . The rationale behind this promoter replacement experiment was to see whether HilC alone could induce HilA expression if expressed from the PhilD promoter. As HilD is capable of inducing HilA expression in absence of HilC or RtsA, we reasoned that HilC may be able to do the same in the absence of HilD if it is transcribed in a manner similar to hilD. However, despite trying designs where different sections of the promoter region were replaced, we were unable to engineer a strain where the PhilA promoter was active in the absence of HilD (data not shown). These results lend credence to our hypothesis regarding HilD dominance used in formulating the model, namely that HilD is necessary for activating the SPI1 promoter under physiological conditions.To explore this issue in more detail, we rewired the SPI1 gene circuit by placing Salmonella is thought to be able to determine its location within the host by sensing a number of environmental factors, key among them oxygen and osmolarity Using a combination of experimental and computational approaches, we found that the SPI1 gene circuit functions as a signal amplifier with an activation threshold. This virulence switch likely ensures that the SPI1 T3SS is assembled only when the bacterium has reached its target site for invasion, the distal small intestine A notable feature of the SPI1 gene circuit is the presence of three, coupled positive feedback loops. At the most fundamental level, positive feedback amplifies the response to an external signal In vivo, loss of either HilC or RtsA does not significantly attenuate intestinal invasion. Yet, loss of both does We first note that these additional feedback loops, namely the ones regulating the expression of HilC and RtsA, do not add redundancy to the circuit, as the loss of HilD effectively shuts off SPI1 gene expression. Rather, they serve to further amplify and accelerate SPI1 gene expression. Only a few studies to date, mostly focused on eukaryotic systems where this regulation is more common, have explored systems employing coupled positive feedback As the loops involving HilC and RtsA only additively contribute to the response, we imagine that the coupling in SPI1 may result instead from the piecewise evolution of the circuit. According to this model, HilC and RtsA were acquired to compensate for the inability of HilD alone to mediate a robust response. The motivation for this model comes from a recent study where a synthetic gene circuit coupling two weak positive feedback loops was engineered Salmonella utilizes two coupled positive feedback loops hilC promoter appear to be different than the ones activating the PhilD promoter, given their dissimilar dynamics. We are tempted therefore to speculate that, in addition to being an amplifier, the SPI1 gene circuit may also function as some sort of coincidence circuit, optimally expressing SPI1 genes only when the activating signals for both the PhilC and PhilD promoter are present. Coupled feedback in this case would reinforce the effect of these signals and further link the two. While such a model alone would not explain why multiple feedbacks loops are present in the SPI1 gene circuit, it may nonetheless provide one possible advantage for such a design.In a related study, we found that the gene circuit controlling the expression of type I fimbriae in In conclusion, we have been able to propose an integrated model for the regulation of SPI1 gene expression. While this system has been studied extensively, an integrated model of its regulation was previously lacking. Using a combination of experimental and computational analyses, we have been able to deconstruct this complex circuit and determine how the individual components contribute towards its integrated function. A key element in our analysis involved rewiring the SPI1 genetic circuit. As the kinetic parameters are unavailable and difficult to perturb, direct validation of our model remains an elusive challenge. However, by rewiring the circuit, we were nonetheless able to test a number of predictions from our mathematical model. Such an approach provides a powerful framework for integrating models with experimental data, particularly when parameters values are lacking or difficult to perturb. Finally, our results provide a detailed examination of a natural system employing coupled positive feedback, a mechanism of control that to date has primarily been investigated in eukaryotes.All experiments were performed in Luria-Bertani broth (LB)  unless otherwise specified. Bacterial strains were grown at 37°C except for strains carrying the temperature sensitive plasmids, pKD46 or pCP20, which were grown at 30°C as described previously Salmonella enterica serovar Typhimurium strains used in this study are isogenic derivatives of strain ATCC 14028 (American Type Culture Collection) and are listed in rtsA5) was made by first transducing the Δ(invH-avrA)2916::cm  allele from JS481 into the strain JS248 (ΔrtsA5) using P22HTint hilC::FRT ΔrtsA5), we first transduced the ΔhilC::cm allele from JS252 (ΔhilC::cm) into the strain JS248 (ΔrtsA5). The antibiotic resistance marker was then removed using pCR20. The strain CR351 (ΔhilE::kan) was made by replacing the hilE gene (genomic region 4763554–4764087) with the kanamycin resistance gene from pKD4 using λ-Red mediated homologous recombination All hilD::PhilC) was made using a two-step counter selection procedure involving the tetRA element from transposon Tn10hilD promoter (genomic region 3017694–3017820) was replaced with the tetRA element using λ-Red mediated homologous recombination. The tetRA marker was then moved into a clean wild-type background (14028) by P22 transduction. Next, the tetRA element was replaced by the PhilC promoter (genomic region 3013780–3013010) using λ-Red mediated homologous recombination and a fusaric acid counter selection, as described previously hilD::PhilC), has the hilD gene with its native ribosome binding site under the control of PhilC promoter. The strain CR354 (ΔPhilD::PhilC ΔhilC::FRT ΔhilE::FRT) was made by P22 transduction, using the strains JS252 (ΔhilC::cm) and CR351 (ΔhilE::kan) as donors. The antibiotic resistance markers were removed by introducing pCP20. Similarly, we also constructed two strains, CR357 and CR358, where the PhilC promoter was replaced by the PhilD promoter. In the first design, CR357 (ΔPhilC::PhilD), the PhilC promoter (genomic region 3013780–3013010) was replaced with the PhilD promoter (genomic region 3017694–3017820) leaving the hilC ribosome binding site intact. In second design, CR358 (ΔPhilC::PhilD hilD RBS), the upstream region of the hilC gene (genomic region 3013780–3013000) was replaced by the upstream region of the hilD gene (genomic region 3017694–3017830). All mutants were subsequently checked using primers that bound outside the region deleted. All chromosomal promoter replacements were verified by amplifying and sequencing the mutated regions.The strain CR352  and then sub-cultured 1∶1000 into fresh LB medium  and grown statically in test tubes at 37°C for 12 hours For single-cell fluorescence measurements, overnight cultures were first grown under SPI1-repressing conditions at 37°C. The cells were then sub-cultured to an OD of 0.05 into fresh LB medium  and grown statically at 37°C. Samples were collected at different time points by resuspending them in phosphate buffered saline (PBS) with 34 µg/mL chloramphenicol in order to arrest translation and then storing on ice. All fluorescent-activated cell sorting (FACS) experiments were performed on a BD LRS II system from BD Biosciences. Data extraction and analysis for the FACS experiments was done using FCS Express Version 3 (De Novo Software). For all FACS experiments, fluorescence values of 30,000 events were recorded and reported as a histogram.hilC and PhilD promoters, we used destabilized GFP transcriptional fusions where the sequence AANDENYAASV was appended to the C-terminus of the protein. This tag reduces the half life of GFP from approximately 24 hours to 110 minutes hilC and PhilD promoters are partially active even when the cells are grown in SPI1-repressing conditions. As a consequence, we were unable to observe the “off” to the “on” transition using “tagless” GFP. We did not run into similar problems with the PhilA and PrtsA promoters and consequently used transcriptional fusions to “tagless” GFP. Qualitatively similar results are obtained when using destabilized GFP transcriptional fusions to these promoters (data not shown).In the flow cytometry experiments involving the PFor time-course luminescence experiments, cultures were grown overnight at 37°C in SPI1-repressing conditions. The overnight cultures were then sub-cultured to an OD of 0.05 into fresh LB medium . A 100 µL aliquot of each culture was then transferred to a 96-well microplate. This is denoted by time 0 h in our kinetic luminescence experiments. In addition, 20 µL of mineral oil was also added to the well to prevent evaporation The major assumptions used in formulating the model are enumerated below.hilA expression. In particular, we ignored the effects of additional external regulators In formulating the model, we focused solely on the interacting SPI1 regulators - HilC, HilD, HilE, and RtsA - and their role in regulating hilA promoter and repressing transcription The model does not account for negative regulation by HilA and SprB. HilA, in particular, negatively regulates its own expression by apparently binding to the PThe model does not distinguish between transcription and translation. Both are lumped together in a single step. As a consequence, the rate of protein synthesis is assumed to be linearly proportional to the concentration of mRNA within the cell. Our justification for this assumption is that, based on a number of unpublished observations, we believe that the regulation of HilD occurs primarily either at the transcriptional or the post-translational level (i.e. the level of HilD protein).HilC, HilD, and RtsA are all AraC-like transcription factors and likely function only in the dimeric form. In the model, we assume for simplicity that the dimers form spontaneously and are stable (i.e. the dimerization reaction is irreversible). As a consequence, the model does not distinguish between the monomeric and dimeric forms; all protein is assumed to be in the dimeric form. We also do not account for the possible formation of heterodimers.hilC is transcribed . To account for HilD dominance (or rather dominant epistasis) in the model, we needed to assume that the SPI1 promoters have two binding sites with occupancy of both required for transcription. We specifically assumed that one site is highly specific for HilD with only weak affinity for HilC and RtsA. This first binding site establishes dominance as it effectively probes for whether HilD is present in the cell. Moreover, because of its high affinity, HilD will occupy this site even when expressed at low levels. Due to their weak affinity, neither HilC nor RtsA will occupy this site under physiological conditions. However, when over expressed, the elevated concentrations of these proteins will compensate for their weak affinity for this site, allowing them to bind. The second site, on the other hand, has moderate affinity for all three regulators (with the affinity for HilD still the highest) and serves to tune expression in proportion to their aggregate concentration. Other alternatives are possible, though this model for promoter regulation offers perhaps the simplest mechanism to explain HilD dominance consistent with what we already know about SPI1 gene expression. Moreover, others have found that the SPI1 promoters contain multiple binding sites for the HilC, HilD, and RtsA hilA promoter, our data suggests that it also extends to the PhilC, PhilD, and PrtsA promoters as detailed below.HilC and RtsA can independently induce HilA expression hilA, PhilC, PhilD, and PrtsA - by HilC, HilD, and RtsA. In the model, we assume that all four promoters have the same two binding sites, one highly specific for HilD and the other much less so (see Assumption 5). While there is no mechanistic data to support this hypothesis, we have found that the promoter activities are linearly proportional to one another when we compared them at varying levels of NaCl induction and in different genetic backgrounds . The simplest explanation for this linear correlation is that all four promoters have the same two binding sites. As a consequence, we used the same mathematical expressions and parameters to model occupancy of the PhilA, PhilC, PhilD, and PrtsA promoters by the SPI1 regulators. Aside from our supporting data, we significantly reduce the number of free parameters in the model by invoking this assumption.The most speculative aspect of the model concerns the mechanism for activation of the SPI1 promoters – PhilE mutant. In the absence of such a mechanism, we found that the steady-state concentrations of HilD and HilA were not affected by HilE, a result contrary to experimental observations.The model assumes that HilE not only binds and inhibits HilD but also promotes its degradation. While there is no experimental data to support such a mechanism, we found it necessary to match our experimental results for the ΔhilD promoter is activated at random times in individual cells, where the activation times are exponentially distributed. In the case of the PhilC promoter, we assumed that it is activated in a deterministic manner. For simplicity, we assumed that both promoters have, on average, the same activation kinetics. Beyond asynchrony in the timing of activation, we do not believe that noise arising from any number of possible sources plays a critical role in SPI1 gene expression beyond introducing variability in the gene expression measurements (see below).The model assumes that the transient heterogeneity observed in the gene expression data is due solely to asynchrony in the timing of the activation signal. To model this behavior, we assumed that the PTo qualitatively compare the simulation results with our flow cytometry data, we employed density estimation using a Gaussian kernel with fixed bandwidth. This method replaces each data point with a Gaussian basis function of constant variance. While this method is typically used to smooth data, namely to approximate a discrete histogram with a continuous function, we employed it to artificially introduce noise into our model. Our motivation was simply to obtain a better qualitative fit to the flow cytometry data where, aside from the heterogeneity, we observed variable gene expression in individual cells. While we do not believe this variability is significant for understanding how the circuit functions, we nonetheless attempted to capture it in our model. As we do not know the origins of this variability , we simply assumed that there was an additive Gaussian noise term in the model, effectively what density estimation does.We note that Mande and coworkers previously published a mathematical model of the SPI1 gene circuit hilD promoter. We included this last state variable, The governing equations for the model are the following:hilD promoter is activated in an individual cell. To model this transition, we employed the Heaviside step function, which has a value zero when the argument is negative and one when positive. Thus, when induced, the PhilD promoter undergoes a step-like increase in activity. We then repeated this procedure multiple times in order to gather statistics for an ensemble of cells.In our simulations, we first generated the pseudo random variable With regards to the model parameters, insufficient data are available to accurately and uniquely estimate them. However, as our goal was simply to construct a model that captured the general trends observed in the data, we simply choose numerical values for the parameters that provided a good qualitative fit. In these regards, the model is only semi-quantitative given the subjective basis of our parameterization. That said, the model captures our current understanding of the SPI1 gene circuit and provides a reasonable fit to the data as documented in the main text.http://www.mathworks.com) using the ode15s routine where the initial conditions of all state variables where set to zero. To account for random initiation times, the model was simulated 1000 times using the built-in random number generator. The Matlab m-file used to generate the figures is provided as supplemental information.The set of coupled ordinary differential equations comprising the model were solved in Matlab 7.2 , PhilC , and PrtsA  promoter activities in wild-type cells as determined using green fluorescent protein (GFP) transcriptional fusions and flow cytometry. (E) Dynamics of the constitutively active PLtetO-1 (pSS098) promoter in wild type cells as determined using GFP and flow cytometry. Note that the dynamics of this promoter are continuous and not switch like. Experiments were performed as described in SPI1 gene expression dynamics in wild type. (A) Normalized activities of the P(0.35 MB PDF)Click here for additional data file.Figure S2hilD (pSS072) promoter activities in wild type (black) and a ΔSPI1 ΔrtsA mutant  as determined using green fluorescent protein (GFP) transcriptional fusions and flow cytometry. (B) Comparison of PhilC  and PrtsA  promoter activities in wild type (solid lines) and a ΔhilD mutant . Note that the PrtsA promoter is off in the absence of HilD. (C) Dynamics of PhilC (pSS073) promoter activities in a ΔhilD mutant (JS253) as determined using green fluorescent protein (GFP) transcriptional fusions and flow cytometry. Note that, in the absence of HilD, the activation of the PhilC promoter is no longer switch-like but instead continuous. Experiments were performed as described in Activation of the SPI1 gene circuit. (A). Comparison of time-course dynamics of P(0.23 MB PDF)Click here for additional data file.Figure S3hilD and PhilA promoter activity in wild type (solid) and ΔhilC ΔrtsA (dashed) mutant. The data from hilA (pSS077) promoter activities in wild type and ΔrtsA (JS248), ΔhilC (JS252), ΔhilC ΔrtsA (CR350), and ΔhilD (CR253) mutants as determined using luciferase transcriptional reporters. (C) Comparison of PhilA (pSS055) promoter activities in wild type (black) and ΔhilC  and ΔrtsA  mutants as determined using green fluorescent protein (GFP) transcriptional fusions and flow cytometry. (D) Comparison of time-course dynamics of PhilD (pSS074) promoter activities in wild type and ΔrtsA (JS248), ΔhilC (JS252), ΔhilC ΔrtsA (CR350), and ΔhilD (CR253) mutants as determined using luciferase transcriptional reporters. (E) Comparison of PhilD (pSS072) promoter activities in wild type (black) and ΔhilC  and ΔrtsA  mutants as determined using green fluorescent protein (GFP) transcriptional fusions and flow cytometry. Experiments were performed as described in HilC and RtsA amplify SPI1 gene expression in an additive manner. (A) Normalized P(0.39 MB PDF)Click here for additional data file.Figure S4hilD and PhilA promoter activities in wild type (solid) and ΔhilE (dashed) mutant. The data from hilD (pSS072) promoter activities in wild type (black) and ΔhilE  mutant as determined using green fluorescent protein (GFP) transcriptional fusions and flow cytometry. Experiments were performed as described in HilE negatively regulates HilD expression. (A) Normalized P(0.12 MB PDF)Click here for additional data file.Figure S5hilD promoter is deterministic. In these simulations, we changed the equation for HilD in the model to:hilD mutant (D). Figures are given as two-dimension heat plots, where the color intensity denotes the density of events. The results for each plot were obtained from 1000 simulations. The expression values are normalized to one and plotted on a log scale. The expression values are given in relative log units (R.L.U.). Mutants were simulated by setting the activity of the respective gene to zero in the model. A detailed description of the model is provided in the Factors determining SPI1 switch. (A) HilA expression at single-cell resolution when activation of the P(0.18 MB PDF)Click here for additional data file.Figure S6Dk and Dα. The parameter Dk specifies the degree by which the SPI1 regulators - HilC, HilD, and RtsA - can activate HilD expression, effectively the strength of positive feedback on HilD expression. The parameter Dα specifies the strength of the signal activating HilD expression. (B) Effect of HilC and RtsA on HilA expression. Plot shows the steady-state concentration of HilD as a function of the parameters Ck, Rk, and Dα. The parameters Ck and Rk specify the degree by which the SPI1 regulators - HilC, HilD, and RtsA - can activate HilC and RtsA expression, respectively. In other words, these parameters set the strength of feedback on HilC and RtsA expression. In these simulations, the parameters Ck and Rk were both varied in tandem: the numerical values for the two are the same. (C) Effect of HilE on HilA expression. Plot shows the steady-state concentration of HilD as a function of the parameters Eα and Dα. (D–E) Effect of HilE and HilD positive feedback on HilD (D) and HilA (E) expression. Plots shows the steady-state concentrations of HilD and HilA as a function of the parameters Eα and Dk. The black lines in the plots (A–C) are used to denote the results obtained using the nominal parameters (aside from Eα). A detailed description of the model is provided in the Parametric analysis of model predicts that SPI1 gene circuit functions as an amplifier and encodes a hard activation threshold. (A) Effect of HilD positive feedback on HilA expression. Plot shows steady-state concentration of HilD as a function of the parameters (0.79 MB PDF)Click here for additional data file.Figure S7hilD (pSS052) promoter activity with PhilA (pSS055), PhilC (pSS053), and PrtsA (pSS054) promoter activities. To induce SPI1 gene expression, cells were first grown overnight in LB/no salt and then sub-cultured into fresh LB at varying concentrations of NaCl to an OD of 0.05 and grown statically for 15 hours. Individual experiments used to construct correlations are given in Panels B–E. (B–E) Comparison of PhilD , PhilC , PhilA , and PrtsA  promoter activities at varying concentration of NaCl and in different mutant backgrounds as determined using GFP transcriptional reporters. Fluorescence values were normalized with the OD600 absorbance to account for cell density. Data is the average of three independent experiments. Error-bars denote standard deviation.SPI1 promoter activities are linear correlated to one another at varying levels of induction and in different mutants. (A) Correlation of P(0.36 MB PDF)Click here for additional data file.


Caulobacter crescentus, CtrA coordinates DNA replication, cell division, and polar morphogenesis and is considered the cell cycle master regulator. CtrA activity varies during cell cycle progression and is modulated by phosphorylation, proteolysis and transcriptional control. In a phosphorylated state, CtrA binds specific DNA sequences, regulates the expression of genes involved in cell cycle progression and silences the origin of replication. Although the circuitry regulating CtrA is known in molecular detail in Caulobacter, its conservation and functionality in the other alpha-proteobacteria are still poorly understood.In the bacterium Caulobacter factors involved in the regulation of CtrA were systematically scanned in genomes of alpha-proteobacteria. In particular, orthologous genes of the divL-cckA-chpT-ctrA phosphorelay, the divJ-pleC-divK two-component system, the cpdR-rcdA-clpPX proteolysis system, the methyltransferase ccrM and transcriptional regulators dnaA and gcrA were identified in representative genomes of alpha-proteobacteria. CtrA, DnaA and GcrA binding sites and CcrM putative methylation sites were predicted in promoter regions of all these factors and functions controlled by CtrA in all alphas were predicted.Orthologs of Caulobacter arranged in a variety of connections. Two schemes appeared to evolve: a complex circuit in Caulobacterales and Rhizobiales and a simpler one found in Rhodobacterales.The regulatory cell cycle architecture was identified in all representative alpha-proteobacteria, revealing a high diversification of circuits but also a conservation of logical features. An evolutionary model was proposed where ancient alphas already possessed all modules found in  Living cells continuously receive and process signals coming from their environment, and by integrating this information into their own internal state, are able to react with appropriate responses which coordinate each function in the cell in order to divide and produce progeny. Regulation of cell cycle progression needs to be a robust but versatile process that integrates different exogenous and endogenous signals and that guarantees fidelity and controlled progression throughout the different phases.Brucella, Rickettsia) and plants (Agrobacterium), photosynthetic bacteria (Rhodobacter) and also several genera metabolizing C1-compounds (Methylobacterium).Bacteria have evolved different regulation systems for cell cycle coordination, probably due to different ecological and evolutionary constraints ,2. AlphaCaulobacter crescentus, has recently become a model organism in these studies , where Fij is the frequency of base i at position j of the L-mer. This score, whose maximum for the best match using a CtrA position weight matrix is ≈1.22, is a measure of the information content of a potential binding site.1) A Position Weight Matrix (PWM) describing CtrA binding sites in  program  on upstr program . We used program : Si = (12) We retained only motifs having a score ≥30% of the maximum score attainable with the given matrix and located in the range -100 to 400 nucleotides from the start codon of a gene.i = (Si - <Si>)/σi, where Si is calculated using the above formula, and <Si> is the average score in an organism over all L-mers and σi is the corresponding standard deviation.3) Then we applied a Z-score transformation to highlight significant occurrences and take into account the background DNA implicitly: ZFor GcrA and DnaA the strategy followed was the same, except that PWMs were obtained differently. For GcrA we obtained a PWM describing a motif enriched upstream of sequences controlled by GcrA as proposed by others . By usinCaulobacter is [TC] [TCG] [AG]TCCACA as previously reported [DnaA recognizes a sequence whose consensus in reported . In the reported .Both GcrA and DnaA were then analyzed as reported in points 2 and 3 for CtrA. CcrM methylation targets were searched using a regular expression modelling the known target of this methyl-transferase (GANTC) .We took advantage of COG  categoriPWM: position weight matrix; BBH: bidirectional best hit; COG: clusters of orthologous groups of proteins; SMART: simple modular architecture research toolEGB planned the project; MB performed the analysis; MF, RF, AM, LF and MB helped with the analytical work and the writing process. EGB and MB wrote the manuscript and created Tables and Figures. All authors read and approved the final version of the manuscript.Table S1. The list of the 65 organisms used in this work.Click here for fileDetailed description of additional material. Legends of additional Tables and Figures.Click here for fileFigure S1. Phylogenetic profiling of BBH hits in the 65-genome dataset.Click here for fileTable S2. Bidirectional Best Hits.Click here for fileFigure S2. Phylogenetic trees of cell cycle related proteins.Click here for fileTable S3. CtrA, DnaA, GcrA and CcrM binding sites.Click here for fileTable S4. Numerical values for Figures Click here for fileTable S5. Numerical values for Figure Click here for fileFigure S3. Control of CcrM on cell cycle genes.Click here for fileFigure S4. Sequence logo of the putative GcrA motif.Click here for file


Epigenomes commonly refer to the sequence of presence/absence of specific epigenetic marks along eukaryotic chromatin. Complete histone-borne epigenomes have now been described at single-nucleosome resolution from various organisms, tissues, developmental stages, or diseases, yet their intra-species natural variation has never been investigated. We describe here that the epigenomic sequence of histone H3 acetylation at Lysine 14 (H3K14ac) differs greatly between two unrelated strains of the yeast Saccharomyces cerevisiae. Using single-nucleosome chromatin immunoprecipitation and mapping, we interrogated 58,694 nucleosomes and found that 5,442 of them differed in their level of H3K14 acetylation, at a false discovery rate (FDR) of 0.0001. These Single Nucleosome Epi-Polymorphisms (SNEPs) were enriched at regulatory sites and conserved non-coding DNA sequences. Surprisingly, higher acetylation in one strain did not imply higher expression of the relevant gene. However, SNEPs were enriched in genes of high transcriptional variability and one SNEP was associated with the strength of gene activation upon stimulation. Our observations suggest a high level of inter-individual epigenomic variation in natural populations, with essential questions on the origin of this diversity and its relevance to gene x environment interactions. Nucleosomes are the basic units of chromatin, with part of the long DNA molecule wrapped around a multiprotein core, which makes unpacked chromatin often portrayed as a string of pearls. This string can carry three types of sequences: DNA, methyl groups on cytosines, and, on every pearl, the presence-or-absence of histone post-translational modifications such as acetylation of lysines . These latter sequences can change dynamically, and the mechanisms involved are heavily studied as they participate in many physiological processes . However, nothing is known about the natural diversity of nucleosomal epigenomes in natural populations. As a model, we compared two unrelated yeast strains for their epigenome of one histone modification. We found a high divergence, which was enriched at regulatory sites and often carried on specific nucleosomes. Although this nucleosome modification is usually associated with high transcription, higher acetylation in one strain did not necessarily imply higher expression of the corresponding gene. However, one nucleosomal variation was associated with a stronger gene activation upon stimulation. These results suggest that nucleosomal epigenomes largely differ between individuals, raising essential questions on the origin of these differences and their contribution to personal responses to environmental changes . Many laboratories are therefore intensively studying if and how information can be coded by epigenomes Divergence of DNA sequences between individuals has been the basis of genetics for half a century. More recently, epimutations were identified where inter-individual differences resided in DNA methylation patterns rather than in the DNA sequence itself, with notable consequences on imprinting and phenotypes Whether epigenomic sequences vary in natural populations has only been poorly investigated. Recent studies showed a rather abundant natural epigenetic variation of methylated DNA in plants, which was shown to correlate to transcriptional differences and to be additively inherited Saccharomyces cerevisiae as a model system, we provide here a first estimate of this variability for one histone post-translational modification at a single-nucleosome resolution. The number of epi-polymorphisms was high, with notable enrichment in regions of conserved DNA sequences and numerous cases where a precise (isolated) nucleosome was targeted. This variability was not correlated to differential transcription but to the degree of transcriptional response to perturbations. Our observations provide a basis for population epigenomics and raise essential questions on the origin of this diversity and its contribution to inter-individual variability in the response to environmental changes.Using two unrelated strains of the yeast S. cerevisiae (BY and RM) as a model system. These strains were previously used to investigate natural genetic variability within S. cerevisiae for various phenotypes such as cellular morphology, sensitivity to drugs, gene expression or telomere length To provide a first estimate of nucleosome-level epigenomic diversity, we used two unrelated strains of the yeast We examined in more details occupancy around transcription start sites and found the stereotyped nucleosome-depleted regions flanked by well-positioned nucleosomes . The typSingle Nucleosome Epi-Polymorphisms’ (SNEP). We chose acetylation of lysine 14 of histone H3 because it was reported to be largely distributed over the genome and not restricted to specific regulatory positions We then searched for nucleosomes bearing differential levels of a specific histone post-translational modification. By analogy to nucleotide polymorphisms, we called these nucleosomes ‘P-value <9.27×10−6 which corresponded to a False Discovery Rate This method identified 5,442 H3K14ac SNEPs at nominal Because some highly polymorphic DNA features are associated with chromatin silencing, specific cases of histone acetylation epi-polymorphisms could be expected. One example is the rDNA locus, a repetitive sequence silenced by the Sir2 histone deacetylase P<0.01. Thus, local correlation seems to be limited and epi-polymorphisms are frequently distributed on specific nucleosomes.If such large position effects were the general source of H3K14ac epipolymorphisms, one would expect SNEPs to cluster together at particular hot spots. We clearly observed local correlations, as epipolymorphisms were 7 times more frequent than expected by chance among nucleosomes adjacent to SNEPs, and this effect could span over 10 nucleosomes upstream and downstream of a SNEP . HoweverNDE2 gene did not have BYac SNEP in promoter nor in terminator region, but had RMac SNEPs at the beginning of its coding region  enrichment of BYac SNEPs in nucleosomes that fully covered a conserved regulatory site Are genes with high inter-strain variability in gene expression the same genes as those having high epigenomic variability? Although SNEP acetylation was not associated to higher gene expression, it remained possible that genes with high expression changes contained more SNEPs than others. We examined this possibility by ranking genes according to their BY/RM expression fold-change and by counting their SNEP content . This shS. cerevisiae genes greatly differ in their divergence of expression across independent mutation accumulation lineages Several studies have examined the evolvability of yeast gene expression levels. For example, when comparing 4 yeast species across 5 different stressful environments, Tirosh et al. showed that genes can have very different inter-species expression divergence The extent of gene x environment interactions in the control of gene expression has been thoroughly estimated by Smith et al. who used the same BY and RM strains as here and compared their transcriptomes between two different steady-state environments: growth in glucose and growth in ethanol t-test P = 3.6×10−7 and 1.2×10−6 for BYac and RMac SNEP, respectively).Expression variability within a given strain background has also been studied in a broad sense by estimating the extent of variation across a large compendium of environmental conditions and/or specific genetic perturbations P = 0.01, Chi-square).Finally, to see if SNEPs were more frequent among nucleosomes known to be evicted upon an environmental change, we used previous maps of nucleosome positioning in normal and stress conditions We reasoned that SNEPs could influence the dynamics of activation or repression. Intuitively, an acetylated nucleosome may be more rapidly evicted than a non-acetylated one upon promoter activation AHA1 gene, which codes for a co-chaperone of Hsp90 known to be activated upon heat-shock AHA1 mRNA by real-time quantitative RT-PCR  lacking SNEP  in the promoter region of the e RT-PCR . Gene ining SNEP . This exS. cerevisiae. Because these strains have a large extent of transcriptional differences, this argues that differences in nucleosome occupancy profiles are not a major source of intra-species variation in gene expression.We observed that nucleosome positioning at promoter regions was similar between two unrelated strains of In contrast, the epigenomic profile of H3K14ac was highly variable and this variability targeted specific nucleosomes. The presence/absence of a modification at a particular nucleosome in a given cell is, by definition, a discrete state. However, we observed quantitative acetylation differences that were often subtle (1.2 to 1.5 fold). This is likely due to high cell-to-cell heterogeneity and high dynamics of the acetylated state: all states from billions of cells were averaged in our samples, and no dynamical information was acquired over time. It is therefore important to interpret SNEPs as differences in the overall acetylation level across a cell population and not as a uniform epigenotype shared by all cells of the sample.Natural epigenetic variation was previously reported at the level of methylated DNA (meDNA), particularly in plants trans-acting variation. Alternatively, accumulation may have occurred as a drift during laboratory culture conditions where fitness selection poorly applied. Future experiments examining a third wild strain will help determine if one of the two patterns is more ‘common’, if the stronger effect (acetylation fold-change) of BYac SNEP is peculiar, and if the abundance of SNEPs is similar in various pairwise comparisons of strains.Since histone-tail modifications are known to be highly reversible and dynamic, the basis and the origin of SNEPs remain to be further investigated. We observed that the two strains had different overall patterns of acetylation along genes, with a preferential acetylation near TSS and TES in the BY strain, while the RM strain had enriched acetylation in the second half of transcribed regions. This pattern difference accounted for many SNEPs and may result from trans-acting factors that act differentially in the two strains. However, 1806 SNEPs could not be attributed to this general inter-strain difference. Focusing on these SNEPs only, we looked again at their genomic distribution, their potential correlation to expression divergence and enrichment in genes with high plasticity . All conobligatory, pure and facilitated qualifications relate to genetic controls that are full, absent or incomplete, respectively. Following this terminology, obligatory SNEPs may result from genetic factors acting in cis or in trans. Known cis-regulations are exemplified by position effects of transposable elements, rDNA repeats or telomeric sequences. Trans-acting genotypes may reside in histone acetyl-transferase or de-acetylase machineries, or in upstream regulatory factors. Such obligatory SNEPs could have been fixed together with their genetic determinants. In contrast, if some SNEPs are pure (independent of genotype) they likely result from their direct selection. As SNEPs seem to relate to the dynamics rather than the steady-state levels of gene expression, this selection may act through the ability to respond to environmental changes . Also, interactions between epigenotypes and genotypes are expected since histone acetylation can modulate the buffering of cryptic genetic variations What is the origin of this epigenomic variability? E. Richards proposed a classification of epigenotypes based on their dependency on DNA variation Acetylation of Lysine 14 of histone H3 at the beginning of protein-coding sequences has unambiguously been associated to high transcriptional activity in several studies FLC locus of Arabidopsis to natural variation in cold-induced acceleration of flowering The abundance of SNEPs in highly-responsive genes and our observation that one SNEP correlated with the dynamics of gene activation upon stimulation suggest a contribution to gene x environment interactions. This is in full agreement with a previous report describing the contribution of H3K27me3 at the In particular, attempts to predict and optimize the response to specific treatments is at the heart of personalized medicine. Chemical inhibitors of histone deacetylase are used in anti-cancer therapies and seem promising to fight other diseases 5′- AAATGTCTTGTCTTCTCTGCTC-3′ and 5′-ACTGTTG<@?show=[fo]?>CGCGAAGTAGT-3′ for HMLalpha1 and 5′-AAGAGCCCAAAGGGAAAATC-3′ and 5′-AGGCTTTGCTTTCTTCTA-3′ for HMRa1. ChIP and non-immunoprecipitated DNA fragments were linearly amplified using T-7 based in-vitro transcription as described previously 18B primer, 1 µl of NEB buffer (2) and 0.4 µl of 5 mM dNTPs and the following thermal cycles were applied; 94°C for 2 min, decreasing to 35°C at -1°C/sec, hold down at 35°C for 2 min and decreasing to 25°C at -0.5°C/sec. Immediately after, 0.4 µl of Klenow enzyme (NEB) were added to the samples which were incubated at 37°C for 90 min. The reaction was halted by adding 5 µl of 0.5 M EDTA (pH 8.0). Products were purified using Qiagen MinElute reaction cleanup kit and eluted in 20 µl nuclease free water. The eluted samples were concentrated to a final volume of 5 µl. The IVT reaction mixture contained 5 µl nuclease free water, 2 µl 10X reaction buffer and 2 µl enzyme mix of MEGAshortscript® T7 kit (Ambion), 6 µl Labeling NTP mix from Affymetrix, and 5 µl T-7 tailed DNA and incubated at 37°C for 16 hrs. Amplified RNAs were purified using RNeasy Mini kit (Qiagen) and eluted in 50 µl of nuclease free water. RNAs (≥15 µg) were hybridized to GeneChip S. cerevisiae Tiling Array from Affymetrix Yeast strains used were BY4716  and the Broad Institute (http://www.broad.mit.edu/annotation/genome/saccharomyces_cerevisiae/Home.html), respectively. The RM genome 8X assembly originates from whole genome shotgun and consists of 17 high-quality supercontigs (hqSC hence after) totalizing 11.7 Mb. The17 hqSC of RM were aligned on the 16 nuclear chromosome sequences of BY by using the nucmer algorithm implemented in MUMmer version 3.0 –maxgap = 1000 –mincluster = 50 considering BY chromosomes as the references and RM hqSCs as the queries. The output of nucmer was then filtered and formatted using the delta-filter and show-coords programs of the MUMmer package. At this stage, the output of our alignment pipeline consisted on a list of clusters of perfect matches between regions of BY chromosomes and RM hqSCs. We implemented an automatic rule to relate RM hqSCs to BY chromosomes by maximizing the coverage and alignment quality chromosome by chromosome. We then dynamically resolved overlapping clusters of perfect matches in order to get the longest aligned fragments of RM hqSCs along each BY chromosome. Visual inspections were also used in a few cases in order to define optimal boundaries of alignments. Detailed results of this genome alignment process as well as the hybrid shell/perl script used to do the genome alignment are available upon request.Genome sequences of S288c (isogenic to BY) and RM were downloaded in December 2007 from NCBI (RepeatMaskerOpen-3.0. 1996–2004 http://www.repeatmasker.org) and were thus excluded, leading to a final core set of 52,280 polymorphisms consisting in 47,011 SNPs (90%), 2,448 insertions (4.5%) and 2,821 deletions (5.5%). These 2,448 insertions corresponded to 238,087 bp that were absent in RM while the 2,821 deletions corresponded to 80,349 bp absent in BY. This discrepancy between BY and RM insertions is mainly due to the heterogeneous content of Ty elements between both genomes (see below).Polymorphisms between BY and RM were detected by base substitution in the final alignment. Since base calling information were not available for RM sequences, we assumed that quality was reasonable and uniform along the RM genome sequence. From the 54,039 polymorphisms found, a few targeted repeated sequences . For RM, predicted gene set was downloaded from the Broad Institute website (http://www.broad.mit.edu/annotation/genome/saccharomyces_cerevisiae/Downloads.html). which were obtained using a combination of mapped ORFs from SGD predictions (http://www.yeastgenome.org), Glimmer http://www.broad.mit.edu/annotation/genome/saccharomyces_cerevisiae/GeneFinding.html#prediction. This original annotation file consisted of 5695 gene loci. 83 were dubious and discarded, as their annotated coding sequence did not code for a protein. 80 other predictions were also discarded because they fell in unaligned regions and could not be mapped to BY. Orthologs were identified using Blastp from WUBlast version 2.0  http://blast.wustl.edu) by aligning protein sequences of the 5532 remaining RM genes against the protein sequences of 6608 BY genes, and selecting reciprocal matches that fulfilled all following criteria: i) E-value <1.e-5, ii) percentage of identity >40% and iii) match length >75% of both protein sequences. This way, 5200 RM genes were called orthologs of BY genes, and 328 genes were RM-specific.BY gene annotations were extracted from chromosomal features defined at NCBI website  and blasted them onto the 17 RM supercontigs. We found 10 matches on RM with 10 Ty2 elements (percentage of identity >90% and match length >95%). No match was found with any other Ty elements even after relaxing these criteria, indicating that Ty elements populating the RM genome are Ty2 only. Of these 10 Ty2 elements found in RM, 3 were located at the same place in BY, 6 were located elsewhere in RM (leading to 6 deletions (insertions) in BY (RM)) and 1 replaced a Ty1 element of BY.Conserved Regulatory Sites (CRS) http://fraenkel.mit.edu/improved_map/p001_c3.gff). Conserved Non-Coding Sites were obtained from the UCSC website (http://genome.ucsc.edu/), using data from table phastConsElements for track MostConserved, and were then defined as the intersection between conserved regions and non-coding regions. Gene expression values of Brem et al. http://www.ncbi.nlm.nih.gov/sites/entrez?db=gds, dataset GSE1990). Regions where nucleosome(s) were remodeled upon heat-shock were extracted from whole-genome nucleosome maps of Shivaswamy et al. The 25 bp array probes were mapped on BY and RM genomes using MUMmer. For each strain, we kept only probes that had unique perfect match on the genome . For nucleosome mapping (see below) we used only the 3 array replicates per strain. Since informative probe sets differed between the two strains, normalization was done separately for each strain and a log2 transformation of probe signals was applied before normalization We positioned nucleosomes in each strain separately using only the 3 dedicated replicates per strain. We then implemented a custom version of the Hidden Markov Model devised by Yuan et al. The most-likely nucleosome occupancy profiles of the two strains  were aligned according to the genome alignments. The BY genome was the reference and the positions of RM nucleosomes on this reference were obtained from the coordinates of RM nucleosomal sequence fragments on the BY genome. Once RM nucleosomes were “mapped” on BY, we used a dynamic algorithm to align RM and BY nucleosomes. The algorithm works chromosome-by-chromosome as follows:Assumption: two nucleosomes are said to be “unambiguously aligned” if the distance between their midpoints is lower than half of their average size.Initiation: find all unambiguously aligned nucleosomes along the chromosome.Recursion: between two consecutive unambiguously aligned nucleosomes:if there is no unaligned nucleosome in the interval, then go to the next unambiguously aligned nucleosome.else:Align nucleosomes by minimizing physical distance between aligned pairs.The remaining nucleosome(s) are considered as insertion(s) (if they are BY nucleosomes) or deletion(s) (if they are RM nucleosomes).Go to the next unambiguously aligned nucleosome.n.Rn where Bn and Rn are the probabilities that the corresponding probes belong to a nucleosome in BY and RM, respectively. Similarly, we computed the likelihood for insertion and deletion of a nucleosome (with respect to BY) as L(insertion)  =  Bn*Rl and L(deletion)  =  Bl*Rn where Bl and Rl are the probabilities that the corresponding probes belong to a linker in BY and RM, respectively. Within each strain, the probability of each state (nucleosome or linker) was derived from probe-level posterior probabilities  averaged over probes covering the target region.More stringent assumptions can be used to define “unambiguously aligned” nucleosomes without significant changes on the final results (data not shown). This strategy aligned 64,294 nucleosomes between the two strains, i.e. ∼95% of RM nucleosomes. Finally, in order to evaluate locally the quality of our alignment, for each pair of aligned nucleosomes we computed their likelihood as L  =  Bkmeans function implemented in the base package of R) using the Euclidean distance metric and 25 repetitions for each number of cluster tested (1< = K< = 10). Visual inspection together with standard clustering validity measures (e.g. ratio of variance within clusters and variance between clusters) were used to choose the optimal number of clusters (K = 6).To generate ijkl is the log2 normalized hybridization intensity of probe k in replicate l for strain i (BY or RM) in experiment type j (nucleosome positioning or ChIP-ChIP), u is the global mean of the signal, ai is the strain effect (BY or RM), bj is the experiment type effect (nucleosome positioning or ChIP-Chip), ck is the probe effect, dij is the interaction term between strain and experiment type and eijkl is the residual. We reasoned that if a nucleosome carries the modification then the corresponding DNA is present in both ChIP and nucleosomal positioning samples and signal expectancies should not differ between experiment types (bj = 0). However, if a nucleosome carries the modification in only one strain, then a significant interaction should be seen between experiment type and strain (dij ≠ 0) (F-statistic to test (H0: dij = 0 vs. HA:dij ≠0) and derived nucleosome-level P-values. A striking enrichment of low P-values was observed  . We therobserved . We applNucleoMiner (for Unix-based platforms) are available on our web site http://www.ens-lyon.fr/LBMC/gisv/snep/EMBL ArrayExpress accession number E-MEXP-1777. Processed data files and the C source code of Figure S1Nucleosome alignment conservation. For 63,706 pairs of BY/RM aligned nucleosomes, we computed the distance between the two midpoints (black histogram and axis), as well as the fraction of the BY nucleosome that overlapped the RM nucleosome (red histogram and axis).(0.22 MB AI)Click here for additional data file.Figure S2The fraction of nucleosomal DNA was computed in 2.5-Kb sliding windows across the genome, excluding windows containing insertions or deletions of 5 bp or more. (A) Histogram of the distribution of BY - RM occupancy differences among all windows. Most differences are small, and few extremes are detected. (B) Genomic location of windows with extreme values .(0.35 MB AI)Click here for additional data file.Figure S3Global nucleosomal occupancy at TSS and TES. Neighborhood regions of TSS (left panels) and TES (right panels) were divided in 10 bp bins. Probes were assigned to bins according to the position of their midpoint. For all probes, the HMM posterior probabilities to be in a nucleosomal state  were summed. For every bin, this cumulated probability was averaged across all probes of the bin. Profiles shown are average profiles across all genes for BY (upper panels) and RM (lower panels). Note that coordinates of transcripts were determined in the BY strain background (0.29 MB AI)Click here for additional data file.Figure S4(acBY/nucBY)/(acRM/nucRM) for BYac SNEPs and its inverse for RMac SNEPs. Most SNEPs show folchanges between 1.2 and 1.5, and BYac SNEPs are associated with higher acetylation differences than RMac SNEPs.The degree of acetylation differences in SNEPs is shown by their cumulative distribution of ‘foldchange’ values, which correspond to (0.20 MB AI)Click here for additional data file.Figure S5log(ac/nuc) value across all probes of the nucleosome. SNEP detection ) is indicated above each nucleosome, with significance cutoff indicated as a dashed line. Brown boxes, coding sequences.Scheme of nucleosome organization in the region of the NDE2 gene. Rectangles represent nucleosomes, colored according to the mean (0.27 MB AI)Click here for additional data file.Figure S6Vm values of Landry et al. For all panels, BYac and RMac SNEP frequencies were counted among nucleosomes located in each gene (coding region plus and minus 250 bp), and averaged in 500-genes sliding windows (y-axis). Genes were sorted (x-axis) either by their BY/RM foldchange in expression, which showed SNEP enrichments at both extremities (A), or by their Expression Divergence Click here for additional data file.Figure S7P-value (significance) of the genotype x interaction term of the ANOVA model of Smith & Kruglyak 2008 sg * P, where sg is the sign of the interaction term. These ranks are reported on the x-axis. SNEP frequency (y-axis) was computed in sliding windows as in For every gene, we considered the (0.29 MB AI)Click here for additional data file.Figure S8SSA3, FES1, and CPR6) were selected based on the following criteria: known to be induced upon heat-shock , possess an HSF1 binding site in the promoter with no BY/RM polymorphism in it, do not have H3K14ac SNEP, do not have a marked BY/RM difference in expression in non-induced cultures. RNA levels of these three genes were measured by real-time quantitative RT-PCR on the same samples as for AHA1 on Kinetics of mRNA expression during heat-shock for three genes lacking SNEPs. Three genes ((0.25 MB AI)Click here for additional data file.Figure S9Properties of SNEPs disagreeing with the patterns of (1.91 MB AI)Click here for additional data file.Figure S10Absence of correlation between H3K14 acetylation differences and amplitudes of gene expression differences. The display is similar as in (1.47 MB AI)Click here for additional data file.Figure S11MAT, HML and HMR loci. Nucleosomes are acetylated at the MAT locus but not at the two silenced HML and HMR loci a1 sequence located at HMR and two copies of the alpha1 sequence: one at HML and the other at MAT. Strains of type a (such as RM) differ only at the MAT locus containing a1 instead of alpha1. Thus, H3K14 acetylated chromatin fractions are expected to be enriched in alpha1 DNA for BY and a1 DNA for RM. (B) Dosage of alpha1 and a1 sequences in ChIP fractions by real-time quantitative PCR (absolute quantification) as explained in methods. (C) Abundance of alpha1 and a1 sequences in the two strains was compared from raw intensity values of 23 and 22 microarray probes, respectively, interrogating the same fragments as those amplified in (B). Ordinate values correspond to log(acBY/nucBY) - log(acRM/nucRM) for each probe, where acX and nucX represent mean intensities across replicates of ChIP and mapping experiments, respectively, performed on strain X.Internal controls provided by the MAT locus. (A) Scheme of the (0.29 MB AI)Click here for additional data file.Figure S12nuc, nucleosome mapping experiments. H3K14ac, ChIP experiments. Normalized hybridization intensities yijkl Click here for additional data file.Figure S13P-values across all nucleosomes interrogated by the ANOVA model described in the Histogram of SNEP (0.25 MB AI)Click here for additional data file.Text S1Densities of BY/RM SNPs in nucleosomal versus linker DNA.(0.05 MB PDF)Click here for additional data file.