We illustrate the usability of our maps for deriving biological hypotheses.Recent sequencing technologies enable joint quantification of promoters and their enhancer regions, allowing inference of enhancer–promoter links. We show that current enhancer–promoter inference methods produce a high rate of false positive links. We introduce FOCS, a new inference method, and by benchmarking against ChIA-PET, HiChIP, and eQTL data show that it results in lower false discovery rates and at the same time higher inference power. By applying FOCS to 2630 samples taken from ENCODE, Roadmap Epigenomics, FANTOM5, and a new compendium of GRO-seq samples, we provide extensive enhancer–promotor maps (The online version of this article (10.1186/s13059-018-1432-2) contains supplementary material, which is available to authorized users. Prominent among them is the ENCODE consortium [Deciphering the regulatory role of the noncoding part of the human genome is a major challenge. With the completion of the sequencing of the genome, efforts have shifted over the past decade towards understanding the epigenome. These efforts aim at understanding regulatory mechanisms outside the protein-coding sequences that allow the production of thousands of different cell types from the same DNA blueprint. Enhancer elements that distally control the activity of target promoters play critical roles in this process. Consequently, large-scale epigenomic projects set out to identify all the nsortium , 2, whicnsortium . Given tnsortium . A commonsortium . HoweverImproved detection of functional enhancers is offered by a recently discovered class of non-coding transcripts, named enhancer RNAs (eRNAs) . eRNAs aFDR-corrected OLS with Cross-validation and Shrinkage), a novel procedure for inference of E–P links based on correlated activity patterns across many samples from heterogeneous sources. FOCS uses a cross-validation scheme in which regression models are learnt on a training set of samples and then evaluated on left-out samples from other cell types. The models are subjected to a new statistical validation scheme that is tailored for zero-inflated data. Finally, validated models are optimally reduced to derive the most important E–P links. We applied FOCS on massive genomic datasets recorded by ENCODE, Roadmap Epigenomics, and FANTOM5, and on a large compendium of eRNA and gene expression profiles that we compiled from publicly available GRO-seq datasets. We demonstrate that FOCS outperforms extant methods in terms of concordance with E–P interactions identified by ChIA-PET, HiChIP, and eQTL data. Collectively, applying FOCS to these four data resources, we inferred ~ 300,000 cross-validated E–P interactions spanning ~ 16,000 known genes. FOCS and our predicted E–P maps are publicly available at http://acgt.cs.tau.ac.il/focs.Here, we present FOCS  Importantly, to avoid over-fitting of the regression models to the training samples, FOCS implements a leave-cell-type-out cross-validation (LCTO CV) procedure, as follows. In a dataset that contains samples from C different cell types, for each promoter FOCS performs C iterations of model learning. In each iteration, all samples belonging to one cell type are left out and the model is trained on the remaining samples. The trained model is then used to predict promoter activity in the left-out samples  to predict the activity of the target promoter in the left-out samples. The first test is a “binary test” in which samples are divided into two sets, positive and negative, containing the samples in which the promoter was active or not, respectively, based on their measured signal . Then, the Wilcoxon signed-rank test is used to compare the predicted promoter activities between these two sets  are considered. Gene models with good predictive power should discriminate well between positive and negative samples (the binary test) and preserve the original activity ranks of the positive samples (the activity level test), and models that pass these tests are regarded as statistically cross-validated. Of note, these two validation tests evaluate each promoter model non-parametrically without assuming any underlying distribution on the data when inferring significance. Next, FOCS corrects the p values obtained by these tests for multiple testing using the Benjamini–Yekutieli (BY) FDR procedure [We implemented and evaluated three alternative regression methods: ordinary least squares (OLS), generalized linear model with the negative binomial distribution (GLM.NB) , and zerrocedure  with q-vrocedure  assumes R2 values obtained by OLS models generated without CV to the values obtained when CV was applied  to outliers. Such promoter models do not pass our CV tests and are considered to have low confidence.Applying FOCS to the ENCODE DHS dataset, we only considered promoters and enhancers that were active  in at least 30 out of the 208 samples  Overall, this dataset contained 92,909 and 408,802 active promoters and enhancers, respectively “”. We firied Fig. . The resr2/R2 where r is the pairwise Pearson correlation between the enhancer and promoter activity patterns and R2 is the coefficient of determination of the entire promoter’s model. In the analysis of the ENCODE DHS data, we included in this step the 70,465 promoters that passed the activity level test (or both tests). In agreement with previous observation [R2 values of these statistically validated models: 54% of the models  had R2 ≥ 0.5 . We note that models that passed the CV tests but have low R2 do contain confident and predictive information on E–P links, though the low R2 suggests that additional, missing regulatory elements play important roles in the regulation of the target promoter.Next, we sought to characterize the configuration of promoter regulation by its enhancers, in terms of the number of regulating enhancers and their relative contribution. For each promoter that passed the validation tests, we now calculated a final model, this time considering all samples Fig. , and est0.5 Fig. ; 61% of k variables , where each variable is assigned a significance level  reflecting its statistical strength. Next, to focus on the most informative E–P interactions, FOCS seeks the strongest enhancers in each model. To this end, FOCS derives, per promoter, an optimally reduced model by applying model shrinkage (“enet) approach, which combines Lasso and Ridge regularizations, since in cases of highly correlated variables , Lasso tends to select a single variable while Ridge gives them more equal coefficients . Next, we compared the performance of FOCS and three alternative methods for E–P mapping. (1) Pairwise: pairwise Pearson correlation > 0.7 between E–P pairs located within ±500 kbp, and accounting for multiple testing using BH  but with enet shrinkage in place of LASSO. Table After optimally reducing the promoter models, FOCS predicted in the ENCODE DHS dataset a total of 167,988 E–P links covering 70,465 promoters and 92,603 distinct enhancers (ρs) was 0.53 after CV. This demonstrates that FOCS can capture promoter models that exhibit non-linear relationships between the promoter and enhancer activities.To evaluate the validity of E–P mappings predicted by each method, we used three external omics resources: physical E–P interactions derived from RNAPII ChIA-PET data, physical E–P interactions derived from YY1 HiChIP experiments, and functional E–P links indicated by eQTL analysis “”. For phHaving demonstrated FOCS proficiency in predicting E–P links on the ENCODE DHS data, we next wished to expand the scope of our E–P mapping. We therefore applied FOCS to three additional large-scale genomic datasets: (1) DHS profiles measured by the Roadmap Epigenomics project, consisting of 350 samples from 73 different cell types and tissues; and (2) FANTOM5 CAGE data that measured expression profiles in 1827 samples from 600 human cell lines and primary cells. The analysis of FANTOM5 data uses eRNA and TSS expression levels for estimating the activity of enhancers and promoters, respectively “”. (3) A R2 ≥ 0.5) when trained on all samples had low predictive power on novel samples , while mentclass2pt{minimR2 ≥ 0.5, but only 11% of the validated models reached this performance in the FANTOM5 dataset  of enhancers in FOCS-derived models had positive Pearson and Spearman correlation with the activity pattern of their target promoters, the models also included cases of negative correlation, suggesting that the regulatory element functions as a repressor  implicitly assumes that transcription rate at promoters is positively related with promoter DHS signal. We therefore examined DHS–expression correlations in cell lines for which both DHS and RNA-seq data were available in the ENCODE project . In all cases, we observed high Spearman but low Pearson correlations  of the variants detected by genome-wide association studies are located in noncoding sequences . Similarn = 302,050) compared to the standard pairwise method with Pearson coefficient r > 0.7 . On average over all datasets, FOCS E–P links show a higher support rate by external validation resources compared to the commonly used pairwise method (r > 0.7). These results demonstrate the improved prediction power and control of false positive E–P links.FOCS predicts ~ 1.5-fold more E–P links  assuming linearity between enhancer and promoter activity patterns.FOCS uses two non-parametric tests to examine the robustness of each promoter model. Using these tests we can correct for multiple promoter models and use them when it is suspected that there is no linear relationship between the E–P activity patterns. Previous methods used the Pearson correlation test  links, which result from negative Spearman correlation between R–P activity patterns. R–P links are less known and are also of high interest.We provide a new compendium of eRNA and gene expression patterns based on 245 GRO-seq profiles from 23 different cell types. This compendium can be used as a genome-wide resource of enhancer activity in a diverse panel of cell lines.ftp://ftp.ebi.ac.uk/pub/databases/ensembl/encode/integration_data_jan2011/byDataType/openchrom/jan2011/combined_peaks/multi-tissue.master.ntypes.simple.hg19.bed).DHS peak locations of enhancers and promoters were taken from a master list of 2,890,742 unique, non-overlapping DHS segments [n = 68,762) and novel  promoter–DHS peaks taken from ftp://ftp.ebi.ac.uk/pub/databases/ensembl/encode/integration_data_jan2011/byDataType/openchrom/jan2011/promoter_predictions.We extracted from the master list the set of known  non-promoter–DHS peaks in the master list were considered as putative regulatory elements, collectively referred to here as enhancer elements. To create enhancer/promoter signal matrices, we used the BAM files of 208 UW DNase-seq samples (106 cell types) from the Gene Expression Omnibus (GEO) dataset GSE29692 [The remaining n = ,777,127 GSE29692 . The numGSE29692 . To reduftp://genome.crg.es/pub/Encode/data_analysis/TSS/Gencodev10_TSS_May2012.gff.gz); 54,650 promoters  were linked to annotated TSSs.We defined for each promoter the set of k = 10 candidate enhancers located within a window of 1 Mb (±500 kb upstream/downstream of the promoter’s center position). We mapped promoters to annotated genes using GencodeV10 TSS annotations  of 350 UW DNase-seq samples (73 cell types) from GEO dataset GSE18927 , 34–36. ftp://genome.crg.es/pub/Encode/data_analysis/TSS/Gencodev10_TSS_May2012.gff.gz) [We defined for each promoter the set of k = 10 candidate enhancers located within a window of ±500 kb. We mapped promoters to annotated genes using GencodeV10 TSS annotations (.gff.gz) ; 17,941 n = 65,423) expression matrices  covering 1827 samples (600 cell types) were downloaded from FANTOM5 DB (http://fantom.gsc.riken.jp/). As in the FANTOM5 paper [Promoter (CAGE tags peak phase 1 and 2) and enhancer  database  and the other for promoters (Mp), measured across the same samples. Activity is measured by DHS signal in ENCODE and Roadmap data, and by expression level in FANTOM5 and GRO-seq data. Samples were labeled with a cell-type label out of C cell types. The output of FOCS is predicted E–P links.The input to FOCS is two activity matrices, one for enhancers  or TSS (in GRO-seq). Formally, let yp be the promoter p normalized activity pattern (measured in counts per million (CPM); yp is a row from Mp) and let Xp be the normalized activity matrix of the corresponding k enhancers . We build an OLS linear regression model yp = Xpβp + εp, where εp is a vector that denotes the errors of the model and βp is the (k + 1) x 1 vector of coefficients (including the intercept) to be estimated.First, FOCS builds for each promoter an OLS regression model based on the C − 1 cell types and testing the predicted promoter activity of the samples from the left-out cell type. This step is repeated C times. The result is a vector of predicted activity values Second, FOCS performs leave-cell-type-out cross-validation (LCTO CV) by training the promoter model based on samples from binary test examines whether p was active (observed activity yp ≥ 1 RPKM) and the samples in which p was inactive (yp < 1 RPKM). (2) The activity level test calculates, for the active samples, the significance of the Spearman correlation between yp. Spearman correlation compares the ranks of the original and predicted activities. We obtain two vectors of p values, one for each test, of length n (the number of promoter models).FOCS tests the predicted activity values using two validation tests. (1) The p value vector the Benjamini–Yekutieli (BY) FDR procedure [R2 ≥ 0.5  as described in   RNAPII ChIA–PET interactions; (2) YY1-HiChIP interactions; and (3) eQTL SNPs.We downloaded 922,997 ChIA-PET interactions  from the Chromatin–Chromatin Spatial Interaction (CCSI) database   for the promoters (relative to the center position in ENCODE/Roadmap/FNATOM5 or to the TSS position in GRO-seq) and the enhancers (±500 bp from the enhancer center). An E–P pair is considered supported by a particular capture interaction if both the promoter and enhancer intervals overlap different anchors of an interaction. An E–P pair is considered supported by an eQTL SNP if the SNP is located within the enhancer’s interval and is associated with the expression of the promoter’s gene. For each predicted E–P pair we checked if the promoter and enhancer intervals are supported by capture interactions and eQTL data. We then measured the fraction of E–P pairs supported by these data resources. See Additional file https://genome.ucsc.edu/).All computational analyses and visualizations were done in the R statistical language environment . We usedAdditional file 1:Figures S1–S14, Tables S1–S3, and Supplemental Methods. (PDF 3127 kb)Additional file 2:Table S4 GO enrichment analyses. (XLSX 29 kb)Additional file 3:Tables S5–S6 GRO-seq and ChIA-PET samples. (XLSX 25 kb)Additional file 4:Review history. (DOCX 473 kb)


Mycobacterium tuberculosis, and other mycobacteria, is the mycobacterial cell envelope with its exceptional complex composition. Mycolic acids are major and very specific components of the cell envelope and play a key role in its architecture and impermeability. Biosynthesis of mycolic acid (MA) precursors requires two types of fatty acid synthases, FAS I and FAS II, which should work in concert in order to keep lipid homeostasis tightly regulated. Both FAS systems are regulated at their transcriptional level by specific regulatory proteins. FasR regulates components of the FAS I system, whereas MabR and FadR regulate components of the FAS II system. In this article, by constructing a tight mabR conditional mutant in Mycobacterium smegmatis mc2155, we demonstrated that sub-physiological levels of MabR lead to a downregulation of the fasII genes, inferring that this protein is a transcriptional activator of the FAS II system. In vivo labelling experiments and lipidomic studies carried out in the wild-type and the mabR conditional mutant demonstrated that under conditions of reduced levels of MabR, there is a clear inhibition of biosynthesis of MAs, with a concomitant change in their relative composition, and of other MA-containing molecules. These studies also demonstrated a change in the phospholipid composition of the membrane of the mutant strain, with a significant increase of phosphatidylinositol. Gel shift assays carried out with MabR and PfasII as a probe in the presence of different chain-length acyl-CoAs strongly suggest that molecules longer than C18 can be sensed by MabR to modulate its affinity for the operator sequences that it recognizes, and in that way switch on or off the MabR-dependent promoter. Finally, we demonstrated the direct role of MabR in the upregulation of the fasII operon genes after isoniazid treatment.One of the dominant features of the biology of  Mycobacterium smegmatis mabR cKD and WT-pMT13 were grown with ATc 2.5 and 25 ng ml−1 and incubated at 37°C with gentle shaking (180 r.p.m.). At T1, aliquots of 5 ml were radiolabelled with 1 µCi ml−1 of [1-14C] acetate  for 1 h at 37°C. Cells were then harvested by centrifugation, washed with phosphate buffer 0.1 M (pH 7.6) and stored at −80°C. FA methyl esters (FAMEs) and MA methyl esters (MAMEs) were obtained after treatment of the radiolabelled cell pellets containing the same number of cells with aqueous tetrabutyl ammonium hydroxide followed by esterification with methyl iodide and extraction with dichloromethane, as previously described -ATP (3000 Ci mmol−1) using T4 polynucleotide kinase. A fixed concentration of His6-MabRTB (1 nM) was pre-incubated with acyl-CoAs at different final concentrations prior to mixing with the 32P-labelled probe (10 000 c.p.m.) in a total volume of 15 µl of binding buffer (25 mM Tris–HCl (pH 8), 1 mM PMSF, 5% (v/v) glycerol, 5 mM MgCl2, 150 mM NaCl and 1 µg poly(dIdC)) at room temperature for 15 min. DNA–protein complexes were solved by electrophoresis on a 5% (w/v) non-denaturing polyacrylamide gel in 0.5× TBE, 5% (v/v) glycerol and run at 200 V at 4°C. The gels were visualized and digitalized with a Storm 840 scanner (Amersham).Purified recombinant His2.9.® fluorometer (Invitrogen). Proteins were electroblotted onto nitrocellulose blotting membranes  for western blot analysis. MabRMS was detected using 1 : 100 dilution of polyclonal anti-MabRMS serum and antigenic polypeptides were visualized using a horseradish-peroxidase-conjugated secondary antibody. To identify RpoBMS, the membrane was probed with a 1 : 20 000 dilution of polyclonal anti-RpoBTB serum, and antigenic polypeptides were visualized using an alkaline-phosphatase-conjugated secondary antibody. Antiserum against His6-MabRMS was elicited in rabbits and anti-RpoB in mice.Purified proteins were analysed by SDS–PAGE . Coomass2.10.M. smegmatis MS26-PfasII800 grown in 7H9 with Kan and Str were diluted in the same medium to an initial OD600 of 0.01 and incubated at 37°C. Once the culture reached OD600 = 0.2, it was divided into three equal fractions and supplemented with FAs at a final concentration of 0.15 mM. Following 6.5 h of incubation, 10 ml of each culture was pelleted, washed and finally resuspended in Z buffer -acetate labelling experiments were carried out with mabR cKD and WT-pMT13 strains. Both strains were grown with ATc 2.5 and 25 ng ml−1, and their lipids were extracted and analysed by radio-TLC. As shown in figure 5a, the de novo synthesis of MA in mabR cKD was strongly inhibited when the cells were grown in the presence of ATc 2.5 ng ml−1, where the levels of MabR were much lower compared with the physiological amounts present in the WT-pMT13 strain. However, FA biosynthesis was not affected, even when MA biosynthesis was extremely compromised, indicating that a normal functioning of the FAS I system is not sufficient for the survival of the cells due to the essential role of MAs in the architecture of the cell wall. On the other hand, although total amount of the de novo MA biosynthesis was not affected in the mabR cKD strain grown in the presence of ATc 25 ng ml−1 , there were clear differences between the relative abundance of the three classes of MAMEs, which could correlate with the slightly lower levels of MabR detected under these conditions (b and 5c). The relative percentages for α-, α′- and epoxy-mycolates from the WT-pMT13 strain grown in the presence of ATc 2.5 and 25 ng ml−1 were identical, 93%, 4.5% and 2.5%, respectively (figure 5c), while the profiles obtained for the mabR cKD strain grown with ATc 25 ng ml−1 were 35%, 62% and 3%, respectively, showing a significant increase in the abundance of α′-mycolates (62%) with a compensatory reduction of α-mycolates  compared with the other MA species (figure 5c).To study the impact of sub-physiological levels of MabR in the nditions b and 5c.3.4.mabR cKD mutant and its parental strain WT-pMT13. Briefly, cell-associated lipids were extracted from the conditional mabR cKD mutant grown in the presence of ATc 2.5 and 25 ng ml−1, and the total lipid content was analysed by high-performance liquid chromatography–mass spectrometry (HPLC-MS). As a control, we extracted and analysed lipids from the parental control strain WT-pMT13 grown in the presence of ATc 2.5 ng ml−1. −1 showed a significant decrease in MAs and a higher proportion of α′ mycolates compared with the control strain. Notably, the distribution of various MAs was shifted to shorter forms in the mutant with reduced levels of MabR . Furthermore, tandem mass analysis (MS–MS) of the individual types of mycolates showed that α-branches of the mycolates from WT-pMT13 and mabR cKD strains grown in the presence of ATc 2.5 ng ml−1 contained mainly 24 carbons, indicating that the length difference of these molecules corresponded to the mero-chains . On the other hand, the relative composition of PL showed a more biased effect towards phosphatidylinositol (PI), which was significantly increased in the presence of sub-physiological levels of MabR .Complex lipids bearing MA moieties, such as glycerol monomycolates (GroMM), monomeromycolyl diacylglycerol (MMDAG) or trehalose monomycolates (TMM), were analysed in the positive-ion mode, and they were also found in lower amounts when the mutant strain had lower levels of MabR . This re3.5.et al. [M. tuberculosis inhibits MA synthesis and is accompanied by a marked upregulation of both AcpM and KasA. Later, transcriptomic studies conducted by several laboratories to explore the genome-wide transcriptional response of M. tuberculosis to different drugs demonstrated that INH and ETH, both potent inhibitors of InhA, markedly induced the transcription of the fasII operon genes [et al. [fasII operon. However, it is still an open question whether or not the transcriptional activation of the fasII operon genes upon INH treatment is mediated by MabR. Therefore, to investigate whether MabR is involved in this regulation, we constructed two transcriptional fusions to the reporter gene lacZ: PfasII800MT::lacZ and PfasII800MTMut2::lacZ. The first transcriptional fusion comprises 774 bp upstream of the TSS of fabD and corresponds to the wild-type sequence of PfasIIMT, which includes the MabR recognition sequence 5′-TTTTGT(N)9ACAAAA-3′; the second transcriptional fusion PfasII800MTMut2::lacZ corresponds to a variant of this promoter region where the palindromic IRs were replaced by the random sequences 5′-CGCCAA(N)9ACACGC-3′ (a). Using gel shift assays, we had previously demonstrated that the mutated IRs were unable to bind MabR [M. smegmatis mc2155, generating two strains (MS-PfasII800 and MS-PfasII800Mut2), which were used for transcriptional activity studies by assaying β-galactosidase activity in their cell-free extracts. As showed in figure 7b, after the treatment of the cultures of both strains with INH 100 μg ml−1 for 90 min, the β-galactosidase activity present in MS-PfasII800 extracts showed a five-fold increase compared with the activity levels found in the non-treated cells, while we did not find variation for MS-PfasII800Mut2 treated or not with INH. This result confirmed that MabR binding sites, and therefore MabR, are required for the transcriptional activation of the fasII operon upon INH treatment. Moreover, this experiment also corroborated the activating role of MabR in the transcriptional regulation of the fasII operon.Mdluli et al.  showed ton genes ,37. More [et al.  showed tCACGC-3′ a. Using ind MabR . Once thfasII could be correlated with a change in the expression level of mabR, total RNA was purified from M. smegmatis cultures treated or not with INH and analysed by quantitative RT-PCR. As shown in figure 7c, mabR expression showed a modest increase (about 30%) after INH treatment.To determine if the INH-mediated induction of P3.6.MT to PfasIIMT . These compounds included palmitoyl-CoA (C16-CoA), stearoyl-coA (C18-CoA), arachidoyl-CoA (C20-CoA), behenoyl-CoA (C22-CoA), lignoceroyl-CoA (C24-CoA) and hexacosanoyl-CoA (C26-CoA). In these experiments, the reaction mixture contained, in addition to the test compounds added at different final concentrations , 32P-labelled PfasIIMT DNA and 1 nM His6-MabRMT. To appreciate even slight differences in the binding affinity of MabR to its DNA probe upon the addition of the test molecules, the concentration of His6-MabRMT used for these gel shift assays was below the one that allows a complete shift of the probe PfasIIMT. As shown in 16-CoA had no effect on the binding affinity of MabR to the probe, while the presence of C18-CoA increased the affinity of the regulator towards PfasIIMT when assayed at a final concentration of 1 µM (a). When the reaction mixture was supplemented with C20-CoA and C22-CoA, the binding affinity was enhanced throughout the range of concentrations studied. Remarkably, although the addition of C24-CoA stabilized the interaction between MabR and the DNA probe, higher concentrations of this acyl-CoA (0.5 and 1 µM) provoked its dissociation or the inhibition of the binding. This effect could be considered as the result of protein denaturation by the detergent action of the long-chain acyl-CoA at higher concentrations, reducing the levels of native MabR available for the formation of the protein–DNA complex. C26-CoA did not show an effect on promoting MabR–DNA interaction. This result could have been obtained either because C26-CoA is not a ligand recognized by MabR or because it could be denaturing the regulatory protein. To tackle this question, MabR was pre-incubated with C26-CoA at a final concentration of 0.5 µM for 5 min and then C20-CoA was added to the final concentration of 0.5 or 1 µM, together with a 32P-labelled probe. In this reaction, no MabR–DNA complex was formed, thus suggesting a denaturing effect of C26-CoA towards MabR, which is then not available to bind the PfasII probe, even when C20-CoA was present in the reaction mixture to promote this interaction .The final products of the bacterial FAS II system are well-established signal molecules that control FAS II activity by binding to the enzymes involved in the biosynthetic pathways ,39 or by of 1 µM a. When t18) could be the signal molecules that modulate the binding between MabR and the PfasII promoter, at least in vitro.Altogether, our studies demonstrate that long-chain acyl-CoAs  or arachidic acid (C20), which are converted into their corresponding CoA derivatives once within the cell. The final strain also carried the replicative plasmid pMR26, which contains a copy of mabRMT under the leaky Pami promoter. In this experiment, cells were grown in 7H9 medium without acetamide to allow the presence of basal levels of MabRMT. Once the cell culture reached OD600 = 0.2–0.3, it was divided into three equal fractions, two of them were supplemented with C16 or C20 to a final concentration of 0.15 mM, and a control culture was left without the addition of FAs. Samples were taken 6.5 h after supplementing the cultures with the specific FAs for β-galactosidase analysis. As shown in figure 8b, the presence of palmitic acid in the growth medium has no enhancing effect on the transcriptional activity of the lacZ gene when compared with the control culture, while arachidic acid led to increased levels of β-galactosidase activity, suggesting an activation of the fasII operon transcription once MabR binds to C20-CoA. This result coincides with the effect observed in the in vitro experiments shown in figure 8a, where C16-CoA had no effect on the in vitro MabR–PfasII interaction while C20-CoA enhanced this binding. All these results point to long-chain acyl-CoAs being the metabolic signals sensed by MabR to exert its role as a transcriptional activator of the fasII operon.The fact that MabR is a transcriptional activator of the 4.M. tuberculosis mycomembrane is a highly efficient permeability barrier containing many antigenic determinants and is essential to cell viability and virulence. The major components of the mycomembrane are the MAs, which play a central role in the process of infection and persistence within the host. During the process of infection, the bacteria must continuously adapt to various environmental conditions by regulating its energy metabolism and cell wall composition. To finely control cell wall biosynthesis, M. tuberculosis employs many sophisticated regulatory strategies such as transcriptional regulation of lipid genes [E. coli [Streptococcus pneumoniae [Bacillus subtilis [The id genes ,19 or moid genes ,41–43; t[E. coli –46, Streeumoniae  and Bacisubtilis . In thisM. smegmatis have been shown to be essential for cell viability [Unlike the transcriptional regulators involved in lipid metabolism in other bacteria, MabR and FasR from iability ,19, suggin vitro and in vivo studies, the latter carried out with the mabR cKD conditional mutant, allowed us to demonstrate that MabR is a transcriptional activator of the fasII operon. These results contradicted our previous studies, where MabR was previously described as a negative regulator of the fasII operon genes [mabR was overexpressed from the amiP promoter, implying that we characterized the system in conditions where MabR levels were very high in comparison with its physiological levels. In this regard, there are examples in the literature where overexpression of transcriptional regulators could lead to a non-physiological response of the regulatory protein due to the misinteraction of the apo form of the regulator with the DNA. In the aforementioned studies, we complemented the M. smegmatis mabR conditional mutant with a mabR copy of either M. tuberculosis or M. smegmatis; interestingly, the two complemented mutants grew similarly to the wild-type strain only in the absence of acetamide (the inducer of the Pami promoter), where the leakiness of the system allowed a weak expression of mabR. However, the colonies were of reduced size and displayed clear morphological differences with the wild-type strain in the presence of acetamide, and in a clear correlation with a lower expression of the fasII genes [fasII region in the absence of its ligand, in that way inhibiting the physiological response of PfasII to the effector-bound form of MabR.In this work, by constructing an MabR conditional mutant regulated by TetR, we were able to downregulate the expression of MabR and study the physiological response of the cell to suboptimal levels of the transcriptional regulator. The complementing on genes . We now II genes . TherefofasII operon genes; for instance, transcription of other genes related with the FAS II system, but encoded in other genome loci, such as inhA (second gene of the mabA-inhA bicistronic operon) and fabH, acting at the intermediate step between FAS I and FAS II activities, was also downregulated. Analysis of the DNA sequences found upstream of the TSS of either the mabA-inhA operon or the fabH gene did not reveal consensus MabR recognition sites. Thus, this cross-regulation might occur at a higher hierarchical level where a more global regulator might act in response to a generalized stress signal generated by the inhibition of MA biosynthesis, turning down other enzymes related to this metabolism. In relation to these results, it has been recently demonstrated that starvation of M. tuberculosis also leads to a generalized downregulation of genes required for the synthesis , modification and transport of mycolates [mabR expression was also turned down, in consonance with the downregulation of the MA biosynthesis genes thus supporting the activator role of this transcriptional regulator.Interestingly, sub-physiological levels of MabR not only impacted on the expression of the ycolates . As thishadABC genes was not modified under conditions of MabR depletion. Whether the lack of response of this operon is related to the existence of a second dehydratase (MSMEG_6754) present in M. smegmatis and found to have functional redundancy with HadB [hadABC operon is more complicated, because they are part of a seven-gene operon together with another four genes involved in translation, and at least in M. tuberculosis they all respond to the alarmone (p)ppGpp which leads to dramatic reprogramming of cell transcription [Interestingly, the expression of the ith HadB  remains cription . Therefofas and fabH observed in our experiments was also found during growth arrest [The opposite response between the expression of h arrest . It was M. smegmatis KasA-overexpressing strain, α′-mycolates decrease with a concomitant increase of α-mycolates [In the condition where the levels of MabR were below its physiological concentration, and the transcription of most of the genes involved in the FAS II system was downregulated, we found that MA biosynthesis was strongly impaired and its relative composition significantly modified, with a high prevalence of α′-MAs. It has been suggested that α′-mycolates are the precursor of α-mycolates, and KasA has been postulated to be the key enzyme responsible for elongating α′-MAs to α-MAs . In a M.ycolates ,52, showycolates ,54. ThusMycobacterium, Corynebacterium, Nocardia, Micromonospora, Streptomyces and Propionibacterium), myxobacteria, and to the genus Treponema. In Mycobacterium sp., PI and metabolically derived molecules of which PI constitutes a lipid anchor to the cell envelope, such as PI mannosides (PIMans), linear lipomannan (LM) and mature branched LM and lipoarabinomannan, are prominent and important PL/lipoglycans [M. smegmatis fas-acpS conditional mutant, where the expression of FAS I synthase was drastically turned down, we also found that the relative amount of PI dropped significantly, while the other PL species had no alteration [In regard to the PL composition found in MabR-deprived cells, there is a relative increase in the population of PI among the PL species. Contrary to phosphatidylethanolamine, phosphatidylglycerol, phosphatidylserine and cardiolipin, which are frequently encountered in all living organisms, PI is an essential phospholipid of eukaryotic cells, but has seldom been found in prokaryotic cells. Actually, the distribution of PI in prokaryotes seems to be confined to some actinomycetes  might accumulate as a response to MA inhibition and induce at least part of the MA biosynthesis genes. In this way, the transcriptional regulator senses and responds to a variation in the signal molecules, which in fact reflect the metabolic state of the cell and adjust its lipid composition in order to adapt the bacteria to a new living condition.Working with two different P by MabR , we demotabolism ,40,58, wfas-acpS operon of Mycobacterium, we demonstrated that long fatty acyl-CoAs (longer than C18-CoA) inhibit the binding of FasR to Pfas, turning down the expression of the fas-acpS operon [18 as key metabolites that are sensed by the two main transcriptional regulators charged to maintain lipid homeostasis in mycobacteria. Considering that both regulatory proteins have been shown to be essential for cell viability in M. smegmatis, it is tempting to speculate that they will also be essential proteins in M. tuberculosis, and if so, they could be considered plausible new targets for the screening of compounds that could disrupt their regular DNA-binding properties and in this way inhibit cell growth.In transcriptional studies carried out with FasR, the transcriptional activator of the S operon . Therefo


S. cerevisiae, the phosphate starvation (PHO) responsive transcription factors Pho4 and Pho2 are required for gene induction and survival during phosphate starvation. In the related human commensal C. glabrata, Pho4 is required but Pho2 is dispensable for survival in phosphate starvation and is only partially required for inducing PHO genes. Phylogenetic survey suggests that reduced dependence on Pho2 evolved in C. glabrata and closely related species. In S. cerevisiae, less Pho2-dependent Pho4 orthologs induce more genes. In C. glabrata, its Pho4 binds to more locations and induces three times as many genes as Pho4 in S. cerevisiae does. Our work shows how evolution of combinatorial regulation allows for rapid expansion of a gene regulatory network’s targets, possibly extending its physiological functions.Although combinatorial regulation is a common feature in gene regulatory networks, how it evolves and affects network structure and function is not well understood. In  The diversity of life on Earth has intrigued generations of scientists and nature lovers alike. Research over recent decades has revealed that much of the diversity we can see did not require the invention of new genes. Instead, living forms diversified mostly by using old genes in new ways – for example, by changing when or where an existing gene became active. This kind of change is referred to as “regulatory evolution”.A class of proteins called transcription factors are hot spots in regulatory evolution. These proteins recognize specific sequences of DNA to control the activity of other genes, and so represent the “readers” of the genetic information. Small changes to how a transcription factor is regulated, or the genes it targets, can lead to dramatic changes in an organism. Before we can understand how life on Earth evolved to be so diverse, scientists must first answer how transcription factors evolve and what consequences this has on their target genes.So far, most studies of regulatory evolution have focused on networks of transcription factors and genes that control how an organism develops. He et al. have now studied a regulatory network that is behind a different process, namely how an organism responds to stress or starvation. These two types of regulatory networks are structured differently and work in different ways. These differences made He et al. wonder if the networks evolved differently too.Saccharomyces cerevisiae), which is commonly used in laboratory experiments, requires both Pho4 and Pho2 to activate about 20 genes when inorganic phosphate is not available in its environment. However, in a related yeast species called Candida glabrata, Pho4 has evolved to depend less on Pho2. He et al. went on to show that, as well as being less dependent on Pho2, Pho4 in C. glabrata activates more than three times as many genes as Pho4 in S. cerevisiae does in the absence of phosphate. These additional gene targets for Pho4 in C. glabrata are predicted to extend the network’s activities, and allow it to regulate new process including the yeast’s responses to other types of stress and the building of the yeast’s cell wall.The chemical phosphate is an essential nutrient for all living things, and He et al. compared how two different species of yeast responded to a lack of phosphate. The key difference was how much a major transcription factor known as Pho4 depended on a so-called co-activator protein named Pho2 to carry out its role. Baker’s yeast (C. glabrata sometimes infects humans, understanding how this yeast’s response to stress has evolved may lead to new ways to prevent and treat this infection.Together these findings show a new way that regulatory networks can evolve, that is, by reducing its dependence on the co-activator, a transcription factor can expand the number of genes it targets. This has not been seen for regulatory networks related to development, suggesting that different networks can indeed evolve in different ways. Lastly, because disease-causing microbes are often stressed inside their hosts and  Evolution of gene regulatory networks (GRNs) is a major source of phenotypic diversity . One comDespite a rich literature on GRN evolution, few studies have documented the evolution of combinatorial regulation and its influence on network structure and function . MoreoveS. cerevisiae, providing a solid foundation for comparative analyses. Third, the two species we focus on, S. cerevisiae and a human commensal and opportunistic pathogen C. glabrata, occupy distinct ecological niches but share ~90% of their gene repertoire and have an average of ~67% protein sequence identity (To approach this question we studied the regulatory divergence in the phosphate starvation (PHO) response network in yeast. For three reasons, this system is well suited for our question. First, starvation/stress response networks differ in architecture from developmental networks, leading us to expect differences in their evolutionary patterns. Second, the GRN controlling the PHO response has been well studied in the model yeast identity , enablinS. cerevisiae activates a phosphate starvation pathway in response to limitation for inorganic phosphate  exist, but unlike Pho4 and Pho2 in S. cerevisiae (hereinafter referred to as ScPho4 and ScPho2), CgPho4 can induce gene expression in the absence of CgPho2 , we inserted coding sequences (CDSs) of the 16 Pho4 orthologs into an S. cerevisiae background lacking both the endogenous ScPho4 CDS and the negative regulator of the PHO pathway, Pho80, under the control of the endogenous ScPho4 promoter. Deletion of PHO80, the cyclin component of the cyclin-dependent-kinase complex, causes constitutive nuclear localization of Pho4 and de-repression of PHO genes, mimicking phosphate starvation .We first confirmed the previously reported result that deleting  by PMU2   were able to induce Pho5 expression in the absence of Pho2  weakly induced Pho5 in a Pho2-independent manner  to identify the binding locations for both ScPho4 and CgPho4 in the S. cerevisiae background lacking the negative regulator Pho80. We identified a total of 115 ChIP-peaks for CgPho4 and 74 peaks for ScPho4, with 72 being bound by both . We performed the same analysis for Cbf1 enrichment in high Pi conditions at the top 25% most accessible E-box motifs – those with the lowest nucleosome occupancy – to avoid confounding nucleosome competition with Cbf1 binding. We found that Cbf1 enrichment at sites bound only by CgPho4 is not significantly different from enrichment at sites bound by both ScPho4 and CgPho4  ScPho4 induced genes required ScPho2 , and the consensus motif is present in 51 of the 100 peaks, with the rest containing a one-bp mismatch (46) or two-bp mismatches (3). CgPho2 bound to more than 500 sites genome-wide, without a strongly enriched sequence motif . With respect to CgPho4 bound sites, CgPho2 binds at the same location for 77 of the 100 peaks . We conclude that CgPho4 binding to DNA is largely independent of CgPho2 in C. glabrata, but a small fraction (18%) of its binding sites show CgPho2 influence.Next we asked if CgPho4 also functions independently of Pho2 in its endogenous genome. To investigate the dependence of CgPho4 binding on CgPho2, we used the high resolution ChIP-exo technique to map CgPho4 binding in the presence and absence of CgPho2, and CgPho2 binding, under both phosphate-replete and phosphate-limited conditions  . We iden00 peaks . Among tckground . We hypockground . AlthougS. cerevisiae, where the majority of gene induction was attributed to the cooperative interaction between ScPho4 and ScPho2  terms associated with the 79 genes bound and induced by CgPho4 in C. glabrata  or evolutionarily unrelated (e.g. the phosphatase function of PHO5 in S. cerevisiae is replaced by that of PMU2 in C. glabrata) , despite a significantly shorter evolutionary time between the two focal species in this study compared to one of the previous studies  are predicted to be involved in responses to non-phosphate related stresses, including osmotic (seven genes) and oxidative (seven genes) stresses. In addition to roles in other stress responses, a smaller group of CgPho4 targets (9 and 3 genes) have potential functions in cell wall synthesis and cell adhesion, two traits that were known to be relevant for survival and virulence in the host , using a standard high efficiency transformation protocol with ~ 40 bp homology sequences . PCR primers were designed based on genome sequences for each species from the Orthogroup website . Strains of the non-S. cerevisiae species were from Aviv Regev lab (Nakaseomyces genus). Abbreviations for species names are as follows: S. mikatae (Smik), S. paradoxus (Spar), C. glabrata (Cgla), C. nivariensis (Cniv), C. bracarensis (Cbra), N. bacillisporus (Nbac), N. delphensis (Ndel), C. castellii (Ccas), N. castellii (Ncas), L. waltii , L. kluyveri (Lklu), K. lactis (Klac), D. hansenii (Dhan), C. albicans , Y. lipolytica (Ylip). Species names were based on YGOB and GRYC (URA3 marker used to knockout the PHO4 CDS with a linear DNA construct containing either ScPho4 or the CDS of CgPho4 fused with the Avitag (GLNDIFEAQKIEWHW) at the C-terminus, separated by a ‘GSGSGS’ linker. Primer sequences for generating the strains are available upon request.All equences . The Phoequences  were mad website , except egev lab  and Ceciand GRYC . Bio-ChIURA3 gene with a random piece of DNA amplified from pUC19 plasmid and selecting on 5-FOA medium. CG1 was subsequently used to generate the rest of C. glabrata strains using homologous recombination with either antibiotic resistance or nutrient markers. The NAT gene conferring resistance to clonNat was amplified from the pFA6a-natNT2 plasmid in the PCR Toolbox collection  for PHO4 with 6xG linker, and a N-terminal V5 tag (GKPIPNPLLGLDST) for PHO2 with 5xGS linker.CG1 was generated from BG99  by inactllection . A stand3 marker . ChIP-exC. albicans strains used in this study are from the transcriptional regulatory deletion library made by Homann et al All nn et al  and orde600 0.3–0.4). To induce the phosphate starvation response, yeast cells were first grown in 10 mM Pi medium to early/mid-logarithmic phase. Cells were then harvested by filtering and washed with 2–3 volumes of no Pi medium pre-warmed to 30°C. Finally, cells were re-suspended in pre-warmed no Pi medium and grown at 30°C for 1 hr before being harvested for downstream analyses. For plate growth assays, yeast cells were grown in 10 mM Pi medium until mid-logarithmic phase, washed 2–3 times and re-suspended in sterile water. A 1:4 dilution series were made with sterile water in 96-well plates. A 48-pin tool was used to transfer ~10 µL of resuspended cell culture onto appropriate solid agar plates. After 24–48 hr of growth at 30°C, pictures were taken using a standard gel box apparatus.Phosphate-free synthetic complete medium was prepared from Yeast Nitrogen Base with ammonium sulfate, without phosphates, without sodium chloride  and supplemented to a final concentration of 2% glucose, 1.5 mg/ml potassium chloride, 0.1 mg/ml sodium chloride and amino acids, as described previously . Monobas600 ~0.1 in the morning and grown to OD600 = 0.6–1. The cell culture was centrifuged, washed and re-suspended in water. A four-fold serial dilution was prepared for each strain and spotted onto an agar plate using a 48-pin tool. Complete synthetic medium was used for pho80∆ strains and phosphate-free medium (see above) used for PHO80 wild-type strains. After overnight growth, the agar plates were overlaid with Fast Blue Salt B stain (Sigma-Aldrich D9805), 1-naphthyl phosphate , and 1% agar in 0.1 m sodium acetate (pH 4.2) , diluted to OD(pH 4.2) . Picture600 ~ 5. Then 30 µL of the re-suspended culture was transferred to a 96-well assay plate in triplicates. The cell re-suspension was incubated with 80 µL of 10 mM p-nitrophenyl phosphate  dissolved in 0.1M sodium acetate (pH = 4.2) for 15 min at 25°C. The reaction was quenched by adding 144 µL of saturated Na2CO3 (pH > 11) followed by 5 min of centrifugation at 3000 g. Finally, 200 µL of the supernatant from each well was transferred to a new plate and OD420 was measured on a plate reader. Phosphatase activity was measured in units expressed as OD420/OD600  were obtained for each sample. Briefly, yeast cells were collected using a cold methanol quenching method (600 = 0.2–0.5) cell cultures were added directly into 30 mL of pre-chilled methanol (~−50°C), and incubated in an ethanol-dry ice bath at the same temperature for at least 20 min. Cells were collected by centrifugation and quickly washed with ice-cold water to remove methanol, and resuspended in RNAlater solution . For each sample, 5*107 cells were collected and mechanically lysed on a Minibeadbeater : Zirconia beads  were added to ~600 µL of cell suspension per sample in a 2 mL screw cap tube to the meniscus. Cells were lysed by four rounds of 1 min bead beating and 2 min of cooling in an ice-water bath. An RNasy Mini kit (Qiagen) was used to isolate total RNA from the lysed cell. RNA-seq libraries were prepared with the TruSeq RNA Library Preparation Kit v2  with the mRNA purification option, following the manufacturer’s protocol. The resulting libraries were sequenced on an Illumina HiSeq 2000, which produced on average 10 million 50 bp single end reads for each sample.All transcriptional profiling experiments were done in the g method . 20 mL oS. cerevisiae with the negative regulator ScPho80 deleted (pho80∆). In addition, CgPho4 ChIP was also performed in S. cerevisiae with both ScPho80 and ScPho2 deleted (pho80∆ pho2∆). One biological sample was analyzed for each strain. Two types of controls were included, i.e. an input sample, which is sonicated chromatin not subject to immune-precipitation (IP), and a mock sample, which uses a strain lacking the epitope tag recognized by the antibody, and is subject to the same IP as done for the biological sample.~100 OD600 units of early log phase cells (OD600 ~ 0.3) were collected in phosphate-replete (10 mM Pi) conditions. Cells were cross-linked with 1% formaldehyde  for 10 min and then quenched with 125 mM glycine for 10 min at room temperature. Cells were collected by centrifugation for 15 min at 6000 rpm in an Avanti J-20 XP high speed centrifuge, with JLA10.500 rotor , immediately washed with cold PBS buffer , and mechanically lysed with 0.5 mm Zirconia beads in lysis buffer . Chromatin was fragmented by sonication using the Covaris E220 Adaptive Focus system  using the 130 µL tube and the following setting: Duty Factor: 10%; Peak Incident Power: 175; Cycles/Burst: 200; Time:150s  or the Anti-V5 antibody  was incubated with the cell lysates for 2 hr up to overnight. For Anti-V5 antibody, Dynabeads Protein G (Thermo Fisher Scientific #10003D) was added to the antibody-lysate mix and incubated for an additional 2 hr at 4°C. The following steps are modified from the Active Motif ChIP-exo vA4 manual (https://www.activemotif.com/documents/1938.pdf): with the chromatin still bound to the beads, the DNA was end-polished and P7-exo adapters are ligated onto the blunt ends. The nicked DNA was repaired and then digested by lambda and RecJf exonulceases to excise DNA in a 5’ to 3’ direction, trimming up to the site of the cross-linking and selectively eliminating the P7 adapter at the 5 ´ end. Following cross-link reversal and elution from the beads, the DNA was made double-stranded by P7 primer extension and a P5-exo adapter was added to the exonuclease-treated ends. The DNA library was PCR amplified and size selected before it was subjected to high-throughput sequencing.ChIP-exo was performed in  methods . All str methods . One bioS. cerevisiae genome version S288C_R64-1-1; C. glabrata genome version s02-m02-r09) using Bowtie  with the option ‘-m 1 --best –strata’  (SCR_006646) , version R64-2-1; gene features for C. glabrata were downloaded from the Candida Genome Database , version s02-m07-r04. The count matrix was imported into R for downstream analyses.The raw reads were mapped to the corresponding species reference genomes   and the _006646) , with thSCR_012802) package to calculate the normalization factors for scaling the raw library sizes  method in the EdgeR  package in R . The raw p-values were then pooled across all genes and all pairwise comparisons, and a Benjamini-Hochberg procedure was used to control the false discovery rate at 0.05, with the function decideTests in LIMMA. The advantage of this approach is that the raw p-value cutoff is consistent across all 16 comparisons.To identify genes induced by each Pho4 ortholog in the age in R . The nore robust . After oThe estimated fold changes were used to produce C. glabrata, we applied the mutant cycle analysis method    greater than three over the mock sample (380 peaks with FE > 4 and 1565 peaks with FE > 2). De novo motif discovery using RSAT (RRID:SCR_008560) Peak-motifs algorithm (SCR_006893)   to autom_005339)  – for Cg_005339)  in both _013291)  to call lgorithm  identifilgorithm . Nor did_006893) . In subsSCR_000259)  and loaded into MochiView, along with the coordinates of the GEM identified ChIP peaks. We also imported previously published datasets for ScPho4 occupancy in S. cerevisiae with or without ScPho2 in no Pi conditions  . We calcnditions . We thenS. cerevisiae  and GSM730527 (Pho4_ChIP_dPho2_NoPi), and scaled the raw library sizes so that the average coverage per base pair is 1. Then these were analyzed along with our CgPho4 ChIP enrichment in pho80∆. For each of the ScPho4 (74) and CgPho4 (115) ChIP peaks, we recorded the maximum value of the normalized read counts for ScPho4 and CgPho4, with or without Pho2, and calculated the ratio between the two. Then the -log2 transformed values were plotted  tool (To investigate the nucleosome and Cbf1 occupancy in le S2 of . We firs81) tool . We then81) tool , and extS. cerevisiae and CgPho4 induced genes in C. glabrata, we assigned each ChIP peak to the nearest gene(s) on both sides, provided that the ChIP peak is upstream (in the 5’ UTR) of the gene, and queried the gene induction results from the RNA-seq experiments. ChIP binding peaks associated with Pho4-dependent gene induction are identified as the direct targets of ScPho4 or CgPho4. The identified target genes are presented in the source data accompanying To identify ScPho4 and CgPho4 induced genes in C. glabrata was based largely on information of the orthologs in the well-annotated S. cerevisiae and C. albicans genomes. Gene Ontology enrichment analysis was performed using the ‘GO term finder’ tool, and mapping genes to GO Slim terms performed using the ‘GO Slim mapper’, both available on the CGD website (http://www.candidagenome.org/). All genomic features were used as the background set. For the enrichment analysis, a threshold of p<0.1 was used for selecting significant GO terms. We focused on the ‘Biological Processes’ to identify the functional groups as reported in the results.Annotation of putative Pho4 targets in  In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by Naama Barkai as the Senior Editor and Reviewing Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Evolution of Reduced Co-Activator Dependence Led to Target Expansion of a Starvation Response Pathway" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.The paper presents a nice set of experiments describing functional divergence of a specific transcription factors activity (Pho4) is budding yeast, and show that this divergence results from differential dependence on its co-activator. Both reviewers appreciated the topic and your study. However, they also noted that, as written, the study appears very specific and is unlikely to appeal to a broad audience.This can be addressed through modified writing, which will introduce, motivate and explain your study in a broader context, perhaps including some of the suggestions given by reviewer #2.In addition, please address all other comments by the reviewers, as described below.Reviewer #1:Summary:S. cerevisiae background. They use traditional assays  as well as genomic approaches such as ChIP-seq and RNA-seq. Overall, the experimental approach seems solid and the claims and conclusion in the paper are generally very well supported by the data. Moreover, the statistical analysis of the genomics data seems solid as well.In this paper, He, Zhou and O'Shea investigate the evolution of Pho-pathway regulation by comparing Pho4 transcription factors from several different yeast species in the eLife with a wide readership. That is, it would be great if the authors could better explain what general implications losing the Pho2-dependence has and what readers, who do not work on phosphate, can learn from this?The main conclusion is that losing a dependence on the transcription factor Pho2 allowed the transcription factor Pho4 to activate more genes and possibly also other stress response pathways in addition to the phosphate starvation pathway and this seems well supported by the data. However, although technically sound the authors could do more to make observations of general interest which is appropriate for a journal such as Reviewer #2:Saccharomyces cerevisiae, the activity of Pho4 is dependent on its co-activator Pho2. In the Candida glabrataclade, this dependency appears to be lost and Pho4 has an expended set of target genes, including genes that are not directly related to phosphate starvation. The authors conclude that the loss of dependency has allowed for the gain of new target genes by this transcriptional regulator. The study is well performed and the evidence is based on several types of experiments that support the underlying model, including the comparison of several species in parallel. The manuscript is well written and the flow is smooth and clear.The authors examine the role of the transcription factors Pho4 and Pho2 in the yeast phosphate response pathways. In the model eLife. Although the title is broad and suggests that the authors study a general phenomenon, the Abstract and the article are narrowed down on S. cerevisiae and the specific PHO pathway. Unless one is interested in the biology of yeast or of this pathway, the interest for the findings could be limited because it may be difficult to see how this relates to larger questions. I do not question the fact that the findings have broader impacts (and exciting) but I could appreciate the broader impact because of my scientific background, not because of the way the paper is presented. I believe this can be largely easily answered by first stating what we know about the evolution of regulatory pathways, what we do not know and why we need to address this lack of knowledge. Then, present the evolution of this pathway across yeast species as an opportunity to address it. For instance, how transcriptional regulators gain new target genes is a very important question, particularly in the view of the constraints that the authors mention in the Discussion . Maybe the impact of the paper would be broadened if these questions were addressed in the Introduction. For instance, combinatorial TF regulation may actually allow for rapid rewiring of transcription programs by changing TF-TF interactions rather than changing many binding sites or the binding specificity of specific TFs.My major concern is the lack of larger perspective on evolution or cell biology that would make the paper appealing to the larger audience of Other comments:Several experiments are performed in genetic backgrounds in which PHO80 is deleted but I could not find the justification for this nor if this had an impact on the results and their interpretation.C. glabrata CgPho4 binds to DNA and activates gene expression largely independent of Pho2”, first paragraph: It could be useful to discuss the fact that the dependency does not need to be qualitative but could be quantitative. Regulatory networks are often described in textbooks in qualitative terms, i.e. this regulates that, but we know, and as shown here, that these relationships are not on or off but gradual.Subsection “In C. glabrata may function beyond phosphate homeostasis”: A great addition to the novelty of the paper and its impact on evolution would be to see whether the genes that are specifically regulated by Pho4 in the C. glabrataclade were "ready" to be Pho4 regulated. For instance, if these genes had already Pho4 binding sites before the loss of Pho2 dependency, this would show that the recruitment of new genes in a circuit proceeds first by the gain of TF binding sites and then by changes in trans-regulators. The authors discuss this possibility but it seems that a simple reconstruction of the history of the TF binding sites through bioinformatics analysis could be enough to test this model, at least partly.Subsection “Pho4 direct targets in S. cerevisiae – was this interaction lost in C. glabrata? This is not required for the paper to be complete but would be a great addition.Finally, it would be interesting to know how the dependency of TF evolves, is it by gain and losses of protein-protein interactions among TFs? It is known for instance that Pho4 and Pho2 interact with each other in  […] Reviewer #1:[…] The main conclusion is that losing a dependence on the transcription factor Pho2 allowed the transcription factor Pho4 to activate more genes and possibly also other stress response pathways in addition to the phosphate starvation pathway and this seems well supported by the data. However, although technically sound the authors could do more to make observations of general interest which is appropriate for a journal such as eLife with a wide readership. That is, it would be great if the authors could better explain what general implications losing the Pho2-dependence has and what readers, who do not work on phosphate, can learn from this?Reviewer #2:[…] The manuscript is well written and the flow is smooth and clear.My major concern is the lack of larger perspective on evolution or cell biology that would make the paper appealing to the larger audience of eLife. Although the title is broad and suggests that the authors study a general phenomenon, the Abstract and the article are narrowed down on S. cerevisiae and the specific PHO pathway. Unless one is interested in the biology of yeast or of this pathway, the interest for the findings could be limited because it may be difficult to see how this relates to larger questions. I do not question the fact that the findings have broader impacts (and exciting) but I could appreciate the broader impact because of my scientific background, not because of the way the paper is presented. I believe this can be largely easily answered by first stating what we know about the evolution of regulatory pathways, what we do not know and why we need to address this lack of knowledge. Then, present the evolution of this pathway across yeast species as an opportunity to address it. For instance, how transcriptional regulators gain new target genes is a very important question, particularly in the view of the constraints that the authors mention in the Discussion . Maybe the impact of the paper would be broadened if these questions were addressed in the Introduction. For instance, combinatorial TF regulation may actually allow for rapid rewiring of transcription programs by changing TF-TF interactions rather than changing many binding sites or the binding specificity of specific TFs.We agree that placing our work in the broad context of regulatory evolution, and discussing the implications to other systems will greatly improve the appeal and impact of our work. To achieve this, we modified the Abstract and the beginning paragraph of the Introduction to place our work in the backdrop of gene regulatory evolution in general. After framing the importance of the problem and challenges, we focus on the central theme of our work – combinatorial regulation. We explained two shortcomings in the literature: few studies have analyzed evolutionary changes in combinatorial regulation and its effects on network function; and a strong bias in previous studies towards developmental networks. Our work targets these gaps by dissecting a known regulatory divergence involving combinatorial control in the phosphate starvation (PHO) response network in yeast. As starvation and stress response networks have very different architectures compared to developmental networks, our study will help identify shared and distinct evolutionary patterns across these two network types. We also significantly modified the first section of Discussion – instead of focusing on the PHO network itself, we compared our findings to existing knowledge of gene regulatory network evolution, pointing out similarities and differences from other types of GRNs. In particular, we focused on two topics – constraint and plasticity in GRN evolution – where we proposed that evolution of co-activator dependence provides a mechanism for rapid gain or loss of network targets compared to individual promoter evolution.We hope that these revisions have improved our manuscript, helping it to reach a broad audience and resonate with researchers studying regulatory evolution in other systems.Other comments:Several experiments are performed in genetic backgrounds in which PHO80 is deleted but I could not find the justification for this nor if this had an impact on the results and their interpretation.PHO80 encodes the cyclin component of the cyclin-dependent-kinase (CDK) complex Pho80-Pho85, which negatively regulates Pho4 nuclear localization through phosphorylation . pho80∆ thus genetically mimics the effect of phosphate starvation by causing Pho4 to be dephosphorylated and constitutively localized inside the nucleus in its functional form (Huang and O’Shea 2005).pho80∆ instead of phosphate starvation serves two purposes in our study. First, for the phosphatase assay , it allowed us to decouple the activity of Pho4 orthologs from the viability of the strain carrying them. This means we can measure Pho4 ortholog activity even if the ortholog cannot activate the PHO response and therefore does not support growth on phosphate-limited medium. Second, because the central goal of our study is to dissect the evolution of Pho4 activity and how it influences network output, pho80∆ allowed us to focus on the downstream targets of Pho4, ignoring starvation-induced but Pho4-independent genes.The use of pho80∆ in place of starvation, we described the role of phosphorylation in regulating Pho4 activity in the Introduction, added two sentences at the end of the first paragraph in the Results section, and explained the reason for using pho80∆ in the second Results section.To help explain the use of pho80∆ background we can recapitulate the genes induced by ScPho4 under phosphate starvation . We conclude that Pho4 targets identified in pho80∆ closely recapitulate those identified under phosphate starvation.We also verified that in the tion see . Four ofSubsection “In C. glabrata CgPho4 binds to DNA and activates gene expression largely independent of Pho2”, first paragraph: It could be useful to discuss the fact that the dependency does not need to be qualitative but could be quantitative. Regulatory networks are often described in textbooks in qualitative terms, i.e. this regulates that, but we know, and as shown here, that these relationships are not on or off but gradual.glabrataclade species . Treated as a quantitative trait, the extent of Pho2-dependence strongly correlates (negatively) with the number of genes induced in the S. cerevisiae background . To emphasize the quantitative nature of the trait, we added a sentence at the end of the first Results section – “Notably, the extent of the reduction varies between the glabrataclade Pho4 orthologs, suggesting that the strength of combinatorial regulation is a quantitative trait that can be fine-tuned by mutations during evolution.” We also mentioned this in the Discussion section titled “Evolutionary Constraint and Plasticity in the PHO response network” – “However, it should be pointed out that the reduction in Pho2-dependence likely evolved gradually , which means cis evolution could have accompanied the trans – while Pho4 expands its targets, promoter evolution could result in fixation of beneficial targets while removing spurious, non-beneficial ones.”We fully agree with the reviewer on this. In fact, our phylogenetic survey using the quantitative phosphatase assay revealed substantial variation in the extent of Pho2 dependence among Pho4 orthologs from the Subsection “Pho4 direct targets in C. glabrata may function beyond phosphate homeostasis”: A great addition to the novelty of the paper and its impact on evolution would be to see whether the genes that are specifically regulated by Pho4 in the C. glabrata clade were "ready" to be Pho4 regulated. For instance, if these genes had already Pho4 binding sites before the loss of Pho2 dependency, this would show that the recruitment of new genes in a circuit proceeds first by the gain of TF binding sites and then by changes in trans-regulators. The authors discuss this possibility but it seems that a simple reconstruction of the history of the TF binding sites through bioinformatics analysis could be enough to test this model, at least partly.S. cerevisiae, and looked for gene orthology relationships. We found that the only CgPho4 targets that are orthologous with one another in the two species backgrounds were those involved in phosphate homeostasis, e.g. PHO84.This is an intriguing question. To rephrase it, the question is “Does CgPho4 gain its new targets by exploiting cryptic DNA motifs not accessible to the ancestral Pho4?” We have considered this question and performed an analysis similar to what reviewer #2 suggested. Specifically, we took CgPho4 direct targets identified both in its own genome and in S. cerevisiae genes that are orthologs of CgPho4 targets in C. glabrata and still retain the ability to be regulated by CgPho4? We think this is most likely because short DNA sequence motifs are rapidly gained and lost during evolution. Cryptic motifs – those that are inaccessible to the TF – are especially vulnerable to such a turnover process due to lack of purifying selection. As a result, the ancestral cryptic Pho4 motifs that were exploited by CgPho4 are almost certainly lost in the modern S. cerevisiae genome, explaining our observation of no overlap in this case.Does this mean all of CgPho4’s new targets in its own genome were de novo gains through promoter evolution? We think not. Then why can’t we identify S. cerevisiae as well as in C. glabrata, there is virtually no overlap between its targets in S. cerevisiae and in C. glabrata, except for those involved in phosphate homeostasis .”In the text, we included a section at the end of the Results section to explain the logic above. “It is worth mentioning that, although CgPho4 induces more genes in Finally, it would be interesting to know how the dependency of TF evolves, is it by gain and losses of protein-protein interactions among TFs? It is known for instance that Pho4 and Pho2 interact with each other in S. cerevisiae – was this interaction lost in C. glabrata? This is not required for the paper to be complete but would be a great addition.We agree that this an outstanding question, and we are actively planning on resolving it. At the moment, although the detailed molecular mechanism of how CgPho4 became less Pho2-dependent is not clear, both our transcriptional and binding assay results are consistent with the hypothesis that CgPho4 evolved to be stronger in DNA-binding and in inducing gene expression, making it less dependent on Pho2 .Regarding the interaction between CgPho4 and CgPho2, it is clearly not lost – our ChIP-exo data showed co-localization of CgPho2 to the majority of CgPho4 bound sites, and that in a small fraction of CgPho4 bound sites, its binding is weakened or lost when CgPho2 is deleted. In fact, loss or reduction of Pho4-Pho2 interaction is not likely to be behind the observed reduced dependence, because a mutant of ScPho4 unable to interact with ScPho2 has significantly decreased, rather than enhanced activity .In the Discussion section titled “Evolutionary Constraint and Plasticity in the PHO response network”, we added a sentence explaining our favored hypothesis – “While the mechanism for this reduction in co-activator dependence is not clear, both transcriptional and binding assays are consistent with the hypothesis that CgPho4 evolved to be stronger in DNA-binding and in inducing gene expression .”


Tracking dynamic protein–chromatin interactions in vivo is key to unravel transcriptional and epigenetic transitions in development and disease. However, limited availability and heterogeneous tissue composition of in vivo source material impose challenges on many experimental approaches.Drosophila imaginal discs and make FLP/FRT-based induction accessible to GAL driver-mediated targeting of specific cell lineages. In a proof-of-principle approach, we utilize ubiquitous DamID expression to describe dynamic transitions of Polycomb-binding sites during wing imaginal disc development and in a scrib tumorigenesis model. We identify Atf3 and Ets21C as novel Polycomb target genes involved in scrib tumorigenesis and suggest that target gene regulation by Atf3 and AP-1 transcription factors, as well as modulation of insulator function, plays crucial roles in dynamic Polycomb-binding at target sites. We establish these findings by DamID-seq analysis of wing imaginal disc samples derived from 10 larvae.Here we adapt cell-type-specific DamID-seq profiling for use in Our study opens avenues for robust profiling of small cell population in imaginal discs in vivo and provides insights into epigenetic changes underlying transcriptional responses to tumorigenic transformation.The online version of this article (10.1186/s13072-018-0196-y) contains supplementary material, which is available to authorized users. Drosophila has contributed tremendously to our understanding of chromatin dynamics during developmental transitions, stem cell differentiation and also tumorigenesis. Yet, like other in vivo model systems, the small size and the heterogeneous fate composition of Drosophila tissues still pose challenges to the detailed tracking of DNA binding sites in different cell populations and lineages in vivo.Understanding the in vivo dynamics of DNA binding by chromatin regulatory proteins is key to elucidate the molecular basis of cell behaviours ranging from differentiation to adaptation and plasticity. The model system Drosophila cell populations from tissues , tubGAL80ts/TM6cen-GAL4, UAS-GFP; tub-GAL80tsscrib1;hsflp122;UAS-FLP(JD2);UAS-FLP(EXEL)(3)hsflp), was induced by a 1-h temperature shift to 37 °C in a water bath. To analyse DamID profiles of young WIDs, a heat shock was performed at 3 days after egg lay (AEL). To analyse DamID profiles of late WIDs, a heat shock was performed at 5 days AEL. To account for the developmental delay characteristic of scrib1 homozygous animals, scrib1 larvae were heat-shocked at 6 days AEL. Afterwards, larvae were kept at 21 °C to maintain a low basal activity of the Hsp70 heat-shock promoter driving expression of Dam and Dam-Pc transcripts. Wing imaginal discs were dissected 48 h after induction of hsflp. Genomic excision of the STOP cassette from DamID constructs as a result of FLP activity was tested with regular PCR protocols on gDNA extracted from WIDs (see below) using the primers hhsp-int (actgcaactactgaaatctgc) and Dam-r (cgctattgatatcggcaagg).Development of embryos was synchronized by an 8-h egg collection on standard fly food at 21 °C. FLP expression, which was controlled by a heat-shock promoter  with proteinase K  and incubated for 4 h at 55 °C. Phenol–chloroform purification and RNase A (QIAGEN) digestion were followed up by a standard ethanol precipitation to obtain pure DNA. Each sample was subsequently run on 1% agarose gel to confirm DNA integrity and to estimate DNA concentrations. DNA from control and experimental samples was isolated at the same time and processed in parallel.Ten Isolation of genomic DNA (gDNA) from WIDs is described above. For each condition and stage, two independent biological samples were processed and analysed as described in  with minR (v. 3.4.0) (https://www.r-project.org/) and bedtools (v. 2.26.0) software (http://bedtools.readthedocs.io/en/latest/#). Analysis for enriched regulatory elements was performed using i-cisTarget (https://gbiomed.kuleuven.be/apps/lcb/i-cisTarget/index.php) [Bioinformatic analysis was performed using dex.php) .DamID-seq fastq files were processed as described previously [5] with the following two modifications. The mapping of reads onto GATC fragments by the software ‘HTSeq-count’ was performed with a higher stringency criterion (by using the ‘intersection_strict’ instead of ‘union’ overlap resolution mode). GATC fragments showing highly discordant values between replicates were excluded from the analysis as described .enriched’, ‘intermediate’ and ‘depleted’ Pc-binding states for each GATC fragment, as described previously  were identified based on Dam-normalized log2-transformed DamID-seq profiles by fitting a three-state hidden Markov model (HMM) to define ‘’ states . Thus, wenriched’, ‘intermediate’ and ‘depleted’). The R code of adapted BioHMM algorithm is provided as Additional file scrib’ datasets, as well as between ‘early’ and ‘late’ development in WT, to assess the dynamics of Polycomb-binding between two samples. To maintain the directionality of differences, the result of this comparison was reported as either ‘gain’, ‘loss’ or ‘no change’ for each GATC fragment between ‘enriched’, ‘intermediate’ and ‘depleted’ HMM states.As the lengths of the genomic GATC fragments (bins) are not of equal size, we used the BioHMM algorithm, a heterogeneous HMM, which takes into account the distance between adjacent bins . This alp val< 0.05) and subsequently divided in upregulated and downregulated expression according to the change in transcript levels. Differential gene expression was provided as log2 of the fold change between WT, scrib1 and Psc/Su(z)2XL26 datasets. ChIP-chip datasets were downloaded from the modENCODE repository (http://www.modencode.org/): Pc in S2 cells (ID 3791), Pc in DmBG3 cells (ID 325), Pc in embryo (ID 3957). Sequence overlap of microarray probe sequences in ChIP-chip datasets and Dam-normalized GATC fragments in DamID-Seq datasets was analysed using bedtool intersect function. Pearson’s correlation between DamID-seq and ChIP-chip data was calculated by correlating the intensity of Dam-normalized Pc-binding at each GATC fragment in either WT or scrib1 datasets to the intensity of Pc-binding at the corresponding microarray probe for the respective Pc ChIP-chip analysis from S2 cells, DmBG3 cells or embryo.RNA-seq datasets were obtained from . Genes wscrib1 were defined as genomic regions spanning 2.5 kb upstream to 1.5 kb downstream of the transcriptional start sites (TSS) of the selected genes. Briefly, the coordinates of the regulatory regions were calculated from the TSS coordinates and the strand on which the TSS mapped on. This information was acquired from Flybase  (genome annotation dm6) using the FB.ID of all differentially expressed genes. Subsequently, genome coordinates of GATC fragments were converted into the appropriate genome annotation  and mapped to the regulatory regions using the intersect function in bedtools2 (no limitations were considered on the amount of overlap between the two coordinates’ sets). Only GATC fragments that overlapped with selected regulatory regions were defined as transcription-associated GATC fragments (taGATCf) and used for the comparative analysis of DamID-seq and RNA-seq data in wild-type and scrib1 WIDs . The entire regulatory region was subsequently classified as gain in Pc-binding, if one or more taGATCf within this region ‘gained’ Pc-binding and other taGATC fragments displayed ‘no change’. Conversely, a regulatory region was classified as loss in Pc-binding, if one or more taGATCf within this region ‘lost’ Pc-binding and other taGATC fragments displayed ‘no change’. Finally, regulatory regions which contained a mix of taGATCf with both gain and loss HHM states were classified as mixed ; group II ; group III ; and group IV .As a result, the described method subdivides regulatory regions into the following four groups: i-cisTarget analysis adhering to an enrichment score threshold = 2 and rank threshold = 10,000. We defined significantly enriched motifs by setting the normalized enrichment scores (NES) > 3. For factors with multiple enriched motifs, we selected only the one with the highest NES. The following features (Databases 3.0 of i-cisTarget) were selected during the analysis: PWMs, TF binding sites, non-TF binding sites, histone modifications. These parameters were common to all icis-Target analysis.We performed our gain or loss of Pc-binding in scrib1 by transitioning in and out of ‘enriched’ HMM states, and no change in Pc-binding in scrib1 by staying ‘enriched’ (excluding ‘depleted’ and ‘intermediate’ HMM states).Figure ‘enriched’ in WT Early profiles after the three-state HMM analysis  and then subdivided into their respective group . The icis-Target analysis was performed on groups II and III independently . Washing steps were performed in 0.1% Triton X-100/PBS (PBT). The following antibodies were incubated overnight at 4 °C: rabbit α-Dcp-1 , mouse α-H3S10p , mouse α-Myc . Secondary antibodies (Molecular Probes), DAPI and phalloidin-TRITC (Sigma) were incubated at room temperature for 2 h. Experimental and control samples were processed together and imaged on the same microscope (Leica TCS SP-5).To detect the Myc-tagged Dam proteins, expression of Adult flies were collected 12 h after eclosion and stored in 2-propanol. Wings were dissected and mounted in Euparal (Sigma) on regular slides for microscopy. Imaging was done using a stereoscopic zoom microscope .Additional file 1. Supplemental figures S1–S5.Additional file 2: SF2. WIG file containing the results of the three-state HMM analysis of wild type wing imaginal discs in late third instar stage for all GATC fragment mapped on dm6 genome annotation. The following values were attributed to the three states: enriched = 1, intermediate = 0, depleted = −1.Additional file 3: SF3. WIG file containing the results of the three-state HMM analysis of scrib1 wing imaginal discs for all GATC fragment mapped on dm6 genome annotation. The following values were attributed to the three states: enriched = 1, intermediate = 0, depleted = −1.Additional file 4: Table S1. Genes in Group I-IV; List of genes with number of GATC fragments within the presumptive regulatory region ) displaying transitions between Pc-binding states  in scrib1 if compared to WT profiles, and changes in gene expression levels of the respective gene in scrib1 to whose TSS the taGATC fragments had been mapped to. Group I ; group II ; group III ; group IV ; m1 ; m2 .Additional file 5: Table S2 i-cisTarget Analysis of group II and III genes; List of regulatory elements identified by i-cisTarget that either represent transcription and chromatin-binding factors or specific histone modifications enriched within the presumptive regulatory region ) of genes belonging to group II ; group III .Additional file 6: SF4 WIG file containing the results of the three-state HMM analysis of wild type wing imaginal discs in early third instar stage for all GATC fragment mapped on dm6 genome annotation. The following values were attributed to the three states: enriched = 1, intermediate = 0, depleted = −1.Additional file 7: SF1 Script developed by A. Ivankin used to perform the three-state HMM analysis based on the previously published BioHMM algorithm .Additional file 8. Pc binding intensities (log2), normalized to Dam-binding and averaged between two replicates, and the corresponding results from the three-state HMM analysis for all three biological samples  mapped on dm3 genome annotation.Additional file 9. Not normalised read counts per GATC fragment for all sequenced samples mapped on dm3 genome annotation.


Probing dynamic processes occurring within the cell nucleus at the quantitative level has long been a challenge in mammalian biology. Advances in bio-imaging techniques over the past decade have enabled us to directly visualize nuclear processes in situ with unprecedented spatial and temporal resolution and single-molecule sensitivity. Here, using transcription as our primary focus, we survey recent imaging studies that specifically emphasize the quantitative understanding of nuclear dynamics in both time and space. These analyses not only inform on previously hidden physical parameters and mechanistic details, but also reveal a hierarchical organizational landscape for coordinating a wide range of transcriptional processes shared by mammalian systems of varying complexity, from single cells to whole embryos. The nucleus is an organelle of central importance to the eukaryotic cell, in which the information encoded in the cell’s genome is stored, organized, expressed, duplicated, and maintained. Each of these processes is highly regulated, often in an interconnected fashion. While we now have a relatively thorough understanding of the molecular machineries and mechanisms driving these processes, our knowledge of how they are organized spatially inside the nucleus remains inadequate. Such a question is particularly pertinent in light of the fact that all of these processes co-exist in the extremely crowded nuclear space, thus suggesting that some degree of functional compartmentalization is essential , 2. MoreAmong the imaging techniques currently available, the most widely used as well as the most direct method is perhaps single-molecule tracking (SMT), which relies on the ability to detect the signal of individual biomolecules labeled with either fluorescent proteins or organic dyes , 5. WhilAnother powerful approach is fluorescence correlation spectroscopy (FCS), which consists of a compendium of related techniques –27 basedA third group of imaging approaches consists of photobleaching-based techniques such as fluorescence recovery after photobleaching (FRAP), fluorescence loss in photobleaching (FLIP), and fluorescence localization after photobleaching (FLAP) –39. WhilEach of these techniques has its respective pros and cons. SMT has the advantage of allowing dynamics to be visualized directly without the need for additional calibrations and corrections commonly associated with the other techniques ; howeverIn this review, we survey some of the key recent studies on imaging mammalian nuclear dynamics in both time and space using these emerging approaches as well as some of the more conventional techniques . To that end, we focus on processes related to transcription, not only because of the tremendous progress made in recent years in understanding its quantitative dynamics in living systems –54, but Eukaryotic transcription is regulated first and foremost through the binding and unbinding of a variety of transcription factors (TFs), as well as their interactions with components of the transcriptional machinery, chief among them RNA polymerases, during transcription initiation. Mammalian TFs have long been known to interact with DNA in a highly dynamic manner . ConcurrA variety of TF dynamics have so far been investigated, including their interactions with genomic DNA, co-activators, and heterodimeric binding partners. Specifically, distinct modes of TF–DNA binding have been resolved, and diffusion constants, on/off rates, DNA residence times, and bound and free fractions have been measured for a diverse range of TFs, including, for example, the glucocorticoid receptor (GR) , 57–62, Using the dynamic parameters uncovered, models for how mammalian TFs search for their specific targets within the nuclear space have been constructed. In consensus with the target search mechanism in bacteria , mammaliWhile the above approaches have been successful mainly in cultured cell systems, much less work has been done in more in vivo contexts. Recently, the nuclear dynamics of two TFs critical for early mammalian development, Oct4 and Sox2, were probed inside intact mouse embryos , 66, 80.Aside from TF binding, the process of transcription itself is also temporally heterogeneous, manifested primarily in the form of “transcriptional bursts” (the production of nascent mRNAs in a pulsatile fashion), in which each pulse consists of a burst of transcript molecules and is separated from the next by a period of inactivity Fig. . Such PoThe dynamics of transcriptional bursts are often gene-specific, and can be regulated at many levels. Single-cell luminescence measurements on a diverse range of mammalian genes have revealed distinct temporal patterns of mRNA synthesis, manifested in both the on/off rates of transcription and the mean numbers of mRNA molecules produced per burst (or burst size) . In the The physiological consequences of transcriptional bursts are profound. They are known to cause cell-to-cell variability in gene expression (“noise”), which could in turn generate opportunities for an otherwise isogenic population of cells to explore different phenotypes or lineage fates , 92. ForSimilar to their temporal heterogeneity, transcriptional processes are also unevenly distributed in the nuclear space, and such spatial organization plays an important role in their regulation. A prime example is transcription mediated by RNA polymerase II (RNAP II). Based on earlier observations that nascent mRNA transcripts tend to localize to discrete foci inside the nucleus, the hypothesis of “transcription factories” Fig. , nuclearRNAP II is by no means the only molecule whose intranuclear localization is regulated. Sox2 binding sites in ES cells, for example, also form 3D clusters in nuclear regions that are segregated from heterochromatin but enriched in RNAP II. Such heterogeneous distribution impacts Sox2’s target search strategy by reducing the global search efficiency while allowing for flexible local adjustments . Along tAnother mode of spatial organization of TF activity takes place in the form of nucleo-cytoplasmic translocation, as exemplified by NF-κB, a key nuclear regulator of cellular stress responses. Upon activation, NF-κB exhibits periodic oscillations of nuclear import from the cytoplasm Fig. , which iSimilar translocation phenomena have been observed for other TFs as well. Oct4, for example, undergoes facilitated nucleo-cytoplasmic transport in early developing embryos. However, fluorescence decay after photoactivation (FDAP) measurements revealed two sub-populations of cells within the embryo that exhibit distinct rates of nuclear export and import as well as an immobile fraction of Oct4; cells with slower Oct4 kinetics are more likely to give rise to the pluripotent cell lineage later on, while those with faster kinetics contribute mostly to the extra-embryonic lineage . For theSpatial organization is also not restricted only to the protein molecules involved in transcription; the structure and positioning of chromatin DNA plays an equally important role in transcriptional regulation , 108. InIn addition to being organized in the temporal and spatial realms, transcriptional dynamics can also be regulated through changes in the concentration of TFs. The tumor suppressor p53, for instance, responds to stress such as DNA damage with oscillatory pulses in its concentration , 116. BaThe oligomerization state of a TF is another factor that may impact its mobility and function. For example, FCS measurements of p53 in single cells reveal that the TF exists in a mixture of oligomeric states; their rapid homo-tetramerization upon DNA damage activates the transcriptional targets of p53 . SimilarLastly, the interplay between transcriptional dynamics and epigenetic modifications are also beginning to be probed at the quantitative level. Recently, it has been found in live cells that histone H3 lysine-27 acetylation could both facilitate the target search of transcriptional activators and promote the transition of RNAP II from the initiation form to the elongation form . H3 argiWe are now at the exciting threshold where advances in quantitative single-cell imaging have empowered us with the means to explore the spatio-temporal dynamics of transcription in unprecedented detail. By generating spatial maps and temporal trajectories at high resolutions in diverse living systems, these approaches not only furnish or revise previous biochemical models with physical parameters and insights, but also shed new light on the multi-faceted landscape for organizing transcription that is shared by mammalian systems ranging from single cells to developing embryos.The transcription-related processes reviewed here constitute only a small fraction of the functions carried out in the mammalian cell nucleus; other equally important and complex processes, such as RNA processing and export, chromatin organization and remodeling, and genome replication and maintenance, are all heavily regulated in both time and space through mechanisms just as versatile as those outlined above. To probe these processes both on their own and in cooperation with each other requires the continuous pushing of the resolution and sensitivity limits of imaging techniques, combined with novel approaches to monitor a large number of molecular species in the nucleus simultaneously. To that end, the recently developed methods capable of highly multiplexed imaging of either protein, DNA, or RNA species offer exciting hopes –129. The


IRF8 and found that IRF8 depletion dramatically inhibits the reactivation of EBV upon lytic induction. We demonstrated that IRF8 depletion suppresses the expression of a group of genes involved in apoptosis and thus inhibits apoptosis induction upon lytic induction by B cell receptor (BCR) stimulation or chemical induction. The protein levels of caspase-1, caspase-3 and caspase-8 all dramatically decreased in IRF8-depleted cells, which led to reduced caspase activation and the stabilization of KAP1, PAX5 and DNMT3A upon BCR stimulation. Interestingly, caspase inhibition blocked the degradation of KAP1, PAX5 and DNMT3A, suppressed EBV lytic gene expression and viral DNA replication upon lytic induction, suggesting that the reduced caspase expression in IRF8-depleted cells contributes to the suppression of EBV lytic replication. We further demonstrated that IRF8 directly regulates CASP1 (caspase-1) gene expression through targeting its gene promoter and knockdown of caspase-1 abrogates EBV reactivation upon lytic induction, partially through the stabilization of KAP1. Together our study suggested that, by modulating the activation of caspases and the subsequent cleavage of KAP1 upon lytic induction, IRF8 plays a critical role in EBV lytic reactivation.Interferon regulatory factor 8 (IRF8), also known as interferon consensus sequence-binding protein (ICSBP), is a transcription factor of the IRF family. IRF8 plays a key role in normal B cell differentiation, a cellular process that is intrinsically associated with Epstein-Barr virus (EBV) reactivation. However, whether IRF8 regulates EBV lytic replication remains unknown. In this study, we utilized a CRISPR/Cas9 genomic editing approach to deplete  Caspase-1 depletion blocks EBV reactivation while KAP1 depletion facilitates reactivation in caspase-1 depleted cells. These results together establish a IRF8/caspase-1/KAP1 axis important for EBV reactivation.Infection with Epstein-Barr virus (EBV) is closely associated with human cancers of both B cell and epithelial cell origin. The EBV life cycle is tightly regulated by both viral and cellular factors. Here, we demonstrate that interferon regulatory factor 8 (IRF8) is required for EBV lytic replication. Mechanistically, IRF8 directly regulates caspase-1 expression and hence caspase activation upon B cell receptor (BCR) stimulation and chemical induction, which leads to the cleavage and de-stabilization of several host factors suppressing lytic replication, including KAP1.  ZTA and RTA gene expression and to affect ZTA/RTA transcriptional activity  showing IRF8/IRF1 binding to CASP1 promoter. ChIP by a nonspecific IgG was include as negative controls. B. The pGL2-CASP1p-1-Luc constructs were co-transfected into Akata (EBV+) cells with either 10 ug of IRF8, IRF1 or IRF8-K108E expression vectors. Luciferase assays were performed 36 hrs post-transfection. The value of cells transfected with an empty vector was set as 1. The results were presented as mean ± standard deviation of triplicate assays. ** p<0.01, *** p<0.001.A. ChIP-PCR analysis using three EBV-positive cells [Akata (EBV(TIF)Click here for additional data file.S6 FigRPKM, Reads Per Kilobase of transcript per Million mapped reads.(TIF)Click here for additional data file.S7 FigCASP1-depleted cell lines showing that 8 out of 13 clones for CASP1-sg1 and 12 out of 14 clones for CASP1-sg2 contain frame shifts. The PAM sequences were highlighted by red and the guide RNA sequences were shown in bold. WT: wild-type; “+” or “-” followed by numbers indicates the number of base pair inserted or deleted; “S” followed by numbers indicates the number of site mutations; “×” followed by numbers indicates the number of clones obtained in the sequencing.The sequencing of (TIF)Click here for additional data file.S8 FigCASP8 mRNA level was slightly increased by CASP1 depletion. The value was normalized by qPCR using specific primers to β-actin. Data are presented as means ± standard deviations of triplicate assays.qPCR analysis showing that (TIF)Click here for additional data file.