CHA1. To assess the generality of the observed function of Clp1 in transcription, we tested the effect of Clp1 on transcription on a genomewide scale using the Global Run-On-Seq (GRO-Seq) approach. GRO-Seq analysis showed the polymerase reading through the termination signal in the downstream region of highly transcribed genes in a temperature-sensitive mutant of Clp1 at elevated temperature. No such terminator readthrough was observed in the mutant at the permissive temperature. The poly(A)-independent termination of transcription of snoRNAs, however, remained unaffected in the absence of Clp1 activity. These results strongly suggest a role for Clp1 in poly(A)-coupled termination of transcription. Furthermore, the density of antisense transcribing polymerase upstream of the promoter region exhibited an increase in the absence of Clp1 activity, thus implicating Clp1 in promoter directionality. The overall conclusion of these results is that Clp1 plays a general role in poly(A)-coupled termination of RNAPII transcription and in enhancing promoter directionality in budding yeast.In budding yeast, the 3′ end processing of mRNA and the coupled termination of transcription by RNAPII requires the CF IA complex. We have earlier demonstrated a role for the Clp1 subunit of this complex in termination and promoter-associated transcription of  The complex has been implicated in cleavage-polyadenylation and coupled termination of transcription4. The termination function of the CF IA complex was demonstrated using a nuclear run-on assay that revealed the readthrough of RNAPII beyond the poly(A) site in the mutants of all four CF IA subunits5. The transcription readthrough phenotype, however, was demonstrated for just a few selected yeast genes. ChIP analysis identified that the whole CF IA complex is localized at the 3′ end of selected genes in accordance with its role in 3′ end processing and termination of transcription6. Genomewide analysis found Pcf11 crosslinked to the 3′ end of a majority of transcriptionally active yeast genes7. These observations suggests that the CF IA complex could be playing a general role in the termination of transcription in budding yeast. More direct evidence, however, was needed in support of a role for the CF IA complex in the termination of transcription on a genomewide scale and to firmly establish it as a general termination factor.The CF IA 3′ end processing complex of budding yeast is composed of four subunits: Rna14, Rna15, Pcf11 and Clp1 as shown in Fig. 9. In yeast, the 5′ end initiated antisense transcripts are rapidly degraded by the RNA surveillance machinery of the cell and are therefore referred to as ‘cryptic unstable transcripts’ (CUTs)10. In contrast, the 3′ end initiated antisense transcripts are stable and belong to the category of ‘stable unannotated transcripts’ (SUTs)8. At least 50% of SUTs in budding yeast are 3′ end initiated antisense transcripts. A number of genes in yeast and mammalian cells exhibit 3′ end initiated antisense transcription under repressed conditions. The GAL10 gene of budding yeast, for example, exhibits robust sense transcription in the presence of galactose12. Upon shifting of cells to glucose containing medium, however, sense transcription is almost completely inhibited, and the 3′ end initiated antisense transcription predominates. It has been proposed that when the 3′ end initiated antisense transcript reaches the promoter end of the gene, it may adversely affect initiation/reinitiation either through transcriptional interference or through histone modifications15. Whether 3′ end initiated antisense transcription is a general feature of termination defective mutants is not clear.A vast majority of RNAPII-transcribed genes in yeast and higher eukaryotes exhibit antisense transcription initiating from both their 5′ as well as the 3′ ends6. Furthermore, the Ssu72 subunit of the CPF complex, which is also linked to 3′ end processing and termination of transcription in budding yeast, has been localized to the promoter end of genes17. The presence of termination factors towards the 5′ end of genes is an evolutionarily conserved feature as a number of 3′ end processing/termination factors of higher eukaryotes also have been found crosslinked to the distal ends of genes18. The promoter occupancy of termination factors remained an enigma until recently when it was demonstrated that these factors provide directionality to transcription by limiting divergent antisense transcription initiating from the promoter region20. The nucleosome free region located at the 5′ end of a majority of protein coding genes in yeast and mammals contains two juxtaposed unidirectional promoters25. The transcription from these promoters initiates in both the downstream sense direction as well as the upstream antisense direction. The downstream sense transcription of the coding region produces mRNA, while the upstream antisense transcription produces upstream antisense RNA (uaRNA)19. Transcription in the upstream antisense direction is terminated within a few hundred bases from the transcription start site by the termination factors residing in the promoter region, while productive transcription in the sense direction is allowed to proceed until the polymerase reaches the 3′ end of the gene26. Thus, promoter linked termination factors provide directionality to the inherently bidirectional eukaryotic promoter region by limiting synthesis of uaRNA. It has been demonstrated that gene looping plays a crucial role in the termination of uaRNA synthesis in budding yeast10. We recently showed that the CF IA complex is involved in providing transcription directionality during transcription of selected yeast genes5. Whether the CF IA complex has a general role in conferring promoter directionality during RNAPII-mediated transcription, however, needed further investigation.Since the CF IA complex is involved in the cleavage-polyadenylation of mRNA and termination of transcription, it is expected to be present near the 3′ end of genes. It was, however, intriguing to find all four subunits of the CF IA complex occupying the 5′ end of genes as wellCHA1 gene in budding yeast5. Here we extend these studies to show that the CF IA complex has a global role in the poly(A)-dependent termination of transcription. Employing the GRO-Seq approach, we further showed that the CF IA complex also influence promoter directionality of subset of yeast genes. How the CF 1A complex affects promoter-associated transcription, however, remains to be elucidated.Using a temperature-sensitive mutant of Clp1, we recently demonstrated the role of the CF IA complex in termination of transcription as well as in the promoter directionality of the 28. In addition, the Rat1 complex is also required to accomplish the termination step. When termination is defective, RNAPII does not dissociate from the template beyond the poly(A) site, but reads through the poly(A) termination signal29. This transcription readthrough phenotype is characteristic of defective termination, and has been widely used to determine the involvement of a factor in the termination of transcription. A reliable approach used for detecting the terminator readthrough phenotype is the nuclear run-on (NRO) assay4. This assay measures the density of transcriptionally active polymerase over a specific genomic locus. The genomewide version of this assay, called ‘Global Run-On-Seq (GRO-Seq), overcomes the limitations in the NRO assay in genome coverage and the strand specificity21. In GRO-Seq, the newly synthesized transcripts incorporate BrUTP, which allow for the affinity purification of these nascent transcripts on an anti-BrUTP column. The resultant nascent RNAs are subjected to high throughput sequencing. The outcome of GRO-Seq is a snapshot of the position as well as the density of the actively engaged polymerase in a strand specific manner.A combination of biochemical and genetic approaches has identified two macromolecular complexes called CF I and CPF being essential for both 3′ end processing and poly(A)-dependent termination of transcription in budding yeast Fig. 2, 27, 28To assess the role of the CF IA subunit Clp1 in transcription on a genomewide scale, we used the GRO-Seq approach. Comparing the GRO-Seq maps of the temperature-sensitive mutant of Clp1 and the isogenic wild type strain at the permissive (25 °C) and non-permissive (37 °C) temperatures of the mutant can reveal if Clp1 is a universal termination factor like the general transcription factors, or its role in termination is restricted to a subset of genes. Accordingly, we performed GRO-Seq analysis in the Clp1 mutant and the isogenic wild type strains in the cells grown at 25 °C and 37 °C. The experiments with the Clp1 mutant were performed in triplicates named TS-1, TS-2 and TS-3. The number of transcriptionally active genes was determined by using an experimentally determined threshold of 5 reads per kilo base. We found that ~5500 of the 6693 annotated ORFs (~82%) in the Saccharomyces Genome Database (SGD) were expressed as tags above the background. The heatmap of pairwise Pearson Correlation Coefficients among all samples shows that all TS-37 samples  are clustered together, while TS-25 , WT-25 and WT-37 samples are clustered together  and GPI18 (YBR004C) genes, which exhibit the termination defect in the GRO-Seq analysis in clp1 mutant at 37 °C  in the mutant and wild type cells grown at 25 °C and 37 °C. RTI was calculated by dividing the number of reads beyond the 3′ end of a genes with the number of reads before the 3′ end within the coding region. A termination defect will result in more GRO-Seq reads downstream of the 3′ end of a genes and therefore a higher RTI value. Accordingly, RTI value increased by about 2-fold in the mutant at 37 °C compared to at 25 °C Fig. . No suchNext, we examined if Clp1 is required for poly(A)-dependent termination of transcription only, or for the poly(A)-independent termination as well. The transcription termination of the snoRNA genes occurs in a poly(A)-independent manner. Most of the genes coding for snoRNA are located in the crowded region of yeast genome. We, therefore, looked at the termination of three snoRNA genes, snR13, snR63 and snR68, which are separated from the neighboring genes by several hundred base pairs. No termination defect was observed for these three genes in the clp1 mutant at elevated temperature Fig. . These rA number of genes both in yeast and mammalian systems exhibit 3′ initiated antisense transcription upon defective termination. It has been shown that the 3′ initiated antisense transcription inhibits the recruitment of transcription machinery at the promoter region for initiation of transcription. We, therefore, examined the role of Clp1 in antisense transcription originating from the 3′ end of genes. We choose the same set of 1038 genes for this analysis whose 5′ and 3′ ends were at least 400 bp away from the neighboring gene. Alignment of GRO-Seq reads to the 3′ end of these genes revealed enhanced antisense transcription in the Clp1 mutant at 37 °C compared to at 25 °C Fig. . No 3′ eCHA1 by facilitating reinitiation and conferring promoter directionality5. We, therefore, examined if Clp1 is facilitating initiation/reinitiation of transcription on a genomewide scale. If Clp1 was facilitating initiation/reinitiation of transcription, we expected a decrease in the density of mRNA transcribing polymerase in the mutant at 37 °C. When we mapped GRO-Seq reads of the mutant and the wild type cells grown at 37 °C, to the annotated 5′ end of genes, there was no decrease in the GRO-Seq signal for sense transcribing polymerase in the mutant at elevated temperature , while remaining approximately 60% of the transcriptionally active genes analyzed in this study did not exhibit any change in promoter directionality at elevated temperature. The overall conclusion of these results is that Clp1 influences the promoter directionality of nearly 40% of transcriptionally active genes analyzed in our study. These include both highly expressed and lowly expressed genes.To compute the number of genes exhibiting a decrease in promoter directionality in terms of enhanced uaRNA transcription in clp1 mutant at elevated temperature, we calculated ratio of GRO-Seq reads in 100 bp promoter upstream region in the antisense direction for individual genes in the mutant at 37 °C and 25 °C. We computed the difference in CPM (Count Per Million) reads in the mutant at the permissive and non-permissive temperatures as described in ‘Methods’ section. Our results indicate that of around 700 transcriptionally active genes analyzed in this study, 330 genes exhibited the CPM difference greater than +1 , while only 13 genes had CPM difference less than −1 Fig. . These rin vitro conditions. In contrast, termination is the least understood step of the transcription cycle. The accessory factors required for termination are not thoroughly characterized. Consequently, termination of transcription under in vitro conditions using purified yeast factors has not been achieved so far in budding yeast. A thorough understanding of the process of termination by RNAPII necessitates identification of the general termination factors in line with the general transcription factors that are required for initiation of transcription. A number of approaches like PAR-CLIP, GRO-Seq, PRO-Seq, and Net-Seq can be used to determine the genomewide role of an identified factor in termination of transcription. All these approaches may give similar results, but there are subtle differences in the precise information they reveal. We used GRO-Seq approach because of its high resolution and sensitivity in detecting defective termination.Of the three steps of the RNAPII transcription cycle , initiation is the most well understood step. The general initiation factors are well characterized, and it is possible to perform initiation of transcription from a defined promoter using highly purified factors under 30. A similar genomewide analysis was performed for CstF64, which is the mammalian homologue of another CF 1A subunit Rna1519. This analysis identified CstF64 as a general termination factor in mammalian RNAPII transcription cycle. Thus, the role of CF 1A complex and its mammalian counterpart in termination of transcription has been conserved during evolution. Besides CF 1A complex, termination of transcription in yeast requires CPF complex. It was recently demonstrated using PAR-CLIP approach that the CPF subunit Ysh1 is required for global termination of transcription31. The termination defective phenotype of Ysh1, however, was quite different from that of Clp1. Unlike Clp1, depletion of Ysh1 did not lead to readthrough transcription, but resulted in the accumulation of polymerase beyond the poly(A) site. These results suggested a role for Ysh1 in dissociating polymerase from the template beyond the poly(A) site. In contrast, CF 1A complex could be facilitating slowing down of polymerase near the poly(A) site, which is an important step in termination of transcription by RNAPII.An important conclusion of the GRO-Seq analysis performed here is that Clp1 is required for termination of transcription of RNPII-transcribed genes in budding yeast. Our analysis further show that Clp1 is required for poly(A)-dependent termination of transcription of relatively highly expressed genes. Since there is no report of Clp1 acting alone or being a part of a complex other than CF IA, we extrapolate these results to propose that the whole CF IA complex, and not its individual components separately, is required for termination of transcription. Our results are corroborated by a recent finding that another CF 1 A subunit Pcf11 is required for termination of transcription on a genomewide scale32. The proximity of the promoter and terminator regions in a gene loop allows promoter-bound factors to influence termination of transcription36. The gene looping also invokes the possibility of termination factors affecting the promoter-associated transcription. Accordingly, it was demonstrated that Ssu72, which is a component of CPF termination complex, affects directionality of promoter-initiated transcription10. We also showed that Clp1 affects transcription directionality of CHA15. Clp1 is also required for gene looping of CHA15. The results of this genomewide analysis demonstrate that, apart from its role in termination, Clp1 also function in regulating promoter directionality of RNAPII-transcribed yeast genes. Clp1 suppresses the promoter-initiated upstream antisense transcription of 330 out of about 700 transcriptionally active genes used in this analysis. The promoter-initiated downstream antisense transcription either remained unaffected or exhibited a marginal increase for a majority of genes in the absence of Clp1 activity. Thus, the role of Clp1 in promoter directionality is similar to that of CPF subunit Ssu7210. CAF-1 complex, which is associated with chromatin assembly pathway, also affected promoter-initiated divergent non-coding transcription of yeast genes in a manner similar to Clp137. Both Ssu72 and CAF-1 complex suppress promoter-initiated uaRNA transcription. The transcription of promoter-associated mRNA, however, either remains unaffected or exhibited a small increase in the absence of Ssu72 or CAF-1 activities. The NET-Seq analysis performed in mammalian cells likewise revealed the role of CstF64 and Xrn2 termination factors in transcription directionality19.During transcription, the distal ends of a gene physically interact with each other forming a looped gene architectureOverall conclusion of these results is that the CF 1A complex plays a role in poly(A)-coupled termination of transcription, and in limiting promoter-associated divergent antisense transcription on a genomewide scale in budding yeast. Not all RNAPII-transcribed yeast genes, however, are dependent on CF 1A complex for either termination of their transcription or promoter directionality. This is expected as there is an alternate pathway for termination of transcription in budding yeast that requires Nrd1-Nab3-Sen1 complex. Nrd1-dependent termination has been shown to affect termination of non-polyadenylated mRNA as well as uaRNA belonging to the category of CUTs. Is it gene looping that governs if termination of transcription and promoter directionality will be accomplished by the CF 1 A complex or the Nrd1 complex remains to be elucidated?MATa his3Δ200 trp1Δ63 leu2Δ0 met15Δ0 ura3Δ0) and clp1-769-5 (MATa ura3Δ0 leu2Δ0 his3Δ1lys2Δ0can1Δ::LEU2-MFA1pr::His3 clp1ts::URA3).The yeast strains used in this study are BY4733  medium to an OD600 of 0.4. The 100 ml culture was transferred to two 50 ml sterile tubes, and centrifuged at 1860 × g for 5 minutes. Cell pellets were resuspended in 50 ml of 25 °C or 37 °C prewarmed YPD medium, and grown at the permissive (25 °C) or non-permissive temperature (37 °C) for 2 hours. This normally takes cells to an OD600~0.8. Equal number of cells were pelleted by centrifugation at 1860 × g for 5 minute at 4 °C. Cell pellets were resuspended in 10 ml of ice-cold TMN buffer , incubated in ice for 10 minutes, centrifuged at 1860 × g for 5 minutes, and then resuspended in 940 μl of DEPC (Diethylpyrocarbonate)-treated ice-cold water. Cells were permeabilized by incubating with 60 μl 10% sarkosyl at 4 °C for 25 minutes with gentle shaking. Permeabilized cells were spun down at 1200 × g for 6 minutes at 4 °C. The pellets were stored at 4 °C till all samples were ready for the next step.The et al., (1997). Cell pellets from step I above were resuspended in 150 μl of transcription run-on reaction buffer  containing 5 μl of freshly added RNase Inhibitor (40 units/μl) and incubated at 30 °C for 5 minutes. The reaction was stopped by adding 500 μl of ice-cold Trizol reagent. About 250 μl of acid-washed glass beads were added to each tube. Cells were immediately lysed by vigorous shaking at 4 °C for 20 minutes. The lysate was transferred to a new 1.5 ml microfuge tube containing 500 μl of Trizol and 200 μl of chloroform. The tubes were shaken vigorously and centrifuged at 14000 rpm at 25 °C for 20 minutes. The supernatant was extracted with phenol-chloroform three times. Total RNA was precipitated by adding 3 volumes of ice-cold 100% ethanol, 2 μl of glycogen (20 mg/ml) and NaCl to a final concentration of 0.3 M followed by overnight incubation at −20 °C. The precipitated RNA was recovered by centrifugation at 16000 × g for 30 minutes at 4 °C. The RNA pellet was washed once with ice-cold 75% ethanol, air dried and resuspended in 55 μl of DEPC-treated water.Transcription run-on reaction was performed by the modification of methods described in Birse The extracted RNA was subjected to partial hydrolysis by incubating with 500 mM NaOH on ice for 20 minutes. The NaOH was neutralized by adding 30 μl of 1 M Tris-HCl (pH 6.6). Partially hydrolyzed RNA was purified using RNeasy kit (Qiagen) to remove any unincorporated nucleotides and to exchange buffer. RNA was eluted from the RNeasy column twice with 50 μl of DEPC treated water.About 25 μl of anti-BrdU-antibody-conjugated agarose beads were washed three times with 500 μl of binding buffer . Beads were blocked by gently shaking with 500 μl of blocking buffer (485 μl binding buffer containing 5 μl of 10% polyvinylpyrrolidone and 10 μl of Ultrapure BSA) for two hours at 4 °C. The blocked beads were washed twice with 500 μl of binding buffer and then resuspended in 400 μl of the same buffer. The RNA from step II was incubated at 65 °C for 5 minutes to get rid of any secondary structure and immediately placed on ice for 2 minutes. The RNA was then bound to the processed anti-BrdU beads with gentle shaking for one hour at 4 °C. Beads were then washed sequentially with 500 μl of binding buffer, 500 μl of low salt buffer  and 500 μl of high salt buffer  once each, followed by two washes of 500 μl of TET buffer . All washes were performed on nutator for three minutes each. All centrifugation steps between the washes were at 194 × g for 1 minute each, with incubation of tubes on ice for 30 seconds before removing wash buffer. The bound nascent RNA was eluted two times with 125 μl and one time with 250 μl of elution buffer  for a total elution time of 10 minutes in 42 °C water bath. The eluted RNA was precipitated with NaCl as described previously.RNA pellet was washed with ice-cold 75% ethanol, air dried and resuspended in 12 μl of DEPC-treated water and quantified using a Nanodrop spectrophotometer.TM complete Kit (Epicenter) following manufacturer’s instructions. DNA Sequencing was performed at the DNA Core Facility of Cornell University.The nascent, purified RNA samples were first depleted of rRNA. The depletion of rRNA was performed by the Ribo-Zero™ Magnetic Kit (Epicenter) following manufacturer’s recommendations. GRO-Seq libraries were constructed using the ScriptSeq38 for quality control of raw fastq files to trim adapters and remove low quality reads. All high quality fastq files were mapped to Saccharomyces genome  using Tophat239  and HTSeq.40 (version 0.6) for calculating read counts. Subsequently DESeq. 2 was performed for read count normalization. Then reads per kilobase of transcript per million (RPKM) mapped reads > = 5 RPKM was done for determining number of expressed genes41.We used CutadaptPheatmap package is applied to generate pairwise Pearson correlation coefficients analysis on normalized count files, and ggplot2 package applied for creating pairwise scatter plot among all replicates.To calculate ‘Read Through Index’ (RTI) score and expression difference of samples regards to their coding strand, again all high quality fastq files were mapped to three modified Gene Transfer format (GTF) files TSS, TTS and gene body regions of Saccer3 genome by Tophat2. In addition HTSeq and DESeq. 2 applied to obtain normalized read count. The output is used to calculate RTI score for each gene as shown in Fig. All scripts for calculating RTI were performed using R and ggplot2 package employed to generate density plot s and scatter plots.To determine the number of genes showing loss of promoter directionality due to increased uaRNA transcription in the mutant at 37 °C, we computed the count of reads in each sample in the 100 bp non-coding window upstream to the TSS. We performed cross-sample normalization by dividing the RAW count values to the total number of aligned reads (in millions) per sample to compute Count Per Million (CPM) reads. Then we computed the Log2 fold-change of mean CPM values between TS-37 vs. TS-25 samples and the sum of CPM values for TS-37 samples minus TS-25 samples. The CPM difference greater than +1 indicates more than 2-fold higher expression in TS-37 samples, while a CPM difference less than −1 shows more than 2-fold higher expression in TS-25 sample. The mean CPM difference value for all genes is +1.136, and the median value is +0.545.The data present in this work was deposited in NCBI’s Gene Expression Omnibus (GEO) database under the accession number GSE71624.Supplemental Information


Drosophila cells by metabolic labeling in combination with cellular fractionation. By mathematical modeling of these data we determined rates of transcription, export and cytoplasmic decay for 5420 genes. We characterized these kinetic rates and investigated links with mRNA features, RNA-binding proteins (RBPs) and chromatin states. We found prominent correlations between mRNA decay rate and transcript size, while nuclear export rates are linked to the size of the 3'UTR. Transcription, export and decay rates are each associated with distinct spectra of RBPs. Specific classes of genes, such as those encoding cytoplasmic ribosomal proteins, exhibit characteristic combinations of rate constants, suggesting modular control. Binding of splicing factors is associated with faster rates of export, and our data suggest coordinated regulation of nuclear export of specific functional classes of genes. Finally, correlations between rate constants suggest global coordination between the three processes. Our approach provides insights into the genome-wide nucleocytoplasmic kinetics of mRNA and should be generally applicable to other cell systems.Eukaryotic mRNAs undergo a cycle of transcription, nuclear export, and degradation. A major challenge is to obtain a global, quantitative view of these processes. Here we measured the genome-wide nucleocytoplasmic dynamics of mRNA in  All mRNAs start from production in the nucleus, undergo exportation through nuclear pores and finally are degraded in the cytoplasm. A comprehensive characterization of the kinetic rates of all mRNAs is an important prerequisite for a global understanding of the regulation of the transcriptome and the cell. By conducting a time-series experiment and building a mathematical model, we trace the dynamics of mRNAs from the nucleus to the cytoplasm and determine the rates at each kinetic step at transcriptome-wide level. This information allows us to associate mRNA kinetic rates with a wealth of biological features and made some intriguing discoveries. We show mRNA decay is positively linked to transcript length while mRNA export is negatively linked to the length of the 3' UTR. We show binding of splicing factors is associated with faster rates of mRNA export. We provide evidence for global coordination between nuclear export an decay of mRNA. We show genes sharing specific functions tend to have similar nucleoplasmic kinetics, in which ribosomal proteins possessing special kinetic features exclusively stand out. Altogether, our integrated approach to quantitatively determine the rates of kinetic steps on a gene-by-gene basis provides a blueprint to obtain the global understanding of RNA regulation. The production, nuclear export and degradation of mRNA are key steps in the control of cytoplasmic mRNA levels. Steady-state levels of transcripts in the cytoplasm are determined by the rates of these three processes. Hence, our understanding of gene regulatory systems requires quantitative knowledge of the relative contributions of each of these steps. Comparison of the kinetic rate constants for these steps across genes may provide insights into mechanisms of differential gene regulation.Recent technological advances have enabled genome-wide measurements of mRNA dynamics , 2 and sThese studies of mRNA kinetics have taken the total cellular mRNA as a single entity to calculate the overall turnover rate, overlooking nucleocytoplasmic transportation, which is thought to be a key aspect of mRNA dynamics and has been shown to be regulated by a variety of evolutionarily conserved mechanisms , 13. HerDrosophila Kc167 cells as a representative model for metazoan cells, because of their ease to perform experiment and the availability of a wealth of genome-wide information. We report kinetic rate constants for 5420 genes and determine the relative contributions of each of transcription, nuclear export and decay to overall cytoplasmic abundance. Moreover, we uncover links between the three kinetic steps and transcript features, interactions of specific RNA-binding proteins and specific gene classes.We chose Drosophila Kc167 cells using 4-thiouridine (4sU). We then isolated nuclear and cytoplasmic fractions from cells at each time point, and determined the relative abundance of “old” (unlabeled) mRNA for thousands of genes by high-throughput sequencing, as a function of time in both fractions. We then fed these measurements into a computational model that describes the process of sequential mRNA transcription, export and decay as a set of differential equations. Parameter fitting of the model to the measurements yielded kinetic rate constants for each of these three steps. Below we describe each step of the approach in more detail.To obtain genome-wide measurements of the nucleocytoplasmic dynamics of mRNA we followed a strategy as outlined in Drosophila . In. InDrosoChromatin is well-known for its role in regulating transcription, but there is also evidence that it may control the downstream fate of RNA. For example, specific histone modifications can influence alternative splicing , 44 and As expected, the modeled transcription rates differ widely among chromatin states, both at TSSs and TTSs . BLUE chS. pombe  [80 [80k′f, And for every hour the nuclear RNA that diffuses into the cytoplasm at the beginning of M phase isTo obtain the kinetic rates of nucleocytoplasmic dynamics, we considered four attributes that ought to be satisfied,mRNA dynamics of the nuclear compartment.mRNA dynamics of the cytoplasmic compartment.quasi-steady-state of the cytoplasmic compartment,The relationship between cytoplasmic decay rate and overall turnover rate,RNA-seq experiments render an over-dispersed non-Gaussian distribution for technical noise . To adjuThus, taking differential dispersions at individual time points into account we compute the difference on logarithmic scale, and minimized the corresponding four-component fitting gradient by least square.We used the Levenberg—Marquardt algorithm to fit experimental data using the ModFit function in the package of FME in R.r2 > 0.8. The consistency of the modeled rates was very high between the original and the down-sampled data, indicating that the performance of modeling is quite resilient to the reduction of sequencing depth  for Drosophila melanogaster genome BDGP5 were used for these analyses.Annotations from BioMart , and calculated the ratio of Pol II signal within 200 bp around TSS and Pol II signal from 201bp to the end of the gene, similar to previous studies [To compute the pausing index of Pol II, we used Pol II Chip-seq data of  studies . To inveChromatin states data were from Filion et al  and Kharhttp://cbl-gorilla.cs.technion.ac.il/). Corresponding p values were retrieved and gene ontology processes with p < 10−11 for at least one kinetic processes were displayed in a heatmap generated by the ‘pheatmap’ R package.GO analysis was performed using the single ranked list method on the Gorilla server Click here for additional data file.S2 FigA) Scatter plot showing the log10 transformed FPKM value of gene expression for Drosophila Kc167 cells unlabeled versus labelled for 480 minutes. (B) Scatter plot showing the log10 transformed FPKM value of gene expression for unfractionated Drosophila Kc167 cells versus the sum of the nuclear and cytoplasmic fractions. Red dots in (A) and (B) mark genes that were excluded from computation of kinetic rate constants; red lines indicate the cutoff functions Click here for additional data file.S3 Fig(A-D) Consistency between two independent biological replicates (rep1 and rep2) of the relative abundance of pre-existing RNA for both nuclear  and cytoplasmic  fractions at 0 min  and 450 min  of labeling. (E) Time series of the relative abundance of cytoplasmic and nuclear pre-existing RNA relative to the yeast RNA spike-in. Error bars depict standard deviation between two biological replicates. (F) Total mRNA abundance (nuclear and cytoplasmic combined) normalized to yeast spike-in as a function of time. Model fitting was used to determine the plateau level (dotted red line) that is eventually reached.  log2 of exon over intron ratio of two genes, Lam and Fmo-2, in both nuclear and cytoplasmic fractions of two biological replicates. (I) Time series of the remaining ribosomal RNA after polyA selection on linear scale. Error bars show standard deviation between two biological replicates.(EPS)Click here for additional data file.S4 Fig(A-C) show the consistency between rate constants determined from the down-sampled and full datasets.Sequence reads were randomly down-sampled to 25 percent and modeling was repeated. (EPS)Click here for additional data file.S5 Fig(A) and intron lengths (B) of genes that are modeled well and not modeled well, using r2 = 0.8 as cutoff.Comparison of transcription rates (EPS)Click here for additional data file.S6 Fig(A-F) Same analysis as in (A-C) and 50% longest (D-F) genes. (G) Boxplot showing log2 transformed export rates of intron-less and intron-containing genes. Central horizontal lines represent median values; box margins represent 25th and 75th percentiles, and whiskers represent the 5th percentile and the 95th percentile. (H) Boxplot showing 3’UTR length of intron-less and intron-containing genes. (I) Log2 transformed ratio between the number intron-less genes and the number of intron-containing genes, for mRNAs bound by the indicated RBPs according to Stoiber et al [(J) Heatmap showing pairwise overlap of mRNA binding by RBPs, sorted by hierarchical clustering. Overlap was calculated as the ratio between the number of the overlapping mRNAs and the number of all mRNAs bound by a particular RBP.er et al . (J) Hea(EPS)Click here for additional data file.S7 Fig(A) transcription rates of genes divided by their chromatin states at their transcription start sites (TSS). Whiskers represent the 5th percentile and the 95th percentile; (B) Between each pair of chromatin states at TSS, a line is drawn if there is statistical difference ;  same as , but for chromatin states at transcription termination sites (TTS). Similar analyses are displayed for nuclear export (E-H) and cytoplasmic decay (I-L).(EPS)Click here for additional data file.S1 Tabler2), for both nuclear and cytoplasmic fractions.Excel spreadsheet. As indicated by the header, for two biological replicates, the table contains the rate of transcription (FPKM/min), export (1/min) and cytoplasmic decay (1/min) for each gene, as well as the goodness of fit which is measured by the coefficient of determination ((XLSX)Click here for additional data file.S2 TableThe layout of the table is identical to that of (XLSX)Click here for additional data file.


Transcription of protein-coding and noncoding DNA occurs pervasively throughout the mammalian genome. Their sites of initiation are generally inferred from transcript 5′ ends and are thought to be either locally dispersed or focused. How these two modes of initiation relate is unclear. Here, we apply permanganate treatment and chromatin immunoprecipitation (PIP-seq) of initiation factors to identify the precise location of melted DNA separately associated with the preinitiation complex (PIC) and the adjacent paused complex (PC). This approach revealed the two known modes of transcription initiation. However, in contrast to prevailing views, they co-occurred within the same promoter region: initiation originating from a focused PIC, and broad nucleosome-linked initiation. PIP-seq allowed transcriptional orientation of Pol II to be determined, which may be useful near promoters where sufficient sense/anti-sense transcript mapping information is lacking. PIP-seq detected divergently oriented Pol II at both coding and noncoding promoters, as well as at enhancers. Their occupancy levels were not necessarily coupled in the two orientations. DNA sequence and shape analysis of initiation complex sites suggest that both sequence and shape contribute to specificity, but in a context-restricted manner. That is, initiation sites have the locally “best” initiator (INR) sequence and/or shape. These findings reveal a common core to pervasive Pol II initiation throughout the human genome. The mammalian transcription machinery assembles into a preinitiation complex (PIC) consisting of general transcription factors such as TFIIB and RNA polymerase II (Pol II) and strand-separated or open DNA . Once PoDespite the high resolution of certain genome-wide assays , their sDrosophila , which includes divergent TSSs that arise upstream of mRNA TSSs and are transcribed from the opposite strand . TranscrHere we use PIP-seq to separate human PICs from PCs on a genomic scale. We use this high-resolution data to assemble a more comprehensive view of how initiation complexes form and initiate transcription across a broad variety of promoter classes.Supplemental Fig. S1; Supplemental Table S1). The initial set of replicates for TFIIB and Pol II PIP-seq were used for peak calling and visualization in all plots due to their high signal-to-noise ratio, although all replicates correlated well compared to the input . Moreover, described patterns were identical between biological replicates .Our objective was to separately detect, on a genomic scale, the open DNA associated with PICs and PCs in human K562 cells. K562 cells were selected due to an abundance of relevant data existing for this cell line. Open DNA can be “marked” in vivo because thymines are more readily oxidized by permanganate when they are single-stranded . When coN = 26,163) . For genes with multiple annotated transcription start sites, the most 5′ coordinate was selected. We found that such regions were not intrinsically biased toward “T” compared with TSS regions having low PIP-seq tags. Indeed, such regions are relatively depleted of “T” (and “A”) and instead are intrinsically enriched with “G” and “C,” which is in line with TSS regions known to reside within CpG islands . We therefore analyzed annotated TSS regions   for intr islands . Thus, PP < 1 × 10−4). Less than 5% of the top 1000 ChIP-exo peaks failed to pass this threshold. Those 5% nevertheless had PIP-seq tags that exceeded the background  . Thus, their status as “false negatives” is due primarily to high thresholding rather than being undetectable.We also considered the corollary that intrinsically T-depleted regions would be refractory to PIP-seq . In order to address this possibility, we called TFIIB-bound regions using two distinct criteria, TFIIB ChIP-exo peaks possessing enriched GRO-cap signal, which specifies active transcription start sites (RNA 5′ ends) . We thenSupplemental Figs. S4A, S5A). In contrast to PIP-seq, tag 5′ ends from ChIP-exo correspond to exonuclease stops (due to blockage by a crosslinked protein) (We next considered the possibility that parts of the PIP-seq assay workup (apart from permanganate treatment) caused a nucleotide bias. Steps with potential bias include formaldehyde crosslinking, chromatin fragmentation by sonication, nuclear extraction, ChIP, DNA ligation, PCR, and DNA sequencing. To this end, we compared the nucleotide composition at position −1 of tag 5′ ends in PIP-seq (where “T” is supposed to be enriched) to that of ChIP-exo (protein)  and thusSupplemental Fig. S4B,C for TFIIB; Supplemental Fig. S5B,C for Pol II; Supplemental Fig. S4D for TFIIB; Supplemental Fig. S5D for Pol II). Thus, the processing steps that are in common with both ChIP-exo and PIP-seq were not creating a collective bias in nucleotide enrichment. In contrast, the TFIIB PIP-seq assay produced a strong −1 “T” enrichment in the TSS region, consistent with its permanganate reactivity. We therefore used only those tags having a “T” at −1 (relative to the tag 5′ end) for calling PIP-seq peaks and for plotting tag 5′ end distributions.As expected, both assays produced tag enrichment around TSSs (due to the ChIP aspect of the assay), including a bimodal distribution that has been attributed to divergent transcription , which degraded the intrinsic pattern resolution.We selected for further study those TFIIB PIP-seq peaks that had a statistically enriched −1 “T” tag density compared to input DNA and that were within 100 bp of a TFIIB location defined by ChIP-exo . By using the same criteria, we also called TFIIB PIP-seq peaks that required enrichment of GRO-cap  signal wSupplemental Fig. S6). Our PIC location is also precisely where GRO-cap 5′ ends mapped  GRO-cap tags in both a focused (±10 bp) window around the PIC, as well as a wider dispersed window (40-bp window immediately flanking the focused region). This differs from the notion that focused and dispersed transcription initiation represents distinct gene classes . Taken together, these results suggest that dispersed transcription initiation is tied primarily to +1 nucleosome positions and less to transcription factor positions. These data support a model previously described in zebrafish  strand A. MoreovSupplemental Figs. S4, S5). We confirmed the validity of this normalization by performing an equivalent PIP-seq data normalization of “−1 G” with “−1 C,” both of which reflect ChIP signals , and also by performing an equivalent ChIP-exo normalization . In all cases, the ChIP signal became fully normalized, as evidenced by the ratio around the TSS becoming essentially 1.0 .To test for strand bias, we generated composite plots of Pol II PIP-seq within the top and bottom quartile of TFIIB PIP-seq signal B. To remIn accord with our strand-bias hypothesis, we observed substantially more Pol II PIP-seq tags on the nontemplate strand, particularly at highly transcribed genes . In order to prevent the strand orientation prediction from being biased by divergent transcription, we compared the relative strand enrichment in a window smaller than the minimum distance observed for divergent transcription (<100 bp) . We succWhile previous studies have shown the presence of the transcription machinery where ncRNA is produced , it has Supplemental Fig. S9A; Supplemental Fig. S9B; Supplemental Fig. S9C; Confirming their enrichment in enhancers regions, these ncRNA locations are predicted to be enhancers (53%) by ChromHMM   as the mod ratio  to quantThose 7mer sequences that were the most similar to an INR within the local search space showed particular enrichment within ±3 bp of a TFIIB PIP-seq-defined PIC B. The saSurprisingly, the centering of the locally best 7mer precisely on TFIIB PIP-seq peaks was quite evident, even where the absolute INR-like scores were no different than the random average , but instead, their flanks were predicted to have narrower minor grooves D. This pTaken together, these results suggest that PICs largely reside on “average nonspecific” DNA that is flanked by local DNA having properties that comparatively resist supporting an open complex. This resistance may include an avoidance of INR-like sequences and having a narrower minor groove. Importantly, this is a local property that is not likely to provide global recruitment specificity, but rather provides local specificity. Nevertheless, a small but substantial fraction of open complexes do reside at consensus INR elements. We envision a genome-wide continuum whereby some balance of core INR-like sequence and minor groove shape are flanked by opposing sequences so as to stand out and provide local specificity. Since we did not observe differences in PIC occupancy at strong versus weak INRs E, we surPervasive transcription has diverse functions throughout the genome, and is typically studied through RNA-based assays . These aOur analyses of TFIIB and Pol II PIP-seq data reveal the capability of PIP-seq to spatially separate PICs from PCs, further supporting the assay's high sensitivity. At mRNA genes, steady-state levels of PCs far exceed PIC levels, which supports the notion that PICs are rapidly converted to PCs. This contrasts with ncRNA promoters where PCs are relatively less abundant, perhaps owing to greater ncRNA/PC turnover. Nevertheless, we find that ncRNA, whether arising from enhancer regions or from divergent transcription upstream of mRNA promoters, is associated with PICs and PCs in a manner that is qualitatively no different than at mRNA promoters. This includes unequal levels of transcription in the two directions, which contrasts with views of enhancers that assume directional equality when performing enhancer averaging . At mRNAUpon examination of GRO-cap data in relation to our high-resolution PICs, we identified focused and dispersed transcription initiation events . In contIntriguingly, while dispersed transcription initiation sites appear to be linked to positions of the +1 nucleosomes, this is not the case for focused initiation sites. We suspect that distinct mechanisms give rise to these two classes, although they are not necessarily independent events. When examining the underlying DNA sequence at focused initiation complexes, the TFIIB-bound PICs were enriched for an INR-like sequence relative to the local sequence. This ranged from canonical INRs to random sequences that were flanked by anti-INR sequences. Additionally, regions that possessed a broader set of INR-like DNA were indeed further discriminated by a locally wide minor groove flanked by DNA having a relatively narrow minor groove. While we did identify TATA-elements upstream of PICs at the expected location (∼30 bp), we did not find them enriched beyond previously described levels . We alsoDrosophila . ChIP-exo libraries were sequenced on an Illumina NextSeq 500, producing (2 × 40 bp) paired-end reads. All alignments to hg19 were performed with BWA using default parameters . AlignmeP < 1 × 10−5 Poisson probability (P < 1 × 10−6) amount of input PIP-seq tags  to RefSeq TSSs using BEDTools , and all biological replicates were merged . AlignedP < 1 × 10−4, using FIMO at default parameters (Position weight matrices (PWMs) of the JASPAR 2016 vertebrate motifs were downloaded from the JASPAR database . Sequencrameters . Motif hThe 8134 called PIC regions were assigned a random strand orientation. Transcription directionality was then predicted by comparing the sum of −1 “T” reads from the downstream (20 to 100 bp) forward strand of the PIC to the sum of the reads upstream (−20 to −100 bp) reverse strand of the PIC. If the higher sum existed downstream, the random orientation was maintained; otherwise, the orientation was switched. The identical analysis was performed using the −1 “A” reads as a negative control. The GRO-cap predicted orientation was used as the gold standard for comparison.2 ratio of divergent transcription was determined by calculating the absolute value of the log2 ratio between the maximum GRO-cap tag peak of the forward and reverse strand in a ±500-bp window relative to the TFIIB called PIC.Divergent PICs were identified by searching for the local TFIIB PIP-seq tag maxima 50–500 bp upstream of the called TFIIB PIP-seq peak. The logP < 1 × 10−4 and default parameters. The control data set was generated by randomly scrambling the DNA sequence of the PIC regions in order to remove nucleotide position information while retaining the local nucleotide content. Log-likelihood ratio (Sequences in 20-bp windows around the TFIIB PIP-seq peaks were extracted using BEDTools  and scanod ratio  scoring od ratio  using a Minor groove width prediction was performed on a 100-bp window centered on TFIIB PIP-seq peaks for each peak using data from the DNAShape webserver .http://www.ncbi.nlm.nih.gov/geo) under accession number GSE76955. Custom code used in analysis with sample inputs is available in the Supplemental Material .All sequencing files and peak files from this study have been submitted to the NCBI Gene Expression Omnibus (GEO; B.F.P. has a financial interest in Peconic, LLC, which utilizes the ChIP-exo technology implemented in this study and could potentially benefit from the outcomes of this research.


Initial studies showed that ligand-activated hormone receptors act by binding to the proximal promoters of individual target genes. Genome-wide studies have now revealed that regulation of transcription by steroid hormones mainly depends on binding of the receptors to distal regulatory elements. Those distal elements, either enhancers or silencers, act on the regulation of target genes by chromatin looping to the gene promoters. In the nucleus, this level of chromatin folding is integrated within dynamic higher orders of genome structures, which are organized in a non-random fashion. Terminally differentiated cells exhibit a tissue-specific three-dimensional (3D) organization of the genome that favors or restrains the activity of transcription factors and modulates the function of steroid hormone receptors, which are transiently activated upon hormone exposure. Conversely, integration of the hormones signal may require modifications of the 3D organization to allow appropriate transcriptional outcomes. In this review, we summarize the main levels of organization of the genome, review how they can modulate the response to steroids in a cell specific manner and discuss the role of receptors in shaping and rewiring the structure in response to hormone. Taking into account the dynamics of 3D genome organization will contribute to a better understanding of the pleiotropic effects of steroid hormones in normal and cancer cells. Similarly to other steroids, Estrogens  exert their action by binding to their cognate nuclear receptors, the Estrogen Receptor (ER), which mainly functions as ligand-activated transcription factor ,2. Upon Enhancers classically regulate transcription through chromatin looping to bring the regulatory machinery in close proximity to the promoters they target . This leIn terminally differentiated cells, 3D genome organization may constitute an epigenetic level controlling signal-induced modifications of transcription, as in the case of the rapid response induced by steroid hormones ,27,28,29Increasing experimental evidences support of a highly compartmentalized organization of the genome within the nucleus in interphase. Both cytological approaches such as Fluorescent In Situ Hybridization (FISH), or biochemical methods deriving from the chromosome conformation capture (3C) technique , have decis  identified by searching for consensus sequences in the genome , suggestMore generally, the precise mechanisms by which transcription factors find their DNA targets within the nuclear environment remains mainly unknown . In addiAn important, still opened, question is to determine whether all the sites detected by ChIP-Seq are functional and participate in transcriptional regulation or whether they are reflecting non-productive binding or chromatin interactions involved in other processes. The use of ChIA-PET, a method allowing the analysis of the spatial contacts between loci bound by a specific factor , showed The organization of the genome in chromosomal domains is therefore both limiting the activity of given regulatory elements to a specific set of target genes and demarcating the 3D space that has to be explored by a regulatory element to engage specific looping with its targets. This can explain the frequent genomic clustering of steroid responsive genes as well as their co-regulation within TADs in response to hormones ,69,70,71There are evidences that architectural proteins, notably CTCF and cohesins, are directly involved in the response to E2 ,73. DeplAs described above, the conformation of the genome can restrain the response to steroid hormones by modulating the binding of the receptors to chromatin as well as by demarcating the target genes that could be regulated in a given cell type. Once bound to their DNA elements, the receptors recruit a plethora of co-factors, which act as nucleosomes remodeling machinery or histones modifying enzymes ,77,78,79TFF1 and CTSD, have been demonstrated by 3C [The existence of functional chromatin loops between distal steroid receptor binding sites and promoters during the activation of model genes, such as ed by 3C ,74,80. Ied by 3C . These eed by 3C ,83,84. Ied by 3C ,85,86. Ted by 3C . Howevered by 3C . Genome-wide studies of chromosome conformation highlighted that contacts between enhancers and promoters occur in a complex fashion: enhancers frequently contact multiple targets and a given gene can be submitted to the activity of several enhancers . The exiAs mentioned above the loops between regulatory elements and promoters appear to be limited to loci laying in the same TAD and exposure to steroid hormones can lead to local restructuration of TADs, reflecting a rewiring of the contacts ,27,28. SAlthough such long-range organizations may be favored, they probably remain highly stochastic and it will be important to determine to what extent they facilitate the transcriptional response. Additional studies of their dynamic will be also necessary to determine to what extent they are reflected by functional modifications of higher levels of structure of the genome.In summary, the way the genome organizes can modulate the activity of steroid receptors and other transcription factors at several levels: accessibility to their binding sites, topological restraint of their potential targets as well as facilitation of effective enhancer/promoter looping. Conversely, the modifications that the receptors exert on chromatin upon exposure to their cognate signal lead to specific restructuration of the chromatin folding, which are probably important for fine-tuning the transcriptional response. Together these observations support a role for steroid receptors as genome organizers, not only at local but also at global scale.In addition to their direct effects on chromatin, it is also important to consider that steroids exert important so-called non-genomic actions, which can actively participate in the final transcriptional output ,97. For Finally, if the 3D structure of the genome plays a role in transcription regulation in normal cells, one could easily hypothesize that its modifications may favor inappropriate responses. Indeed, chromosomal rearrangements could have dramatic influence on gene expression by modifying the normal landscape of action of enhancers ,104. Jux


Klebsiella pneumoniae (K. pneumoniae) is an opportunistic pathogen that can adhere to host cells or extracellular matrix via type 1 and type 3 fimbriae. KP1_4563 is a gene encoding a hypothetical protein in K. pneumoniae NTUH-K2044. KP1_4563 is located between the type 1 and type 3 fimbrial gene clusters and is likely associated with fimbrial function given its putative conserved domains of unknown function (DUF1471). Cyclic AMP receptor protein (CRP) regulates virulence-related gene expression and is a crucial transcriptional regulator in many bacteria. The predicted DNA recognition motif of CRP is present in the KP1_4563 promoter region. This study aimed to investigate the function of KP1_4563 in fimbriae and its transcriptional regulation mechanism by CRP. We generated Kp-Δ4563 mutant and complementation strains. We utilized phenotype and adhesion assays to evaluate the role of KP1_4563 in fimbriae. We conducted quantitative RT-PCR (qRT-PCR), LacZ fusion, electrophoretic mobility shift, and DNase I footprinting assays to study the transcriptional regulation of KP1_4563 gene by CRP. We found that KP1_4563 negatively regulates the function of type 3 fimbriae. Compared with NTUH-K2044, the absence of KP1_4563 enhanced the ability of Kp-Δ4563 to adhere to A549 cells. CRP negatively regulates KP1_4563 by directly binding to its promoter region. KP1_4563 plays an important role in type 3 fimbrial function. This novel insight will assist in the development of strategies for preventing K. pneumoniae infection. Klebsiella pneumoniae (K. pneumoniae), a common hospital-acquired and potentially community-acquired pathogen, causes catheter-associated urinary tract infections, pneumonia, bacteremia, surgical wound infections, pyogenic liver abscesses and bacterial meningitis  ATP and T4 polynucleotide kinase. In the final 10 μl of the reaction volume, the labeled DNA fragment (1000 to 2000 cpm/μl) was incubated with increasing amounts of purified His–CRP protein and 20 mM cAMP at room temperature for 20 min in 5× binding buffer. Three controls were included in the EMSA experiment: 1) cold probe  as a specific DNA competitor, 2) negative probe (the unlabeled coding region of the 16S rRNA gene) as a non-specific DNA competitor, and 3) unrelated protein as a non-specific protein competitor. After incubation, the samples were analyzed by electrophoresis on 4% native polyacrylamide gels in 0.5× TBE buffer at 80–120 voltage and 4°C. Radioactivity was detected via autoradiography after exposure to Kodak film at -70°C.The His–CRP protein was purified as previously described , 55. ForKP1_4563 gene was labeled with [γ-32P] ATP and T4 polynucleotide kinase. The putative promoter region of KP1_4563 was amplified by PCR with 32P-labeled primers. In the final 10 μl of the reaction volume, the purified PCR products  were incubated with increasing amounts of purified His–CRP protein with 20 mM cAMP in 5× binding EMSA buffer. The reaction volume was incubated at room temperature for 30 min. Optimized DNase I (Promega) was then added to the reaction mixture, and it was incubated at room temperature for 30–70s. The reaction was quenched by adding 9 μl of stop solution . Then, the mixture was incubated at room temperature for 1 min. The digested DNA was extracted with phenol/chloroform and analyzed on 6% polyacrylamide gels with 8 M urea. Radioactivity was detected as above. Footprints were identified by comparison with sequence ladders.For the DNase I footprinting , 57, theAll sequences obtained in this study were deposited in GenBank under the accession number NC_012731.P<0.05 was considered statistically significant.Statistical analyses were conducted with SPSS 22.0 software. 4563 and Kpc-Δ4563 strains. We then utilized phenotype and adhesion assays to investigate the role of KP1_4563 in fimbriae.We successfully generated Kp-ΔKP1_4563 on type 3 fimbrial function, we performed MRHA, a sensitive functional assay for type 3 fimbriae. The minimum bacterial density (CFU/ml) that was required to agglutinate erythrocytes is showed in KP1_4563 from K2044 increased the MRHA activity. The minimum bacterial density of Kp-Δ4563 required to agglutinate erythrocytes was 6.67×107 CFU/ml, which was approximately 10-fold lower than that of K2044 . The MRHA activity weakened when Kp-Δ4563 was complemented with pBAD33-p4563 plasmid, and the minimum bacterial density of Kpc-Δ4563 required to agglutinate erythrocytes was 3.67×108 CFU/ml. Two controls were included in this experiment, E. coli DH5α (type 1 fimbriae expression) and PBS (negative control), which both failed to mediate visible agglutination.To address the influence of KP1_4563 on the function of type 1 fimbriae was determined via the mannose-sensitive agglutination assays of guinea pig RBCs. The highest bacterial densities  of K2044, Kp-Δ4563, and Kpc-Δ4563 examined all failed to induce the visible mannose-sensitive agglutination of guinea pig RBCs. Two controls were included in this experiment, E. coli DH5α, which mediates visible mannose-sensitive agglutination, and PBS, which does not induce agglutination. These results indicated that KP1_4563 is irrelevant to the function of type 1 fimbriae.The effect of Saccharomyces cerevisiae (the receptor compound) was performed to investigate the function of fimbriae. As showed in 4563 bound to mannan more strongly. Binding was reduced when Kp-Δ4563 was complemented with the expression-complementary pBAD33-p4563 plasmid. These results indicated that KP1_4563 likely negatively regulates the function of type 3 fimbriae in an unknown manner.Type 1 and type 3 fimbriae both bind to yeast surfaces . A bindi4563, and Kpc-Δ4563 to adhere to A549 human lung cancer cells were analyzed  in the Kp-Δcrp mutant. The results of the LacZ fusion assays showed that the activity of β-galactosidase in CCW01Δcrp/placZ15-p4563 (6.8×104 Miller units) increased approximately 6-fold relative to that of CCW01/placZ15-p4563 . This study describes, for the first time, the role of KP1_4563 in type 3 fimbrial function and elucidates the transcriptional regulation of KP1_4563 by CRP. The function of KP1_4563 reported here will facilitate understanding of the functions of other DUF1471 proteins’ function. Understanding the effect of the KP1_4563 on type 3 fimbrial function will aid in the development of strategies for preventing K. pneumoniae infection.The region between the ae C3091 . KP1_4564563, and Kpc-Δ4563. The majority of the K. pneumoniae strains express the type 1 and type 3 fimbriae [KP1_4563 gene on fimbriae. Unexpectedly, K2044, Kp-Δ4563, and Kpc-Δ4563 all failed to mediate the visible mannose-sensitive agglutination of guinea pig RBCs at the highest tested bacterial density . These results indicated that KP1_4563 does not influence type 1 fimbrial function. The minimum bacterial density of Kp-Δ4563 required to agglutinate tannic acid-treated human erythrocytes was approximately 10-fold lower than that of K2044. The results of the mannan-binding assay further confirmed the negative regulatory role of KP1_4563 in type 3 fimbriae.We investigated fimbrial types in K2044, Kp-Δfimbriae –60. Typefimbriae , 49. ThiKP1_4563 on adhesion in K. pneumoniae. As expected, the ability of Kp-Δ4563 to adhere to A549 cells was dramatically enhanced. The results showed that the absence of KP1_4563 increased bacterial adhesion to A549 cells.The ability of bacteria to adhere to host structures plays a major role in the development of infections. Given that type 3 fimbriae can adhere to epithelial cells in the respiratory tract, A549 human lung epithelial cell lines were selected as target cells to identify the effect of KP1_4563 gene by CRP. The results indicated that CRP negatively regulates KP1_4563 by directly binding to the promoter region of KP1_4563 and that KP1_4563 negatively regulates the function of type 3 fimbriae in an unknown manner. Overall, CRP may indirectly and positively regulate the function of type 3 fimbriae. These results corroborate the importance of CRP in regulating virulence-related genes in K. pneumoniae [We studied the transcriptional regulation mechanism of the eumoniae .crp lead to a huge attenuation of the ability to agglutinate yeast cells [KP1_4563 gene at the molecular level, which might aid in understanding the relationship of CRP and fimbriae. Lin reported that CRP down-regulates type 3 fimbriae expression indirectly through the c-di-GMP signaling pathway [mrkJ gene is immediately adjacent to the mrkABCDF operon that encodes the structural and assembly components of type 3 fimbriae. MrkJ has homology to EAL domain-containing phosphodiesterases (PDEs). Overexpression of mrkJ results in a significant decrease in the intracellular concentration of c-di-GMP and down-regulates type 3 fimbriae expression [KP1_4563 negatively regulates the function of type 3 fimbriae without affecting the intracellular concentration of c-di-GMP, but did show that KP1_4563 protein has the putative conserved DUF1471 domains. In E. coli K-12, the YcfR protein has DUF1471 domains, and deleting ycfR caused changes in bacterial cell surface structures and properties by affecting cell surface protein gene expression, which further affects cell aggregation. The remarkable changes in the ycfR mutant may be due to regulation by CRP, as EMSA results showed that CRP binds to the upstream region of the ycfR gene [KP1_4563 regulates the function of type 3 fimbriae, which may be associated with changes in bacterial cell surface structures and properties. Further study is needed to elucidate the precise role of KP1_4563 in type 3 fimbrial function.CRP has been reported to be required for fimbrial production, and the deletion of st cells . Our res pathway . c-di-GMpression . BioinfocfR gene . We suspKP1_4563 negatively regulates type 3 fimbrial function, but does not influence type 1 fimbrial function. Adherence to A549 cells is considerably enhanced in the absence of KP1_4563. Moreover, CRP negatively regulates the transcription of KP1_4563 by directly binding to the upstream KP1_4563 promoter region.In conclusion, we found that


Ten-eleven translocation (TET) enzymes oxidise DNA methylation as part of an active demethylation pathway. Despite extensive research into the role of TETs in genome regulation, little is known about their effect on transposable elements (TEs), which make up nearly half of the mouse and human genomes. Epigenetic mechanisms controlling TEs have the potential to affect their mobility and to drive the co-adoption of TEs for the benefit of the host.We performed a detailed investigation of the role of TET enzymes in the regulation of TEs in mouse embryonic stem cells (ESCs). We find that TET1 and TET2 bind multiple TE classes that harbour a variety of epigenetic signatures indicative of different functional roles. TETs co-bind with pluripotency factors to enhancer-like TEs that interact with highly expressed genes in ESCs whose expression is partly maintained by TET2-mediated DNA demethylation. TETs and 5-hydroxymethylcytosine (5hmC) are also strongly enriched at the 5′ UTR of full-length, evolutionarily young LINE-1 elements, a pattern that is conserved in human ESCs. TETs drive LINE-1 demethylation, but surprisingly, LINE-1s are kept repressed through additional TET-dependent activities. We find that the SIN3A co-repressive complex binds to LINE-1s, ensuring their repression in a TET1-dependent manner.Our data implicate TET enzymes in the evolutionary dynamics of TEs, both in the context of exaptation processes and of retrotransposition control. The dual role of TET action on LINE-1s may reflect the evolutionary battle between TEs and the host.The online version of this article (doi:10.1186/s13059-016-1096-8) contains supplementary material, which is available to authorized users. Transposable elements (TEs) are mobile genetic elements that are present in organisms from all kingdoms of life and account for nearly half of the human and murine genomes. Retrotransposons, which act through an RNA intermediate, are recognised as major evolutionary contributors to genome structure and organisation. Namely, retrotransposons bear enormous potential for affecting gene expression at multiple levels, from creating novel transcription factor (TF) binding sites to regulating post-transcriptional processes , 2. The On the other hand, active TEs such as LINE-1 (or L1) elements carry substantial mutagenic potential, as evidenced by numerous examples of disease-causing retrotransposition events . SomaticDuring early embryogenesis, TEs are kept under control by multiple epigenetic mechanisms such as DNA methylation, histone modifications and small RNA-mediated mechanisms . The larWe have previously shown that L1 elements are enriched in 5hmC in mouse ESCs , 32, raiPrevious studies have established that TET1 primarily binds at CpG-rich gene promoters in mouse ESCs , 30. HowTo investigate the putative role of TETs at TE-derived ESC enhancers, we first analysed uniquely mapped ChIP-seq data at individual copies of TE classes associated with TET1 and NOS. TET1 binding was strongly correlated with the levels of both enhancer-associated histone marks  and NOS Fig. . As enhaOrl [Orl, which revealed an enrichment at the 5′ UTR, matching the pattern of TET1 binding , which Orl , 41. OnlOrl . TET bining Fig. . Moreoveing Fig. , a patteOrl Fig. , showingWe then asked whether a similar mechanism could be implicated in the regulation of human L1 elements. Using TAB-seq data from human ESCs , we uncoTet1 KO and Tet2 KO ESCs [Tet1/Tet3 double knockout blastocysts, confirming that TETs maintain L1 hypomethylation in vivo -treated DNA , 44. TET KO ESCs , which sTet1/Tet3 double knockout blastocysts further supported these findings  and enhancer elements (RLTR13D6 and others), potentially involving TETs in the exaptation process. Indeed, our data show that TET2 helps to control the expression of genes interacting with TE-derived enhancers, likely by maintaining a hypomethylated state that is permissive for TF binding. TET2 binding to specific TEs seems to have conferred them with an additional means for affecting gene expression that could have contributed to their exaptation by the host genome Fig. .Fig. 6MoAt L1 elements, TET binding displays a striking pattern whereby it targets exclusively the 5′ UTR of young, active L1 clades. Moreover, the same pattern is apparent in human ESCs (as judged by the presence of 5hmC), which is suggestive of an evolutionarily convergent mechanism that allows for L1 activation and expansion. Parallel evolution of L1 silencing mechanisms is also seen for KAP1 action, which targets and silences older L1 elements in both mouse and human through the binding of KRAB-ZFPs , 17. IntGiven that young L1s are derepressed in DNMT-null ESCs , 16, TETCould TET-dependent repressive activities constitute a host strategy for ensuring L1 silencing upon TET-mediated DNA demethylation? In one possible evolutionary scenario, as a newly formed L1 clade (not necessarily the most recent ones) escaped from the repressive action of KRAB-ZFP/KAP1, permissive TET-binding conditions would have been created that would lead to DNA demethylation, contributing to L1 expansion Fig. . SelectiIt remains possible that in other cell types TET-mediated control of DNA methylation regulates L1 transcription due to differences in the underlying epigenome. For example, TET1 and TET2 silencing during early ESC differentiation may be important to ensure full L1 repression through DNA methylation. And in neurons, where 5hmC levels are particularly high , TET-medWe have unveiled several novel roles of TET enzymes in the regulation of TEs, both catalytically dependent and independent. TETs mediate DNA demethylation of several TEs, but also serve as a hub for additional epigenetic activities, both of which impact on the evolution of the TE-host relationship. The intriguing dual nature of TET action on L1 elements appears to reflect the competing interests of host and TEs, raising new questions on TE-driven evolution of epigenetic mechanisms.Tet1 (Dharmacon siGENOME Mouse Tet1 Cat. D-062861-01-0020) and Sin3a siRNAs  were used at 125 nM, whereas Ogt esiRNAs  were used at 50 nM. Knockdown was confirmed by RT-qPCR and western blot. PUGNAc  treatment was performed either with 100 μM of DMSO-dissolved PUGNAc or matched amount of DMSO in control conditions and its efficiency was tested by western blot. A list of antibodies used can be found in Additional file E14 ESCs were grown in feeder-free conditions using DMEM-based medium with 15% FBS and 1000 U/mL ESGRO LIF (Millipore). For shRNA-mediated gene knockdown, ESCs were infected with viral particles carrying pLKO.1 constructs harbouring gene-specific shRNAs or a non-targeting sequence . After 24 h, cells were selected with 10 μg/mL puromycin or 50 μg/mL hygromycin for 3–9 days see ‘’. For siRNA was extracted using Quick-RNA™ MiniPrep (Zymo Research D7003) and DNAse treated in-column either with the DNAse provided on the kit or with the TURBO DNA-free™ Kit . RNA (1 μg) was retrotranscribed using SuperScript® III Reverse Transcriptase  and the cDNA was diluted 1/10 for qPCRs using MESA BLUE MasterMix  on a LightCycler® 480 Instrument II (Roche). A list of primers used can be found in Additional file ChIP was performed as described in , with moRibosomal RNA-depleted RNA-seq libraries were prepared from 400–600 ng of total RNA using the low input ScriptSeq Complete Gold Kit (Epicentre). For small RNA-seq, RNA was isolated with QIAzol (QIAGEN) and libraries prepared from 400–600 ng of RNA using the NEBNext small RNA library prep kit (NEB), followed by size selection  using gel extraction. Libraries were sequenced on an Illumina NextSeq 500 with single-end 75 bp reads.4 (Alpha Aesar) in 0.5 M NaOH for 1 h. Following bisulphite conversion of both DNA fractions with the EpiTect Bisulfite kit (QIAGEN), a two-step PCR amplification was used: a first PCR amplifies the region of interest and adds part of the sequencing adaptors; a second PCR on pooled amplicons then completes the adaptors and adds sample barcodes, allowing for multiplexing , chr5:70814798–70821450 , chr6:107895070–107901677 .Orl sequence (accession number D84391) or the L1.4 sequence (accession number L19092). ChIP-seq and small RNA-seq data were aligned using Bowtie2 v2.1.0 [External datasets were downloaded from GEO  overlapping each TE class (using the Repeatmasker annotation) were summed. This was compared with the overlaps obtained using a matched list of regions  randomly distributed anywhere in the genome (for inclusive mapping) or within mappable regions of the genome, as defined by the uniqueome  (for uniRaw read counts for each gene or repeat (from RepeatMasker annotation) were generated in Seqmonk with the RNA-seq quantitation pipeline. Reads from repeats belonging to the same class were pooled. DESeq2 was used for differential expression analysis and for generating normalised gene and repeat expression values. To couple TE-derived enhancers to gene expression values, promoter capture Hi-C data from ESCs , as proc


Escherichia coli K-12 MG1655, fimB and hlyE (clyA). In both cases, SlyA activates gene expression by antagonizing repression by the nucleoid-associated protein H-NS. Here, the transcript profiles of aerobic glucose-limited steady-state chemostat cultures of E. coli K-12 MG1655, slyA mutant and slyA over-expression strains are reported. The transcript profile of the slyA mutant was not significantly different from that of the parent; however, that of the slyA expression strain was significantly different from that of the vector control. Transcripts representing 27 operons were increased in abundance, whereas 3 were decreased. Of the 30 differentially regulated operons, 24 have previously been associated with sites of H-NS binding, suggesting that antagonism of H-NS repression is a common feature of SlyA-mediated transcription regulation. Direct binding of SlyA to DNA located upstream of a selection of these targets permitted the identification of new operons likely to be directly regulated by SlyA. Transcripts of four operons coding for cryptic adhesins exhibited enhanced expression, and this was consistent with enhanced biofilm formation associated with the SlyA over-producing strain.SlyA is a member of the MarR family of bacterial transcriptional regulators. Previously, SlyA has been shown to directly regulate only two operons in  The MarR family of transcription regulators are widespread throughout the Bacterial and Archeal kingdoms . MarR faSalmonella enterica serovar Typhimurium LT2 SlyA protein is one of the best characterized members of the MarR family. The S. enterica serovar Typhimurium slyA mutant is attenuated for virulence, is hypersensitive to oxidative stress and is impaired for survival in macrophages , the bacteria were lysed by two passages through a French pressure cell (16 000 psi) and the extract was clarified by centrifugation . The His-tagged SlyA protein was isolated from the cell-free extract by affinity chromatography on a HiTrap chelating column (1 ml) attached to an AKTA prime according to the standard manufacturer’s protocol . The eluted SlyA was buffer exchanged into 20 mM Tris/HCl, pH 7.4, containing 200 mM NaCl by repeated dilution and Vivaspin 6 concentration (Sartorius Stedim Biotech). The protein was judged to be >90 % pure by Coomassie blue-stained SDS-PAGE, and protein concentration was estimated by the BioRad protein reagent protocol  to Hybond-C extra nitrocellulose membranes . The membranes were soaked in a blocking solution, which contained 5 % (w/v) dried skimmed milk in PBS  and 0.05 % (v/v) Tween 20, for 16 h at 4 °C. The blocking solution was then removed and the membranes were washed in PBS containing 0.05 % (v/v) Tween 20 before exposure to a 1 : 1000 dilution of the SlyA antibody  in blocking solution for 1 h at room temperature. After four washes with PBS containing 0.05 % (v/v) Tween 20, the membranes were soaked in blocking solution containing anti-rabbit secondary antibody provided in the Pierce ECL Western Blotting kit, and the presence of SlyA was visualized according to the manfacturer’s standard protocol (Thermo Scientific).Cultures  of protocol . SlyA pr−2 and detection of labelled DNA using the Nucleic Acid Detection Module (Thermo Scientific).The LightShift Chemiluminescent EMSA kit (Thermo Scientific) was used according to the manufacturer’s instructions. Biotin-labelled DNA of target promoter regions was amplified from genomic DNA using the appropriate oligonucleotide primer pairs . The corE. coli K-12 (hlyE and fimB) by antagonizing H-NS repression ..paaA-K),aline pH . HoweverS. enterica serovar Typhimurium, the majority of genes affected by SlyA encode proteins associated with the bacterial cell envelope and are important for virulence and survival within murine macrophages. Although it has been previously shown that the majority of genes regulated by SlyA in S. enterica serovar Typhimurium are not present in E. coli K-12 [E. coli SlyA was evident here. Of the 30 operons that exhibited altered expression in SlyA-expressing bacteria, 13 (43 %) were associated with cell surface/membrane functions  encoding fimbrial-like adhesins , yad and yeh operons resulted in enhanced biofilm formation on abiotic surfaces, whereas sfm promoted binding to eukaryotic cells [elf, sfm, yad and yeh transcripts upon expression of SlyA are consistent with the cryptic status of these genes under normal laboratory conditions, and suggest that these chaperone-usher fimbriae are functional under environmental conditions that enhance slyA expression such that SlyA can operate as an H-NS antagonist  or the slyA expression plasmid pGS2468 under conditions that mirrored the transcript profiling experiment. The data showed a fourfold increase in biofilm formation when slyA was over-expressed, consistent with the transcript profiling data  were associated with LeuO binding sites identified in the SELEX-chip study of Shimada et al. [leuO resulting in an increase in expression of the entire LeuO regulon, i.e. indirect regulation by SlyA, or (2) SlyA and LeuO have overlapping regulons as a consequence of the fact that they both operate by antagonizing H-NS-mediated repression. To further investigate the extent of direct SlyA-mediated regulation in E. coli K-12 MG1655, binding of SlyA to 10 promoter regions was examined by EMSA.The changes in transcript profiles that were observed upon over-production of SlyA could have resulted from direct interaction of SlyA with the promoter regions of the corresponding genes or indirectly via SlyA-regulated factors. For example, one of the genes up-regulated upon SlyA over-production, gulation . Of the a et al. . This sthlyE-C0299 intergenic region was shown previously (slyA-ydhIJK and ssuE-B-elfADCG-ycbUVF) were shown to bind SlyA in EMSA  values for SlyA binding at these promoters were similar, at ~50–100 nM. These experiments indicate that these operons are likely to be directly regulated by SlyA. The sgcXBCQ-sgcAER genes are separated by an sRNA ryjB on the opposite DNA strand  for the analysis . This cois [6TAA . All the in EMSA  possesse in EMSA . Site-di binding ; all theE. coli K-12 MG1655 cultures grown under precisely controlled conditions such that any potential effects associated with changes in growth rate/growth phase could not confound the interpretation of the data obtained. Enhanced transcript abundance for several cryptic fimbrial operons in a SlyA over-producing strain and an over-representation of H-NS-repressed genes were consistent with the current model of SlyA-mediated gene activation. The SlyA protein was shown to bind at nine intergenic regions controlling the expression of 11 operons, thus expanding the number of known directly SlyA-regulated genes in E. coli MG1655 from 2 to 13.SlyA proteins have been shown to play important roles in regulating gene expression in a wide range of bacterial species. The most common mechanism for SlyA-mediated activation of gene expression is through antagonism of H-NS repression. Here, transcript profiling has revealed the breadth of the SlyA regulon (directly and indirectly regulated genes) in