The core promoter of hepatitis B virus (HBV) genome is a critical region for transcriptional initiation of 3.5 kb, pregenome and precore RNAs and for the viral replication. Although a number of host-cell factors that potentially regulate the viral promoter activities have been identified, the molecular mechanisms of the viral gene expression, in particular, regulatory mechanisms of the transcriptional repression remain elusive. In this study, we identified LUC7 like 3 pre-mRNA splicing factor  as a novel interacting partner of HBV enhancer II and basal core promoter (ENII/BCP), key elements within the core promoter, through the proteomic screening and found that LUC7L3 functions as a negative regulator of ENII/BCP. Gene silencing of LUC7L3 significantly increased expression of the viral genes and antigens as well as the activities of ENII/BCP and core promoter. In contrast, overexpression of LUC7L3 inhibited their activities and HBV replication. In addition, LUC7L3 possibly contributes to promotion of the splicing of 3.5 kb RNA, which may also be involved in negative regulation of the pregenome RNA level. This is the first to demonstrate the involvement of LUC7L3 in regulation of gene transcription and in viral replication. Hepatitis B virus (HBV) infection is a major cause of acute and chronic hepatitis and is closely associated with development of cirrhosis and hepatocellular carcinoma (HCC) worldwide. HBV is a small enveloped virus with a partially double-stranded DNA genome. The 3.2 kb HBV genome transcribes four major species of unspliced RNA transcripts; 3.5, 2.4, 2.1 and 0.7 kb from the core, preS1, preS2/S and X promoters, respectively, as well as a variety of spliced viral RNAs. Core promoter produces 3.5-kb transcripts; the pregenome and precore RNAs (termed as pgRNA in this study). HBV pgRNA not only serves as the mRNA that codes for core and polymerase proteins but can be packaged into nucleocapsid as a pregenomic RNA intermediate. Two enhancers; ENI and ENII are also involved in modulation of the viral transcription basically via up-regulating the activities of the HBV promoters. The transcriptional regulation of HBV genes is prerequisite for the control of the viral replication12The core promoter and ENII are located in the region that overlaps with the X gene. The core promoter can be divided into two elements; basal core promoter (BCP)(nucleotide [nt] 1743–1849) sufficient to initiate transcription and an upper regulatory region (nt 1643–1742). The latter region is composed of the core upstream regulatory sequence (CURS) that stimulates the BCP and of negative regulatory element (NRE). ENII is located at nt 1685–1773, immediately upstream of the BCP and overlaps with the CURS of the core promoter. ENII stimulates the viral promoter activities in the manner independent  or dependent (BCP) on position/orientation. ENII function is the results of various interactions with liver-enriched or ubiquitous transcription factors including the nuclear receptor superfamily. Nevertheless, further studies to elucidate regulatory mechanisms for accurate HBV gene modulation on the complex interplay between cellular factors and their corresponding sites within these critical elements are required23The course of chronic HBV infection frequently leads to alteration of the viral genome. Several nucleotide mutations that naturally occurred in the ENII/BCP region have been shown affecting viral function in cultured cells5678101112In this study, to further understand the molecular mechanisms underlying pivotal roles of ENII/BCP in HBV replication and possibly in the viral pathogenesis, we employed a proteome-based screening to identify candidate proteins interacting with ENII/BCP of the HBV genome. One of the identified proteins, LUC7 like 3 pre-mRNA splicing factor  was found to be a negative regulator of ENII/BCP activity. The silencing or overexpression of LUC7L3 regulated HBV replication. Ectopic expression of the siRNA-resistant LUC7L3 apparently restored down-regulation of the core promoter activity mediated by the gene silencing. Mutational analyses demonstrated that the N-terminal zinc finger motif within LUC7L3 is involved in the negative regulation of pgRNA expression and that the nt 1666–1700 region in ENII is important for LUC7L3-mediated negative regulation of the ENII/BCP activity. In addition, it is likely that LUC7L3 plays a role in promotion of the pgRNA splicing. Our data suggested a role of LUC7L3 as a novel negative regulator in HBV replication process.To identify novel host factors which are involved in regulating the transcription of 3.5-kb pregenome/precore RNA (pgRNA) of HBV, we first carried out proteomics screening in which nuclear proteins binding to biotinylated capture DNA of HBV sequence covering enhancer II and basal core promoter (ENII/BCP)(nt 1627–1817) were isolated using the magnetic microbeads separation system . Among 8LUC7L3, a human homolog of yeast U1 snRNP-associated factor, is known as a nuclear protein that is possibly involved in pre-mRNA splicing14The functional relevance of LUC7L3 on transcriptional regulation of pgRNA was further assessed. Knockdown of LUC7L3 resulted in significant increase in the ENII/BCP activity, likewise the activity of entire core/pregenome promoter . Effect Next we investigated loss- and gain-of-functions of LUC7L3 with respect to the replication and production of HBV. After 48 h of siRNA transfection, cells were transfected with a plasmid carrying a 1.24-fold HBV genome derived from genotype A, B or C . Further 48-h culture later, the supernatants and cells were harvested and used to determine the particle-associated HBV DNA by qPCR and HBs and HBc antigens by immunoblotting, respectively. The viral DNA levels in the supernatants were 5- to 9-fold higher in the cultures with knockdown of LUC7L3 compared with those with introducing negative control siRNA , indicatAs represented in The subcellular distribution of LUC7L3 and its mutants was also determined . As previn vitro synthesized LUC7L3 and a series of oligonucleotide probes resulted in no specific signal for the direct LUC7L3-DNA binding  were transiently transfected with 0.5 μg of plasmid DNA mixed with Lipofectamine LTX . ACIN1-specific siRNA, BCLAF1-specific siRNA, CNBP1-specific siRNA, CTCF-specific siRNA, HDGFRP2-specific siRNA, HMGN1-specific siRNA, LUC7L3-specific siRNA (siLUC7) and the negative control RNA (siNC) was provided by Bonac . siRNAs for HNF4α (siHNF4a), C/EBPα (siC/EBPa) and the negative control RNA (siNC-2) were purchased from Ambion . The synthetic siRNAs (50 pmol) were transfected into cells using ScreenFect A (Wako). Total RNAs and proteins of cells were prepared at 72 h post-transfection. Cell viability was measured by using a CellTiter-Glo luminescent cell viability assay (Promega).HuH7 and 293T cells were maintained in Dulbecco modified Eagle medium with 10% fetal bovine serum. Cells , which was used for normalizing transfection efficiency. At 48 h post-transfection, luciferase activities in cell lysates were measured with the dual luciferase reporter assay kit (Promega).Cells were transiently co-transfected with the firefly luciferase reporter and pGL4.75 carrying To determine HBV pgRNA and spliced RNAs derived from pgRNA, total RNAs were extracted from transfected cells with TRI-reagent  according to the manufacture’s instruction. The RNAs were treated with DNase I  and RNase inhibitor (TaKaRa). cDNA templates were synthesized using SuperScript VILO cDNA synthesis kit (Invitrogen), and qPCR was carried out using the SYBR qPCR Mix kit  with the following primer sets; 5′-TCCCTCGCCTCGCAGACG-3′ and 5′-GTTTCCCACCTTATGAGTC-3′ for unspliced, 3.5-kb pgRNA, and 5′-CCGCGTCGCAGAAGATCT-3′ and 5′-CTGAGGCCCACTCCCATAGG-3′ for spliced RNAs derived from pgRNA. The GAPDH gene was adopted as an internal standard for the assay and the primers; 5′-AACAGCCTCAAGATCATCAGC-3′ and 5′-GGATGATGTTCTGGAGAGCC-3′ were used. For semi-quantitative RT-PCR, the cDNA templates were amplified with the following primers, 5′-AGCCTCCAAGCTGTGCCTTGGGTG-3′ and 5′-AACCACTGAACAAATGGCACTAGTAAACTGAGC-3′. Agarose gel electrophoresis was used for the separation of PCR products corresponding to unspliced and spliced forms of pgRNA.To quantify the particle-associated HBV DNA, culture supernatants collected were treated with PNE buffer  on ice for 1 h. After centrifugation at 12000 rpm for 15 min, the pellets were treated with DNase I and RNase for 1 h at 37 °C to remove free nucleic acids. The samples were then lysed with the lysis buffer  containing proteinase K (Wako) at 56 °C overnight. DNAs in the lysates were isolated by phenol/chloroform extraction and ethanol precipitation. The viral DNA was then analyzed by qPCR with primers used for the analysis of unspliced pgRNA as indicated above.Expression of LUC7L3 mRNA in various tissue was examined by using the multiple tissue cDNA (MTC) human panels I and II . The panels contained a set of normalized single-strand cDNAs, produced from poly(A)+ RNA from various normal human tissues. qPCRs were performed using the SYBR qPCR Mix kit . Expression of LUC7L3 mRNA was standardized by that of RNA polymerase II (RPII) gene. The primers used were 5′-TCAAGCCGAACATCAGACAG-3′ and 5′-GCTTCTGCTTCTTCGTCGAT-3′ for LUC7L3 and 5′-GCACCACGTCCAATGACAT-3′ and 5′-GTGCGGCTGCTTCCATAA-3′ for RPII. For RT-qPCR to determine mRNAs of SMC5 and SMC6, total cellular RNAs isolated by TRI reagent were transcribed using SuperScript VILO cDNA Synthesis Kit. Aliquots of cDNAs were subjected to 45 cycles of PCR amplification using THUNDERBIRD SYBR qPCR mix. The primers used were 5′-AGAAGCAAGATGTTATAGAAAGGAAAG-3′ and 5′-TCCTCTGTCGGTCAAGCTCT-3′ for SMC5 and 5′-TGCATCAATTCTGGACAAAGA-3′ and 5′- TGCTTCTTGGTACTGCCTCA-3′ for SMC6. The RNA expression data were normalized to levels of reference gene GAPDH using the comparative threshold cycle method.Extraction of protein-free DNAs that contain cccDNA and protein-free relaxed circular (rc)DNA was carried out by using a modified Hirt extraction procedure41Nuclear proteins that interact with the DNA sequence corresponding to the ENII/BCP (nt 1627–1817) were isolated using FactorFinder Kit  according to the manufacturer’s instructions. The biotin-labeled capture ENII/BCP DNA probe was generated by PCR with biotinylated primers; 5′-CGTGAACGCCCACCGGAACC-3′ and 5′-GTTGCATGGTGCTGGTGAAC-3′. The streptavidin-conjugated microbeads were used to isolate DNA-protein complexes, followed by mass-spectrometry (MS) analysis to identify proteins using mass spectrometry Q Exactive Hybrid Quadrupole-Orbitrap Mass Spectrometer  with Xcalibur (version 2.2). The Proteome Discoverer software  was used to generate peak lists from the raw MS data files. The resulting peak lists were subsequently submitted to a SEQUEST search engine (Thermo Fisher Scientific) and compared against the human protein sequences in the UniProt protein database  to identify peptides. The SEQUEST search parameters were as follows: variable modifications including oxidation of methionine and carbamidomethyl of cysteine; peptide mass tolerance of ±10 ppm; fragment mass tolerance of ±0.02 Da.For detection of the DNA-LUC7L3 interaction, nuclear extracts obtained from cells transfected with a plasmid expressing FLAG-tagged LUC7L3 or its deletion mutant were used. The DNA-protein complexes were analyzed by immunoblotting with anti-FLAG antibody. To prepare ampicillin DNA probe as an unrelated capture DNA probe, PCR was performed using biotinylated primers; 5′-TTCGTTCGTCCATAGTGGCCTG-3′ and 5′-CATAGACTGGATGGAGGCGGAC-3′, and pGL4.10 (Promega) as a template.How to cite this article: Li, Y. et al. LUC7L3/CROP inhibits replication of hepatitis B virus via suppressing enhancer II/basal core promoter activity. Sci. Rep.6, 36741; doi: 10.1038/srep36741 (2016).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


E. coli genes based on experimental data. We find large differences in transcription speed between individual genes and a strong influence of both cellular location as well as the relative importance of genes for cellular function on transcription speeds. Investigating factors influencing transcription speeds we observe both codon composition as well as factors associated to DNA topology as most important factors influencing transcription speeds. Moreover, we show that differences in transcription speeds are sufficient to explain the timing of regulatory responses during environmental shifts and highlight the importance of the consideration of transcription speeds in the design of experiments measuring transcriptomic responses to perturbations.Dynamics in the process of transcription are often simplified, yet they play an important role in transcript folding, translation into functional protein and DNA supercoiling. While the modulation of the speed of transcription of individual genes and its role in regulation and proper protein folding has been analyzed in depth, the functional relevance of differences in transcription speeds as well as the factors influencing it have not yet been determined on a genome-wide scale. Here we determined transcription speeds for the majority of  High-throughput experiments recently uncovered transcription speeds varying between 5 and 60 nucleotides per second5 and single-gene-targeted experiments demonstrated gene- as well as growth-rate specific influences7.Gene expression is a complex process, involving the timely interaction of many different proteins. Early on in the elucidation of this process it was noted that RNAP transcribes genes with different chain elongation rates (speeds)8 and the speeds of transcription and translation are linked6, control by tRNAs is possible. Gene sequence has an influence as well: E. coli rRNA operon gene sequences are transcribed with 65 nt/s and intergenic regions with 250–400 nt/s9. RNAP pausing sites depend on associated 10 nt hairpin structures10. Similar associations have been observed in eukaryotes. In Drosophila melanogaster exon-intron junctions are frequently transcribed slower than the remainder of the gene11 and in Saccharomyces cerevisiae exons are transcribed slower than introns12. A third factor influencing the transcription speed is the downstream DNA supercoiling level, the amount of twist and writhe of DNA: the accumulation of positive supercoiling slows down the transcribing RNAP13. Binding of DNA gyrase to specific sites and its ATP-dependent activity contravene excessive positive supercoiling16.The molecular causes for differences in transcription speed are unclear. As ribosomes bind to RNAPS. cereviseae exon-intron junctions is important for correct splicing12. Proper protein and RNA folding in E. coli can depend on a localized RNAP deceleration17. These mechanisms apply widely, but are insufficient to explain the large range of observed speeds, since the deceleration for protein folding happens only in some cases18 and RNA folding mostly applies to ncRNA or regions outside of genes20.The speed differences seem to be necessary on a molecular level. Slow transcription at 22, requires more ATP for DNA gyrase to remove positive supercoils16 and it increases the likelyhood of toxic R-loops upstream of RNAP which also limits gene evolvability23. However, high transcription speeds reduce the time until a functional product is produced and thereby enables a faster reaction to environmental cues.High transcription speed is costly for the cell. It increases the amount of supercoiling produced by transcriptionE. coli5, and Global Run-On-Sequencing24 time series and ChIP-Seq were used to infer transcription speeds from transcription unit clearance waves after inhibition of transcription initiation in mouse embryonic stem cells12 and human breast cancer cell lines25. In addition, Fuchs et al.26 and Veloso et al.27 independently reported a method that also relies on a transcription stop and restart intervention before taking measurements. All of these approaches are limited to a minimum transcript length.Transcription speed has been inferred using various methods before: A short interval-time series of RNA-Seq after halting trancription initiation was analyzed for lags in RNA level drop on 482 genes in E. coli from a collection of data sets integrated by an algebraic model without transcript length limits. We establish that the transcription speed reflects the relative importance of a gene and we identify the ontological groups subject to fast transcription and slow transcription for a defined growth condition. We propose an ecological purpose of different transcription speeds: The delay between the regulation of transcription initiation and a change of gene expression6 determines the reaction time of an organism to changes in its immediate environment. A case example of sequential down-regulation that can be largely explained with delays caused by different transcription speeds highlights the importance of considering the transcription speed in experimental setups.In this study, we infer transcription speeds on 2791 genes in E. coli K-12 MG1655 data sets whose experimental conditions were similar  at a typical growth rate of 60 min/doubling in this medium. While this work relies on the ranking of speed, the absolute speed provides the basis of our coarse-grained speed calculation validation. The RNAP footprint of 40 nt we assume in the model influences the outcome of this validation.The data we use reflects absolute numbers of mRNA and RNAP density with an unknown linear scaling. Hence the model also returns transcription speed with an unknown scaling. We estimated this scaling by matching the speed at the reference gene 46 and were assumed significant for p ≤ 0.05. Only genes with non-zero transcription speed and background-corrected unscaled RNAP ChIP-chip signal >0.5 were analyzed. We did the Gene Set Enrichment Analysis (GSEA) using the PANTHER overrepresentation test48, release date 15. July 2016 with Bonferroni-corrected p-values and the Gene Ontology annotation with release date 30. November 2016. For each of the GO-classification trees Biological Process, Cellular Compartment and Molecular Function we removed unclassified genes and those with zero RNAP speed. We then used these as reference gene sets and for extracting the bottom and top 25% sets of transcription speed. We did the statistical analysis, modeling and data processing using the BioJava 3.0.4 library49 and R 3.450 with the packages stat50 and psych51. We used bowtie 0.12.752 with switches -a -S -trim5 3 -trim3 10 and the NCBI SRA toolkit34 for the reproduction of the RNA-seq read alignment in Chen et al.5. We used the R packages ggplot253 and ggrepel54 for plotting and Biostrings55 as a convenient source of the universal genetic code table.All reported correlations are Spearman rank correlation coefficients with two-sided p-values. All reported p-values of comparisons between groups are calculated using the Mann-Whitney U-test. The p-values determined for amino acid and codon content are adjusted for multiple hypothesis-testing by the Benjamini-Hochberg methodAll data generated or analysed during this study are included in the Supplementary Information.et al.56. The set contains promoter activities of 1920 E. coli promoters fused to gfp and shows little stationarity57. GFP fluorescence intensitiy and OD was measured in intervals of 14 min to a total of 54 time points both in M9 minimal and M9 rich medium. The promoter activity was calculated as dGFP/dt/OD56.To check the robustness of our model against violations of the stationarity assumption, we validated it against experimental data from Zaslaver gfp-mRNA is not. Megerle et al.58 have estimated the half life of this mRNA to be 6 min, making about 20% of the mRNA, and hence 20% of the increase in GFP, at each time step a leftover from the previous time step. We calculated two sets of promoter activities for all genes at each time point. Once we assumed stationarity and used equation (ρ > 0.98) for both M9 rich and minimal medium. Even correcting every measurement with that of the 10th preceding measurement, corresponding to 154 minutes between measurements and simulating increasing fluctuations in the time series, the correlation coefficient between the real promoter activity and our approximation at each seperate time step was still >0.95 in minimal medium and >0.75 in rich medium . The genes with a determined non-zero speed in both the data of Chen et al. and our data have a mean speed of 24.59 nt/s (SD = 12.73) in the exponential phase on LB medium, 19.15 nt/s (SD = 13.29) in the stationary phase on LB medium and 24.48 nt/s (SD = 17.47) in the growth conditions of our data set.Using equation  we obtai60 and arrived at a sum of 6041 mRNA molecules (4876 mRNA molecules when we included operons with weak evidence). This is in the physiological range of 103 to 104 molecules, but closer to the approximately 8000 molecules expected for exponential growth on LB medium than the 3000 molcules expected in M9 minimal medium62. Assuming a total of 2400 mRNAs per cell62 we obtain 517 transcribing RNAPs (or 640 with weak-evidence operons). This is within the range reported by Stracy et al.60.To validate our scaling we computed the absolute amount of mRNA given a total of 1300 transcribing RNAP per cell63.Hence, there is a tendency to an overestimation of transcription speed. However, the calculated transcription speed at rRNA-genes are below the expected value . This is likely due to an overestimation of half lives as free rRNA in the original experiment did not decay through RNA maturation and incorporation into ribosomeset al.5 for each operon containing at least 3 genes to the time-series data for the mRNA half life28. Briefly, each time series shows a linear decay on a log-scale after some delay. The delay depends on the transcription speed, which we varied for each operon between 1 and 90 nt/s to find the one that gives optimal linear fits (maximum R2) to the linear decay curves. In an ideal correspondence between the optimization-derived speed and our model-derived speed, a linear fit through them will have the slope = 1.00 and approximately so in the presence of measurement error. With a forced zero intercept we observed a linear fit with slope = 0.93 and its 95 % confidence interval encloses the ideal slope. The optimization-derived and our model-derived transcription speed were in good correspondence to each other  as done in Chen her Fig. .As a second, indirect approach to validate the determined transcription speed, we used them to assess the relative importance of genes in response to environmental challenges. We hypothesized that genes who’s transcription needs to be rapidly adjusted in response to an environmental cue, e.g. because they perform essential cellular functions, and those that are regulatory hubs in the gene regulatory network should have a faster transcription speed in order to minimize response times. To verify this hypothesis, we analyzed the transcription speed of transcription factor proteins relative to their number of target genes and genes essential for growth compared to non-essential genes.ρ = 0.23, p = 0.018, Fig. ρ = 0.33, p = 0.001, without self-regulation, Fig. p = 0.234, data not shown).We calculated regulation delays, that is, the time between the initiation of transcription and the completion of the transcript, for all genes (cumulatively for multi-gene operons) as the length divided by transcription speed. The regulation delay for most genes was <5 min, but 11% of genes had a delay of >5 min Fig. . We founp = 1.5e − 16, Fig. Compared to all genes in the sample, essential genes are transcribed significantly faster  and enriched in the top 25%  and “plasma membrane” is enriched in the bottom 25%  and depleted in the top 25%  of transcription speed  is significantly lower than the mean speed of cytosol-targeted genes  and 3rd base , but not that of the 2nd base .Nucleoid associated proteins like H-NS have been suspected to act as a roadblock to RNAPet al.73 and differing regulation delays is one mechanism to explain this observation. They used three measurement time points up to 30 min post-intervention and in each one observed additional differentially regulated genes73. We find our calculated delays to predict the observed sequential regulation well in case of upregulation, but not in the case of down-regulation . Durfee et al. report73 and we can substantiate a sequential down-regulation of flagellum genes as a consequence of regulation delays when the stringent response is invoked (Fig. et al. with regard to a scaling factor. There are two reasons for this. First, the microarray probes in Durfee et al.73 are at different positions in the genes, possibly at the start site in some cases, and they might indicate significant regulation before the gene is cleared of RNAP. Second, the mRNA half-lifes were determined on a time series of 8 minutes28, which is shorter than a portion of the regulation delays. The effect would be an overestimation of half-lifes and regulation delays, although we saw no such bias in the raw data of Bernstein et al.We then analyzed genes that have a high RNAP density and a low mRNA level, such that much of the measured mRNA level originates from nascent mRNA see Fig. . A gene ked Fig. . However74, relying on better accessible measurements, to estimate the transcription speed. We trained it with default parameters and the variables CAI, tAI, 1st and 3rd base GC content, gene length, z-scored mRNA level, RNAP density and ratio of mRNA level to RNAP density. The Random Forest performed well on protein-coding genes in a 5-fold cross-validation scheme (RRSE = 0.32, Fig. 5. The performance (RRSE = 0.65, Fig. As measurements of mRNA half lifes require an elaborate experimental setup, we trained a Random Forest4, went largely unexplained and prompted an investigation into its causes and uses. Our approach is the first to shed light on transcription speed on a genome scale without limitations on gene length, including most of the active genes in Escherichia coli on minimal medium. Our model of transcription speed is robust against violations of the stationary state assumption and will be unreliable only when RNAP density is very low or when the transcription speed is very low. At low RNAP density, the measurement error will be amplified. At low transcription speed the regulation delay might become so large that it conflicts with the accurate determination of mRNA half-lifes.The differences in transcription speed observed more than fourty years agoOur model of the transcription speed includes the calculation of the steady state promoter activity. This promoter activity model for the first time enables a large scale inference and will thereby provide insights into factors that determine promoter strengths on a genome-wide scale. The analysis of the transcription speed set we obtained revealed that essential genes are transcribed very fast and that the transcription speed of transcription factor proteins that represent regulatory hubs is higher than those with a low number of regulatory targets. Many of the essential genes and regulatory hubs control resource intensive components of metabolism or maintenance. Low regulation delays help the fast adaptation of these molecular processes to changing conditions, but they require high speed transcription. High transcription speeds produce more positive DNA supercoiling that requires more ATP to be removed by DNA gyrase. In consequence, the optimal speed is achieved when this cost roughly matches the overall energetic benefit of a low regulation delay. Hence, a high speed implies a large benefit and thus high importance.in vivo on a genome scale the supercoiling-induced speed reduction found by Chong et al.13. The low transcription speed of membranic proteins is caused by the structural coupling of transcription, translation and transfer of the nascent peptide chain across the membrane. This structural coupling fully blocks the rotation of RNAP and causes the highest production of genomic supercoiling. Thus, compared to other genes the transcription speed of membrane-targeted proteins needs to be particularly low to avoid the potentially toxic side-effects of excessive supercoiling. This might be a reason for the expression of sugar transporters regardless of sugar presence, although the literature reports a high variability in expression of this group39 and only rare constitutive expression75. Because the structural coupling is so influential on transcription speed but effectively limited to a group of genes, the relative importance of gene products by transcription speed should be evaluated only within this group, but not between membranic proteins and cytosolic proteins.The high speed range of the speed distribution is functionally enriched with genes targeting the cytosol while the low speed range is enriched with genes targeting the cell membrane. This reflects E. coli, which we confirmed here genome wide. Wobble-base paring is known to slow down translation, and thus transcription, through a lower efficiency of aminoacyl-tRNA binding compared to non wobble-base pairing tRNA76. The aminoacyl-tRNA concentration is rate-limiting in translation77, hence the influence of the amino acid concentration in conjunction with the codon distribution. Transcription speed also has a negative feedback on itself through the transcription-coupled production of positive DNA supercoiling. Downstream positive supercoiling slows down transcription13. Positive supercoiling is released by the ATP-dependent enzyme DNA gyrase, which binds certain sites and prefers positively supercoiled DNA79. Thereby, the transcription speed not only depends on the DNA supercoiling level, but also on the presence of DNA gyrase binding sites and the energy state of the cell80. Our findings confirm this effect on a genome scale.The transcription speed is controlled largely by aminoacyl-tRNA concentrations in conjunction with the codon distributions and wobble-base pairing in 8. This limits the speed of the trailing ribosomes, but since ribosomes rarely queue81, translation is likely not faster than transcription in general. Translation dynamics, however, seem to dominate transcription dynamics, hence we can assume that translation speed matches transcription speed, as was shown before on a smaller scale6, even without a spatial coupling. Large scale experimental translation speed data in E. coli is to our knowledge not available. Large scale predictions of the translation speed are available82, but these are based on the codon distribution and tRNA copy number, which we already incorporated in our analysis.Our calculated transcription speed can be directly translated into the translation speed of the nascent transcript’s leading ribosome by virtue of the spatial couplinget al.5 indicates large variability between different media and growth conditions. Thus, to accurately determine the transcription speed, experimental data from matching growth conditions is required and a prediction of transcription speed across conditions is relatively inaccurate. Contributing factors likely are changes in DNA supercoiling level between conditions that strongly influences the transcription speed.We confirm on a genome-wide level that environmental conditions have a strong influence on the transcription speed. The speed scaling validation in Fig. 73, the regulation delays we obtained are sufficient to explain most of the step-wise down-regulation. They explain the regulation pattern qualitatively since the regulation delays are overall in the correct order but consistently higher than those observed in direct measurements.Transcription speed needs to be accounted for in time-series measurements with respect to time-series development and the timing of gene expression measurements after interventions. We observed delays as long as 20 min, much longer than the usual 5 min between intervention and first expression measurement. The majority of genes has a delay below 5 min, but accurate figures of expression change additionally depend on the transition time to post-intervention steady state. This transition time depends on the mRNA half life and the extent of changes in transcriptional activity. In the case of flagellum gene regulation during stringent responseet al.5 allows to calculate the RNAP density without ChIP techniques. Extending this combination by RNAP ChIP-Seq measurements is even better. It would yield accurate speed references for scaling, accurate low speed figures, absolute RNAP and mRNA numbers and more accurate high speed figures. The data from such an experiment could be used well to discern the complicated interactions of DNA supercoiling and transcription and the adjacent problem of topological domain formation.Complementing our model with the appraoch of Chen In summary, our work provides, for the first time, a genome-wide assessment of the mRNA-transcription speed, a method for determination without limits on transcript length based on experimental data sets and a comprehensive characterization of cellular factors influencing it. As we have shown, information on transcription speed can be used to determine the relative importance of genes for cellular function since such genes tend to be transcribed at faster speed as well as for the decision on the optimal timing of determining gene expression changes after a perturbation since the strength of effects strongly depends on the time it takes for transcription of affected genes to complete.Supplementary Figures and TablesSupplementary Dataset 1


Germline mutations in the coding sequence of the tumour suppressor APC gene give rise to familial adenomatous polyposis  and are associated with many other oncopathologies. The loss of APC function because of deletion of putative promoter 1A or 1B also results in the development of colorectal cancer. Since the regions of promoters 1A and 1B contain many single nucleotide polymorphisms (SNPs), the aim of this study was to perform functional analysis of some of these SNPs by means of an electrophoretic mobility shift assay (EMSA) and a luciferase reporter assay.First, it was shown that both putative promoters of APC (1A and 1B) drive transcription in an in vitro reporter experiment. From eleven randomly selected SNPs of promoter 1A and four SNPs of promoter 1B, nine and two respectively showed differential patterns of binding of nuclear proteins to oligonucleotide probes corresponding to alternative alleles. The luciferase reporter assay showed that among the six SNPs tested, the rs75612255 C allele and rs113017087 C allele in promoter 1A as well as the rs138386816 T allele and rs115658307 T allele in promoter 1B significantly increased luciferase activity in the human erythromyeloblastoid leukaemia cell line K562. In human colorectal cancer HCT-116 cells, none of the substitutions under study had any effect, with the exception of minor allele G of rs79896135 in promoter 1B. This allele significantly decreased the luciferase reporter’s activityOur results indicate that many SNPs in APC promoters 1A and 1B are functionally relevant and that allele G of rs79896135 may be associated with the predisposition to colorectal cancer.The online version of this article (doi:10.1186/s12863-016-0460-8) contains supplementary material, which is available to authorized users. The adenomatous polyposis coli (APC) gene is mapped to chromosome 5q and encodes a protein consisting of 2843 amino acid residues that has been implicated in various cellular functions , 2. In pGermline mutations in this tumour suppressor gene (APC) give rise to familial adenomatous polyposis (FAP). The latter is an autosomal-dominant colorectal-cancer predisposition syndrome and accounts for ~1% of newly diagnosed cases of colorectal cancer. FAP is characterised by the development of multiple adenomatous polyps (from hundreds to thousands) in the large intestine . The greMany other oncopathologies are also associated with mutations in APC’s coding sequence. For example, in the case of pancreatic acinar cell carcinomas, there is a report of a frameshift mutation characterised by an insertion of 170 nucleotides after nucleotide 4177 and resulting in a truncated protein . LikewisAPC in 28-year-old proband of Canadian Mennonite FAP family. This large deletion results in allele silencing and was also detected among the proband’s other clinically affected siblings while the unaffected ones do not carry the same deletion  (Promega) with T4 ligase. The constructs were all confirmed by DNA sequencing.The 6 pairs of mutagenic oligonucleotide primers used in this study are listed in Table For luciferase reporter assays HCT −116 and K562 cells were used. The plasmids under study were co-transfected with pRL-TK using Screen FectA . Luciferase activity was measured 24 h after transfection by using the dual-luciferase reporter assay kit .STATISTICA 6 was used for statistical analysis.Previously, we developed a method for whole-genome identification of regulatory regions on the basis of the assumption that enrichment of a genomic region for peaks from high-throughput sequencing of chromatin immunoprecipitation material (ChIP-Seq) indicates that this is a regulatory region . ApplicaSimilar results were obtained with HCT-116 cells although in this case, both promoters showed even higher activity. In particular, under the control of promoter 1A, expression of the luciferase gene increased 10-fold, whereas under control of promoter 1B as much as 80-fold in comparison with the reference plasmid  and rs138386816: C > T and rs79896135: C > G (from 1B)—that showed differential patterns of binding of nuclear proteins to oligonucleotide probes representing the alternative alleles. Besides, we analysed two SNPs from promoter 1B (rs115658307: C > T and rs78429131: T > G) that did not change the protein binding pattern, because it is known that substitutions that do not affect the binding of nuclear-extract proteins can nevertheless influence the expression of a reporter [To evaluate the effects of rs75612255 T > C, rs113017087 T > C, rs138386816: C > T, rs79896135: C > G, rs115658307: C > T, and rs78429131: T > G on transcriptional activity, we prepared a series of reporter plasmids containing the corresponding substitutions in promoter 1A or 1B  was originally identified as a gene mutated in colorectal cancers associated with the FAP syndrome, hence the name [the name , 25. Nowthe name –28. Morethe name , adhesiothe name , chromosthe name , apoptosthe name , and neuthe name . Multifuthe name , 23 are In the present study, we for the first time obtained direct evidence of promoter activity of the distal (1B)  and proximal (1A)  regions of APC, which include start sites for APC’s alternative transcripts containing exon 1A or 1B. For these experiments, we cloned these regions into the luciferase expression vector pGL4.10. The analysis was conducted on two human cell lines: the colorectal cancer HCT-116 cells and erythromyeloblastoid leukaemia K562 cells: in both cases, activity of promoter 1B was substantially (approximately sixfold-eightfold) higher than that of promoter 1A. This result is consistent with the in vivo data showing that the levels of expression from promoter 1B are 15- to 250-fold higher as compared with transcripts generated from promoter 1A in healthy human tissues  , 23. In Next, we studied eleven randomly selected SNPs of promoter 1A and four from promoter 1B by the EMSA. Nuclear extracts were prepared from four human cell lines . These cell lines originate from different tissues; this situation expanded the range of nuclear proteins that can bind to specific DNA sites. The results of the EMSA revealed that respectively nine and two of the selected SNPs—in at least one cell line—manifest substantial differences in patterns of binding of nuclear proteins to oligonucleotide probes corresponding to alternative alleles. These differences represented either an increase or decrease in the affinity of some TFs, or even disappearance or appearance of binding sites for TFs. Such alterations in TF binding patterns can strongly influence gene expression , 34–36. Overall, our functional analysis uncovered new possible mechanisms of resistance/susceptibility to oncopathologies with involvement of APC because we demonstrated the effect of a number of rare SNPs located in the alternative promotes of this gene both on nuclear protein binding and promoter activity.


D. melanogaster embryos and cell lines has revealed a rich and detailed landscape of both cis- and trans-regulatory elements and factors. However, TSS profiling has not been investigated in an orthogonal in vivo setting. Here, we present a comprehensive dataset that links TSS dynamics with nucleosome occupancy and gene expression in the wandering third instar larva, a developmental stage characterized by large-scale shifts in transcriptional programs in preparation for metamorphosis.High-resolution transcription start site (TSS) mapping in D. melanogaster, but also identify notable exceptions. Furthermore, we identify thousands of novel initiation events occurring at unannotated TSSs that can be classified into functional categories by their local density of histone modifications. Interestingly, a sub-class of these unannotated TSSs overlaps with functionally validated enhancer elements, consistent with a regulatory role for “enhancer RNAs” (eRNAs) in defining developmental transcription programs.The data recapitulate major regulatory classes of TSSs, based on peak width, promoter-proximal polymerase pausing, and cis-regulatory element density. We confirm the paucity of divergent transcription units in High-depth TSS mapping is a powerful strategy for identifying and characterizing low-abundance and/or low-stability RNAs. Global analysis of transcription initiation patterns in a developing organism reveals a vast number of novel initiation events that identify potential eRNAs as well as other non-coding transcripts critical for animal development.The online version of this article (10.1186/s12864-018-4510-7) contains supplementary material, which is available to authorized users. Transcription initiation constitutes the first step in gene expression, and thus its fidelity is of utmost importance for proper regulation of gene expression. Initiation begins when the pre-initiation complex (PIC) assembles on exposed DNA at a promoter region upstream of the transcription start site (TSS) . Throughcis elements in the promoter results in displacement of nucleosomes. Additional descriptive characteristics of transcription initiation activity, such as the breadth or distribution of initiating polymerases across a given domain [Transcription initiation can be regulated at several levels. Prior to RNA polymerase II (RNA pol II) engaging with DNA at the TSS, nucleosome depletion directly upstream of the TSS facilitates assembly of the PIC and other general transcription factors. This stereotypical ‘minus-1’ nucleosome depleted region (NDR) is conserved across eukaryotes , 11, andn domain , 13, corD. melanogaster S2 cells, appear to be largely devoid of divergent initiation [Furthermore, transcription initiation has been shown to occur in divergent directions, with uncertain consequences for gene expression , 15. In itiation .A final initiation-related regulatory step occurs after PIC assembly, when RNA pol II transcribes ~ 50-100 nt into the gene body before it is subject to promoter proximal pausing. Pausing can act as a regulatory step to help integrate signals or it can prepare promoters for rapid activation , 21. AltSaccharomyces cerevisiae, genetic perturbations that cause cryptic initiation in coding regions are tolerated [S. cerevisiae , it is Drosophila melanogaster 3rd instar larvae [Here, we present a detailed characterization of matched Start-seq , ATAC-ser larvae . From thD. melanogaster larvae, we carried out rRNA-depleted total nuclear RNA-seq, Start-seq, which quantifies short, capped, nascent RNAs that represent newly initiated species [To characterize the genome-wide landscape of gene expression, transcription initiation, and chromatin accessibility in third instar  species , 21, and species , as prev species . For eve species . Additio species . Importa species , enablinNucleosomes are barriers to transcription factor binding and PIC assembly . Accordicis-regulatory motif, which is characteristic of many paused promoters [We hypothesized that discrepancies between ATAC-seq, Start-seq, and nuclear RNA-seq could be due to the influence of promoter proximal polymerase pausing on the relationship between nucleosome depletion and transcription initiation, as has been shown previously . Specifiromoters , is posiTo test the relationship between pausing and chromatin accessibility, we partitioned genes into quartiles based on their PI values, and quantified ATAC-seq chromatin accessibility and predicted nucleosome occupancy in a window surrounding TSSs in those quartiles. We found that genes in the most highly paused quartile have the highest ATAC-seq signal at the minus-1 nucleosome position, despite also having the highest predicted nucleosome occupancy Fig. , which iD. melanogaster genome build. We termed these clusters observed promoter TSSs (obsTSSs). The remaining 3123 high-confidence TSSs that failed to map to an annotated promoter region were considered novel unannotated TSSs (nuTSSs).As observed in S2 cells , Start-sDrosophila embryos and embryonic cell lines have revealed the presence of different TSS “shapes,” as defined by the breadth of the distribution of initiation signals within a given TSS [Previous studies of iven TSS , 19, 36.iven TSS , 37. To iven TSS  and Ni eD. melanogaster embryos [D. melanogaster TSSs are indeed largely “sharp” and focused, at least at the third instar larval stage. These results contrast with previous findings that broad TSSs are well represented in the fruit fly transcriptome but are consistent with the absence of CpG island promoters in Drosophila, a feature that is characteristic of less-focused TSSs in mammals [We noted that the other groups employed different library preparation methods, and also relied on smoothing-density estimates for signal quantitation in peak clusters, and therefore their results may not be directly comparable. To determine whether library preparation or clustering parameters might have contributed to differences in “peaked” promoter identification, we directly compared the two methods. We subsampled reads from our Start-seq dataset, a Start-seq dataset from S2 cells , and a C embryos , such th embryos  CAGE pea embryos  Start-se mammals .D. melanogaster TSSs do not conform to the typical sharp/broad duality seen in mammals, we reasoned that TSS peak shape might correlate differently with sequence motifs and gene expression outcomes in flies. Therefore, we sought to generate a numeric shape metric that would allow us to elucidate relationships between TSS shape and other aspects of gene expression. To do so, we adapted the approach taken by Hoskins et al. [Given our confirmation in larvae that s et al.  to assigs et al. , and argDrosophila promoters [To determine whether peak shape or pausing index is associated with particular sequence motifs, we searched the regions flanking obsTSSs for a suite of motifs that were previously shown to be enriched at romoters , and theromoters . obsTSS romoters  enrichedDrosophila development that have not been previously analyzed, we show that transcription factor and other cis-regulatory motifs are reliably correlated with TSS shape and polymerase pausing. Taken together with previous work [Drosophila.Interestingly, the motifs enriched in obsTSS Cluster 1 (and to a lesser extent Cluster 2) correlate positively with each other , we were able to identify bona-fide Start-seq signal at thousands of locations across the genome that did not correspond to an annotated TSS. To systematically analyze these locations, we applied several metrics to peaks that did not fall in a TSS cluster that matched to an existing obsTSS . We identified a total of 11,916 distinct nuTSSs, including 3123 that met a relatively stringent false-discovery-rate (FDR) threshold of 9 reads within every biological replicate. In general, nuTSSs exhibit NDRs comparable in shape to those found at obsTSSs, despite residing at loci with a higher intrinsic likelihood of nucleosome occupancy  taken at a matched stage of development, we found that the most informative set of PTMs included H3K4me1, H3K4me3, H3K27ac, and H3K36me3. Hierarchical clustering based on these four marks resulted in seven categories  co-localize with bona-fide TSSs, and therefore we surmised that enrichment of particular histone PTMs at nuTSSs might provide an indication of nuTSS functions. Therefore, we measured the enrichment of a battery of histone PTMs at nuTSSs, and conducted unsupervised hierarchical clustering. Out of several modEncode ChIP-seq tracks  originating from developmentally-regulated enhancers , 15, 17.osophila . As mentD. melanogaster, a model system wherein eRNAs have not been characterized systematically. To determine whether E-nuTSSs overlap with functional enhancer regions, we curated D. melanogaster 3rd instar larval enhancers from the FlyLight collection [If E-nuTSSs represent bona-fide eRNAs, they would expand the ensemble of known regulatory RNAs in llection , 49. TheD. melanogaster [D. melanogaster enhancers are generally characterized by nucleosome depletion, and that these NDRs are more likely to be transcribed than other regions of comparable chromatin accessibility. However, at our present sequencing depth, and in a heterogeneous cell population, we find only a small fraction of these enhancer NDRs are associated with highly expressed eRNAs.The low fraction of enhancers that overlapped with high-confidence E-nuTSSs indicates that TSS profiling alone is unlikely to be useful as a tool for predicting enhancers as compared with other methods. For instance, open chromatin data have been used to detect tissue-specific enhancers in nogaster . We therWe hypothesized that the highly expressed eRNAs we were able to detect might be located within enhancers that are active in a large fraction of cells. Therefore, we examined expression data for the enhancer regions that contained nuTSSs compared to those that did not. Relative to the 3063 enhancers that did not contain a high-confidence nuTSS, enhancers that contained a nuTSS were more likely to be enriched for expression in all five imaginal disc categories  as well in the optic lobe, all of which represent large, broad-based cell populations . Howeverevalent , 36; andrevalent , we wereDrosophila, this finding suggests a convergent evolutionary force that promotes a broad modality of transcription initiation specifically at ubiquitously expressed genes. Although direct comparisons of peak width across sequencing platforms are difficult, most of the peaks that are considered “sharp” in embryos are also considered sharp in larvae, whereas the shape of broad peaks is less well conserved. Combined with the more consistent sequence context of sharp promoters . Notably, more than half of the divergent TSS pairs we identified correspond to a bidirectional promoter pair in which annotated genes are transcribed in opposite directions from the same promoter region. There is evidence of this phenomenon in the human transcriptome [Previous studies have found that ectional , despiteectional –17. Howecriptome , 52, butIt has been suggested that divergent transcription may serve to strengthen the recruitment of transcription factors and other initiation machinery to the site of the sense-directed gene, thereby increasing its expression . Though We showed that around 18% of validated larval enhancers from the Janelia FlyLight collection , 49, alsFrom our ATAC-seq data, we find that NDRs are more successful at detecting validated enhancers than are nuTSSs alone. From a practical perspective, using nucleotide-resolution TSSs to identify developmentally regulated enhancers may have the further benefit of improving the resolution of enhancer identification. Though enhancer length is variable, and the functional fraction of a given enhancer is undoubtedly longer than the spatially restricted region identified by a nuTSS, single-nucleotide resolution provides a helpful starting point for defining minimal sequence requirements for activation. It remains to be seen whether sequencing nuTSSs to higher depth could reliably identify spatially restricted enhancers. Future studies that carefully benchmark enhancer detection methods with experimentally validated enhancers will be instructive in this regard. Furthermore, it is possible that incorporating nuTSSs with other existing methods of enhancer detection, such as nucleosome depleted regions, may improve our ability to identify novel enhancers, particularly those that are active broadly in several tissues within a complex mixture of cells. Indeed, our annotation of nuTSSs with their enrichment for enhancer-associated histone PTMs already partially achieves this goal.With regard to elucidating eRNA function, it is important to note that Start-seq signal was generally more likely to occur within enhancer-overlapping NDRs than it was at comparable NDRs elsewhere in the genome. In contrast, similarly sized random genomic regions display limited transcriptional activity. Moreover, Henriques et al.  recentlyD. melanogaster eRNAs or perhaps the decay of one of the two presumptive eRNAs is much more rapid than the other. Nevertheless, the data point to a novel modality for eRNA genesis and function, perhaps distinct from the existing hypothesis that eRNAs originate from “underdeveloped” promoter regions that have not yet accumulated the cis-regulatory elements necessary to discriminate against antisense transcription [Strikingly, we found that nuTSSs present within annotated enhancers are strongly unidirectional. In contrast, previous reports showed that, in S2 cells, putative eRNAs associated with computationally predicted intronic enhancers were significantly more divergent than active promoters . Becausecription .In summary, high-depth TSS profiling in a developing organism is a powerful strategy for identifying novel transcription initiation events corresponding to potential eRNAs as well as other non-coding transcripts. Further characterization of these low-abundance and/or low-stability RNAs will be important for understanding animal development.D. melanogaster larvae as previously described [For all libraries, nuclei were isolated from whole 3rd instar escribed . For Nucescribed , 21, and2, 0.1% (v/v) Igepal CA-630). Homogenate was directly used as input to the Nextera DNA library preparation kit (NEB) for tagmentation of chromatinized DNA, as described in Buenrostro et al. [ATAC-seq libraries were prepared as previously described . For eacwww.modencode.org). In all cases where possible, data were derived from the 3rd instar larval time point as determined by modEncode developmental staging procedures. GEO accession numbers for modEncode data used in this study are as follows: H3K4me1: GSM1147329–32; H3K4me3: GSM1200083–86; H3K27ac: GSM1200071–74; H3K36me3: GSM1147189–92; H3: GSM1147289–92; BEAF32: GSM1256853–56. All histone PTM ChIP-seq data was normalized to H3 ChIP-seq data using the Deeptools bigwigCompare utility. Predicted nucleosome occupancy data was obtained from genome-wide nucleosome prediction tracks in D. melanogaster generated by the Eran Segal laboratory (https://genie.weizmann.ac.il/software/nucleo_genomes.html). For ChIP-seq, ATAC-seq, and predicted nucleosome occupancy, metagene plots were generated using the Deeptools package [All raw data (fastq files) from ATAC-seq, Start-seq, and nuclear RNA-seq are available in the Gene Expression Omnibus (GEO) archive at NCBI under the following accession number: GSE96922. All ChIP-seq data were downloaded from modEncode ( package . All bro package .D. melanogaster dm3 genome build with Bowtie2 [R) mapping to each spike-in transcript t \in T in each replicate i \in n (Rti), we normalized Rti to the geometric mean of all {Rt1,…,Rtn} to generate the normalized transcript value Nti. Finally, for each replicate we calculated a replicate normalization score Si by calculating the geometric mean of all {N1i,…,NTi}. For initial analysis in Figs. D. melanogaster TSSs defined from a previous study [Reads from start-seq FASTQ files were clipped to the first 26 nt to remove adapter sequence, then mapped to the  Bowtie2 . We used Bowtie2 . For theus study .ni, an edge was established with a neighboring nucleotide nj if it occurred within 5 nt of ni. Then clusters were formed by including all nucleotides n that occurred between terminal nucleotides upstream (nu) and downstream (nd) that were bound to the cluster by only a single edge, and thus terminated the “chain.” For each cluster, the cluster “summit” was identified as the nucleotide containing the most mapped reads, and secondary and tertiary peaks were identified as containing the second- and third-most reads, respectively of any nucleotide in the cluster, if applicable. From the summit, we calculated the proportion of reads in the cluster contained within 2 nt on either side of the summit.To generate Start-seq peak clusters likely to belong to the same TSS, we first calculated an FDR cutoff for bona-fide Start-seq peak detection at 9 normalized reads per nucleotide per biological replicate, based on sequencing depth. Then we clustered nucleotides meeting this threshold as follows: For each nucleotide To compare our peak clusters with those reported in Hoskins et al.  and NechD. melanogaster embryos were obtained from Hoskins et al. [To assign Start-seq peak clusters to observed TSSs (obsTSSs) or novel TSSs (nuTSSs), we searched for overlap between our peak clusters and a list of coordinates for the first exons from every transcript in the dm3 gene annontation, and assigned those that overlapped as obsTSSs. All other clusters that did not map to a defined first exon were assigned as nuTSSs. Promoter regions defined by CAGE in s et al. , and “ins et al. .N = the number of single nucleotides i in the peak cluster, and pi = the proportion of total reads in the cluster mapping to nucleotide i. To calculate peak pausing index (PI), we divided the normalized start-seq signal mapping to the obsTSS peak cluster by the nuclear RNA-seq RPKM calculated for its corresponding gene.To calculate peak shape index (SI), we adapted a formula from Hoskins et al. :\documenX and each motif y, a single motif score Xy was determined by selecting the highest log odds score among all {Xy1..Xyn} detected in the assigned FASTA sequence. Those obsTSSs for which a sequence with no more than 4 mismatches to a motif was not detected were assigned a score Xy equal to the lowest log odds score for the all obsTSSs tested for the motif in question. Xy values were converted to z-scores using the following formula:ZXy = z-score for obsTSS X and motif y, μy = mean log odds score for motif y, and ∂y = standard deviation of log odds scores for motif y. The resultant z-scores were visualized using the heatmap.2 utility in the gplots R package (https://CRAN.R-project.org/package=gplots), with column clustering. Correlation coefficients for motifs were generated using the “cor” function in the R base package, and were visualized using the heatmap.2 utility.To discover motifs proximal to obsTSSs, we first sourced transcription factor motifs from FitzGerald et al. . We thenTo find instances of divergent transcription, pairs of neighboring TSSs oriented in opposite directions were ordered by distance of the reverse-strand TSS from the forward-strand TSS , ATAC-seq signal was plotted in a 20 kb window surrounding the forward-strand TSS using the Deeptools “computeMatrix reference-point” utility with default parameters , and sigTo cluster nuTSSs by histone post-translational modification (PTM) density, PTM enrichment for H3K4me1, H3K4me3, H3K27ac, and H3K36me3 ChIP-seq signal at each nuTSS was calculated by mapping ChIP and input reads to a 200 nt window on either side of the nuTSS using the bedtools multicov tool , then diTo analyze transcription elongation from nuTSSs, we quantified the number of strand-specific nuclear RNA-seq reads mapping within either 200 nt upstream or 200 nt downstream of the nuTSS. Then, the enrichment of downstream over upstream reads was calculated using DESeq2 . Though http://flweb.janelia.org/cgi-bin/flew.cgi) by querying based on anatomical expression in the larval CNS or in imaginal discs, and selecting all enhancers with validated expression in any one of those tissues. Each enhancer was annotated with the tissues in which it was reported to express according to the FlyLight database. Genomic coordinates of enhancer regions were obtained from the Bloomington Stock Center website (http://flystocks.bio.indiana.edu/bloomhome.htm). Meta-analysis of Start-seq signal probability in enhancer regions was conducted as follows: ATAC-seq peaks were called using Macs2 [Functionally validated enhancers were obtained from the FlyLight database


Dspp) as a differentiation marker of odontoblasts is regulated by BMP-2. However, the intimate mechanism is still unknown. Transcription factors Dlx3 and Osx are essential for odontoblasts differentiation. We hypothesized that BMP-2 regulation of Dspp transcription was mediated by Dlx3 and/or Osx in odontoblasts. In the present investigation, we found that BMP-2 stimulated expression and nuclear translocation of Dlx3 and Osx in odontoblasts both in vitro and in vivo. Osx was a downstream target of Dlx3 and both of them stimulated Dsp expression. Both Dlx3 and Osx were able to activate Dspp promoter from nucleotides (nt) −318 to +54 by transfections of luciferase reports containing different lengths of mouse Dspp promoters. The binding of Dlx3 and Osx with nt −318 to +54 of Dspp promoter was verified by chromatin immunoprecipitation in vivo. Two Dlx3 binding sites and one Osx binding site on Dspp promoter were found by EMSA. Furthermore, the exact biological function of these binding sites was confirmed by site-directed mutagenesis. At last, the protein-protein interaction between Dlx3 and Osx in odontoblasts was detected by co-immunoprecipitation. In conclusion, in this study we found a novel signaling pathway in which BMP-2 activates Dspp gene transcription via Dlx3/Osx pathway.Dentin sialophosphoprotein ( Dspp) gene encodes the major non-collagenous protein in dentin matrix, which is expressed predominantly in preodontoblasts and odontoblasts and transiently in preameloblasts, and at low levels in osteoblasts4. DSPP is a highly phosphorylated protein that is cleaved into 3 proteins, dentin sialoprotein (DSP), dentin glycoprotein (DGP), and dentin phosphoprotein (DPP), immediately after secretion6. DSP is the N-terminal portion of DSPP and is a 95-kDa glycoprotein that was first identified within the extracellular matrix of dentin5. DGP was identified as an 81-amino acid segment between DSP and DPP6. DPP is rich in aspartic acid and phosphoserine and binds to calcium. Therefore, DPP is strongly associated with mineral phase of dentin, acting as an important initiator and modulator of dentin apatite crystal formation, which makes DPP a specific marker for terminal differentiated odontoblasts8. Several human and mouse genetic studies have indicated that Dspp is important for dentin mineralization. Mutations of human DSPP gene are associated with human hereditary disorders such as dentinogenesis imperfecta type II (DGI-II), type III (DGI-III), and dentin dysplasia type II (DD-II)13. Dspp knockout mice exhibit dentin mineralization defects that are very similar to human DGI-III as well as impaired cranial bone development15. These studies demonstrate that Dspp plays a crucial role in tooth development and mineralization, in particular dentinogenesis.The dentin sialophosphoprotein , then shifts to the dental mesenchymal papilla and is involved in specifying the fate of the dental mesenchymal cells at later stage of tooth development18. During the late stage of tooth development, Bmp-2 becomes more intense in the terminal differentiated odontoblasts and regulates the differentiation of odontoblasts20. The odontoblasts do not mature properly and fail to form proper dentin with normal dentinal tubules and activate terminal differentiation with the deletion of the Bmp-2 gene in odontoblasts16. Studies have shown that Dspp in odontoblasts is regulated by BMP signaling, especially BMP-2, in vitro and in vivo22. Beads socked in human recombinant BMP-2 induce the mRNA expression of Dspp, after implantation onto dental papilla in organ culture23. Dspp expression was reduced in the Bmp-2 knock out mice16. Previously we also have found that BMP-2 up-regulates Dspp transcription through its regulatory region in mouse preodontoblast cells3. However, the intimate molecular mechanisms of BMP-2 regulating Dspp transcription in preodontoblasts or odontoblasts have not been completely understood.Bone morphogenetic proteins (BMPs) are structurally related to the transforming growth factor β (TGF-β) superfamily. Among the BMP family members, BMP-2 plays important roles during odontogenic differentiation26. Both Dlx3 and Osx are suggested as downstream targets of BMP-2 signaling in osteogenic cells28. Although both odontoblasts and osteoblasts originate from mesenchymal cells and share many physical similarities, the molecular mechanisms regulating odontoblasts differentiation are different from osteoblasts. For example, BMPs are able to stimulate expression of Runx2 in osteoblastic cells but not in odontoblasts29. Whether Dlx3 and Osx mediate the BMP-2 induced Dspp expression in odontoblasts is still not known. During tooth morphogenesis, Dlx3 mRNA is initially expressed in the dental epithelium, and is later expressed in both the dental epithelium and the dental mesenchyme25. Mutations of DLX3 in human cause Tricho-Dento-Osseous (TDO) syndrome, which is an autosomal dominant disorder characterized by defects in ectodermal derivatives such as hair (kinky hair), teeth (enamel hypoplasia and taurodontism) and bone (increased bone density in cranium and long bones)32. Deletion of Dlx3 in neural crest, from which dental mesenchyme is derived, leads to severe dentin hypoplasia and dysplasia and remarkably down-regulated Dspp expression33. During tooth development, Osx expression was initiated at dental papilla, and remained highly expressed in differentiating odontoblasts at later stages21. Previously, we found overexpression of Dlx3 or Osx in odontogenic cells induced cell differentiation and Dspp expression35,while knock-down of Osx caused the down-regulation of Dsp expression36.Transcription factors Dlx3 and Osx  are essential for osteoblast and odontoblast differentiationDspp transcription was mediated by Dlx3 and/or Osx signaling pathway in odontoblasts.Based on these biological functions of Dlx3 and Osx during odontoblasts differentiation and tooth development, we hypothesized that BMP-2 regulation of Bmp2-cKO°d mice. With the knock-out of Bmp2 gene in odontoblasts  gene family, it is yet not known if Dlx3 is able to regulate Osx in odontoblasts. We therefore tested the relationship between Dlx3 and Osx in MD10-F2 cells. The results showed that overexpression of Dlx3 was able to induce Osx and Dsp expression  −318 to +54.To confirm whether induction of Dsp expression by Dlx3 and Osx is through stimulating ted Fig.  and trantor Fig. . Both DlDspp promoter, we performed ChIP assay. The immunoprecipitated and purified DNA was used as a template. The PCR amplifying bands with primers corresponding to the 5′-flanking region (from nt −318 to +54) of mouse Dspp gene were detected, and the band is more intense in cells transfected with pcDNA-Dlx3 or pcDNA-Osx-Flag plasmids  and one putative Osx binding sites using computer software program and these sequences are highly conserved in several species, including mouse, human and rat  and Dlx3IIOligo (−76/−55) covering the Dlx3I and Dlx3II binding sites, OsxOligo (−154/−132) covering the Osx binding site on Dspp promoter. After incubation, the complexes representing protein-probe binding were detected , p318(Dlx3II Del), and p318(Osx Del). Then p318 or p318 mutant constructs were transiently transfected into MD10F-2 cells, and their transcription activity was evaluated with the co-transfection of pcDNA-Dlx3 and/or pcDNA-Osx-Flag. The results showed that deletion of either Dlx3I or Osx binding site suppressed the p318 response to both Dlx3 and Osx, but deletion of Dlx3II binding site only suppressed the p318 response to Dlx3 but not to Osx  motifs in the proximal regions53. Previously, we found BMP-2 regulates Dspp expression through the activation of the heterotrimeric transcription factor NF-Y, and NF-Y binds to a BMP-2 response element in the mouse Dspp promoter, especially between nt −97 and −723. In the present investigation, we found that in addition to NF-Y, BMP-2 also regulates Dspp expression also through the Dlx3 and Osx binding sites on Dspp promoter. Two Dlx3 binding sites  and one Osx binding site were identified in the proximal mouse Dspp promoter that mediates BMP-2-stimulated Dspp expression. EMSA and ChIP analyses verified that Dlx3 and Osx bind to their target sites in the mouse Dspp promoter. To further evaluated the function of these binding sequences, direct deletion in the Dspp promoter was used, and showed that deletion of either Dlx3 or Osx binding site decreased Dspp promoter activity in MD10-F2 cells, which means each Dlx3 or Osx binding site in the Dspp promoter was functional. Duverger et al.33 demonstrated the direct binding of Dlx3 to Dspp promoter, which is in consistent with our findings. In distal-less family, Dlx5 also mediates Dspp expression regulated by BMP signal22. Although both Dlx3 and Dlx5 can bind to TAAT box, but the two Dlx3 binding sites found in the present research only respond to Dlx3 but not to Dlx522, which indicates Dlx3 and Dlx5, as distal-less family members, show different mechanism in regulation of Dspp transcription. Both Dlx3 and Osx can increase the promoter activity of p318, but not p591 and p1318. One possibility is that there are Dlx3 and Osx binding sites at upstream of nt −318 on Dspp promoter, but Dlx3 and Osx act as suppressors when binding to these binding sites. Another possibility is that there are strong negative regulatory elements for the binding of other transcription factors at upstream of nt −318 on Dspp promoter, and Dlx3 and Osx as co-factors could increase the suppressing activity of these elements. Cao et al.22 found that deletion of the region from −791 to −427 strongly increased basal Dspp promoter activity, and inferred there might be a strong negative regulatory element between nt −794 and −427. Narayanan et al.8 also identified a repression domain between nt −700 and −400 of Dspp promoter. Further analysis of this region identified two binding elements, which have 90% homology to the DNA binding sites for two bZIP transcription factors, Nrf1 and the CCAAT enhancer-binding protein (C/EBP) β. A possible response element for Yy1, a negative transcription factor, was also found (at position of nt −622) in this repression region of Dspp promoter, and the basal promoter activity of Dspp increased after the Yy1 binding sites was deleted22.Both positive and negative regulatory mechanisms are required for the spatial and temporal expression of Dspp transcription. Mutational studies suggest that the Dlx3 and Osx depend on each other to increase Dspp promoter activity. Dlx3 could not activate the promoter activity by binding itself to the Dspp promoter containing the mutated Osx binding site. All these results indicate the bindings of Dlx3 and Osx to their binding sites on Dspp promoter are necessary for each of these two transcription factors to activate Dspp promoter.By means of co-immunoprecipitation assay we have clearly demonstrated that Dlx3 associates and functionally cooperates with Osx during regulation of Dspp gene transcription via Dlx3/Osx signaling pathway: BMP-2 interacting with its receptor on cell membrane induces Dlx3 expression, then Dlx3 induces Osx expression; subsequently, both Dlx3 and Osx are activated, enter nucleus and bind to their target sites in Dspp promoter; then Dlx3 interacts with Osx and synergistically stimulate Dspp transcription.In conclusion, in this study we provide the evidence that BMP-2 activates Bmp2f/f and 3.6Col1a1-Cre mice have been described previously55. To conditionally knock out Bmp2 gene in odontoblasts, 3.6Col1a1-Cre;Bmp2f/f (referred to as Bmp2-cKOod) mice were generated by crossing 3.6Col1a1-Cre;Bmp2f/+ allele with Bmp2f/f allele. Mice at postnatal day 3 (P3) were put to death. Mandibles of the mice were immediately dissected and fixed with 4% paraformaldehyde (PFA) for 24 hours at 4 °C. After washes with PBS, samples were demineralized in 8% EDTA for 2 weeks. Then, the tissues were processed for paraffin embedding and sectioned at 5 µm.All experimental procedures involving the use of animals were reviewed and approved by the Institutional Animal Care at the University of Texas Health Science Center at San Antonio. All experiments were performed in accordance with the relevant guidelines and regulations. The generations of 56. Cells were grown at 33 °C under 5% CO2 in alpha minimum essential medium (α-MEM) supplemented with 10% fetal bovine serum, 100 units/ml penicillin/streptomycin, 50 µg/ml ascorbic acid and 10 mM sodium β-glycerophosphate. For BMP-2 induction, cells were starved overnight in FBS-free medium, then cells were induced with 100 ng/ml of recombinant human BMP-2 protein (R&D) for the indicated times. Then the cells were subjected to Western blot or immunofluorescence staining.A mouse immortalized preodontoblast cell line, MD10-F2 was usedBmp2-cKOod mice were analyzed by immunohistochemistry as described previously57. MD10-F2 cells cultured on glass slides were fixed with cold methanol/acetone (1:1) for 10 minutes and permeabilized with 0.5% Triton-X for 15 minutes. To block the non-specific binding of antibodies, slides were incubated with 10% goat serum for 30 minutes, followed by primary antibodies, anti-Dlx3 (Abcam) and anti-Osx (Santa Cruz), overnight at 4oC. After washing with PBS, slides were incubated with secondary IgG antibodies conjugated to Alexa-Fluor 568 (Invitrogen) were added and incubated for 1 hour. Hoechst (Pierce) was used to stain nucleus.The expressions of Dlx3 and Osx in mandibular first molars of both wild type and 2. Anti-Dlx3, anti-Osx, and anti-Dsp (Santa Cruz) was used as primary antibodies. β-actin antibody (Santa Cruz) was used as a loading control.Western blot was performed with whole cell lysates from MD10-F2 cells. Cells were washed with cold PBS and lysed with RIPA buffer . Proteins (40 μg/well) were resolved by 10% SDS-PAGE and transferred to a Trans-blot membrane (Bio-Rad). Western blot was performed as described earlierDspp promoter were generated using standard cloning procedures as described previously58. Briefly, the 1372-bp BglII-HindIII fragment of the mouse Dspp gene from nucleotides (nt) −1318 to +54 was cloned into the BglII and HindIII sites of pGL3-Basic luciferase vector (Promega) and designated p1318. The 646-bp EcoRI-HindIII fragment of the mouse Dspp gene from nt −591 to +54 was cloned into the EcoRI and HindIII sites of the pGL3-Basic and called p591. The 372-bp XhoI-HindIII fragment of the mouse Dspp gene from nt −318 to +54 was cloned into the XhoI and HindIII sites of pGL3-Basic vector and called p318. All these various plasmids contained part of the exon 1 noncoding region of Dspp gene.All reporter gene constructs containing 5′ deletions of the Dlx3 cDNA or Osx cDNA containing a Flag tag was subcloned into pcDNA 3.1 vector . All constructs were confirmed by DNA sequencing.For the generation of expression constructs, the full length of Dspp promoter was performed using the Quickchange® II Site-Directed Mutagenesis Kit (Stratagene). Mutant plasmids, p318(Dlx3I Del), p318(Dlx3II Del), and p318(Osx Del), were generated by site-directed deletion at −228/−221, −68/−64, and −148/−143 sites of p318 construct as a template. Primers for mutant plasmids were as follows: p318(Dlx3I Del), 5′-CTTCCAGGAGCAAGTGAAAGTAAGTCTAGTCC-3′, and 5′-GGACTAGACTTACTTTCACTTGCTCCTGGAAG-3′; p318 (Dlx3II Del), 5′-GCAGTGATTGGTTGAGAATCAAACTGAATTTAAATTTCAGC-3′, and 5′-GCTGAAATTTAAATTCAGTTTGATTCTCAACCAATC ACTGC-3′; p318(Osx Del), 5′-GCGAGCGAGGGAGTCGTTATTACAGCCTC-3′, and 5′-GAGGCTGTAATAACGACTC CCTCGCTCGC-3′. All constructs were checked by DNA sequencing and purified using Plasmid Midi Kit (Qiagen).Site directed deletion targeting the Dlx3 and Osx binding sites in the pcDNA-Dlx3 or pcDNA-Osx-Flag or empty vector using Lipofectamine 2000 transfection reagent (Invitrogen). To knock down the endogenous Dlx3 expression, small interfering RNA (siRNA) targeted mouse Dlx3 or negative control siRNA (Santa Cruz) were transfected into MD10-F2 cells at 60 nM concentration with Lipofectamine 2000. Cell lysates were collected 24 or 48 hours after transfection and protein levels of Dlx3, Osx and Dsp were analyzed by Western blot.MD10-F2 cells were transfected with pGL3-Basic plasmid and pRL-TK Renilla luciferase reporter (Promega), as well as pcDNA-Dlx3 or/and pcDNA-Osx-Flag were co-transfected into cells using the Lipofectamine 2000 for 48 hours. After that, the cells were collected and lysed in passive lysis buffer (Promega). The luciferase assay was performed using the Dual Luciferase Reporter Assay System (Promega) according to the manufacture’s protocols. Firefly and Renilla luciferase activities were qualified using the Glomax Luminometer (Promega). Firefly luciferase activities was normalized against Renilla luciferase activities. All luciferase assays were performed in triplicate at least three times.Cell were seeded in 48-well plates and cultured overnight. Then wild-type or mutant reporter plasmids or empty TM kit (Active Motif). Briefly, MD10-F2 cells were co-transfected with pcDNA-Dlx3, pcDNA-Osx-Flag expression plasmids and p318 plasmid. After 72 hours of transfection, cells were washed in PBS and incubated for 10 minutes with 1% formaldehyde. After quenching the reaction with 0.1 M glycine, the cross-linked material was sonicated into chromatin fragment of an average length of 200–800 bp. After pre-cleaning, 1% of each sample was saved as input fraction. The chromatin solution was precleared by adding protein G beads with salmon sperm DNA for 2 hours at 4 °C. Then immunoprecipitations were performed with protein G beads and 5 μg of anti-Dlx3 or Osx antibodies overnight at 4 °C. Anti-TFIIB antibody and negative control IgG  were used as positive and negative controls. Immunoprecipitated materials were washed, and cross-links were reversed by incubating samples for 5 hours at 65 °C in 200 mM NaCl and 10 μg of Rnase A to eliminate RNA. Recovered material was treated with proteinase K, and the DNA fragment was purified by Qiagen columns (Qiagen). The purified DNA was analyzed by PCR amplifying promoter regions (−318/+54) of the Dspp gene using the following primer pairs: forward −3185′-GAAATGCAGGGTGACAGAGTCTAAGTGGCT-3′−289, and reverse+545′-CGAGGGG ACTTTGAAAATCCAGATT-3′+30. The sequence of positive control PCR primers was provided by the kit, which flank the GAPDH promoter. The sequence of negative control PCR primers was also provided by the kit, which flank a region of genomic DNA between the GAPDH gene and the chromosome condensation-related SMC-associated protein (CNAP1) gene.ChIP were performed according to the instructions provided by ChIP-ITet al.59. Protein concentration was determined by using the Bradford assay60. Oligonucleotides used in EMSA were synthesized as listed in Table Dlx3 (Con)Oligo and Osx (Con)Oligo were set as positive control, which were verified containing two copies of the Dlx3 binding sites and one GC-rich Osx binding site, respectively61. Dlx3IOligo, Dlx3IIOligo, and OsxOligo consist of the putative Dlx3 or Osx binding region of Dspp promoter. These oligonucleotides were labeled with [γ-32P]ATP. Double-stranded probes were generated by annealing [γ-32P]ATP—labeled complementary oligonucleotides. EMSA was performed as described previously58. For the competition binding reactions, the unlabeled competitor in 100-fold molar excesses of the labeled probe was included in the reaction. Antibody super-shift experiments were performed with anti-Dlx3 and anti-Osx antibodies. The antibodies were added to recombinant Dlx3 protein or the nuclear extracts 10 min prior to the addition of the radiolabeled prob. Free and protein-bound DNA complexes were loaded onto a 5% native polyacrylamide gel in 1 × Tris/boric acid/EDTA (TBE) buffer, electrophoresed, dried, and exposed to X-ray film.Recombinant Dlx3 protein was purchased from Novus Biologicals. Nuclear extracts from MD 10-F2 cells were prepared using the method of Dignam pcDNA-Dlx3 and pcDNA-Osx-Flag. After 72 hours of transfection, cells were lysed with RIPA lysis buffer, and centrifuged at 12,000 rpm at 4 °C for 20 minutes. The supernatant was collected and precleared with 40 μl of protein A/G plus agarose beads (Santa Cruz) at 4 °C for 30 minutes. Immunoprecipitation was performed with anti-Dlx3, anti-Osx, anti-Flag (Sigma) antibodies or negative control IgG (3 μg each) and immunocomplexes were precipitated with 50 μl of protein A/G plus agarose beads. Beads were washed three times with wash buffer . Protein was eluted by boiling in 50 μl of sample buffer for 10 minutes and analyzed by Western blot.MD10-F2 cells cultured in 10-cm dish were transfected with 10 μg of t test with SPSS. For Western blot, densitometry of immunoblot bands were analyzed with ImageJ software.Quantitative data were presented as means ± SD from three independent experiments and analyzed with Analysis of Variance (ANOVA) or Student’s Supplementary data