Bifidobacterium breve represents a common member of the infant gut microbiota and its presence in the gut has been associated with host well being. For this reason it is relevant to investigate and understand the molecular mechanisms underlying the establishment, persistence and activities of this gut commensal in the host environment.Bifidobacterium breve UCC2003 was performed employing a combination of RNA tiling array analysis and cDNA sequencing. Canonical −10 (TATAAT) and −35 (TTGACA) sequences were identified upstream of transcribed genes or operons, where deviations from this consensus correspond to transcription level variations. A Random Forest analysis assigned the −10 region of B. breve promoters as the element most impacting on the level of transcription, followed by the spacer length and the 5’-UTR length of transcripts. Furthermore, our transcriptome study also identified rho-independent termination as the most common and effective termination signal of highly and moderately transcribed operons in B. breve.The assessment of vegetative promoters in the bifidobacterial prototype Bifidobacterium, our dataset constitutes a transcriptomic reference to be used for future investigations of gene expression in members of this genus.The present study allowed us to identify genes and operons that are actively transcribed in this organism during logarithmic growth, and link promoter elements with levels of transcription of essential genes in this organism. As homologs of many of our identified genes are present across the whole genus The online version of this article (10.1186/s12864-017-4387-x) contains supplementary material, which is available to authorized users. The development of Next Generation Sequencing (NGS) technologies has facilitated a genome-wide view of the transcriptional activities of an organism. The microarray-based technology for transcriptome analysis, which until recently was the tool of choice when assessing global transcription patterns of a given organism, has in recent years gradually been replaced by the alternative, NGS-based RNA-Seq approach .Transcription levels in bacteria may vary considerably from gene to gene, and may also vary in response to (changes in) environmental conditions. In this context a key role is played by the RNA polymerase (RNAP) which is responsible for gene transcription. However, to initiate transcription RNAP requires (reversible) association with a sigma subunit  in order to recognize the promoter sequence at two conserved DNA sequences that are located approximately 10 and 35 bp upstream of the transcriptional start site (TSS) , 3. OnceEscherichia coli and Bacillus subtilis) several different sigma factors (between 7 and 10) have been identified, being responsible for global modulation of transcriptional patterns in response to changing growth conditions and environmental challenges [E. coli have employed RNA sequencing (RNA-Seq) to identify and assess promoters recognized by the vegetative sigma-70 or RpoD sigma factor, which is responsible for transcription of housekeeping genes active during the exponential growth phase [In well characterized bacteria  rho-dependent transcriptional termination, which requires the presence of a stem-loop mediated pause site and the termination factor rho, and ii) rho-independent transcriptional termination, which involves a stem-loop structure followed by a poly-T sequence [http://rna.igmors.u-psud.fr/toolbox/arnold/) [Transcription termination in bacteria is caused by one of two principal mechanisms: sequence , 8. Rho-arnold/) .Bifidobacterium breve UCC2003 as a bifidobacterial prototype which has now become one of the most intensely characterized strains from a functional genomics perspective. Recently, genes have been identified which are essential for normal vegetative growth of this particular strain by employing a so-called TraDIS approach, which utilizes a mutant library of random Tn5 insertions combined with NGS to map the Tn5 insertion locations [Bifidobacteria enjoy an ever increasing scientific interest due to the purported beneficial effects they elicit on their (human) host . Howeverocations . This stB. breve UCC2003 cells using two different approaches involving strand-specific tiling arrays and RNA-Seq analyses. The data generated from these analyses facilitated an in-depth investigation of the (vegetative) transcriptional landscape of this strain and reveal the principal features responsible for transcriptional initiation in this strain.In the current study, we determined the global transcriptome of exponentially growing B. breve UCC2003 genome sequence. In this manner an array containing a total of 230,722 probes  were obtained from Agilent Technologies and used for this study. Probes designed on known housekeeping genes, i.e. dnaA, dnaN, recF, gyrB and gyrA [Array probes that were 60 bp in length and overlapping with a 22 nt sliding window were designed across the forward and reverse strand of the and gyrA , were usB. breve UCC2003 was inoculated into 2% glucose MRS (Difco) medium, grown until mid-log phase  and harvested by centrifugation. Cell disruption followed by DNA/RNA isolation was performed as described in a previous study [https://www.r-project.org/) [An overnight culture of us study . A totalus study , with thct.org/) .https://www.bioconductor.org).Background correction of raw data was performed using the convolution model (normexp + offset method in Limma) and a linear model with empirical Bayes statistics was employed to fit the log ratios and retrieve the highly expressed probes (mRNA) compared to the baseline (gDNA), as from the relative manual (B. breve UCC2003 cultures grown in MRS (Difco) following the same protocol as mentioned above for the tiling array experiment. The obtained cell pellet was resuspended in 1 ml of QIAZOL  and placed in a tube containing 0.8 g of glass beads . The cells were lysed by shaking the mix on a BioSpec homogenizer at 4 °C for 2 min (maximum setting). The mixture was then centrifuged at 12,000 rpm for 15 min, and the RNA-containing upper phase was recovered. RNA was further purified by phenol extraction and ethanol precipitation [Total RNA was isolated from pitation . QualityBifidobacterium reference genome through BWA [http://htseq.readthedocs.io/en/release_0.9.1/) and analysis of the RPKM values was performed using the formula RPKM = numReads/ [For RNA sequencing, 2.5 μg of total RNA was treated by the Ribo-Zero Magnetic kit (Illumina) to remove ribosomal RNA, followed by purification of the rRNA-depleted sample by ethanol precipitation. RNA was further processed according to the manufacturer’s instructions. The yield of rRNA depletion was checked by Tape station 2200 (Agilent Technologies). Then, 400 ng of the rRNA-depleted RNA sample was fragmented using a Bioruptor NGS ultrasonicator  followed by size evaluation using Tape station 2200 (Agilent Technologies). A whole transcriptome library was constructed using the TruSeq Stranded RNA LT Kit (Illumina). Samples were loaded into a Flow Cell V3 150 cycles (Illumina) as reported by the technical support guide. The reads were depleted of adapters, quality filtered  and aligned to the ough BWA . Countin000,000) .B. breve, transcriptional start sites (TSSs) and termination sites (TTSs) were deduced from the combination of tiling arrays and RNA-Seq data. Array probes were first aligned to the full nucleotide sequence of B. breve UCC2003 (Genbank: NC_020517) using BLAT aligner [B. breve UCC2003 genome. For each expressed gene or operon the start and end coordinates of the transcript were obtained from the first to the last base position from probes that elicit significant hybridization signals  at which mRNA signal discriminates from the gDNA baseline and compared to the RNA-Seq mapped reads. In the case of RNA-Seq transcriptional starts and ends were defined at the first and last base of reads where an increase or drop in sequence coverage was observed. The best fit to a canonical −10 and −35 promoter sequence in B. breve was first searched using Meme Suite (http://meme-suite.org/) over a region of 62 bp upstream of the TSS of a training set of 75 high/medium level transcribed housekeeping genes. The obtained predicted canonical promoter was then used to compile a promoter list for the remaining transcribed genes using a combination of Meme (http://meme-suite.org/) and manual annotation in Artemis (http://www.sanger.ac.uk/science/tools/artemis). All manually annotated promoters were first re-aligned with Meme to ensure that −10 and −35 were detected at their optimal position.In order to define a transcriptional unit (TU) in http://www.sanger.ac.uk/science/tools/artemis). Where appropriate, additional terminators to the ones predicted above were included following a manual search for the presence of polyT stretches downstream of putative stem-loop structures at the end of transcripts.Rho-independent transcriptional terminators were first predicted using ARNold  and manuhttps://cran.r-project.org). Random forest classification was performed to identify signature classifiers for discrimination of not-expressed, low, medium and highly expressed genes. This classification model, consisting of 5000 decision trees was trained on random subsets of properties and sequences of promoter regions of expressed genes and 270,000 promoter-sized kmers in the intergenic region of B. breve UCC2003 (excluding the promoters) as negative control. The classifiers chosen for RF were: spacer length (bp), leader length (distance between promoter and start of the gene), AT % of the −35 upstream region, AT % of the −35 signal, AT% of the spacer, AT % of the −10 region and AT % of the −10 downstream region and all aligned bases in the 62 bp promoter region. Three classes were chosen based on the differential hybridization level between mRNA signal vs gDNA baseline: high (>10 fold), medium (3 < 10 fold) and low (<3 fold) level of transcription.Parameters associated with the predicted promoter sequences were first retrieved and stored in variables to be used as classifiers in Random Forest (RF) analysis. Information contained in these classifiers was extracted from the promoter region  resulting in a 62 basepair region (on average), which was manually aligned with the predicted transcription start and the −10 and −35 regions. Random forest (RF) analysis was then performed using the RandomForest v4.6–10 package in R  with orthologues previously retrieved from seven fully sequenced and publicly available B. breve genomes as well as 46 Bifidobacterium type strains [In order to assess if the identified transcribed genes are part of the  strains –24.B. breve UCC2003 genes, the list of transcribed genes in our datasets was compared with genes that were deemed essential based on analysis of an insertional mutant library of this strain combined with TraDIS sequencing [http://circos.ca/).In order to combine our expression data with information related to essential quencing . A circohttp://www.rnaspace.org/) and the Rfam database (http://rfam.xfam.org/) and the entire nucleotide sequence of B. breve UCC2003 as input.Prediction of sRNAs and RNA-based regulatory elements was performed using the RNAspace web server (http://www.sanger.ac.uk/science/tools/artemis) was used to inspect the results and for data representation.Artemis v16 , was determined in this study using two different technologies involving a hybridization-based approach availing of whole-genome tiling arrays integrated with data obtained by (Illumina) high throughput RNA sequencing  showed that 84.3% of the genes identified as being transcribed in the tiling array approach were also identified by the RNA-Seq approach, confirming good reproducibility between these two technologies , as well as 47 tRNA and 6 rRNA genes were transcribed under the applied conditions in i) presence of mRNA secondary structures influencing the cDNA synthesis, ii) ambiguity in read mapping, iii) random hexamer priming in cDNA generation which biases the nucleotide composition at the start of sequencing reads, and/or iv) fragmentation during library preparation [Despite the above mentioned corresponding data generated by the two distinct transcriptome methods, some differences were observed. For example, we observed a non-even distribution of RNA-Seq reads across the same transcript, which is not (or much less so) noted for the tiling array approach, where the hybridization signals seem to be relatively evenly spread across a single transcript Fig.  panel a.paration –27. An aparation .Fig. 2BThe 22 bp sliding window used to design the array probes introduced a degree of uncertainty in the determination of the exact transcriptional start site (TSS), although a precise location of the TSS could in most cases be verified by RNA-Seq information (see below). Despite the high level of concordance obtained between the tiling array and RNA-Seq data sets, we found higher reproducibility for those genes exhibiting high or medium transcription levels. In fact, genes eliciting a low transcriptional level in tiling arrays were sometimes hard to identify as their hybridization signals were very close to baseline signal levels.B. breve, array and RNA-Seq data were first independently processed and subsequently compared. In the specific case of tiling arrays, TSSs were assigned at the first base position of the first significantly expressed probe in the 5’-UTR region of a transcript. As the tiling array employed in the current study was designed based on a 22 bp sliding window, this was inevitably going to impact on the TSS-assignment accuracy. For this reason TSSs were also determined from RNA-Seq data and compared, which revealed that the vast majority of discrepancies between the obtained predictions ranged between 25 and 50 bp, where a given TSS predicted from RNA-Seq was always located downstream of the corresponding array-based TSS. As the arrays were designed based on 60 bp probes with 22 bp overlap, we expected an overestimation of TSS in tiling arrays of at least 22 bp. Our comparison of TSS prediction performed with these two approaches revealed that the associated error of predicting TSSs from our array design does not exceed the size of one probe  [With the objective of defining TSSs of vegetatively transcribed genes and operons in obe Fig.  panel a.br_1668) –31 returB. breve during exponential growth.Integration of data generated using tiling arrays and RNA-Seq platforms allowed us to determine the (approximate) TSS of 413 genes and/or operons (including 2 rRNA loci and 33 tRNAs) transcribed in B. breve, the alignment of a 62 bp region upstream each determined TSS was performed followed by conserved motif finding in Meme (http://meme-suite.org/). This search was carried out with the idea of covering the binding region required for the RNA polymerase to initiate transcription [In order to establish whether such determined TSSs would allow us to predict a promoter consensus sequence for cription , 32.Our analysis identified typical bacterial vegetative promoter consensus motifs of TATAAT (−10 region) and TTGACA (−35 region), located within an optimum of 17 bp spacer distance, being especially conserved upstream of highly/medium expressed (housekeeping) genes Fig.  panel b.st, adenine in 2nd and thymine in 6th position , the identification of transcriptional start sites (TSS) was followed by the analysis of putative transcriptional termination sites (TTSs).In the case of transcriptional termination, we found that the array data represented the 3′ termination signal of operons better than the RNA-Seq information (and for this reason we did not include the latter data when predicting TTSs), probably because the tiling array data were based on direct mRNA labelling, without the necessity of cDNA synthesis, and for this reason these results are not affected by interfering factors such as 5′ enrichment of reads and/or uneven distribution of coverage along a single transcript Fig.  panel a.Based on our analysis, we were able to identify a total of 224 rho-independent terminators , each constituted by a GC-rich stem loop structure followed by the characteristic polyT region. In the case of bi-directional terminators we observed that the stem loop region is preceeded by polyA .E. coli and Bacillus subtilis [Of the 413 identified TUs, 83 were classified as highly transcribed (see Methods section) of which 70 contained a rho-independent terminator, while of the 153 classified as moderately transcribed TUs, 81 were predicted to be subject to rho-independent termination. As regards to weakly transcribed TUs, only 73 terminators out of the 177 identified were classified as rho-independent, suggesting that rho-independent terminators represent strong termination signals especially for bifidobacterial genes and operons exhibiting high (and perhaps medium) level transcription , we also observed that TTSs often locate at GC-rich stem-loops structures, not otherwise classified as rho-independent terminators. It can be argued that they constitute rho-dependent terminations or RNA polymerase pausing sites prior to the release of the transcription machinery, even though we did not identify a clear consensus among these regions. Nevertheless this observation suggests a possible involvement of GC-rich stem-loops as pausing sites for the RNA polymerase followed by its release.As rho-independent terminators seem to represent the most frequently adopted strategy by In the case of convergently expressed operons (simultaneously transcribed in a tail-to-tail orientation), we often observed that no termination signal was detectable. However, in these cases we often noticed overlap between the 3’-UTR region of these opposing transcripts . Deduced (vegetative growth-associated) TU length in B. breve was shown to vary from over 9 Kbp to ~200 bp (when constituted by a single gene), with a calculated average size of 1636 bp.The transcriptome analysis performed in this study also allowed an investigation into the main features associated with TUs in B. breve TUs, the 5’-UTR region is nearly always longer than the untranslated 3′ end   Fig.  panel b.ly) Fig.  panel b.B. breve, the information obtained from the identification of promoters was stored in variables and used to generate classifiers for Random Forest (RF) analysis.With the aim of investigating factors that have the most obvious impact on the level of gene transcription in The classifiers chosen for RF were based on spacer length (bp), 5’-UTR length (distance between promoter and start of the gene), AT % of the −35 and corresponding upstream region, AT % of the −35 signal, AT% of the spacer, AT % of the −10 region and AT % of the −10 downstream region.All promoters were grouped based on the level of RNA signal strength vs gDNA baseline (expressed as fold-change or FC). Such analyses revealed transcription levels being classified as high (FC > 10), medium (3 < FC < 10) or low (FC < 3), which were then subjected to RF (Random Forest) analysis performed on 5000 trees (representative of the whole sequence space).st, 2nd and 6th position for the −10 region, while the −35 region showed higher importance in the first three base positions despite having less impact on gene transcription . Our analysis also revealed that the 5’-UTR length may impact gene transcription, in fact a longer leader length seems to be associated with highly expressed genes , suggesting that the de novo prediction of transcription level cannot be deduced from the promoter with sufficient accuracy. However, our analysis highlighted certain trends in the degree of conservation of the promoter. Highly transcribed genes seem to possess a promoter that best resembles the canonical bifidobacterial consensus, while medium expressed genes possess a more degenerate −35, but a still highly conserved −10 region. Interestingly, weakly transcribed genes seem to possess both degenerated −10 and −35, with an AT-rich region located in the TATA box  gene expression as applied to the whole genus  and the 7.2 Kbp ATP synthase operon (Bbr_0323–30), both characterized by an untranslated leader region of remarkable length . Another notable example of essential TUs is represented by the ribosomal rRNA genes, organized in two 5.6 Kb regions in B. breve UCC2003 and exhibiting a transcription level that is higher than any other gene (>50 fold) in the genome  are present   and one partially functioning (BbrI) systems [Another intriguing feature revealed by our dataset is the (constitutive) transcription of the CRISPR/Cas spacers in  systems . Of thes systems . We obseB. breve such as RNAseP, transfer-messenger RNA (tmRNA) and 4.5S RNA  with associated promoter and rho-independent terminator  or 4.5S RNA we assigned this sRNA to a 104 bp highly transcribed region (>50 fold) and we were able to identify the corresponding promoter while the associated transcriptional terminator is characterized by a stem loop structure , a thiamine pyrophosphate (TPP) or T-box and a YKOK riboswitch. In the case of the identified FMN riboswitch, we located the expression of this element in the 5’-UTR region of two genes (locus tags Bbr_1328–29) resembling the modular organization of an energy coupling factor (ECF) transporter  (AdditioIn the case of TPP-sensing riboswitch or T-box we assigned it to an expressed 5’-UTR region of an operon involved in thiamine biosynthesis (Bbr_0674–77), suggesting also in this case its involvement in the regulation of these genes  we identified this element in an expressed region located at the 5’-UTR of an ABC transporter (Bbr_0406–07), where the permease-encoding gene (Bbr_0407) belongs to the category COG4986 of “inorganic ion transport”, suggesting also in this case the involvement of these genes in bacterial metal ion homeostasis  and TTGACA (−35 region) with an optimum spacer length of 17 bp upstream the TSS of transcribed genes or operon. Random Forest analysis revealed the parameters with the highest impact on transcription levels in B. breve, being the AT % of the −10 region the classifier with highest importance followed by the spacer length and the 5’-UTR length of transcripts. Our analysis highlights how the consensus of the promoter region appears to degenerate from the canonical consensus with the decrease in transcriptional level, however, prediction of transcription levels is still difficult and may require the inclusion of other  features of the promoter region.Based on the obtained transcriptome data we were able to map transcriptional start and termination sites (TTS) of the identified B. breve (and perhaps Bifidobacterium), especially for highly and moderately transcribed operons. It also showed that there may be other strategies of transcriptional termination responsible for modulating gene expression in B. breve.Our study also described how rho-independent termination represents the most common and effective termination signal adopted by B. breve demonstered how the vast majority of those also appear to be expressed in our dataset, in particular those housekeeping genes of which orthologues can be found across the Bifidobacterium genus.Furthermore, the alignment of our dataset with a recently published study on the essential genes of B. breve UCC2003 , indicating that RNA-mediated regulation of gene expression also occurs in this organism (and genus). Altogether this study has generated a detailed and robust dataset to be used as a reference for transcription in the genus Bifidobacterium.Finally, our analysis also allowed the identification of a number housekeeping sRNAs and regulatory RNA elements not previously identified in Additional file 1: Table S1.Transcribed genes as determined by RNA-Seq and tiling array analyses. A .docx document containing the list of genes detected as expressed in RNA-Seq and Tiling arrays experiments. For each gene the fold-change  and RKPM values are also indicated. (DOCX 135 kb)Additional file 2: Figure S1.a) Bar chart showing B. breve UCC2003 genes detected as transcribed in RNA-Seq, but not in tiling arrays with associate gene count and level of expression (RPKM). b) Distribution of genes exhibiting discrepant transcription between RNA-Seq and tiling array approaches as grouped by level of transcription (RPKM). A red horizontal line indicates the baseline of transcription background, while in purple the average RPKM of transcribed genes is indicated. (PDF 94 kb)RNA-Seq and tiling array comparisons. Additional file 3: Table S2.B. breve and associated promoters. A .docx document containing the list of predicted transcriptional units (TUs) in B. breve and corresponding (predicted) promoters. For each TU also the transcription level and transcriptional termination is indicated. (DOCX 143 kb)Predicted transcriptional units (TUs) in Additional file 4: Figure S2.B. breve termination of transcription. Artemis plot showing the different strategies of transcriptional termination of convergently expressed genes (dashed purple line) in B. breve: a) Double-stranded rho-independent termination observed between two convergently expressed genes employing a bidirectional terminator. In this case a clear-cut transcriptional termination is observed in either forward and reverse strands. b) Single stranded rho-independent termination observed between two convergently transcribed genes employing a strand specific terminator. In this case transcriptional termination is only observed in the forward strand. c) Tail-to-tail termination observed between two convergently transcribed genes without a termination signal. In this case no specific point of transcriptional termination can be observed. (PDF 474 kb)Additional file 5: Table S3.B. breve. A .docx document containing the list of essential genes of B. breve UCC2003 with associated level of transcription as detected from our transcriptomic study. (DOCX 41 kb)Transcription of essential genes in Additional file 6: Figure S3.B. breve ribosomal operon and CRISPR-Cas system transcription. Artemis plot showing the level of transcription of a) the rRNA operon and b) the CRISPR/Cas system in B. breve UCC2003 as detected in tiling arrays. The relative TU is indicated by a dashed purple line. (PDF 282 kb)Additional file 7: Figure S4.B. breve sRNA expression. Artemis plot showing the sRNA transcription in B. breve of a) Ribonuclease P, b) tmRNA, and c) 4.5S SRP RNA. In all cases tiling array signals of forward (red) and reverse (blue) strand are indicated. (PDF 701 kb)Additional file 8: Figure S5.B. breve regulatory RNA expression. Artemis plot showing the regulatory RNA transcription in B. breve of a) FMN, b) TPP, and c) YKOK elements. In all cases tiling array signals of forward (red) and reverse (blue) strand are indicated. (PDF 732 kb)


Lytic infection by the Epstein-Barr virus (EBV) poses numerous health risks, such as infectious mononucleosis and lymphoproliferative disorder. Proteins in the bromodomain and extraterminal (BET) family regulate multiple stages of viral life cycles and provide promising intervention targets. Synthetic small molecules can bind to the bromodomains and disrupt function by preventing recognition of acetylated lysine substrates. We demonstrate that JQ1 and other BET inhibitors block two different steps in the sequential cascade of the EBV lytic cycle. BET inhibitors prevent expression of the viral immediate-early protein BZLF1. JQ1 alters transcription of genes controlled by the host protein BACH1, and BACH1 knockdown reduces BZLF1 expression. BET proteins also localize to the lytic origin of replication (OriLyt) genetic elements, and BET inhibitors prevent viral late gene expression. There JQ1 reduces BRD4 recruitment during reactivation to preclude replication initiation. This represents a rarely observed dual mode of action for drugs. Modern antiviral drugs generally share two characteristics: the target is a single protein, and that target acts at one distinct step in the viral life cycle . AcyclovBZLF1 is necessary and sufficient to promote downstream events  family regulate multiple stages of viral life cycles. The bovine papilloma virus protein E2 binds the human protein BRD4 directly and colocalizes on mitotic chromosomes to attach viruses for proper segregation , 10. Mut14–JQ1  and I-BEA). Cells treated with 1 μm JQ1, I-BET, or RVX-208 alone similarly yield a low percentage of cells positive for BZLF1. Antibodies raised against human immunoglobulin cross-link the B cell receptor and reactivate EBV from latency (50 of 20 ± 9 nm (B), a concentration consistent with the affinity of the small molecule for BET bromodomains . We controlled for pleiotropic toxicity by verifying that JQ1-treated cells show similar growth, 97 ± 12%, and viability, 102 ± 1%, compared with untreated cells. Vehicle-treated lines display a transcription profile consisting predominantly of low-abundance lytic gene signals from background spontaneous reactivation . Thus, we know that the lack of BZLF1 protein induction detected by flow cytometry after JQ1 treatment  is due to lack of BZLF1 gene expression rather than post-transcriptional regulation. We also confirmed that replication of viral DNA is inhibited upon JQ1 pretreatment by deep sequencing all DNA extracted from cells and calculating the increase in viral genomes. The pattern of the results is similar to that seen with lytic gene transcription: viral DNA increases with antibody exposure but decreases back to background levels with JQ1 pretreatment prior to antibody exposure . Thus, JQ1 completely suppresses the EBV lytic cycle even at downstream readouts, speaking to the strong efficacy of inhibition.JQ1 blocking BZLF1 protein production should also prevent downstream transcription of lytic genes and replication of viral DNA. To confirm this inhibition of transcription, we performed RNA-seq on cells induced by antibody with or without 1 μreatment C. We contivation , 24. CelBZLF1 expression, we suspected that BET proteins would localize to the BZLF1 promoter. To test this possibility and to simultaneously check for potential binding elsewhere, we probed the entire EBV genome for BRD2, BRD3, and BRD4 occupancy with ChIP-seq (A). Contrary to our suspicion, we did not detect noticeable enrichment in the region near the BZLF1 transcription start site at ∼90 kb on the EBV genome. Much to our surprise, however, we detected strong signals of occupancy for all three BET proteins at ∼41 and ∼144 kb within the 3′ edge of the two OriLyt elements (A) are also consistent with bromodomain function. Acetylated H3K9 and H3K27, which may serve as bromodomain-binding partners, are enriched at these sites. The BET protein-bound regions lack peaks of H3K4 trimethylation, suggesting the assembly of enhancer-like as opposed to promoter-like chromatin  elements. Given that BET inhibitors prevent ChIP-seq A. Contraelements  geneticaelements A are alshromatin .POUF2AF1 and BCL6 genes in B cell lymphoma lines . JQ1 also reduces BRD4 binding to the viral OriLyt elements ∼2-fold. Although this reduction of occupancy in the EBV genome is weaker than effects observed in the human genome, similar behavior at the viral OriLyt elements further validates our identification of this enriched occupancy as BET protein-binding events.We also measured the effect of JQ1 on BRD4 occupancy at the OriLyt elements. Although our initial ChIP-seq protocol readily detected BRD4 occupancy on the high-copy number EBV episome, we could not readily identify occupancy on the human genome. To improve sensitivity so that we could compare binding in response to BET inhibitor treatment between sites on both genomes, we increased the number of cells processed and repeated the ChIP-seq experiment. Among many other sites, BRD4 has been shown to bind superenhancers of the ma lines . In our  regions B. JQ1 alBZLF1 promoter to drive expression, then we surmise that BET inhibitor activity at that site occurs through an indirect mechanism involving other host proteins. Our RNA-seq data identified ∼3000 differentially regulated human genes in response to 1 μm JQ1 treatment. Pathway analysis reveals 10 enriched groups of genes either known or predicted to be controlled by the transcription factors FOXP3, STAT6, SPI1, KLF1, E2F4, EBF1, BACH1, YY1, NFE2L2, and TAL1. We followed up on these leads by depleting individual proteins and measuring the effect on viral reactivation. Lentivirus shRNA reduces BACH1 expression by ∼70% compared with a control non-targeting shRNA (E). This BACH1 knockdown reduces the increase in BZLF1 expression induced by gemcitabine ∼2-fold (F). Other candidate factors may also mediate the effect of BET inhibitors on viral transcription, but our preliminary results suggest that JQ1 perturbs BACH1 function to decrease immediate-early protein production.If BET proteins do not directly bind the ng shRNA E. This B ∼2-fold F. Other BZLF1 expression. Because BRD2, BRD3, and BRD4 bind the lytic origins of replication, we hypothesized that BET inhibitors could perturb the function of that genetic element. Prevention of BZLF1 expression in MutuI cells by JQ1 precludes testing for this effect because blocking immediate-early transcription abrogates all downstream events, including lytic DNA replication . RNA-seq verified that, as expected, incubation with doxycycline and consequent lytic cycle induction cause transcription to increase throughout the EBV genome (B).To demonstrate that BET inhibitors act at a second step in the viral life cycle, we determined that JQ1 blocks EBV DNA replication despite ectopic lication . To perfkata-Zta , an EBV-ve cells A. RNA-seV genome B.m JQ1 before exposure to doxycycline, total transcription of BZLF1 does not change , whereas expression is reduced at many other genes (B). Again, we controlled for pleiotropic toxicity by verifying that JQ1-treated cells showed similar growth, 79 ± 23%, and viability, 101 ± 1%, compared with untreated cells. The overlapping nature of EBV gene organization often confounds analysis, so we measured expression only at non-overlapping RNA segments. We found that, although none of 18 early genes are perturbed by JQ1 pretreatment, 12 of 18 late genes show significantly decreased expression . I-BET pretreatment reduced EBV DNA content by ∼2-fold, and this weaker inhibition of DNA replication relative to that by JQ1 is consistent with the smaller RNA-seq perturbations. RVX-208 yields no reduction, again emphasizing the primary role of bromodomain 1 in inhibition of the EBV life cycle.To directly verify a replication defect, we measured the viral DNA content of cells. We found that 1 μnt alone C. I-BET LNGFR) gene under control of the same bidirectional tetracycline-inducible promoter that expresses the BZLF1 protein. Magnetic purification of this cell surface marker allows for separation of cells containing latent and reactivated EBV. When Akata-Zta cells are pretreated with acyclovir before exposure to doxycycline, we detect more DNA content near both OriLyt elements in cells containing lytic episomes (A). The signal arises from abortive DNA replication that is initiated prior to incorporation of acyclovir and consequent chain termination during elongation. When 1 μm JQ1 pretreatment is added, this abortive replication is not detected. Thus, JQ1 blocks the initiation of lytic DNA replication.We further probed the step at which lytic replication is blocked by measuring the appearance of newly synthesized portions in preparations of bulk DNA. Akata-Zta cells contain the low-affinity nerve growth factor receptor (episomes A. The siB), we suspected that larger perturbations may occur during reactivation. We therefore measured differences in BRD4 occupancy between latent and lytic episomes with ChIP-seq (B). Akata-Zta cells were reactivated with doxycycline but also pretreated with acyclovir to prevent lytic synthesis of linear genomes that may confound measurement of protein binding to circular episomes. Populations containing latent and lytic EBV were separated based on LNGFR expression. In the absence of JQ1, BRD4 occupancy increases ∼4–12-fold at the lytic origins of replication upon reactivation. In the presence of JQ1, occupancy increases only ∼1–2-fold. JQ1 reduces the change in enrichment by ∼4–5-fold. This prevention of BRD4 recruitment may underlie the defect in lytic DNA replication initiation.Although JQ1 reduces BRD4 binding to the OriLyt elements during latency B, we susChIP-seq B. Akata-BET inhibitors display specific activity against EBV. Dual-action transcriptional inhibition does not appear to be the result of a nonspecific antiviral host response against episomal DNA viruses. JQ1 reduces replication of the John Cunningham polyomavirus through inhibition of BRD4-mediated NF-κB coactivation . OppositBZLF1 promoter (F). The second block likely occurs by directly preventing lytic DNA replication (C) is not as robust as inhibition at the first block (A), the combined effects result in complete suppression of all stages of the EBV lytic cycle (C and D). Such dual-action inhibitors may be more effective than current drugs and result in a decreased risk of resistance. Many BET inhibitors are currently in clinical trials for cancer, and repurposing them against EBV may be useful in treating lytic replication during infectious mononucleosis and post-transplantation lymphoproliferative disorder.We propose a model  wherein promoter . JQ1 altpromoter F. The selication , an assolication . We favolication , but we lication C is not st block A, the coic cycle , C and DSystems pharmacology approaches attempt to target key nodes in a network to most efficiently disrupt a biological process. The EBV lytic cycle progresses as a sequential cascade. Immediate-early expression of transcription factors to bind multiple viral promoters serves as an initial amplification step to activate early genes. DNA replication drives a second amplification step necessary for the transcription of late genes. Our studies of viral chromatin led to the serendipitous discovery that BET inhibitors target both key nodes in the EBV lytic cascade. These small molecules have several properties that may underlie the ability to perturb multiple steps in a viral pathway: nanomolar affinity for different complexes, involvement in epigenetics that dysregulates diverse genes, and binding to a host rather than a viral protein. Only a few other similar lead compounds also exist. GNF-2 targets both the Abl kinase and viral glycoprotein E to inhibit dengue virus replication . 17-Dimem HEPES and 2 g/liter NaHCO3 supplemented with 10% (v/v) Tet system-approved fetal bovine serum (Clontech) in 5% CO2 at 37 °C. Growth was measured with a hemocytometer, and viability was measured by trypan blue exclusion.MutuI  cells wem JQ1 (EMD Millipore), I-BET/GSK525762A (EMD Millipore), RVX-208 , or vehicle was added for 1 h before induction. Where lytic genome replication elongation is specifically prevented, 200 μm acyclovir (Sigma-Aldrich) was added for 1 h before induction. The JQ1 dose response was determined by performing pretreatment with vehicle, 10 nm, 100 nm, 1 μm, or 10 μm for 1 h before induction with 1 μg/ml gemcitabine (Sigma-Aldrich) or vehicle for 3 days. Data were fit to the sigmoidal equation a + (b − a)/(1 + (x/c)d) (where c = IC50) using KaleidaGraph version 4.5.2.To induce reactivation of the lytic cycle, log-phase cultures were treated with 10 μg/ml goat anti-human IgG, IgM, IgA secondary antibody (Thermo Fisher Scientific), 500 ng/ml doxycycline (Sigma-Aldrich), or vehicle for 1 day. Where BET inhibitor pretreatment is noted, 1 μm HEPES, 2 g/liter NaHCO3, 10% (v/v) fetal bovine serum, and 2 μg/μl puromycin in 5% CO2 at 37 °C.Lentivirus shRNA was used to deplete BACH1 from MutuI cells. pLKO.1-hPGK-Puro-CMV-tGFP plasmids contained either a BACH1-targeting shRNA of the sequence CCGGCCAGCAAGAATGCCCAAGAAACTCGAGTTTCTTGGGCATTCTTGCTGGTTTTT  or a non-targeting control shRNA of the sequence CCGGGCGCGATAGCGCTAATAATTTCTCGAGAAATTATTAGCGCTATCGCGCTTTTT . Lentiviruses were produced by transfecting plasmids into 293T cells, and infection-based titers were measured with GFP expression  .EBV immediate-early gene expression was measured by flow cytometry on a FACSCalibur (BD Biosciences) after staining for BZLF1 .Protein knockdown was measured with Western blotting using the SuperSignal West Pico Chemiluminescent Substrate (Thermo Fisher Scientific), a ChemiDoc MP Imaging System (Bio-Rad), and ImageLab version 5.2.1 (Bio-Rad). BACH1 was detected using the F-9 antibody  at 1:300 dilution and rabbit anti-mouse-HRP  at 1:5000 dilution. For normalization, actin was detected using anti-β-actin  at 1:10,000 dilution and rabbit anti-mouse-HRP  at 1:10,000 dilution.RNA-seq libraries were prepared and sequenced as described . EBV tram JQ1, 1 μm I-BET, or vehicle. Transcriptional changes were calculated by measuring normalized nucleotide counts for each lytic transcript integrated over only exons spanning regions that did not overlap with any other transcript  at room temperature for 10 min. Cells were then washed and lysed, and the resulting nuclei were frozen. Upon thawing, volumes were adjusted to 36% to reflect the starting cell number. Chromatin was digested with micrococcal nuclease for 5 min prior to shearing using a Bioruptor water bath sonicator (Diagenode). After sonication, tubes were vortexed and spun down for 10 min at 4 °C at 18,000 × g. Aliquots of input samples with 10 mm Tris, 1 mm EDTA, pH 7.5, added to 80 μl were combined with 100 μl of 50 mm Tris-HCl, pH 7.5, 10 mm EDTA, 1% (w/v) SDS and 20 μl of 20 mg/ml Pronase in 100 mm Tris, 150 mm NaCl, pH 7.5, for reverse cross-linking. 10–15 μl of epitope-specific antibodies per ChIP were preincubated with 150 μl of protein G Dynabeads (Invitrogen) and 450 μl of Buffer A containing 10 mm Tris-HCl, 1 mm EDTA, 150 mm NaCl, 5% (v/v) glycerol, 0.1% (w/v) sodium deoxycholate, 0.1% (w/v) SDS, 1% (v/v) Triton X-100, pH 8.0, rotating at 4 °C for 1 h. ChIP antibodies used recognized BRD4 , BRD2 , BRD3 , H3K27ac , H3K9ac , and H3K4me3 . Dynabeads were transferred to 2.5 ml of cold Buffer A without SDS but supplemented with 25 μl of Halt protease inhibitor mixture (Thermo Fisher Scientific). 277.5 μl of chromatin, the proportion equivalent to 1.5 × 107 cells, was added to the beads in buffer and incubated on a rotator for 4 h at 4 °C. Beads were collected on a magnetic rack and washed consecutively with the following ice-cold buffers: Buffer A with Halt protease inhibitor mixture, then Buffer A with 500 mm NaCl and Halt protease inhibitor mixture, and finally 20 mm Tris-HCl, 1 mm EDTA, 250 mm LiCl, 0.5% (v/v) Nonidet P-40, 0.5% (w/v) sodium deoxycholate, Halt protease inhibitor mixture, pH 8.0. Complexes were eluted from the beads in 300 μl of 10 mm Tris, 1 mm EDTA, 0.7% (w/v) SDS, pH 8.0, at room temperature. For reverse cross-linking, the eluate was added to 450 μl of 50 mm Tris, 10 mm EDTA, 0.45% (w/v) SDS, pH 7.0, and 82.5 μl of 20 mg/ml Pronase in 100 mm Tris, 150 mm NaCl, pH 7.5. Reverse cross-linking of input and ChIP eluate samples was performed by incubation at 42 °C for 2 h and then at 65 °C overnight. DNA was purified by silica-based membrane affinity with a MinElute PCR purification kit (Qiagen).ChIP-seq methods were based on standard protocols  with theChIP-seq libraries were prepared with either the Ovation Ultralow Library System (NuGen) or Ovation Ultralow Library System V2 (NuGen). Quantification and size distribution of libraries were observed using the Bioanalyzer High Sensitivity DNA kit (Agilent). Size selection of the predominant DNA peak between ∼200 and 400 bp was performed either by gel extraction or magnetic bead purification. For gel extraction, the band from a 1% agarose gel in Tris acetate-EDTA was excised and purified with silica-based membrane affinity using a MinElute gel extraction kit (Qiagen). For magnetic bead purification, solutions were treated with two steps of solid-phase reversible immobilization using Agencourt RNAClean XP beads (Nugen). Libraries were sequenced on a HiSeq system (Illumina).8 treated cells were processed per ChIP. Initial buffer volumes were scaled up accordingly, but purification occurred with 10 μg of BRD4-specific antibody preincubated with 100 μl of protein G Dynabeads. Other steps were identical to the protocol performed with fewer cells.For immunoprecipitation experiments measuring the effect of JQ1 treatment on BRD4 occupancy in the MutuI line, 1 × 10TM accession number NC_007605.1) genomes using Bowtie 0.12.8  genomes using Bowtie 0.12.8  and subsequently sheared using an S2 Focused ultrasonicator (Covaris) to obtain fragments ∼200 bp in length. Libraries were prepared, DNA was sequenced, and reads were mapped as described for ChIP-seq. Peaks on the EBV genome were visualized after normalization to the total number of mapped reads. Every experimental condition was measured with two independent biological replicates and yielded ∼30-40 million mapped sequences for each data set.K. M. K. and J. L. M. designed research. K. M. K., S. A. M., A. H., S. G. F., J. J. S., S. M. L., D. M. M., and J. L. M. performed research. K. M. K., S. A. M., A. H., S. G. F., S. M. L., D. M. M., and J. L. M. analyzed data. K. M. K. and J. L. M. wrote the paper.


SIX1 gene belongs to the family of six homeodomain transcription factors (TFs), that regulates the extracellular signal-regulated kinase 1/2 (ERK1/2) pathway and mediate skeletal muscle growth and regeneration. Previous studies have demonstrated that SIX1 is positively correlated with body measurement traits (BMTs). However, the transcriptional regulation of SIX1 remains unclear. In the present study, we determined that bovine SIX1 was highly expressed in the longissimus thoracis. To elucidate the molecular mechanisms involved in bovine SIX1 regulation, 2-kb of the 5′ regulatory region were obtained. Sequence analysis identified neither a consensus TATA box nor a CCAAT box in the 5′ flanking region of bovine SIX1. However, a CpG island was predicted in the region −235 to +658 relative to the transcriptional start site (TSS). An electrophoretic mobility shift assay (EMSA) and chromatin immunoprecipitation (ChIP) assay in combination with serial deletion constructs of the 5′ flanking region, site-directed mutation and siRNA interference demonstrated that MyoD, PAX7 and CREB binding occur in region −689/−40 and play important roles in bovine SIX1 transcription. In addition, MyoG drives SIX1 transcription indirectly via the MEF3 motif. Taken together these interactions suggest a key functional role for SIX1 in mediating skeletal muscle growth in cattle.The  In terms of gene regulation, the making of muscle in vertebrates is mainly driven by the action of the myogenic regulatory factor (MRF) family, myoblast determining factors (MyoD), Myf5, myogenin (MyoG) and MRF42. The MRFs serve as myogenic determination factors and control the proliferation and differentiation fate of muscle cells derived from myogenic precursor cells2. Sequence-specific TFs, such as myocyte-specific enhancer binding factor 2 (MEF2)4, paired box 7 (PAX7)5 and TEA DNA binding domain factor 4 (TEAD4)6, are coordinated in part by the action of MRFs and are regulated by transcriptional activation via the binding of MRFs to promoters. However, MEF3, which is also recognized by TFs of the SIX family, is the most abundant, and the MEF3 element is specifically enriched in promoters targeted by MRF7.Skeletal muscle development is a complex process regulated by a multitude of genes and sequence-specific transcription factors (TFs)8. Among them, SIX1 is localized in both the cytoplasm and the nucleus of mesenchymal stem cells during embryogenesis and is involved in controlling the development of multiple tissue types and organs14. Importantly, the function of SIX1 is tied to skeletal muscle development. SIX1-null mice die at birth due to hypoplasia and abnormal primary myogenesis caused by the reduction and delayed activation of MRF genes in the limb buds15. Furthermore, SIX1 drives the transformation of slow-twitch towards fast-twitch (glycolytic) fate during myogenesis development16. Consistent with these effects, loss of SIX1 gene function in zebrafish and mice causes abnormal fast-twitch muscle formation10. Taken together, these data indicate that SIX1 is critical for skeletal myogenesis and skeletal muscle development.The SIX family of TFs includes six members designated SIX1 to SIX6SIX1 in regulating the formation of muscles and other tissues, there is limited information regarding the transcriptional regulation of bovine SIX1 during myogenesis. Exquisitely orchestrated gene expression programmes resulting from the concerted interplay of regulatory elements at promoters and enhancers mediate differentiation and development17. In this study, we analysed the molecular mechanisms involved in the regulation of the SIX1 gene via the 5′ regulatory region. In addition, the coding sequence (CDS) of bovine SIX1 was cloned, and the relative mRNA expression pattern of bovine SIX1 in the tissue was determined. Our results provide a solid basis for further research on the regulatory roles of SIX1 in mediating beef skeletal muscle development.Despite the clear role of SIX1 gene products in various tissues, cDNA from 10 bovine tissues and organs, including: liver, heart, spleen, lung, kidney, abomasum, small intestine, subcutaneous fat, testicular and longissimus thoracis muscle were performed by qPCR , we identified an open reading frame (ORF) of 855 bp, which encoded 284 amino acids (aa) with a calculated molecular weight of 32.18 kDa and an isoelectric point (pI) of 9.24. In addition, the bovine SIX1 protein contains two putative domains: the N-terminal SD domain and the homeodomain. The N-terminal SD domain resides in aa 9 to 118, and the homeodomain resides in aa 125 to 186 , CREB, PAX7, MyoD, MEF2 and TEAD4   Fig. . HoweverSIX1 promoter both in vitro and vivo. As shown in Fig. We used EMSAs and ChIP assays to determine if MyoD, PAX7, CREB and MyoG bind to the SIX1 gene plays an important role in embryonic myogenesis and has very high expression levels and specific expression patterns in the skeletal muscle of pigs, humans and ducks21. In the present study, bovine SIX1 was found to be highly expressed in the longissimus thoracis muscle, thereby indicating that the SIX1 gene might play a functional role in mediating the development of bovine skeletal muscle. The bovine SIX1 aa sequence shares a 99% similarity with sequences in the goat, pig, dog and human, and bovine SIX1 contains two putative domains: the N-terminal SD domain and the homeodomain. These results indicate that the function of SIX1 is primarily coordinated by specific DNA-binding factors and cooperative interactions with co-factors8. Additionally, these results show that the SIX1 gene is highly conserved and has similar functions in ruminants.The SIX1 gene at the transcriptional level, we cloned and analysed its 5′ regulatory region. The results revealed that there was no consensus TATA box or CCAAT box; however, there was a CpG island containing the promoter region from −235 to +658 relative to the TSS. Consistent with these observations TATA boxes are enriched in a minority of mammalian gene promoters23, and only 10–20% of mammalian promoters contain a functional TATA box24. DNA methylation is involved in stable gene silencing , either through interference with TF binding or through the recruitment of repressors that specifically bind sites containing methylated CG26. A previously a study has shown that the porcine SIX1 gene is highly associated with DNA methylation during myoblast differentiation27. Thus, we hypothesized that the GC-rich region of the bovine SIX1 gene promoter is subject to epigenetic regulation.To further elucidate the regulation of the bovine SIX1 gene using online prediction software identified potential TF binding sites for MyoD, CREB and PAX7 at −2000/−1300, −1300/−689 and −689/−40. However, the luciferase activity decreased significantly compared with the other serial reporter constructs only when the promoter sequence was deleted in the −689/−40 fragment. Therefore, we hypothesized that the potential TFs MyoD, PAX7 and CREB and MEF3 motif at −689/−40 to play major roles in regulating the transcriptional activity of the bovine SIX1 gene. MyoD belongs to the MRF family and plays key roles in muscle plasticity and regeneration. MyoD expression is strongly induced early after injury as satellite cells become activated in part through the action of the TF SIX128. Additionally, MyoD co-expressed with PAX7 regulates satellite cell physiology by inducing self-renewal29 and maintains a population of functional satellite cells in the undifferentiated state30. In the absence of MyoD and PAX7, satellite cells die and thus fail to repopulate, leading to severe skeletal muscle defects31. Furthermore, MyoD and PAX7 expression levels are increased by activation of the Notch signalling pathway, and increased MyoD and PAX7 expression levels promote self-renewal and proliferation while inhibiting differentiation33. However, SIX1 overproduction represses proliferation and promotes the differentiation of satellite cells. There appears to be some distinction between MyoD and PAX7 regarding the function of SIX1 in the postnatal stage of myogenesis development28. In the present study, MyoD and PAX7 mutation and knock down increased the basal promoter activity. The EMSA and ChIP results showed that MyoD and PAX7 were capable of binding to SIX1 with high affinity in C2C12 cells, thereby suggesting that MyoD and PAX7 may have compensatory mechanisms in the function of the SIX1 gene and may contribute to determining the fate of bovine skeletal muscle cells.An analysis of the region comprising positions −2000 to + 170 of the bovine 34. Moreover, signals from damaged skeletal muscle tissues induce CREB phosphorylation and target gene expression in primary mouse myoblasts. Activated CREB localizes to both myogenic precursor cells and newly regenerating myofibres within regenerating areas35. Additionally, a previous study showed that CREB can interact directly with MyoD, inducing transactivation attenuated by interference with its dimerization36. CREB is constitutively expressed in 3T3-L1 fibroblasts and regulates the adipocyte-specific genes phosphoenol pyruvate carboxykinase (PEPCK), fatty acid binding protein (FABP (aP2/422)), fatty acid synthetase (FAS) andcyclooxygenase (COX)−2, which induce adipogenesis as “adipocyte-specific” markers during the early phase of adipogenesis38. In the present study, we observed that CREB mutation and knock down reduced the transcriptional activity of the SIX1 gene in C2C12 and 3T3-L1 cells, respectively, while the EMSA and ChIP results showed that CREB could bind to this sequence. These results indicate that CREB plays an important role in regulating the expression of the bovine SIX1 gene in skeletal muscle and adipocyte cells.CREB plays key roles in the differentiation of embryonic skeletal muscle progenitors and in the survival of adult skeletal muscle40. Strikingly, the transcriptional activity of the SIX1 gene decreased upon MEF3 motif mutation. However, MyoD binds to the −689/−40 promoter fragment of SIX1 and is a negative regulator. Therefore, we hypothesized that MyoG may have been responsible for this regulatory activity. MyoG is a muscle-specific TF and is up-regulated during myoblast differentiation into multinucleated myotubes41. A recent study reported that MyoG interacts with SIX1 via the E-box and MEF3 motifs during embryonic development and plays a role that is parallel to that played by the SIX family during terminal differentiation of myoblasts40. Our results strongly support this hypothesis since MEF3 mutation or MyoG knock down led to a significant reduction in the basal activity of the −689/+170 promoter region. However, MyoG overexpression resulted in a boost in promoter activity. Although there is no evidence of potential MyoG TF binding sites in the −689/+170 promoter region of the bovine SIX1 gene, the EMSA and ChIP results showed that MyoG could bind with high affinity to the MEF3 motif. Taking these results together, we conclude that MyoG plays an important role in regulating SIX1 indirectly via the MEF3 motif. In addition, the MRFs and the SIX1 TF form a feed-forward regulatory network that is responsible for inducing the transcription of many muscle function genes, as shown in the proposed model in Fig. SIX1 gene.Another important motif is MEF3, which the most abundant of the MRFs including MyoD and MyoG. MEF3 initiates the expression of muscle-specific proteins by inducing target genesSIX1 gene and identified its transcription initiation sites. The aa sequence of bovine SIX1 shares high similarity with its homologues in goat and sheep. The SIX1 gene is highly expressed in the longissimus thoracis muscle and is regulated by multiple TFs, including MyoD, PAX7, CREB and MyoG. Epigenetic modifications in the SIX1 promoter and their effects on the transcription of the gene remain to be investigated. Our results provide a foundation for a better understanding of the transcriptional regulation and biological function of the bovine SIX1 gene.In summary, we cloned the promoter and CDS sequences of the bovine All animal procedures were performed according to guidelines laid down by the China Council on Animal Care, and the protocols were approved by the Experimental Animal Manage Committee (EAMC) of Northwest A&F University.longissimus thoracis muscle and testicular tissue) were obtained from three Qinchuan foetal bovine. β-actin was used as the endogenous reference. Total RNA and cDNA were obtained using an RNA kit and PrimeScript™ RT Reagent kit  , respectively. qPCR reaction mixtures (20 μL) contained SYBR Green Real-time PCR Master Mix (TaKaRa) and gene-specific primers . The total protein samples were quantified using the Pierce BCA Protein Assay Kit (Thermo Scientific) and 50 μg of total protein was separated by electrophoresis on a 10% SDS-polyacrylamide gel, followed by transfer to nitrocellulose. After blocking in defatted milk powder, the membranes were incubated with the SIX1 antibody  and β-actin antibody . The blots were washed and subsequently treated with a peroxidase labelled secondary antibody. The signals were detected by exposure of X-ray films to chemical luminescence using the ChemiDoc™ XRS+ System .SIX1 CDS was cloned from longissimus dorsi muscle cDNA using specific primers . PCR amplifications were performed using genomic DNA from Qinchuan cattle blood as a template, using KOD DNA Polymerase  to amplify the 5′-regulatory region sequence. The potential TF binding sites were analysed using the Genomatix suite (http://www.genomatix.de/). CpG islands were predicted using MethPrimer (http://www.urogene.org/methprimer/). Structural and phylogenetic tree analyses of SIX1 were performed using the SMART database (http://smart.embl-heidelberg.de/) and MEGA 5.1 (http://www.megasoftware.net), respectively.The complete SIX1-P1/R (−2000/+170), SIX1-P2/R (−1300/+170), SIX1-P3/R (−689/+170) and SIX1-P4/R (−40/+170) were designed to contain unidirectional deletions of the bovine SIX1 promoter. The PCR complexes were generated using specific primers that included the sequences of the KpnI and BglII restriction sites and the SIX1-PF/PR products as a template. PCR fragments were then cloned into the pMD19-T (simple) vector (Takara) and ligated into the luciferase reporter construct pGL3-basic vector digested with the same restriction enzymes KpnI and BglII (Takara). These plasmids were named pGL3-P1, pGL3-P2, pGL3-P3 and pGL3-P4.The fragment primers 2 in an atmospheric incubator. Cells were grown overnight in 24-well plates with growth medium without antibiotics until reaching 80–90% confluence at a density of 1.2 × 105 cells. In each well, the transfection reagent was mixed with 800 ng of the expression construct , 10 ng of pRL-TK normalizing vector, and 2 μL of X-tremeGENE HP DNA transfection reagent ; the samples were then incubated with 100 μL of opti-DMEM . The pGL3-basic vector served as a negative control. At 6 h after transfection, we replaced the media with DMEM with 2% horse serum (HS)  and incubated for 42 h to induce differentiation of the C2C12 myoblasts into myotubes. Cell lysates were collected 48 h post-transfection and used for the measurement of the relative transcriptional activity of each fragment with the Dual-Luciferase Reporter Assay System , according to the manufacturer’s instructions. The relative luciferase activities were determined using a NanoQuant Plate™ . Experiments were conducted in parallel and in triplicate.C2C12 and 3T3-L1 cells were maintained in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% newborn calf serum  and antibiotics (100 IU/mL penicillin and 100 µg/mL streptomycin) at 37 °C and 5% CODpnI and then amplified with XL10-Gold competent cells (Stratagene) and sequenced.The potential TF-binding sites for MyoD, PAX7, MEF3 and CREB motif were mutated with the corresponding primers Table  using th46 and synthesized with the control siRNA . The NC siRNA served as a negative control. The siRNA sequences are presented in Table HindIII and Xbal restriction sites to allow ligation into the pcDNA3.1 vector digested with these restriction enzymes. For MyoG overexpression, C2C12 cells cultured in 24-well plates were transiently co-transfected with 400 ng each of pcDNA3.1-MyoG and of the corresponding pGL3 −689/+170. The pcDNA3.1 plasmid served as a negative control.siRNAs against MyoD, PAX7, CREB and MyoG were designed as described previously47. To obtain nuclear extracts, QCMCs and adipocytes were treated using the Nuclear Extract kit  according to the manufacturer’s protocol. A LightShift Chemiluminescent EMSA Kit  was used for the EMSAs according to the manufacturer’s protocol with modifications. Briefly, 200 fmol of 5′-biotin labelled probe (listed in Table Qinchuan cattle myoblast cells (QCMCs) and adipocytes were isolated from Qinchuan foetal bovine samples as described previously[−ΔCt(Ct[ChIP]−(Ct[Input]−Log2(Input Dilution Factor)))] 48. We used normal rabbit IgG and intragenic DNA fragment of SIX1 exon 2 as negative controls.The ChIP assays were performed using the SimpleChIP® Enzymatic Chromatin IP kit  according to the manufacturer’s protocol. QCMCs and adipocytes from Qinchuan foetal bovine samples (n = 3) were used. The protein-DNA complexes were cross-linked with 37% formaldehyde and neutralized with glycine. After digestion of the DNA with micrococcal nuclease into fragments of approximately 150–900 bp in length, the fragmented chromatin samples were suspended in ChIP dilution buffer as an input. The cross-linked chromatin samples were immunoprecipitated with 4 μg of MyoD, PAX7, CREB or MyoG antibodies and with normal rabbit IgG overnight at 4 °C. The immunoprecipitated products were isolated with protein G agarose beads, and the bound chromatin was then collected by salt washing. The eluted ChIP Elution Buffer was then digested with proteinase K and purified for PCR analysis. All ChIP primers used in the standard PCR and quantitative real-time PCR experiment are listed in Table Western blots showing the different of bovine Six1 expression in tissues and organs and control.