Onset of transcription is followedby cellular and morphological changes involving cell speciation and gastrulation.Previous genome-wide surveys of transcriptional changes only assessed geneexpression levels; however, recent studies have shown the necessity to mapisoform-specific transcriptional changes. Here, we perform isoform discovery andquantification on transcriptome sequences from before and after zebrafish zygoticgenome activation (ZGA).Zebrafish embryos are transcriptionally silent until activation of the zygoticgenome during the 10pou5f1, sall4 and dnmt1. Genes involvedin cell-cell interactions such as f11r and magi1 display isoformswitches with alterations of coding sequences. We also detect over 1000transcripts that acquire a longer 3’ terminal exon when transcribed by thezygote compared to their maternal transcript counterparts. ChIP-sequencing datamapped onto skipped exon events reveal a correlation between histone H3K36trimethylation peaks and skipped exons, suggesting epigenetic marks being part ofalternative splicing regulation.We identify novel isoforms and isoform switches during ZGA for genes related tocell adhesion, pluripotency and DNA methylation. Isoform switching events includealternative splicing and changes in transcriptional start sites and in 3’untranslated regions. New isoforms are identified even for well-characterizedgenes such as The novel isoforms and isoform switches reported here include regulators oftranscriptional, cellular and morphological changes taking place around ZGA. Ourdata display an array of isoform-related functional changes and represent avaluable resource complementary to existing early embryo transcriptomes. At the time of ZGA, development proceeds from a controlby mRNAs synthesized during oogenesis and stored in the egg  tomRNAs produced by the embryo’s own genome. Following ZGA, blastomeres divide lessfrequently and more asynchronously, they start to differentiate and migrate to form thethree germ layers of the gastrulating embryo . OurRNA-seq data are available under [NCBI GEO GSE22830].RNA-seq: RNA-sequencing; ChIP-seq: Chromatin immunoprecipitation sequencing; ZGA:Zygotic genome activation; MBT: Mid-blastula transition; CDS: Coding sequence; UTR:Untranslated region; TSS: Transcription start site; AS: Alternative splicing; TTS:Transcription termination site; FPKM: Fragments per kilobase per million mappedfragments; ORF: Open reading frame; GO: Gene ontology; FDR: False discovery rate; ES:Exon skipping; AD: Alternative donor site; AA: Alternative acceptor site; IR: Intronretention.The authors declare that they have no competing interests.HA designed the study, wrote the manuscript, made the figures and performed allbioinformatics analysis, including writing python scripts. OØ performed RT-qPCR,cDNA cloning and sequencing, contributed to figures and the manuscript. ISA performedChIP experiments. LFM collected embryos, isolated RNA and made figures. SM designed thestudy, initiated the RNA-seq experiment and generated all the basic data. PC supervisedChIP-seq work, designed the study and wrote parts of the manuscript. PA designed thestudy, wrote the manuscript and supervised the work. All authors approved the finalmanuscript.Supplementary figures 1–9.Click here for fileNon-redundant gene annotations file containing a merger of all Ensembl andassembled transcripts.Click here for fileExpression values pre-ZGA and post-ZGA for all isoforms in the‘all’ gene annotation.Click here for fileGene annotation for robustly expressed isoform (>3 FPKM pre-ZGA orpost-ZGA).Click here for fileTest results for whether loci with multiple TSSs exhibited a shift in TSSusage between pre-ZGA and post-ZGA.Click here for fileGO term analysis results from DAVID of genes marked with H3K4me3.Click here for fileGO term analysis results from DAVID of genes marked with H3K27me3.Click here for fileAnatomical ontology results from DAVID of genes marked with H3K4me3.Click here for fileAnatomical ontology results from DAVID of genes marked with H3K27me3.Click here for fileResults from a statistical test of whether genes exhibit a significantchange in alternative splicing between pre-ZGA and post-ZGA.Click here for fileResults from sequencing of cloned dnmt1 fragments.Click here for fileResults from sequencing of cloned pou5f1 fragments.Click here for fileAll last exons with evidence for an extended 3’UTR post-ZGA relativeto the pre-ZGA.Click here for fileDifferent scripts used in the analyses.Click here for fileA list with description of the primers.Click here for file


GH) and prolactin (PRL) gene transcription remain unclear. In this study, we detected the effects of SCFAs  on the activity of the cAMP/PKA/CREB signaling pathway, GH, PRL, and Pit-1 gene transcription in dairy cow anterior pituitary cells (DCAPCs). The results showed that SCFAs decreased intracellular cAMP levels and a subsequent reduction in PKA activity. Inhibition of PKA activity decreased CREB phosphorylation, thereby inhibiting GH and PRL gene transcription. Furthermore, PTX blocked SCFAs- inhibited cAMP/PKA/CREB signaling pathway. These data showed that the inhibition of GH and PRL gene transcription induced by SCFAs is mediated by Gi activation and that propionate is more potent than acetate and butyrate in inhibiting GH and PRL gene transcription. In conclusion, this study identifies a biochemical mechanism for the regulation of SCFAs on bovine GH and PRL gene transcription in DCAPCs, which may serve as one of the factors that regulate pituitary function in accordance with dietary intake.Short-chain fatty acids (SCFAs) play a key role in altering carbohydrate and lipid metabolism, influence endocrine pancreas activity, and as a precursor of ruminant milk fat. However, the effect and detailed mechanisms by which SCFAs mediate bovine growth hormone ( Growth in vivo and in vitro effects including mediate inflammation, alter carbohydrate and lipid metabolism, regulate cell proliferation and differentiation, influence endocrine pancreas activity, provide an additional source of energy for the body, and as a precursor of ruminant milk fat [in vivo and in vitro actions on pituitary hormone secretion function. Addition of sodium-butyrate to milk formula increased the secretion of GH and insulin level in pre-weaning calves [et al. found that addition of propionate or butyrate to the anterior pituitary cells of the goat cultured in vitro inhibited GHRH-induced GH release and GH production [Short-chain fatty acids (SCFAs), including acetate, propionate, and butyrate, are formed during bacterial fermentation of carbohydrates in the gut or rumen of various animal species . Dietarymilk fat –13. In ag calves . The sodg calves . By contoduction . Therefoq and Gi/o, and their activation can induce an increase in intracellular calcium concentration and suppress cellular cyclic adenosine 3′,5′-monophosphate (cAMP) accumulation [et al. has proved that GPR41 and GPR43 mRNA are expressed in bovine pituitary gland [GH and PRL [GH gene include binding sites for Pit-1, specificity protein 1 (Sp1), cAMP-response element binding protein (CREB), and thyroid hormone response element (TRE) [PRL gene include binding sites for Pit-1, estrogen response element (ERE), and Ets binding sites (EBS) [Pit-1 promoter contains a binding site for Pit-1 and two CREB binding sites [GH and PRL gene transcription level directly or indirectly. We hypothesize that SCFAs may mediate bovine GH and PRL gene transcription via the G protein signaling pathway. Therefore, the objective of this study was to determine the effects of SCFAs on the activity of G protein signaling pathway, GH, PRL, and Pit-1 gene transcription in dairy cow anterior pituitary cells (DCAPCs). The results of this study could provide important information for understanding the role of the G protein signaling pathway in SCFAs mediate bovine pituitary function.In 2003, two orphan G protein coupled receptors (GPCRs), GPR41 and GPR43 have been identified as cell-surface receptors for SCFAs . Both GPmulation . Wang etry gland . Pituitant (TRE) ,22. The es (EBS) . The Pitng sites . Thus, t2.2.1.GH and PRL showed a decreasing trend in the SCFAs-treated groups. The mRNA levels of GH were significantly lower in the 0.1 and 0.5 mmol/L acetate and 0.5, 1.0, 2.5 and 5.0 mmol/L butyrate groups than in the control groups (p < 0.05), and the mRNA levels of GH were markedly lower in the 1.0, 2.5 and 5.0 mmol/L acetate and 0.1, 0.5, 1.0, 2.5 and 5.0 mmol/L propionate groups than in the control groups (p < 0.01), respectively. The mRNA levels of PRL were significantly lower in the 1.0, 2.5 and 5.0 mmol/L acetate, 0.1 and 5.0 mmol/L propionate and 0.1, 0.5, 1.0 and 5.0 mmol/L butyrate groups than in the control groups (p < 0.05), and the mRNA levels of PRL were markedly lower in the 0.5 mmol/L acetate, 0.5, 1.0, 2.5 mmol/L propionate and 1.0 and 2.5 mmol/L butyrate groups than in the control groups (p < 0.01), respectively. The transcription of the Pit-1 gene had no obvious change treated by either acetate, propionate or butyrate at 0.1, 0.5, 1.0, 2.5 and 5.0 mmol/L (p > 0.05), respectively. The transcription of the GH and PRL gene were significantly higher in the PTX + acetate group than in the 1.0 mmol/L acetate treatment group (p < 0.05). The mRNA levels of GH and PRL were markedly higher in the PTX + propionate group than in the propionate treatment group (p < 0.01). The mRNA levels of GH and PRL were significantly higher in the PTX + butyrate group than in the butyrate treatment group (p < 0.01). Taken together, these results strongly suggest that SCFAs significantly down-regulate the expression of GH and PRL gene in DCAPCs, and the mRNA levels of GH and PRL were significantly higher by prior PTX incubation.The mRNA levels of 2.2.p < 0.05), respectively. The cAMP level was significantly higher in the PTX + propionate group than in the non PTX treatment group (p < 0.01). Overall, these results indicate that SCFAs significantly decrease the intracellular cAMP concentration, and the intracellular cAMP levels were significantly decreased by prior PTX incubation.The cAMP levels were lower in the SCFAs-treated groups than in the control group, and both were the lowest at the 3 h of the SCFAs-treated groups . The cAM2.3.p < 0.05), respectively. The PKA activity was markedly lower in the propionate group than in the control group (p < 0.01). The PKA activity was significantly increased in the PTX + propionate group than non PTX group (p < 0.05). The PKA activity had no obvious change in the PTX + acetate or butyrate group than non PTX treatment group (p > 0.05), respectively.The PKA activity was lower in the SCFAs-treated groups than in the control group, and the inhibiting effect of SCFAs was blocked by PTX . The PKA2.4.p < 0.01), and the phosphorylation levels of CREB were markedly higher by prior PTX treatment (p < 0.01).The phosphorylation levels of CREB  were mar3.in vivo and in vitro actions on pituitary secretion function [GH and PRL gene transcription remain unclear. More emphasis should be put on GH and PRL gene transcription regulation to elucidate the role of the G protein signaling pathway in SCFAs mediate bovine pituitary function.SCFAs, primarily acetate, propionate and butyrate, are organic acids produced within the intestinal lumen or rumen of ruminant by bacterial fermentation of mainly undigested dietary carbohydrates . There hfunction –16. Howeαs, Gαi, Gαq, and Gα12 with respect to sequence homology and functional similarities of their α subunits [αi results in an inhibition of adenylyl cyclase, hence, decrease in cAMP production [αi-proteins prevents the coupling to their cognate GPCRs and consequently disrupts the signal transduction cascade. Here, we demonstrated that PTX could inhibit SCFAs-induced decrease in cAMP level. This indicates that SCFAs exert their actions through coupled to Gαi subunit. The biological effects of PTX that result from ADP-ribosylation of the Gαi-proteins are diverse, some of which can be attributed to the pretreatment timing and load dosing. Almost all cell types Gαi-protein were nearly completely ADP-ribosylated after a 2 h incubation with PTX (200 ng/mL) [GPCRs are cell surface receptors which serve in the transduction of extracellular stimuli into intracellular signals. They mediate extracellular stimuli into intracellular signals and are indispensable among membrane proteins because they constitute the largest and most diverse groups of receptor proteins. Thus, GPCRs represent one of the most important families of drug targets. The regulatory subunit of G proteins has four classes: Gsubunits . The actoduction . In this0 ng/mL) . In most0 ng/mL) . TherefoDecrease in cAMP levels results in a subsequent reduction in PKA activity. Then, PKA catalyzes the phosphorylation of serine residues on CREB to activate pit-1 and thus initiate a response within the cell . Our datGH gene transcription level directly or activate Pit-1 to trigger the transcription of the GH and PRL gene indirectly [GH and PRL gene in DCAPCs, and the mRNA levels of GH and PRL were significantly higher by prior PTX incubation. Based on these observations, we conclude that SCFAs inhibit GH and PRL gene transcription via cAMP/PKA/CREB signaling pathway.In the nucleus, phosphorylated CREB could change directly . In thisPit-1 gene was not significantly changed, suggesting that the change of Pit-1 activity may play an important role in the regulation of GH and PRL transcription by SCFA. Three serine-threonine phosphorylation sites have been identified in Pit-1:serine 115 (S115), threonine 219 (T219), and threonine 220 (T220). The S115 and T220 sites are phosphorylated both in cells treated with cAMP or phorbol esters or by directly phosphorylating purified Pit-1 proteins with PKA and PKC [1p, GHF-1p, and GH-1p sites was reduced [PRL gene transcription via increase the phosphorylation levels of Pit-1. The CREB binding protein (CBP) acts as a cofactor for Pit-1-dependent activation of the hGH promoter by the GHRH signaling pathway and PKA [PRL gene. Thus, further studies are needed to understand the exact mechanisms mediating bovine PRL gene by CREB.However, the expression of  and PKC –37. Phos and PKA . CBP act and PKA . For theet al. found that addition of sodium-butyrate to milk formula increased the secretion of GH and insulin level in pre-weaning calves [et al. found that ruminal or mesenteric venous infusion of SCFAs significantly suppressed GH secretion in a dose-dependent manner [in vivo experiments according to the difference physiological state of the animal. Furthermore, the network integrating hormone, neurotransmitters, cytokines, and nutrient signaling in control of GH and PRL synthesis and secretion is complex and make interpretation difficult. It seems, therefore, that existing in vivo approaches are not effective for the investigation the effect and exact mechanisms of SCFAs on the GH and PRL synthesis and secretion in the dairy cows. Cell culture is one of the most convenient tools for understanding the mechanisms of pituitary hormone synthesis and secretion, which provides a powerful tool to uncover new information. Based on these reasons, we established a dairy cow GH and PRL secreting anterior pituitary cell model, which can be used successfully for the study of the mechanisms of hypothalamic factors, peripheral hormones, cytokines, or dietary nutrients regulating GH or PRL synthesis and release [GH and PRL gene transcription in DCAPCs, which may serve as one of the factors that regulate pituitary function in accordance with dietary intake.SCFAs are the major energy source for dairy cows, which promote rumen development in calves and rumen epithelial papilla growth in lambs . Zhao ang calves . Howevert manner –44. Factt manner ,45. Thus release . Mean ac release ,48. In t4.4.1.133), anti-CREB antibodies, and PKA assay kit were purchased from Millipore .Fetal bovine serum (FBS), bovine serum albumin (BSA), trypsogen, and DMEM medium were purchased from Gibco . Sodium-acetate, sodium-propionate, sodium-butyrate, pertussis toxin (PTX), hyaluronidase, Dnase, and I type collagenase were provided by the Sigma Aldrich . Cell culture plates were purchased from Corning Incorporated . Bovine cAMP assay kit was purchased from R & D Systems . Anti-phospho CREB  to which was added 50 μg streptomycin/mL, and 100 IU penicillin/mL at 5 °C within 2 h. The posterior pituitary was removed with precision medical scissors and tweezers to obtain integrated anterior pituitary glands. Anterior pituitary glands were located were diced into small pieces less than 1 mm4.3.5 cells), serum starved for 24 h, and then stimulated with 0, 0.1, 0.5, 1.0, 2.5, and 5.0 mmol/L acetate, propionate, or butyrate for 24 h. PTX is the ADP-ribosylating toxin produced by the whooping cough causing bacterium Bordetella pertussis. ADP-ribosylation of the α subunit of heterotrimeric Gαi proteins locks the α subunits into an inactive state, thus it is unable to inhibit adenylyl cyclase [GH, PRL, Pit-1, and GAPDH genes . The relative expression level for GH, PRL, and Pit-1 was calculated relative to GAPDH  using the comparative cycle threshold method [For real-time PCR analysis, DCAPCs were grown in 6-well plates , serum starved for 24 h, and then stimulated with 1.0 mmol/L acetate, propionate, or butyrate for the assigned period . The cAMP-dependent pathway is a GPCR-triggered signaling cascade. Thus, the intracellular cAMP was extracted from the cells and the concentration was assayed by microplate reader  using rat cAMP assay kit according to the manufacturer’s instructions. DCAPCs were also treated with or without prior PTX incubation (100 ng/L) for 2 h, and then stimulated with 1.0 mmol/L acetate, propionate, or butyrate for 3 h. The assay is based on the competition between unlabeled cAMP and a fixed quantity of horseradish peroxidase (HRP)-labeled cAMP for a limited number of binding sites of a cAMP specific antibody.DCAPCs were grown in 6-well plates .cAMP works by activating protein kinase A (PKA), then, leads to the phosphorylation of cAMP response element binding protein (CREB). Thus, the PKA activity and phosphorylation levels of CREB were determined in this study. DCAPCs were cultured in the presence or absence of acetate, propionate, or butyrate (1.0 mmol/L) in FBS-F12 for 3 h. The cells were also treated with prior PTX incubation (100 ng/L) for 2 h compared with groups without prior PTX incubation. Activity of PKA was determined using a radioactive method using PKA assay kit according to the manufacturer’s instructions. Briefly, the lysates were incubated for 30 min at 30 °C with 100 μm PKA-specific substrate, and 10 μCi of [γ-4.6.5 cells), serum starved for 24 h, and then stimulated with 1.0 mmol/L acetate, propionate, or butyrate for 6 h. Cells were also treated with prior PTX incubation (100 ng/L) for 2 h compared with groups without prior PTX incubation. Nuclear proteins were extracted by nuclear protein extraction kit according to the manufacturer’s instructions . Protein concentrations were determined by bicinchoninic acid (BCA) protein assay kit . Aliquotes of cell lysates containing 20 μg protein were separated in 10% polyacrylamide gels, electrophoretically transferred to PVDF membranes  using Bio-Rad criterion blotter . Membranes were blocked in 5% non-fat, dried milk in TBST  at room temperature for 1 h and then incubated with either anti-phospho CREB (1:3000 dilution) or anti-CREB (1:2000 dilution) at 4 °C overnight. Blots were then washed and incubated with horseradish peroxidase-labeled secondary antibodies  at 37 °C for 1 h. Immunoreactive bands were detected with enhanced chemiluminescence (ECL) western blotting detection reagents . Blots were exposed to X-ray film for radiography of the bands. Blots were digitally detected and measured using a LAS3000 Bioimage Analyzer .DCAPCs were grown in 6-well plates . Groups were compared by one-way analysis of variance (ANOVA) followed by the least significant difference test. A 5.GH and PRL gene transcription in DCAPCs, putatively according to the mechanism shown graphically in αi and βγ subunit. The exchange of GTP from GDP results in activation of the Gαi, thereby inhibiting adenylyl cyclase activity. Inactivated adenylyl cyclase results in a decrease of intracellular cAMP levels and a subsequent reduction in PKA activity. Inhibition of PKA activity inhibits CREB phosphorylation, thereby leading to a decrease of bovine GH gene transcription. The change of the phosphorylation levels of CREB may decrease the phosphorylated CBP or CBP complex interacts with Pit-1 resultant inhibition of transcription of the bovine PRL gene. Consequently, SCFAs inhibit bovine GH and PRL gene transcription in DCAPCs. A-protomer of PTX penetrates into the host cells result in inactivation of Gαi, hence, inhibits SCFAs mediated signaling pathway. In addition, inhibition of PKA activity may result in the decreased of Ca2+ influx through decreased l-type Ca2+ channel mean open time [αi-mediated inhibition of Ca2+ channel. This study identifies a biochemical mechanism for the regulation of SCFAs on GH and PRL gene transcription in DCAPCs, which may serve as one of the factors that regulate bovine pituitary function in accordance with dietary intake. Future studies will further research the role and mechanism of SCFAs in regulating bovine pituitary hormone secretion.In summary, the results of this study indicate that SCFAs, as signaling molecules, significantly decrease pen time . Therefo


Transmembrane protein 174 (TMEM174) is a type III transmembrane protein with no clear signal peptide. The N and C terminals are located inside the cell. Our previous study demonstrated high expression of TMEM174 in the kidney and its potential involvement in renal cancer based on its capacity to stimulate cell proliferation. However, the mechanism by which TMEM174 promotes proliferation at the transcriptional level remains to be elucidated. In the present study, the TMEM174 promoter region was amplified from whole blood DNA. Six different regions of the regulatory sequences of the TMEM174 promoter region including ~2.5 kb of the upstream region were cloned into the dual luciferase expression vector pGL3-basic. Comparison of the activity of these fragments in dual luciferase reporter assays revealed higher levels of activity for the fragments spanning −186 to +674, −700 to +674, −1,000 to +674 and −2,500 to +1 bp. Lower levels of activity were detected for the fragments spanning −466 to +674 and −890 to +674 bp. The highest activity was detected for the fragment spanning −186 to +674 bp. Electrophoretic mobility shift assay (EMSA) was performed to determine effective transcription factor binding sites. Specific binding of the cyclic-AMP response element binding (CREB) within the TMEM174 gene promoter region was demonstrated, although binding of the activator protein-1 (AP-1) was also detected in this region. In conclusion, these results suggest that the core promoter region of the human TMEM174 gene is located within the region spanning −186 to +674 bp and that the transcription factors CREB and AP-1 are involved in the transcriptional regulation of this gene. Transmembrane protein 174 (TMEM174) was identified among a large pool of genes by high-throughput cell screening technology, which is used to isolate functional genes and to provide insight into the mechanisms of gene function . The TMEThe cyclic-AMP response element binding (CREB) protein is a transcription factor that affects a spectrum of cellular activities. including glucose homeostasis, growth factor-dependent survival, proliferation, differentiation and memory ,4. CREB AP-1 family members are important upstream activators of the ERK signaling pathway , which aThese studies contribute to the understanding of the mechanism by which cell proliferation is promoted at the protein level. However, details of the mechanism at the transcriptional level remain to be elucidated. Identification of the core promoter is a critical first step in this process, facilitating subsequent determination of the important transcription factors.In the present study, the mechanism by which TMEM174 promotes cell proliferation at the transcriptional level was investigated. The promoter region and transcription factor binding sites were predicted by bioinformatics analysis. Among the large numbers of binding sites predicted, such as CREB, AP-1, nuclear factor-κB (NF-κB) and Oct1, CREB and AP-1 were identified with potential binding sites for interaction with the TMEM174 promoter region by electrophoretic mobility shift assay (EMSA).http://www.ncbi.nlm.nih.gov/mapview) and using TFEARCH online software (http://www.cbrc.jp/research/db/TFSEARCH.html) to predict the candidate promoter region and transcription factor binding sites. Fragments were amplified for cloning from whole blood genomic DNA. The primers used are shown in Primers were designed for the amplification of fragments of various lengths corresponding to the 4.8 kb sequence upstream of the TMEM174 gene . The recombined vectors were sequenced by SinoGenoMax Co., Ltd. .The PCR amplification reaction system  conditio4/well) in complete Dulbecco’s modified Eagle’s medium  containing 10% fetal bovine serum (FBS) and were transfected the next day with Vigofect (Vigorous Biotechnology) according to the instructions provided by the manufacturer. A total of 104 ng plasmid DNA/well was transfected, including 100 ng recombinant pGL3 plasmid or pGL3-basic (empty vector control) and 4 ng pRL-TK (Promega) containing the Renilla luciferase gene as an internal control. Transfections were performed in triplicate. The cells were lysed in standard 1X lysis buffer (Promega) for 30 min at room temperature (RT) and the cell lysates were assayed for both firefly and Renilla luciferase activity using the Dual-Luciferase Reporter assay kit (Promega) according to the instructions provided by the manufacturer. Fluorescence was detected using a GENius Pro microtiter plate reader . Relative luciferase activity was determined by normalizing the activity of the firefly luciferase activity  against the Renilla luciferase activity . Each experiment was performed at least three times. The F/R value was calculated respectively and an average value was obtained as the recombined plasmid activity. pGL3-basic was used as the negative control and its activity was defined as 1. The activities of all other recombined plasmids were compared with the negative control.293T cells were seeded in 96-well plates (1×10http://www-bimas.cit.nih.gov/molbio/proscan/). Experimental groups were as follows: CREB probe/nuclear extract, AP-1 probe/nuclear extracts, CREB probe/nuclear extract + competitive probe and AP-1 probe/nuclear extract + competitive probe. Free probe was used as a negative control. Nuclear extracts (5 μg) were incubated with 2 μl (200 nM) biotin-labeled DNA probe and mixed with 10X binding buffer (2 μl), 50% glycerol (1 μl), MgCl2 (1 μl), Poly (dI.dC) (1 μl) and 1% NP-40 (1 μl) for 10 min at RT. Bound DNA complexes were separated by non-denaturing polyacrylamide gel (6.5%) electrophoresis and transferred to a positively charged nylon membrane. Cross-linking was performed under ultraviolet (UV) light for 20 min and the membranes were probed with a streptavidin-horseradish peroxidase (HRP) conjugate and incubated with the chemiluminescent substrate for band detection. For competition experiments, unlabeled competitor oligonucleotides were pre-incubated (200-fold excess) with the labeled probe.Nuclear extracts of HepG2 cells were prepared using Nuclear Extract kits . Protein concentrations were determined using BCA protein assay kits (Vigorous Biotechnology). The oligonucleotide probes  used in EMSA were designed according to a computer-based search with the software Promoter Scan . This method allows the rapid identification of functional genes from a large pool and provides insights into to the function of these genes. However, this method is limited in its application for subsequent studies of the mechanism of gene function due to the potential omission of the role of such proteins in other pathways.in situ hybridization analysis showed that TMEM174 is highly expressed in some types of renal cancer, such as squamous cell carcinoma with necrosis, papillary renal cell carcinoma and transitional cell carcinoma, and that TMEM174 is expressed in the majority of renal cancers and pyelonephritis (data not shown). These data indicate that TMEM174 plays a role in the development of renal cancer.RNA NF-κB, STAT3, AP-1, CREB and nuclear factor erythroid 2-related factor (Nrf2) are transcription factors that regulate tumor cell proliferation, transformation, survival, invasion, angiogenesis, metastasis, chemoresistance and radioresistance . In the In conclusion, the results of the present study indicate that the core promoter region of the human TMEM174 gene is located in the region spanning −186 to +674 bp and that the transcription factors CREB and AP-1 are involved in the transcriptional regulation of this gene. These findings provide further insight into the mechanism of TMEM174 gene regulation, with the identification of CREB representing a basis for further studies concerning this mechanism.


Drosophila embryos and show that massive recruitment of RNA polymerase II (Pol II) with widespread pausing occurs de novo during the midblastula transition. However, ∼100 genes are strongly occupied by Pol II before this timepoint and most of them do not show Pol II pausing, consistent with a requirement for rapid transcription during the fast nuclear cycles. This global change in Pol II pausing correlates with distinct core promoter elements and associates a TATA-enriched promoter with the rapid early transcription. This suggests that promoters are differentially used during the zygotic genome activation, presumably because they have distinct dynamic properties.Massive zygotic transcription begins in many organisms during the midblastula transition when the cell cycle of the dividing egg slows down. A few genes are transcribed before this stage but how this differential activation is accomplished is still an open question. We have performed ChIP-seq experiments on tightly staged DOI:http://dx.doi.org/10.7554/eLife.00861.001 Fertilized eggs—zygotes—develop into embryos via several distinct stages. In many animals, the zygote initially undergoes rapid rounds of genome replication; however, this hectic activity is not controlled by the zygote itself. Instead, the mother deposits RNA molecules in the egg as it forms inside her, and after the egg has been fertilized, these RNA molecules are translated into proteins that guide the development of the early embryo. Only at a stage called midblastula transition does the zygote take over control by transcribing its own RNA molecules.Fruit flies start to transcribe their own genes en masse after completing thirteen rounds of DNA replication. However, some genes are already transcribed during the rapid cycles of DNA replication earlier in development. How these early genes are transcribed, and how the embryo shifts to more widespread transcription during the midblastula transition, are not well understood. In particular, it is not known if the molecular machinery needed to transcribe the genes is recruited a long time before transcription starts, or if it is recruited ‘just in time’. Here, Chen et al. explore how genes are switched on in the fruit fly zygote.Genes are transcribed by a protein complex called RNA polymerase, which binds to DNA sequences, called promoters, within the genes. Chen et al. used a technique called ChIP-Seq to determine how much RNA polymerase was bound to the DNA before, during and after the midblastula transition. Before the transition—from about eight rounds of DNA replication onward—RNA polymerase was bound to only about 100 genes, and was active in most of these cases. In contrast, after the transition, RNA polymerase had been recruited to the promoters of around 4000 genes . However, it was often found in a paused, rather than active, form, at these genes, which is thought to help ensure that their transcription can occur on a precise schedule.Chen et al. then used computer analyses to test the theory that differences in the DNA sequences of the gene promoters might determine which genes the RNA polymerase bound to, and whether or not the polymerase underwent pausing or became active immediately. Strikingly, there were clear differences in the sequence motifs that recruited RNA polymerase to the promoters of genes that were transcribed immediately and those that showed pausing of the polymerase. Moreover, genes that were transcribed before the midblastula transition were shorter, on average, than those transcribed after. This suggests that transcription during the rapid genome replication cycles has to occur quickly and therefore lacks pausing. Together, these findings present a biological rationale for differences in how genes are first transcribed during fruit fly development.DOI:http://dx.doi.org/10.7554/eLife.00861.002 Xenopus, zebrafish and Drosophila, development begins with rapid, synchronous cell divisions without gap phases , the key subunit of TFIID, which binds to the promoter prior to Pol II recruitment, as well as Serine 5-phosphorylation of the C-terminal domain of Pol II (Ser5-P Pol II), which marks transcriptional initiation, can only be first detected during nuclear cycles 8–12, when significant transcription of pre-MBT genes occurs. This was the first indication that Pol II is recruited to promoters de novo during the zygotic genome activation.Drosophila embryos required for ChIP-seq can be collected by conventional means, such collections always contain a fraction (5–20%) of older embryos due to maternal egg holding and thus cannot be used to study very early stages of embryogenesis (We next performed chromatin immunoprecipitation experiments coupled to deep sequencing (ChIP-seq) to analyze the occupancy of Pol II, TBP and histone modifications in pre-MBT embryos (nuclear cycles 8–12), MBT embryos (nuclear cycles 13–14), as well as post-MBT embryos as control . Althougogenesis . To elimSupplementary file hosted by Dryad ). This shows that there is massive de novo recruitment of Pol II during the MBT.The ChIP-seq data from these hand-sorted embryos have robust and reproducible signals in replicates . Despite 7.6 Mb; ). These Supplementary file hosted by Dryad [7.6 Mb]).To obtain a complete list of pre-MBT genes occupied by Pol II before the MBT, we identified all genes with at least twofold enrichment of Pol II over input at the transcription start site across four Pol II ChIP-seq replicates. From this list, we removed 12 genes that were likely false positives as a result of Pol II read-through from a nearby gene and added 10 genes that were missed due to un-annotated alternative start sites  when thSupplementary file hosted by Dryad [7.6 Mb]). Genes in the first group  have highest expression during cellularization (nuclear cycle 14) and tend to diminish in expression thereafter (SNCF and the average profile in ac and the average profile in sim and the average profile in We then classified the pre-MBT genes based on their Pol II occupancy during development and identified three distinct groups (ereafter . These gSupplementary file hosted by Dryad [7.6 Mb]). We first subtracted from them the 3163 genes that are also maternally expressed  because they are known to be enriched for broadly expressed housekeeping genes  and more often intronless  compared to MBT-zygotic genes. We also noticed that pre-MBT genes frequently use the transcription start site that yields the shortest transcript   and pro<0.0003) . This suDrosophila nuclei prior to gene activation. In both immunostainings and ChIP-seq data, the signal of H3K4me3, a modification associated with gene activation, only starts to be detectable during the MBT   . Core prnt (DRE) . As expent (DRE) . We notint (DRE) .10.7554/Supplementary file hosted by Dryad [7.6 Mb]). Interestingly, only pre-MBT genes that initially show the non-paused profile  are significantly enriched for Inr and TATA. Furthermore, only pre-MBT genes that are paused at some point  show enrichment for GAGA, Inr and PB  regulates the onset of zygotic gene expression . NotablyTo further consolidate the differences between pre-MBT and MBT genes, we performed de novo motif analysis with MEME on the 200 bp centered on the transcription start site . For preFinally, an analysis of the co-occurrences of core promoter elements similar to previous analyses  also supThis suggests a model in which rapid pre-MBT transcription without Pol II pausing is mediated by Zelda bound close to a TATA-enriched promoter . In contftz ).Manual inspection of some of these transcripts showed that the Pol II signal originated from a different gene’s TSS  Pol IIle cells . The remTSS enrichment at least twofold above WCE in all three replicates. If multiple transcripts for the same gene met these criteria, we selected the one with the highest Pol IITSS signal (breaking ties using the mean Pol IITU enrichment).To identify genes bound by Pol II in the MBT embryo, we selected all transcripts with Pol IIMBT genes were classified into three groups using gene expression values calculated from previously published single-embryo RNA-seq experiments . We classified as ‘maternal’ all MBT genes with an RPKM of at least 1 in nuclear cycle 10. We classified as ‘MBT active’ all non-maternal MBT genes with an RPKM of at least 5 in nuclear cycle 14D. The remaining MBT genes were classified as ‘MBT paused’.Drosophila melanogaster genome were downloaded from http://hgdownload-test.cse.ucsc.edu/goldenPath/dm3/phastCons15way/. For each mRNA transcript in Flybase r5.47, the mean phastCons score along the transcript’s length was used as a relative measure of conservation. These transcripts were then organized into groups as described in the ‘Identification and classification of pre-MBT genes’ and ‘Identification and classification of MBT genes’ sections.PhastCons scores  from theFor For Using supplemental Table 17 from Schuettengruber et al. , the 441Sequences surrounding all FlyBase r5.47 transcription start sites  were scanned for the core promoter elements listed in For each group of transcripts analyzed for promoter element composition, an enrichment and p-value were calculated for each promoter element. Enrichment was calculated as follows, where G is the group of transcripts tested and PE is a particular promoter element:For enrichment values less than one, the negative reciprocal of the enrichment value was used (indicating depletion instead of enrichment). To calculate a p-value for the observed frequency of each promoter element in each group of transcripts, a Fisher test was performed. Enrichments and depletions with a p<0.05 (after correcting for multiple testing with the Benjamini and Hochberg method) were deemed significant.For Zelda, we calculated enrichment and p-values via random sampling. Enrichment values for each group of transcripts were calculated by dividing the number of Zelda sites per transcript in each group by the average number of Zelda sites per transcript in the genome. To calculate p-values for each group of transcripts, we randomly selected an equal number of transcripts from the entire genome 10,000 times and calculated the enrichment value for each random sample. The p-value was then calculated as the portion of random samples with higher Zelda enrichment than the transcript group.hclust function using Euclidean distance. For the Zelda motif, which can occur multiple times in a single promoter region, the presence of at least one motif within 2 kb upstream was scored.The enrichments (Observed/Expected) and p-values (Fisher test) for the co-occurrence of promoter elements were calculated for all pairs of core promoter elements in three sets of genes. For the ‘FitzGerald’ set of genes, we extracted the gene and overlap counts for a subset of motifs listed in Fasta sequence files were generated for each of the classified Pre-MBT and MBT groups, based on regions ±100 bp surrounding the FlyBase r5.47 transcription start sites . Using MEME v4.8.1  the fast-mod zoops -dna -nmotifs 50 -revcomp -maxw 12 -maxsize 5000000 -oc meme/The resulting motifs were then compared against the TRANSFAC 2011.4 database and the Drosophila Genome Project in situ expression database from http://insitu.fruitfly.org/  in a Linux virtual machine hosted by Amazon Web Services. Instructions for accessing the virtual machine can be found at http://research.stowers.org/zeitlingerlab/data. The analysis code is available on GitHub at https://github.com/zeitlingerlab/chen_elife_2013.All ChIP-seq and MNase-seq data have been deposited with the NCBI Gene Expression Omnibus under accession number spreadsheet summarizing our classification and analysis of pre-MBT and MBT gene groups is available via Dryad digital repository. The first sheet gives an explanation for all column headings. The second sheet lists all data for all our annotated genes, including our ten custom transcripts. It includes the classifications into pre-MBT and MBT gene groups, the Pol II ChIP-seq enrichment values at the transcription start site (TSS) and transcription unit (TU) for all replicates, phastCon conservation scores, and the presence or absence of all core promoter motifs analyzed in this study, as well as the presence of the TATA element identified by de novo motif analysis.A  review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent  The authors need to provide some statistics on reproducibility among the biological replicates used.2) While the authors will release a GEO accession, having supplements at the journal site is a good idea. Supplementary file 1 is critical , but is lacking in ways that will make it difficult to use. There should be another sheet in the workbook, or a separate readme file that has all the def lines and nomenclature explained. For example, I have no idea what “Custom_slam” means in a column where all the rest of the cells appear to have FBtr#s. I can guess what most of the columns labels mean, but you should not make the reader do this. Additionally, many of the important data fields in Supplementary file 1 are conclusions rather than data . These should be replaced with numbers used for the classification system as outlined in the methods. The classification scheme itself is somewhat ad hoc and in a different study, by a different lab, so it is almost certain that some of the same genes will be classified differently. These types of data handling issues have muddled many areas of genomics and can be ameliorated by giving others data, allowing them to apply a different classifier.3) Write something about the nucleosome positioning in 4) The core promoter enrichments are clear in 5) In 6) There are a confusing number of core promoter and MBT classification names. This is certainly not all the fault of the authors, but it would help the reader if the names from the traditional literature and the computational studies were both used or listed somewhere to help match Ohler and FitzGerald, for example. This could be added to the “promoter element table”. The authors should not send the reader to another paper for a term that they could easily define in the manuscript (dispersed and focused are never described). The long descriptors (“pre-MBT not-paused genes”) could be replaced in the figures with data in the heat maps. Almost anything that would make the high-dimensional naming of promoters easier to follow would be greatly appreciated. 1) The authors need to provide some statistics on reproducibility among the biological replicates used.TSS enrichments in We agree that additional information about the reproducibility of our ChIP-seq experiments would be useful. In addition to the existing scatterplots comparing two replicates each of pre-MBT and MBT Pol II2) While the authors will release a GEO accession, having supplements at the journal site is a good idea. Supplementary file 1 is critical , but is lacking in ways that will make it difficult to use. There should be another sheet in the workbook, or a separate readme file that has all the def lines and nomenclature explained. For example, I have no idea what “Custom_slam” means in a column where all the rest of the cells appear to have FBtr#s. I can guess what most of the columns labels mean, but you should not make the reader do this. Additionally, many of the important data fields in Supplementary file 1 are conclusions rather than data . These should be replaced with numbers used for the classification system as outlined in the methods. The classification scheme itself is somewhat ad hoc and in a different study, by a different lab, so it is almost certain that some of the same genes will be classified differently. These types of data handling issues have muddled many areas of genomics and can be ameliorated by giving others data, allowing them to apply a different classifier.We apologize for the lack of full descriptions in the spreadsheet and we have substantially revised it to improve its clarity and usefulness. As suggested, we have added an additional sheet containing complete descriptions of each column. In addition, we have added all data columns used in our gene classification schemes.3) Write something about the nucleosome positioning inWe now say in the legend of We did not emphasize this result in the main text because the strong positioning of nucleosomes at promoters with dispersed initiation has been observed before  and the 4) The core promoter enrichments are clear in, but are there any dependencies of the type mentioned in the Discussion? For example, in the case of preMBT dual genes, do they have an Inr and a PB element? One or the other?Thank you for the suggestion. We have now added a figure on the co-occurrence of promoter elements . Our resWe have added this analysis and mention this result in the main text: “Finally, an analysis of the co-occurrences of core promoter elements similar to previous analyses  also supThe figure also shows that Inr and PB significantly co-occur. Note, however, that most co-occurrences are only significant when analyzing a large data set and not in selected pre-MBT or MBT gene sets. Thus, it is likely that promoters do not generally require a pair of motifs with a perfect match to the consensus sequence . We interpret a co-occurrence of two motifs as evidence that they are found in the same promoter type and can function together.5) In, the locations of the putative DPE, MTE and PB sites are distributed throughout the promoter region. It is known, however, that these sequences function only in a specific downstream location. Therefore, DPE, MTE and PB-like sequences that are not located in their characteristic downstream location should probably be disregarded.We agree that instances of DPE, MTE and PB motifs found upstream of the transcription start site in the MBT active gene group are unlikely to be functional. That’s why we selected a specific window relative to the transcription start site when we scanned for known motif sequences in We apologize for the confusion this has caused and added to the legend of 6) There are a confusing number of core promoter and MBT classification names. This is certainly not all the fault of the authors, but it would help the reader if the names from the traditional literature and the computational studies were both used or listed somewhere to help match Ohler and FitzGerald, for example. This could be added to the “promoter element table”. The authors should not send the reader to another paper for a term that they could easily define in the manuscript (dispersed and focused are never described). The long descriptors (“pre-MBT not-paused genes”) could be replaced in the figures with data in the heat maps. Almost anything that would make the high-dimensional naming of promoters easier to follow would be greatly appreciated.We also noticed that promoter studies and their classifications are not well known to a general audience. We have therefore now defined focused and dispersed promoters in the text, and mention their alternative names (peaked and broad promoters):Drosophila, dispersed initiation is typically found at broadly expressed housekeeping genes with constitutive promoters.”“Focused transcription initiates within a very narrow window and often at a single nucleotide , while dispersed transcription initiates from several weak transcription start sites within a ∼150 nucleotide region . In We also evaluated whether we could simplify our nomenclature but only found one instance where it was not completely consistent. We had labeled the Ohler motifs as either Motif1, etc. or Ohler1, etc. We have now consistently referred to these motifs as Ohler1, etc. to make sure the source is clear . Overall, we feel that our choice, description, and presentation of the core promoter elements is systematic:• We only include core promoter elements in the analysis that are relatively well understood to avoid confusion.• We are always using traditional names if available  and use the Ohler nomenclature only if no other name is known .• If possible, we always mention promoter elements that belong to the same promoter type together.• In the figures, core promoter elements are sorted and grouped by function for clarity.


Genome-wide rhythms in RNA polymerase II loading and dynamic chromatin remodeling underlie periodic gene expression during diurnal cycles in the mouse liver. Interactions of cell-autonomous circadian oscillators with diurnal cycles govern the temporal compartmentalization of cell physiology in mammals. To understand the transcriptional and epigenetic basis of diurnal rhythms in mouse liver genome-wide, we generated temporal DNA occupancy profiles by RNA polymerase II (Pol II) as well as profiles of the histone modifications H3K4me3 and H3K36me3. We used these data to quantify the relationships of phases and amplitudes between different marks. We found that rhythmic Pol II recruitment at promoters rather than rhythmic transition from paused to productive elongation underlies diurnal gene transcription, a conclusion further supported by modeling. Moreover, Pol II occupancy preceded mRNA accumulation by 3 hours, consistent with mRNA half-lives. Both methylation marks showed that the epigenetic landscape is highly dynamic and globally remodeled during the 24-hour cycle. While promoters of transcribed genes had tri-methylated H3K4 even at their trough activity times, tri-methylation levels reached their peak, on average, 1 hour after Pol II. Meanwhile, rhythms in tri-methylation of H3K36 lagged transcription by 3 hours. Finally, modeling profiles of Pol II occupancy and mRNA accumulation identified three classes of genes: one showing rhythmicity both in transcriptional and mRNA accumulation, a second class with rhythmic transcription but flat mRNA levels, and a third with constant transcription but rhythmic mRNAs. The latter class emphasizes widespread temporally gated posttranscriptional regulation in the mouse liver. In mammalian organs such as the liver, many metabolic and physiological processes occur preferentially at specific times during the 24-hour daily cycle. The timing of these rhythmic functions depends on a complex interplay between the endogenous circadian clock and environmental timing cues relayed through the master circadian clock in the suprachiasmatic nucleus, or via feeding rhythms. These rhythms can be implemented on several regulatory levels, and here we aimed at a better understanding of the transcriptional and epigenetic changes that regulate diurnal rhythms. We performed genome-wide analysis of the locations of RNA polymerase II (Pol II) and the epigenetic histone modifications H3K4me3 and H3K36me3 at specific times of day, relating these data to mRNA expression levels. Our analyses show that Pol II transcriptional rhythms are biphasic in mouse liver, having predominant peak activities in the morning and evening. Moreover, dynamic changes in histone marks lag transcription rhythms genome-wide, indicating that the epigenetic landscape can be remodeled during the 24-hour cycle. Finally, a quantitative analysis of temporal Pol II and mRNA accumulation profiles indicates that posttranscriptional regulation significantly contributes to the amplitude and phase of mRNA accumulation profiles. While many studies have analyzed how transcription and chromatin states are modified during irreversible cell differentiation processes, our work highlights how these states can evolve reversibly in a system exhibiting periodicity in time. In most mammalian organs, widespread diurnal rhythms in mRNA expression underlie the temporal compartmentalization of cellular and physiological processes Here we quantified in time the recruitment of Pol II to gene promoters, the loading of Pol II in gene bodies, the levels of H3K4 and H3K36 trimethylation, as well as the accumulation of mRNAs at six time points around a 24-h cycle. We mapped H3K4 and H3K36 trimethylation because these marks are known to accumulate to high levels just downstream of active TSSs in the first case Bmal1 was maximally occupied by Pol II, both at the promoter and within the gene body, at Zeitgeber Time (ZT) 22 (ZT0 corresponds to light on and ZT12 to light off), whereas its direct target and repressor, the nuclear receptor RevErbα, reached peak occupancy levels at ZT6 , H3K4me3, and H3K36me3 in mouse liver revealed that Pol II occupancy correlated well with known transcription phases of core clock and clock-control genes  and S1. s at ZT6 , consists at ZT6 , S2. In s at ZT6 . As expePol II transcription starts with recruitment of the polymerase complex to gene promoters to form closed complexes. This initial step is followed by the isomerization to the open complex, abortive transcription, promoter escape, release from possible proximal pausing, elongation, and transcript termination  either hypophosphorylated or phosphorylated on Serine (Ser) 5 in the closed complex, open complex, and paused states, and that of Pol II density within gene bodies, likely to represent elongating enzymes with CTDs increasingly phosphorylated on Ser 2 in addition to Ser 5 see . Our mRNThe net transcription rate can be controlled on several levels: at the level of Pol II recruitment to promoters by transcription factors as studied in vitro Per2 gene dropped close to background levels at nadir time ZT2, whereas H3K4me3 levels remained relatively high . Even at this permissive false discovery rate (FDR), this set shows excellent overlap with previously published transcriptome data (n = 752) shows equally convincing overlap  were rhythmic with regard to both transcription and mRNA accumulation. Class 2 genes (n = 668) were transcribed rhythmically, but their RNAs accumulated at almost constant levels. Finally, class 3 genes (n = 217) were constitutively transcribed, but their RNAs accumulated in a rhythmic manner (Alb) gene Fus, Tfrc, and Spon2, whose cyclic accumulation was confirmed by a quantitative RT-PCR analysis (Rdbp (class 2 gene) showed flat mRNA accumulation despite rhythmic transcription  preceded peaks of H3K4me3 by 1 h, H3K36me3 by 3 h, and mRNA accumulation by 3 h. While this was unknown for the H3K36me3 mark, a similar finding for H3K4me3 was previously reported for the per mRNA decay has previously been shown to contribute to the high amplitude accumulation of Drosophila per mRNAs The comparison between mRNA accumulation and Pol II occupation indicated that posttranscriptional mechanisms could strongly contribute to rhythmic diurnal gene expression. Thus, for class 2 genes, Pol II occupancy was cyclic, whereas mRNA accumulation was nearly flat. This would be expected for long-lived transcripts whose syntheses were diurnally rhythmic or, alternatively, in situations where a temporally varying mRNA degradation rate worked against transcription. For class 3 genes, mRNA abundance oscillated in spite of virtually constant polymerase occupancy, suggesting that cyclic pre-mRNA processing efficiency or mRNA decay was subject to diurnal regulation. Consistent with this notion, a rhythmic regulation of Drosophila Bmal1 mRNA accumulates with an about 30-fold amplitude, while due to a long half-life, BMAL1 protein levels only fluctuate about 3-fold in abundance Gstm1 and Bhmt, which encode relatively stable mRNAs but oscillating proteins, the phases of Pol II occupancy and protein accumulation would be compatible with such a mechanism. Conceivably, the proposed scenario could apply to additional proteins that, due to their low abundance, have escaped detection in reference We have limited our analysis to transcription and mRNA accumulation and can thus not address to what extent the proteins issued by the investigated genes also accumulate in a cyclic fashion. However, the same arguments we have made about RNA synthesis and accumulation also apply for protein synthesis and accumulation. Thus, mRNAs with accumulation rhythms can only produce strongly oscillating proteins if these proteins are short-lived. For example, ad libitum. They were then phase-entrained to a 12 h/12 h LD regimen with water ad libitum but food access between ZT12 and ZT24 for 7 d . At each ZT2, ZT06, ZT10, ZT14, ZT18, ZT22, and ZT26, five mice were anesthetized with isoflurane and decapitated. The livers were perfused with 2 ml of PBS through the spleen and immediately collected. A small piece of liver tissue (approx. 100 mg) was snap-frozen in liquid nitrogen and kept at −80°C for RNA extraction. The remaining liver tissue was immediately homogenized in PBS containing 1% formaldehyde for chromatin preparation. All animal care and handling was performed according to the State of Geneva's law for animal protection.C57/BL6 male, 12–14-wk-old (at time of sacrifice), mice were housed in a 12 h light/12 h dark (LD) regimen for 2 wk with water and food available Perfused livers were processed for chromatin preparation as described in A total of 1 ml of each chromatin suspension (containing about 60 µg of DNA) was incubated with 10 µg of anti-RPB2, 1.5 µg of anti-H3K36me3, or 1.5 µg of anti-H3K4me3 in buffer A (20 mM Tris/HCl (pH 7.5), 150 mM NaCl, 2 mM EDTA) overnight at 4°C on a rotating wheel. Ten µl of protein A bead suspension (25% slurry in buffer A), pre-blocked with 10 µg/ml of salmon sperm DNA and BSA at 4°C overnight, was then added and the incubation was continued for 1 h at room temperature on a rotating wheel. The beads were then washed with dialysis buffer and ChIP wash buffer as described http://www.Bioconductor.org). Normalized expression signals were calculated from Affymetrix CEL files using RMA normalization method About 100 mg of snap-frozen liver tissue were used for RNA preparation with the TRIzol reagent (Invitrogen) according to the manufacturer's instructions and purified with a miRNeasy Mini Kit (Qiagen). For each time point, 500 ng of total RNA from each of the five mouse livers were pooled and analyzed on Mouse Gene 1.0ST arrays according to the manufacturer's instructions (Affymetrix). All statistical analyses were performed with the statistical language R and various Bioconductor packages  (Invitrogen) following the supplier's instructions. Five percent of this cDNA was PCR amplified  with the Sybr Green master mix (Applied Biosystems), and raw threshold-cycle (Ct) values were calculated with SDS 2.0 software (Applied Biosystems). Mean values were calculated from triplicate PCR assays for each sample and normalized to those obtained for Mus musculus NCBI m37 genome assembly ) using Bowtie At each time point, DNA sequenced reads were mapped to the mouse genome , gene bodies  and immediately downstream of the PASs (PAS to +1 kb). The H3K4me3 signals were quantified in a region extending from ±1 kb around TSSs. As the H3K36me3 signal was found mostly in the gene bodies towards the end of the genes, the last 40% of gene bodies were used for quantification. For each of these features, the signal densities were then quantile normalized across time points data in . For thex(t) = A0+A24cos(2π/(24 h) * (t−tmax)) to perform least squared fitting of temporal profiles. A0 is the mean signal, A24 the amplitude of the diurnal oscillation, and tmax the peak time. Such fits are conveniently done using Fourier series.We used the function In p value associated with 24 h rhythmic profiles was computed using the Fisher test for one specific period p values (see above) for each of those k = 5 marks and combined them using the statistics k degree of freedom p value was used to estimate False Discovery Rates (FDR) via the linear step-up method p value <0.25; for Pol II the p value was obtained from the promoter and gene body cycling p values, combined with the Fisher Chi-squared test with k = 2). This set of 1,560 genes was further split into three classes according to the relative amplitudes B = 24A/A0 computed from the cosine fits. We defined class 3 genes (n = 217) as those with B(mRNA)/B(Pol II)>2; class 2 (n = 668) with B(mRNA)/B(Pol II)<0.5; and class 1 genes (n = 675) as the remaining set. All selections are provided in http://cyclix.vital-it.ch.The 24-h spectral power and phase were computed by using established methods m(t):s(t) is taken as a cosine function with frequency ω = 2π/24h−1 mean s0, and relative amplitude b. k is a constant mRNA degradation rate. This linear model predicts that the mRNA accumulate as a cosine function with mean m0, relative amplitude B, and delay φ with respect to synthesis. The tangent of delay is given by the ratio of the frequency to the degradation rate, which constrains the delay between 0 and 6 h . Moreover the ratio of relative amplitudes is given by the cosine of the phase delay.To study amplitude and phase relationships between transcription and mRNA accumulation, we used a simplified kinetic model for the accumulation of the mRNA The analysis was performed using GOstats_2.22.0 and the following databases org.Mm.eg.db_2.7.1, KEGG.db_2.7.1, GO.db_2.7.1. In http://cyclix.vital-it.ch.Illumina sequencing data for the ChIP-seq and mRNA are available at GEO as the super series GSE35790. Additional processed data and visualization tools are provided at Figure S1Per1 gene, which spans 11 kb on chromosome 11, with the thin lines above the profiles indicating the position-specific temporal maxima. The gene structure (RefSeq transcripts) is shown below the panel. The Per1 gene has two alternative TSSs, both with Pol II and H3K4me3 peaks, and both promoters are thus likely active. Maximal Pol II density in gene body is at ZT10. (B) As in (A) but for the constitutively expressed Tbp gene, which spans 17.5 kb on chromosome 17. (C) Per 1 gene, temporal profiles of the quantifications of the different signals. (D) Tbp gene, temporal profiles of the quantifications of the different signals.Pol II, H3K4me3, and H3K36me3 profiles measured around the clock. (A) The density profiles of Pol II (red), H3K4me3 (green), and H3K36me3 (blue) are indicated for the (TIFF)Click here for additional data file.Figure S2Spatial H3K4me3 and H3K36me3 profiles around transcription start sites (TSSs) and polyadenylation sites (PASs) stratified according to mRNA expression levels and CpG content at promoter. (A) Average H3K4me3 signals per transcription unit split by quartile, based on the total mRNA expression level measured by microarray hybridization. Each quartile is represented by a distinct color shading from light (lowest quartile) to dark (upper quartile). (B) As in (A) but around polyadenylation sites (PASs). (C–D) As in (A) and (B) but for the average H3K36me3 signal. (E–F) Average Pol II signals per transcription unit split by quartile, based on the CpG content in windows spanning  around the TSS. Each quartile is represented by a distinct color shading from light (lowest quartile) to dark (upper quartile). (G–H) Input signals at the TSS and PAS.(TIFF)Click here for additional data file.Figure S3Temporal relationship of Pol II promoter loadings and gene body density. (A) H3K36me3 signal in the most 3′-proximal 40% of gene bodies versus Pol II promoter occupancy at ZT2. There is a strong correlation between high Pol II loading at the promoter and high levels of H3K36me3 in the most 3′-proximal 40% of gene bodies. (B) mRNA expression versus Pol II promoter occupancy at ZT2 shows two distinct populations. (C) Pol II occupancies at promoters and in gene bodies vary in synchrony in a genome-wide fashion. Each gene is represented by a line indicating the orientation and the amplitude of changes during a diurnal cycle. Orientation is indicated by the color (key in upper right corner). (D–G) The genes in (A) were separated into groups according to orientation, from genes showing the largest variation in Pol II occupancy within gene bodies  to genes showing the largest variation within promoter regions . Panel G shows genes for which gene body occupancy decreased as promoter region occupancy increased. Most highly expressed genes show patterns of concomitant changes in both promoter and body occupancy (panels D and E). (H) Idem as (C) but for the PAS1K signals against the promoter Pol II signals.(TIFF)Click here for additional data file.Figure S4x,y,z) and gene bodies (w) as predicted by a simplified model of transcription. The model describes the (reversible) recruitment of polymerases to promoters, the (irreversible) transition to the open complex, the (irreversible) promoter escape, and the (irreversible) transition from a pausing to an elongating state, after which the polymerases travel to the end of the transcript. We investigate the two scenarios in which either the recruitment or the elongation rates are subject to cyclic circadian variation. (B) The mathematical model of transcription and its parameters: fk (forward recruitment rate), bk (backward recruitment rate), ok (isomerization rate), ik (promoter escape rate), dk (de-pausing rate), v (elongation rate). (C) Simulated temporal variation of occupancy in promoter proximal positions  and gene body (w) when the recruitment rate is varied in a circadian manner. (D) Idem when the de-pausing rate is varied in a circadian manner. The numerical values used in the simulation are fk = 1/min; bk = 0; ok = 0.1/s; ik = 1/min; dk = 1/min; v = 1/(6 s).A model predicts temporal patterns for recruitment- or initiation-regulated rhythmic transcription. (A) Temporal variation of Pol II occupancy at promoter proximal positions ((TIFF)Click here for additional data file.Figure S5Temporal relationship of H3K4me3 and H3K36me3 marks with Pol II promoter occupancy. (A and C) Each gene is represented by a line indicating the average orientation and amplitude of changes during a diurnal cycle. Orientation is also indicated by the color (key in upper right corner). (B and D) Binned representation of (A) and (C), respectively. Panel B is identical to (TIFF)Click here for additional data file.Figure S6n = 284, red) and the rhythmic transcripts in Hughes et al. 2009 (blue) and Hughes et al. 2012 (green). In all pairwise comparisons, the percentage overlaps refer to the smallest of the two sets. Indicated p values for the overlaps are computed using the hypergeometric test. The gene sets and overlaps are given in n = 752, red). (E) Number of genes in the set as a function of the q-values (Fisher combined probability test with Benjamini-Hochberg FDR correction).Comparison of gene selection with the Hughes et al. 2009 (TIFF)Click here for additional data file.Figure S7x-axis).Stratification of rhythmically transcribed genes into subgroups. (A) Transcription phases for the 400 strongest BMAL1 sites in Rey et al. 2011 (TIFF)Click here for additional data file.Figure S8n = 752, p<0.018, FDR = 0.5).Temporal relationships of Pol II, H3K4me3, H3K36me3 profiles, and mRNA accumulation in mouse liver. Idem as (TIFF)Click here for additional data file.Figure S9n = 892, red) and the rhythmic transcripts in Hughes et al. 2009 (blue) and Hughes et al. 2012. In all pairwise comparisons, the percentage overlaps refer to the smallest of the two sets. Indicated p values for the overlaps are computed using the hypergeometric test. The gene sets and overlaps are given in Comparison of class 1 and class 3 genes with the Hughes et al. 2009 (TIFF)Click here for additional data file.Figure S10Pol II occupancy profiles at the Per1 and Cry2 genes. (A) Profile of Per1, entire locus. (B) Profile of Per1, region around the PAS. (C) Profile of Cry2, entire locus. (D) Profile of Cry2, region around the PAS. The location of ChIP-qPCR primers used in Click here for additional data file.Movie S1Animated profiles for the Bmal1, Reverbα (Nr1d1), and mPer1 genes for the Pol2 (red) occupancy and H3K4me3 (green) and H3K36me3 (blue) marks. The data were interpolated in time using spline interpolation (every 30 min).(GIF)Click here for additional data file.Movie S2Idem as Movie S1 with only the measured time points (no interpolation).(GIF)Click here for additional data file.Table S1Summary of the number of mapped reads per time point per mark.(XLS)Click here for additional data file.Table S2List of the transcription units selected to define the density profiles.(XLS)Click here for additional data file.Table S3Pol II, H3K4me3, and H3K63me3 quantifications after normalization. The signals for each transcript per time point and per mark are given as described in Materials and Methods.(TXT)Click here for additional data file.Table S4A0 values use background-subtracted signals .Data and gene lists for (TXT)Click here for additional data file.Table S5Gene Ontology analysis for the gene in classes 1–3: biological processes.(XLS)Click here for additional data file.Table S6Gene Ontology analysis for the gene in classes 1–3: molecular function.(XLS)Click here for additional data file.Table S7Gene Ontology analysis for the gene in classes 1–3: cellular component.(XLS)Click here for additional data file.Table S8PCR primers used for qRT-PCR validations.(XLS)Click here for additional data file.


Multiplex experimental assays coupled to computational predictions are being increasingly employed for the simultaneous analysis of many specimens at the genome scale, which quickly generates very large amounts of data. However, inferring valuable biological information from the comparisons of very large genomic datasets still represents an enormous challenge.As a study model, we chose the NFI/CTF family of mammalian transcription factors and we compared the results obtained from a genome-wide study of its binding sites with chromatin structure assays, gene expression microarray data, and in silico binding site predictions. We found that NFI/CTF family members preferentially bind their DNA target sites when they are located around transcription start sites when compared to control datasets generated from the random subsampling of the complete set of NFI binding sites. NFI proteins preferably associate with the upstream regions of genes that are highly expressed and that are enriched in active chromatin modifications such as H3K4me3 and H3K36me3. We postulate that this is a causal association and that NFI proteins mainly act as activators of transcription. This was documented for one member of the family (NFI-C), which revealed as a more potent gene activator than repressor in global gene expression analysis. Interestingly, we also discovered the association of NFI with the tri-methylation of lysine 9 of histone H3, a chromatin marker previously associated with the protection against silencing of telomeric genes by NFI.Taken together, we illustrate approaches that can be taken to analyze large genomic data, and provide evidence that NFI family members may act in conjunction with specific chromatin modifications to activate gene expression. The size of the control dataset can then be chosen to parallel that of the experimental set, to simplify the statistical analysis [High-throughput assays are being widely employed in various fields of biology. For example in genomics, DNA microarrays are used to simultaneously measure the expression levels of nearly all genes of a genome ,2. Recenanalysis ,15.in vitro, the specificity of this family of proteins may be different from that observed in the cell, where interactions with other transcription factors may take place and DNA accessibility may be restrained by chromatin. Here we assessed the in vivo binding preferences of NFI/CTF, its global functional properties regarding the regulation of gene expression and the relationship of NFI binding sites with different histone methylation markers typical of either an open or closed chromatin structure.As a study group of proteins, we chose NFI/CTF family of mammalian transcription factors. NFI/CTF represents a family of transcription-replication factors comprising polypeptides encoded by four paralogous genes located on different chromosomes in mammals  ,17. NFI Statistical analysis in genomics often relies on the sub-sampling of datasets, which requires random sampling algorithms. We devised a random sampling algorithm that can be conveniently applied to large genomic datasets. The random sampling algorithm C++ source code is available as a text file online  and 5 kb of upstream sequences.We first used this random sampling method to compare data from a ChIP-Seq experiment performed on primary mouse embryonic fibroblasts for the NFI transcription factor family relative to the in silico predictions of its binding sites. The mouse genome (NCBI build 37 or mm9) was found to contain a set of 61,492 NFI predicted sites that were defined using a previously established position weight matrix ,28. The in vivo binding site occupancy was estimated using the average ChIP-Seq tag count covering each subset of NFI predicted sites. When tag counts occurring at TSS-proximal binding sites were compared to those that do not occur near known TSS, a higher tag counts was noted at the predicted sites, but also in regions extending several kb away from the known binding sites . Thus, we conclude that NFI preferentially occupies promoters or upstream regulatory regions of genes that exhibit high expression levels in the cell.Next, we assessed whether genes that bind NFI may have some common features regarding their gene expression levels. We selected 39,807 roblasts . Again, To test whether the preferential binding of NFI to upstream regions of expressed genes may be associated with specific chromatin modifications, we used ChIP-Seq data for 4 different histone H3 methylations, as previously obtained from mouse embryonic fibroblasts , to be awt-Eko) were determined for each gene within defined groups and plotted as an absolute value. The group of genes activated by NFI-C showed the highest median ΔE value, implicating this factor as a potent gene activator , the group of NFI-C suppressed genes had levels of expression that did not differ significantly from the control subgroups , again indicating that NFI-C acts as a moderate inhibitor of gene expression . These results suggest that NFI-C member of the NFI family acts globally as an effective gene activator.The preferential association of NFI with expressed genes prompted us to test whether NFI family members may have the potential to activate gene expression, or whether their binding is rather the consequence of histone modifications and chromatin accessibility at promoters. For this purpose, we used gene expression microarray data from mouse embryonic fibroblasts in which one of the NFI factors (termed NFI-C) was knocked out by insertional mutagenesis . We focur Figure . NFI-C sn Figure . OverallIn this study, we used a random sampling procedure as a general method to obtain reliable control datasets in the analysis of high-throughput genomic assays. We find that datasets of more than 100 individual values can be used without decreasing the robustness of statistical analysis, and that independently generated random subsets of data have statistically indistinguishable global properties. Thus, subsampling can provide a convenient way to display and compare the noise and signals from experimental and control datasets of the same size.First, we showed that NFI binds preferentially those predicted sites that are located upstream of the initiation sites of transcription Figure . SeveralWe also found that NFI occupy promoters or upstream regions of the group of genes that are significantly more expressed than the representative randomly selected control groups. Since correlation does not necessarily imply causal relationship, this observation does not allow the conclusion that NFI-family members actually activate the expression of these genes. For instance, NFI might bind highly expressed genes to suppress in part their expression, but still leaving relatively high transcription levels. However, taken together with previous observations that NFI activates the expression of many genes in higher eukaryotes ,32,34-37in vivo NFI binding sites are located up to 5 kb from their TSS, which is a relatively large distance, NFI might act as well through some of the types of remote regulation, for instance by the establishment of a chromatin domain boundary that would prevent the propagation of a silencing chromatin structure towards the promoter [The hypothesis that NFI family members may directly activate genes appears to be true for at least one of the member of the family (i.e. NFI-C), as mRNA profiling analysis performed on wild-type and NFI-C knock-out cells revealed that NFI-C is a more potent gene activator than a repressor. The 1000 genes that are most up-regulated by NFI-C had significantly higher change in their expression levels than the top 1000 down-regulated genes. In addition, up-regulated genes showed significantly higher expression levels than representative control gene samples selected from the total gene population, implicating again that this factor is a potent activator of gene expression. Since the selected promoter ,38.Histone H3 methylations such as the H3K4me3 and H3K36me3 modifications were found to be enriched around the TSS of NFI-occupied genes when compared with control gene groups. This finding is consistent with the model that NFI acts predominantly as an activator of transcription, since H3K4me3 and H3K36me3, but not H3K27me3, were proposed as markers of active gene transcription . This in2, DMEM , Supplementary 10% FBS , 1% v/v nonessential amino-acids , 1% v/v L-glutamine .Mouse embryonic fibroblasts (MEF) were extracted from mouse embryos of 14.5 days. Cells from WT (wild-type) and NFI-C knock-out embryos were cultured under the following conditions: 37°C, 5% COChromatin was extracted from approximately 20,000,000 primary mouse embryonic fibroblasts grown in culture and cross-linked using 11% formaldehyde. Extracted chromatin was fragmented to the average fragment size of 1000 bp using high-frequency sound sonication on VibraCell-75455 (Bioblock Scientific). ChIP was performed as described before  using thChIP DNA was processed using the contents of the ChIP-Seq Sample Prep Kit (Illumina). Size range of templates was selected by loading the entire processed sample on a 2% agarose gel and excising the gel region of 50-400 bp. PCR amplification of the gel-extracted DNA was performed for 18 cycles using the adapter-specific primers. Each sample was loaded into 3 separate flow cell channels of the Illumina Cluster Station and then subjected to sequencing-by-synthesis on the Illumina Genome Analyzer sequencing system. For each of the samples, sequence reads from independent channels were pooled together.In vivo NFI sites were defined using the ChIP-peak tool and applying the following parameters: window width - 300 bp, vicinity range - 300 bp, peak threshold - 5 tags, count cut-off - 1 tag, repeat filtering-on. Correlation analyses were made using ChIP-cor tool and applying the following parameters: window width - 50 bp, count cut-off - 1, normalization - global. Galaxy tools were used to operate on the genomic intervals [Clustering and correlation analyses of mapped reads were performed using ChIP-peak and ChIP-cor tools available on the ChIP-Seq Analysis Server ,41. In vntervals ,43.Random sampling algorithm was written in C++ language and compiled in Microsoft Visual Studio as detailed in the Additional file ChIP-Seq data for the wild type and NFI-C knock-out mouse embryonic fibroblasts were deposited at the Gene Expression Omnibus (GEO) repository under the accession number GSE15844. Gene expression microarray data for the wild type and NFI-C knock-out mouse embryonic fibroblasts were taken from the GEO repository under the accession number GSE15871. ChIP-Seq data for histone methylations in mouse embryonic fibroblasts  were taken from GEO repository under the accession number GSE12241.NFI: Nuclear Factor I; NFI-C: Nuclear Factor I - C; CTF: CAAT box transcription factors; ChIP: Chromatin immuno-precipitation; ChIP-Seq: Chromatin immuno-precipitation sequencing; MEFs: Mouse embryonic fibroblasts; TSS: Transcriptional start site; TFs: Transcription factors; NFI-C KO: Nuclear Factor I - C knock out; WT: Wild typeMP conceived the study, carried out the ChIP-Seq experiments, participated in the data analysis and drafted the manuscript. PP developed the random sampling algorithm and participated in the data analysis. CS, GA and PB developed ChIP-Seq analysis tools and participated in the data analysis. AG, GP made substantial contributions to the data collection and interpretation of data. CM, PB and NM participated in the design of the study, made substantial contributions to the interpretation of data, provided essential data revisions, and helped to draft the manuscript. All authors have read and approved the final manuscript..Click here for file.Click here for file


A sizeable fraction of the identified genetic factors was also associated with nearby gene expression, which was correlated with the distance between the transcription start site (tss) and the boundary that faces the tss. Taken together, the variation in the width of accessible chromatin regions may arise in conjunction with the modulation of the boundary nucleosomes by post-translational modifications or by chromatin regulators and in association with the activity of nearby gene transcription.Regulatory regions maintain nucleosome-depleted, open chromatin status but simultaneously require the presence of nucleosomes for specific histone modifications. It remains unclear how these can be achieved for proper regulatory function. Here we demonstrate that nucleosomes positioned within accessible chromatin regions near the boundaries provide platforms for histone modifications while preventing the occlusion of regulatory elements. These boundary nucleosomes were particularly enriched for active or poised regulatory marks in human, such as histone acetylations, H3K4 methylations, H3K9me3, H3K79me2, and H4K20me1. Additionally, we found that based on a genome-wide profiling of ∼100 recombinant yeast strains, the location of open chromatin borders tends to vary mostly within 150 bp upon genetic perturbation whereas this positional variation increases in proportion to the sequence preferences of the underlying DNA for nucleosome formation. More than 40% of the local boundary shifts were associated with genetic variation in  Open chromatin formation and regulation are intimately coupled with nucleosome remodelling and modification. Regulatory regions such as promoters and enhancers maintain nucleosome-free, open chromatin states whilst at the same time the presence of nucleosomes is required for specific histone modifications. In this work, we carried out detailed analyses of our data of open chromatin maps for ∼100 different yeast strains and whole-genome nucleosome occupancy along with the public data of open chromatin and nucleosome positioning in human generated in the ENCODE project. We observed nucleosomes positioned within accessible chromatin regions near their boundaries. These boundary nucleosomes appeared to carry various histone methylations without hampering the binding of DNA regulators and sequence preferences for these nucleosomes were associated with variation in the width of accessible chromatin. The end positions of open chromatin domains, particularly with high intrinsic preferences for nucleosome formation, were more flexible than the middle point, changing mostly within 150 bp upon genetic perturbation. By using quantitative trait loci (QTL) mapping, we identified genetic variants that are associated with the variation in the width of open chromatin and examined its relationship with nearby gene expression. Open chromatin provides access to a wide spectrum of DNA binding proteins for genetic regulation processes such as transcription, repair, recombination, and replication. In this regard, open chromatin profiling has been widely used to identify the location of regulatory regions, including promoters, enhancers, insulators, silencers, replication origins, and recombination hotspots While chromatin opening is required at regulatory regions, promoters and enhancers carry specific histone modifications that are associated with regulatory activity and particular functionality in vivo nucleosome positioning, and transcription factor (TF) binding in the GM12878 lymphoblastoid cell line. Chromatin accessibility was measured based on next-generation sequencing of DNA isolated by two different methods, namely the DNase I hypersensitivity assay in vitro purely based on naked yeast and human DNA To understand the mechanisms by which various histone modifications specifically mark regulatory regions that should be in nucleosome-free states, we set out for integrative analyses of recent data generated as part of the ENCODE project, including chromatin accessibility, histone modifications, histone variant H2A.Z, in vivo nucleosomes by means of MNase-mediated purification of mononucleosomes  nucleosome positioning whereas >25.6% had nucleosome positioning in vivo (in vitro than in vivo occupancy) is maintained  open chromatin regions while forming a bimodal peak in longer regions (>1 kb)  . On the e region .in vivo TF binding events.When examined according to TF binding sites (TFBSs) in the human cells, two strongly positioned nucleosomes were found 200 bp away on average from empirical TFBSs (based on the ChIP-seq of ∼90 TFs), and periodic nucleosome phasing was observed in the surrounding regions  mapping for the end-to-end distances of the open chromatin boundaries that were identified in BY4746 and were <100 bp away from their homologous sites in all the other strains. At a false discover rate (FDR) of 0.01, 39.2% of the boundary shifts were significantly associated with at least one genetic marker in trans-linkages included chromatin remodelers and transcription regulators  was two times higher than that in the accessibility QTL mapping, implying that underlying DNA sequences play a significant role in the regulation of open chromatin boundaries. Sixty-six boundary shifts were associated in cis with 226 genetic markers while 853 boundaries were in trans with 431 genetic markers. Interestingly, only for 4.5% of the 66 cis-associated boundaries and 5.0% of the 853 trans-associated boundaries, the relevant chromatin region was also identified in the accessibility QTL mapping. This supports that the variation in boundary locations does not simply reflect the variation in chromatin accessibility despite a possible mechanistic correlation between peak size and peak width. While different target chromatin regions were identified in the two QTL mappings, there was a considerable overlap of responsible regulatory loci. Among the 431 regulatory loci that were associated in trans with boundary variations, 52.4% were also responsible for chromatin accessibility in trans, and 58.0% of these dual chromatin QTLs were trans-expression QTLs as well. On the other hand, 15.0% of cis-QTLs for boundary variations were cis-QTLs for chromatin accessibility. The overlapping fraction is low because a single marker cannot usually cover multiple different chromatin regions in cis. However, 97.1% of these dual chromatin QTLs were cis-expression QTLs. This cross-confirmation suggests that the regulatory loci identified in each QTL mapping may be functional with many of them exerting effects on transcription regulation.We then compared the results of the boundary QTL mapping with those of the QTL mapping for chromatin accessibility as previously performed for the same dataset TAT1  for the GM12878 lymphoblastoid cell line. Chromosomal coordinates of the validated DNase I peaks were refined by interrogating the base-pair resolution map of DHS tags obtained from the UCSC Genome Browser . Specifically, the average number of the DHS tags mapped outside of the peak boundaries across all the validated DNase I peaks was obtained and then the end positions of each DNase I peak were adjusted such that the maximum number of the DHS tags mapped outside of the adjusted ends would not exceed the expected (average) number obtained. To identify open chromatin in yeast, we obtained the BY-RM cross strains from the original authors We obtained 46,080 genomic regions enriched for DNase I hypersensitivity as identified by F-Seq In vivo nucleosome occupancy in the GM12878 lymphoblastoid cells was obtained from the UCSC Genome Browser (Nucleosome Position by MNase-seq from ENCODE/Stanford/NYU). The MNase-seq reads were extended to 147 bp and then mapped across the boundary of open chromatin. We used the NPS package In vitro nucleosome positioning was identified in a previous study in vivo nucleosome profiling in yeast, the MNase-mediated purification of mononucleosomes was carried out. The mononucleosomal DNA fragments were sequenced by Illumina Genome Analyzer, subjected to 36 cycles of single-read sequencing. We used Genetrack software in vitro nucleosomes in yeast in vitro nucleosome positioning.Histone modification data for the GM12878 lymphoblastoid cell line were downloaded from the ENCODE Histone Modification Tracks. Data for H3K4me1, H3K4me2, H3K4me3, H3K9ac, H3K9m3, H3K27ac, H3K27me3, H3K79me2, and H4K20me1, and H2A.Z were downloaded. The raw reads were extended to 200 bp and the number of the extended reads mapped to the body and flanking regions of open chromatin was obtained. To handle different sizes of open chromatin, the body regions were divided into the same number of bins with varying lengths. The profiles of transcription factor binding were obtained from the ENCODE Transcription Factor Binding Tracks. All the data available for the GM12878 cell line were generated by either HudsonAlpha Institute for Biotechnology (HAIB) or Stansford/Yale/USC/Harvard (SYDH). The peaks of transcription binding were identified by the MACS software (HAIB) or the PeakSeq algorithm (SYDH). The number of peaks was obtained for the body and flanking regions of open chromatin in a similar manner as the histone modification plots. The Human/Mouse/Rat Conserved Transcription Factor Binding Sites track of the UCSC Genome Browser provided 3.8 million evolutionarily conserved binding sites of 250 transcription factors as inferred based on the Transfac Matrix Database (v7.0). To predict actual binding sites of the transcription factors, we first identified enriched regions for transcription factor binding by using the peak finding functionality of the HOMER package cis- and trans-associations.For the 4,897 open chromatin loci identified in the BY strain, the end-to-end distances to the nearest open chromatin sites in the other strains were obtained. A total of 918 boundaries were less than 100 bp away from the closest homologous site in all the other 95 strains. The nearest end-to-end distances for these 918 boundaries across the 95 strains were used as the quantitative trait. The genotypes of the genetic markers from the original study http://dir.nhlbi.nih.gov/papers/lmi/epigenomes/hgtcell.aspx, http://dir.nhlbi.nih.gov/papers/lmi/epigenomes/hgtcellacetylation.aspx, and http://dir.nhlbi.nih.gov/papers/lmi/epigenomes/hgtcellnucleosomes.aspx, respectively. MNase-seq nucleosomes and H2A.Z-containing nucleosomes were identified by using the NPS package Regarding the human T cell chromatin data All the data used in this work is summarized in Figure S1Normalized frequency of C/G dinucleotides and A/T dinucleotides across the boundaries of open chromatin in yeast.(PDF)Click here for additional data file.Figure S2In vitro (above) and in vivo (below) nucleosome patterns in human within open chromatin regions that are shorter than 500 bp (left) and are longer than 1 kb (right). The maximum boundaries (for <500 bp) and the minimum boundaries (for >1 kb) are shaded in yellow.(PDF)Click here for additional data file.Figure S3in vivo mononucleosomes, H2A.Z-containing nucleosomes and transcription binding across the flanking regions and body of open chromatin regions in GM12878 cells.Overlay of (PDF)Click here for additional data file.Figure S4Association of histone modifications with H2A.Z in T cells. Histone methylation, H2A.Z occupancy, histone acetylation, and MNase-digested nucleosome data in resting T cells were obtained. MNase-seq nucleosomes and H2A.Z-containing nucleosomes were identified by using the NPS package. Histone modification levels were estimated for individual positioned nucleosomes based on overlapping sequence read counts and the relative enrichment of each type of histone modification on H2A.Z nucleosomes was computed.(PDF)Click here for additional data file.Figure S5Distribution of center-to-center distances (above) and end-to-end distances (below) of open chromatin regions detected in technical replicates of the laboratory strain of yeast (boxplots) in comparison with those of randomly shuffled open chromatin regions in various strains from their homologous site in the laboratory strain (dotted curves).(PDF)Click here for additional data file.Table S1The percentage of nucleosome-containing open chromatin regions.(PDF)Click here for additional data file.Table S2trans-linkages (except MCM5 and MCM6) in QTL mapping of the end-to-end distances between homologous sites of open chromatin regions. We identified the genes that are located within 10 kb upstream or downstream of the genomic region covered by the genetic marker. To identify potential regulators, we used the Gene Ontology term ‘DNA binding’ and also selected genes that are known to be involved in transcription and chromatin regulation.Regulatory factors with >5 (PDF)Click here for additional data file.Table S3Summary of datasets used in this work.(PDF)Click here for additional data file.