Taken together, our study provides a guideline for rational design the promoter-targeting siRNA to sequence-specifically enhance gene expression.Small interfering RNAs (siRNAs) are widely used to repress gene expression by targeting mRNAs. Some reports reveal that siRNAs can also activate or inhibit gene expression through targeting the gene promoters. Our group has found that microRNAs (miRNAs) could activate gene transcription via interaction with the TATA-box motif in gene promoters. To investigate whether siRNA targeting the same region could upregulate the promoter activity, we test the activating efficiency of siRNAs targeting the TATA-box motif of 16 genes and perform a systematic analysis to identify the common features of the functional siRNAs for effective activation of gene promoters. Further, we try various modifications to improve the activating efficiency of siRNAs and find that it is quite useful to design the promoter-targeting activating siRNA by following several rules such as (a) complementary to the TATA-box-centered region; (b) UA usage at the first two bases of the antisense strand; (c) twenty-three nucleotides (nts) in length; (d) 2′-O-Methyl (2′-OMe) modification at the 3′ terminus of the antisense strand; (e) avoiding mismatches at the 3′ end of the antisense strand. The optimized activating siRNAs potently enhance the expression of  WAF1/CIP1 (p21) or VEGF showed long-lasting and sequence-specific induction of targeted genes SiRNAs are widely used to repress genes expression by targeting mRNAs + T cells isolated from HIV-1-infected patients receiving suppressive highly active antiretroviral therapy (HAART) Recently, our group revealed that an HIV-1-encoded miRNA, miR-H3, could target the TATA-box region in HIV-1 5′ LTR and enhance viral replication. Moreover, the chemically-synthesized siRNAs targeting the same site could activate HIV-1 viral production from the CD4in vitroin vivoCaenorhabditis elegansFor rational design of the conventional siRNAs to degrade target mRNAs, many guidelines have been developed. For instance, the G/C content of the siRNA sense strand should be 30–50%; bases at positions 1–3 should be A/U; the 10th and 19th bases of the sense strand should be A or U; the 1st base of the sense strand should be G/C A large amount of chemical modifications have been developed to improve the properties of siRNAs Although several criteria have been suggested for efficient design of repressive siRNAs that target mRNAs, there is no suggestion about the effective design of activating siRNAs that target gene promoters. In this study, we attempted to investigate the role of TATA-box-targeting siRNAs in the regulation of gene expression and increase their efficiency by testing various modifications including the target site, sequence characters, length and chemical modifications. We identified several helpful characteristics and have combined them together to obtain a reasonable strategy for rational designing siRNAs to activate the gene promoters specifically.This research was approved by the Ethics Review Board of Sun Yat-Sen University. Healthy donors were comprised of a group of local volunteers, who were seronegative and had no reported history of chronic illness or intravenous drug use. All mouse experiments were approved by the Sun Yat-Sen University Institutional Animal Care and Use Committee. BALB/c mice were obtained from the Animal Experimental Center of Sun Yat-Sen University. All mice were maintained under specific pathogen-free conditions and used at 8–10 weeks of age.http://epd.vital-it.ch/) which is a collection of experimentally defined eukaryotic POL II promoters http://www.bioinformatics.org/yapp/cgi-bin/yapp.cgi). The sequences of the two mutated IL-2 promoter (−50 to +1) were as follows: IL-2mt-27/−25, CACAAATTGCATCTCTTGTTCAAGAGTTCTAATATTTTTCCAGAATTAACAG; IL-2mt-17/−16, TACTCTTGTTCAAGAGTTCTAATATTTTTCCAGAATTAACAGTATAAATTGC. Mutations were italicized and underlined.The sequences of gene promoters were downloaded from the Eukaryotic Promoter Database  with the sequence −400 to +1 bp relative to the TSS of the human IL-2 promoter. The vectors containing HIV-1, apolipoprotein E (APOE), insulin (INS), c-Myc, or other gene promoters were generated in the same way. Hsa-let-7i mimics, ssRNAs and the corresponding negative control RNAs were purchased from Genepharma . SiRNAs targeting the TATA-box motif of each gene were synthesized and chemically modified by Ribobio . The sequences of the small RNAs used in this study are listed in The + T lymphocytes were then isolated from PBMCs with human CD4+ T Lymphocyte Enrichment kit (BD). The mouse primary CD4+ T cells were isolated from spleens of 8- to 10-week-old female BALB/c mice with mouse CD4+ T Lymphocyte Enrichment kit (BD). The isolated human or mouse primary CD4+ T cells were grown in RPMI 1640 media supplemented with 10% fetal bovine serum (FBS), 50 U/ml penicillin and 50 µg/ml streptomycin in a humidified incubator with 5% CO2.Jurkat and HEK293T cells were obtained from ATCC  and cultured according to the recommendations. Human peripheral blood mononuclear cells (PBMCs) were isolated from the whole blood of healthy donors with Ficoll-Hypaque Solution . The human primary CD4+ T cells with Lipofectamine RNAiMAX (Invitrogen) at final concentrations of 120 nM. At 12 hrs post transfection, Jurkat cells were treated with 50 ng/ml phorbol 12-myristate 13-acetate  and 1 µM ionomycin (Sigma) for 36 or 84 hrs to activate the cells. Human CD4+ T cells were activated with 1 µg/ml anti-CD3 (R&D Systems) and 5 µg/ml anti-CD28 (R&D Systems) for 84 hrs. Mouse CD4+ T cells were stimulated with 2 µg/ml anti-mouse CD3 (R&D Systems) and 1 µg/ml anti-mouse CD28 (R&D Systems) for 84 hrs.Small RNAs and plasmids were transfected into HEK293T cells with Lipofectamine 2000 (Invitrogen) according to the manufacturer’s protocol. SiRNAs were transfected into Jurkat and human or mouse primary CD44 cells per well and grown to 40–60% confluence overnight. Five to ten ng of gene promoter-driven firefly luciferase (FL) reporter and 2 ng renilla luciferase (RL) vector were co-transfected with activating siRNAs or negative control siRNA at final concentrations of 25 nM into HEK293T cells using Lipofectamine 2000 (Invitrogen) by following the manufacturer’s instructions. At 36 hrs post transfection, FL and RL activities were measured with the Dual-Glo Luciferase assay system (Promega) according to the instructions of the manufacturer. FL signals (sample) were normalized to RL signals (transfection control).HEK293T cells were seeded in 48-well plates (Corning) at a density of 2.5×10+ T cells were isolated with TRIzol reagent (Invitrogen) and then subjected to cDNA synthesis using PrimeScript RT reagent Kit (Takara). Quantitative PCR was performed with SYBR Premix ExTaq II Kit (Takara) by using the CFX96 Real-Time System (Bio-Rad). The instructions of the manufacturer were followed. Quantification was normalized to mRNA levels of the housekeeping gene GAPDH or β-actin. The sequences of the primers used in this study are listed in the supplementary data.Total RNAs from Jurkat or primary CD4After transfection, cells were collected and lysed with lysis buffer . The lysates were then separated by 15% SDS-PAGE and analyzed by immunoblotting using primary antibodies specific for human IL-2  or β-actin . The LI-COR Odyssey scanner was used to detect and quantify fluorescent signals as previously described IL-2 promoter activity. We wondered whether siRNAs targeting the TATA-box region in gene promoters could also activate their promoter activities. For this purpose, we randomly designed three siRNAs targeting a ∼30 bp range around the individual TATA-box of sixteen genes: siRNAs targeting the TATA-box-centered position (CEN), siRNAs targeting the positions in upstream (UP) or downstream (DN) of the TATA-box in each gene promoter  stands for the enhancement of siRNA on promoter activity in percent compared with NC) (Table S1 in The most effective siRNA for each gene was picked up and the promoter activities relative to NC were listed in IL-2 core promoter (IL-2 promoter (IL-2-pro) or the mutated IL-2 promoter (IL-2mt-27/−25 or IL-2mt-17/−16) were co-transfected with siRNA IL2-CEN into HEK293T cells. The dual-luciferase assay indicated that the siRNA IL2-CEN had no enhancement effect on the mutated promoters any longer, especially the mutation which impaired the binding in seed sequence (IL-2mt-17/−16)  . Further-17/−16) .IL-2 promoter by let-7i for its regulation function (see the related manuscript). Therefore, it is intriguing to examine whether the regulation of the activating siRNAs is also through an AGO-dependent way. When AGO1 or AGO2 was knocked down by siRNAs, the activation of IL-2 promoter transcription by the activating siRNA was significantly impaired . To confirm this result, we chemically synthesized various siRNAs against different sites of human IL-2, INS or APOE promoter  and 1–3-A/U in the antisense strand  effects .IL-2 promoter activity more potently than siRNAs with other lengths  at their 3′ termini could accumulate in the nucleus et al. reported that the 3′ end of the antisense strand of siRNA hTF167i had a general tolerance to mutations (G/C transversions), while the 5′ end (within the seed region) exhibited low tolerance to mutations IL-2 or INS promoter and named them according to the mutated position relative to the 5′ end of the antisense strand  and ionomycin (1 µM). All the chemical modified siRNAs enhanced IL-2 mRNA expression in Jurkat cells at two days post transfection  and normalized to human β-actin and mouse GAPDH respectively. Human IL-2 protein levels were analyzed by Western blot and normalized to human β-actin. Compared to normal siRNAs, the optimized siRNA 5ua23Me3 enhanced IL-2 mRNA and protein expression potently in human CD4+ T cells  (see the related manuscript). The specific target sites of the functional activating siRNAs in our study may enable the TATA-box-targeting siRNAs to facilitate the recruitment of PICs members onto the TATA-box in gene promoters and subsequently promote the transcription.TATA-box represents the most conserved and a wide-spread core promoter In consistence with previous studies, the optimized siRNAs with U/A in the 5′ termini of antisense strands exerted enhanced activities , becauseThe results above may be due to the different thermodynamic properties of the binding of RNA-RNA and RNA-DNA hybrids. RNA/RNA duplex is more stable than DNA/DNA double helix and RNA/DNA hybrid duplex with the same nearest-neighbor sequence et al. showed that 2′-F sugar was generally well-tolerated on the antisense strand, whereas siRNAs with three 2′-OMe residues at the 3′ ends of the antisense strands were more active than the 5′-modified ones et al. found that siRNAs with 2′-OMe modification at position 2 in the antisense strands can reduce off-target effects et al., except that our functional activating siRNAs contained four 2′-OMe modified bases at the 3′ ends of the antisense strands  or DNA repair genes (BRCA2), may provide a new safe treatment strategy for many diseases such as cancer and diabetes. Further studies should be conducted to find the target position(s) and the sequence characteristics of activating siRNAs that could enhance the promoter activities of the genes without the TATA-box motif.In conclusion, we have demonstrated that the siRNAs targeting the TATA-box-centered region effectively activated 14 out of 16 genes promoter activities, and the siRNA targeting TATA-box-centered region, with UA at the first two bases of the antisense strand, 23 nts in length, and with 2′-OMe modification at the 3′ terminus of the antisense strand, is most effective in activating gene transcription. Taken together with other reports, these results imply the potential of using appropriate modified siRNAs for technical or therapeutic applications. For instance, it is well known that some primary cells  are very difficult to allow the entrance of transfected plasmids File S1Supporting Figures and Tables. Table S1. Feature analysis of siRNAs. For each siRNA, only antisense strand is shown. Efficiency was the FL/RL  ratio relative to the negative control siRNA (NC) for each siRNA. SiRNAs were classified according to their individual fold upregulation of the efficiency in percent (E (%)) compared with NC. Relative position was the distance of 3′ end of siRNA antisense strand to 5′ end of TATA box. Nuclear import sequences in the 3′ region of antisense strand were in bold. S-G/C, N-A/G/C/U. Table S2. Sequences of single-stranded oligonucleotides targeting human IL-2 promoter and negative control. Nucleotides in bold were corresponding to the TATA-box motif. Modified bases were in bold lowercase. ss, single strand; as, antisense; s, sense. Table S3. Sequences of siRNAs targeting the TATA-box of c-Myc, NPPA, or IL-6 with or without modified nuclear import sequence (Mip). Modified bases were in lowercase. Nuclear import sequences in the 3′ region of the antisense strand of modified siRNA were in bold. Figure S1. Effects of inhibiting AGO1, AGO2 or both genes on the activation of IL-2 promoter by siRNA. After transfection of 50 nM siRNAs for AGO1or AGO2 or the negative control siRNA (NC) into HEK293T cells for 24 h (1st transfection), 25 nM siRNA IL-2-CEN or NC were co-transfected with IL-2 promoter-driven firefly luciferase (FL) and renilla luciferase (RL) constructs (2nd transfection). Thirty-six hrs later, promoter activities were determined by dual-luciferase assay. P-values were calculated using the two tailed unpaired Student’s t-test with equal variances. n = 3. *, p<0.05. **, p<0.01. ***, p<0.001. Figure S2. Distribution of functional activating siRNAs with low G/C content. The frequency of siRNAs with low G/C content (30%<G/C<50%) in the antisense strand in each functional activating class . Figure S3.Impacts of ssRNAs (single strand RNAs) on IL-2 promoter activity. SiRNAs were co-transfected into HEK293T cells with the target promoter-driven firefly luciferase (FL) and renilla luciferase (RL) constructs. Thirty-six hrs later, promoter activities were determined by dual-luciferase assay. ss-asRNA, single strand antisense RNA; ss-sRNA, single strand sense RNA. P-values were calculated using the two tailed unpaired Student’s t-test with equal variances. n = 3. *, p<0.05. **, p<0.01. ***, p<0.001. Figure S4. Nuclear import sequence modified siRNAs showed no significant improvement of neither the enrichment in the nucleus nor the activation of gene promoters. (A) Analysis of reported nuclear import sequence in 3′ ends of siRNAs antisense strands. S-G/C, N-A/G/C/U. ND, not detected. Subcellular distributions of antisense strands of siRNAs with (middle) or without (left) modified nuclear import sequence (Mip) targeting (B) c-Myc, (C) NPPA or (D) IL-6 promoter. Thirty-six hrs after siRNAs were transfected, nuclear and cytoplasmic fractions were separated. The expression levels of siRNAs, tRNA-Lys and U78 were then tested with qRT-PCR. Effects of siRNAs on gene promoter activities were determined with dual-luciferase assay as described above (right). P-values were calculated using the two tailed unpaired Student’s t-test with equal variances. n = 3. *, p<0.05. **, p<0.01. Figure S5. The mRNA levels of IL-4 and IL-5 were not affected. “On target” and “Off target” activities. At 12 hrs post transfection of 120 pmol siRNAs into Jurkat cells, cells were subsequently treated with PMA (50 ng/ml) and ionomycin (1 µM) for (A–B) 2 days or (C–D) 4 days. The mRNA levels of IL-4 and IL-5 were determined by qRT-PCR as described above. P-values were calculated using the two tailed unpaired Student’s t-test with equal variances. n = 3. *, p<0.05.(DOCX)Click here for additional data file.


The HSA21 encoded Single-minded 2 (SIM2) transcription factor has key neurological functions and is a good candidate to be involved in the cognitive impairment of Down syndrome. We aimed to explore the functional capacity of SIM2 by mapping its DNA binding sites in mouse embryonic stem cells. ChIP-sequencing revealed 1229 high-confidence SIM2-binding sites. Analysis of the SIM2 target genes confirmed the importance of SIM2 in developmental and neuronal processes and indicated that SIM2 may be a master transcription regulator. Indeed, SIM2 DNA binding sites share sequence specificity and overlapping domains of occupancy with master transcription factors such as SOX2, OCT4 (Pou5f1), NANOG or KLF4. The association between SIM2 and these pioneer factors is supported by co-immunoprecipitation of SIM2 with SOX2, OCT4, NANOG or KLF4. Furthermore, the binding of SIM2 marks a particular sub-category of enhancers known as super-enhancers. These regions are characterized by typical DNA modifications and Mediator co-occupancy (MED1 and MED12). Altogether, we provide evidence that SIM2 binds a specific set of enhancer elements thus explaining how SIM2 can regulate its gene network in neuronal features. Down syndrome (DS) results from trisomy of human chromosome 21 (T21). It is the most frequent live-born aneuploidy, affecting 1 in 750 newborns. DS patients are characterized by a broad range of phenotypes including mental retardation, short stature, muscle hypotonia, congenital heart defects or Alzheimer disease neuropathology [Drosophila melanogaster as a key regulator of the midline cell development in the central nervous system (CNS) [Sim2 is expressed early during development in many tissues affected in DS such as developing forebrain, ribs, vertebrae, limb skeletal muscles or kidney [SIM2 is expressed during the early fetal life in the central nervous system and in key brain structures involved in learning and memory processes [SIM2 and its known function in Drosophila suggest that it may be a good candidate to explain some of the DS cognitive features. Interestingly, the transgenic mice harboring three copies of Sim2 exhibit some of the DS phenotypes namely a moderate impairment of learning and memory as well as a reduced exploratory behavior and sensitivity to pain [Sim2 -/- mutant mice die rapidly after birth due to breathing failure and display rib, vertebral and craniofacial abnormalities [SIM2 is a member of the basic helix-loop-helix Per-Arnt-Sim (bHLH/PAS) family of transcription factors. The proteins of this family contain a basic DNA binding domain adjacent to a helix-loop-helix region and a PAS region, essential for the dimerization of the proteins and the proper formation of active transcription factor complexes . They arem (CNS) –8. Interem (CNS)  but alsoem (CNS) , 10. To em (CNS) . The SIMem (CNS) –16. Theyem (CNS) . The murr kidney . Similarrocesses , 18. The to pain –21. Sim2malities , 23.Sim2 under the control of an inducible system. Using this model, we have identified 1229 regions occupied by SIM2 and showed that the associated target genes fulfill molecular functions related to the DS phenotypes. More importantly, we observed that a significant fraction of SIM2 binding sites overlaps with genomic regions occupied by master transcription factors involved in the genetic control of the pluripotent state, namely SOX2, OCT4, NANOG or KLF4. These regions are characterized by typical enhancer signatures and our data demonstrate that the binding of SIM2 could also predict a super-enhancer activity. Altogether, we provide new evidence of the SIM2 functional capacity.In order to better understand how SIM2 can participate in some DS features, we have further explored its regulatory role in mammalian cells. An accurate list of SIM2 target genes in normal and trisomic cells is required for understanding its role in genetic regulation. We have mapped the SIM2 DNA binding sites using chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-Seq) in a mouse embryonic stem cell (mES cell) line that stably overexpresses The EBRTcH3 (EB3) parental mouse ES line and the clones overexpressing Sim2-Flag have been previously described in , 25.2 atmosphere. Medium was changed every day and cells were passed every 1 or 2 days using 1X Trypsin-EDTA (Sigma #T4174).mES cells were grown on 0.1% gelatin (Sigma #G1890) coated dishes in DMEM high glucose medium (Life technologies #41965) supplemented with 15% Fetal Bovine Serum , 2mM L-glutamine (Life technologies #25030), 1mM Sodium pyruvate (Life technologies #11360), 100units/ml penicillin/streptomycin (Bioconcept #4-01F00-H), 0.1mM 2-mercaptoethanol (Life technologies #31350), 1000units/ml Leukemia Inhibitory Factor  and 1μg/ml tetracycline (Sigma #T7660). Cells were incubated at 37°C in a 5% COCulture medium was changed for tetracycline-free medium three hours before passing the cells in order to eliminate the residual tetracycline. Cells were passed using 1X Trypsin-EDTA (Sigma #T4174). Five million cells were plated in each new dish and cultured in the tetracycline-free medium for 26h starting from passage time.Cells were grown in presence or absence of tetracycline for 26 hours and collected using Trypsin-EDTA. Pellets were washed with PBS and 300’000 cells from each line were collected in 300μl of PBS supplemented with 2% FBS for the measure of Venus fluorescence by FACS .Total RNA was isolated 26h post induction, concurrently with the crosslinking experiment. RNA samples were prepared using TRIzol reagent (Life technologies #15596) as per the manufacturer’s instructions. Quality was assessed using the Agilent 2100 Bioanalyzer  and quantity was measured on a Qubit instrument (Life technologies). RNA was extracted from each of the SIM2 clones  and from three independent cultures of the EB3 clone.Reverse transcription was performed using 1μg of total RNA and the SuperScript II Reverse Transcriptase (Life technologies #18064). PCR was performed on 1μl of cDNA diluted 10 times using the following primers: TTCGAATGAAGTGCGTCTTG (forward) and ACATGTTGCTGTGGAGCTTG (reverse) for mSim2 and TGCCTCATCTGGTACTGCTG (forward) and GAACATGCTGCTCACTGGAA (reverse) for mArnt. The PCR program was the following: 94°C for 5min followed by 10 cycles of 94°C for 30s, 60°C (∆-1) for 30s, 72°C for 30s, followed by 25 cycles of 94°C for 30s, 50°C for 30s, 72°C for 30s and a final elongation step at 72°C for 7min.ChIP was performed using the SimpleChIP Enzymatic Chromatin IP Kit  according to manufacturer’s instructions. Briefly, mES cells SIM2  and mES cells EB3 were cultured in absence of tetracycline for 26h. After the induction, 50 million cells from each clone were crosslinked using 1% formaldehyde. Digestion of the chromatin was performed with 5μl of Micrococcal Nuclease for 20 minutes at 37°C, followed by sonication . Independent chromatin immunoprecipitations were performed on SIM2  and EB3 chromatin preparations using the equivalent of 20μg of chromatin DNA per IP. Each chromatin preparation was incubated at 4°C overnight with 6μg of anti-FLAG M2 antibody (Sigma #F3165). The next day, samples were incubated with 30μl of Protein G Magnetic Beads for 2h at 4°C, beads were washed and chromatin was eluted from the antibody/Protein G beads complexes. A 2% input sample of each chromatin preparation was saved before the immunoprecipitation as a control. Both input and eluted chromatin samples were reverse crosslinked in presence of Proteinase K for 2 hours at 65°C and DNAs were purified on columns.ChIP experiments against OCT4, SOX2, NANOG, MED1 and MED12 were performed in the same conditions (same cross-linked pellets) on SIM2 A6 cells and EB3 cells using 6μg of the following antibodies: anti-Nanog (D2A3) XP , anti-Oct3/4 (N-19) (Santa Cruz #sc-8628), anti-Sox2 (Santa Cruz #sc-17320), anti-Med1 (CRSP1/TRAP220) (Bethyl #A300-793A) and anti-Med12 (Bethyl #A300-774A).Preparation of the libraries for high-throughput sequencing was performed using the ChIP-Seq DNA sample Prep Kit (Illumina #IP-102-1001), following the manufacturer’s instructions with some modifications. Libraries were prepared starting from 1.08ng of DNA from SIM2  anti-FLAG ChIP, SIM2  input, EB3 anti-FLAG ChIP and EB3 input samples. 8ng of starting material were used from ChIP and input DNA from OCT4, SOX2, NANOG, MED1 and MED12 ChIP experiments (Sim2 A6 and EB3 cells). Enrichment of the DNA fragments by PCR was performed using reagents and adapters from the TruSeq RNA Sample Preparation kit (Illumina #RS-122-2001) according to the following program: 98°C for 30s followed by 18 cycles of 98°C for 10s, 60°C for 30s and 72°C for 30s, followed by a final elongation step at 72°C for 5min. PCR clean up was done on Agencourt AMPure XP beads (Beckman Coulter #A63880). Libraries were validated on an Agilent Technologies 2100 Bioanalyzer (DNA1000 chip). Libraries were sequenced on Illumina HiSeq 2000, in single-end sequencing 1x36bp or 1x50bp (4 samples per lane).mRNA-Sequencing libraries were prepared from 500ng of total RNA using the TruSeq RNA Sample Preparation kit (Illumina #RS-122-2001) following Illumina’s instructions. Libraries were sequenced on one lane of the Illumina HiSeq 2000 in paired-end sequencing 2x100bp.SIM2 A6 cells and EB3 cells were grown in tetracycline-free media for 26h and harvested using Trypsin-EDTA. Total protein extract was collected in lysis buffer  after 1h at 4°C and centrifugation for 30min at 4°C (13000rpm). 50μl of beads  were prepared for the immunoprecipitation by coupling with 2μg of antibody for 30min at room temperature. Immunoprecipitation was performed overnight at 4°C using 500μg of protein extract and the beads coupled to the following antibodies: anti-Sox2 (Y-17) (Santa Cruz #sc-17320), anti-Oct3/4 (N-19) (Santa Cruz #sc-8628), anti-Klf4 (R&D systems #AF3158), anti-Nanog (N-term and C-term) (Bethyl #A310-110A). Beads were washed four times for 5min at 4°C in 50mM TrisHCl pH 8, 250mM NaCl, 1% Triton X-100. Elution was performed in 200mM TrisHCl pH 8, 6% SDS, 15% glycerol, 3% ß-mercaptoethanol for 10min at 95°C. 5μl of the immunoprecipitated extract were analyzed by western blot using an anti-Flag antibody coupled to HRP . Each experiment was performed in duplicate.http://biowhat.ucsd.edu/homer/ngs/index.html) [For each sequenced library, reads generated from the sequencing were mapped against the mouse genome (mm9) using the BWA (Burrows-Wheeler Aligner)  alignmenex.html)  for the Each identified peak was assigned to the closest gene(s) by calculating the distance separating the center of the peak from the TSS. All peaks located in intergenic or intronic regions were assigned to both the closest upstream and downstream genes. Peaks located in exonic or promoter regions were assigned to the unique gene to which they belong.For OCT4, SOX2, NANOG, MED1 and MED12, peak finding was performed in HOMER by comparing the ChIP tags to the input tags in SIM2 A6 cells and EB3 cells independently. Each peak was then assigned to the closest gene by calculating the distance separating the center of the peak from the TSS.Sim2 expressing cells  and EB3 control cells (3 independent replicates) was performed using the default parameters of EdgeR [mRNA-Seq reads were mapped against the mouse genome (mm9) using the default parameters of BWA. For each gene, a custom pipeline was used to calculate the exon coverage. This coverage was normalized in reads per kilobase per million (RPKM). Differential expression analysis between of EdgeR . A gene Sim2 expressing cells and EB3 cells (mRNA-Seq data). The GSEA analysis consisted in testing if a particular gene set was randomly distributed in this ranked list or enriched at the beginning or the end of the distribution. A positive enrichment score (ES) reflects enrichment in the upregulated genes whereas a negative ES reflects enrichment in the downregulated genes. This enrichment was considered significant if the FDR corrected p-value was less than 0.05 (after 1000 or 10000 permutations).Gene ontology analyses were performed using DAVID  , 30. Genet al. [https://bitbucket.org/henryhzhang/rcircos).ChIA-PET data were taken from Zhang et al. . Circulahttp://ccg.vital-it.ch/chipseq/documents.php). The relative abundance of each tested feature is reported in a 40kb window around the SIM2 DNA binding sites by comparing the position of the SIM2 peaks with the position of the target features. SOX2, NANOG, OCT4, MED1 and MED12 peak positions were taken from our ChIP-seq data in Sim2 expressing cells. Other transcription factor peak coordinates were taken from Chen et al. [et al. [http://genome.ucsc.edu/): P300 and PolII data are from ES-Bruce4 cells (LICR), H3K4me1 and H3K27ac data are from ES-E14 cells (LICR), H3K4me3 data are from ES-E14 cells (SYDH) and DNAseI HS data are from ES-E14 cells [Enrichment around Sim2 DNA binding sites was performed using the ChIP-Cor Analysis Module of the ChIP-Seq Web Server  as well as the EBRTcH3 (EB3) parental line as a negative control  analysis revealed that 26 hours of induction were sufficient to promote the expression of Venus in the four lines  using the BWA aligner (Burrows-Wheeler Aligner)  to analyng lines . The majng lines . We founng lines . A totalng lines .In order to identify putative SIM2 target genes, we assigned each of the 1229 peaks to the closest gene(s) by calculating the distance separating the center of the peak from the TSS. Peaks located in intergenic or intronic regions were assigned to both the closest upstream and downstream TSSs whereas peaks located in exons or promoter regions were assigned to a unique gene. In total, 1992 different genes were associated to one or more SIM2 binding site(s). We used DAVID   to perfoSim2. We used mRNA-sequencing to study the transcriptome of the A6, B8, C4 and EB3 lines. Total RNA was collected concurrently with the ChIP-Seq experiment and sequenced on the Illumina instrument. The reads generated were mapped against the mouse genome using BWA and normalized in RPKM (Reads per Kilobase per Million) in order to compare the expression level of each gene between Sim2 expressing and non-expressing cells. We first verified the expression level of Sim2 in both conditions and confirmed its overexpression in A6, B8 and C4  as opposed to the EB3 cells which showed very low levels of endogeneous Sim2 transcripts   . We also of 19.7 . This shSim2 expressing and non-expressing cells. This analysis revealed that 300 RefSeq genes were significantly upregulated and 230 genes significantly downregulated when Sim2 is overexpressed in mES cells (FDR<0.05)  and can be considered as direct targets of SIM2 (We then used EdgeR  to perfoDR<0.05) . However of SIM2 . A gene trans to regulate the expression of distant targets. In contrast, 265 interactions occur between loci located on the same chromosome. Most of these intra-chromosomal interactions (63%) connected SIM2 binding loci and gene promoters distant from less than 100kb. Overall, the RNA polymerase II ChIA-PET datasets identified 310 unique transcribed gene promoters that physically interact with at least one SIM2 binding site. Interestingly, 22 of those genes were significantly dysregulated by the overexpression of Sim2 (EdgeR FDR<0.05)  has been developed to identify such long-range interactions , 42. We DR<0.05) . We consDR<0.05) .We then investigated whether SIM2 preferentially binds to specific DNA motifs. Using the HOMER algorithm, we identified five motifs significantly enriched in the SIM2 DNA binding sites  and the EB3 parental line. The numbers of binding sites identified for each factor are summarized in Sim2 expressing cells, 82% of SIM2 peaks overlap with a DNA binding site for NANOG, 46.2% with a binding site for OCT4 and 44.75% with a binding site for SOX2 (100bp window)  . Compari window) . AltogetES cells  Fig. Theet al. [A previous study has described the co-binding of the OSN master transcription factors in ES cells as predictive for enhancer activity . In ordeet al.  S1 Fig)et al. [Set al. [Sim2 expressing cells. We revealed a significant overlap between SIM2 binding sites and each of these factors  in the In this study, we have shown how the identification and characterization of the SIM2 DNA binding sites improve the understanding of its molecular function and potential role in the manifestations of DS.Sim2 expressing cells, we have established a set of 22 genes that could be considered as direct targets of SIM2. Among them, several genes are known to be involved in molecular functions that could possibly be related to DS manifestations. For instance, the OTX2 gene (Orthodenticle Homeobox 2) is known as an important transcription factor for the control of brain and craniofacial development [OTX2 mutations present a microphthalmia syndrome associated to multiple features resembling those of DS such as developmental delay, hypotonia or short stature [ARID1B gene (AT Rich Interactive Domain 1B), a member of the SWI/SNF chromatin remodeling complex, has been recently associated to cognitive impairment [SYNGR1 gene (Synaptogyrin 1) also constitutes an interesting target gene given its role in synaptic plasticity, as revealed by the Syngr1 knockout mice [We have identified 1229 binding loci for SIM2 and shown that a significant fraction of target genes located in the vicinity were involved in neuronal development processes. These results suggest that SIM2 may be a candidate gene for some DS phenotypes, in particular the cognitive impairment. These findings validate the hypotheses established so far with studies performed in Drosophila and in mouse models –8, 19–23elopment . Mutatioelopment , 47. Pat stature . Similarpairment  and morepairment , 51. Finout mice .The enrichment for enhancer marks at the SIM2 binding loci as well as the relatively small number of peaks located in promoter regions show that SIM2 is mainly recruited to distant regulatory elements for the regulation of its target genes. Here, we reported that SIM2 could bind genomic loci occupied by master transcription factors involved in the control of ES cell pluripotency. Co-immunoprecipitation experiments even suggest that SIM2 can physically interact with these factors and raise the possibility that they are part of the same protein complex. Interestingly, a study using the Drosophila model validated this hypothesis by showing functional interactions between SIM, SOX and POU transcription factors for the control of midline gene expression . An inteSox2, Nanog, Klf4 and Esrrb genes belong to the list of targets associated to SIM2 binding sites in our results.This colocalization also raises the possibility that SIM2 interferes with the binding of the master transcription factors and thus could modify the pluripotent state of mES cells. This function has been previously reported for other factors including CDX2  . Indeed,Interestingly, we have observed that SIM2 can also mark a particular subtype of enhancers called super-enhancers. Those are known to be associated with genes, mostly transcription factors, essential for the maintenance of the ES cell identity . This obOur data open interesting perspectives for the understanding of the mechanisms underlying the DS phenotypes and emphasize the benefit of using an ES cell model to study the function of HSA21 transcription factors.S1 Figet al. [et al. [et al. [Pie charts give the number of SIM2 peaks overlapping with the binding sites of each of the transcription factors, typical enhancers or super-enhancers (in grey) (100bp window). Typical enhancers and super enhancers data were taken from Whyte et al. . MED1 Ch [et al.  and othe [et al. .(TIF)Click here for additional data file.S2 FigSim2 expressing cells (A6) or EB3 cells were immunoprecipitated by using antibodies directed against each of the pluripotency factors  or IgG as a negative control for co-immunoprecipitation. Associated proteins were immunoblotted using an anti-FLAG antibody. Red star shows the SIM2-FLAG protein, blue star the signal given by the recognition of the IgG heavy chains. Ø: Beads only; kDa: kilodaltons; protein lysat: protein lysat was loaded as an input control for the immunoblot.Cellular protein extracts from (TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(PDF)Click here for additional data file.


Using a nuclease-dead Cas9 mutant, we show that Cas9 reproducibly induces chromatin accessibility at previously inaccessible genomic loci. Cas9 chromatin opening is sufficient to enable adjacent binding and transcriptional activation by the settler transcription factor retinoic acid receptor at previously unbound motifs. Thus, we demonstrate a new use for Cas9 in increasing surrounding chromatin accessibility to alter local transcription factor binding. Active gene regulatory elements are invariably found in regions of heightened chromatin accessibility, which are characterized by loosened contact between DNA and nucleosomes . RegulatChromatin accessibility is regulated in part by pioneer factors, a class of TFs which are capable of binding to inaccessible, nucleosome-bound DNA and inducing accessibility , 6, 7. WWhile previous work has uncovered the importance of chromatin accessibility in governing TF binding, it is unknown if altering chromatin accessibility is sufficient to alter settler factor binding. An alternative possibility is that changes in chromatin accessibility imply changes in other aspects of chromatin state such as histone modification  and DNA In this work, we assess the ability of the CRISPR/Cas (CRISPR) genome editing system to alter chromatin accessibility. In CRISPR, a short guide RNA (sgRNA) with sequence complementarity to a 17–20 base-pair (bp) genomic sequence complexes with the Cas9 protein to cleave DNA at that complementary genomic site, inducing local sequence mutants or enabling site-specific homologous recombination , 11. CasIn spite of the utility of dCas9 fusion proteins in a panoply of applications, it is unknown whether dCas9 by itself is able to induce chromatin accessibility after binding to previously inaccessible chromatin. If dCas9 is able to induce chromatin accessibility, it could be used to artificially open chromatin, permitting inquiry into the causal implications of chromatin accessibility and its role in affecting gene expression. The ability to focally open chromatin would be a step toward altering gene expression and cell fate predictably, allowing endogenous transcription factors and signaling pathways to populate the newly accessible chromatin. Additionally, if dCas9 opens chromatin, it may disrupt the native gene regulatory architecture at targeted loci, which may lead to erroneous results in CRISPR-based live imaging and mass spectrometry approaches. Lastly, determining whether dCas9 is able to open previously inaccessible chromatin is important in determining how likely it is to function when targeted to these inaccessible regions in the genome.Mouse embryonic stem cell (mESC) culture was performed according to previously published protocols . UndiffeThe Eco0109I-XbaI fragment from pX330 , 16 and 7 nuclei. 50–125 bp hypersensitive DNA was collected using E-Gel SizeSelect Agarose 2% gels (Life Technologies). qPCR was performed with Dynamo Flash SYBR Green qPCR kit (Thermo Scientific) using positive and negative control primers to normalize for variability in signal-to-noise of the DNase hypersensitivity assay. All experiments used in this paper had >20-fold enrichment of positive over negative control primers. Primer sequences used in this work are listed in DNase hypersensitivity analysis was performed as described previously . 10–100 Chromatin immunoprecipitation was performed according to the “Mammalian ChIP-on-chip” protocol (Agilent) using a polyclonal antibody against retinoic acid receptor that recognizes all protein isoforms  and Protein G Dynabeads (Life Technologies). 10–100 million cells were used for each experiment. qPCR using positive and negative control primers was performed to ensure ChIP enrichment, and all experiments used in this paper had >10-fold enrichment. Primer sequences used in this work are listed in mESCs with a Tol2 transposon-integrated reporter construct containing 2x RAR binding site, minimal Hsp68 promoter, and GFP were generated previously . CAGGS-dTo address whether dCas9 is capable of opening previously closed chromatin, we built two Tol2 transposon-based vectors , 17 alloUsing this system, we targeted dCas9 to 16 genomic locations with minimal prior chromatin accessibility in mESC as measured by DNase hypersensitivity analysis , 17, 18 To determine the spatial pattern of dCas9-induced chromatin opening, we assessed chromatin accessibility +/-150 bp from the sgRNA site at four previously closed loci. We found that dCas9 opens chromatin most strongly within +/-20 bp of the sgRNA , yet accIs dCas9-induced chromatin accessibility functional? To assess whether dCas9-opened chromatin can facilitate the binding of a settler factor, we recruited dCas9 to three genomic loci within 40 bp of natively unbound motifs for the settler factor retinoic acid receptor (RAR)  S3 Fig)S3 Fig). We next asked whether the ability of dCas9 to alter local binding of RAR increases the ability of RAR to transcriptionally activate nearby genes. We used a GFP reporter system in which two strong RAR motifs are placed in front of a minimal promoter and GFP reporter gene and inserted into the genome using Tol2 transposon-based integration . We havedCas9 consistently opens chromatin surrounding sgRNA sites. This opened chromatin enables binding and RA-dependent gene activation by the settler factor RAR at previously obscured motifs . It is wSince dCas9 is a bacterial protein, it is likely that its chromatin opening ability stems directly from its known ability to bind strongly to and mechanically unwind DNA ,19,20. IOur studies cannot distinguish whether it is the mechanical strand displacement and RNA-DNA heteroduplex formation  or the sIt is highly likely that dCas9 binding enables adjacent TF binding of many more TFs than the one we assayed, RAR. On the one hand, the fact that dCas9 disrupts the native transcription factor binding profile argues for caution in the interpretation of results from CRISPR-based approaches aimed at studying native genome function including those based on imaging and mass spectrometry, as dCas9 may disrupt the very events being measured. On the other hand, our demonstration that dCas9-induced chromatin opening can increase RA-induced gene expression introduces a paradigm of conditional gene activation dependent on the two independent parameters of dCas9 recruitment and RA administration, a form of transcriptional AND gating. Extended to other TFs, the ability of dCas9 to artificially induce chromatin accessibility will enable future studies of how TF binding and gene regulatory activities change at enhancers and promoters as chromatin becomes accessible and future work manipulating gene regulatory networks through rational alteration of chromatin accessibility.S1 FigImmunofluorescence for V5 tag reveals strong, uniform, nuclear expression of dCas9-V5 after transposon integration (right panels) but not in wildtype mESC (left panels).(PNG)Click here for additional data file.S2 FigDNase-seq signal in wildtype mESC (ENCODE ES-CJ7) in the 300 bp centered around the 16 guide RNA sites targeted in this work is uniformly weak, indicating that these regions are inaccessible in mESC. Reads per base are shown on the y-axis, and the 300 bp of genomic sequence surrounding the sgRNA sequences is shown on the x-axis. Rarb and Cyp26a1 loci, used as ChIP positive controls, are shown as comparisons that reside in accessible chromatin.(PNG)Click here for additional data file.S3 FigRAR ChIP signal in wildtype mESC (129P2/OlaHsd mESC used in this work) in the 300 bp regions surrounding the three RAR motif-adjacent sgRNAs targeted by dCas9. Reads per base are shown on the y-axis, and the 300 bp genomic regions centered around sgRNAs are shown on the x-axis. All three have minimal RAR ChIP prior to dCas9 recruitment. Rarb and Cyp26a1 are used in the DNase-qPCR analysis as positive controls, and ChIP signal at sgRar1-3 loci becomes equivalently strong to these loci after dCas9 recruitment.(PNG)Click here for additional data file.S4 FigAnti-retinoic acid receptor (RAR) ChIP followed by qPCR at three loci  in the presence of sgRNAs targeting each locus . ChIP-qPCR values are normalized to control ChIP without sgRNA. Three replicates were performed for all experiments, and a two-tailed Student’s t-test was used to calculate significance, and values with P<0.01 are denoted with a *.(PNG)Click here for additional data file.S5 FigGFP expression in Tol2 RAR-GFP reporter cells in the absence of RA, normalized to cells without sgRNA. The presence of sgRNA recruiting dCas9 upstream (sgRAR up) or downstream (sgRAR down) of the RAR site has no measurable effect on GFP expression in the absence of RA.(PNG)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.


The current study aimed to isolate key transcription factors (TFs) in caerulein-induced pancreatitis, and to identify the difference between wild type and Mist1 knockout (KO) mice, in order to elucidate the contribution of Mist1 to pancreatitis. The gene profile of GSE3644 was downloaded from the Gene Expression Omnibus database then analyzed using the t-test. The isolated differentially expressed genes (DEGs) were mapped into a transcriptional regulatory network derived from the Integrated Transcription Factor Platform database and in the network, the interaction pairs involving at least one DEG were screened. Fisher’s exact test was used to analyze the functional enrichment of the target genes. A total of 1,555 and 3,057 DEGs were identified in the wild type and Mist1KO mice treated with caerulein, respectively. DEGs screened in Mist1KO mice were predominantly enriched in apoptosis, mitogen-activated protein kinase signaling and other cancer-associated pathways. A total of 188 and 51 TFs associated with pathopoiesis were isolated in Mist1KO and wild type mice, respectively. Out of the top 10 TFs , 7 TFs, including S-phase kinase-associated protein 2 (Skp2); minichromosome maintenance complex component 3 (Mcm3); cell division cycle 6 (Cdc6); cyclin B1 (Ccnb1); mutS homolog 6 (Msh6); cyclin A2 (Ccna2); and cyclin B2 (Ccnb2), were expressed in the two types of mouse. These TFs were predominantly involved in phosphorylation, DNA replication, cell division and DNA mismatch repair. In addition, specific TFs, including minichromosome maintenance complex component 7 (Mcm7); lymphoid-specific helicase (Hells); and minichromosome maintenance complex component 6 (Mcm6), that function in the unwinding of DNA were identified to participate in Mist1KO pancreatitis. The DEGs, including Cdc6, Mcm6, Msh6 and Wdr1 are closely associated with the regulation of caerulein-induced pancreatitis. Furthermore, other identified TFs were also involved in this type of regulation. Pancreatitis is an inflammatory disease associated with auto-digestion of the gland. It is widely accepted that the injury is initiated within pancreatic acinar cells subsequent to premature intracellular activation of the digestive enzymes , which rCaerulein is a polypeptide present in the brain and gastrointestinal tract, which has been demonstrated experimentally to result in pancreatitis. Caerulein administered via an intraperitoneal injection has been reported to induce acute edematous pancreatitis in mice, leading to the release of actin in pancreatic acinar cells, disruption to the transport of digestive enzymes and increased cellular permeability . In addiIt has previously been demonstrated that the knockout of certain TFs may also result in inflammation. For example, Mist1 (an exocrine-specific TFs) is important for the successful differentiation and functioning of pancreatic acinar cells . AnotherIn the current study, microarray data from caerulein-induced wild type and Mist1KO mice were used to isolate the differentially expressed genes (DEGs). Key regulatory TFs were then screened from regulatory networks that were constructed by DEGs, combined with the TFs in the database. Comparisons between the sample groups allowed the identification of important TFs, which may aid in the elucidation of the regulatory mechanisms in Mist1KO mice and contribute to knowledge of the pathogenic mechanisms underlying pancreatitis.http://www.ncbi.nlm.nih.gov/geo/). This expression dataset was created by Kowalik et al  online software (http://cran.r-project.org/web/packages/gplots/).Gene ontology functional enrichment analysis was conducted, using Database for Annotation, Visualization and Integrated Discovery   and was In contrast, Mcm6 was observed in Mist1KO mice only. Mcm6, which belongs to the MCM family, is involved in the initiation of eukaryotic genome replication. The Mcm6 protein constructs an MCM complex with Mcm2, 4 and 7, which perform DNA helicase activity and may act to enzymatically unwind DNA ,28. In aIn conclusion, high expression of Cdc6 and Mcm6 may lead to increased cell proliferation and pancreatitis. Msh6 and Wdr1 may be involved in the regulation of pancreatitis through DNA mismatch repair and acinar cells, respectively. Several key TFs that were not DEGs were also identified, these were considered to be involved in the regulation of caerulein-induced pancreatitis through target genes. Further investigation into these genes may contribute a more complete understanding of the regulatory network in pancreatitis and how this contributes to its pathogenic mechanisms.


Escherichia coli and related species, the transcription factor FlhDC is the master regulator of a multi-tiered transcription network. FlhDC activates transcription of a number of genes, including some flagellar genes and the gene encoding the alternative Sigma factor FliA. Genes whose expression is required late in flagellar assembly are primarily transcribed by FliA, imparting temporal regulation of transcription and coupling expression to flagellar assembly. In this study, we use ChIP-seq and RNA-seq to comprehensively map the E. coli FlhDC and FliA regulons. We define a surprisingly restricted FlhDC regulon, including two novel regulated targets and two binding sites not associated with detectable regulation of surrounding genes. In contrast, we greatly expand the known FliA regulon. Surprisingly, 30 of the 52 FliA binding sites are located inside genes. Two of these intragenic promoters are associated with detectable noncoding RNAs, while the others either produce highly unstable RNAs or are inactive under these conditions. Together, our data redefine the E. coli flagellar regulatory network, and provide new insight into the temporal orchestration of gene expression that coordinates the flagellar assembly process.Flagellar synthesis is a highly regulated process in all motile bacteria. In  Escherichia coli; however, many questions remain. We have used an unbiased, genome-wide approach to comprehensively identify all of the binding sites and regulatory targets for the two key regulators of flagellar synthesis, FlhDC and FliA. Our results redefine the flagellar regulatory network, and suggest that FliA binds many sites that are not associated with productive transcription. This work is important because it suggests possible new functions for FliA outside of the transcription of canonical mRNAs, and it provides new insight into the temporal orchestration of gene expression that coordinates the flagellar assembly process.Flagella are surface-associated appendages that propel bacteria and are involved in diverse functions such as chemotaxis, surface attachment, and host cell invasion. Flagella are incredibly complex macromolecular machines that are energetically costly to produce, assemble, and power. Flagellar production is tightly regulated and flagellar components are only synthesized when flagellar motility is advantageous. Regulation also ensures that flagellar components are produced in roughly the same order in which they are needed, increasing efficiency of the assembly process. The transcriptional regulation of flagellar genes has been studied extensively in the model organism  Bacterial flagellar synthesis and motility are highly regulated processes involving positive and negative input at the transcriptional, post-transcriptional, translational, and post-translational levels. This complex regulatory system allows for sequential production of flagellar components in roughly the order they are required for assembly. One of the primary ways this temporality is established is through the underlying hierarchical transcription network reviewed . Promote4C2) serves as the master flagellar regulator in Escherichia coli, Salmonella enterica, and other related enteric bacteria flhDC operon is considered the sole Class 1 transcription unit. FlhDC expression and activity is regulated at a number of levels, allowing it to serve as an integrator of environmental, nutritional, and growth-phase signals The atypical transcription factor FlhDC  encode important regulatory factors and the structural components of the membrane-spanning basal body and associated export apparatus fliA encodes an alternative σ-factor flgM encodes its cognate anti-σ-factor Class 2 promoters are directly activated by FlhDC and transcribed by RNA polymerase (RNAP) containing the primary σ-factor, σflgKL, fliDST, flgMN, fliC, tar-tap-cheRBYZ (meche), and motAB-cheAW (mocha)) and can initiate transcription of its own operon, fliAZY. These Class 3 operons encode products needed in late flagellar assembly such as the subunits of the flagellar filament (flagellin), components of the motor, and chemotaxis-related regulatory factors. In addition to these classical Class 3 operons, FliA has been predicted or shown to drive transcription of genes involved in chemotaxis , FlgM is exported out of the cell, freeing FliA and allowing initiation of FliA-dependent transcription from Class 3 promoters. This coupling of transcription and assembly allows for efficient “just-in-time” expression kinetics, as has been described for some metabolic pathways  trgtsr, aerotaxaer as an FlhDC target modAflxAIn addition to their roles in the flagellar transcription network, FlhDC and FliA have been implicated in the regulation of non-flagellar genes. Studies have reported a host of non-flagellar genes and processes, such as cell division and anaerobic metabolism, regulated by FlhDC, or by FlhD alone E. coli flagellar network has been studied extensively over the past few decades, many issues remain to be addressed. These include (i) the ability of FlhD to have regulatory effects independent of FlhC, (ii) the extent of co-regulation by FlhDC and FliA and the relative contributions of each at known complex promoters such as fliAZY and fliLMNOPQR, and (iii) the extent and composition of the non-flagellar regulons of FlhDC and FliA. Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) is a powerful technique to study the genome-wide localization of DNA-binding proteins. ChIP-seq provides high-resolution information about binding location and relative binding affinity in vivoin vivo measurements incredibly valuable flhD, ΔflhC, and ΔfliA derivatives of E. coli MG1655. These data reveal new binding sites and regulatory targets for both FlhDC and FliA, including the discovery of two FliA-dependent non-coding RNAs. We have comprehensively determined the direct and indirect regulons of these three proteins and demonstrate that this information can be used to build a detailed map of this hierarchical transcription network.Though the flhDC. Spontaneous highly motile isolates were recovered following incubation on motility agar, as has been described previously flhDC, we sequenced the upstream and coding regions of each isolate  do not support the existence of any directly regulated targets not identified by ChIP-seq.While motile isolates were obtained for each tagged strain, all three tagged strains were less motile than a similarly selected wild-type strain, suggesting that epitope-tagging resulted in moderate functional impairment . It is pE. coli MG1655 grown to mid-exponential phase (OD600 0.5–0.7) in Lysogeny Broth (LB). The genome-wide binding profiles of the two proteins are shown in yecR, a gene of unknown function, consistent with previous reports ampH/sbmA, yciK/sohB, and gntR). The remaining novel binding site is located inside csgC and immediately upstream of a transcription start site for the adjacent gene, ymdAWe used ChIP-seq to assess genome-wide binding of FlhD and FlhC in p-value≤0.05) for all loci. ChIP-qPCR was also used to evaluate FlhDC occupancy at previously predicted binding sites fliDST promoter was the only site to show significant occupancy by either protein  but significant changes in ampH and sbmA levels are detected between wild type and ΔflhC; however, these changes are not detected in the ΔflhD strain or supported by complementation experiments  was identified in all 52 binding regions and matches previously reported FliA promoter motifs well . This strongly suggests that a majority of low-scoring ChIP-seq peaks represent genuine sequence-specific FliA binding events.A subset of the putative FliA binding sites identified by ChIP-seq was also tested using ChIP-qPCR. Of the 13 novel FliA binding sites tested, 11 showed significant enrichment in these targeted experiments . The 2 nifs well , Table 2ifs well . The twoinsB-4/cspH convergent intergenic region and inside proK), that have unusually localized motifs, suggesting that they might not be genuine FliA promoters.Not only were motifs identified for all ChIP-seq peaks, but these motifs were also localized in a striking pattern relative to the ChIP-seq peaks . For mosWhile this manuscript was in preparation, FliA ChIP-chip data was published by another group fliA strain and compared to the isogenic motile wild-type. Analysis of differential gene expression was performed with Rockhopper fliA strain  RNA encoded in the same orientation as the gene (hypD (hypD gene and overlaps ∼500 nt of the 5′ end of the hypD open reading frame (ORF). The hypD antisense RNA is too weakly expressed to be detected by Rockhopper, despite the program's ability to identify novel antisense RNAs. The remaining novel FliA binding sites were not associated with detectable FliA-dependent changes in gene expression under these conditions  showing that the FliA promoters are capable of transcribing the target genes. However, this effect was smaller for fliL than for any other dual regulated gene. Furthermore, overexpression of fliA in the ΔfliA strain (hashed green bar) reduced fliL expression by 2.10-fold compared to the empty vector control , suggesting a possible repressive interaction . Therefore, with the exception of the dual targets already discussed, our data do not support widespread dual regulation of Class 2 genes.As dual regulation has been suggested for all Class 2 targets t tested . While npression . Since fc region  so we coampH, ymdA, sbmA, gntR, ykfB, yjcZ, ynjH, hypD, and uhpT. Motility on semi-solid agar was determined for each deletion strain. None of the deletions resulted in a substantial change in motility , while the remaining two were not (gntR and ampH/sbmA). It is likely that the two latter sites are functional, perhaps under different conditions, because the probability of two spurious sites occurring in intergenic regions is very small (only ∼11% of the genome is intergenic sequence). None of the genes surrounding the 4 novel binding sites have an obvious functional connection to flagellar synthesis, and deletion of these genes had no detectable effect on motility (p<0.0001). FliZ is the only transcription factor regulated directly by either FlhDC or FliA, and we do not see regulation of described FliZ targets To our knowledge, this study provides the first look at genome-wide nditions . Our datmotility . The RNAin vivo, even at sites that are bound in vitro . Coupled with the σ7°ChIP data presented here . This suggests, as will be discussed below, that a large number of promoter sequences bind FliA but are relatively inactive. Similar to FlhDC, the RNA-seq data provide little evidence for an extensive indirect FliA regulon. Only 40 genes were differentially regulated but not associated with FliA binding. The magnitude of indirect regulation was dramatically smaller than direct regulation  has been reported to bind many intragenic sites, but these sites only account for ∼25% of the total sites bound . A FliA promoter upstream of yjdA has been incorrectly predicted, based on expression data uhpT transcribes a small RNA overlapping the 3′ end of the ORF in the sense orientation . Two of these promoters have been accurately predicted before  and the developmental stage at which the resulting proteins are utilized has been described reviewed . Howeverflagella . Our resflagella  but it iWe have identified many new targets of FlhDC and FliA, including many non-canonical FliA binding sites. Additionally we have shed new light on the complex topology of the network by systematically defining Class 2, Class 3, and dual-regulated targets. Finally, we have demonstrated that the combined application of ChIP-seq and RNA-seq to related regulators provides sufficient data to build a transcriptional network model from scratch or redefine even the best-characterized networks, such as the flagellar transcription network.Bacterial strains used in this work are listed in thyA), which has a poorly motile phenotype. FliA was N-terminally tagged by inserting a 3×FLAG tag after the third amino acid. FlhD and FlhC were tagged by inserting 3×FLAG tags into internal loop regions  at a precise location upstream of flhDC. thyA insertions were confirmed by PCR and sequencing (Wadsworth Center Applied Genomic Technologies Core).Primers used in strain construction are described in  regions . InternaflhD, ΔflhC, and ΔfliA) were also generated using recombineering. First, DMF35 was generated from AMD052 by motility selection, as described below. With DMF35 as the common parent strain, FRUIT was used to generate scarless deletions of flhD (DMF38) and fliA (DMF40). The flhC deletion strain (DMF58) was generated by amplifying the flhC::kan allele from the Keio collection thyA was reintroduced at its native locus and strains were cured of all plasmids. Strains DMF50–57 were also generated from AMD052, using FRUIT to replace the genes of interest with the thyA gene. Note that these strains lack thyA at its native locus, but have a thyA+ phenotype. Overexpression plasmids were generated by cloning the ORF of interest into pBAD24 NheI and SphI (NEB) using the In-Fusion method (Clontech). All deletion strains and plasmids were verified by PCR and sequencing (Wadsworth Center Applied Genomic Technologies Core).Deletion strains  onto soft TB agar (0.3%) and incubated at 30°C. Motile subpopulations began emerging between 20 and 24 hours. We typically observed 1–5 motile subpopulations on each plate. Motile cells were collected from stabs, yielding motile strains DMF35, DMF11, DMF14, and RPB081. IS element insertion was verified by PCR and sequencing with oligonucleotides JW3100 and JW5358  were spotted onto 155 mm TB soft agar (0.2%) plates and incubated at 30°C. Each plate included DMF36 for reference and 1–4 other strains of interest. Each assay was performed 5 times from independent overnight cultures. Images were taken at hourly intervals from 4–6 hours post-inoculation. Representative images are shown in 600 of 0.5–0.7. Cultures were harvested, crosslinked, sonicated, and immunoprecipitated as previously described 70  antibodies were used for all immunoprecipitations, both for ChIP-qPCR and ChIP-seq. ChIP and input DNA was purified using Zymo PCR Clean and Concentrate kit. Samples were analyzed using qPCR as previously described bglB control region and regions of interest are described in  Strains DMF11, DMF14, RPB081, and DMF36 were used for all ChIP experiments. Subcultures were grown in LB at 37°C with aeration to an ODCultures for ChIP-seq experiments were grown as described for ChIP-qPCR. ChIP-seq libraries were constructed and sequenced as previously described Sequences were aligned to the MG1655 genome (NC_000913.2) using the CLC Genomics Workbench. Mapped reads were piled up and written to a .gff file using a custom Python script and viewed in SignalMap (Nimblegen). All ChIP-seq images presented in this study are captured from SignalMap and manipulated in the image editing software GIMP to highlight baselines (zero reads) and fill gaps in the data resulting from image artifacts.1T was selected as the threshold. For the second replicate, a value 2T was selected as the threshold. Values for 1T and 2T were considered between 1 and 1000. For each combination of values for 1T and 2T, the number of genome positions with values ≥1T in the first replicate and with values ≥2T in the second replicate was determined. The false discovery rate was estimated using the null hypothesis that no regions are enriched. The combination of thresholds yielding the highest number of true positive positions, with an estimated false discovery rate of less than 0.01, was selected. Once 1T and T2 were chosen, peak calling was performed as previously described  were excluded from RNA-seq analysis since rRNA was removed during library preparation.Sequence reads were mapped and visualized as described above. Differential expression analysis was performed using Rockhopper 600 of 0.4–0.6. Arabinose was added to a final concentration of 0.2% and cultures were incubated at 37°C for an additional 10 minutes. RNA was isolated as follows. 10 ml of culture were harvested by centrifugation, resuspended in 1 ml Trizol reagent (Life Technologies) and incubated at room temperature for 5 min. Samples were centrifuged, supernatants were transferred to new tubes, and 200 µL of chloroform was added. Samples were mixed well, incubated at room temperature for 3 minutes, and centrifuged. The aqueous layers were transferred to new tubes and mixed with 500 µL isopropanol. Samples were mixed well, incubated at room temperature for 10 minutes, and centrifuged to collect precipitated RNA. Pellets were washed once with 75% ethanol and then dried. RNA was resuspended in H2O and treated with TURBO DNase (Life Technologies) as described above. RNA was reverse transcribed using SuperScript III reverse transcriptase (Invitrogen) with 100 ng of random hexamer, according to the manufacturer's instructions. A control reaction lacking reverse transcriptase (no RT) was performed for each sample. cDNA and no RT samples were diluted and used as templates for qPCR. qPCR was performed with an ABI 7500 Fast real time PCR machine. Oligonucleotides used to amplify target genes, and the control (mreB) are listed in −ΔΔCt method mreB) yielding ΔCt values, then 1.9−ΔCt (assuming imperfect PCR efficiency) was calculated for each target in each strain. Finally, expression values in all strains were normalized to the average 1.9−ΔCt value in motile MG1655+pBAD. qRT-PCR experiments utilized 3–4 biological replicates per strain and all qPCR reactions were performed in triplicate.Strains pDMF9–18 were used for qRT-PCR experiments. Subcultures were grown in LB+100 µg/ml ampicillin at 37°C with aeration to an ODFigure S1Strain construction and validation. (A) Tag locations for internal 3×FLAG-tagging of FlhD and FlhC. Black lines represent unstructured regions, red cylinders represent α-helices, and blue boxes represent β-sheets. Gold stars represent Zn-binding cysteine residues. Insets show amino acid sequence surrounding tag insertion sites. (B) Location, identity, and direction of IS element insertions present in each motility-selected strain. The boxes represent regions that are duplicated during insertion. (C) Soft agar motility of motile MG1655 and strains in which a thyA cassette has been inserted in each of the IS element insertion locations described above. (D) Soft agar motility of motile MG1655 and epitope-tagged strains. (E) Soft agar motility of motile MG1655, isogenic deletions, and complemented strains. Note that ΔflhC+pflhC is non-motile due to disruption of the promoter of the motAB-cheAW operon.(PDF)Click here for additional data file.Figure S2Presence of FlhDC motif and in vitro binding is not predictive of in vivo binding. Stafford et al.in vitro binding. With the exception of fliD and yecR (not shown), none of the predicted sites showed in vivo FlhDC binding in targeted ChIP-qPCR assays .(PDF)Click here for additional data file.Figure S3Genome-wide expression in ΔflhD and ΔflhC. Expression of all genes in ΔflhD versus ΔflhC. Gene expression values represent normalized expression values calculated by Rockhopper.(PDF)Click here for additional data file.Figure S4Comparison of FliA motifs from this study and Cho et al.. Motifs were generated from sequence surrounding binding sites identified only in our study (n = 27) or only in Cho et al. (n = 29). (A) FliA binding sites unique to our study yielded a highly significant motif  similar to that described for FliA. (B) The best-scoring motif for FliA binding regions unique to Cho et al is not significantly enriched, and shows no similarity to described FliA motifs . It should also be noted that Cho et al failed to detect some well-characterized FliA promoters such as those upstream of fliAZY and fliC.(PDF)Click here for additional data file.Figure S5flgJK is dual regulated but flgBAE is not. (A) RT-PCR using an upstream primer within flgJ and a downstream primer within flgK yielded a band of the expected size in motile MG1655, but not in ΔflhD. This confirms that flgKL can be transcribed as part of the upstream FlhDC-dependent operon. Lanes labeled “colony” are a colony (genomic DNA) PCR control, “+RT” are RT-PCR, and “−RT” are controls in which no reverse transcriptase was added during cDNA synthesis. (B) RT-PCR using an upstream primer within cheZ and a downstream primer within flhBAE yielded very little product. Product slightly increased, relative to the −RT control, when fliA was overexpressed. This small amount of read-through at very high FliA levels is unlikely to be physiologically relevant.(PDF)Click here for additional data file.Figure S6All class 2 genes are not transcribed by FliA. Expression of flgA, flgB, and fliF cannot be rescued by overexpression FliA in ΔflhD. Expression of flhB is moderately increased in the ΔflhD+pBAD-fliA strain, potentially due to read-through from the upstream FliA-dependent tar-tap-cheRBYZ (see RBYZ see .(PDF)Click here for additional data file.Figure S7Novel FlhDC and FliA targets are not required for motility. Soft agar motility of motile MG1655 and single gene deletions of FlhDC and FliA target genes. Images are representative of 5 biological replicates per strain.(PDF)Click here for additional data file.Table S1Complete ChIP-qPCR data.(XLS)Click here for additional data file.Table S2Complete qRT-PCR data.(XLS)Click here for additional data file.Table S3Comparison of previously predicted FliA promoters to ChIP-seq binding sites.(XLS)Click here for additional data file.Table S4Strains.(XLS)Click here for additional data file.Table S5Oligonucleotides: strain construction and validation.(XLS)Click here for additional data file.Table S6Oligonucleotides: ChIP-qPCR.(XLS)Click here for additional data file.Table S7Oligonucleotides: qRT-PCR.(XLS)Click here for additional data file.File S1flhDC locus in motile MG1655 and epitope-tagged strains.Sequence of (DOCX)Click here for additional data file.


Cell-to-cell variation in gene expression levels (noise) generates phenotypic diversity and is an important phenomenon in evolution, development and disease. TATA-box binding protein (TBP) is an essential factor that is required at virtually every eukaryotic promoter to initiate transcription. While the presence of a TATA-box motif in the promoter has been strongly linked with noise, the molecular mechanism driving this relationship is less well understood. Through an integrated analysis of multiple large-scale data sets, computer simulation and experimental validation in yeast, we provide molecular insights into how noise arises as an emergent property of variable binding affinity of TBP for different promoter sequences, competition between interaction partners to bind the same surface on TBP (to either promote or disrupt transcription initiation) and variable residence times of TBP complexes at a promoter. These determinants may be fine-tuned under different conditions and during evolution to modulate eukaryotic gene expression noise. TATA boxes in gene promoters are associated with high level of cell-to-cell variation in gene expression. Through integration of multiple data sets, the authors now provide insights into how the interactions of TBP with DNA and other proteins can lead to noisy expression. Gene expression noise is the measure of cell-to-cell variability in the expression level of a gene in a population of genetically identical cells that are grown in the same environment25291112When a promoter becomes accessible after chromatin reorganization and TF binding, RNA polymerase II can be recruited in different ways1516182223Previously, genes were classified as TATA-box containing or TATA-less based on predicted TATA-box sequences somewhere in their promoterAt an accessible promoter, TBP forms transcriptionally permissive assemblies either as part of a TFIID or SAGA co-activator complex. TFIID is a ∼1.2 MDa complex3216163235In terms of noise (measured as coefficient of variation (CV); ratio of s.d. to mean abundance), we observed that TATA-box genes are noisier and display higher expression level than the newly classified TATA-like genes . In fact5/A5 subset) of the TATA-box  the TBP structure in the DNA bound and the unbound forms are highly similar (irrespective of either a TATA-box or TATA-like sequence), (b) the DNA of both TBS types are bent to the same extent and (c) the complexes are very similar in terms of buried surface area, interaction energy and number of interacting residues . Despite5 subset . These oin vivo was higher in promoters of the T5 compared to the A5 subset . Furthermore, Taf1p makes more unique contacts with TBP than Mot1p  differential affinity of TBP to TBS sequences, (b) competition between TBP interaction partners  and (c) variable residence time of the TBP complexes (Mot1p and SAGA residence time in particular) on noise . The simFirst, we modelled the effect of the affinity of TBP to different TBS sequences (affinity parameter) in situations that are reflective of the observations described above, that is, (a) Mot1p is more likely to outcompete SAGA (competition parameter), (b) longer and intermediate residence times for TFIID and SAGA, respectively (residence time parameter) and (c) lower residence time for Mot1p at promoters since Mot1p enzymatic activity is high in cells , a TATA-box gene is less noisy since SAGA more often wins the competition and/or SAGA residence times are higher. This can happen when a strong transcriptional activator effectively recruits and tethers SAGA to the promoter  the TBP complexes that can assemble at a promoter . Howeveret al.Gene expression noise data were obtained from Newman The TBP-binding site (TBS) classification status (TATA-box or TATA-like) for 4,231 mRNA-coding genes was obtained from Rhee and Pughet al.The genome-wide binding profiles of TBP, Mot1p, TFIID (Taf1p subunit), SAGA (Spt20p subunit) and Pol II across the entire yeast genome were acquired from van Werven Every gene was classified according to whether it was TFIID regulated or SAGA regulated. As an intuitive measure of regulation, genes that have an occupancy value above the median of all genes' promoters for a factor (Taf1p or Spt20p) are considered to be regulated by that factor. Genes with an occupancy value below the median are considered as not regulated by that factor. This classification for TFIID regulation and SAGA regulation, respectively, leads to four possible states for a promoter: TFIID and SAGA regulated (+/+), only TFIID regulated (+/−), only SAGA regulated (−/+) and neither TFIID nor SAGA regulated (−/−). Finally rescaling and centring was applied for visual clarity and does not have an effect on calculating the respective median of SAGA and TFIID occupancy. Genes where either factor could not be detected were excluded from the analysis.http://the_brain.bwh.harvard.edu/uniprobe/downloads.php). The authors generated a microarray chip with a set of synthetic double-stranded DNA sequences that together represent all possible 10-mers of DNA. This unbiased set of sequences on the PBM chip can then be assessed for their protein binding levels, when incubating the chip with a GST-tagged protein. Binding of TBP to the DNA probes was detected using a GFP-tagged antibody to the GST, which in turn indicates the intrinsic binding preference of the TBP for all possible 8-mer sequences (the sequence length that is bound by TBP). To deconvolute and approximate which of the contained motifs is bound by TBP (every probe host multiple motifs), the median value of the many replicate measurements of the same motif on different probes was calculated. This strategy has been shown to be a good indicator42The raw data of the PBM chips was obtained from the Bulyk group website  with standard parameters, putting the structures of the complexes on the same energetic ‘footing' important for comparisons. The atomic contacts between TBP and its interaction partners were then characterized with the internal module of the Chimera software (https://www.cgl.ucsf.edu/chimera/). Furthermore, the accessible surface area of TBP and the buried surface area on complex formation was calculated using the Hotregions webserverhttp://www.bioinf.manchester.ac.uk/naccess/) internally. Finally, for the different complexes containing TBP, the energy contributions of the interfaces were quantitatively estimated at the residue level using FoldX 3.0 with standard parameters.The atomic coordinates of TBP in complex with Taf1p (4B0A)et al.TBP turnover data at 542 gene promoters were obtained from van Werven χ2 test when comparing enrichments. The Mann–Whitney test (or Wilcoxon rank-sum test) was used to assess whether two samples were from the same population. It is a non-parametric test and does not assume a defined distribution. Statistical tests were corrected for multiple testing using the Benjamini and Hochberg method. The χ2 test for goodness-of-fit compares observed ratios with expected ratios for nominal scale data. It helps to assess whether there are significant differences between the expected frequencies and observed frequencies in one or more categories.Statistical analysis was done using the R statistical package. Statistical significance was assessed using the Wilcoxon rank-sum test when comparing distributions and the Distributions were represented by box plots, which highlight informative statics. The median value for each sample is shown with a horizontal black line. Boxes enclose values between the first and third quartile. The interquartile range (IQR) is calculated by subtracting the first quartile from the third quartile. All values that are 1.5 × IQR lower than the first quartile or 1.5 × IQR greater than the third quartile are considered to be outliers and were removed only from the figures to improve visualization.Discrete time stochastic modelling of gene expression was performed to determine the impact of affinity, competition and residence time of TBP on noise. Markov chains (MCs) were used to model a graph based on our findings with five distinct microstates: free promoter (f), TBP:TBS (T), TBS:TBP:Mot1p (M), TBS:TBP:SAGA (S) and TBS:TBP:TFIID (D). The transition probabilities to switch between the microstates were chosen to be reflective of the cellular conditions in yeast cells. Every simulation was conducted for 150 time points and for 500 cells (see SPT3 subunit of the SAGA complex from the MATα haploid Yeast strain Y6545 using nourseothricin (Nat) resistance plasmid pAG35  and lacking HIS. Sporulation was then induced by transferring cells to nitrogen starvation plates for 5 days. Haploid cells containing all desired mutations were selected by transferring cells to plates containing all selection markers alongside the toxic amino-acid derivatives Canavanine and Thialysine (Sigma-Aldrich) to select against remaining diploids and lacking Leucine to select for only spores with an ‘a' mating type (Cohen and SchuldinerSPT3 by PCR. To manipulate the collection in high-density format (384), we used a RoToR bench top colony arrayer (Singer Instruments).We deleted the AG35 ref . SynthetSPT3 deleted GFP-tagged yeast strains (see details below) were measured using flow cytometry. The comparison between the fluorescence emitted by wild type GFP-tagged strain (with SPT3) and the knockout shows the impact of the deletion. To process the cytometry data, the protocols from Newman et al.et al.et al.−1 was used. GFP was excited at 488 nm and the fluorescence was collected through a 505-nm long-pass filter and 525-nm band-pass filter (Chroma Technology). Thousands of events were recorded from each well in the plate. The flow cytometry experiments were repeated in duplicates. The processing of the raw data was performed as reported beforeSPT3 knockout were averaged and the s.e. was estimated.Wild type (WT) and How to cite this article: Ravarani, C. N. J. et al. Affinity and competition for TBP are molecular determinants of gene expression noise. Nat. Commun. 7:10417 doi: 10.1038/ncomms10417 (2016).Supplementary Figures 1-7, Supplementary Notes 1-5, Supplementary Discussion, Supplementary Methods and Supplementary References


ANGPTL3) gene variants with coronary artery disease, levels of low-density lipoprotein cholesterol (LDL-C), triglycerides and ANGPTL3 mRNA transcript. Extensive linkage disequilibrium at the locus, however, has hindered efforts to identify the potential functional variants. Using regulatory annotations from ENCODE, combined with functional in vivo assays such as allele-specific formaldehyde-assisted isolation of regulatory elements, statistical approaches including eQTL/lipid colocalisation, and traditional in vitro methodologies including electrophoretic mobility shift assay and luciferase reporter assays, variants affecting the ANGPTL3 regulome were examined. From 253 variants associated with ANGPTL3 mRNA expression, and/or lipid traits, 46 were located within liver regulatory elements and potentially functional. One variant, rs10889352, demonstrated allele-specific effects on DNA-protein interactions, reporter gene expression and chromatin accessibility, in line with effects on LDL-C levels and expression of ANGPTL3 mRNA. The ANGPTL3 gene lies within DOCK7, although the variant is within non-coding regions outside of ANGPTL3, within DOCK7, suggesting complex long-range regulatory effects on gene expression. This study illustrates the power of combining multiple genome-wide datasets with laboratory data to localise functional non-coding variation and provides a model for analysis of regulatory variants from GWAS.Genome-wide association studies have confirmed the involvement of non-coding angiopoietin-like 3 ( •ANGPTL3 SNP locus.Over 200 variants exist in strong linkage disequilibrium with the lead •rs10889352 affects DNA-protein interactions from liver nuclear extract.•rs10889352 affects reporter gene expression in liver cells.•rs10889352 is associated with chromatin accessibility.•ANGPTL3 gene expression and lipid levels is likely due to rs10889352.Effects on  Examining the ANGPTL3 locus, it can be seen that there are over 250 SNPs in very strong LD with the lead TG GWAS SNP that span ∼250 kb and include 3 genes ANGPTL3, DOCK7 and USP1  ANGPTL3 locus, and examined these variants for functionality in the laboratory.The lead SNP, rs2131925 is unlikely to represent the functional SNP at the locus, with 92 SNPs in perfect LD, and 202 SNPs in very strong LD (r22.12 ≥ 0.8) with the GWAS lead SNP (rs2131925) for TG and LDL-C ANGPTL3 reaching genome-wide significance (p < 5 × 108) for LDL-C or TG levels using a large-scale meta-analysis of >100,000 individuals were identified Variants in strong LD  were examined in relation to variants associated with lipid biomarkers and 2.4l-glutamine and 10% foetal bovine serum . EBV-transformed lymphoblast cell lines from the Centre d'Etude du Polymorphism Humain (CEPH) panel (Coriell Cell Repositories), were cultured in RPMI 1640 (PAA) with 2 mM l-glutamine and 15% foetal bovine serum (PAA) at 37 °C, 5% CO2. Cell viability was verified using the ADAM-MC cell counter , and minimum cell viability for experiments was ≥99%. Cytokine stimulation was performed by overnight incubation in serum-free media, followed by addition of 5 ng/ml IL-1β, two hours prior to cell fixing.HepG2 cells were cultured in high-glucose Dulbecco's modified Eagle's medium  supplemented with 2 mM 2.5A/C)CACTACTACGTATAC and rs10889352: 5′-GCAAACAGAAAAAAA(T/C)ATAGTACTAGTAAAA-3’.Nuclear extracts were obtained from HepG2 cells using the NE-PER Nuclear and Cytoplasmic Extraction Reagents Kit  as described in the manual, with the addition of Complete Protease Inhibitor  to buffers CER I and NER I. EMSA probes were designed using 15 bp of genomic sequence on each side of the candidate SNP, and reverse complement for each forward strand was synthesised and annealed . Forward sequence for rs6690733: 5′-ATCAAAAGGGGGTAA.Probes were labelled using the Biotin 3′-End DNA Labelling Kit  as described in the manual, and complementary labelled oligonucleotides annealed by heating to 95 °C for 10 min, followed by slow cooling (−1 °C/min to 30 °C). Each binding reaction consisted of 2 μl 10× binding buffer , 1 μg p[dI-dC], 2 nmol biotin-labelled DNA, made to a total of 20 μl with H2.6To determine which proteins may be interacting with EMSA oligonucleotide probes, the MatInspector  2.7ANGPTL3 regulatory elements surrounding the SNPs of interest, oligonucleotides were designed with the addition of BamHI and SalI restriction sites at the 5’- and 3’-ends and PCR amplification of homozygous wild-type individuals performed. Primer sequences for cloning: rs6690733 - 5′-GGGGCTACTCTTCAAATTCCCT-3′ forward, 5′-GGCCAGCTTCTCTCCCCTAT-3′ reverse; rs10889352 – 5′-TGACACAGGCTAGGTGACAAA-3′ forward, 5′-AGAAGCAGATGCCACCAAGG-3′ reverse. PCR products were digested with BamHI and SalI, and ligated into the BamHI/SalI-linearized pGL3 Promoter vector (Promega), downstream of the SV40 late poly(A) signal. Allelic variants were created using the Site-Directed-Mutagenesis kit (Invitrogen), the variant verified by sequencing and insert digested and re-inserted into a fresh pGL3 vector. The size of insert for rs6690733 was 249 bp and 192 bp for rs10889352.For the putative 4/well in a 96 well plate and grown to confluence in high-glucose Dulbecco's modified Eagle's medium  supplemented with 2 mM l-glutamine and 10% fetal bovine serum (FBS). Cells were transfected with 250 ng pGL3 reporter construct with 10 ng pRLTK as a transfection control. Transfection was carried out using Opti-Mem serum-free media  and Lipofectamine 2000  as described in the manual. Media was replaced 24 h following transfection, with serum-containing media, and the cells left for 2 days before harvesting. Cells were lysed using Passive Lysis Buffer  and luciferase expression was determined using the Dual Luciferase Reporter Assay System , and measured in the Tropix TR717 Microplate Luminometer . Luciferase activity was determined as the mean of 12 transfections with the assay performed in triplicate.HepG2 cells were seeded at 2.0 × 102.8The methodology for allele-specific FAIRE used for this study is described elsewhere 2.9http://www.sph.umich.edu/csg/abecasis/public/lipids2010/) and the statistical methodology is described in full in Luciferase expression was normalised to the pRLTK control vector, and a 2-sided t-test performed between each run of 12 samples. Assays were performed in triplicate. Analysis of FAIRE-gen was performed using a paired t-test between the BAF from SNPs of all heterozygous individuals, comparing non-FAIRE vs FAIRE-treated chromatin. Imputation of genotypes not covered on the array was carried out as described 33.1ANGPTL3 locus, all variants from the 1000 Genomes Project 2 > 0.8) with the GWAS lead SNPs  were identified, providing 202 SNPs  were selected. All 106 LDL-C associated SNPs were present within the subset associated with TG  affecting gene regulatory processes is likely to be responsible for both associations. The potential for this selection of 253 variants to affect gene regulation was assessed by examining data from the ENCODE Project and Roadmap Epigenomics 14 13. Since none of these variants occur within ANGPTL3 coding regions, 5′ or 3’ UTR, or predicted splice sites, allele-specific regulation is likely to be through effects on promoter, enhancer or silencer elements, rather than effects on mRNA stability. Since the variants are also associated with DOCK7 transcript levels, a common enhancer/silencer element is more likely to be affected than an ANGPTL3 promoter element. To identify variants that may affect such regulatory elements, ENCODE and Roadmap Epigenomics datasets were searched for universal signatures of regulatory potential, including DNaseI hypersensitivity (DHS) peaks, histone modifications and FAIRE-seq peaks, and also for transcription factor binding sites. Variants present within, or close (500 bp) to these signatures were also selected for further analysis. 33 SNPs were present directly within an annotated regulatory region in liver cells, and 13 were present close to a regulatory annotation and were included for further analysis due to possible longer-range regulatory roles  were performed for each of the alleles, using biotin-labelled double-stranded oligonucleotide probes comprising 30bp genomic sequence surrounding each variant, with the variant located directly at the centre of each probe. Each oligonucleotide probe was incubated with a HepG2 liver nuclear extract, and the resulting complexes run on a polyacrylamide gel. From a pool of 46 SNPs, 44 either did not result in transcription factor binding from either allele, or both alleles resulted in identical binding of equal intensity, with the binding site likely to be located outside of the polymorphic site. Two variants demonstrated replicated allele-specific binding to the liver nuclear extract, rs6690733 and rs10889352, indicating changes in transcription factor binding potential, and a possible regulatory role . Interes17, which use extensive libraries of matrix descriptions for transcription factor binding sites to identify matches in DNA sequences. Both variants are predicted to alter binding sites for a number of transcription factors  downstream of luc reporter gene (acting as a putative enhancer), driving the SV40 promoter in the pGL3 promoter vector. Constructs were transiently transfected into the HepG2 cell line and luciferase activity measured.The two SNPs associated with TG levels, 10) in gene expression for the minor alleles  was performed. The allelic imbalance was assessed from heterozygotes following FAIRE-enrichment for open chromatin, compared to non-enriched chromatin, using lymphoblast chromatin samples from 20 unrelated individuals.Variants that affect gene transcription which are not located either within a transcription factor protein-coding sequence or regulatory RNA-coding sequence (e.g. miRNA), as is the case at the p = 0.002), suggesting that the T allele allows for an increased regulatory potential than the C allele. This data fits with the increased binding of nuclear proteins to the T allele, and higher expression levels in reporter assay, along with the T allele associated with higher ANGPTL3 transcript levels. The other potentially functional variant at the locus, rs6690733, however did not demonstrate effects on chromatin accessibility (p = 0.53), and is therefore a less plausible casual candidate SNP.Interestingly, rs10889352 T > C did indeed demonstrate a robust allele-specific effect on chromatin accessibility  from of the 46 variants selected from ENCODE, and effects of this manageable number of variants on gene expression were examined by luciferase reporter assays. Both variants also showed allele-specific effects on gene expression, with each allele that showed increased expression levels also associated with increases in in vivo and in vitro analyses. The variant rs10889352 is located outside of the coding region for ANGPTL3, within an intron of DOCK7 (in silico tools to correctly predict transcription factor binding. It does, however, restrict the use of chromatin immunoprecipitation to demonstrate in vivo interactions, another technique that would help characterise functional variants and determine the precise mechanism of action.Despite >200 variants in very strong LD with the lead SNP, only one SNP demonstrated likely functionality based on all bioinformatics, of DOCK7 , and cloof DOCK7 . Severalof DOCK7 . Higher in vitro analysis, and although we replicated DNA-protein interactions with an additional liver cell line, primary hepatoctyes may provide a different result.The study described here utilises multiple lines of evidence to demonstrate SNP functionality. Where it is necessary to isolate individual SNPs for analysis, methods such as EMSA and reporter assays are important, but take no account of chromatin accessibility, requiring such information from sources such as ENCODE and Roadmap Epigenomics. FAIRE-gen largely overcomes this weakness, with long-range LD no longer a limiting factor. A potential limitation with the assay described here is the use of allele-specific FAIRE data from lymphoblasts for examining traits seen in the liver. However, although absolute levels of accessible chromatin are tissue-specific, and largely colocalise with tissue-specific transcription factor binding sites ANGPTL3 locus.This study demonstrates the complexity of identification of SNP functionality in regions where strong LD is present, and where several variants in near-complete LD may be having additive/opposite effects. Such information will be important when examining loci in populations with different LD structures, where knowledge of the functional variant will be able to improve the risk prediction for individuals in these populations compared using a lead tag SNP derived from another population. The methodology described here provides an outline to localise potentially causal regulatory variants based on the use of complementary tools. Reducing the number of functional variants at GWAS loci will allow for the emerging genome-editing techniques such as transcription activator-like effector nucleases British Heart Foundation  and the European Commission [GA No 278397 to PH].This work was supported by the None.