Tg) and thyroperoxidase (Tpo) promoters, but FoxE1 binding to regulatory regions other than Tg and Tpo promoters remains almost unexplored. Improving knowledge of the regulatory functions of FoxE1 is necessary to clarify its role in endocrine syndromes and cancer susceptibility.FoxE1 is a thyroid-specific forkhead transcription factor essential for thyroid gland development, as well as for the maintenance of the thyroid differentiated state in adults. FoxE1 recognizes and binds to a short DNA sequence present in thyroglobulin (FoxE1 and obtained new insights into FoxE1 transcriptional networks in thyroid follicular cells. After validation, we confirmed Adamts9, Cdh1, Duox2 and S100a4 as upregulated genes and Casp4, Creld2, Dusp5, Etv5, Hsp5a, Nr4a2 and Tm4sf1 as downregulated genes when FoxE1 was silenced. In promoter regions of putative FoxE1-regulated genes and also in the promoters of the classical thyroid genes Nis, Pax8 and Titf1, we performed an in silico search of the FoxE1 binding motif that was in close proximity to the NF1/CTF binding sequence, as previously described for other forkhead factors. Using chromatin immunoprecipitation we detected specific in vivo FoxE1 binding to novel regulatory regions in two relevant thyroid genes, Nis and Duox2. Moreover, we demonstrated simultaneous binding of FoxE1 and NF1/CTF to the Nis upstream enhancer region, as well as a clear functional activation of the Nis promoter by both transcription factors.In order to further investigate downstream FoxE1 targets, we performed a genome-wide expression screening after knocking-down Nis and Duox2 in executing the transcriptional program triggered by FoxE1. Furthermore, this study points out the important role of FoxE1 in the regulation of a large number of genes in thyroid cells.In search for potential downstream mediators of FoxE1 function in thyroid cells, we identified two novel direct FoxE1 target genes. To our knowledge, this is the first evidence regarding the implication of  Coordinated expression of thyroid transcription factors Pax8, FoxE1/Ttf2 and Ttf1/Nkx2-1 is essential for maintaining the differentiated thyroid function, which involves synthesis and secretion of thyroid hormones. These factors are encoded by genes with paired box, forkhead box and homeobox domains, respectively. Thyroid hormones are iodinated, and therefore thyroid cells actively concentrate iodide through a sodium dependent co-transporter, Nis, a glycoprotein located in the basal membrane. The iodide is transported to the apical membrane, where thyroperoxidase (Tpo) iodinates the tyrosine residues of the main thyroid protein thyroglobulin (Tg) that serves as a storage for thyroid hormones FoxE1, formerly known as thyroid transcription factor 2 or Ttf2, is a thyroid-specific transcription factor that belongs to the forkhead/winged-helix family FoxE1, as a member of the Fox family, is able to interact with nucleosomes through its winged-helix DNA binding domain and to alter chromatin structure, creating a locally exposed domain necessary for the action of other transcription factors Foxe1-null mice exhibit an ectopic (50%) or completely absent (50%) thyroid gland and severe cleft palate. Consequently, FoxE1 seems to be crucial either for the migration of the thyroid cell precursors or for repressing differentiation until migration has completed FOXE1 mutations cause the Bamforth–Lazarus syndrome (OMIM 241850), which is associated with congenital hypothyroidism, cleft palate and spiky hair, with or without choanal atresia, bifid epiglottis and ocular hypertelorism FOXE1 variations have been associated with susceptibility to several types of cancer Human (Tg) (Tpo)Tg and Tpo genes; however, it can also act as a promoter-specific transcriptional repressor of both genes FoxE1 was initially identified as a nuclear protein Tg and Tpo promoters share the core sequence AAACA Tpo promoter FoxE1 forms part of an interaction-complex together with the transcription factor NF1/CTF, whose final result is to turn on the expression of the Tpo gene in response to external hormonal stimuli Putative FoxE1-binding sites previously identified in the Tg and Tpo promoters remains almost unexplored. Only two studies have reported other FoxE1 targets, but both were conducted in heterologous expression systems Nevertheless, FoxE1 binding to DNA sequences other than the FoxE1 knock-down cells followed by a search of direct target genes containing in their promoters both FoxE1 and NF1/CTF binding sites. The results obtained in this study provide new insights into FoxE1 transcriptional networks in differentiated thyroid cells and predict involvement of FoxE1 in relevant biological processes and pathways. These data may lead to a better understanding of thyroid biology.In order to further investigate FoxE1 downstream targets in thyroid epithelial cells, we performed a genome-wide screening using expression arrays in Tg, Tpo, and Nis) as well as thyroid-specific transcription factors  PCCl3 cells, a continuous line of rat thyroid follicular cells HeLa cells were grown in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 5% fetal bovine serum.PCCl3 cells were transfected with 25 nM of FoxE1 siRNA (Rat FoxE1 ON-TARGETplus SMARTpool) or with scrambled siRNA (ON-TARGETplus Non-targeting Pool) using DharmaFECT 1 Transfection Reagent and following the manufacturer’s protocol .FoxE1 silencing, duplicate samples were harvested at different time points  after transfection, and total protein was extracted. FoxE1 silencing was tested by western blotting using a polyclonal FoxE1 antibody  (data not shown). Once 48 hours was defined as the best time point for FoxE1 silencing, we performed additional transfections, four biological replicates for each condition, to isolate total protein and total RNA using TRIzol reagent  following the manufacturer’s recommended protocol.In order to establish optimal conditions for FoxE1-dependent gene expression was tested using expression arrays (Agilent SurePrint Rat 60 K). We established two main comparisons: FoxE1-silenced PCCl3 cells (siFoxE1 PCCl3) vs. scrambled siRNA-treated PCCl3 cells (siScramble PCCl3) and FoxE1-silenced PCCl3 cells vs. wild type PCCL3 cells (wt PCCl3). This last condition was included to consistently analyse expression array signals in basal cellular conditions. Each comparison was performed using quadruplicates and dye swaps  for RNA quality evaluation, amplification, labelling and hybridization to Agilent SurePrint Rat 60 K arrays according to the manufacturer’s protocols.http://www.bioconductor.org). At a later stage, we used the file “SurePrint G3 Rat GE 8×60 K Microarray” to obtain the annotations of the rat genome from Agilent. Genes that showed adjusted p-values <0.005 both in siFoxE1 vs. wild type and in siFoxE1 vs. siScramble PCCl3 cells were considered differentially expressed. Functional analysis of Gene Ontology (GO) terms was carried out using the FatiGO tool and gene set enrichment analysis was performed using FatiScan http://www.ncbi.nlm.nih.gov/geo/) database under accession number GSE42497.Signal quantification was carried out with Agilent Feature Extraction Software 10.7 , using default analysis parameters for Agilent’s whole rat genome 60 K gene expression arrays. To normalize the data set, we performed loess within-array normalization and quantiles between-array normalization. Differential expression analysis was done using Bioconductor’s limma package  and western blots after performing four independent experiments of FoxE1, Tpo, Tg, Nis, Pax8 and Titf1) as well as for 12 putative FoxE1-regulated genes, using beta-glucuronidase (Gus) for gene expression normalization . RNA was reverse transcribed with random primers and quantitative PCR reactions were assembled in triplicate using the SYBR Green PCR Kit  following the manufacturer’s protocol. Expression level changes were studied for 6 thyroid-specific genes  Nonidet, 0.1% (wt/vol) sodium deoxycholate, 0.1% SDS). Protein extracts (30 µg) were separated on 10% SDS-PAGE gels and immunodetected after western blotting. FoxE1 antibody was from Biopat , Actin and Cdh1 antibodies were from Santa Cruz Biotechnology, Inc. , and Nis and Duox2 antibodies were generous gifts from Dr. N. Carrasco  Tpo promoter [5′-TTGG-(5–30)bp-ACAAA-3′] http://www.ensembl.org).We searched for FoxE1 binding sites in promoter regions (+/−1000 bp relative to the transcription start site) of genes statistically significantly regulated by FoxE1 . We performed an automated search of genes containing both the FoxE1 core binding site [5′-ACAAA-3′] and the NF1/CTF binding motif [5′-TTGG -3′] localised between 5 to 30 bp from the FoxE1 site, as previously described for FoxE1 binding within the Chromatin immunoprecipitation (ChIP) and sequential ChIP (ReChIP) were performed using the Diagenode HighCell ChIP kit following the manufacturer’s standard protocol. Cross-linked PCCl3 chromatin was immunoprecipitated using polyclonal antibodies against FoxE1  and NF1 . Two independent ChIP experiments were performed using two different batches of Biopat FoxE1 antibodies.ReChIP was used to analyse simultaneous binding of FoxE1 and NF1/CTF proteins to a common DNA region. After a first immunoprecipitation with FoxE1 antibody using the Diagenode HighCell ChIP kit, samples were rinsed with RIPA buffer, and the first antibody was stripped from the beads by incubating in 1% SDS at 65°C for 15 minutes. Then a second round of immunoprecipitation with NF1 antibody was performed using the same Diagenode HighCell ChIP kit and following the manufacturer’s standard protocol.Tpo and Tg were used as positive IP controls whereas intron and promoter regions of Gad1 (glutamate decarboxylase 1) and Afm  were used as negative controls. qPCR reactions were assembled in triplicate using the SYBR Green PCR Kit , following the manufacturer’s protocol. The enrichment of target sequences in ChIP material was calculated relative to the Afm and Gad1 negative controls, and normalized to their relative amplification in the input sample FoxE, NF1 and FoxE1/NF1 -immunoprecipitated and input samples were analysed by real-time PCR using specific primers for the analysed regions see . The kno5 cells/well in 6-well tissue culture plates, 24 h before transfection. Transfections were performed by calcium phosphate co-precipitation as described previously Renilla vector were cotransfected to assess transfection efficiency. After 48 h, cells were harvested, lysed, and analysed for Luciferase and Renilla activities by the Dual-Luciferase reporter assay system . The promoter activity in cells transfected with expression vectors was determined as the ratio between luciferase and Renilla activities, relative to the ratio obtained in cells transfected with the corresponding control vectors. The results shown are the average ± SD of six different experiments performed in triplicate. Statistical significance was determined by t test analysis (two-tailed), and differences were considered significant at p<0.05. Western blots were done where indicated to determine the levels of expression of the transfected expression vectors (data not shown).HeLa cells were plated at a density of 2×10FoxE1. Silencing was performed for 48 h after transfection. Data from four independent silencing experiments were analysed. We obtained a FoxE1 silencing grade of more than 85%  was downregulated, in the absence of FoxE1, in both comparisons.In order to further investigate FoxE1 function and to identify putative FoxE1 target genes in thyroid cells, we performed whole genome microarray analysis in PCCl3 thyroid cells after knocking down than 85% . Statistd probes . Array rd probes  and 2. AGene ontology (GO) analysis using FatiGO identified common GO terms, including endoplasmic reticulum (ER) overload response (GO:0006983), response to ER stress (GO:0034976), cellular response to glucose starvation (GO:0042149), protein folding (GO:0006457), heat shock protein binding, and oxidoreductase activity acting on sulfur group of donors, disulfide as acceptor (GO:0016671).FoxE1 silencing experiments, 48 h after siRNA transfection. First of all, we analysed expression levels of FoxE1, in order to check and confirm its inhibition at the mRNA level. We also checked the mRNA expression status of the FoxE1 target genes Tpo and Tg, as well as mRNA levels of the classical, most representative, thyroid genes: the thyroid iodide symporter gene, Nis, and thyroid transcription factors Pax8 and Ttf1 , a cell-cell adhesion glycoprotein; Crip2, a repressor of NF-kappaB; dual oxidase 2 (Duox2), a glycoprotein located at the apical membrane of thyroid follicular cells; S100a4, a calcium-binding protein; caspase 4 (Casp4), an apoptosis-related cysteine peptidase; the cysteine-rich with GF-like domains protein 2, Creld2; dual specificity phosphatase 5 (Dusp5); transcription factor Etv5; 70 kDa heat shock protein 5 (Hsp5a); steroid-thyroid hormone-retinoid receptor Nr4a2; and transmembrane 4L six family member 1, Tm4sf1.Moreover, we validated 12 genes randomly chosen from the list of putative FoxE1 targets, 5 of which were upregulated and 7 of which were downregulated after ilencing . Genes sFoxE1 silencing, qRT-PCR analysis confirmed downregulation of FoxE1 as well as its previously described target genes Tpo and TgNis, and its mRNA levels were decreased even more than Tg mRNA levels . However, AAACA is a short sequence that can be easily found at random in the genome. We therefore performed an automatic search in the promoter sequences for FoxE1 binding sites in close proximity (5–30 bp) to the NF1/CTF binding motif, [5′-TTGG-(5–30)bp-ACAAA-3′], as previously described for FoxE1 binding to the Duox2, Cdh1 and Nr4a2 for performing ChIP in vivo analysis of FoxE1 binding to their promoters.FoxE1-NF1/CTF binding sequences were present in promoter regions of 26 out of 55 differentially FoxE1-regulated genes. Results are summarized in We also wanted to study in more detail the role of FoxE1 in Nis regulation . As we din vivo FoxE1 binding to Duox2, Cdh1, Nr4a2 and Nis promoter sequences, we performed chromatin immunoprecipitation (ChIP) using a polyclonal antibody against FoxE1. We analysed immunoprecipitated DNA of two independent experiments using qPCR. Results of IP ratios normalized to the Afm negative control are shown in Gad1 as a negative control (data not shown). Previously described FoxE1 binding sequences in the Tg and Tpo promoters were used as positive controls.In order to analyse Tg and Tpo promoters with relative immunoprecipitation (IP) ratios of 22.8 and 3.6, respectively. We found binding of FoxE1 to chromatin regions containing the Nis upstream enhancer element (relative IP ratio: 12.6). We also observed positive FoxE1 binding to two DNA sites located in the Duox2 gene; one in the promoter sequence at −351 bp (relative IP ratio: 28.1) and the other in the third exon at +696 bp (relative IP ratio: 9.3) (Cdh1 or Nr4a2.The FoxE1 antibody precipitated FoxE1 target sequences in the io: 9.3)  and 4. WNis and Duox2 promoter regions, we performed ChIP assays using a polyclonal antibody against NF1. Next, we also performed ReChIP experiments with FoxE and NF1 antibodies. We quantified immunoprecipitated DNA regions using qPCR. The previously described FoxE1 binding sequence in the Tg promoter was used as positive control.In order to better understand whether FoxE1 and NF1/CTF are bound together on the in vivo NF1/CTF binding to either of the two putative FoxE1 binding sequences in Duox2 (data not shown). Nevertheless, we detected positive binding of NF1/CTF to the NUE. Moreover, we observed simultaneous FoxE1 and NF1/CTF binding to the NUE region. Results of IP ratios normalised to the Afm negative control are shown in Gad1 was used as a negative control (data not shown).In our experimental conditions, we did not observe As we wanted to study in depth the role of FoxE1 and NF1/CTF in Nis regulation, we cotransfected HeLa cells with FoxE1 and NF1/CTF expression vectors, together with a luciferase expression vector containing the NUE. Forty-eight hours later, cells were lysed and luciferase levels were measured. The results showed a significant 2-fold increase in the activity of NUE when FoxE1 or NF1/CTF were expressed individually in HeLa cells; thus, the effect was additive when both transcription factors were transfected together .in vivo FoxE1 binding to regulatory regions of two relevant thyroid genes, Nis and Duox2. These FoxE1 core binding sequences are located close to a NF1/CTF transcription factor binding site. Moreover, we demonstrate that FoxE1 and NF1/CTF are simultaneously bound to the Nis upstream enhancer and that they cooperate in the regulation of NIS-promoter activity.In summary, we present the first description of specific FOXE1 mutations cause the Bamforth–Lazarus syndrome (OMIM 241850) FOXE1 variations have been associated with susceptibility to several types of cancer Tg and Tpo promoters. However, little is known about other downstream targets of FoxE1 that could explain the phenotypes observed in humans carrying variations in FoxE1. Only two studies reporting novel genes regulated by FoxE1 have been published until now in vivo, but importantly, instead of a heterologous system we used a thyroid cell line. Results obtained by performing a genome-wide approach followed by chromatin immunoprecipitation analysis, led us to the identification of direct FoxE1 target genes in thyroid cells.FoxE1 is a forkhead transcription factor essential for thyroid differentiation and function. In humans, Adamts9, Cdh1, Duox2 and S100a4) showed an increased expression, whereas the remaining seven genes  showed decreased mRNA levels when FoxE1 is silenced. Moreover, we have not ruled out that the other 43 statistically significant genes are true FoxE1 targets, and in future studies we will take advantage of these additional array results.In this study, we found 54 putative novel FoxE1 target genes. We obtained a very restrictive list of significantly regulated genes, based on overlapping expression array data of two comparisons. This restrictive list did not allow us to obtain relevant information about main pathways affected by FoxE1 action. We identified and validated eleven genes regulated by FoxE1; four of them Click here for additional data file.Table S2Oligonucleotides used for ChIP analysis.(DOC)Click here for additional data file.Table S3Statistically significant probes (p<0.005) common to both comparisons .(DOC)Click here for additional data file.Dataset S1Microarray results for siFoxE1 PCCl3 vs siScramble PCCl3 comparison.(XLS)Click here for additional data file.Dataset S2Microarray results for siFoxE1 PCCl3 vs wt PCCl3 comparison.(XLS)Click here for additional data file.


IME1 and IME2, under all growth conditions and activates the transcription of NDT80 only during vegetative growth. In contrast, the effects of Rpd3L depends on nutrients; it represses or activates transcription in the presence or absence of a nitrogen source, respectively. Further, we show that transcriptional activation does not correlate with histone H4 deacetylation, suggesting an effect on a nonhistone protein. Comparison of rpd3-null and catalytic-site point mutants revealed an inhibitory activity that is independent of either the catalytic activity of Rpd3 or the integrity of Rpd3L and Rpd3S. In budding yeasts, the histone deacetylase Rpd3 resides in two different complexes called Rpd3L (large) and Rpd3S  that exert opposing effects on the transcription of meiosis-specific genes. By introducing mutations that disrupt the integrity and function of either Rpd3L or Rpd3S, we show here that Rpd3 function is determined by its association with either of these complexes. Specifically, the catalytic activity of Rpd3S activates the transcription of the two major positive regulators of meiosis,  Histone deacetylation plays a pivotal role in the regulation of transcription. Deacetylation of lysine residues in the N-terminal tail domain of histones H3 and H4 correlates with transcriptional repression of most promoters, while transcriptional activation correlates with acetylation of these residues -4. In soHSP82 , these results indicate that the effect of Rpd3L and Rpd3S on IME2 is indirect and is mediated via their effect on IME1. However, the rco1Δ sds3Δ-double mutant exhibited a specific phenotype, that is, the transcription of IME2 increased whereas that of IME1 was reduced (Table 3), suggesting that the effect of Rpd3 on IME2 transcription is also direct. Moreover, the results of ChIP assays revealed that Rpd3 binds the IME2 promoter in SA medium  while at later times by Rpd3L (Sds3). Moreover, the epistatic relationship between RCO1 and SDS3 (no additive effect when both genes were deleted) suggests that these complexes deacetylate the same substrate. We expected that deleting DEP1, which is a specific component of Rpd3L, would exhibit the same phenotype as deleting SDS3, and that deletion of RPD3 will give the same phenotype as deletion of both RCO1 and SDS3, namely reduced transcription. However, this was not the case, because early in meiosis, Dep1 and Rpd3 functioned as positive regulators, but at later times as negative regulators , or indirectly (the reduced level of Ime2 delayed the transcription of NDT80). Next, we determined the effects of Rpd3L and Rpd3S on the transcription of detected . This reay NDT80 . Therefoe strain . Because on IME2 , we suggNDT80 in the rco1Δ sds3Δ-double mutant was similar to that observed for the sds3Δ or the rpd3Δ isogenic strains for 12 hours in SPM   . The resNDT80 transcription similar to the level of the rco1Δ strain  would suppress rpd3Δ and promote NDT80 transcription. This hypothesis was discarded, because rpd3Δ/rpd3Δ rad17Δ/rad17Δ diploids remained sporulation deficient and did not express NDT80 or SPS1 (a mid-meiosis-specific gene)  .IME4 [IME1 [NDT80 promoter drives the transcription of an antisense RNA , we posdiploids . Moreovediploids . NDT80 transcription correlated with histone H4 acetylation rather than deacetylation  IME1, the master transcriptional activator, is essential for the transcription of all meiosis-specific genes; 2) IME2, a representative of the network of early genes, is essential for the transcription of middle and late genes; and 3) NDT80, an early-middle gene, is essential for the transcription of middle and late genes -Rpd3 repressed transcription of a synthetic gene, albeit repression depended on the integrity and function of Rpd3S and Rpd3L . This inIME1, IME2, and NDT80 responded uniquely to Rpd3 present in Rpd3L and Rpd3S. Their expression was decreased in mutants that expressed only Rpd3L . The noncoding RNA transcribed from the IME1 promoter represses, through the Set3 histone deacetylase, IME1 transcription in non-MATa/MATα strains [IME2 transcription, because recruitment of Rpd3 to the IME2 promoter by Ume6 induced deacetylation of histones H3 and H4, and consequently repression [NDT80 promoter expresses an antisense RNA [NDT80 antisense RNA to induce transcription of the NDT80 ORF was rejected, because the induction of NDT80 transcription did not correlate with reduced levels of this RNA (NDT80 antisense RNA expression first peaked simultaneously with that of the NDT80 ORF mRNA and was not affected by Rpd3. The presence of a second antisense peak depended on Rpd3 and correlated with decreased NDT80 transcription (NDT80 transcription may be mediated through this RNA (3).. Acetylation regulates the activities of a wide array of nonhistone proteins as well as transcription factors [NDT80 transcription depends on four transcriptional activators, Ime1, Ime2, Ndt80, and Rpd3 as well as on the negative regulator Sum1  High strains . This repression . The NDTense RNA . The posthis RNA . NDT80 acription , indicat factors ,64. Thesporulate , and thedivision . FurtherRPD3 caused persistent transcription  the histone deacetylase activity of Rpd3 that is mediated by Rpd3S activates transcription, 2) in Rpd3L the catalytic activity of Rpd3 switches from negative to positive, depending on the availability of a nitrogen source, and 3) a noncatalytic activity of Rpd3 represses transcription.Table S1Mass spectroscopic analysis of Rpd3 isolated from cells cultured in SPM for six hours. Cells grown in SA to a density of 1 × 107 cell/ml were shifted to SPM. After six hours in SPM, proteins were crosslinked with 1% formaldehyde for 15 min. Rpd3 complexes isolated by IP were subjected to gel electrophoresis, excised from the gel, and analyzed using mass spectroscopy. Strains: MATa/MATα RPD3-13xmyc-URA3/RPD3-13xmyc-URA3 (Y1767) and MATa/MATα rpd3Δ::HIS3/rpd3 Δ::HIS3 . The results for strain Y1767 are shown. Sf - score for each peptide was calculated by a neural network algorithm that incorporates the Xcorr, DeltaCn, Sp, RSp, peptide mass, charge state, and the number of matched peptides for the search. P (pep) displays the probability of finding a match as good as or better than a random match.(DOCX)Click here for additional data file.


Integration of retroviruses occurs in most genomic regions with weak but statistical significant preferences for target site sequences. HIV-1 preferentially targets genes, particularly the transcriptionally active ones. Avian sarcoma and leukosis viruses (ASLV) integrate with only slight preference for genes while murine leukemia virus uniquely favors integration in close proximity to upstream or downstream transcription start sites. Genomic features as well as the “epigenetic landscape“ at the site of integration are crucial for the outcome of each integration event, i. e. stable long terminal repeat-driven expression of the provirus or silencing and transcriptional suppression. The current availability of assembled genome sequences with epigenomic characteristics and reliable cloning of retrovirus integration sites enables to study the interplay between transcription signals of the retrovirus and enhancing or suppressive influences of the adjacent cellular DNA.We studied the integration sites of ASLV-derived vectors integrated into normal or DNA methylation-deficient human cells. Integration sites from cell clones containing single provirus were cloned and their genomic features as well as epigenetic landscape were correlated with expression or silencing of the provirus. Dnmt3b turned out to be the major player in silencing the long terminal repeat-driven transcription. Proviruses integrated into H3K4me3-rich CpG islands associated with promoters of active genes display long-term stability of expression and are resistant to the transcriptional silencing after over expression of Dnmt3b. Stability of expression and resistance to the silencing decreases with the distance from transcription start site. By contrast, proviruses integrated into the intergenic regions tend to the spontaneous transcriptional silencing even in DNA methyltransferase-deficient cells.


Translation regulation plays important roles in both normal physiological conditions and diseases states. This regulation requires cis-regulatory elements located mostly in 5′ and 3′ UTRs and trans-regulatory factors ) which recognize specific RNA features and interact with the translation machinery to modulate its activity. In this paper, we discuss important aspects of 5′ UTR-mediated regulation by providing an overview of the characteristics and the function of the main elements present in this region, like uORF (upstream open reading frame), secondary structures, and RBPs binding motifs and different mechanisms of translation regulation and the impact they have on gene expression and human health when deregulated. Translation regulation also plays a significant role during development and cell differentiation by altering the levels of expression of specific mRNA subsets during a particular time window while the majority of transcripts remain unchanged . In this paper, we will focus on the importance of 5′ UTR mediated regulation and the different functional elements present in this region with the exception of IRES which is discussed in a different article of this issue. The main regulatory elements in 5′ UTR are secondary structures (including IRES), binding sites for RNA binding proteins, uAUGs and uORFs .The average length of 5′ UTRs is ~100 to ~220 nucleotides across species . High GCTranslation initiation in eukaryotes requires the recruitment of ribosomal subunits at either the 5′ m7G cap structure. The initiation codon is generally located far downstream, requiring ribosomal movement to this site. This movement appears to be nonlinear for some mRNAs  segments of the 5′ UTR as they move in the direction of the AUG). Shunting could allow mRNAs containing uAUGs or hairpin structures to be translated efficiently. Important examples are provided by the cauliflower mosaic virus  and adenBRCA1 (breast cancer 1) and βTGF- (transforming growth factor β). BRCA1 is a tumor suppressor, frequently mutated in breast cancer with functions in cell cycle, apoptosis, and DNA damage repair. BRAC1 produces two different transcripts that derive from two different promoters and therefore display differences in their 5′ UTR. A shorter transcript is expressed in cancerous as well as noncancerous breast tissue and efficiently translated, while a longer transcript is predominantly expressed in breast cancers. The presence of several uAUGs and a more complex structure dramatically affect the translation of this longer transcript. This causes an overall decrease in BRAC1 levels in tumor cells, leading to a relief in growth inhibition [βTGF- is implicated in a large number of processes that include cell proliferation, migration, wound repair, development, tumorigenesis and immunosuppression. There are three known isoforms: β1, β2, and β3. β3 TGF-produces two alternative transcripts: a 3.5 kb transcript with a very long 5′ UTR (1.1 kb) and a 2.6 kb transcript with a shorter 5′ UTR (0.23 kb). The presence of 11 uORFs in the longer transcript dramatically inhibits its translation while the shorter transcript is efficiently translated [Genes presenting differences in the 5′ UTR of their transcripts are relatively common. 10–18% of genes express alternative 5′ UTR by using multiple promoters , 12 whilhibition . TGF-β ianslated , 16.Secondary structures can function as major regulatory tools in 5′ UTRs. A correlation with gene function has been suggested; secondary structures have been determined to be particularly prevalent among mRNAs encoding transcription factors, protooncogenes, growth factors, and their receptors and proteins poorly translated under normal conditions. >90% of transcripts in these classes have 5′ UTRs containing stable secondary structures with average free energies less than −50 kcal/mol. 60% of these stable secondary structures are positioned very close to the cap structure , 18. Staβ1TGF- provides a good example of translation inhibition mediated by secondary structure [β1TGF- depends on increased binding of the RNA binding protein YB-1 to the β1TGF- transcript [β1TGF- with high affinity thanks to its GC content and cooperates with the stem loop to inhibit β1TGF- translation by facilitating duplex formation [ The gene tructure , 22. An anscript . It was ormation . The human genome is predicted to encode circa 1,000 RNA binding proteins (RBPs) with a large percentage of them implicated in translation. They could be categorized into two main groups: RBPs that are part of the basic translation machinery and required for the translation of all expressed mRNAs  and RBPs that function in a more selective way by controlling either positively or negatively the levels of translation of specific target mRNAs . Regarding this later group, it has been observed that RBPs can use distinct mechanisms to increase or inhibit translation. Although several exceptions are known, it can be said that RBPs often recognize specific motifs in UTRs and interact with the translation machinery to control expression. Interference with translation normally takes place during the initiation step (reviewed in ).The best characterized example of RBP-mediated regulation involving 5′ UTRs is provided by the iron regulatory proteins (IRP 1 and 2). These proteins recognize a highly conserved stem loop structure with circa 30 nucleotides, known as the iron response element (IRE). The most important features include a hexanucleotide loop with the sequence CAGYCX  and a 5 bp upper stem that is separated from a lower stem of variable length by an unpaired cytosine. This regulation is crucial in maintaining cellular iron homeostasis as a large number of mRNAs connected to iron storage and metabolism including ferritin, mitochondrial aconitase, succinate dehydrogenase-iron protein, erythroid 5-aminolevulinate synthetase (eALAS), and an iron-exportin molecule named ferroportin (FPN1) have their expression modulated by this system. When cellular iron levels are low, IRP1 and IRP2 bind the IRE and block translation of the downstream ORF. When intracellular iron levels are high, the RNA binding activity of both IRPs is reduced . IREs temsl-2) gene in Drosophila, a main player in dosage compensation. The female-specific RNA binding protein sex lethal (SXL) participates in multiple aspects of msl-2 regulation where msl-2 expression must be prevented , but when present, SXL binds a poly U stretch a few nucleotides away from the uAUG and increases this repression to more than 14-fold. SXL acts by boosting translation initiation at the uAUG and not by acting as a simple steric arrest of scanning ribosomes. This effect may take place via an interaction between SXL and translation initiation factors; possibly members of elF3 component as indicated by a two-hybrid screening. This mechanism potentially affects a large number of mRNAs; 268 transcripts in Drosophila were determined to contain SXL binding motifs associated with uAUG spaced at an appropriate distance. For instance, a reporter construct containing the 5′UTR of the gene Irr47 was repressed ~4-fold by SXL protein [ RBP-mediated regulation can be very elaborate and involve multiple steps. One good example showing the crosstalk between factors and distinct regulatory processes is the male-specific-lethal 2 . Competition between the two proteins determines final levels of p21 expression and establishes if cells will proliferate or undergo growth arrest and senescence. Binding of CUGBP1 to p21 mRNA is dramatically increased in senescence compared to young fibroblast cells. Protein levels do not change during the process and this increase in activity is due to phosphorylation. On the other hand, CRT IPs showed a four-to-fivefold reduction of activity in senescence cells due to a decrease in expression. Both proteins were shown to affect p21 translation. However, while CUGBP1 functions as an activator, CRT acts as a repressor. Since the two proteins have opposing activity in senescent cells, they were examined to see if they compete for interaction with p21 mRNA and to control its translation. Increasing amounts of one protein were able to reverse the binding of the other protein to p21 mRNA and its effect on translation; affinity to the binding site is rather different as CUGBP1 had to be present in the binding reactions at a four-to-eightfold molar excess to CRT to antagonize its binding to p21 mRNA and impact its translation . uORFs and uAUGs are major regulatory elements in 5′ UTRs. As their names suggest, uORFs are sequences defined by a start and stop codons upstream of the main coding region while uAUGs are start codons without an in-frame downstream stop codon located upstream of the main coding region. A large percentage of the human transcriptome contains uORF and/or uAUGs, with values ranging between 44 and 49% , 39. Simde novo fungal arginine biosynthesis in high arginine concentration [Although, overall it has been suggested that uORFs are negatively correlated with protein production , 38, 41 ntration . In thisntration , 47. AAPntration . Anotherntration , 50. Anontration . It has ntration . A conneβ3 transcript was determined to be associated with arrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC) [SRY) [IRF6) [PRKAR1A) [SPINK1) [β (HBB) [Several mutations that eliminate or create uORFs that end up altering protein levels have been connected to human diseases. Their relevance was discussed recently . Predispa (ARVC) . Anothera (ARVC)  using reC) [SRY) , Van der) [IRF6) , Carney PRKAR1A) , Heredit[SPINK1) , and Tha[β (HBB) . This lihttp://utrdb.ba.itb.cnr.it/) [Only a small fraction of the posttranscriptional regulatory elements located in human 5′ UTRs have been characterized. Those identified UTR elements are catalogued in a web-resource maintained by Graziano Pesole's group called UTRdb (cnr.it/) . In vivocnr.it/) . Althougcnr.it/) ), as deeAnother choice to map UTR elements regulating translation is to use purely computational methods based on analyzing the UTR sequences. These methods are based on identifying degenerate ribonucleotide patterns that have the expected properties of RBP binding sites. Similar methods have been applied for nearly 30 years to identify transcriptional regulatory in promoter sequences. These methods are reaching maturity, are very widely used, and have assisted greatly in compiling databases about transcriptional regulation  , 67. AltUltimately the best methods for identifying post-transcriptional regulatory elements will emerge from complementary application of experimental and computational techniques.We summarized the basic statistics for uORFs in human transcriptome (NCBI build37.3). Table 1.a shows the number of appearances of uORF-like sequences which contains AUG in 5′ UTRs in terms of mRNAs and genes. Also, we separated out two distinctive uORF-like sequences with matching termination codon in 5′ UTRs or without matching termination codon in 5′ UTRs. The detailed information for these two cases is listed in Table 1.b. Finally, Table 1.c shows how many uORF-like sequences are appeared on an individual 5′ UTR of each mRNA.Click here for additional data file.


The three distinct types of kidneys, pronephros, mesonephros and metanephros, develop consecutively in vertebrates. The earliest form of embryonic kidney, the pronephros, is derived from intermediate mesoderm and the first expressed genes localized in the pronephros anlage are the transcription factors osr1, osr2, hnf1b, lhx1 and pax8, here referred to as the early nephrogenic transcription factors. However, the pathway inducing nephrogenesis and the network of theses factors are poorly understood. Treatment of the undifferentiated animal pole explant  of Xenopus with activin A and retinoic acid induces pronephros formation providing a powerful tool to analyze key molecular events in nephrogenesis.We have investigated the expression kinetics of the early nephrogenic transcription factors in activin A and retinoic acid treated animal caps and their potential to induce pronephric differentiation. In treated animal caps, expression of osr1, osr2, hnf1b and lhx1 are induced early, whereas pax8 expression occurs later implying an indirect activation. Activin A alone is able to induce osr2 and lhx1 after three hours treatment in animal caps while retinoic acid fails to induce any of these nephrogenic transcription factors. The early expression of the five transcription factors and their interference with pronephros development when overexpressed in embryos suggest that these factors potentially induce nephrogenesis upon expression in animal caps. But no pronephros development is achieved by either overexpression of OSR1, by HNF1B injection with activin A treatment, or the combined application of LHX1 and PAX8, although they influenced the expression of several early nephrogenic transcription factors in some cases. In an additional approach we could show that HNF1B induces several genes important in nephrogenesis and regulates lhx1 expression by an HNF1 binding site in the lhx1 promoter.The early nephrogenic transcription factors play an important role in nephrogenesis, but have no pronephros induction potential upon overexpression in animal caps. They activate transcriptional cascades that partially reflect the gene activation initiated by activin A and retinoic acid. Significantly, HNF1B activates the lhx1 promoter directly, thus extending the known activin A regulation of the lhx1 gene via an activin A responsive element. During vertebrate development three kidney types of increasing complexity  form successively from the intermediate mesoderm, located between the paraxial mesoderm (developing somites) and the lateral plate . The proAll components of the pronephros arise from intermediate mesoderm, but the signals that direct pattering of the presumptive pronephric mesoderm towards pronephric lineages are unknown. Experiments showed that the anterior somites are crucial for pronephros development and provide an essential first signal. If the anterior somites are removed  or separin vitro system genes are induced with similar kinetics as in vivo [Xenopus is a very attractive model organism to analyse key molecular events in nephrogenesis, because most genes essential for pronephros development in Xenopus embryos are also crucial for the formation of the more complex mammalian kidneys -9. A cla in vivo -18.In Xenopus the first genes expressed in the pronephros anlage are the transcription factors osr1 and osr2, members of the odd-skipped family of proteins , hnf1b, It should be noted that each of these five early nephrogenic transcription factors plays also a crucial role in the development of other organs. The prominent role of these nephrogenic transcription factors is partially also evident in mammalian systems. Whereas null mutation of Osr1 in mice exhibit agenesis of the kidney , Osr2 knTo further explore the role of these five nephrogenic transcription factors we have now analyzed the kinetics of their induction in animals caps differentiated into pronephric tissue by activin A and retinoic acid. We then have overexpressed these transcription factors in animal caps and analyzed their potential to induce each other and to stimulate pronephric differentiation in these explants. To allow discrimination between injected mRNAs and endogenous mRNAs we used the human mRNAs that are functionally equivalent, but are not detectable with the Xenopus probes. We use capital letters for these human transcription factors to make a clear distinction. In addition we identified genes induced by HNF1B in these early embryonic cells.-4 M retinoic acid for three hours induces differentiation of pronephric tissues [Since simultaneous treatment of Xenopus animal caps with 10 ng/ml of activin A and 10 tissues , we expl tissues .To clarify which nephrogenic transcription factor transcripts are induced by retinoic acid or activin A alone, we analyzed animal caps treated with either retinoic acid or activin A for three hours Figure . TreatmeSince the murine Osr2 gene is expressed in two splice variants referred to as Osr2A and Osr2B , we searTo examine the morphogenetic potential of the mammalian Osr1 and Osr2A in developing Xenopus embryos, we injected mRNA encoding the human OSR1 or the murine Osr2A proteins into one blastomere of the two-cell stage embryo using GFP mRNA as a marker to identify the injected side. About half of the OSR1 and more than the half of the Osr2A injected embryos showed gastrulation defects that were more severe with higher doses of mRNA. Injected embryos surviving to the free swimming tadpole stage 45  were immin vitro as previously reported [Since in our experiments expression of OSR1 had a lower lethality compared to Osr2A and induces ectopic kidney tissues Figure , we invereported . However-7). However, in contrast to experiments using the Xenopus factors [It is known that embryonic overexpression of Xenopus lhx1 or pax8 induces enlargement of pronephros and coexpression of both factors leads to a synergistic effect . This en factors ,25, no e factors . To prov factors  was 6- oSince differential addition of retinoic acid and activin A to animal caps has shown that activin A alone induces osr2, lhx1 and frequently also osr1, but never hnf1b that requires retinoic acid in addition Figure , we wondSince numerous target genes of hnf1b have been defined  or postuin silico analysis with JASPAR [The Xenopus lhx1 gene is known to be regulated in early embryogenesis by activin A via an activin response element (ARE) present in the first intron ,43. Sinch JASPAR  we identh JASPAR . As showh JASPAR ,47.To extend these findings to Xenopus embryonic cells we tested some constructs in animal caps that were derived from controls or HNF1B injected eggs. All constructs retaining the HNF1 binding site were transactivated by injected HNF1B, including the minimal construct Ex1(-120/+3), whereas the reporter Ex1(-117/+3) containing the partially deleted HNF1 site was not inducible Figure . From thin vitro, because pronephros differentiation can be induced by treatment with activin A and retinoic acid [Animal caps are a suitable system to analyse nephrogenesis oic acid . Activinoic acid . This faoic acid ,50 has moic acid . On the oic acid . In Xenooic acid  and it woic acid , whereasoic acid .Although the animal cap assay represents a powerful system to analyse key molecular events in nephrogenesis, it has some limitations, since pronephros differentiation does not occur in all animal caps. In our hands treated animal caps revealed pronephric induction rate of about 60-85% comparable to about 80% described previously ,18. Signin vivo, an observation made for other induced RNAs previously [Our analysis of animal caps treated with activin A and retinoic acid revealed the known induction of lhx1  and pax8 , but alseviously ,57. Thuseviously  and pax8eviously .In animal caps activin A can induce osr2 and lhx1 alone reflecting its strong inducing activity in animal caps . In contThe fast induction of osr2, hnf1b and lhx1 reflects most likely a direct activation by activin A and retinoic acid, since the induction of these transcripts was not inhibited by cycloheximide treatment Figure , a findiPrevious experiments showed that it is possible to induce tissue differentiation by overexpressing transcription factors in animal caps. For instance overexpressed Xbra leads to mesoderm differentiation with muscle, mesothelium and mesenchyme ,64, wherin vivo [In other experiments the failure of injected transcription factor to induce differentiation in animal caps could be overcome by adding growth factors. For example the induction of neural crest differentiation by Pax3 and Zic1 requires Wnt signaling  or Neptuin vivo , its inain vivo . Similarin vivo . Since Oin vivo ,25 overeIn our experiments we overexpressed either the human or murine transcription factors in order to distinguish the activity of the endogenous gene from the injected RNA, because previous experiments have shown equivalence between Xenopus and human hnf1b. This we confirmed in principle for OSR1 and Osr2A Figure  as well Investigating the influence of HNF1B on 26 potential hnf1b target genes we could show the activation of nine genes in injected animal caps  and LHX1 (RC210977) in pCMV6 were obtained from OriGene Technologies.The myc-Rc/CMVHNF1B expression vector has been described previously . The mouCTCGAGGCTTAATGGTT-3' (forward), Ex1(-117/+3): 5'-CCGCTCGAGGGTTTACCAG-3' (forward) and 5'-CCCAAGCTTTCCCTTTGGTTAT-3' (reverse) were inserted into the XhoI and HindIII digested pGL3-Basic vector (Promega). The restriction sites for cloning are underlined. Both constructs were verified by sequencing.The pRL-Con renilla luciferase construct  and the in vitro transcribed with RNA polymerases . The restriction enzymes and RNA polymerases used are given in Additional file The expression vectors encoding OSR1, Osr2A, HNF1B, LHX1 and PAX8 proteins and the GFP encoding expression vector (pCSGFP2) were linearized and 3)2, 0.83 mM MgSO4, 3 mM HEPES, pH 7.8) containing recombinant human activin A  and all-trans retinoic acid  or only in Steinberg's solution for controls and for explants from mRNA injected embryos. After three-times washing with Steinberg's solution the explants were cultured in Steinberg's solution at 20°C until they were equivalent to stage 40-42 in normal embryos (four days) and used for whole-mount immunostaining.Xenopus late blastulae, stage 9 , were deRNA from pools of 30 animal caps was isolated with peqGold RNAPure (PeqLab) followed by phenol/chloroform extraction. For cDNA synthesis the High Capacity cDNA Reverse Transcription Kit (Applied Biosystems) was used. SYBR-Green real-time PCR was performed on a 7900HT Sequence Detection System (Applied Biosystems) using Power-SYBRGreen Mix (Applied Biosystems). Templates were determined in duplicate and for primers used see Additional file Whole-mount immunostaining of four day cultured animal caps or animals at the swimming larval stage were done as described . The difThe HEK293 (HNF1B) cell line  contains50 pg reporter constructs and 150 pg HNF1B mRNA were coinjected into the animal region of Xenopus embryos at the two-cell stage together with renilla luciferase as internal control. Animal caps were cut, cultured in Steinberg's solution for four hours and pools of four caps were assayed by the Dual-Luciferase Reporter Assay (Promega). As the absolute levels of luciferase activity varied between pools of animal caps , the MaCD carried out all the experiments with the animal caps and the injected embryos and drafted the manuscript. SS made the transfection assays in cell culture. GUR conceived the study, participated in its design and coordination and helped to finish the manuscript. All authors read and approved the final manuscript.Table S1: Expression vectors with restriction enzymes and RNA polymerases used for RNA synthesis.Click here for fileTable S2: List of primers used for quantitative RT-PCR.Click here for file


APC or CTNNB1 are highly frequent in colon cancer and cause aberrant stabilization of CTNNB1, which activates the transcription of Wnt target genes by binding to chromatin via the TCF/LEF transcription factors. Here we report an integrative analysis of genome-wide chromatin occupancy of CTNNB1 by chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) and gene expression profiling by microarray analysis upon RNAi-mediated knockdown of CTNNB1 in colon cancer cells.Deregulation of canonical Wnt/CTNNB1 (beta-catenin) pathway is one of the earliest events in the pathogenesis of colon cancer. Mutations in + intestinal stem cell signature, and is significantly enriched in normal intestinal stem cells as well as in clinical colorectal cancer samples. Interestingly, while the expression of the CTNNB1 target gene set does not correlate with survival, elevated expression of negative feedback regulators within the signature predicts better prognosis.We observed 3629 CTNNB1 binding peaks across the genome and a significant correlation between CTNNB1 binding and knockdown-induced gene expression change. Our integrative analysis led to the discovery of a direct Wnt target signature composed of 162 genes. Gene ontology analysis of this signature revealed a significant enrichment of Wnt pathway genes, suggesting multiple feedback regulations of the pathway. We provide evidence that this gene signature partially overlaps with the Lgr5Our data provide a genome-wide view of chromatin occupancy and gene regulation of Wnt/CTNNB1 signaling in colon cancer cells. APC gene or activating mutations in the CTNNB1 gene MYC and CCND1Wnt/CTNNB1 signaling is a conserved pathway that plays fundamental roles in embryonic development, tissue homeostasis and maintenance of stem cells. In normal intestine, this pathway is essential for the development and maintenance of intestinal stem cells Thus, identification and characterization of CTNNB1 target genes genome-wide have been an important pursuit in CRC studies. Transcriptional profiling was used to identify Wnt/CTNNB1 target genes in colon cancer cells In the current study, we combine ChIP-seq with microarray analysis using the SW480 human colon cancer cell line, where deregulated Wnt signaling activity has been well documented. We report a significant correlation between CTNNB1 binding and expression regulation, and the identification of a core set of 162 genes as ‘Wnt direct target genes’ that are bound and activated by CTNNB1. The Wnt target gene signature was enriched in normal intestinal stem cells and CRCs but failed to predict prognosis of CRC patients. Our study provides an important resource for understanding the biology of Wnt signaling.APC gene that results in a high level of constitutively stabilized CTNNB1 protein in the nuclei MYC and LEF1  across the entire genome (p<0.001) when compared to the distribution of randomly generated peaks of similar sizes (3.5±0.2%). Consistent with a previous report SW480 cells contain an inactivating mutation in the and LEF1 . DNAs frand LEF1 . Model-Be genome . Approxie genome . Enrichmp  =  1.78e-50), consistent with previous work reporting the enrichment of AP-1 motifs in CTNNB1-binding elements Using Multiple EM for Motif Elicitation (MEME), a de novo motif discovery program Axin2 is a classical target and contains multiple TCF/LEF binding regions in its promoter and first intron AXIN2, with their positions corresponding well with the reported binding sites (MYCLEF1Using the UCSC Genome browser (NCBI36/hg18) ng sites . We alsoMYCLEF1.p  =  0) between CTNNB1 binding and gene expression changes after CTNNB1 knockdown, and this was true with both siRNAs , pathways in cancer , or Hedgehog signaling pathway  (Gene Ontology (GO) term enrichment by Database for Annotation, Visualization, and Integrated Discovery (DAVID) , WNT11) .AXIN2, LEF1, NKD1 and NKD2 have been described as negative or positive feedback regulators of the pathway WNT6 and WNT11 encode Wnt ligands that have potential tumor-promoting roles even in CRCs with APC mutations APCDD1, NOTUM, PPP1R2, and ZNRF3. APCDD1 is reported to negatively regulate the pathway at the ligand-receptor interaction step and its mutation is responsible for an autosomal dominant hair loss disease, hereditary hypotrichosis simplex NOTUM encodes a secreted α/β-hydrolase, which is known to antagonize Wnts by releasing glypicans from the cell surface PPP1R2 gene product inhibits the protein phosphatase 1 complex, which regulates the phospholyration status of GSK3 ZNRF3 encodes an E3 ubiquitin ligase that specifically promotes ubiquitination and degradation of Frizzled receptors NOTUM and PPP1R2 to be bona fide targets of CTNNB1. Taken together, these data suggest that Wnt signaling is subject to complex feedback regulations.GO analysis also identified genes encoding Wnt signaling components as CTNNB1 targets and we generated an extended list of potential feedback regulators . AXIN2, + intestinal cells are actively cycling stem cells and the Wnt/CTNNB1 signaling pathway plays a critical role in their self-renewal + intestinal stem cells p<8.9e-07, representation factor > 4.1; comparing to random chance) was found: 17 genes (∼10%) in the CTNNB1 target gene signature were also part of the Lgr5+ intestinal stem cell signature  er group . TogetheOur unbiased genome-scale screening for CTNNB1 target genes combining ChIP-seq and microarray analyses provides an important resource for colon cancer research. Despite the significant correlation between chromatin binding and gene expression regulation, our work uncovers a small number (162) of genes as direct, functional CTNNB1 targets in SW480 colon cancer cells. This number is considerably smaller than that of CTNNB1-bound loci or of CTNNB1-responsive genes, suggesting that not all bound loci are functionally utilized in SW480 cells for transcriptional regulation, and that much of the changes in gene expression occurs as indirect consequences of CTNNB1 depletion. We might have missed some modestly regulated targets by requiring the gene expression change to occur upon treatment with two different siRNAs and by using stringent cut-off values. Some of the CTNNB1-bound loci may be regulated in a context-specific manner; for example, genes that have additional layers of regulation such as those silenced by DNA methylation or predominantly controlled by other transcriptional/translational regulators in SW480 cells may not show a significant decrease in transcription upon CTNNB1 depletion. It remains possible that such loci are regulated by CTNNB1 in other cellular contexts.AXIN2 and WNT11) were found in our list of 10 Wnt pathway genes (p<4.8e-07), a substantial number of target genes was specific to each study. This may be in part due to a difference in parameter setting or potential noises in the analysis, but could also reflect a real difference in CTNNB1 function between the CRC cell lines (HCT116 vs. SW480) used in the two studies. Indeed, a difference in the DNA methylation patterns of Wnt target genes between HCT116 and SW620, a lymph node metastatic variant of SW480, has been reported Our validation experiments suggest an 80% success rate in the identification of bona fide CTNNB1 targets using the genomic approach. A difference in knockdown efficiency among si/shRNAs or off-target effects of si/shRNAs may account for the “false positives”. The CTNNB1 target signature of 162 genes presents some interesting features. Most striking is the presence of feedback regulators of the pathway. Feedback regulation of the Wnt/CTNNB1 pathway has been well-established based on studies of individual genes ay genes . FurtherEven for a relatively small number of genes in the 162-target-signature, the related functional terms appear undefined and diverse. This is consistent with the divergent biological effects of Wnt/CTNNB1 signaling It is widely recognized that deregulation of the Wnt/CTNNB1 pathway is a key initiation step of intestinal tumorigenesis Our data provide a genome-wide view of gene regulation by Wnt/CTNNB1 signaling in CRCs. The target gene analysis revealed multiple layers of feedback regulation of the Wnt/CTNNB1 pathway. The CTNNB1 direct target gene signature was highly expressed in intestinal stem cell populations and cancer cells, but did not show significant correlation with the survival of CRC patients. This study provides an important resource to understand the complex and divergent functions of Wnt/CTNNB1 pathway.SW480 cells were obtained from ATCC and maintained in Dulbecco’s modified eagle medium supplemented with 10% fetal bovine serum.ChIP was performed according to the protocol from Millipore with some modifications. SW480 cells were cross-linked first with a cocktail of protein-protein crosslinkers  The validation experiment was performed using ChIP-PCR with primers listed in p-value was generated and propagated throughout the system.The analysis was published previously http://genome.ucsc.edu/index.html). Peak identification was accomplished using MACS mfold 10, bandwidth 300 bp, p-value 10−5 and false discovery rate (FDR) of 5%. For each peak, the distance from the peak to the nearest TSS was determined, and plotted. The TSSs were taken from a RefSeq file obtained from NCBI. The background was determined by placing peaks at random locations on the genome and by determining distances to TSS. For motif analysis, DNA sequences were retrieved using Galaxy (http://main.g2.bx.psu.edu) and used for motif search using MEME After Illumina sequencing, reads were mapped to a reference genome by Ambry Genetics using ELAND, allowing one mismatch. Short sequence reads that mapped to simple and complex repeats or that were not unique by chance were removed from the analysis. BED files were created and used as input for downstream data processing, as well as for visualization in the UCSC Genome Browser (The ChIP-seq and microarray data have been deposited into the Gene Expression Omnibus (GEO) database. The accession numbers are GSE 53927 and GSE53656, respectively.Total RNA was extracted using the RNeast kit  and cDNA was prepared from 1 μg total RNA using Retroscript High Capacity cDNA Reverse Transcription kit (Life Technologies) according to manufacturer’s instructions. Quantitative PCR was performed using a CFX96 qPCR system and SsoAdvanced SYBR Green supermix (Bio-rad). House keeping gene GAPDH was used for normalization. Primers used for gene expression analysis are listed in A cohort of 232 unique CRC expression profiles with clinical outcome data was obtained from GSE17538 The data sets supporting the results of this article are included in the article and its supplemental files.Table S1List of direct and functional β-catenin targets.(XLS)Click here for additional data file.Table S2GO terms enriched in DAVID analysis.(XLS)Click here for additional data file.Table S3RT-qPCR/ChIP-PCR primers used in this study.(DOC)Click here for additional data file.


Chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq), enables unbiased and genome-wide mapping of protein-DNA interactions and epigenetic marks. The first step in ChIP-seq data analysis involves the identification of peaks . The next step consists of interpreting the biological meaning of the peaks through their association with known genes, pathways, regulatory elements, and integration with other experiments. Although several programs have been published for the analysis of ChIP-seq data, they often focus on the peak detection step and are usually not well suited for thorough, integrative analysis of the detected peaks.To address the peak interpretation challenge, we have developed ChIPseeqer, an integrative, comprehensive, fast and user-friendly computational framework for in-depth analysis of ChIP-seq datasets. The novelty of our approach is the capability to combine several computational tools in order to create easily customized workflows that can be adapted to the user's needs and objectives. In this paper, we describe the main components of the ChIPseeqer framework, and also demonstrate the utility and diversity of the analyses offered, by analyzing a published ChIP-seq dataset.ChIPseeqer facilitates ChIP-seq data analysis by offering a flexible and powerful set of computational tools that can be used in combination with one another. The framework is freely available as a user-friendly GUI application, but all programs are also executable from the command line, thus providing flexibility and automatability for advanced users. The use of chromatin immunoprecipitation in combination with high-throughput sequencing (ChIP-seq) has enabled the study of genome-wide mapping of protein-DNA interaction and epigenetic marks. By sequencing millions of immunoprecipitated DNA fragments in a single experiment, ChIP-seq outperforms the array-based ChIP-chip (Chromatin Immunoprecipitation followed by DNA microarray hybridization) technology in terms of quality, specificity, and coverage -3, and hAlthough numerous approaches already exist for the analysis of ChIP-seq data, many of them focus on the peak detection process and provide few or no tools for the interpretation of ChIP-seq peaks -8,12-19.ChIPseeqer is available as a set of standalone command line tools. For advanced users, command line tools provide great flexibility and automatability. For less advanced users, we have made these tools available via a graphical user interface (GUI), developed using the multi-platform QT framework . The bunMany computations performed in ChIPseeqer involve assessing overlaps between hundreds or thousands of genomic regions , and therefore, efficient algorithms are needed to quickly determine and characterize these overlaps. In ChIPseeqer, fast comparison on genomic intervals is performed using interval trees ,40, ordeSAM, BAM, eland, extended eland, bed and export. ChIPseeqer also provides gene-based annotation from multiple sources and databases, such as RefSeq, Ensembl, UCSC Genes, and AceView. Finally, four species are currently supported, namely Homo sapiens, Mus musculus, Drosophila melanogaster, and Saccharomyces cerevisiae. Support for additional species can be added to the framework as described in Additional file One of the advantages of the framework is the support of different formats that are well-established in deep sequencing experiments, such as ChIPseeqer modules section. The framework includes a peak detection program as well as tools for performing quality control of the raw reads . Thus, the novelty of the framework is the capability to combine these modules and design specific workflows that enable multi-step bioinformatics analyses of ChIP-seq data, according to the user's objectives and hypotheses. Figure ChIPseeqer is a comprehensive and fully integrated framework offering a dry-lab workbench for the processing and analysis of ChIP-seq data. Table (1) running the peak detection algorithm for a TF  ChIP-seq dataset,ChIPseeqerMotifMatch module,(2) finding the peaks that have a specific motif  using the ChIPseeqerAnnotate, and(3) identifying the peaks that bind at the promoters of known RefSeq genes using ChIPseeqeriPAGE, in order to find biological processes in which the given TF is likely involved. Another workflow  performing pathways analysis on these genes with er marks  are repoTo illustrate the power and flexibility of ChIPseeqer, we analyzed a published ETS1 ChIP-seq , performChIPseeqerAnnotate module and the RefSeq annotation  and de novo motif discovery. In the supervised analysis on the promoter peaks ChIPseeqerMotifMatch module), we determined subsets of ETS1 peaks that contain motif occurrences for other ETS family members  [ChIPseeqerMotifMatch reveals that a large fraction of ETS1 peaks (more than 73%) contain such matches  for the distal peaks revealed that multiple motifs appear from the same regions, among which ETS-domain motifs , but also motifs resembling binding elements recognized by non-ETS related factors  ,47,48, uB, ELK1)  and UniPB, ELK1)  databasenhancers . The RUNdistal peaks obtained from the ChIPseeqerAnnotate analysis. To better identify enhancers, we also analyzed the CBP ChIP-seq dataset described by Hollenhorst et al. [+ T cells described by Barski et al. [CompareIntervals module, we identified such peaks . We used the two lists of genes as input, one list for each group. Using the Gene Ontology annotation, we noticed a higher enrichment of T and B cell related pathways [Moreover, to examine whether these regions are also conserved , we performed conservation analysis using the s scores . The conpathways  in the dpathways , a databpathways  and "Thypathways .CompareGenes, a tool in our framework that allows comparisons on the gene level, we also looked for genes that have ETS1 peaks in their promoters  and in intergenic regions (163 putative enhancer peaks). This analysis gave us 39 genes .• Specific pathways are preferentially related to genes with ETS1 binding in promoters or intergenic regions.• It is straightforward to characterize ETS1 binding to genomic regions with enhancers signatures .• It is possible to determine a list of genes that may be regulated by an enhancer-boundPerformance Evaluation section in Additional file TF, through a chromatin-looping event. Although these analyses could have been performed by combining several published tools or custom scripts, in ChIPseeqer this is fast . This module generates lists of peaks found in each of these classes of genomic regions. This analysis is controlled by default or user-defined parameters controlled by the user, such as the promoter window around the TSS and the annotation database. For example, promoters are defined by default as 4kb-long regions, around the TSS but not extended further than the downstream extremity of the genes. Moreover, ChIPseeqerAnnotate reports peaks overlapping with the first intron, since it has been reported that some first introns play a vital role in transcriptional control and splicing [ChIPseeqerAnnotate can be directly used in other tools within the framework to perform subsequent analyses. This can be useful to focus further analyses on specific classes of peaks (e.g. promoter peaks or intergenic peaks); ChIPseeqerAnnotate lets users extract these subsets of peaks. ChIPseeqerAnnotate has other interesting features. It generates a gene-based matrix that summarizes the number of peaks found in each gene part  and can help the user identify quickly the peak binding occurring in their gene of interest. We have also developed tools that merge and combine this matrix output, in order to extract, for example, the genes with both promoter and intergenic peaks. The expected fraction of peaks in different genomic categories  is also provided, based on the fraction of the genome in each of these regions, so that it can be compared to the observed fraction of the input peaks in each category. Finally, several widely used gene annotations are supported, such as the RefSeq, Ensembl, UCSCGenes, and AceView . This feature enables comparing the peak-gene association results between different databases.This module associates a set of ChIP-seq peaks, given as input, with the closest genes in the genome, and determines where each peak is located in these genes. In particular, splicing . The tooChIPseeqerAnnotate. We have integrated two pathways analysis modes in the framework that involve: (1) looking for a given pathway of interest within the genes associated with input peaks and (2) looking for any pathways that are enriched in these genes. Pathways annotations are obtained from the Gene Ontology [Pathways analysis can help elucidate important biological mechanisms associated with genome-wide binding and histone modification patterns. and can be performed after peaks have been associated with genes, as described in Ontology , KEGG daOntology , BiocartOntology , the SigOntology , and theOntology . ImportaChIPseeqerPathwayMatch then finds all genes that belong to the selected pathway and outputs the corresponding ChIP-seq peaks. These peaks can then be used as input to other modules . The hypergeometric distribution is used to assess the statistical significance of the pathway association: it determines whether the input peaks are associated with more genes in the query pathway than expected by chance  analysis based on known motifs or user-defined motif patterns, and (2)  library .ChIPseeqerMotifMatch seeks to overcome some of these limitations. To perform known motif analysis in ChIPseeqerMotifMatch, the user either selects the desired motif from a compiled dataset of ~250 TF binding sites (defined as position-specific weight matrices) from JASPAR [Several software tools support searching for peaks that match a specific motif, but they often have limitations that restrict their usability. For example, in Cistrome  it is nom JASPAR  and UniPm JASPAR  and a usm JASPAR , and peam JASPAR .De novo motif analysis is performed using FIRE, an information-theoretic methodology for identification and characterization of regulatory elements [elements . In ordeChIPseeqerNongenicAnnotate provides such capabilities; it can determine the subset of input peaks that overlap with peaks obtained from TF or histone modification ChIP-seq datasets of the ENCODE project [http://genome.ucsc.edu/ENCODE/terms.html). ChIPseeqerNongenicAnnotate can perform additional integrative analyses. For example, extensive literature has shown that TF binding sites and specific histone modifications can be associated with repeated elements [ChIPseeqerNongenicAnnotate module and track-based data from the UCSC Genome Browser, users can quickly and easily determine which of their input ChIP-seq peaks overlap with: (1) known repeated sequences (identified by RepeatMasker [intersection between any two tracks), ChIPseeqerNongenicAnnotate facilitates these analyses and allows their integration with other analyses within the framework.The ability to integrate the results of a ChIP-seq study with existing and publicly available ChIP-seq is an important. For example, this integration could suggest transcription factor, co-factors or histone modification that should be further explored because of their extensive overlap with a set of ChIP-seq peaks. In the ChIPseeqer framework, the  project ,74, as welements  and otheelements . Filterielements  and moreatMasker ), (2) CpChIPseeqerCons, a tool that estimates the conservation for a given set of peaks and outputs the peaks whose average conservation score is greater than a user-defined threshold (default is set to 0.5). The most useful aspect of this module, is estimating the conservation level of sequences adjacent to the input peaks, or of randomly selected sequences, thus allowing global assessment of peak conservation.Cross-species conservation analysis is necessary in order to discover functional genomic elements  and also to prioritize the most promising genomic elements for experimental validation. For these reasons, we have developed ChIPseeqerCons is producing conservation profiles for regions around the summit of the peaks (default is 2kb-long regions), and for random intervals: the average conservation score is estimated for every n-sized bins of the regions (default is n = 10 nucleotides). By plotting the resulting conservation profiles, we can easily compare the level of conservation between the input peaks and randomly generated genomic regions  calculate the read density profile of the regions , (2) count the maximum or average number of ChIPseq reads for each genomic region. These tools can also perform RPKM-style read count normalization  prior toChIPseeqerDensityMatrix lets the users explore and analyse the average read density profiles, either for user-defined regions around the TSS or TES of the genes (default is set to 4 kb around the TSS), or around the summit of the given peaks (default is set to 2 kb window centred to the peak summit). For each region, bins of n nucleotides (default is 10 nucleotides) are created and the average number of reads for each bin is counted. For example, if 4 kb regions are extracted around the TSS of genes, the result of this module will be a matrix that contains for each promoter the average number of reads for 400 bins of 10 nucleotides each. Clustering the promoters of the resulting matrix is then performed based on their read density profiles, using either the built-in Self-Organizing Map algorithm [ChIPseeqerCluster). The results can be directly visualized using Postscript/PDF heatmaps produced using our built-in visualization tools or using TreeView [lgorithm , or by ilgorithm ,82 . These tools can be used to compare sets of peaks, and quickly: (1) identify overlapping peaks, (2) merge sets of peaks, or (3) determine peaks in the first set that do not overlap with any peaks from the second set (i.e. find unique peaks). Moreover, as described in previous section, these tools can assess the significance of the overlap between two sets of peaks using randomization tests that take into account the genomic distribution of peaks. In addition to simply counting how many peaks overlap between two peak lists, we provide tools that can also quantify the extent to which two sets of peaks overlap by estimating the pairwise Jaccard similarity coefficient between pairs of ChIP-seq datasets (ChIPseeqerComputeJaccardIndex). The Jaccard index is estimated as the number of peaks that overlap between two peak files, divided by the union of the two files. The larger the coefficient, the more similar two datasets are in terms of overlapping peaks  that can be easily used on the genes-based output of ChIPseeqerAnnotate. Finally, the annotation of peaks against a collection of ENCODE ChIP-seq datasets (ChIPseeqerNongenicAnnotate), as well as the read density analysis across multiple datasets (ChIPseeqerReadCountMatrix), both described in previous paragraphs, were also developed in the context of integration and comparison of multiple ChIP-seq experiments.As more and more ChIP-seq datasets become publicly available, the need for data integration and comparison is becoming essential. Such integration can reveal how different TFs cooperate to regulate gene expression , as wellChIPseeqerPlotAverageReadDensityInGenes, ChIPseeqerPlotAveragePeaksNumberInGenes) for the visualization of reads density and peaks number in gene parts . The description of these tools and examples of the visualization they provide can be found at the ChIPseeqer tutorial [Visualization is tightly integrated to all modules of the framework in order to facilitate ChIP-seq data exploration and summarize the results of each analysis. ChIPseeqer includes tools for creating UCSC Genome Browser tracks representing peak location and genome-wide read densities. It also includes tools for drawing pie charts summarizing the genomic distribution of the peaks, creating motif and pathway enrichment tables and conservation plots , and can be combined with each other. The variety of tools and the flexibility offered by their parameters Figure  makes itC. elegans, zebrafish, chicken, rat), as well as more visualization components. Moreover, facilitating the integration of ChIP-seq data with gene expression data, obtained from microarray or RNA-sequencing (RNA-seq) experiments, also represents an important avenue for future improvement of the framework.ChIPseeqer is a continuously developing project and we are actively working on implementing several additional components. For example, more species will be supported soon , explore the data from multiple perspectives , and address specific biological questions, such as "http://physiology.med.cornell.edu/faculty/elemento/lab/CS_files/ChIPseeqer-2.0.tar.gz The system requirements, instructions on how to install and run the software, and a detailed tutorial are also provided at http://physiology.med.cornell.edu/faculty/elemento/lab/chipseq.shtml The ETS1 and CBPdatasets used in this paper can be found at the GEO (GSE17954), and the H3K4me1 and H3K4me3 datasets are available at http://dir.nhlbi.nih.gov/papers/lmi/epigenomes/hgtcell.aspx ChIPseeqer is freely available and can be downloaded at kb: kilobase; ChIP-chip: Chromatin Immunoprecipitation followed by DNA microarray hybridization; ChIP-seq: Chromatin Immunoprecipitation followed by sequencing; GUI: Graphical user interface; RNA-seq: RNA sequencing; TES: Transcription end site; TSS: Transcription start site; TF: Transcription factor.EGG implemented the ChIPseeqer GUI version and parts of the command-line version, performed the analysis of the ChIP-seq datasets and drafted the manuscript. OE implemented parts of the command-line version, supervised the project and edited the manuscript. All authors read and approved the final manuscript.This file describes in detail the quality control analysis tools and the peak detection algorithm, implemented within the ChIPseeqer framework, as well as performance evaluation results for several tools of the framework.Click here for file